GKD-C Adaptive-Lookback Stochastic [Loxx]Giga Kaleidoscope GKD-C Adaptive-Lookback Stochastic is a Metamorphosis module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-C Adaptive-Lookback Stochastic
The Adaptive-Lookback Stochastic uses a swing pivot lookback algorithm to adjust the periiod input bar-bar-bar thereby converting the regular Stochasitc oscillator into an adaptive Stochatic oscillator.
What is the Adaptive Lookback Period?
The adaptive lookback period is a technique used in technical analysis to adjust the period of an indicator based on changes in market conditions. This technique is particularly useful in volatile or rapidly changing markets where a fixed period may not be optimal for detecting trends or signals.
The concept of the adaptive lookback period is relatively simple. By adjusting the lookback period based on changes in market conditions, traders can more accurately identify trends and signals. This can help traders to enter and exit trades at the right time and improve the profitability of their trading strategies.
The adaptive lookback period works by identifying potential swing points in the market. Once these points are identified, the lookback period is calculated based on the number of swings and a speed parameter. The swing count parameter determines the number of swings that must occur before the lookback period is adjusted. The speed parameter controls the rate at which the lookback period is adjusted, with higher values indicating a more rapid adjustment.
The adaptive lookback period can be applied to a wide range of technical indicators, including moving averages, oscillators, and trendlines. By adjusting the period of these indicators based on changes in market conditions, traders can reduce the impact of noise and false signals, leading to more profitable trades.
The adaptive lookback period is a powerful technique for traders and analysts looking to optimize their technical indicators. By adjusting the period based on changes in market conditions, traders can more accurately identify trends and signals, leading to more profitable trades. While there are various ways to implement the adaptive lookback period, the basic concept remains the same, and traders can adapt and customize the technique to suit their individual needs and trading styles.
What is the Stochastic Oscillator?
The Stochastic Oscillator is a popular technical analysis indicator developed by George Lane in the 1950s. It is a momentum indicator that compares a security's closing price to its price range over a specified period. The main idea behind the Stochastic Oscillator is that, in an upward trending market, prices tend to close near their high, while in a downward trending market, prices tend to close near their low. The Stochastic Oscillator ranges from 0 to 100 and is primarily used to identify overbought and oversold conditions or potential trend reversals.
The Stochastic Oscillator is calculated using the following formula:
%K = ((C - L14) / (H14 - L14)) * 100
Where:
%K: The Stochastic Oscillator value.
C: The most recent closing price.
L14: The lowest price of the last 14 periods (or any other chosen period).
H14: The highest price of the last 14 periods (or any other chosen period).
Additionally, a moving average of %K, called %D, is calculated to provide a signal line:
%D = Simple Moving Average of %K over 'n' periods
The Stochastic Oscillator generates signals based on the following conditions:
1. Overbought and Oversold Levels: The Stochastic Oscillator typically uses 80 and 20 as overbought and oversold levels, respectively. When the oscillator is above 80, it is considered overbought, indicating that the market may be overvalued and a price decline is possible. When the oscillator is below 20, it is considered oversold, indicating that the market may be undervalued and a price rise is possible.
2. Bullish and Bearish Divergences: A bullish divergence occurs when the price makes a lower low, but the Stochastic Oscillator makes a higher low, suggesting a potential trend reversal to the upside. A bearish divergence occurs when the price makes a higher high, but the Stochastic Oscillator makes a lower high, suggesting a potential trend reversal to the downside.
3. Crosses: Buy signals are generated when %K crosses above %D, indicating upward momentum. Sell signals are generated when %K crosses below %D, indicating downward momentum.
The Stochastic Oscillator is commonly used in combination with other technical analysis tools to confirm signals and improve the accuracy of predictions.
When using the Stochastic Oscillator, it's important to consider a few best practices and additional insights:
1. Confirmation with other indicators: While the Stochastic Oscillator can provide valuable insights into potential trend reversals and overbought/oversold conditions, it is generally more effective when used in conjunction with other technical indicators, such as moving averages, RSI (Relative Strength Index), or MACD (Moving Average Convergence Divergence). This can help confirm signals and reduce the chances of false signals or whipsaws.
2. Timeframes: The Stochastic Oscillator can be applied to various timeframes, such as daily, weekly, or intraday charts. Adjusting the lookback period for the calculation can also alter the sensitivity of the indicator. A shorter lookback period will make the oscillator more sensitive to price movements, while a longer lookback period will make it less sensitive. Traders should choose a timeframe and lookback period that aligns with their trading strategy and risk tolerance.
3. Variations: There are two primary variations of the Stochastic Oscillator: Fast Stochastic and Slow Stochastic. The Fast Stochastic uses the original %K and %D calculations, while the Slow Stochastic smooths %K with an additional moving average and uses this smoothed %K as the new %D. The Slow Stochastic is generally considered to generate fewer false signals due to the additional smoothing.
4. Overbought and Oversold: It's important to remember that overbought and oversold conditions can persist for an extended period, especially during strong trends. This means that the Stochastic Oscillator alone should not be relied upon as a definitive buy or sell signal. Instead, traders should wait for additional confirmation from other indicators or price action before entering or exiting a trade.
The Stochastic Oscillator is a valuable momentum indicator that helps traders identify potential trend reversals and overbought/oversold conditions in the market. However, it is most effective when used in combination with other technical analysis tools and should be adapted to suit the specific needs of the individual trader's strategy and risk tolerance.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Full GKD Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Composite RSI
Confirmation 2: uf2018
Continuation: Vortex
Exit: Rex Oscillator
Metamorphosis: Fisher Transform, Universal Oscillator, Aroon, Vortex .. combined
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest:
GKD-BT Giga Stacks Backtest:
GKD-BT Full Giga Kaleidoscope Backtest:
GKD-BT Solo Confirmation Super Complex Backtest:
GKD-BT Solo Confirmation Complex Backtest:
GKD-BT Solo Confirmation Simple Backtest:
Cerca negli script per "GOLD"
GKD-M Accuracy Alchemist [Loxx]Giga Kaleidoscope GKD-M Accuracy Alchemist is a Metamorphosis module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-M Accuracy Alchemist
What is the Accuracy Alchemist?
The Accuracy Alchemist is designed to process up to 10 GKD-C indicators and create a compound signal that can be utilized in a GKD-BT backtest. It achieves this by applying an individual Solo Confirmation Simple backtest to each GKD-C indicator provided. The compound signal is derived from the cumulative accuracy rate of each candle within a specified date range.
To illustrate this process, consider the following scenario:
The Fisher Transform indicator has a 65% win rate for long positions on the current ticker.
The Vortex indicator has a 45% success rate on the current candle.
Suppose a long signal is generated by the Vortex indicator. However, this signal is disregarded because its accuracy is lower than that of the Fisher Transform. Now, imagine that the subsequent candle produces a long signal from the Fisher Transform indicator. This signal will be exported to the backtest. The GKD-C indicator that exhibits the highest accuracy for the current candle is chosen to generate the signal. The dominant indicator, determined by its accuracy, will always be used to generate signals. However, it is important to note that the current dominant indicator might not retain its dominance in the future if its accuracy rate falls below that of other indicators connected within the Accuracy Alchemist indicator.
The Accuracy Alchemist provides a comprehensive table that displays the dominant indicator based on accuracy, highlighted in green for the highest long accuracy rate and in red for the highest short accuracy rate. Additionally, the table presents the cumulative long and short accuracy rates for all indicators.
The functionality of the Accuracy Alchemist extends to several GKD-BT backtests, allowing for seamless integration. These backtests include:
-Solo Confirmation Simple
-Solo Confirmation Complex
-Solo Confirmation Super Complex
-Full GKD (as a Confirmation 1 indicator only)
-Confirmation Stack (as a Confirmation 1 indicator only)
By incorporating the Accuracy Alchemist, you gain the ability to evaluate and compare GKD-C Confirmation indicators within your full GKD trading system. It serves as an ideal tool to assess the performance of different confirmation indicators and aids in the selection process for determining which indicators to incorporate into your trading strategy.
Take Profit and Stoploss
The GKD system utilizes volatility-based take profits and stop losses, where each take profit and stop loss is calculated as a multiple of volatility. Users have the flexibility to adjust the multiplier values in the settings to suit their preferences. Accuracy Alchemist tests the accuracy of GKD-C Confirmation indicators and therefore has only 1 take profit and 1 stoploss. You can adjust the multipliers of both in the settings
Setting up Accuracy Alchemist
To use this indicator, you must import GKD-C Confirmation indicators and then activate them in the Accuracy Alchemist settings. Import the value "Input into NEW GKD-BT Backtest" from a GKD-C indicator and then activate it by checking the box next to the import. See below:
Volatility Types Included
17 types of volatility are included in this indicator
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Full GKD Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Composite RSI
Confirmation 2: uf2018
Continuation: Vortex
Exit: Rex Oscillator
Metamorphosis: Fisher Transform, Universal Oscillator, Aroon, Vortex .. combined
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest:
GKD-BT Giga Stacks Backtest:
GKD-BT Full Giga Kaleidoscope Backtest:
GKD-BT Solo Confirmation Super Complex Backtest:
GKD-BT Solo Confirmation Complex Backtest:
GKD-BT Solo Confirmation Simple Backtest:
GKD-C Composite RSI [Loxx]Giga Kaleidoscope GKD-C Composite RSI is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ What is the Composite RSI?
The Composite Relative Strength Index (Composite RSI) is a sophisticated adaptation of the traditional Relative Strength Index (RSI). This advanced indicator combines the benefits of smoothing techniques with the relative strength index to offer a more detailed perspective of market conditions. To fully comprehend the scope of Composite RSI, it's crucial to first understand the traditional RSI and its limitations.
The Relative Strength Index (RSI) is a widely used momentum oscillator that gauges the speed and change of price movements. Developed by J. Welles Wilder, the RSI is a scale from 0 to 100, with high and low levels typically set at 70 and 30, respectively. When the RSI climbs above 70, the asset is often considered overbought, suggesting a potential price decrease. Conversely, when the RSI falls below 30, the asset is deemed oversold, indicating a potential price increase.
While the RSI is beneficial in various market conditions, it is not without its limitations. One of the main criticisms of the traditional RSI is that it can produce false signals during trending markets. This is primarily due to the fact that the RSI only considers a single timeframe and does not account for volatility in the market.
The Composite RSI aims to address these limitations. This advanced indicator uses smoothing techniques and depth analysis to provide a more nuanced view of the market. As the provided pseudocode suggests, the Composite RSI calculates the Relative Strength (RS) over a given period and a certain depth, incorporating the average upward and downward changes in the price.
By using the Composite RSI, traders can better interpret market conditions and make more informed decisions. Its application of smoothing techniques helps to filter out market noise and reduce the likelihood of false signals. Furthermore, by considering multiple periods (the depth), the Composite RSI provides a more comprehensive view of market momentum.
While the traditional RSI remains a valuable tool in technical analysis, the Composite RSI offers a more nuanced and comprehensive approach to assessing market conditions. By incorporating smoothing techniques and depth analysis, the Composite RSI provides a more reliable and robust measure of market momentum, enhancing the decision-making process for traders and investors alike.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Full GKD Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Composite RSI
Confirmation 2: uf2018 as shown
Continuation: Vortex
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to create your GKD trading system. Each indicator contains a proprietary signal generation algorithm that only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest:
GKD-BT Giga Stacks Backtest:
GKD-BT Full Giga Kaleidoscope Backtest:
GKD-BT Solo Confirmation Super Complex Backtest:
GKD-BT Solo Confirmation Complex Backtest:
GKD-BT Solo Confirmation Simple Backtest:
GKD-BT Full Giga Kaleidoscope Backtest [Loxx]Giga Kaleidoscope GKD-BT Full Giga Kaleidoscope Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Full Giga Kaleidoscope Backtest
The Full Giga Kaleidoscope Backtest module enables users to backtest Full GKD Long and Short signals, allowing the creation of a comprehensive NNFX trading system consisting of two confirmation indicators, a baseline, a measure of volatility/volume, and continuations.
This module offers two types of backtests: Trading and Full. The Trading backtest allows users to evaluate individual Long and Short trades one by one. On the other hand, the Full backtest enables the analysis of Longs or Shorts separately by toggling between them in the settings, providing insights into the results for each signal type. The Trading backtest simulates actual trading conditions, while the Full backtest evaluates all signals regardless of their Long or Short nature.
Additionally, the backtest module allows testing with 1 to 3 take profits and 1 stop loss. The Trading backtest supports 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also includes a trailing take profit feature.
Regarding the percentage of trade removed at each take profit, the backtest module incorporates the following predefined values:
Take profit 1: 50% of the trade is removed.
Take profit 2: 25% of the trade is removed.
Take profit 3: 25% of the trade is removed.
Stop loss: 100% of the trade is removed.
After achieving each take profit, the stop loss level is adjusted accordingly. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into effect after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest module also provides the option to restrict testing to a specific date range, allowing for simulated forward testing using past data. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. Historical take profit and stop loss levels are displayed as overlaid horizontal lines on the chart for reference.
To utilize this strategy, follow these steps:
1. GKD-B Baseline Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-B Baseline."
2. GKD-V Volatility/Volume Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-V Volatility/Volume."
3. Adjust the "Confirmation 1 Type" in the GKD-C Confirmation Indicator to "GKD New."
4. GKD-C Confirmation 1 Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 1 module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-C Confirmation 1."
5. Adjust the "Confirmation 2 Type" in the GKD-C Confirmation 2 Indicator to "GKD New."
6. GKD-C Confirmation 2 Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 2 module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-C Confirmation 2."
7. Adjust the "Confirmation Type" in the GKD-C Continuation Indicator to "GKD New."
8. GKD-C Continuation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Continuation module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-C Confirmation."
The GKD system utilizes volatility-based take profits and stop losses, where each take profit and stop loss is calculated as a multiple of volatility. Users have the flexibility to adjust the multiplier values in the settings to suit their preferences.
In a future update, the Full Giga Kaleidoscope Backtest module will include the option to incorporate a GKD-E Exit indicator, completing the full trading strategy.
█ Full Giga Kaleidoscope Backtest Entries
Within this module, there are ten distinct types of entries available, which are outlined below:
Standard Entry
1-Candle Standard Entry
Baseline Entry
1-Candle Baseline Entry
Volatility/Volume Entry
1-Candle Volatility/Volume Entry
Confirmation 2 Entry
1-Candle Confirmation 2 Entry
PullBack Entry
Continuation Entry
Each of these entry types can generate either long or short signals, resulting in a total of 20 signal variations. The user has the flexibility to enable or disable specific entry types and choose which qualifying rules within each entry type are applied to price to determine the final long or short signal.
The following section provides an overview of the various entry types and their corresponding qualifying rules:
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Volatility Types Included
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Full Giga Kaleidoscope Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Vorext as shown on the chart above
Confirmation 2: Coppock Curve as shown on the chart above
Continuation: Fisher Transform as shown on the chart above
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
GKD-BT Solo Confirmation Super Complex Backtest [Loxx]Giga Kaleidoscope GKD-BT Solo Confirmation Super Complex Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Solo Confirmation Super Complex Backtest
The Solo Confirmation Super Complex Backtest module allows users to perform backtesting on Full GKD Long and Short signals using GKD-C confirmation indicators. These signals are further refined by GKD-B Baseline and GKD-V Volatility/Volume indicators and augmented by an additional GKD-C Confirmation indicator acting as a Continuation indicator. This module serves as a comprehensive tool that falls just below a Full GKD trading system. The key difference is that the GKD-BT Solo Confirmation Super Complex utilizes a single GKD-C Confirmation indicator, while the Full GKD system employs two GKD-C Confirmation indicators. Both the Solo Confirmation Super Complex and the Full GKD systems incorporate an extra GKD-C Confirmation indicator to identify Continuation signals, which provide both longs and shorts on developing trends following an initial trend change.
This module encompasses two types of backtests: Trading and Full. The Trading backtest permits users to evaluate individual trades, whether Long or Short, one at a time. Conversely, the Full backtest allows users to analyze either Longs or Shorts separately by toggling between them in the settings, enabling the examination of results for each signal type. The Trading backtest emulates actual trading conditions, while the Full backtest assesses all signals, regardless of being Long or Short.
Additionally, this backtest module provides the option to test the core GKD-C Confirmation and GKD-C Continuation indicators with 1 to 3 take profits and 1 stop loss. The Trading backtest allows for the use of 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also offers the capability to apply a trailing take profit.
In terms of the percentage of trade removed at each take profit, this backtest module has the following hardcoded values:
Take profit 1: 50% of the trade is removed.
Take profit 2: 25% of the trade is removed.
Take profit 3: 25% of the trade is removed.
Stop loss: 100% of the trade is removed.
After each take profit is achieved, the stop loss level is adjusted. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into play after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest module also offers the capability to restrict by a specific date range, allowing for simulated forward testing based on past data. Additionally, users have the option to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. On the chart, historical take profit and stop loss levels are represented by horizontal lines overlaid for reference.
To utilize this strategy, follow these steps:
1. GKD-B Baseline Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline module into the GKD-BT Solo Confirmation Super Complex Backtest module setting named "Import GKD-B Baseline."
2. GKD-V Volatility/Volume Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume module into the GKD-BT Solo Confirmation Super Complex Backtest module setting named "Import GKD-V Volatility/Volume."
3. Adjust the "Confirmation Type" in the GKD-C Confirmation Indicator to "GKD New."
4. GKD-C Confirmation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation module into the GKD-BT Solo Confirmation Super Complex Backtest module setting named "Import GKD-C Confirmation."
5. Adjust the "Confirmation Type" in the GKD-C Continuation Indicator to "GKD New."
6. GKD-C Continuation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Continuation module into the GKD-BT Solo Confirmation Super Complex Backtest module setting named "Import GKD-C Continuation."
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings.
In a future update, the option to include a GKD-E Exit indicator will be added to this module to complete a full trading strategy.
█ Solo Confirmation Super Complex Backtest Entries
Within this module, there are eight distinct types of entries available, which are outlined below:
Standard Entry
1-Candle Standard Entry
Baseline Entry
1-Candle Baseline Entry
Volatility/Volume Entry
1-Candle Volatility/Volume Entry
PullBack Entry
Continuation Entry
Each of these entry types can generate either long or short signals, resulting in a total of 16 signal variations. The user has the flexibility to enable or disable specific entry types and choose which qualifying rules within each entry type are applied to price to determine the final long or short signal. You'll notice that these signals are different form the core GKD signals mentioned towards the end of this description. Signals from the GKD-BT Solo Confirmation Super Complex Backtest are modifided to add additional qualifications to make your finalized trading strategy more dynamic and robust.
The following section provides an overview of the various entry types and their corresponding qualifying rules:
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle:
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Basline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle:
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Baseline agrees
6. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle:
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
█ Volatility Types Included
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Solo Confirmation Complex Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Vortex as shown on the chart above
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
Fundur - Market Sentiment BIndicator Overview
The Market Sentiment B indicator is a sophisticated multi-timeframe momentum oscillator that provides comprehensive market analysis through advanced wave theory and sentiment measurement. Unlike traditional single-timeframe indicators, Market Sentiment B analyzes 11 different timeframes simultaneously to create a unified view of market momentum and sentiment.
What Makes Market Sentiment B Unique
Multi-Timeframe Convergence : The indicator combines data from 11 different periods (8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987) based on mathematical sequences that naturally occur in market cycles.
Advanced Wave Analysis : The histogram component tracks momentum waves with precise peak and trough identification, allowing traders to spot both major moves and smaller precursor waves.
Sentiment Extremes Detection : When all 11 timeframes reach extreme levels simultaneously, the indicator highlights these rare conditions with background coloring, signaling potential major reversals.
Dynamic Zone Analysis : The indicator divides market conditions into Premium (80+), Discount (20-), and Liquidity zones (40-60), providing clear context for trade entries and exits.
Core Components
1. Market Sentiment B Line (Main Signal)
The primary oscillator line that represents the averaged sentiment across all timeframes. This line uses advanced mathematical filtering to smooth out noise while preserving important trend changes.
Key Features:
Oscillates between 0-100
Color-coded: Green when rising, Red when falling
Shows divergences with colored dots
Premium zone: 80+, Discount zone: 20-
2. Momentum Waves (Secondary Signal)
A smoothed version of the Market Sentiment B line that acts as a trend-following component. This line helps identify the underlying momentum direction.
Key Features:
Blue coloring during bullish expansion (above 50 and rising)
Orange coloring during bearish expansion (below 50 and falling)
Filled areas show expansion and contraction phases
Critical 50-line crossovers signal momentum shifts
3. Histogram (Wave Analysis)
The difference between Market Sentiment B and Momentum Waves, displayed as a histogram that reveals the relationship between current sentiment and underlying momentum.
