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
Cerca negli script per "stop loss"
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
Ruckard TradingLatinoThis strategy tries to mimic TradingLatino strategy.
The current implementation is beta.
Si hablas castellano o espanyol por favor consulta MENSAJE EN CASTELLANO más abajo.
It's aimed at BTCUSDT pair and 4h timeframe.
STRATEGY DEFAULT SETTINGS EXPLANATION
max_bars_back=5000 : This is a random number of bars so that the strategy test lasts for one or two years
calc_on_order_fills=false : To wait for the 4h closing is too much. Try to check if it's worth entering a position after closing one. I finally decided not to recheck if it's worth entering after an order is closed. So it is false.
calc_on_every_tick=false
pyramiding=0 : We only want one entry allowed in the same direction. And we don't want the order to scale by error.
initial_capital=1000 : These are 1000 USDT. By using 1% maximum loss per trade and 7% as a default stop loss by using 1000 USDT at 12000 USDT per BTC price you would entry with around 142 USDT which are converted into: 0.010 BTC . The maximum number of decimal for contracts on this BTCUSDT market is 3 decimals. E.g. the minimum might be: 0.001 BTC . So, this minimal 1000 amount ensures us not to entry with less than 0.001 entries which might have happened when using 100 USDT as an initial capital.
slippage=1 : Binance BTCUSDT mintick is: 0.01. Binance slippage: 0.1 % (Let's assume). TV has an integer slippage. It does not have a percentage based slippage. If we assume a 1000 initial capital, the recommended equity is 142 which at 11996 USDT per BTC price means: 0.011 BTC. The 0.1% slippage of: 0.011 BTC would be: 0.000011 . This is way smaller than the mintick. So our slippage is going to be 1. E.g. 1 (slippage) * 0.01 (mintick)
commission_type=strategy.commission.percent and commission_value=0.1 : According to: binance . com / en / fee / schedule in VIP 0 level both maker and taker fees are: 0.1 %.
BACKGROUND
Jaime Merino is a well known Youtuber focused on crypto trading
His channel TradingLatino
features monday to friday videos where he explains his strategy.
JAIME MERINO STANCE ON BOTS
Jaime Merino stance on bots (taken from memory out of a 2020 June video from him):
'~
You know. They can program you a bot and it might work.
But, there are some special situations that the bot would not be able to handle.
And, I, as a human, I would handle it. And the bot wouldn't do it.
~'
My long term target with this strategy script is add as many
special situations as I can to the script
so that it can match Jaime Merino behaviour even in non normal circumstances.
My alternate target is learn Pine script
and enjoy programming with it.
WARNING
This script might be bigger than other TradingView scripts.
However, please, do not be confused because the current status is beta.
This script has not been tested with real money.
This is NOT an official strategy from Jaime Merino.
This is NOT an official strategy from TradingLatino . net .
HOW IT WORKS
It basically uses ADX slope and LazyBear's Squeeze Momentum Indicator
to make its buy and sell decisions.
Fast paced EMA being bigger than slow paced EMA
(on higher timeframe) advices going long.
Fast paced EMA being smaller than slow paced EMA
(on higher timeframe) advices going short.
It finally add many substrats that TradingLatino uses.
SETTINGS
__ SETTINGS - Basics
____ SETTINGS - Basics - ADX
(ADX) Smoothing {14}
(ADX) DI Length {14}
(ADX) key level {23}
____ SETTINGS - Basics - LazyBear Squeeze Momentum
(SQZMOM) BB Length {20}
(SQZMOM) BB MultFactor {2.0}
(SQZMOM) KC Length {20}
(SQZMOM) KC MultFactor {1.5}
(SQZMOM) Use TrueRange (KC) {True}
____ SETTINGS - Basics - EMAs
(EMAS) EMA10 - Length {10}
(EMAS) EMA10 - Source {close}
(EMAS) EMA55 - Length {55}
(EMAS) EMA55 - Source {close}
____ SETTINGS - Volume Profile
Lowest and highest VPoC from last three days
is used to know if an entry has a support
VPVR of last 100 4h bars
is also taken into account
(VP) Use number of bars (not VP timeframe): Uses 'Number of bars {100}' setting instead of 'Volume Profile timeframe' setting for calculating session VPoC
(VP) Show tick difference from current price {False}: BETA . Might be useful for actions some day.