Key Features:
Green bars: Positive momentum (Market Sentiment above Momentum Waves)
Red bars: Negative momentum (Market Sentiment below Momentum Waves)
Wave height labels show the strength of each wave
Divergence patterns identify potential reversals
4. Divergence System
Advanced divergence detection that identifies both regular and hidden divergences, with special "Golden Divergences" for the strongest signals.
Types:
Regular Divergences : Price makes new highs/lows while indicator doesn't
Hidden Divergences : Continuation patterns in trending markets
Golden Divergences : High-probability reversal signals (orange dots)
5. Zone Analysis
The indicator divides market conditions into distinct zones:
Premium Zone (80-100) : Potential selling area
Liquidity Zone (40-60) : Neutral/consolidation area (highlighted in orange)
Discount Zone (0-20) : Potential buying area
Extreme Conditions : Background coloring when all timeframes align
Setup Guide
Initial Installation
Open TradingView and navigate to your desired chart
Click the "Indicators" button or press "/" key
Search for "Fundur - Market Sentiment B"
Click on the indicator to add it to your chart
The indicator will appear in a separate pane below your chart
Essential Settings Configuration
Main Settings
Show Histogram Wave Values : Enable to see wave strength numbers
Wave Value Text Size : Choose from tiny, small, normal, or large
Wave Label Offset : Adjust label positioning (default: 2)
Market Sentiment Thresholds
Only Show Indicators at Market Sentiment Extremes : Filter signals to extreme zones only
Extreme levels are automatically set at 80 (high) and 20 (low)
Small Wave Strategy
Enable Small Wave Swing Strategy : Focus on smaller, early-warning waves
Small Wave Label Color : Customize the color for small wave labels
Divergence Analysis
Show Regular Divergences : Enable standard divergence detection
Show Gold Divergence Dots : Enable high-probability golden signals
Show Divergence Dots : Show all divergence markers
Histogram Settings
Enable Histogram : Toggle the histogram display
Divergence Types : Choose which types to display (Bullish/Bearish Reversals and Continuations)
Recommended Initial Setup
Enable all main components (Histogram, Divergences, Momentum Waves)
Set wave value text size to "small" for clarity
Enable golden divergence dots for premium signals
Start with all alert categories enabled, then customize based on your trading style
Basic Trading Guide
Understanding the Zones
Premium Zone Trading (80-100)
When to Consider Selling:
Market Sentiment B enters 80+ zone
Bearish divergences appear
Histogram shows weakening momentum (smaller green waves)
Background turns red (extreme conditions)
What to Look For:
Bearish pivot signals (orange triangles pointing down)
Golden divergence dots at tops
Momentum Waves turning bearish
Discount Zone Trading (0-20)
When to Consider Buying:
Market Sentiment B enters 0-20 zone
Bullish divergences appear
Histogram shows strengthening momentum (smaller red waves)
Background turns green (extreme conditions)
What to Look For:
Bullish pivot signals (blue triangles pointing up)
Golden divergence dots at bottoms
Momentum Waves turning bullish
Liquidity Zone Trading (40-60)
Consolidation and Breakout Zone:
Orange-filled area indicates neutral sentiment
Wait for clear breaks above 60 or below 40
Use for range-bound trading strategies
Look for momentum wave direction changes
Key Signal Types
1. Zone Crossovers
Above 60 : Bullish momentum building
Below 40 : Bearish momentum building
50-line crosses : Primary trend changes
2. Divergence Signals
Golden dots : Strongest reversal signals that align accross different timeframes
Colored dots : Standard divergence warnings
Hidden divergences : Trend continuation signals
3. Histogram Patterns
Increasing green bars : Building bullish momentum
Increasing red bars : Building bearish momentum
Smaller waves : Early warning signals of deteriorating interest
Basic Entry Rules
Long Entries
Market Sentiment B in discount zone (0-20) OR
Bullish divergence confirmed OR
Break above 40 from oversold conditions OR
Golden divergence dot at bottom
Short Entries
Market Sentiment B in premium zone (80-100) OR
Bearish divergence confirmed OR
Break below 60 from overbought conditions OR
Golden divergence dot at top
Exit Rules
Exit longs when entering premium zone
Exit shorts when entering discount zone
Close positions on opposite divergence signals
Use histogram wave tops/bottoms for fine-tuning exits
Advanced Analysis Setups
Setup 1: Scalping Configuration
Purpose : Quick intraday trades focusing on small moves
Settings :
Enable Small Wave Strategy
Show indicators only at extremes: OFF
Combine multiple alerts: ON
Focus on 1-5 minute timeframes
Signals to Watch :
Small wave histogram peaks/troughs
Quick zone crossovers (40/60 line breaks)
Momentum wave direction changes
Short-term divergences
Setup 2: Swing Trading Configuration
Purpose : Medium-term trend following and reversal trading
Settings :
Show indicators only at extremes: ON
Enable all divergence types
Focus on 15-minute to 4-hour timeframes
Golden divergence alerts: HIGH priority
Signals to Watch :
Premium/discount zone entries
Golden divergence signals
Extreme condition backgrounds
Major histogram wave formations
Setup 3: Position Trading Configuration
Purpose : Long-term trend identification and major reversal spots
Settings :
Only alert in extremes: ON
Focus on golden divergences only
Use daily and weekly timeframes
Minimize noise with extreme filtering
Signals to Watch :
Extreme condition backgrounds (red/green)
Major golden divergence signals
Long-term momentum wave trends
Weekly/monthly zone transitions
Setup 4: Reversal Hunting Configuration
Purpose : Catching major market turns at key levels
Settings :
Enable all divergence types
Show golden divergence dots: ON
Extreme filtering: ON
Small wave strategy: OFF
Signals to Watch :
Multiple divergence confirmations
Golden divergence + extreme zones
All-timeframe extreme conditions
Major histogram wave exhaustion
Setup 5: Trend Following Configuration
Purpose : Riding momentum in established trends
Settings :
Momentum waves: HIGH priority
Hidden divergences: ON
Continuation patterns focus
Zone crossover alerts
Signals to Watch :
Momentum wave expansion phases
Hidden divergence continuations
Liquidity zone breakouts
Sustained momentum patterns
Alert System
The Market Sentiment B indicator features a comprehensive alert system with over 30 different alert types organized into logical categories.
Alert Categories
Market Sentiment B Line Alerts
Golden Divergences : Highest priority reversal signals
Standard Divergences : Regular divergence patterns
Bearish/Bullish Pivots : Momentum pivot points
Premium/Discount Zone : Zone entry/exit alerts
Extreme Conditions : Rare all-timeframe extremes
Liquidity Zone : 40-60 zone movement alerts
Momentum Waves Alerts
Premium/Discount Zones : 80+/20- level alerts
Liquidity Zone Movement : 40-60 zone alerts
Expansion Phases : Bullish/bearish expansion alerts
Direction Changes : 50-line crossover alerts
Cross Alerts : MSB vs Momentum crossovers
Histogram Alerts
State Changes : Bullish/bearish turns
Peak/Trough Detection : Wave top/bottom alerts
Divergence Alerts : Histogram-specific divergences
Hidden Divergences : Continuation pattern alerts
Smaller Wave Alerts : Early warning signals
Alert Configuration Tips
For Day Trading
Enable quick state change alerts
Focus on histogram and small wave alerts
Use combined alerts to reduce noise
Disable extreme-only filtering
For Swing Trading
Enable zone crossover alerts
Focus on divergence and pivot alerts
Use extreme-only filtering
Prioritize golden divergence alerts
For Position Trading
Enable only golden divergences and extreme conditions
Use extreme-only filtering
Focus on major zone transitions
Disable minor wave alerts
Trading Strategies
Strategy 1: Premium/Discount Zone Reversal
Setup : Wait for Market Sentiment B to reach extreme zones
Entry :
Long: Enter discount zone (0-20) with bullish divergence
Short: Enter premium zone (80-100) with bearish divergence
Exit : Opposite zone reached or momentum wave reversal
Risk Management : Stop loss at recent swing high/low
Strategy 2: Golden Divergence Power Plays
Setup : Wait for golden divergence dots to appear
Entry : Enter in direction opposite to divergence (reversal play)
Confirmation : Wait for momentum wave to confirm direction
Exit : When sentiment reaches opposite zone
Risk Management : Tight stops below/above divergent pivot
Strategy 3: Momentum Wave Trend Following
Setup : Identify strong momentum wave expansion phases
Entry : Enter on pullbacks to 50-line during expansion
Continuation : Hold while expansion phase continues
Exit : When expansion phase ends or opposite expansion begins
Risk Management : Trail stops using wave peaks/troughs
Strategy 4: Small Wave Early Entry
Setup : Enable Small Wave Strategy for early signals
Entry : Enter on small wave formations before major moves
Confirmation : Wait for main sentiment line to follow
Exit : When major wave forms or opposite signal appears
Risk Management : Quick exits if main indicator doesn't confirm
Strategy 5: Extreme Condition Contrarian
Setup : Wait for background color changes (extreme conditions)
Entry : Counter-trend when ALL timeframes are extreme
Confirmation : Look for early divergence signs
Exit : When background color disappears
Risk Management : Position size smaller due to counter-trend nature
FAQ & Troubleshooting
Frequently Asked Questions
Q: Why don't I see any signals on my chart?
A: Check if "Only Show Indicators at Market Sentiment Extremes" is enabled. If so, signals only appear when the indicator is above 80 or below 20.
Q: What's the difference between golden and standard divergences?
A: Golden divergences (orange dots) are higher-probability signals that meet additional criteria for strength and momentum alignment. Standard divergences are regular price/indicator disagreements.
Q: How do I reduce alert noise?
A: Enable "Only Alert In Extremes" in the alert settings, or use "Combine Multiple Alerts" to consolidate multiple signals into single messages.
Q: What timeframe works best with this indicator?
A: The indicator works on all timeframes. For day trading, use 1-15 minutes. For swing trading, use 1-4 hours. For position trading, use daily or weekly.
Q: Why are the histogram wave values important?
A: Wave values show the strength of momentum. Declining wave values (smaller peaks) often precede trend changes, while increasing values confirm trend strength.
Troubleshooting Common Issues
Issue: Indicator not loading
Solution: Ensure you're using TradingView Pro or higher
Check that max_bars_back is set appropriately
Refresh the chart and re-add the indicator
Issue: Too many alerts firing
Solution: Enable extreme-only filtering
Disable less important alert categories
Use combined alerts feature
Issue: Missing divergence signals
Solution: Check that divergence detection is enabled
Ensure you're looking in the correct zones
Verify that extreme filtering isn't hiding signals
Issue: Histogram not displaying
Solution: Check that "Enable Histogram" is turned ON
Verify histogram divergence types are enabled
Ensure the chart has sufficient historical data
Best Practices
Start Simple : Begin with basic zone trading before using advanced features
Paper Trade First : Test strategies with paper trading before risking capital
Combine with Price Action : Use the indicator alongside support/resistance levels
Respect Risk Management : Never risk more than you can afford to lose
Keep Learning : Market conditions change; adapt your usage accordingly
Performance Optimization
Use appropriate timeframes for your trading style
Enable only necessary alert types
Consider using extreme filtering during high-volatility periods
Regularly review and adjust settings based on market conditions
Conclusion
The Market Sentiment B indicator represents a sophisticated approach to market analysis, combining multiple timeframes, advanced wave theory, and comprehensive divergence detection into a single powerful tool. Whether you're a scalper looking for quick opportunities or a position trader seeking major reversals, this indicator provides the insights needed to make informed trading decisions.
Remember that no indicator is perfect, and the Market Sentiment B should be used as part of a comprehensive trading plan that includes proper risk management, fundamental analysis awareness, and sound money management principles.
Happy Trading!
Disclaimer: Trading involves substantial risk and is not suitable for all investors. Past performance is not indicative of future results. Always practice proper risk management and never trade with money you cannot afford to lose.
DC History & Daily Cross CountOkay, here is a technical document for the Pine Script indicator we developed. This can be used as a guide or description when publishing the script on TradingView or elsewhere.
Technical Document: SMA Cross Signals & Static DC History (Death Cross)
Version: 1.0
Date: April 14, 2025
Indicator Name: Specific Static DC History + Live Signals
Pine Script Version: 5
1. Overview
This TradingView indicator is designed to provide traders with visual signals for Simple Moving Average (SMA) crossovers, specifically focusing on the "Death Cross", while also presenting relevant historical context via a static data table and a real-time daily cross counter.
It combines several features:
Plotting of a fast and a slow Simple Moving Average (SMA).
Visual identification and marking of "Death Cross" events (Fast SMA crossing below Slow SMA) directly on the price chart.
A customizable table displaying static, pre-defined historical performance data of the S&P 500 following specific Death Crosses that occurred between 2016 and 2022.
An optional label that counts the total number of SMA crosses (both Golden Crosses and Death Crosses) occurring during the current trading day/session, including extended hours if enabled by the user on their chart.
2. Features
Customizable SMA Lengths: User-defined periods for both the Fast (default 50) and Slow (default 200) SMAs.
Death Cross Signals: Clear visual markers (red triangles above the bar and optional background shading) when the Fast SMA closes below the Slow SMA.
Optional SMA Plotting: Ability to show or hide the SMA lines themselves.
Static Historical Performance Table: Displays fixed historical return data (1 Week, 1 Month, 3 Months, 6 Months, 1 Year) following specific S&P 500 Death Crosses that occurred on 1/11/2016, 12/7/2018, 3/30/2020, and 3/14/2022. Note: This data is static and does not change based on the current chart.
Customizable Table Position: User can select the on-screen corner for the data table.
Daily SMA Cross Counter: Optionally displays a label showing the cumulative number of times the Fast SMA has crossed above (Golden Cross) or below (Death Cross) the Slow SMA during the current trading day/session.
Extended Hours Compatibility: The Daily Cross Counter includes crosses from pre-market and after-hours sessions if the user has "Extended Trading Hours" enabled on their TradingView chart settings.
3. Technical Explanation
SMA Calculation: The script uses the built-in ta.sma(source, length) function, calculating the Simple Moving Average based on the close price of each bar for the user-defined fastLen and slowLen.
Death Cross Detection: A Death Cross is detected using ta.crossunder(fastMA, slowMA). This function returns true on the first bar where the value of fastMA is less than the value of slowMA, after previously being greater than or equal to it. The comparison is based on the calculated SMA values at the close of each bar.
Golden Cross Detection: Similarly, ta.crossover(fastMA, slowMA) is used to detect Golden Crosses for the daily counter.
Visual Signals: The plotshape() function draws a red triangle above the bar where deathCross is true. The bgcolor() function applies a transparent red background to the bar where deathCross is true.
Static Table Data: The historical performance data for the 4 specified dates (Jan 2016 - Mar 2022) is hardcoded into array variables within the script. This data was derived from a prior analysis (based on the initially provided image, source likely Dow Jones Market Data or similar) and is not calculated dynamically from the chart. The script iterates through these arrays and populates a table object on the last bar.
Daily Cross Counter:
A var int dailyCrossCount variable holds the count, ensuring persistence across bars within a day.
ta.change(time("D")) detects the start of a new daily session based on the chart's symbol and session settings. When true, the dailyCrossCount is reset to 0.
On each bar, if either deathCross or goldenCross is true, the dailyCrossCount is incremented.
A label object displays the dailyCrossCount and is updated on the last bar (barstate.islast).
Extended Hours Inclusion: The script inherently uses the data series provided by the chart. If the chart is configured to include Extended Trading Hours (ETH), the close prices used for SMA calculations will reflect ETH data, and crosses occurring during ETH will be detected and counted.
4. Settings (Inputs)
Show Static Data Table (2016-2022) (Checkbox): Toggles the visibility of the table containing the fixed historical performance data. (Default: On)
Table Position (Dropdown): Selects the corner or side of the chart where the static data table will be displayed. (Default: top_right)
Plot 50/200 SMAs (Checkbox): Toggles the visibility of the Fast and Slow SMA lines on the chart. (Default: On)
Fast MA Length (Integer Input): Sets the lookback period for the Fast Simple Moving Average. (Default: 50)
Slow MA Length (Integer Input): Sets the lookback period for the Slow Simple Moving Average. (Default: 200)
Show Daily Cross Count (Checkbox): Toggles the visibility of the label displaying the number of SMA crosses detected during the current day's session. (Default: On)
5. How to Use / Interpretation
Apply the indicator to your desired chart (e.g., SPY, QQQ, /ES).
Use the plotted SMA lines (if enabled) and the red triangle/background signals to identify potential trend changes indicated by Death Crosses based on your chosen MA lengths. Remember that these are lagging indicators.
Refer to the static data table for historical context only. It shows how the S&P 500 performed following specific Death Crosses between 2016 and 2022. This data is fixed and does not predict future performance.
Use the "Today's SMA Crosses" label (if enabled) to gauge the frequency of interaction between the chosen SMAs during the current session. A higher number might indicate choppier conditions or potential shifts on the chart's timeframe.
Important: For the Daily Cross Counter to reflect pre-market/after-hours activity, ensure "Extended Trading Hours" (ETH) is enabled in your TradingView chart settings.
Be aware that the number of crosses detected by the script (based on bar closes) may differ from visual interpretations of lines touching intraday, especially on lower timeframes.
6. Limitations
Static Table Data: The performance data in the table is fixed to the 4 provided historical instances (2016-2022) and is not calculated dynamically or updated. It serves only as a historical reference point.
Lagging Indicators: Moving Averages and their crosses are lagging indicators and may not signal trend changes precisely at tops or bottoms.
Cross Calculation: Crosses are based on the closing price of each bar. Intraday price movements briefly piercing an SMA may not register as a confirmed cross.
Daily Counter Definition: The definition of "Today" depends on the chart's session timing, which might not align perfectly with a calendar day.
Whipsaws: On lower timeframes or during volatile periods, MA crosses can generate frequent signals (whipsaws) which may be less reliable.
7. Disclaimer
This indicator is provided for informational and educational purposes only. It does not constitute financial advice or a recommendation to buy or sell any security. Trading involves significant risk, and past performance (including the historical data presented in the table) is not indicative of future results. Always conduct your own thorough research and consult with a qualified financial advisor before making any trading decisions.
The Silver Lining – GSR🍯 This tool converts the Gold/Silver Ratio (GSR) into a precision timing lens for short-term traders operating inside digital silver markets. It reveals structural dominance, trend exhaustion, and regime inflection by comparing the GSR to its smoothed baseline and historical percentile rhythm. On high timeframes (1D+), it reflects macroeconomic sentiment shifts 📈.
🧐 The lower the timeframe, the higher the alpha; the 15m and 1h charts are where you will the hidden pots of gold. For LTF traders, it becomes a hyper-responsive bias filter — especially when paired with volatility-based confirmation systems like SUPeR TReND 2.718, as shown.
🧠 The core logic compares the GSR (gold ÷ silver) against a user-defined moving average (VWMA or EMA). A color-coded fill shifts based on direction: amber when gold leads, teal when silver gains strength. Percentile bands (20th, 50th, 80th) map structural zones — helping traders anchor trades based on confluence, not hype.
📊 In the example chart, four theoretical long trades are shown on the 1h chart, manually drawn on the 15m timeframe. Each begins when the GSR reverses from the 80th percentile or breaks below its MA. The trades occur precisely as silver tested support, with confirmation from SUPeR TReND’s trend shift. Although idealized, these aren’t guesses — they are compression-to-expansion sequences backed by macro relative strength flow. Several yielded gains exceeding 4%.
🏆 Best-case long trades occur when GSR rotates down through the 50th percentile and silver catches a reactive bid. Shorts appear when GSR rises through the upper percentile band while silver fails to hold key intraday levels. The percentile bands function like behavioral tiers:
🥈 Below 20th = Silver Dominance
⚠️ Around 50th = Crossover Area
🥇 Above 80th = Gold Dominance
🥈 Why silver? It’s faster, more emotional, and more manipulated than gold — which paradoxically makes it more tradable on low timeframes. Its range-bound nature is ideal for rinse-and-repeat systems. Because we trade the derivative (XAGUSD), there’s no friction or delivery constraint — just price action, clean and liquid.
⚖️ The underlying strategy isn’t just technical; it’s alchemical. The system begins with short-term trading in digital silver and funnels gains into physical gold — converting volatility into wealth. Over time, this establishes a perpetual motion model: when profits allow, trade silver, extract value, cash out and convert into gold. The account stays active, and the hedge keeps growing.
🔁 The Silver Lining isn’t a signal engine. It’s a structural overlay. It tells you when the market’s invisible bias is shifting — so your tactics stay aligned with macro rhythm.
🌊 Silver moves fast. Gold moves first. The Silver Lining helps you bridge that gap — with clarity, confluence, and edge.
Multi-Band Comparison (Uptrend)Multi-Band Comparison
Overview:
The Multi-Band Comparison indicator is engineered to reveal critical levels of support and resistance in strong uptrends. In a healthy upward market, the price action will adhere closely to the 95th percentile line (the Upper Quantile Band), effectively “riding” it. This indicator combines a modified Bollinger Band (set at one standard deviation), quantile analysis (95% and 5% levels), and power‑law math to display a dynamic picture of market structure—highlighting a “golden channel” and robust support areas.
Key Components & Calculations:
The Golden Channel: Upper Bollinger Band & Upper Std Dev Band of the Upper Quantile
Upper Bollinger Band:
Calculation:
boll_upper=SMA(close,length)+(boll_mult×stdev)
boll_upper=SMA(close,length)+(boll_mult×stdev) Here, the 20-period SMA is used along with one standard deviation of the close, where the multiplier (boll_mult) is 1.0.
Role in an Uptrend:
In a healthy uptrend, price rides near the 95th percentile line. When price crosses above this Upper Bollinger Band, it confirms strong bullish momentum.
Upper Std Dev Band of the Upper Quantile (95th Percentile) Band:
Calculation:
quant_upper_std_up=quant_upper+stdev
quant_upper_std_up=quant_upper+stdev The Upper Quantile Band, quant_upperquant_upper, is calculated as the 95th percentile of recent price data. Adding one standard deviation creates an extension that accounts for normal volatility around this extreme level.
The Golden Channel:
When the price crosses above the Upper Bollinger Band, the Upper Std Dev Band of the Upper Quantile immediately shifts to gold (yellow) and remains gold until price falls below the Bollinger level. Together, these two lines form the “golden channel”—a visual hallmark of a healthy uptrend where the price reliably hugs the 95th percentile level.
Upper Power‑Law Band
Calculation:
The Upper Power‑Law Band is derived in two steps:
Determine the Extreme Return Factor:
power_upper=Percentile(returns,95%)
power_upper=Percentile(returns,95%) where returns are computed as:
returns=closeclose −1.
returns=close close−1.
Scale the Current Price:
power_upper_band=close×(1+power_upper)
power_upper_band=close×(1+power_upper)
Rationale and Correlation:
By focusing on the upper 5% of returns (reflecting “fat tails”), the Upper Power‑Law Band captures extreme but statistically expected movements. In an uptrend, its value often converges with the Upper Std Dev Band of the Upper Quantile because both measures reflect heightened volatility and extreme price levels. When the Upper Power‑Law Band exceeds the Upper Std Dev Band, it can signal a temporary overextension.
Upper Quantile Band (95% Percentile)
Calculation:
quant_upper=Percentile(price,95%)
quant_upper=Percentile(price,95%) This level represents where 95% of past price data falls below, and in a robust uptrend the price action practically rides this line.
Color Logic:
Its color shifts from a neutral (blackish) tone to a vibrant, bullish hue when the Upper Power‑Law Band crosses above it—signaling extra strength in the trend.
Lower Quantile and Its Support
Lower Quantile Band (5% Percentile):
Calculation:
quant_lower=Percentile(price,5%)
quant_lower=Percentile(price,5%)
Behavior:
In a healthy uptrend, price remains well above the Lower Quantile Band. It turns red only when price touches or crosses it, serving as a warning signal. Under normal conditions it remains bright green, indicating the market is not nearing these extreme lows.
Lower Std Dev Band of the Lower Quantile:
This line is calculated by subtracting one standard deviation from quant_lowerquant_lower and typically serves as absolute support in nearly all conditions (except during gap or near-gap moves). Its consistent role as support provides traders with a robust level to monitor.
How to Use the Indicator:
Golden Channel and Trend Confirmation:
As price rides the Upper Quantile (95th percentile) perfectly in a healthy uptrend, the Upper Bollinger Band (1 stdev above SMA) and the Upper Std Dev Band of the Upper Quantile form a “golden channel” once price crosses above the Bollinger level. When this occurs, the Upper Std Dev Band remains gold until price dips back below the Bollinger Band. This visual cue reinforces trend strength.
Power‑Law Insights:
The Upper Power‑Law Band, which is based on extreme (95th percentile) returns, tends to align with the Upper Std Dev Band. This convergence reinforces that extreme, yet statistically expected, price moves are occurring—indicating that even though the price rides the 95th percentile, it can only stretch so far before a correction or consolidation.
Support Indicators:
Primary and Secondary Support in Uptrends:
The Upper Bollinger Band and the Lower Std Dev Band of the Upper Quantile act as support zones for minor retracements in the uptrend.
Absolute Support:
The Lower Std Dev Band of the Lower Quantile serves as an almost invariable support area under most market conditions.
Conclusion:
The Multi-Band Comparison indicator unifies advanced statistical techniques to offer a clear view of uptrend structure. In a healthy bull market, price action rides the 95th percentile line with precision, and when the Upper Bollinger Band is breached, the corresponding Upper Std Dev Band turns gold to form a “golden channel.” This, combined with the Power‑Law analysis that captures extreme moves, and the robust lower support levels, provides traders with powerful, multi-dimensional insights for managing entries, exits, and risk.
Disclaimer:
Trading involves risk. This indicator is for educational purposes only and does not constitute financial advice. Always perform your own analysis before making trading decisions.
Enhanced Economic Composite with Dynamic WeightEnhanced Economic Composite with Dynamic Weight
Overview of the Indicator :
The "Enhanced Economic Composite with Dynamic Weight" is a comprehensive tool that combines multiple economic indicators, technical signals, and dynamic weighting to provide insights into market and economic health. It adjusts based on current volatility and recession risk, offering a detailed view of market conditions.
What This Indicator Does :
Tracks Economic Health: Uses key economic and market indicators to assess overall market conditions.
Dynamic Weighting: Adjusts the importance of components like stock indices, gold, and bonds based on volatility (VIX) and yield curve inversion.
Technical Signals: Identifies market momentum shifts through key crossovers like the Golden Cross, Death Cross, Silver Cross, and Hospice Cross.
Recession Shading: Marks known recessions for historical context.
Economic Factors Considered :
TIP (Treasury Inflation-Protected Securities): Reflects inflation expectations.
Gold: A safe-haven asset, increases in weight during volatility or rising momentum.
US Dollar Index (DXY): Measures USD strength, fixed weight of 10%, smoothed with EMA.
Commodities (DBC): Indicates global demand; weight increases with momentum or volatility.
Volatility Index (VIX): Reflects market risk, inversely related to market confidence.
Stock Indices (S&P 500, DJIA, NASDAQ, Russell 2000): Represent market performance, with weights reduced during high volatility or negative yield spread.
Yield Spread (10Y - 2Y Treasuries): Predicts recessions; negative spread reduces stock weighting.
Credit Spread (HYG - TLT): Indicates market risk through corporate vs. government bond yields.
How and Why Factors are Weighted:
Stock Indices get more weight in stable markets (low VIX, positive yield spread), while safe-haven assets like gold and bonds gain weight in volatile markets or during yield curve inversions. This dynamic adjustment ensures the composite reflects current market sentiment.
Technical Signals:
Golden Cross: 50 EMA crossing above 200 SMA, signaling bullish momentum.
Death Cross: 50 EMA below 200 SMA, indicating bearish momentum.
Silver Cross: 21 EMA crossing above 50 EMA, plotted only if below the 200-day SMA, signaling potential upside in downtrend conditions.
Hospice Cross: 50 EMA crosses below 21 EMA, plotted only if 21 EMA is below 200 SMA, a leading bearish signal.
Recession Shading:
Recession periods like the Great Recession, Early 2000s Recession, and COVID-19 Recession are shaded to provide historical context.
Benefits of Using This Indicator:
Comprehensive Analysis: Combines economic fundamentals and technical analysis for a full market view.
Dynamic Risk Adjustment: Weights shift between growth and safe-haven assets based on volatility and recession risk.
Early Signals: The Silver Cross and Hospice Cross provide early warnings of potential market shifts.
Recession Forecasting: Helps predict downturns through the yield curve and recession indicators.
Who Can Benefit:
Traders: Identify market momentum shifts early through crossovers.
Long-term Investors: Use recession warnings and dynamic adjustments to protect portfolios.
Analysts: A holistic tool for analyzing both economic trends and market movements.
This indicator helps users navigate varying market conditions by dynamically adjusting based on economic factors and providing early technical signals for market momentum shifts.
STRX - Macro TimesSTRX - Macro Times
The STRX - Macro Times is an advanced indicator designed to highlight key moments in financial markets based on specific macroeconomic time frames for Forex, Indices, and Gold. With this tool, you can optimize your trading decisions by monitoring periods of increased volatility and activity in the markets, leveraging the most strategic time windows to operate.
Key Features:
Highlighting Forex, Indices, and Gold Sessions:
The STRX - Macro Times automatically colors the candles on the chart during crucial time intervals for Forex, Indices, and Gold markets, helping you easily spot periods of heightened economic and financial activity. This allows you to focus on times when the market is most liquid and volatile, enhancing your trading performance.
Pre-set Macro Times:
The indicator is programmed to highlight three different key time windows for each market:
Forex: Major sessions from 8:30 to 10:00, 12:00 to 13:00, and 15:00 to 15:30.
Indices: Key times from 9:00 to 10:00, 15:45 to 16:15, and 19:00 to 20:00.
Gold: Strategic moments from 8:30 to 10:00, 14:30 to 16:00, and 20:00 to 21:30.
Total Customization:
You can enable or disable the coloring for different markets (Forex, Indices, Gold) based on your trading preferences. This allows you to focus only on the markets you follow, simplifying chart analysis and optimizing your response time to market changes.
Clear and Intuitive Visual Coloring:
The chart bars are colored in white, creating a clear visual distinction to recognize the most relevant time windows. This makes it easy to identify macroeconomic periods without wasting time manually calculating opportunity windows.
With STRX - Macro Times, you’ll have a strategic advantage in trading by focusing on periods of high volatility and improving the efficiency of your operations in the most active markets. This indicator is perfect for those looking to enhance their strategy and operate in sync with the key moments of the global market.
GKD-C Wavelet Oscillator [Loxx]The Giga Kaleidoscope GKD-C Wavelet Oscillator is a Confirmation module included in AlgxTrading's "Giga Kaleidoscope Modularized Trading System."
█ GKD-C Wavelet Oscillator, a brief overview
The Wavelet Oscillator is an advanced technical analysis tool that integrates wavelet transformations with the Kalman filter to provide a nuanced understanding of market trends and momentum. At the heart of this oscillator is the Haar wavelet transform, a mathematical technique that breaks down price data into different frequency components. The Haar transform works by analyzing the price series in pairs, calculating the average and difference between adjacent data points, effectively separating the underlying signal (trend) from noise or minor fluctuations. This decomposition allows the oscillator to isolate significant price movements and reconstruct them with greater clarity through the inverse Haar transform. The Kalman filter is then applied to further smooth the signal, refining the data and reducing the impact of short-term volatility.
This process enhances the oscillator's ability to detect subtle shifts in market dynamics that might be missed by conventional indicators. The GKD-C Wavelet Oscillator utilizes these refined signals to generate two types of trading signals: Zero-line crosses, where the oscillator moves above or below a central reference point, indicating potential bullish or bearish momentum, and Signal crosses, where the current oscillator value crosses its previous value, signaling possible trend reversals. These features make the Wavelet Oscillator particularly effective in identifying key turning points in the market, providing traders with a powerful tool for anticipating and responding to changes in price momentum within the GKD trading system. (Read the sections below to learn how traders can test these different signal types using AlgxTrading's GKD trading system.)
GKD-C Wavelet Oscillator in Zero-line crosses mode
GKD-C Wavelet Oscillator in Signal crosses mode
To explain the features included in the GKD-C Wavelet Oscillator, let's first dive into the details of the Giga Kaleidoscope (GKD) Modularized Trading System.
█ Giga Kaleidoscope (GKD) Modularized Trading System
The GKD Trading System is a comprehensive, algorithmic trading framework from AlgxTrading, designed to optimize trading strategies across various market conditions. It employs a modular approach, incorporating elements such as volatility assessment, trend identification through a baseline, multiple confirmation strategies for signal accuracy, and volume analysis. Key components also include specialized strategies for entry and exit, enabling precise trade execution. The system allows for extensive backtesting, providing traders with the ability to evaluate the effectiveness of their strategies using historical data. Aimed at reducing setup time, the GKD system empowers traders to focus more on strategy refinement and execution, leveraging a wide array of technical indicators for informed decision-making.
🔶 Core components of a GKD Algorithmic Trading System
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system. The GKD algorithm is built on the principles of trend, momentum, and volatility. There are eight core components in the GKD trading algorithm:
🔹 Volatility - In the GKD trading system, volatility is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. There are 17+ different types of volatility available in the GKD system including Average True Range (ATR), True Range Double (TRD), Close-to-Close, Garman-Klass, and more.
🔹 Baseline (GKD-B) - The baseline is essentially a moving average and is used to determine the overall direction of the market. The baseline in the GKD trading system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other GKD indicators.
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards or price is above the baseline, then only long trades are taken, and if the baseline is sloping downwards or price is below the baseline, then only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
🔹 Confirmation 1, Confirmation 2, Continuation (GKD-C) - The GKD trading system incorporates technical confirmation indicators for the generation of its primary long and short signals, essential for its operation.
The GKD trading system distinguishes three specific categories. The first category, Confirmation 1, encompasses technical indicators designed to identify trends and generate explicit trading signals. The second category, Confirmation 2, a technical indicator used to identify trends; this type of indicator is primarily used to filter the Confirmation 1 indicator signals; however, this type of confirmation indicator also generates signals*. Lastly, the Continuation category includes technical indicators used in conjunction with Confirmation 1 and Confirmation 2 to generate a special type of trading signal called a "Continuation"
In a full GKD trading system all three categories generate signals. (see the section “GKD Trading System Signals” below)
🔹 Volatility/Volume (GKD-V) - Volatility/Volume indicators are used to measure the amount of buying and selling activity in a market. They are based on the trading Volatility/Volume of the market, and can provide information about the strength of the trend. In the GKD trading system, Volatility/Volume indicators are used to confirm trading signals generated by the various other GKD indicators. In the GKD trading system, Volatility is a proxy for Volume and vice versa.
Volatility/Volume indicators reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by GKD-C confirmation and GKD-B baseline indicators.
🔹 Exit (GKD-E) - The exit indicator in the GKD system is an indicator that is deemed effective at identifying optimal exit points. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
🔹 Backtest (GKD-BT) - The GKD-BT backtest indicators link all other GKD-C, GKD-B, GKD-E, GKD-V, and GKD-M components together to create a GKD trading system. GKD-BT backtests generate signals (see the section “GKD Trading System Signals” below) from the confluence of various GKD indicators that are imported into the GKD-BT backtest. Backtest types include: GKD-BT solo and full GKD backtest strategies used for a single ticker; GKD-BT optimizers used to optimize a single indicator or the full GKD trading system; GKD-BT Multi-ticker used to backtest a single indicator or the full GKD trading system across up to ten tickers; GKD-BT exotic backtests like CC, Baseline, and Giga Stacks used to test confluence between GKD components to then be injected into a core GKD-BT Multi-ticker backtest or single ticker strategy.
🔹 Metamorphosis (GKD-M)** - The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, GKD-E, or GKD-V slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
*(see the section “GKD Trading System Signals” below)
**(not a required component of the GKD algorithm)
🔶 What does the application of the GKD trading system look like?
Example trading system:
Volatility: Average True Range (ATR) (selectable in all backtests and other related GKD indicators)
GKD-B Baseline: GKD-B Multi-Ticker Baseline using Hull Moving Average
GKD-C Confirmation 1: GKD-C Advance Trend Pressure
GKD-C Confirmation 2: GKD-C Dorsey Inertia
GKD-C Continuation: GKD-C Stochastic of RSX
GKD-V Volatility/Volume: GKD-V Damiani Volatmeter
GKD-E Exit: GKD-E MFI
GKD-BT Backtest: GKD-BT Multi-Ticker Full GKD Backtest
GKD-M Metamorphosis: GKD-M Baseline Optimizer
**all indicators mentioned above are included in the same AlgxTrading package**
Each module is passed to a GKD-BT backtest module. In the backtest module, all components are combined to formulate trading signals and statistical output. This chaining of indicators requires that each module conform to AlgxTrading's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the various indictor types in the GKD algorithm.
🔶 GKD Trading System Signals
🔹 Standard Entry requires a sequence of conditions including a confirmation signal from GKD-C, baseline agreement, price criteria related to the Goldie Locks Zone, and concurrence from a second confirmation and volatility/volume indicators.
🔹 1-Candle Standard Entry introduces a two-phase process where initial conditions must be met, followed by a retraction in price and additional confirmations in the subsequent candle, including baseline, confirmations 1 and 2, and volatility/volume criteria.
🔹 Baseline Entry focuses on signals generated by the GKD-B Baseline, requiring agreement from confirmation signals, specific price conditions within the Goldie Locks Zone, and a timing condition related to the confirmation 1 signal.
🔹 1-Candle Baseline Entry mirrors the baseline entry but adds a requirement for a price retraction and subsequent confirmations in the following candle, maintaining the focus on the baseline's guidance.
🔹 Volatility/Volume Entry is predicated on signals from volatility/volume indicators, requiring support from confirmations, price criteria within the Goldie Locks Zone, baseline agreement, and a timing condition for the confirmation 1 signal.
🔹 1-Candle Volatility/Volume Entry adapts the volatility/volume entry to include a phase of initial signal and agreement, followed by a retracement phase that seeks further agreement from the system's components in the subsequent candle.
🔹 Confirmation 2 Entry is based on the second confirmation signal, requiring the first confirmation's agreement, specific price criteria, agreement from volatility/volume indicators, and baseline, with a timing condition for the confirmation 1 signal.
🔹 1-Candle Confirmation 2 Entry adds a retracement requirement to the confirmation 2 entry, necessitating additional agreements from the system's components in the candle following the signal.
🔹 PullBack Entry initiates with a baseline signal and agreement from the first confirmation, with a price condition related to volatility. It then looks for price to return within the Goldie Locks Zone and seeks further agreement from the system's components in the subsequent candle.
🔹 Continuation Entry allows for the continuation of an active position, based on a previously triggered entry strategy. It requires that the baseline hasn't crossed since the initial trigger, alongside ongoing agreements from confirmations and the baseline.
█ GKD-C Wavelet Oscillator, a deep dive
Now that you have a basic understanding of the GKD trading system. let's dive deeper into the features included in the GKD-C Wavelet Oscillator
🔶 GKD-C Wavelet Oscillator Modes aka "Confirmation Type"
The GKD-C Wavelet Oscillator has 4 modes: Confirmation for confirmation 1 and 2; Continuation; Multi-ticker for multi-ticker confirmation 1 and 2; and Optimizer.
🔹 Confirmation: When in this mode, the GKD-C Wavelet Oscillator generates confirmation 1 and 2 signals. These values can then be exported to a GKD-BT backtest strategy.
Signal Key: L = Long, S = Short
GKD-C Wavelet Oscillator in Confirmation mode
Confirmation Exports
GKD-C Wavelet Oscillator in attached to a GKD-BT backtest strategy
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolated per ticker and trading side, long or short**
🔹 Continuation: When in this mode, the GKD-C Wavelet Oscillator generates continuation signals.
Signal Key: L = Long, S = Short, CL = Continuation Long, CS = Continuation Short
GKD-C Wavelet Oscillator in Continuation mode
Continuation Exports
🔹 Multi-ticker: When in this mode, the GKD-C Wavelet Oscillator generates multi-ticker confirmation 1 and 2. This mode allows users to generate confirmation 1 and 2, and continuation signals for up to 10 different tickers. These values can then be exported to a GKD-BT Multi-ticker backtest.
Signal Key: L = Long, S = Short
GKD-C Wavelet Oscillator in Multi-ticker mode
Multi-ticker Exports
GKD-C Wavelet Oscillator attached to the GKD-BT Multi-ticker SCS Backtest
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolated per ticker and trading side, long or short**
🔹 Optimizer: When in this mode, the GKD-C Wavelet Oscillator generates optimization signals. These signals allow the user to backtest a range of input values. These values are exported to a GKD-BT optimizer backtest.
Signal Key: L = Long, S = Short
GKD-C Wavelet Oscillator in Optimizer mode
Optimizer Inputs and Exports
GKD-C Wavelet Oscillator attacked to the GKD-BT Optimizer SCS Backtest
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolated per ticker and trading side, long or short**
█ Conclusion
The GKD-C Wavelet Oscillator serves as a multi-modal component of the GKD trading system allowing traders to optimize and backtest acorss a range of input parameters and tickers. These features decrease total build time required to create a custom GKD algorithmic trading system by allowing users to spend more time trading and less time guessing.
█ How to Access
You can see the Author's Instructions below to learn how to get access.