(VP) Number of bars {100}: If 'Use number of bars (not VP timeframe)' is turned on this setting is used to calculate session VPoC.
(VP) Volume Profile timeframe {1 day}: If 'Use number of bars (not VP timeframe)' is turned off this setting is used to calculate session VPoC.
(VP) Row width multiplier {0.6}: Adjust how the extra Volume Profile bars are shown in the chart.
(VP) Resistances prices number of decimal digits : Round Volume Profile bars label numbers so that they don't have so many decimals.
(VP) Number of bars for bottom VPOC {18}: 18 bars equals 3 days in suggested timeframe of 4 hours. It's used to calculate lowest session VPoC from previous three days. It's also used as a top VPOC for sells.
(VP) Ignore VPOC bottom advice on long {False}: If turned on it ignores bottom VPOC (or top VPOC on sells) when evaluating if a buy entry is worth it.
(VP) Number of bars for VPVR VPOC {100}: Number of bars to calculate the VPVR VPoC. We use 100 as Jaime once used. When the price bounces back to the EMA55 it might just bounce to this VPVR VPoC if its price it's lower than the EMA55 (Sells have inverse algorithm).
____ SETTINGS - ADX Slope
ADX Slope
help us to understand if ADX
has a positive slope, negative slope
or it is rather still.
(ADXSLOPE) ADX cut {23}: If ADX value is greater than this cut (23) then ADX has strength
(ADXSLOPE) ADX minimum steepness entry {45}: ADX slope needs to be 45 degrees to be considered as a positive one.
(ADXSLOPE) ADX minimum steepness exit {45}: ADX slope needs to be -45 degrees to be considered as a negative one.
(ADXSLOPE) ADX steepness periods {3}: In order to avoid false detection the slope is calculated along 3 periods.
____ SETTINGS - Next to EMA55
(NEXTEMA55) EMA10 to EMA55 bounce back percentage {80}: EMA10 might bounce back to EMA55 or maybe to 80% of its complete way to EMA55
(NEXTEMA55) Next to EMA55 percentage {15}: How much next to the EMA55 you need to be to consider it's going to bounce back upwards again.
____ SETTINGS - Stop Loss and Take Profit
You can set a default stop loss or a default take profit.
(STOPTAKE) Stop Loss % {7.0}
(STOPTAKE) Take Profit % {2.0}
____ SETTINGS - Trailing Take Profit
You can customize the default trailing take profit values
(TRAILING) Trailing Take Profit (%) {1.0}: Trailing take profit offset in percentage
(TRAILING) Trailing Take Profit Trigger (%) {2.0}: When 2.0% of benefit is reached then activate the trailing take profit.
____ SETTINGS - MAIN TURN ON/OFF OPTIONS
(EMAS) Ignore advice based on emas {false}.
(EMAS) Ignore advice based on emas (On closing long signal) {False}: Ignore advice based on emas but only when deciding to close a buy entry.
(SQZMOM) Ignore advice based on SQZMOM {false}: Ignores advice based on SQZMOM indicator.
(ADXSLOPE) Ignore advice based on ADX positive slope {false}
(ADXSLOPE) Ignore advice based on ADX cut (23) {true}
(STOPTAKE) Take Profit? {false}: Enables simple Take Profit.
(STOPTAKE) Stop Loss? {True}: Enables simple Stop Loss.
(TRAILING) Enable Trailing Take Profit (%) {True}: Enables Trailing Take Profit.
____ SETTINGS - Strategy mode
(STRAT) Type Strategy: 'Long and Short', 'Long Only' or 'Short Only'. Default: 'Long and Short'.
____ SETTINGS - Risk Management
(RISKM) Risk Management Type: 'Safe', 'Somewhat safe compound' or 'Unsafe compound'. ' Safe ': Calculations are always done with the initial capital (1000) in mind. The maximum losses per trade/day/week/month are taken into account. ' Somewhat safe compound ': Calculations are done with initial capital (1000) or a higher capital if it increases. The maximum losses per trade/day/week/month are taken into account. ' Unsafe compound ': In each order all the current capital is gambled and only the default stop loss per order is taken into account. That means that the maximum losses per trade/day/week/month are not taken into account. Default : 'Somewhat safe compound'.
(RISKM) Maximum loss per trade % {1.0}.
(RISKM) Maximum loss per day % {6.0}.
(RISKM) Maximum loss per week % {8.0}.
(RISKM) Maximum loss per month % {10.0}.
____ SETTINGS - Decimals
(DECIMAL) Maximum number of decimal for contracts {3}: How small (3 decimals means 0.001) an entry position might be in your exchange.
EXTRA 1 - PRICE IS IN RANGE indicator
(PRANGE) Print price is in range {False}: Enable a bottom label that indicates if the price is in range or not.
(PRANGE) Price range periods {5}: How many previous periods are used to calculate the medians
(PRANGE) Price range maximum desviation (%) {0.6} ( > 0 ): Maximum positive desviation for range detection
(PRANGE) Price range minimum desviation (%) {0.6} ( > 0 ): Mininum negative desviation for range detection
EXTRA 2 - SQUEEZE MOMENTUM Desviation indicator
(SQZDIVER) Show degrees {False}: Show degrees of each Squeeze Momentum Divergence lines to the x-axis.
(SQZDIVER) Show desviation labels {False}: Whether to show or not desviation labels for the Squeeze Momentum Divergences.
(SQZDIVER) Show desviation lines {False}: Whether to show or not desviation lines for the Squeeze Momentum Divergences.
EXTRA 3 - VOLUME PROFILE indicator
WARNING: This indicator works not on current bar but on previous bar. So in the worst case it might be VP from 4 hours ago. Don't worry, inside the strategy calculus the correct values are used. It's just that I cannot show the most recent one in the chart.
(VP) Print recent profile {False}: Show Volume Profile indicator
(VP) Avoid label price overlaps {False}: Avoid label prices to overlap on the chart.
EXTRA 4 - ZIGNALY SUPPORT
(ZIG) Zignaly Alert Type {Email}: 'Email', 'Webhook'. ' Email ': Prepare alert_message variable content to be compatible with zignaly expected email content format. ' Webhook ': Prepare alert_message variable content to be compatible with zignaly expected json content format.
EXTRA 5 - DEBUG
(DEBUG) Enable debug on order comments {False}: If set to true it prepares the order message to match the alert_message variable. It makes easier to debug what would have been sent by email or webhook on each of the times an order is triggered.
HOW TO USE THIS STRATEGY
BOT MODE: This is the default setting.
PROPER VOLUME PROFILE VIEWING: Click on this strategy settings. Properties tab. Make sure Recalculate 'each time the order was run' is turned off.
NEWBIE USER: (Check PROPER VOLUME PROFILE VIEWING above!) You might want to turn on the 'Print recent profile {False}' setting. Alternatively you can use my alternate realtime study: 'Resistances and supports based on simplified Volume Profile' but, be aware, it might consume one indicator.
ADVANCED USER 1: Turn on the 'Print price is in range {False}' setting and help us to debug this subindicator. Also help us to figure out how to include this value in the strategy.
ADVANCED USER 2: Turn on the all the (SQZDIVER) settings and help us to figure out how to include this value in the strategy.
ADVANCED USER 3: (Check PROPER VOLUME PROFILE VIEWING above!) Turn on the 'Print recent profile {False}' setting and report any problem with it.
JAIME MERINO: Just use the indicator as it comes by default. It should only show BUY signals, SELL signals and their associated closing signals. From time to time you might want to check 'ADVANCED USER 2' instructions to check that there's actually a divergence. Check also 'ADVANCED USER 1' instructions for your amusement.
EXTRA ADVICE
It's advised that you use this strategy in addition to these two other indicators:
* Squeeze Momentum Indicator
* ADX
so that your chart matches as close as possible to TradingLatino chart.
ZIGNALY INTEGRATION
This strategy supports Zignaly email integration by default. It also supports Zignaly Webhook integration.