GKD-BT Optimizer SCSC Backtest [Loxx]The Giga Kaleidoscope GKD-BT Optimizer SCSC Backtest (Solo Confirmation Super Complex) is a Backtest module included in AlgxTrading's "Giga Kaleidoscope Modularized Trading System." (see the section Giga Kaleidoscope (GKD) Modularized Trading System below for an explanation of the GKD trading system)
**the backtest data rendered to the chart above and all screenshots below use $5 commission per trade and 10% equity per trade with $1 million initial capital**
█ GKD-BT Optimizer SCSC Backtest
The GKD-BT Optimizer SCSC Backtest is a comprehensive backtesting module designed to optimize the combination of key GKD indicators within AlgxTrading's "Giga Kaleidoscope Modularized Trading System." This module facilitates precise strategy refinement by allowing traders to configure and optimize the following critical GKD indicators:
GKD-B Baseline
GKD-V Volatility/Volume
GKD-C Confirmation 1
GKD-C Continuation
Each indicator is equipped with an "Optimizer" mode, enabling dynamic feedback and iterative improvements directly into the backtesting environment. This integrated approach ensures that each component contributes effectively to the overall strategy, providing a robust framework for achieving optimized trading outcomes.
The GKD-BT Optimizer supports granular test configurations including a single take profit and stop loss setting, and allows for targeted testing within specified date ranges to simulate forward testing with historical data. This feature is essential for evaluating the resilience and effectiveness of trading strategies under various market conditions.
Furthermore, the module is designed with user-centric features such as:
Customizable Trading Panel: Displays critical backtest results and trade statistics, which can be shown or hidden as per user preference.
Highlighting Thresholds: Users can set thresholds for Total Percent Wins, Percent Profitable, and Profit Factor, which helps in quickly identifying the most relevant metrics for analysis.
The detailed setup ensures that traders can not only adjust their strategies based on historical performance but also fine-tune their approach to meet specific trading objectives.
🔶 To configure this indicator: ***all GKD indicators listed below are all included in the AlgxTrading trading system package***
1. Add GKD-C Confirmation, GKD-B Baseline, GKD-V Volatility/Volume, and GKD-C Continuation to your chart
2. In the GKD-B Baseline indicator, change "Baseline Type" to "Optimizer"
3. In the GKD-V Volatility/Volume indicator, change "Volatility/Volume Type" to "Optimizer"
4. In the GKD-C Confirmation 1 indicator, change "Confirmation Type" to "Optimizer"
5. In the GKD-C Continuation indicator, change "Confirmation Type" to "Optimizer"
An example of steps 2-5. In the screenshot example below, we change the value "Confirmation Type" in the GKD-C Fisher Transform indicator to "Optimizer"
6. In the GKD-BT Optimizer SCSC Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline indicator into the field "Import GKD-B Baseline indicator"
7. In the GKD-BT Optimizer SCSC Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume indicator into the field "Import GKD-V Volatility/Volume indicator"
8. In the GKD-BT Optimizer SCSC Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 1 indicator into the field "Import GKD-C Confirmation 1 indicator"
9. In the GKD-BT Optimizer SCSC Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Continuation indicator into the field "Import GKD-C Continuation indicator"
An example of steps 6-9. In the screenshot example below, we import the value "Input into NEW GKD-BT Backtest" from the GKD-C Fisher Transform indicator into the GKD-BT Optimizer SCSC Backtest
10. Decide which of the 5 indicators you wish to optimize in first in the GKD-BT Optimizer SCSC Backtest. Change the value of the import from "Input into NEW GKD-BT Backtest" to "Input into NEW GKD-BT Optimizer Signals"
An example of step 10. In the screenshot example below, we chose to optimize the Confirmation 1 indicator, the GKD-C Fisher Transform. We change the value of the field "Import GKD-C Confirmation 1 indicator" from "Input into NEW GKD-BT Backtest" to "Input into NEW GKD-BT Optimizer Signals"
11. In the GKD-BT Optimizer SCSC Backtest and under the "Optimization Settings", use the dropdown menu "Optimization Indicator" to select the type of indicator you selected from step 12 above: "Baseline", "Volatility/Volume", "Confirmation 1", or "Continuation"
12. In the GKD-BT Optimizer SCSC Backtest and under the "Optimization Settings", import the value "Input into NEW GKD-BT Optimizer Start" from the indicator you selected to optimize in step 12 above into the field "Import Optimization Indicator Start"
13. In the GKD-BT Optimizer SCSC Backtest and under the "Optimization Settings", import the value "Input into NEW GKD-BT Optimizer Skip" from the indicator you selected to optimize in step 12 above into the field "Import Optimization Indicator Skip"
An example of step 11. In the screenshot example below, we select "Confirmation 1" from the "Optimization Indicator" dropdown menu
An example of steps 12 and 13. In the screenshot example below, we import "Import Optimization Indicator Start" and "Import Optimization Indicator Skip" from the GKD-C Fisher Transform indicator into their respective fields
🔶 This backtest includes the following metrics
Net profit: Overall profit or loss achieved.
Total Closed Trades: Total number of closed trades, both winning and losing.
Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
🔶 Summary of notable settings not already explained above
🔹 Backtest Properties
These settings define the financial and logistical parameters of the trading simulation, including:
Initial Capital: Specifies the starting balance for the backtest, setting the baseline for measuring profitability and loss.
Order Size: Determines the size of trades, which can be fixed or a percentage of the equity, affecting risk and return.
Order Type: Chooses between fixed contract sizes or a percentage-based order size, allowing for static or dynamic trading volumes.
Commission per Order: Accounts for trading costs, subtracting these from profits to provide a more accurate net performance result.
🔹 Signal Qualifiers
This group of settings establishes criteria related to the strategy's Baseline, and Volatility/Volume indicators in relation to the GKD-C Confirmation 1 indicator, which is crucial for validating trade signals. These include:
Maximum Allowable Post Signal Baseline Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the trend position of the Baseline, then should the Baseline "catch-up" to the long/short trend of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
Maximum Allowable Post Signal Volatility/Volume Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the position of the Volatility/Volume, then should the Volatility/Volume "catch-up" with the long/short of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
🔹 Signal Settings
Signal Options: These settings allow users to toggle the visibility of different types of entries based on the strategy criteria, such as standard entries, baseline entries, and continuation entries.
Standard Entry Rules Settings: Detailed criteria for standard entries can be customized here, including conditions on baseline agreement, price within specific zones, and agreement with other confirmation indicators.
1-Candle Rule Standard Entry Rules Settings: Similar to standard entries, but with a focus on conditions that must be met within a one-candle timeframe.
Baseline Entry Rules Settings: Specifies rules for entries based on the baseline, including conditions on confirmation agreement and price zones.
Volatility/Volume Entry Rules Settings: This includes settings for entries based on volatility or volume conditions, with specific rules on confirmation agreement and baseline agreement.
Continuation Entry Rules Settings: This group outlines the conditions for continuation entries, focusing on agreement with baseline and confirmation indicators since the entry signal trigger.
🔹 Volatility Settings
Volatility PnL Settings: Parameters for defining the type of volatility measure to use, its period, and multipliers for profit and stop levels.
Volatility Types Included
Standard Deviation of Logarithmic Returns: Quantifies asset volatility using the standard deviation applied to logarithmic returns, capturing symmetric price movements and financial returns' compound nature.
Exponential Weighted Moving Average (EWMA) for Volatility: Focuses on recent market information by applying exponentially decreasing weights to squared logarithmic returns, offering a dynamic view of market volatility.
Roger-Satchell Volatility Measure: Estimates asset volatility by analyzing the high, low, open, and close prices, providing a nuanced view of intraday volatility and market dynamics.
Close-to-Close Volatility Measure: Calculates volatility based on the closing prices of stocks, offering a streamlined but limited perspective on market behavior.
Parkinson Volatility Measure: Enhances volatility estimation by including high and low prices of the trading day, capturing a more accurate reflection of intraday market movements.
Garman-Klass Volatility Measure: Incorporates open, high, low, and close prices for a comprehensive daily volatility measure, capturing significant price movements and market activity.
Yang-Zhang Volatility Measure: Offers an efficient estimation of stock market volatility by combining overnight and intraday price movements, capturing opening jumps and overall market dynamics.
Garman-Klass-Yang-Zhang Volatility Measure: Merges the benefits of Garman-Klass and Yang-Zhang measures, providing a fuller picture of market volatility including opening market reactions.
Pseudo GARCH(2,2) Volatility Model: Mimics a GARCH(2,2) process using exponential moving averages of squared returns, highlighting volatility shocks and their future impact.
ER-Adaptive Average True Range (ATR): Adjusts the ATR period length based on market efficiency, offering a volatility measure that adapts to changing market conditions.
Adaptive Deviation: Dynamically adjusts its calculation period to offer a nuanced measure of volatility that responds to the market's intrinsic rhythms.
Median Absolute Deviation (MAD): Provides a robust measure of statistical variability, focusing on deviations from the median price, offering resilience against outliers.
Mean Absolute Deviation (MAD): Measures the average magnitude of deviations from the mean price, facilitating a straightforward understanding of volatility.
ATR (Average True Range): Finds the average of true ranges over a specified period, indicating the expected price movement and market volatility.
True Range Double (TRD): Offers a nuanced view of volatility by considering a broader range of price movements, identifying significant market sentiment shifts.
🔹 Other Settings
Backtest Dates: Users can specify the timeframe for the backtest, including start and end dates, as well as the acceptable entry time window.
Volatility Inputs: Additional settings related to volatility calculations, such as static percent, internal filter period for median absolute deviation, and parameters for specific volatility models.
UI Options: Settings to customize the user interface, including table activation, date panel visibility, and aesthetics like color and text size.
Export Options: Allows users to select the type of data to export from the backtest, focusing on metrics like net profit, total closed trades, and average profit per trade.
█ Giga Kaleidoscope (GKD) Modularized Trading System
The GKD Trading System is a comprehensive, algorithmic trading framework from AlgxTrading, designed to optimize trading strategies across various market conditions. It employs a modular approach, incorporating elements such as volatility assessment, trend identification through a baseline, multiple confirmation strategies for signal accuracy, and volume analysis. Key components also include specialized strategies for entry and exit, enabling precise trade execution. The system allows for extensive backtesting, providing traders with the ability to evaluate the effectiveness of their strategies using historical data. Aimed at reducing setup time, the GKD system empowers traders to focus more on strategy refinement and execution, leveraging a wide array of technical indicators for informed decision-making.
🔶 Core components of a GKD Algorithmic Trading System
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system. The GKD algorithm is built on the principles of trend, momentum, and volatility. There are eight core components in the GKD trading algorithm:
🔹 Volatility - In the GKD trading system, volatility is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. There are 17+ different types of volatility available in the GKD system including Average True Range (ATR), True Range Double (TRD), Close-to-Close, Garman-Klass, and more.
🔹 Baseline (GKD-B) - The baseline is essentially a moving average and is used to determine the overall direction of the market. The baseline in the GKD trading system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other GKD indicators.
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards or price is above the baseline, then only long trades are taken, and if the baseline is sloping downwards or price is below the baseline, then only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
🔹 Confirmation 1, Confirmation 2, Continuation (GKD-C) - The GKD trading system incorporates technical confirmation indicators for the generation of its primary long and short signals, essential for its operation.
The GKD trading system distinguishes three specific categories. The first category, Confirmation 1 , encompasses technical indicators designed to identify trends and generate explicit trading signals. The second category, Confirmation 2 , a technical indicator used to identify trends; this type of indicator is primarily used to filter the Confirmation 1 indicator signals; however, this type of confirmation indicator also generates signals*. Lastly, the Continuation category includes technical indicators used in conjunction with Confirmation 1 and Confirmation 2 to generate a special type of trading signal called a "Continuation"
In a full GKD trading system all three categories generate signals. (see the section “GKD Trading System Signals” below)
🔹 Volatility/Volume (GKD-V) - Volatility/Volume indicators are used to measure the amount of buying and selling activity in a market. They are based on the trading Volatility/Volume of the market, and can provide information about the strength of the trend. In the GKD trading system, Volatility/Volume indicators are used to confirm trading signals generated by the various other GKD indicators. In the GKD trading system, Volatility is a proxy for Volume and vice versa.
Volatility/Volume indicators reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by GKD-C confirmation and GKD-B baseline indicators.
🔹 Exit (GKD-E) - The exit indicator in the GKD system is an indicator that is deemed effective at identifying optimal exit points. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
🔹 Backtest (GKD-BT) - The GKD-BT backtest indicators link all other GKD-C, GKD-B, GKD-E, GKD-V, and GKD-M components together to create a GKD trading system. GKD-BT backtests generate signals (see the section “GKD Trading System Signals” below) from the confluence of various GKD indicators that are imported into the GKD-BT backtest. Backtest types include: GKD-BT solo and full GKD backtest strategies used for a single ticker; GKD-BT optimizers used to optimize a single indicator or the full GKD trading system; GKD-BT Multi-ticker used to backtest a single indicator or the full GKD trading system across up to ten tickers; GKD-BT exotic backtests like CC, Baseline, and Giga Stacks used to test confluence between GKD components to then be injected into a core GKD-BT Multi-ticker backtest or single ticker strategy.
🔹 Metamorphosis (GKD-M) ** - The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, GKD-E, or GKD-V slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
*see the section “GKD Trading System Signals” below
**not a required component of the GKD algorithm
🔶 What does the application of the GKD trading system look like?
Example trading system:
Volatility: Average True Range (ATR) (selectable in all backtests and other related GKD indicators)
GKD-B Baseline: GKD-B Multi-Ticker Baseline using Hull Moving Average
GKD-C Confirmation 1 : GKD-C Advance Trend Pressure
GKD-C Confirmation 2: GKD-C Dorsey Inertia
GKD-C Continuation: GKD-C Stochastic of RSX
GKD-V Volatility/Volume: GKD-V Damiani Volatmeter
GKD-E Exit: GKD-E MFI
GKD-BT Backtest: GKD-BT Multi-Ticker Full GKD Backtest
GKD-M Metamorphosis: GKD-M Baseline Optimizer
**all indicators mentioned above are included in the same AlgxTrading package**
Each module is passed to a GKD-BT backtest module. In the backtest module, all components are combined to formulate trading signals and statistical output. This chaining of indicators requires that each module conform to AlgxTrading's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the various indictor types in the GKD algorithm.
🔶 GKD Trading System Signals
Standard Entry requires a sequence of conditions including a confirmation signal from GKD-C, baseline agreement, price criteria related to the Goldie Locks Zone, and concurrence from a second confirmation and volatility/volume indicators.
1-Candle Standard Entry introduces a two-phase process where initial conditions must be met, followed by a retraction in price and additional confirmations in the subsequent candle, including baseline, confirmations 1 and 2, and volatility/volume criteria.
Baseline Entry focuses on signals generated by the GKD-B Baseline, requiring agreement from confirmation signals, specific price conditions within the Goldie Locks Zone, and a timing condition related to the confirmation 1 signal.
1-Candle Baseline Entry mirrors the baseline entry but adds a requirement for a price retraction and subsequent confirmations in the following candle, maintaining the focus on the baseline's guidance.
Volatility/Volume Entry is predicated on signals from volatility/volume indicators, requiring support from confirmations, price criteria within the Goldie Locks Zone, baseline agreement, and a timing condition for the confirmation 1 signal.
1-Candle Volatility/Volume Entry adapts the volatility/volume entry to include a phase of initial signal and agreement, followed by a retracement phase that seeks further agreement from the system's components in the subsequent candle.
Confirmation 2 Entry is based on the second confirmation signal, requiring the first confirmation's agreement, specific price criteria, agreement from volatility/volume indicators, and baseline, with a timing condition for the confirmation 1 signal.
1-Candle Confirmation 2 Entry adds a retracement requirement to the confirmation 2 entry, necessitating additional agreements from the system's components in the candle following the signal.
PullBack Entry initiates with a baseline signal and agreement from the first confirmation, with a price condition related to volatility. It then looks for price to return within the Goldie Locks Zone and seeks further agreement from the system's components in the subsequent candle.
Continuation Entry allows for the continuation of an active position, based on a previously triggered entry strategy. It requires that the baseline hasn't crossed since the initial trigger, alongside ongoing agreements from confirmations and the baseline.
█ Conclusion
The GKD-BT Optimizer SCSC Backtest is a critical tool within the Giga Kaleidoscope Modularized Trading System, designed for precise strategy refinement and evaluation within the GKD framework. It enables the optimization and testing of various trading indicators and strategies under different market conditions. The module's design facilitates detailed analysis of individual trading components' performance, allowing for the optimization of indicators like Baseline, Volatility/Volume, Confirmation, and Continuation. This optimization process aids traders in identifying the most effective configurations, thereby enhancing trading outcomes and strategy efficiency within the GKD ecosystem.
█ How to Access
You can see the Author's Instructions below to learn how to get access.
GKD-BT Optimizer Full GKD Backtest [Loxx]The Giga Kaleidoscope GKD-BT Optimizer Full GKD Backtest is a Backtest module included in AlgxTrading's "Giga Kaleidoscope Modularized Trading System." (see the section Giga Kaleidoscope (GKD) Modularized Trading System below for an explanation of the GKD trading system)
**the backtest data rendered to the chart above and all screenshots below use $5 commission per trade and 10% equity per trade with $1 million initial capital**
█ GKD-BT Optimizer Full GKD Backtest
The GKD-BT Optimizer Full GKD Backtest is a comprehensive backtesting module designed to optimize the combination of key GKD indicators within AlgxTrading's "Giga Kaleidoscope Modularized Trading System." This module facilitates precise strategy refinement by allowing traders to configure and optimize the following critical GKD indicators:
GKD-B Baseline
GKD-V Volatility/Volume
GKD-C Confirmation 1
GKD-C Confirmation 2
GKD-C Continuation
Each indicator is equipped with an "Optimizer" mode, enabling dynamic feedback and iterative improvements directly into the backtesting environment. This integrated approach ensures that each component contributes effectively to the overall strategy, providing a robust framework for achieving optimized trading outcomes.
The GKD-BT Optimizer supports granular test configurations including a single take profit and stop loss setting, and allows for targeted testing within specified date ranges to simulate forward testing with historical data. This feature is essential for evaluating the resilience and effectiveness of trading strategies under various market conditions.
Furthermore, the module is designed with user-centric features such as:
Customizable Trading Panel: Displays critical backtest results and trade statistics, which can be shown or hidden as per user preference.
Highlighting Thresholds: Users can set thresholds for Total Percent Wins, Percent Profitable, and Profit Factor, which helps in quickly identifying the most relevant metrics for analysis.
The detailed setup ensures that traders can not only adjust their strategies based on historical performance but also fine-tune their approach to meet specific trading objectives.
🔶 To configure this indicator: ***all GKD indicators listed below are all included in the AlgxTrading trading system package***
1. Add GKD-C Confirmation, GKD-B Baseline, GKD-V Volatility/Volume, GKD-C Confirmation 2, and GKD-C Continuation to your chart
2. In the GKD-B Baseline indicator, change "Baseline Type" to "Optimizer"
3. In the GKD-V Volatility/Volume indicator, change "Volatility/Volume Type" to "Optimizer"
4. In the GKD-C Confirmation 1 indicator, change "Confirmation Type" to "Optimizer"
5. In the GKD-C Confirmation 2 indicator, change "Confirmation Type" to "Optimizer"
6. In the GKD-C Continuation indicator, change "Confirmation Type" to "Optimizer"
An example of steps 2-6. In the screenshot example below, we change the value "Confirmation Type" in the GKD-C Fisher Transform indicator to "Optimizer"
7. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline indicator into the field "Import GKD-B Baseline indicator"
8. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume indicator into the field "Import GKD-V Volatility/Volume indicator"
9. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 1 indicator into the field "Import GKD-C Confirmation 1 indicator"
10. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 2 indicator into the field "Import GKD-C Confirmation 2 indicator"
11. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Continuation indicator into the field "Import GKD-C Continuation indicator"
An example of steps 7-11. In the screenshot example below, we import the value "Input into NEW GKD-BT Backtest" from the GKD-C Coppock Curve indicator into the GKD-BT Optimizer Full GKD Backtest
12. Decide which of the 5 indicators you wish to optimize in first in the GKD-BT Optimizer Full GKD Backtest. Change the value of the import from "Input into NEW GKD-BT Backtest" to "Input into NEW GKD-BT Optimizer Signals"
An example of step 12. In the screenshot example below, we chose to optimize the Confirmation 1 indicator, the GKD-C Fisher Transform. We change the value of the field "Import GKD-C Confirmation 1 indicator" from "Input into NEW GKD-BT Backtest" to "Input into NEW GKD-BT Optimizer Signals"
13. In the GKD-BT Optimizer Full GKD Backtest and under the "Optimization Settings", use the dropdown menu "Optimization Indicator" to select the type of indicator you selected from step 12 above: "Baseline", "Volatility/Volume", "Confirmation 1", "Confirmation 2", or "Continuation"
14. In the GKD-BT Optimizer Full GKD Backtest and under the "Optimization Settings", import the value "Input into NEW GKD-BT Optimizer Start" from the indicator you selected to optimize in step 12 above into the field "Import Optimization Indicator Start"
15. In the GKD-BT Optimizer Full GKD Backtest and under the "Optimization Settings", import the value "Input into NEW GKD-BT Optimizer Skip" from the indicator you selected to optimize in step 12 above into the field "Import Optimization Indicator Skip"
An example of step 13. In the screenshot example below, we select "Confirmation 1" from the "Optimization Indicator" dropdown menu
An example of steps 14 and 15. In the screenshot example below, we import "Import Optimization Indicator Start" and "Import Optimization Indicator Skip" from the GKD-C Fisher Transform indicator into their respective fields
🔶 This backtest includes the following metrics
Net profit: Overall profit or loss achieved.
Total Closed Trades: Total number of closed trades, both winning and losing.
Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
🔶 Summary of notable settings not already explained above
🔹 Backtest Properties
These settings define the financial and logistical parameters of the trading simulation, including:
Initial Capital: Specifies the starting balance for the backtest, setting the baseline for measuring profitability and loss.
Order Size: Determines the size of trades, which can be fixed or a percentage of the equity, affecting risk and return.
Order Type: Chooses between fixed contract sizes or a percentage-based order size, allowing for static or dynamic trading volumes.
Commission per Order: Accounts for trading costs, subtracting these from profits to provide a more accurate net performance result.
🔹 Signal Qualifiers
This group of settings establishes criteria related to the strategy's Baseline, Volatility/Volume, and Confirmation 2 indicators in relation to the GKD-C Confirmation 1 indicator, which is crucial for validating trade signals. These include:
Maximum Allowable Post Signal Baseline Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the trend position of the Baseline, then should the Baseline "catch-up" to the long/short trend of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
Maximum Allowable Post Signal Volatility/Volume Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the position of the Volatility/Volume, then should the Volatility/Volume "catch-up" with the long/short of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
Maximum Allowable Post Signal Confirmation 2 Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the trend position of the Confirmation 2, then should the Confirmation 2 "catch-up" to the long/short trend of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
🔹 Signal Settings
Signal Options: These settings allow users to toggle the visibility of different types of entries based on the strategy criteria, such as standard entries, baseline entries, and continuation entries.
Standard Entry Rules Settings: Detailed criteria for standard entries can be customized here, including conditions on baseline agreement, price within specific zones, and agreement with other confirmation indicators.
1-Candle Rule Standard Entry Rules Settings: Similar to standard entries, but with a focus on conditions that must be met within a one-candle timeframe.
Baseline Entry Rules Settings: Specifies rules for entries based on the baseline, including conditions on confirmation agreement and price zones.
Volatility/Volume Entry Rules Settings: This includes settings for entries based on volatility or volume conditions, with specific rules on confirmation agreement and baseline agreement.
Confirmation 2 Entry Rules Settings: Settings here define the rules for entries based on a second confirmation indicator, detailing the required agreements and conditions.
Continuation Entry Rules Settings: This group outlines the conditions for continuation entries, focusing on agreement with baseline and confirmation indicators since the entry signal trigger.
🔹 Volatility Settings
Volatility PnL Settings: Parameters for defining the type of volatility measure to use, its period, and multipliers for profit and stop levels.
Volatility Types Included
Standard Deviation of Logarithmic Returns: Quantifies asset volatility using the standard deviation applied to logarithmic returns, capturing symmetric price movements and financial returns' compound nature.
Exponential Weighted Moving Average (EWMA) for Volatility: Focuses on recent market information by applying exponentially decreasing weights to squared logarithmic returns, offering a dynamic view of market volatility.
Roger-Satchell Volatility Measure: Estimates asset volatility by analyzing the high, low, open, and close prices, providing a nuanced view of intraday volatility and market dynamics.
Close-to-Close Volatility Measure: Calculates volatility based on the closing prices of stocks, offering a streamlined but limited perspective on market behavior.
Parkinson Volatility Measure: Enhances volatility estimation by including high and low prices of the trading day, capturing a more accurate reflection of intraday market movements.
Garman-Klass Volatility Measure: Incorporates open, high, low, and close prices for a comprehensive daily volatility measure, capturing significant price movements and market activity.
Yang-Zhang Volatility Measure: Offers an efficient estimation of stock market volatility by combining overnight and intraday price movements, capturing opening jumps and overall market dynamics.
Garman-Klass-Yang-Zhang Volatility Measure: Merges the benefits of Garman-Klass and Yang-Zhang measures, providing a fuller picture of market volatility including opening market reactions.
Pseudo GARCH(2,2) Volatility Model: Mimics a GARCH(2,2) process using exponential moving averages of squared returns, highlighting volatility shocks and their future impact.
ER-Adaptive Average True Range (ATR): Adjusts the ATR period length based on market efficiency, offering a volatility measure that adapts to changing market conditions.
Adaptive Deviation: Dynamically adjusts its calculation period to offer a nuanced measure of volatility that responds to the market's intrinsic rhythms.
Median Absolute Deviation (MAD): Provides a robust measure of statistical variability, focusing on deviations from the median price, offering resilience against outliers.
Mean Absolute Deviation (MAD): Measures the average magnitude of deviations from the mean price, facilitating a straightforward understanding of volatility.
ATR (Average True Range): Finds the average of true ranges over a specified period, indicating the expected price movement and market volatility.
True Range Double (TRD): Offers a nuanced view of volatility by considering a broader range of price movements, identifying significant market sentiment shifts.
🔹 Other Settings
Backtest Dates: Users can specify the timeframe for the backtest, including start and end dates, as well as the acceptable entry time window.
Volatility Inputs: Additional settings related to volatility calculations, such as static percent, internal filter period for median absolute deviation, and parameters for specific volatility models.
UI Options: Settings to customize the user interface, including table activation, date panel visibility, and aesthetics like color and text size.
Export Options: Allows users to select the type of data to export from the backtest, focusing on metrics like net profit, total closed trades, and average profit per trade.
█ Giga Kaleidoscope (GKD) Modularized Trading System
The GKD Trading System is a comprehensive, algorithmic trading framework from AlgxTrading, designed to optimize trading strategies across various market conditions. It employs a modular approach, incorporating elements such as volatility assessment, trend identification through a baseline, multiple confirmation strategies for signal accuracy, and volume analysis. Key components also include specialized strategies for entry and exit, enabling precise trade execution. The system allows for extensive backtesting, providing traders with the ability to evaluate the effectiveness of their strategies using historical data. Aimed at reducing setup time, the GKD system empowers traders to focus more on strategy refinement and execution, leveraging a wide array of technical indicators for informed decision-making.
🔶 Core components of a GKD Algorithmic Trading System
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system. The GKD algorithm is built on the principles of trend, momentum, and volatility. There are eight core components in the GKD trading algorithm:
🔹 Volatility - In the GKD trading system, volatility is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. There are 17+ different types of volatility available in the GKD system including Average True Range (ATR), True Range Double (TRD), Close-to-Close, Garman-Klass, and more.
🔹 Baseline (GKD-B) - The baseline is essentially a moving average and is used to determine the overall direction of the market. The baseline in the GKD trading system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other GKD indicators.
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards or price is above the baseline, then only long trades are taken, and if the baseline is sloping downwards or price is below the baseline, then only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
🔹 Confirmation 1, Confirmation 2, Continuation (GKD-C) - The GKD trading system incorporates technical confirmation indicators for the generation of its primary long and short signals, essential for its operation.
The GKD trading system distinguishes three specific categories. The first category, Confirmation 1 , encompasses technical indicators designed to identify trends and generate explicit trading signals. The second category, Confirmation 2 , a technical indicator used to identify trends; this type of indicator is primarily used to filter the Confirmation 1 indicator signals; however, this type of confirmation indicator also generates signals*. Lastly, the Continuation category includes technical indicators used in conjunction with Confirmation 1 and Confirmation 2 to generate a special type of trading signal called a "Continuation"
In a full GKD trading system all three categories generate signals. (see the section “GKD Trading System Signals” below)
🔹 Volatility/Volume (GKD-V) - Volatility/Volume indicators are used to measure the amount of buying and selling activity in a market. They are based on the trading Volatility/Volume of the market, and can provide information about the strength of the trend. In the GKD trading system, Volatility/Volume indicators are used to confirm trading signals generated by the various other GKD indicators. In the GKD trading system, Volatility is a proxy for Volume and vice versa.
Volatility/Volume indicators reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by GKD-C confirmation and GKD-B baseline indicators.
🔹 Exit (GKD-E) - The exit indicator in the GKD system is an indicator that is deemed effective at identifying optimal exit points. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
🔹 Backtest (GKD-BT) - The GKD-BT backtest indicators link all other GKD-C, GKD-B, GKD-E, GKD-V, and GKD-M components together to create a GKD trading system. GKD-BT backtests generate signals (see the section “GKD Trading System Signals” below) from the confluence of various GKD indicators that are imported into the GKD-BT backtest. Backtest types include: GKD-BT solo and full GKD backtest strategies used for a single ticker; GKD-BT optimizers used to optimize a single indicator or the full GKD trading system; GKD-BT Multi-ticker used to backtest a single indicator or the full GKD trading system across up to ten tickers; GKD-BT exotic backtests like CC, Baseline, and Giga Stacks used to test confluence between GKD components to then be injected into a core GKD-BT Multi-ticker backtest or single ticker strategy.
🔹 Metamorphosis (GKD-M) ** - The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, GKD-E, or GKD-V slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
*see the section “GKD Trading System Signals” below
**not a required component of the GKD algorithm
🔶 What does the application of the GKD trading system look like?
Example trading system:
Volatility: Average True Range (ATR) (selectable in all backtests and other related GKD indicators)
GKD-B Baseline: GKD-B Multi-Ticker Baseline using Hull Moving Average
GKD-C Confirmation 1 : GKD-C Advance Trend Pressure
GKD-C Confirmation 2: GKD-C Dorsey Inertia
GKD-C Continuation: GKD-C Stochastic of RSX
GKD-V Volatility/Volume: GKD-V Damiani Volatmeter
GKD-E Exit: GKD-E MFI
GKD-BT Backtest: GKD-BT Multi-Ticker Full GKD Backtest
GKD-M Metamorphosis: GKD-M Baseline Optimizer
**all indicators mentioned above are included in the same AlgxTrading package**
Each module is passed to a GKD-BT backtest module. In the backtest module, all components are combined to formulate trading signals and statistical output. This chaining of indicators requires that each module conform to AlgxTrading's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the various indictor types in the GKD algorithm.
🔶 GKD Trading System Signals
Standard Entry requires a sequence of conditions including a confirmation signal from GKD-C, baseline agreement, price criteria related to the Goldie Locks Zone, and concurrence from a second confirmation and volatility/volume indicators.
1-Candle Standard Entry introduces a two-phase process where initial conditions must be met, followed by a retraction in price and additional confirmations in the subsequent candle, including baseline, confirmations 1 and 2, and volatility/volume criteria.
Baseline Entry focuses on signals generated by the GKD-B Baseline, requiring agreement from confirmation signals, specific price conditions within the Goldie Locks Zone, and a timing condition related to the confirmation 1 signal.
1-Candle Baseline Entry mirrors the baseline entry but adds a requirement for a price retraction and subsequent confirmations in the following candle, maintaining the focus on the baseline's guidance.
Volatility/Volume Entry is predicated on signals from volatility/volume indicators, requiring support from confirmations, price criteria within the Goldie Locks Zone, baseline agreement, and a timing condition for the confirmation 1 signal.
1-Candle Volatility/Volume Entry adapts the volatility/volume entry to include a phase of initial signal and agreement, followed by a retracement phase that seeks further agreement from the system's components in the subsequent candle.
Confirmation 2 Entry is based on the second confirmation signal, requiring the first confirmation's agreement, specific price criteria, agreement from volatility/volume indicators, and baseline, with a timing condition for the confirmation 1 signal.
1-Candle Confirmation 2 Entry adds a retracement requirement to the confirmation 2 entry, necessitating additional agreements from the system's components in the candle following the signal.
PullBack Entry initiates with a baseline signal and agreement from the first confirmation, with a price condition related to volatility. It then looks for price to return within the Goldie Locks Zone and seeks further agreement from the system's components in the subsequent candle.
Continuation Entry allows for the continuation of an active position, based on a previously triggered entry strategy. It requires that the baseline hasn't crossed since the initial trigger, alongside ongoing agreements from confirmations and the baseline.
█ Conclusion
The GKD-BT Optimizer Full GKD Backtest is a critical tool within the Giga Kaleidoscope Modularized Trading System, designed for precise strategy refinement and evaluation within the GKD framework. It enables the optimization and testing of various trading indicators and strategies under different market conditions. The module's design facilitates detailed analysis of individual trading components' performance, allowing for the optimization of indicators like Baseline, Volatility/Volume, Confirmation, and Continuation. This optimization process aids traders in identifying the most effective configurations, thereby enhancing trading outcomes and strategy efficiency within the GKD ecosystem.
█ How to Access
You can see the Author's Instructions below to learn how to get access.
GKD-C Derivative Oscillator [Loxx]The Giga Kaleidoscope GKD-C Derivative Oscillator is a Confirmation module included in AlgxTrading's "Giga Kaleidoscope Modularized Trading System."
█ GKD-C Derivative Oscillator, a brief overview
The Derivative Oscillator is a technical analysis tool used in trading that merges the concepts of the Relative Strength Index (RSI) and the double smoothed moving average. Essentially, it operates by taking the difference between a short-term moving average of the asset's price and a longer-term moving average, which is then double smoothed with exponential moving averages (EMAs). This process refines the RSI, aiming to provide clearer signals regarding the momentum and potential trend reversals of a security's price. The GKD-C Derivative Oscillator produces two types of signals: Zero-line or Signal crosses. (read the sections below to learn how traders can test these different signal types using AlgxTrading's GKD trading system)
GKD-C Derivative Oscillator in Zero-line crosses mode
GKD-C Derivative Oscillator in Signal crosses mode
To explain the features included in the GKD-C Derivative Oscillator , let's first dive into the details of the Giga Kaleidoscope (GKD) Modularized Trading System.
█ Giga Kaleidoscope (GKD) Modularized Trading System
The GKD Trading System is a comprehensive, algorithmic trading framework from AlgxTrading, designed to optimize trading strategies across various market conditions. It employs a modular approach, incorporating elements such as volatility assessment, trend identification through a baseline, multiple confirmation strategies for signal accuracy, and volume analysis. Key components also include specialized strategies for entry and exit, enabling precise trade execution. The system allows for extensive backtesting, providing traders with the ability to evaluate the effectiveness of their strategies using historical data. Aimed at reducing setup time, the GKD system empowers traders to focus more on strategy refinement and execution, leveraging a wide array of technical indicators for informed decision-making.
🔶 Core components of a GKD Algorithmic Trading System
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system. The GKD algorithm is built on the principles of trend, momentum, and volatility. There are eight core components in the GKD trading algorithm:
🔹 Volatility - In the GKD trading system, volatility is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. There are 17+ different types of volatility available in the GKD system including Average True Range (ATR), True Range Double (TRD), Close-to-Close, Garman-Klass, and more.
🔹 Baseline (GKD-B) - The baseline is essentially a moving average and is used to determine the overall direction of the market. The baseline in the GKD trading system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other GKD indicators.
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards or price is above the baseline, then only long trades are taken, and if the baseline is sloping downwards or price is below the baseline, then only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
🔹 Confirmation 1, Confirmation 2, Continuation (GKD-C) - The GKD trading system incorporates technical confirmation indicators for the generation of its primary long and short signals, essential for its operation.
The GKD trading system distinguishes three specific categories. The first category, Confirmation 1 , encompasses technical indicators designed to identify trends and generate explicit trading signals. The second category, Confirmation 2 , a technical indicator used to identify trends; this type of indicator is primarily used to filter the Confirmation 1 indicator signals; however, this type of confirmation indicator also generates signals*. Lastly, the Continuation category includes technical indicators used in conjunction with Confirmation 1 and Confirmation 2 to generate a special type of trading signal called a "Continuation"
In a full GKD trading system all three categories generate signals. (see the section “GKD Trading System Signals” below)
🔹 Volatility/Volume (GKD-V) - Volatility/Volume indicators are used to measure the amount of buying and selling activity in a market. They are based on the trading Volatility/Volume of the market, and can provide information about the strength of the trend. In the GKD trading system, Volatility/Volume indicators are used to confirm trading signals generated by the various other GKD indicators. In the GKD trading system, Volatility is a proxy for Volume and vice versa.
Volatility/Volume indicators reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by GKD-C confirmation and GKD-B baseline indicators.
🔹 Exit (GKD-E) - The exit indicator in the GKD system is an indicator that is deemed effective at identifying optimal exit points. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
🔹 Backtest (GKD-BT) - The GKD-BT backtest indicators link all other GKD-C, GKD-B, GKD-E, GKD-V, and GKD-M components together to create a GKD trading system. GKD-BT backtests generate signals (see the section “GKD Trading System Signals” below) from the confluence of various GKD indicators that are imported into the GKD-BT backtest. Backtest types include: GKD-BT solo and full GKD backtest strategies used for a single ticker; GKD-BT optimizers used to optimize a single indicator or the full GKD trading system; GKD-BT Multi-ticker used to backtest a single indicator or the full GKD trading system across up to ten tickers; GKD-BT exotic backtests like CC, Baseline, and Giga Stacks used to test confluence between GKD components to then be injected into a core GKD-BT Multi-ticker backtest or single ticker strategy.
🔹 Metamorphosis (GKD-M) ** - The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, GKD-E, or GKD-V slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
*(see the section “GKD Trading System Signals” below)
**(not a required component of the GKD algorithm)
🔶 What does the application of the GKD trading system look like?
Example trading system:
Volatility: Average True Range (ATR) (selectable in all backtests and other related GKD indicators)
GKD-B Baseline: GKD-B Multi-Ticker Baseline using Hull Moving Average
GKD-C Confirmation 1 : GKD-C Advance Trend Pressure
GKD-C Confirmation 2: GKD-C Dorsey Inertia
GKD-C Continuation: GKD-C Stochastic of RSX
GKD-V Volatility/Volume: GKD-V Damiani Volatmeter
GKD-E Exit: GKD-E MFI
GKD-BT Backtest: GKD-BT Multi-Ticker Full GKD Backtest
GKD-M Metamorphosis: GKD-M Baseline Optimizer
**all indicators mentioned above are included in the same AlgxTrading package**
Each module is passed to a GKD-BT backtest module. In the backtest module, all components are combined to formulate trading signals and statistical output. This chaining of indicators requires that each module conform to AlgxTrading's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the various indictor types in the GKD algorithm.
🔶 GKD Trading System Signals
🔹 Standard Entry requires a sequence of conditions including a confirmation signal from GKD-C, baseline agreement, price criteria related to the Goldie Locks Zone, and concurrence from a second confirmation and volatility/volume indicators.
🔹 1-Candle Standard Entry introduces a two-phase process where initial conditions must be met, followed by a retraction in price and additional confirmations in the subsequent candle, including baseline, confirmations 1 and 2, and volatility/volume criteria.
🔹 Baseline Entry focuses on signals generated by the GKD-B Baseline, requiring agreement from confirmation signals, specific price conditions within the Goldie Locks Zone, and a timing condition related to the confirmation 1 signal.
🔹 1-Candle Baseline Entry mirrors the baseline entry but adds a requirement for a price retraction and subsequent confirmations in the following candle, maintaining the focus on the baseline's guidance.
🔹 Volatility/Volume Entry is predicated on signals from volatility/volume indicators, requiring support from confirmations, price criteria within the Goldie Locks Zone, baseline agreement, and a timing condition for the confirmation 1 signal.
🔹 1-Candle Volatility/Volume Entry adapts the volatility/volume entry to include a phase of initial signal and agreement, followed by a retracement phase that seeks further agreement from the system's components in the subsequent candle.
🔹 Confirmation 2 Entry is based on the second confirmation signal, requiring the first confirmation's agreement, specific price criteria, agreement from volatility/volume indicators, and baseline, with a timing condition for the confirmation 1 signal.
🔹 1-Candle Confirmation 2 Entry adds a retracement requirement to the confirmation 2 entry, necessitating additional agreements from the system's components in the candle following the signal.
🔹 PullBack Entry initiates with a baseline signal and agreement from the first confirmation, with a price condition related to volatility. It then looks for price to return within the Goldie Locks Zone and seeks further agreement from the system's components in the subsequent candle.
🔹 Continuation Entry allows for the continuation of an active position, based on a previously triggered entry strategy. It requires that the baseline hasn't crossed since the initial trigger, alongside ongoing agreements from confirmations and the baseline.
█ GKD-C Derivative Oscillator, a deep dive
Now that you have a basic understanding of the GKD trading system. let's dive deeper into the features included in the GKD-C Derivative Oscillator
🔶 GKD-C Derivative Oscillator Modes aka "Confirmation Type"
The GKD-C Derivative Oscillator has 4 modes: Confirmation for confirmation 1 and 2; Continuation; Multi-ticker for multi-ticker confirmation 1 and 2; and Optimizer.