ZIGNALY INTEGRATION - Email integration example
What you would write in your alert message:
||{{strategy.order.alert_message}}||key=MYSECRETKEY||
ZIGNALY INTEGRATION - Webhook integration example
What you would write in your alert message:
{ {{strategy.order.alert_message}} , "key" : "MYSECRETKEY" }
CREDITS
I have reused and adapted some code from
'Directional Movement Index + ADX & Keylevel Support' study
which it's from TradingView console user.
I have reused and adapted some code from
'3ema' study
which it's from TradingView hunganhnguyen1193 user.
I have reused and adapted some code from
'Squeeze Momentum Indicator ' study
which it's from TradingView LazyBear user.
I have reused and adapted some code from
'Strategy Tester EMA-SMA-RSI-MACD' study
which it's from TradingView fikira user.
I have reused and adapted some code from
'Support Resistance MTF' study
which it's from TradingView LonesomeTheBlue user.
I have reused and adapted some code from
'TF Segmented Linear Regression' study
which it's from TradingView alexgrover user.
I have reused and adapted some code from
"Poor man's volume profile" study
which it's from TradingView IldarAkhmetgaleev user.
FEEDBACK
Please check the strategy source code for more detailed information
where, among others, I explain all of the substrats
and if they are implemented or not.
Q1. Did I understand wrong any of the Jaime substrats (which I have implemented)?
Q2. The strategy yields quite profit when we should long (EMA10 from 1d timeframe is higher than EMA55 from 1d timeframe.
Why the strategy yields much less profit when we should short (EMA10 from 1d timeframe is lower than EMA55 from 1d timeframe)?
Any idea if you need to do something else rather than just reverse what Jaime does when longing?
FREQUENTLY ASKED QUESTIONS
FAQ1. Why are you giving this strategy for free?
TradingLatino and his fellow enthusiasts taught me this strategy. Now I'm giving back to them.
FAQ2. Seriously! Why are you giving this strategy for free?
I'm confident his strategy might be improved a lot. By keeping it to myself I would avoid other people contributions to improve it.
Now that everyone can contribute this is a win-win.
FAQ3. How can I connect this strategy to my Exchange account?
It seems that you can attach alerts to strategies.
You might want to combine it with a paying account which enable Webhook URLs to work.
I don't know how all of this works right now so I cannot give you advice on it.
You will have to do your own research on this subject. But, be careful. Automating trades, if not done properly,
might end on you automating losses.
FAQ4. I have just found that this strategy by default gives more than 3.97% of 'maximum series of losses'. That's unacceptable according to my risk management policy.
You might want to reduce default stop loss setting from 7% to something like 5% till you are ok with the 'maximum series of losses'.
FAQ5. Where can I learn more about your work on this strategy?
Check the source code. You might find unused strategies. Either because there's not a substantial increases on earnings. Or maybe because they have not been implemented yet.
FAQ6. How much leverage is applied in this strategy?
No leverage.
FAQ7. Any difference with original Jaime Merino strategy?
Most of the times Jaime defines an stop loss at the price entry. That's not the case here. The default stop loss is 7% (but, don't be confused it only means losing 1% of your investment thanks to risk management). There's also a trailing take profit that triggers at 2% profit with a 1% trailing.
FAQ8. Why this strategy return is so small?
The strategy should be improved a lot. And, well, backtesting in this platform is not guaranteed to return theoric results comparable to real-life returns. That's why I'm personally forward testing this strategy to verify it.
MENSAJE EN CASTELLANO
En primer lugar se agradece feedback para mejorar la estrategia.
Si eres un usuario avanzado y quieres colaborar en mejorar el script no dudes en comentar abajo.
Ten en cuenta que aunque toda esta descripción tenga que estar en inglés no es obligatorio que el comentario esté en inglés.
CHISTE - CASTELLANO
¡Pero Jaime!
¡400.000!
¡Tu da mun!
AccumulationPro Money Flow StrategyAccumulationPro Money Flow Strategy identifies stock trading opportunities by analyzing money flow and potential long-only opportunities following periods of increased money inflow. It employs proprietary responsive indicators and oscillators to gauge the strength and momentum of the inflow relative to previous periods, detecting money inflow, buying/selling pressure, and potential continuation/reversals, while using trailing stop exits to maximize gains while minimizing losses, with careful consideration of risk management and position sizing.