🔹 Confirmation: When in this mode, the GKD-C Derivative Oscillator generates confirmation 1 and 2 signals. These values can then be exported to a GKD-BT backtest strategy.
Signal Key: L = Long, S = Short
GKD-C Derivative Oscillator in Confirmation mode
Confirmation Exports
GKD-C Derivative Oscillator in attached to a GKD-BT backtest strategy
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolated per ticker and trading side, long or short**
🔹 Continuation: When in this mode, the GKD-C Derivative Oscillator generates continuation signals.
Signal Key: L = Long, S = Short, CL = Continuation Long, CS = Continuation Short
GKD-C Derivative Oscillator in Continuation mode
Continuation Exports
🔹 Multi-ticker: When in this mode, the GKD-C Derivative Oscillator generates multi-ticker confirmation 1 and 2. This mode allows users to generate confirmation 1 and 2, and continuation signals for up to 10 different tickers. These values can then be exported to a GKD-BT Multi-ticker backtest.
Signal Key: L = Long, S = Short
GKD-C Derivative Oscillator in Multi-ticker mode
Multi-ticker Exports
GKD-C Derivative Oscillator attached to the GKD-BT Multi-ticker SCS Backtest
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolated per ticker and trading side, long or short**
🔹 Optimizer: When in this mode, the GKD-C Derivative Oscillator generates optimization signals. These signals allow the user to backtest a range of input values. These values are exported to a GKD-BT optimizer backtest.
Signal Key: L = Long, S = Short
GKD-C Derivative Oscillator in Optimizer mode
Optimizer Inputs and Exports
GKD-C Derivative Oscillator attacked to the GKD-BT Optimizer SCS Backtest
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolated per ticker and trading side, long or short**
█ Conclusion
The GKD-C Derivative Oscillator serves as a multi-modal component of the GKD trading system allowing traders to optimize and backtest acorss a range of input parameters and tickers. These features decrease total build time required to create a custom GKD algorithmic trading system by allowing users to spend more time trading and less time guessing.
█ How to Access
You can see the Author's Instructions below to learn how to get access.
Temporal Value Tracker: Inception-to-Present Inflation Lens!What we're looking at here is a chart that does more than just display the price of gold. It offers us a time-traveling perspective on value. The blue line, that's our nominal price—it's the straightforward market price of gold over time. But it's the red line that takes us on a deeper journey. This line adjusts the nominal price for inflation, showing us the real purchasing power of gold.
Now, when we talk about 'real value,' we're not just philosophizing. We're anchoring our prices to a point in time when the journey began—let's say when gold trading started on the markets, or any inception point we choose. By 'shadowing' certain years—say, from the 1970s when the gold standard was abandoned—we can adjust this chart to reflect what the inflation-adjusted price means since that key moment in history.
By doing so, we're effectively isolating our view to start from that pivotal year, giving us insight into how gold, or indeed any asset, has held up against the backdrop of economic changes, policy shifts, and the inevitable rise in the cost of living. If you're analyzing a stock index like the S&P 500, you might begin your inflation-adjusted view from the index's inception date, which allows you to measure the true growth of the market basket from the moment it started.
This adjustment isn't just academic. It influences how we perceive value and growth. Consider a period where the nominal price skyrockets. We might toast to our brilliance in investment! But if the inflation-adjusted line lags, what we're seeing is nominal growth without real gains. On the other hand, if our red line outpaces the blue even during stagnant market periods, we're witnessing real growth—our asset is outperforming the eroding effects of inflation.
Every asset class can be evaluated this way. Stocks, bonds, real estate—they all have their historical narratives, and inflation adjustment tells us if these stories are tales of genuine growth or illusions masked by inflation.
So, as informed traders and investors, we need to keep our eyes on this inflation-adjusted line. It's our measure against the silent thief that is inflation. It ensures we're not just keeping up with the Joneses of the market, but actually outpacing them, building real wealth over time
CE - Market Performance TableThe 𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 is a sophisticated market tool designed to provide valuable insights into the current market trends and the approximate current position in the Macroeconomic Regime.
Furthermore the 𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 provides the Correlation Implied Trend for the Asset on the Chart. Lastly it provides information about current "RISK ON" or "RISK OFF" periods.
Methodology:
𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 tracks the 15 underlying Stock ETF's to identify their performance and puts the combined performances together to visualize 42MACRO's GRID Equity Model.
For this it uses the below ETF's:
Dividends (SPHD)
Low Beta (SPLV)
Quality (QUAL)
Defensives (DEF)
Growth (IWF)
High Beta (SPHB)
Cyclicals (IYT, IWN)
Value (IWD)
Small Caps (IWM)
Mid Caps (IWR)
Mega Cap Growth (MGK)
Size (OEF)
Momentum (MTUM)
Large Caps (IWB)
Overall Settings:
The main time values you want to change are:
Correlation Length
- Defines the time horizon for the Correlation Table
ROC Period
- Defines the time horizon for the Performance Table
Normalization lookback
- Defines the time horizon for the Trend calculation of the ETF's
- For longer term Trends over weeks or months a length of 50 is usually pretty accurate
Visuals:
There is a variety of options to change the visual settings of what is being plotted and the two table positions and additional considerations.
Everything that is relevant in the underlying logic that can help comprehension can be visualized with these options.
Market Correlation:
The Market Correlation Table takes the Correlation of the above ETF's to the Asset on the Chart, it furthermore uses the Normalized KAMA Oscillator by IkkeOmar to analyse the current trend of every single ETF.
It then Implies a Correlation based on the Trend and the Correlation to give a probabilistically adjusted expectation for the future Chart Asset Movement. This is strengthened by taking the average of all Implied Trends.
With this the Correlation Table provides valuable insights about probabilistically likely Movement of the Asset, for Traders and Investors alike, over the defined time duration.
Market Performance:
𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 is the actual valuable part of this Indicator.
It provides valuable information about the current market environment (whether it's risk on or risk off), the rough GRID models from 42MACRO and the actual market performance.
This allows you to obtain a deeper understanding of how the market works and makes it simple to identify the actual market direction.
Utility:
The 𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 is divided in 4 Sections which are the GRID regimes:
Economic Growth:
Goldilocks
Reflation
Economic Contraction:
Inflation
Deflation
Top 5 Equity Style Factors:
Are the values green for a specific Column? If so then the market reflects the corresponding GRID behavior.
Bottom 5 Equity Style Factors:
Are the values red for a specific Column? If so then the market reflects the corresponding GRID behavior.
So if we have Goldilocks as current regime we would see green values in the Top 5 Goldilocks Cells and red values in the Bottom 5 Goldilocks Cells.
You will find that Reflation will look similar, as it is also a sign of Economic Growth.
Same is the case for the two Contraction regimes.
Chuff by Monty V2This is the second version of the indicator ‘Chuff by Monty’ we made a while back.
This indicator uses multiple previously available indicators as well as some newly calculated scripts to provide information on chart that uses one indicator slot but is telling you more than what one indicator could’ve.
The indicator also includes alerts which can be used to find potential signals generated by the indicator, So be sure to check those out as well.
Features of the indicator:
Ichimoku Cloud
TK Crosses: Label on chart when the Conversion Line (Tenken Sen) and Base line (Kijun Sen) is crossing each other. There are five types of crosses that are marked in the indicator. Each will have Bullish or Bearish aspect but, you have to look at the image below to really understand which is worth considering signal.
TK Lines: This checkbox will enable the Conversion and Base line, the crosses of which are labeled through the TK Crosses Checkbox above.
Bullish TK: Now when the orange line crosses the red line to the up side which should be flat at that time, this generates a bullish signal showing that this can lead price to the upwards direction. And a label print as Bullish TK in red color.
Bearish TK: Now when the orange line crosses the red line to the down side which should be flat at that time, this generates a bearish signal showing that this can lead price to the downwards direction. And a label print as Bearish TK in Golden color.
Kumo Cloud: There are two types of clouds in the indicator as well, This is calculated with a bit different approach then conventional Ichimoku Cloud indicator. Both red and green Kumo cloud acts as resistance and support respectively.
Trading Edge to Edge: This phenomenon in ichimoku suggests that when there’s a close in the cloud as 1. For longs, Green candles should close in the red cloud and at that time, The other side of the cloud should be flat and 2. For shorts, Red candles should close in the green cloud and at that time, the other side of the cloud should be flat. This opens up the window for the price to go to the other flat side of the cloud after retesting the cloud through the inside. An example is shared in the snapshot.
Divergences:
This part of the indicator uses 10 different types of oscillators including MACD, Histogram, RSI, Stochastic, CCI, Momentum, On-Balance Volume, Volume Weighted MACD, CMF, Money Flow Index and EXT to calculate divergences. By default, the indicator will show hidden and regular divergences at once, but you can choose to have just hidden or just regular divergences as per your liking. I specifically hard coded the indicator to calculate divergences from candle closes rather than from wicks, so that’s what it’s doing.
Harmonic Patterns:
I personally trade three and only three harmonics, these are bat, butterfly and Gartley. This part of indicator will analyze each swing and check if these swings are falling in any of those three harmonic pattern ranges. As we all know that these patterns don’t complete their retracement to the last digit exactly each time, so there’s a liberty range that has 10% error flexibility. Which means that if a retracement is supposed to be at 0.618, the error flexibility will check it in a range of +10% and -10% of 0.618 which comes out to be 0.556-0.678. Three of the harmonics, bullish (Green) and bearish (Red) is posted in the snapshot.
You can trade these harmonics by either waiting for the indicator to print them, either by putting an alert for each type of a harmonic pattern or by pre-predicting a harmonic which is taught in our community’s premium discord discord.gg .
Golden/Death cross:
Just like TK Crosses, this will print you Goldencross and Deathcross labels each time 55sma and 200sma cross each other. If the 55sma is crossing the 200sma to the upside, A Goldencross label will appear and if 55sma is crossing the 200sma to the downside, A Deathcross label will appear. Golden cross means the coin is turning bullish and can go high. Death cross shows that the coin is turning bearish and the price can fall.
Moving Averages:
Default lengths are 13EMA, 21EMA, 55SMA, 200SMA and 355SMA. You can change it as you like but I use these lengths for my analysis. One feature that this set of moving average has is that each MA is labeled as it’s length and the calculation method (SMA or EMA). So, when you are analyzing with multiple Moving Averages enabled, you can easily know which MA is which.
SR Band:
It has three mods. Fast/Weak which is going to be quick and sensitive to the price but will a weak support and resistance area. Slow/Strong which is going to be slow and less sensitive to the price but will be a very strong support and resistance area. The normal settings which is set as default is kind of in-between these two. You can use this SR band as a way of getting in and out of the trades as it represents Supports and resistances. The colors of the band changes when the price is above, below and is in the band.
Here’s an example trade using the confluences provided by the indicator.
This is how that trade would’ve looked like with indicator:
And this is how that trade would’ve looked like without the indicator:
Do ask questions in the comment section about the indicator or the trading strategy here if you feel like this is too complex. I’ll be glad to help.
All the settings and features which were worth customizing are customizable in this version of the indicator. Feel free to change those settings as per your liking.
Thank you.
GKD-BT Solo Confirmation Complex Backtest [Loxx]Giga Kaleidoscope GKD-BT Solo Confirmation Complex Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Solo Confirmation Complex Backtest
The Solo Confirmation Complex Backtest module enables users to perform backtesting on Standard Long and Short signals from GKD-C confirmation indicators, filtered by GKD-B Baseline and GKD-V Volatility/Volume indicators. This module represents a complex form of the Solo Confirmation Backtest in the GKD trading system. It includes two types of backtests: Trading and Full. The Trading backtest allows users to test individual trades, both Long and Short, one at a time. On the other hand, the Full backtest allows users to test either Longs or Shorts by toggling between them in the settings to view the results for each signal type. The Trading backtest simulates real trading, while the Full backtest tests all signals, whether Long or Short.
Additionally, this backtest module provides the option to test the GKD-C Confirmation indicator with 1 to 3 take profits and 1 stop loss. The Trading backtest allows for the use of 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also offers the capability to apply a trailing take profit.
In terms of the percentage of trade removed at each take profit, this backtest module has the following hardcoded values:
Take profit 1: 50% of the trade is removed.
Take profit 2: 25% of the trade is removed.
Take profit 3: 25% of the trade is removed.
Stop loss: 100% of the trade is removed.
After each take profit is achieved, the stop loss level is adjusted. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into play after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest module also offers the capability to restrict by a specific date range, allowing for simulated forward testing based on past data. Additionally, users have the option to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. On the chart, historical take profit and stop loss levels are represented by horizontal lines overlaid for reference.
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings.
To utilize this strategy, follow these steps:
1. GKD-B Baseline Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline module into the GKD-BT Solo Confirmation Complex Backtest module setting named "Import GKD-B Baseline indicator."
Adjust the "Confirmation Type" in the GKD-C Confirmation Indicator to "GKD New."
2. GKD-C Confirmation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation module into the GKD-BT Solo Confirmation Complex Backtest module setting named "Import GKD-C Confirmation indicator."
3. GKD-V Volatility/Volume Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume module into the GKD-BT Solo Confirmation Complex Backtest module setting named "Import GKD-V Volatility/Volume indicator."
4. The Solo Confirmation Complex Backtest module exclusively supports Standard Entries, both Long and Short. However, please note that this module uses a modified version of the Standard Entry. In this modified version, long and short signals are directly imported from the Confirmation indicator, and then baseline and volatility filtering is applied.
The GKD-B Baseline filter ensures that only trades aligning with the GKD-B Baseline's current trend are accepted. This filter takes into consideration the Goldie Locks Zone, which allows trades where the closing price of the last candle has moved within a minimum XX volatility and a maximum YY volatility range. The GKD-V Volatility/Volume filter allows only trades that meet a minimum threshold of ZZ GKD-V Volatility/Volume, which varies based on the specific GKD-V Volatility/Volume indicator used.
The Solo Confirmation Complex Backtest execution engine determines whether signals from the GKD-C Confirmation indicator are accepted or rejected based on two criteria:
1. The GKD-C Confirmation signal must be qualified by the direction of the GKD-B Baseline trend and the GKD-B Baseline's sweet-spot Goldie Locks Zone.
2. Sufficient Volatility/Volume, as indicated by the GKD-V Volatility/Volume indicator, must be present to execute a trade.
The purpose of the Solo Confirmation Complex Backtest is to test a GKD-C Confirmation indicator in the presence of macro trend and volatility/volume filtering.
Volatility Types Included
17 types of volatility are included in this indicator
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Solo Confirmation Complex Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Volatility-Adaptive Rapid RSI T3
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
GKD-BT Solo Confirmation Simple Backtest [Loxx]Giga Kaleidoscope GKD-BT Solo Confirmation Simple Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Solo Confirmation Simple Backtest
The Solo Confirmation Simple Backtest module enables users to perform Standard Long and Short signals on GKD-C confirmation indicators. This module represents the simplest form of Backtest in the GKD trading system. It includes two types of backtests: Trading and Full. The Trading backtest allows users to test individual trades, both long and short, one at a time. On the other hand, the Full backtest allows users to test either longs or shorts by toggling between them in the settings to view the results for each signal type. The Trading backtest simulates real trading, while the Full backtest tests all signals, whether long or short.
Additionally, this backtest module provides the option to test the GKD-C indicator with 1 to 3 take profits and 1 stop loss. The Trading backtest allows for the use of 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also offers the capability to apply a trailing take profit.
In terms of the percentage of trade removed at each take profit, this backtest module has the following hardcoded values:
Take profit 1: 50% of the trade is removed
Take profit 2: 25% of the trade is removed
Take profit 3: 25% of the trade is removed
Stop loss: 100% of the trade is removed
After each take profit is achieved, the stop loss level is adjusted. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into play after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest also offers the capability to restrict by a specific date range, allowing for simulated forward testing based on past data. Additionally, users have the option to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. On the chart, historical take profit and stop loss levels are represented by horizontal lines overlaid for reference.
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
To utilize this strategy, follow these steps:
1. Adjust the "Confirmation Type" in the GKD-C Confirmation Indicator to "GKD New."
2. Import the value "Input into NEW GKD-BT Backtest" into the GKD-BT Solo Confirmation Simple Backtest module (this strategy backtest).
**The GKD-BT Solo Confirmation Simple Backtest module exclusively supports Standard Entries, both Long and Short. However, please note that this module uses a modified version of the standard entry, where long and short signals are directly imported from the Confirmation indicator without any baseline or volatility filtering applied.**
Volatility Types Included
17 types of volatility are included in this indicator
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Solo Confirmation Simple Backtest as shown on the chart above
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Volatility-Adaptive Rapid RSI T3
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
GKD-C Volatility-Adaptive Rapid RSI T3 [Loxx]Giga Kaleidoscope GKD-C Volatility-Adaptive Rapid RSI T3 is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-C Volatility-Adaptive Rapid RSI T3
Adaptive Momentum: Mastering Market Dynamics with Advanced RSI Techniques
The Volatility-Adaptive Rapid RSI T3 is a sophisticated technical indicator that combines the concepts of Rapid RSI, Volatility Adaptation, and T3 smoothing. This combination results in a more responsive, accurate, and adaptable momentum oscillator compared to the regular RSI.
The Rapid RSI is a variation of the RSI designed to provide faster and more responsive signals. It does this by modifying the way average gains and losses are calculated, using a simple moving average (SMA) instead of an exponential moving average (EMA). This makes the Rapid RSI more sensitive to recent price changes, allowing traders to identify overbought and oversold conditions more quickly.
Volatility adaptation is a concept that adjusts the parameters of an indicator based on the current market volatility. In the context of the Volatility-Adaptive Rapid RSI T3, the volatility is calculated using the standard deviation of price changes over a specified period. This value is then used to adjust the T3 smoothing period, making the indicator more adaptive to changing market conditions. When the market is volatile, the indicator will respond more quickly to price changes, while in less volatile markets, the indicator will be less sensitive, reducing the likelihood of false signals.
T3 smoothing, developed by Tim Tilson, is a powerful and flexible moving average technique that aims to reduce lag and improve the responsiveness of an indicator. It utilizes a combination of multiple exponential moving averages with varying degrees of weighting to create a smoother and more accurate representation of the underlying data. The T3 smoothing method is applied to the price data before the Rapid RSI calculation, enhancing the overall responsiveness of the indicator.
By combining these three concepts, the Volatility-Adaptive Rapid RSI T3 offers several advantages over the regular RSI:
1. Faster and more responsive signals: The Rapid RSI and T3 smoothing components allow the indicator to respond more quickly to price changes, potentially leading to earlier entry and exit points.
2. Adaptability to market conditions: The volatility adaptation feature enables the indicator to adjust its sensitivity based on the current market volatility. This helps to reduce false signals in less volatile markets and increase responsiveness in more volatile markets.
2. Smoother representation of price data: The T3 smoothing technique provides a more accurate and smoother representation of the underlying data, making it easier to identify trends and potential reversals.
In conclusion, the Volatility-Adaptive Rapid RSI T3 is a powerful technical indicator that offers several improvements over the regular RSI. Its responsiveness, adaptability, and smoothing capabilities make it a valuable tool for traders seeking to identify overbought and oversold conditions more accurately. However, it is essential to remember that no indicator is perfect, and using the Volatility-Adaptive Rapid RSI T3 in conjunction with other technical indicators and analysis tools can provide more reliable trading signals.
Additional Features
This indicator allows you to select from 33 source types. They are as follows:
Close
Open
High
Low
Median
Typical
Weighted
Average
Average Median Body
Trend Biased
Trend Biased (Extreme)
HA Close
HA Open
HA High
HA Low
HA Median
HA Typical
HA Weighted
HA Average
HA Average Median Body
HA Trend Biased
HA Trend Biased (Extreme)
HAB Close
HAB Open
HAB High
HAB Low
HAB Median
HAB Typical
HAB Weighted
HAB Average
HAB Average Median Body
HAB Trend Biased
HAB Trend Biased (Extreme)
What are Heiken Ashi "better" candles?
Heiken Ashi "better" candles are a modified version of the standard Heiken Ashi candles, which are a popular charting technique used in technical analysis. Heiken Ashi candles help traders identify trends and potential reversal points by smoothing out price data and reducing market noise. The "better formula" was proposed by Sebastian Schmidt in an article published by BNP Paribas in Warrants & Zertifikate, a German magazine, in August 2004. The aim of this formula is to further improve the smoothing of the Heiken Ashi chart and enhance its effectiveness in identifying trends and reversals.
Standard Heiken Ashi candles are calculated using the following formulas:
Heiken Ashi Close = (Open + High + Low + Close) / 4
Heiken Ashi Open = (Previous Heiken Ashi Open + Previous Heiken Ashi Close) / 2
Heiken Ashi High = Max (High, Heiken Ashi Open, Heiken Ashi Close)
Heiken Ashi Low = Min (Low, Heiken Ashi Open, Heiken Ashi Close)
The "better formula" modifies the standard Heiken Ashi calculation by incorporating additional smoothing, which can help reduce noise and make it easier to identify trends and reversals. The modified formulas for Heiken Ashi "better" candles are as follows:
Better Heiken Ashi Close = (Open + High + Low + Close) / 4
Better Heiken Ashi Open = (Previous Better Heiken Ashi Open + Previous Better Heiken Ashi Close) / 2
Better Heiken Ashi High = Max (High, Better Heiken Ashi Open, Better Heiken Ashi Close)
Better Heiken Ashi Low = Min (Low, Better Heiken Ashi Open, Better Heiken Ashi Close)
Smoothing Factor = 2 / (N + 1), where N is the chosen period for smoothing
Smoothed Better Heiken Ashi Open = (Better Heiken Ashi Open * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Open * (1 - Smoothing Factor))
Smoothed Better Heiken Ashi Close = (Better Heiken Ashi Close * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Close * (1 - Smoothing Factor))
The smoothed Better Heiken Ashi Open and Close values are then used to calculate the smoothed Better Heiken Ashi High and Low values, resulting in "better" candles that provide a clearer representation of the market trend and potential reversal points.
It's important to note that, like any other technical analysis tool, Heiken Ashi "better" candles are not foolproof and should be used in conjunction with other indicators and analysis techniques to make well-informed trading decisions.
Heiken Ashi "better" candles, as mentioned previously, provide a clearer representation of market trends and potential reversal points by reducing noise and smoothing out price data. When using these candles in conjunction with other technical analysis tools and indicators, traders can gain valuable insights into market behavior and make more informed decisions.
To effectively use Heiken Ashi "better" candles in your trading strategy, consider the following tips:
Trend Identification: Heiken Ashi "better" candles can help you identify the prevailing trend in the market. When the majority of the candles are green (or another color, depending on your chart settings) and there are no or few lower wicks, it may indicate a strong uptrend. Conversely, when the majority of the candles are red (or another color) and there are no or few upper wicks, it may signal a strong downtrend.
Trend Reversals: Look for potential trend reversals when a change in the color of the candles occurs, especially when accompanied by longer wicks. For example, if a green candle with a long lower wick is followed by a red candle, it could indicate a bearish reversal. Similarly, a red candle with a long upper wick followed by a green candle may suggest a bullish reversal.
Support and Resistance: You can use Heiken Ashi "better" candles to identify potential support and resistance levels. When the candles are consistently moving in one direction and then suddenly change color with longer wicks, it could indicate the presence of a support or resistance level.
Stop-Loss and Take-Profit: Using Heiken Ashi "better" candles can help you manage risk by determining optimal stop-loss and take-profit levels. For instance, you can place your stop-loss below the low of the most recent green candle in an uptrend or above the high of the most recent red candle in a downtrend.
Confirming Signals: Heiken Ashi "better" candles should be used in conjunction with other technical indicators, such as moving averages, oscillators, or chart patterns, to confirm signals and improve the accuracy of your analysis.
In this implementation, you have the choice of AMA, KAMA, or T3 smoothing. These are as follows:
Kaufman Adaptive Moving Average (KAMA)
The Kaufman Adaptive Moving Average (KAMA) is a type of adaptive moving average used in technical analysis to smooth out price fluctuations and identify trends. The KAMA adjusts its smoothing factor based on the market's volatility, making it more responsive in volatile markets and smoother in calm markets. The KAMA is calculated using three different efficiency ratios that determine the appropriate smoothing factor for the current market conditions. These ratios are based on the noise level of the market, the speed at which the market is moving, and the length of the moving average. The KAMA is a popular choice among traders who prefer to use adaptive indicators to identify trends and potential reversals.
Adaptive Moving Average
The Adaptive Moving Average (AMA) is a type of moving average that adjusts its sensitivity to price movements based on market conditions. It uses a ratio between the current price and the highest and lowest prices over a certain lookback period to determine its level of smoothing. The AMA can help reduce lag and increase responsiveness to changes in trend direction, making it useful for traders who want to follow trends while avoiding false signals. The AMA is calculated by multiplying a smoothing constant with the difference between the current price and the previous AMA value, then adding the result to the previous AMA value.
T3
The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm.
The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction.
The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Volatility-Adaptive Rapid RSI T3 as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Volatility-Adaptive Rapid RSI T3
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
]█ Setting up the GKD
The GKD system involves chaining indicators together. These are the steps to set this up.
Use a GKD-C indicator alone on a chart
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
Use a GKD-V indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Use a GKD-B indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Baseline (Baseline, Backtest)
1. Import the GKD-B Baseline into the GKD-BT Backtest: "Input into Volatility/Volume or Backtest (Baseline testing)"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline"
Volatility/Volume (Volatility/Volume, Backte st)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Solo"
2. Inside the GKD-V indicator, change the "Signal Type" setting to "Crossing" (neither traditional nor both can be backtested)
3. Import the GKD-V indicator into the GKD-BT Backtest: "Input into C1 or Backtest"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Volatility/Volume"
5. Inside the GKD-BT Backtest, a) change the setting "Backtest Type" to "Trading" if using a directional GKD-V indicator; or, b) change the setting "Backtest Type" to "Full" if using a directional or non-directional GKD-V indicator (non-directional GKD-V can only test Longs and Shorts separately)
6. If "Backtest Type" is set to "Full": Inside the GKD-BT Backtest, change the setting "Backtest Side" to "Long" or "Short
7. If "Backtest Type" is set to "Full": To allow the system to open multiple orders at one time so you test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Solo Confirmation Simple (Confirmation, Backtest)
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
1. Import the GKD-C indicator into the GKD-BT Backtest: "Input into Backtest"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Solo Confirmation Simple"
Solo Confirmation Complex without Exits (Baseline, Volatility/Volume, Confirmation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
6. Import the GKD-C into the GKD-BT Backtest: "Input into Exit or Backtest"
Solo Confirmation Complex with Exits (Baseline, Volatility/Volume, Confirmation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Import the GKD-C indicator into the GKD-E indicator: "Input into Exit"
6. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
7. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Full GKD without Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
9. Import the GKD-E into the GKD-BT Backtest: "Input into Exit or Backtest"
Full GKD with Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Import the GKD-C Continuation indicator into the GKD-E indicator: "Input into Exit"
9. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
10. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Baseline + Volatility/Volume (Baseline, Volatility/Volume, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Baseline + Volatility/Volume"
2. Inside the GKD-V indicator, make sure the "Signal Type" setting is set to "Traditional"
3. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline + Volatility/Volume"
5. Import the GKD-V into the GKD-BT Backtest: "Input into C1 or Backtest"
6. Inside the GKD-BT Backtest, change the setting "Backtest Type" to "Full". For this backtest, you must test Longs and Shorts separately
7. To allow the system to open multiple orders at one time so you can test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Requirements
Inputs
Confirmation 1: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Continuation: GKD-C Confirmation indicator
Solo Confirmation Simple: GKD-B Baseline
Solo Confirmation Complex: GKD-V Volatility / Volume indicator
Solo Confirmation Super Complex: GKD-V Volatility / Volume indicator
Stacked 1: None
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 1
Outputs
Confirmation 1: GKD-C Confirmation 2 indicator
Confirmation 2: GKD-C Continuation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest
Solo Confirmation Complex: GKD-BT Backtest or GKD-E Exit indicator
Solo Confirmation Super Complex: GKD-C Continuation indicator
Stacked 1: GKD-C, GKD-V, or GKD-B Stacked 2+
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 2+ or GKD-BT Backtest
Additional features will be added in future releases.
GKD-C Adaptive-Lookback Variety RSI [Loxx]Giga Kaleidoscope GKD-C Adaptive-Lookback Variety RSI is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-C Adaptive-Lookback Variety RSI
What is the Adaptive Lookback Period?
The adaptive lookback period is a technique used in technical analysis to adjust the period of an indicator based on changes in market conditions. This technique is particularly useful in volatile or rapidly changing markets where a fixed period may not be optimal for detecting trends or signals.
The concept of the adaptive lookback period is relatively simple. By adjusting the lookback period based on changes in market conditions, traders can more accurately identify trends and signals. This can help traders to enter and exit trades at the right time and improve the profitability of their trading strategies.
The adaptive lookback period works by identifying potential swing points in the market. Once these points are identified, the lookback period is calculated based on the number of swings and a speed parameter. The swing count parameter determines the number of swings that must occur before the lookback period is adjusted. The speed parameter controls the rate at which the lookback period is adjusted, with higher values indicating a more rapid adjustment.
The adaptive lookback period can be applied to a wide range of technical indicators, including moving averages, oscillators, and trendlines. By adjusting the period of these indicators based on changes in market conditions, traders can reduce the impact of noise and false signals, leading to more profitable trades.
In summary, the adaptive lookback period is a powerful technique for traders and analysts looking to optimize their technical indicators. By adjusting the period based on changes in market conditions, traders can more accurately identify trends and signals, leading to more profitable trades. While there are various ways to implement the adaptive lookback period, the basic concept remains the same, and traders can adapt and customize the technique to suit their individual needs and trading styles.
This indicator includes 10 types of RSI
1. Regular RSI
2. Slow RSI
3. Ehlers Smoothed RSI
4. Cutler's RSI
5. Rapid RSI
6. Harris' RSI
7. RSI DEMA
8. RSI TEMA
9. RSI T3
10. Jurik RSX
Regular RSI
The Relative Strength Index (RSI) is a widely used technical indicator in the field of financial market analysis. Developed by J. Welles Wilder Jr. in 1978, the RSI is a momentum oscillator that measures the speed and change of price movements. It helps traders identify potential trend reversals, overbought, and oversold conditions in a market.
The RSI is calculated based on the average gains and losses of an asset over a specified period, typically 14 days. The formula for calculating the RSI is as follows:
RSI = 100 - (100 / (1 + RS))
Where:
RS (Relative Strength) = Average gain over the specified period / Average loss over the specified period
The RSI ranges from 0 to 100, with values above 70 generally considered overbought (potentially indicating that the asset is overvalued and may experience a price decline) and values below 30 considered oversold (potentially indicating that the asset is undervalued and may experience a price increase).
Slow RSI
Slow RSI is a modified version of the Relative Strength Index (RSI) indicator that aims to provide a smoother, more consistent signal than the traditional RSI. The Slow RSI is designed to be less sensitive to sudden price movements, which can cause false signals.
To calculate Slow RSI, we first calculate the up and down values, just like in traditional RSI and Ehlers RSI. The up and down values are calculated by comparing the current price to the previous price, and then adding up the positive and negative differences.
Next, we calculate the Slow RSI value using the formula:
SlowRSI = 100 * up / (up + dn)
where "up" and "dn" are the total positive and negative differences, respectively.
This formula is similar to the one used in traditional RSI, but the dynamic lookback period based on the average of the up and down values is used to smooth out the signal.
Finally, we apply smoothing to the Slow RSI value by taking an exponential moving average (EMA) of the Slow RSI values over a specified period. This EMA helps to reduce the impact of sudden price movements and provide a smoother, more consistent signal over time.
Ehler's Smoothed RSI
Ehlers RSI is a modified version of the Relative Strength Index (RSI) indicator created by John Ehlers, a well-known technical analyst and author. The purpose of Ehlers RSI is to reduce lag and improve the responsiveness of the traditional RSI indicator.
To calculate Ehlers RSI, we first smooth the prices by taking a weighted average of the current price and the two previous prices. This smoothing helps to reduce noise in the data and produce a more accurate signal.
Next, we calculate the up and down values differently than in traditional RSI. In traditional RSI, the up and down values are based on the difference between the current price and the previous price. In Ehlers RSI, the up and down values are based on the difference between the current price and the price two bars ago. This approach helps to reduce lag and produce a more responsive indicator.
Finally, we calculate Ehlers RSI using the formula:
EhlersRSI = 50 * (up - down) / (up + down) + 50
The result is a more timely signal that can help traders identify potential trends and reversals in the market. However, as with any technical indicator, Ehlers RSI should be used in conjunction with other analysis tools and should not be relied on as the sole basis for trading decisions.
Cutler's RSI
Cutler's RSI (Relative Strength Index) is a variation of the traditional RSI, a popular technical analysis indicator used to measure the speed and change of price movements. The main difference between Cutler's RSI and the traditional RSI is the calculation method used to smooth the data. While the traditional RSI uses an exponential moving average (EMA) to smooth the data, Cutler's RSI uses a simple moving average (SMA).
Here's the formula for Cutler's RSI:
1. Calculate the price change: Price Change = Current Price - Previous Price
2. Calculate the average gain and average loss over a specified period (usually 14 days):
If Price Change > 0, add it to the total gains.
If Price Change < 0, add the absolute value to the total losses.
3. Calculate the average gain and average loss by dividing the totals by the specified period: Average Gain = Total Gains / Period, Average Loss = Total Losses / Period
4. Calculate the Relative Strength (RS): RS = Average Gain / Average Loss
5. Calculate Cutler's RSI: Cutler's RSI = 100 - (100 / (1 + RS))
Cutler's RSI is not necessarily better than the regular RSI; it's just a different variation of the traditional RSI that uses a simple moving average (SMA) instead of an exponential moving average (EMA) quantifiedstrategies.com. The main advantage of Cutler's RSI is that it is not data length dependent, meaning it returns consistent results regardless of the length of the period, or the starting point within a data file quantifiedstrategies.com.
However, it's worth noting that Cutler's RSI does not necessarily outperform the traditional RSI. In fact, backtests reveal that Cutler's RSI is no improvement compared to Wilder's RSI quantifiedstrategies.com. Additionally, using an SMA instead of an EMA in Cutler's RSI may result in the loss of the "believed" advantage of weighting the most recent price action aaii.com.
Both Cutler's RSI and the traditional RSI can be used to identify overbought/oversold levels, support and resistance, spot divergences for possible reversals, and confirm the signals from other indicators investopedia.com. Ultimately, the choice between Cutler's RSI and the traditional RSI depends on personal preference and the specific trading strategy being employed.
Rapid RSI
Rapid RSI is a technical analysis indicator that is a modified version of the Relative Strength Index (RSI). It was developed by Andrew Cardwell and was first introduced in the October 2006 issue of Technical Analysis of Stocks & Commodities magazine.
The Rapid RSI improves upon the regular RSI by modifying the way the average gains and losses are calculated. Here's a general breakdown of the Rapid RSI calculation:
1. Calculate the upward change (when the price has increased) and the downward change (when the price has decreased) for each period.
2. Calculate the simple moving average (SMA) of the upward changes and the SMA of the downward changes over the specified period.
3. Divide the SMA of the upward changes by the SMA of the downward changes to get the relative strength (RS).
4. Calculate the Rapid RSI by transforming the relative strength (RS) into a value ranging from 0 to 100.
By using the simple moving average (SMA) instead of the slow exponential moving average (RMA) as in the regular RSI, the Rapid RSI tends to be more responsive to recent price changes. This can help traders identify overbought and oversold conditions more quickly, potentially leading to earlier entry and exit points. However, it is important to note that a faster indicator may also produce more false signals.
Harris' RSI
Harris RSI (Relative Strength Index) is a technical indicator used in financial analysis to measure the strength or weakness of a security over time. It was developed by Larry Harris in 1986 as an alternative to the traditional RSI, which measures the price change of a security over a given period.
The Harris RSI uses a slightly different formula from the traditional RSI, but it is based on the same principles. It calculates the ratio of the average gain to the average loss over a specified period, typically 14 days. The result is then plotted on a scale of 0 to 100, with high values indicating overbought conditions and low values indicating oversold conditions.
The Harris RSI is believed to be more responsive to short-term price movements than the traditional RSI, making it useful for traders who are looking for quick trading opportunities. However, like any technical indicator, it should be used in conjunction with other forms of analysis to make informed trading decisions.
The calculation of the Harris RSI involves several steps:
1. Calculate the price change over the specified period (usually 14 days) using the following formula:
Price Change = Close Price - Prior Close Price
2. Calculate the average gain and average loss over the same period, using separate formulas for each:
Average Gain = (Sum of Gains over the Period) / Period
Average Loss = (Sum of Losses over the Period) / Period
Gains are calculated as the sum of all positive price changes over the period, while losses are calculated as the sum of all negative price changes over the period.
3. Calculate the Relative Strength (RS) as the ratio of the Average Gain to the Average Loss:
RS = Average Gain / Average Loss
4. Calculate the Harris RSI using the following formula:
Harris RSI = 100 - (100 / (1 + RS))
The resulting Harris RSI value is a number between 0 and 100, which is plotted on a chart to identify overbought or oversold conditions in the security. A value above 70 is generally considered overbought, while a value below 30 is generally considered oversold.
DEMA RSI
DEMA RSI is a variation of the Relative Strength Index (RSI) technical indicator that incorporates the Double Exponential Moving Average (DEMA) for smoothing. Like the regular RSI, the DEMA RSI is a momentum oscillator used to measure the speed and change of price movements, and it ranges from 0 to 100. Readings below 30 typically indicate oversold conditions, while readings above 70 indicate overbought conditions.
The DEMA RSI aims to improve upon the regular RSI by addressing its limitations, such as lag and false signals. By using the DEMA, a more responsive and faster RSI can be achieved. Here's a general breakdown of the DEMA RSI calculation:
1. Calculate the price change for each period, as well as the absolute value of the change.
2. Apply the DEMA smoothing technique to both the price change and its absolute value, separately. This involves calculating two sets of exponential moving averages and combining them to create a double-weighted moving average with reduced lag.
3. Divide the smoothed price change by the smoothed absolute value of the price change.
4. Transform the result into a value ranging from 0 to 100 to obtain the DEMA RSI.
The DEMA RSI is considered an improvement over the regular RSI because it provides faster and more responsive signals. This can help traders identify overbought and oversold conditions more accurately and potentially avoid false signals.
In summary, the main advantages of these RSI variations over the regular RSI are their ability to reduce noise, provide smoother lines, and be more responsive to price changes. This can lead to more accurate signals and fewer false positives in different market conditions.
TEMA RSI
TEMA RSI is a variation of the Relative Strength Index (RSI) technical indicator that incorporates the Triple Exponential Moving Average (TEMA) for smoothing. Like the regular RSI, the TEMA RSI is a momentum oscillator used to measure the speed and change of price movements, and it ranges from 0 to 100. Readings below 30 typically indicate oversold conditions, while readings above 70 indicate overbought conditions.
The TEMA RSI aims to improve upon the regular RSI by addressing its limitations, such as lag and false signals. By using the TEMA, a more responsive and faster RSI can be achieved. Here's a general breakdown of the TEMA RSI calculation:
1. Calculate the price change for each period, as well as the absolute value of the change.
2. Apply the TEMA smoothing technique to both the price change and its absolute value, separately. This involves calculating two sets of exponential moving averages and combining them to create a double-weighted moving average with reduced lag.
3. Divide the smoothed price change by the smoothed absolute value of the price change.
4. Transform the result into a value ranging from 0 to 100 to obtain the TEMA RSI.
The TEMA RSI is considered an improvement over the regular RSI because it provides faster and more responsive signals. This can help traders identify overbought and oversold conditions more accurately and potentially avoid false signals.
T3 RSI
T3 RSI is a variation of the Relative Strength Index (RSI) technical indicator that incorporates the Tilson T3 for smoothing. Like the regular RSI, the T3 RSI is a momentum oscillator used to measure the speed and change of price movements, and it ranges from 0 to 100. Readings below 30 typically indicate oversold conditions, while readings above 70 indicate overbought conditions.
The T3 RSI aims to improve upon the regular RSI by addressing its limitations, such as lag and false signals. By using the T3, a more responsive and faster RSI can be achieved. Here's a general breakdown of the T3 RSI calculation:
1. Calculate the price change for each period, as well as the absolute value of the change.
2. Apply the T3 smoothing technique to both the price change and its absolute value, separately. This involves calculating two sets of exponential moving averages and combining them to create a double-weighted moving average with reduced lag.
3. Divide the smoothed price change by the smoothed absolute value of the price change.
4. Transform the result into a value ranging from 0 to 100 to obtain the T3 RSI.
The T3 RSI is considered an improvement over the regular RSI because it provides faster and more responsive signals. This can help traders identify overbought and oversold conditions more accurately and potentially avoid false signals.
Jurik RSX
The Jurik RSX is a technical indicator developed by Mark Jurik to measure the momentum and strength of price movements in financial markets, such as stocks, commodities, and currencies. It is an advanced version of the traditional Relative Strength Index (RSI), designed to offer smoother and less lagging signals compared to the standard RSI.
The main advantage of the Jurik RSX is that it provides more accurate and timely signals for traders and analysts, thanks to its improved calculation methods that reduce noise and lag in the indicator's output. This enables better decision-making when analyzing market trends and potential trading opportunities.