Setup Instructions:
1. Configuring the Strategy Properties:
Click the "Settings" icon (the gear symbol) next to the strategy name.
Navigate to the "Properties" tab within the Settings window.
Initial Capital: This value sets the starting equity for the strategy backtesting. Keep in mind that you will need to specify your current account size in the "Inputs" settings for position sizing.
Base Currency: Leave this setting at its "Default" value.
Order Size: This setting, which determines the capital used for each trade during backtesting, is automatically calculated and updated by the script. You should leave it set to "1 Contract" and the script will calculate the appropriate number of contracts based on your risk per trade, account size, and stop-loss placement.
Pyramiding: Set this setting at 1 order to prevent the strategy from adding to existing positions.
Commission: Enter your broker's commission fee per trade as a percentage, some brokers might offer commission free trading. Verify Price for limit orders: Keep this value as 0 ticks.
Slippage: This value depends on the instrument you are trading, If you are trading liquid stocks on a 1D chart slippage might be neglected. You can Keep this value as 1 ticks if you want to be conservative.
Margin for long positions/short positions: Set both of these to 100% since this strategy does not employ leverage or margin trading.
Recalculate:
Select the "After order is filled" option.
Select the "On every tick" option.
Fill Orders: Keep “Using bar magnifier” unselected.
Select "On bar close". Select "Using standard OHLC"
2. Configuring the Strategy Inputs:
Click the "Inputs" tab in the Settings window.
From/Thru (Date Range): To effectively backtest the strategy, define a substantial period that includes various bullish and bearish cycles. This ensures the testing window captures a range of market conditions and provides an adequate number of trades. It is usually favorable to use a minimum of 8 years for backtesting. Ensure the "Show Date Range" box is checked.
Account Size: This is your actual current Account Size used in the position sizing table calculations.
Risk on Capital %: This setting allows you to specify the percentage of your capital you are willing to risk on each trade. A common value is 0.5%.
3. Configuring Strategy Style:
Select the "Style" tab.
Select the checkbox for “Stop Loss” and “Stop Loss Final” to display the black/red Average True Range Stop Loss step-lines
Make sure the checkboxes for "Upper Channel", "Middle Line", and "Lower Channel" are selected.
Select the "Plots Background" checkboxes for "Color 0" and "Color 1" so that the potential entry and exit zones become color-coded.
Having the checkbox for "Tables" selected allows you to see position sizing and other useful information within the chart.
Have the checkboxes for "Trades on chart" and "Signal Labels" selected for viewing entry and exit point labels and positions.
Uncheck* the "Quantity" checkbox.
Precision: select “Default”.
Check “Labels on price scale”
Check “Values in status line”
Strategy Application Guidelines:
Entry Conditions:
The strategy identifies long entry opportunities based on substantial money inflow, as detected by our proprietary indicators and oscillators. This assessment considers the strength and momentum of the inflow relative to previous periods, in conjunction with strong price momentum (indicated by our modified, less-lagging MACD) and/or a potential price reversal (indicated by our modified, less-noisy Stochastic). Additional confirmation criteria related to price action are also incorporated. Potential entry and exit zones are visually represented by bands on the chart.
A blue upward-pointing arrow, accompanied by the label 'Long' and green band fills, signifies a long entry opportunity. Conversely, a magenta downward-pointing arrow, labeled 'Close entry(s) order Long' with yellow band fills, indicates a potential exit.
Take Profit:
The strategy employs trailing stops, rather than fixed take-profit levels, to maximize gains while minimizing losses. Trailing stops adjust the stop-loss level as the stock price moves in a favorable direction. The strategy utilizes two types of trailing stop mechanisms: one based on the Average True Range (ATR), and another based on price action, which attempts to identify shifts in price momentum.
Stop Loss:
The strategy uses an Average True Range (ATR)-based stop-loss, represented by two lines on the chart. The black line indicates the primary ATR-based stop-loss level, set upon trade entry. The red line represents a secondary ATR stop-loss buffer, used in the position sizing calculation to account for potential slippage or price gaps.
To potentially reduce the risk of stop-hunting, discretionary traders might consider using a market sell order within the final 30 to 60 minutes of the main session, instead of automated stop-loss orders.