What is Adaptive-Lookback Variety RSI
This indicator allows the user to select from 9 different RSI types and 33 source types. The various RSI types is enhanced by injecting an adaptive lookback period into the caculation making the RSI able to adaptive to differing market conditions.
Additional Features
This indicator allows you to select from 33 source types. They are as follows:
Close
Open
High
Low
Median
Typical
Weighted
Average
Average Median Body
Trend Biased
Trend Biased (Extreme)
HA Close
HA Open
HA High
HA Low
HA Median
HA Typical
HA Weighted
HA Average
HA Average Median Body
HA Trend Biased
HA Trend Biased (Extreme)
HAB Close
HAB Open
HAB High
HAB Low
HAB Median
HAB Typical
HAB Weighted
HAB Average
HAB Average Median Body
HAB Trend Biased
HAB Trend Biased (Extreme)
What are Heiken Ashi "better" candles?
Heiken Ashi "better" candles are a modified version of the standard Heiken Ashi candles, which are a popular charting technique used in technical analysis. Heiken Ashi candles help traders identify trends and potential reversal points by smoothing out price data and reducing market noise. The "better formula" was proposed by Sebastian Schmidt in an article published by BNP Paribas in Warrants & Zertifikate, a German magazine, in August 2004. The aim of this formula is to further improve the smoothing of the Heiken Ashi chart and enhance its effectiveness in identifying trends and reversals.
Standard Heiken Ashi candles are calculated using the following formulas:
Heiken Ashi Close = (Open + High + Low + Close) / 4
Heiken Ashi Open = (Previous Heiken Ashi Open + Previous Heiken Ashi Close) / 2
Heiken Ashi High = Max (High, Heiken Ashi Open, Heiken Ashi Close)
Heiken Ashi Low = Min (Low, Heiken Ashi Open, Heiken Ashi Close)
The "better formula" modifies the standard Heiken Ashi calculation by incorporating additional smoothing, which can help reduce noise and make it easier to identify trends and reversals. The modified formulas for Heiken Ashi "better" candles are as follows:
Better Heiken Ashi Close = (Open + High + Low + Close) / 4
Better Heiken Ashi Open = (Previous Better Heiken Ashi Open + Previous Better Heiken Ashi Close) / 2
Better Heiken Ashi High = Max (High, Better Heiken Ashi Open, Better Heiken Ashi Close)
Better Heiken Ashi Low = Min (Low, Better Heiken Ashi Open, Better Heiken Ashi Close)
Smoothing Factor = 2 / (N + 1), where N is the chosen period for smoothing
Smoothed Better Heiken Ashi Open = (Better Heiken Ashi Open * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Open * (1 - Smoothing Factor))
Smoothed Better Heiken Ashi Close = (Better Heiken Ashi Close * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Close * (1 - Smoothing Factor))
The smoothed Better Heiken Ashi Open and Close values are then used to calculate the smoothed Better Heiken Ashi High and Low values, resulting in "better" candles that provide a clearer representation of the market trend and potential reversal points.
It's important to note that, like any other technical analysis tool, Heiken Ashi "better" candles are not foolproof and should be used in conjunction with other indicators and analysis techniques to make well-informed trading decisions.
Heiken Ashi "better" candles, as mentioned previously, provide a clearer representation of market trends and potential reversal points by reducing noise and smoothing out price data. When using these candles in conjunction with other technical analysis tools and indicators, traders can gain valuable insights into market behavior and make more informed decisions.
To effectively use Heiken Ashi "better" candles in your trading strategy, consider the following tips:
Trend Identification: Heiken Ashi "better" candles can help you identify the prevailing trend in the market. When the majority of the candles are green (or another color, depending on your chart settings) and there are no or few lower wicks, it may indicate a strong uptrend. Conversely, when the majority of the candles are red (or another color) and there are no or few upper wicks, it may signal a strong downtrend.
Trend Reversals: Look for potential trend reversals when a change in the color of the candles occurs, especially when accompanied by longer wicks. For example, if a green candle with a long lower wick is followed by a red candle, it could indicate a bearish reversal. Similarly, a red candle with a long upper wick followed by a green candle may suggest a bullish reversal.
Support and Resistance: You can use Heiken Ashi "better" candles to identify potential support and resistance levels. When the candles are consistently moving in one direction and then suddenly change color with longer wicks, it could indicate the presence of a support or resistance level.
Stop-Loss and Take-Profit: Using Heiken Ashi "better" candles can help you manage risk by determining optimal stop-loss and take-profit levels. For instance, you can place your stop-loss below the low of the most recent green candle in an uptrend or above the high of the most recent red candle in a downtrend.
Confirming Signals: Heiken Ashi "better" candles should be used in conjunction with other technical indicators, such as moving averages, oscillators, or chart patterns, to confirm signals and improve the accuracy of your analysis.
In this implementation, you have the choice of AMA, KAMA, or T3 smoothing. These are as follows:
Kaufman Adaptive Moving Average (KAMA)
The Kaufman Adaptive Moving Average (KAMA) is a type of adaptive moving average used in technical analysis to smooth out price fluctuations and identify trends. The KAMA adjusts its smoothing factor based on the market's volatility, making it more responsive in volatile markets and smoother in calm markets. The KAMA is calculated using three different efficiency ratios that determine the appropriate smoothing factor for the current market conditions. These ratios are based on the noise level of the market, the speed at which the market is moving, and the length of the moving average. The KAMA is a popular choice among traders who prefer to use adaptive indicators to identify trends and potential reversals.
Adaptive Moving Average
The Adaptive Moving Average (AMA) is a type of moving average that adjusts its sensitivity to price movements based on market conditions. It uses a ratio between the current price and the highest and lowest prices over a certain lookback period to determine its level of smoothing. The AMA can help reduce lag and increase responsiveness to changes in trend direction, making it useful for traders who want to follow trends while avoiding false signals. The AMA is calculated by multiplying a smoothing constant with the difference between the current price and the previous AMA value, then adding the result to the previous AMA value.
T3
The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm.
The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction.
The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Adaptive-Lookback Variety RSI as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Adaptive-Lookback Variety RSI
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
]█ Setting up the GKD
The GKD system involves chaining indicators together. These are the steps to set this up.
Use a GKD-C indicator alone on a chart
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
Use a GKD-V indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Use a GKD-B indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Baseline (Baseline, Backtest)
1. Import the GKD-B Baseline into the GKD-BT Backtest: "Input into Volatility/Volume or Backtest (Baseline testing)"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline"
Volatility/Volume (Volatility/Volume, Backte st)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Solo"
2. Inside the GKD-V indicator, change the "Signal Type" setting to "Crossing" (neither traditional nor both can be backtested)
3. Import the GKD-V indicator into the GKD-BT Backtest: "Input into C1 or Backtest"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Volatility/Volume"
5. Inside the GKD-BT Backtest, a) change the setting "Backtest Type" to "Trading" if using a directional GKD-V indicator; or, b) change the setting "Backtest Type" to "Full" if using a directional or non-directional GKD-V indicator (non-directional GKD-V can only test Longs and Shorts separately)
6. If "Backtest Type" is set to "Full": Inside the GKD-BT Backtest, change the setting "Backtest Side" to "Long" or "Short
7. If "Backtest Type" is set to "Full": To allow the system to open multiple orders at one time so you test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Solo Confirmation Simple (Confirmation, Backtest)
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
1. Import the GKD-C indicator into the GKD-BT Backtest: "Input into Backtest"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Solo Confirmation Simple"
Solo Confirmation Complex without Exits (Baseline, Volatility/Volume, Confirmation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
6. Import the GKD-C into the GKD-BT Backtest: "Input into Exit or Backtest"
Solo Confirmation Complex with Exits (Baseline, Volatility/Volume, Confirmation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Import the GKD-C indicator into the GKD-E indicator: "Input into Exit"
6. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
7. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Full GKD without Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
9. Import the GKD-E into the GKD-BT Backtest: "Input into Exit or Backtest"
Full GKD with Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Import the GKD-C Continuation indicator into the GKD-E indicator: "Input into Exit"
9. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
10. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Baseline + Volatility/Volume (Baseline, Volatility/Volume, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Baseline + Volatility/Volume"
2. Inside the GKD-V indicator, make sure the "Signal Type" setting is set to "Traditional"
3. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline + Volatility/Volume"
5. Import the GKD-V into the GKD-BT Backtest: "Input into C1 or Backtest"
6. Inside the GKD-BT Backtest, change the setting "Backtest Type" to "Full". For this backtest, you must test Longs and Shorts separately
7. To allow the system to open multiple orders at one time so you can test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Requirements
Inputs
Confirmation 1: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Continuation: GKD-C Confirmation indicator
Solo Confirmation Simple: GKD-B Baseline
Solo Confirmation Complex: GKD-V Volatility / Volume indicator
Solo Confirmation Super Complex: GKD-V Volatility / Volume indicator
Stacked 1: None
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 1
Outputs
Confirmation 1: GKD-C Confirmation 2 indicator
Confirmation 2: GKD-C Continuation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest
Solo Confirmation Complex: GKD-BT Backtest or GKD-E Exit indicator
Solo Confirmation Super Complex: GKD-C Continuation indicator
Stacked 1: GKD-C, GKD-V, or GKD-B Stacked 2+
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 2+ or GKD-BT Backtest
Additional features will be added in future releases.
GKD-C Sentiment Zone Oscillator [Loxx]Giga Kaleidoscope GKD-C Sentiment Zone Oscillator is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-C Sentiment Zone Oscillator
The Sentiment Zone Oscillator (SZO) is a technical indicator used in financial markets to measure the sentiment of traders and investors. It is primarily used to identify potential market reversals and overbought or oversold conditions, by analyzing the underlying sentiment of market participants. The SZO was developed by Walid Khalil and David Steckler and was first introduced in the Stocks & Commodities magazine in May 2011.
The SZO is calculated using a combination of moving averages and the Rate of Change (ROC) indicator. The basic idea behind the SZO is to compare the current price to its recent average price and then normalize this value using a moving average. The resulting oscillator ranges between -1 and 1, where positive values indicate bullish sentiment and negative values indicate bearish sentiment. Here's a step-by-step explanation of how to calculate the SZO:
Choose the time period for the calculation. The default period is 14 days, but you can adjust this to fit your trading strategy.
1. Calculate the Rate of Change (ROC) for the chosen period. The ROC is calculated as the percentage change in price from the current period to the previous period. The formula for ROC is:
2. ROC = * 100
3. Calculate the Simple Moving Average (SMA) of the ROC for the chosen period. The SMA is the average of the ROC values for the given period.
4. Calculate the Exponential Moving Average (EMA) of the SMA for the chosen period. The EMA is a type of weighted moving average that gives more weight to recent data points. The formula for EMA is:
EMA = (Current SMA - Previous EMA) * (2 / (Period + 1)) + Previous EMA
5. Calculate the Sentiment Zone Oscillator (SZO) by normalizing the EMA value between -1 and 1. The formula for SZO is:
SZO = (EMA - 50) / 50
Interpretation of the Sentiment Zone Oscillator:
-Values above 0.5 indicate strong bullish sentiment, suggesting that the market may be overbought and a potential reversal could occur.
-Values below -0.5 indicate strong bearish sentiment, suggesting that the market may be oversold and a potential reversal could occur.
-Values between -0.5 and 0.5 indicate neutral sentiment, meaning that the market is in a consolidation phase and no clear trend is present.
Traders and investors can use the SZO to identify potential entry and exit points in the market, as well as to gauge the overall market sentiment. It is important to note that the SZO should not be used in isolation, but rather as a complementary tool alongside other technical indicators and fundamental analysis.
This version expands on typical calculation for SZO by allowing 63+ different smoothing methods for price and the SZO. This allows the user to choose something different than the standard SMA and EMA. This version also expands the interpretation of the SZO by allowing the user to select from varoius signal types: Middle, Quantile middle, Quantile Levels, Floating Levels, or Floating middle.
Additional Features
This indicator allows you to select from 33 source types. They are as follows:
Close
Open
High
Low
Median
Typical
Weighted
Average
Average Median Body
Trend Biased
Trend Biased (Extreme)
HA Close
HA Open
HA High
HA Low
HA Median
HA Typical
HA Weighted
HA Average
HA Average Median Body
HA Trend Biased
HA Trend Biased (Extreme)
HAB Close
HAB Open
HAB High
HAB Low
HAB Median
HAB Typical
HAB Weighted
HAB Average
HAB Average Median Body
HAB Trend Biased
HAB Trend Biased (Extreme)
What are Heiken Ashi "better" candles?
Heiken Ashi "better" candles are a modified version of the standard Heiken Ashi candles, which are a popular charting technique used in technical analysis. Heiken Ashi candles help traders identify trends and potential reversal points by smoothing out price data and reducing market noise. The "better formula" was proposed by Sebastian Schmidt in an article published by BNP Paribas in Warrants & Zertifikate, a German magazine, in August 2004. The aim of this formula is to further improve the smoothing of the Heiken Ashi chart and enhance its effectiveness in identifying trends and reversals.
Standard Heiken Ashi candles are calculated using the following formulas:
Heiken Ashi Close = (Open + High + Low + Close) / 4
Heiken Ashi Open = (Previous Heiken Ashi Open + Previous Heiken Ashi Close) / 2
Heiken Ashi High = Max (High, Heiken Ashi Open, Heiken Ashi Close)
Heiken Ashi Low = Min (Low, Heiken Ashi Open, Heiken Ashi Close)
The "better formula" modifies the standard Heiken Ashi calculation by incorporating additional smoothing, which can help reduce noise and make it easier to identify trends and reversals. The modified formulas for Heiken Ashi "better" candles are as follows:
Better Heiken Ashi Close = (Open + High + Low + Close) / 4
Better Heiken Ashi Open = (Previous Better Heiken Ashi Open + Previous Better Heiken Ashi Close) / 2
Better Heiken Ashi High = Max (High, Better Heiken Ashi Open, Better Heiken Ashi Close)
Better Heiken Ashi Low = Min (Low, Better Heiken Ashi Open, Better Heiken Ashi Close)
Smoothing Factor = 2 / (N + 1), where N is the chosen period for smoothing
Smoothed Better Heiken Ashi Open = (Better Heiken Ashi Open * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Open * (1 - Smoothing Factor))
Smoothed Better Heiken Ashi Close = (Better Heiken Ashi Close * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Close * (1 - Smoothing Factor))
The smoothed Better Heiken Ashi Open and Close values are then used to calculate the smoothed Better Heiken Ashi High and Low values, resulting in "better" candles that provide a clearer representation of the market trend and potential reversal points.
It's important to note that, like any other technical analysis tool, Heiken Ashi "better" candles are not foolproof and should be used in conjunction with other indicators and analysis techniques to make well-informed trading decisions.
Heiken Ashi "better" candles, as mentioned previously, provide a clearer representation of market trends and potential reversal points by reducing noise and smoothing out price data. When using these candles in conjunction with other technical analysis tools and indicators, traders can gain valuable insights into market behavior and make more informed decisions.
To effectively use Heiken Ashi "better" candles in your trading strategy, consider the following tips:
Trend Identification: Heiken Ashi "better" candles can help you identify the prevailing trend in the market. When the majority of the candles are green (or another color, depending on your chart settings) and there are no or few lower wicks, it may indicate a strong uptrend. Conversely, when the majority of the candles are red (or another color) and there are no or few upper wicks, it may signal a strong downtrend.
Trend Reversals: Look for potential trend reversals when a change in the color of the candles occurs, especially when accompanied by longer wicks. For example, if a green candle with a long lower wick is followed by a red candle, it could indicate a bearish reversal. Similarly, a red candle with a long upper wick followed by a green candle may suggest a bullish reversal.
Support and Resistance: You can use Heiken Ashi "better" candles to identify potential support and resistance levels. When the candles are consistently moving in one direction and then suddenly change color with longer wicks, it could indicate the presence of a support or resistance level.
Stop-Loss and Take-Profit: Using Heiken Ashi "better" candles can help you manage risk by determining optimal stop-loss and take-profit levels. For instance, you can place your stop-loss below the low of the most recent green candle in an uptrend or above the high of the most recent red candle in a downtrend.
Confirming Signals: Heiken Ashi "better" candles should be used in conjunction with other technical indicators, such as moving averages, oscillators, or chart patterns, to confirm signals and improve the accuracy of your analysis.
In this implementation, you have the choice of AMA, KAMA, or T3 smoothing. These are as follows:
Kaufman Adaptive Moving Average (KAMA)
The Kaufman Adaptive Moving Average (KAMA) is a type of adaptive moving average used in technical analysis to smooth out price fluctuations and identify trends. The KAMA adjusts its smoothing factor based on the market's volatility, making it more responsive in volatile markets and smoother in calm markets. The KAMA is calculated using three different efficiency ratios that determine the appropriate smoothing factor for the current market conditions. These ratios are based on the noise level of the market, the speed at which the market is moving, and the length of the moving average. The KAMA is a popular choice among traders who prefer to use adaptive indicators to identify trends and potential reversals.
Adaptive Moving Average
The Adaptive Moving Average (AMA) is a type of moving average that adjusts its sensitivity to price movements based on market conditions. It uses a ratio between the current price and the highest and lowest prices over a certain lookback period to determine its level of smoothing. The AMA can help reduce lag and increase responsiveness to changes in trend direction, making it useful for traders who want to follow trends while avoiding false signals. The AMA is calculated by multiplying a smoothing constant with the difference between the current price and the previous AMA value, then adding the result to the previous AMA value.
T3
The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm.
The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction.
The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Sentiment Zone Oscillator as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Sentiment Zone Oscillator
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
]█ Setting up the GKD
The GKD system involves chaining indicators together. These are the steps to set this up.
Use a GKD-C indicator alone on a chart
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
Use a GKD-V indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Use a GKD-B indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Baseline (Baseline, Backtest)
1. Import the GKD-B Baseline into the GKD-BT Backtest: "Input into Volatility/Volume or Backtest (Baseline testing)"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline"
Volatility/Volume (Volatility/Volume, Backte st)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Solo"
2. Inside the GKD-V indicator, change the "Signal Type" setting to "Crossing" (neither traditional nor both can be backtested)
3. Import the GKD-V indicator into the GKD-BT Backtest: "Input into C1 or Backtest"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Volatility/Volume"
5. Inside the GKD-BT Backtest, a) change the setting "Backtest Type" to "Trading" if using a directional GKD-V indicator; or, b) change the setting "Backtest Type" to "Full" if using a directional or non-directional GKD-V indicator (non-directional GKD-V can only test Longs and Shorts separately)
6. If "Backtest Type" is set to "Full": Inside the GKD-BT Backtest, change the setting "Backtest Side" to "Long" or "Short
7. If "Backtest Type" is set to "Full": To allow the system to open multiple orders at one time so you test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Solo Confirmation Simple (Confirmation, Backtest)
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
1. Import the GKD-C indicator into the GKD-BT Backtest: "Input into Backtest"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Solo Confirmation Simple"
Solo Confirmation Complex without Exits (Baseline, Volatility/Volume, Confirmation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
6. Import the GKD-C into the GKD-BT Backtest: "Input into Exit or Backtest"
Solo Confirmation Complex with Exits (Baseline, Volatility/Volume, Confirmation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Import the GKD-C indicator into the GKD-E indicator: "Input into Exit"
6. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
7. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Full GKD without Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
9. Import the GKD-E into the GKD-BT Backtest: "Input into Exit or Backtest"
Full GKD with Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Import the GKD-C Continuation indicator into the GKD-E indicator: "Input into Exit"
9. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
10. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Baseline + Volatility/Volume (Baseline, Volatility/Volume, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Baseline + Volatility/Volume"
2. Inside the GKD-V indicator, make sure the "Signal Type" setting is set to "Traditional"
3. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline + Volatility/Volume"
5. Import the GKD-V into the GKD-BT Backtest: "Input into C1 or Backtest"
6. Inside the GKD-BT Backtest, change the setting "Backtest Type" to "Full". For this backtest, you must test Longs and Shorts separately
7. To allow the system to open multiple orders at one time so you can test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Requirements
Inputs
Confirmation 1: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Continuation: GKD-C Confirmation indicator
Solo Confirmation Simple: GKD-B Baseline
Solo Confirmation Complex: GKD-V Volatility / Volume indicator
Solo Confirmation Super Complex: GKD-V Volatility / Volume indicator
Stacked 1: None
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 1
Outputs
Confirmation 1: GKD-C Confirmation 2 indicator
Confirmation 2: GKD-C Continuation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest
Solo Confirmation Complex: GKD-BT Backtest or GKD-E Exit indicator
Solo Confirmation Super Complex: GKD-C Continuation indicator
Stacked 1: GKD-C, GKD-V, or GKD-B Stacked 2+
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 2+ or GKD-BT Backtest
Additional features will be added in future releases.