Order Types:
Market Orders are intended for use with this strategy, specifically when the candle and signal on the chart stabilize within the final 30 to 60 minutes of the main trading session.
Position Sizing:
A key aspect of this strategy is that its position size is calculated and displayed in a table on the chart. The position size is calculated based on stop-loss placement, including the stop-loss buffer, and the capital at risk per trade which is commonly set around 0.5% Risk on Capital per Trade.
Backtesting:
The backtesting results presented below the chart are for informational purposes only and are not intended to predict future performance. Instead, they serve as a tool for identifying instruments with which the strategy has historically performed well.
It's important to note that the backtester utilizes a tiny portion of the capital for each trade while our strategy relies on a diversified portfolio of multiple stocks or instruments being traded at once.
Important Considerations:
Volume data is crucial; the strategy will not load or function correctly without it. Ensure that your charts include volume data, preferably from a centralized exchange.
Our system is designed for trading a portfolio. Therefore, if you intend to use our system, you should employ appropriate position sizing, without leverage or margin, and seek out a variety of long opportunities, rather than opening a single trade with an excessively large position size.
If you are trading without automated signals, always allow the chart to stabilize. Refrain from taking action until the final 1 hour to 30 minutes before the end of the main trading session to minimize the risk of acting on false signals.
To align with the strategy's design, it's generally preferable to enter a trade during the same session that the signal appears, rather than waiting for a later session.
Disclaimer:
Trading in financial markets involves a substantial degree of risk. You should be aware of the potential for significant financial losses. It is imperative that you trade responsibly and avoid overtrading, as this can amplify losses. Remember that market conditions can change rapidly, and past performance is not indicative of future results. You could lose some or all of your initial investment. It is strongly recommended that you fully understand the risks involved in trading and seek independent financial advice from a qualified professional before using this strategy.
Custom Strategy: ETH Martingale 2.0Strategic characteristics
ETH Little Martin 2.0 is a self-developed trading strategy based on the Martingale strategy, mainly used for trading ETH (Ethereum). The core idea of this strategy is to place orders in the same direction at a fixed price interval, and then use Martin's multiple investment principle to reduce losses, but this is also the main source of losses.
Parameter description:
1 Interval： The minimum spacing for taking profit, stop loss, and opening/closing of orders. Different targets have different spacing. Taking ETH as an example, it is generally recommended to have a spacing of 2% for fluctuations in the target.
2 Base Price： This is the price at which you triggered the first order. Similarly, I am using ETH as an example. If you have other targets, I suggest using the initial value of a price that can be backtesting. The Base Price is only an initial order price and has no impact on subsequent orders.
3 Initial Order Amount： Users can set an initial order amount to control the risk of each transaction. If the stop loss is reached, we will double the amount based on this value. This refers to the value of the position held, not the number of positions held.
4 Loss Multiplier： The strategy will increase the next order amount based on the set multiple after the stop loss, in order to make up for the previous losses through a larger position. Note that after taking profit, it will be reset to 1 times the Initial Order Amount.
5. Long Short Operation: The first order of the strategy is a multiple entry, and in subsequent orders, if the stop loss is reached, a reverse order will be opened. The position value of a one-way order is based on the Loss Multiplier multiple investment, so it is generally recommended that the Loss Multiplier default to 2.
Improvement direction
Although this strategy already has a certain trading logic, there are still some improvement directions that can be considered:
1. Dynamic adjustment of spacing: Currently, the spacing is fixed, and it can be considered to dynamically adjust the spacing based on market volatility to improve the adaptability of the strategy. Try using dynamic spacing, which may be more suitable for the actual market situation.
2. Filtering criteria: Orders and no orders can be optimized separately. The biggest problem with this strategy is that it will result in continuous losses during fluctuations, and eventually increase the investment amount. You can consider filtering out some fluctuations or only focusing on trend trends.
3. Risk management: Add more risk management measures, such as setting a maximum loss limit to avoid huge losses caused by continuous stop loss.
4. Optimize the stop loss multiple: Currently, the stop loss multiple is fixed, and it can be considered to dynamically adjust the multiple according to market conditions to reduce risk.
QuantBuilder | FractalystWhat's the strategy's purpose and functionality?
QuantBuilder is designed for both traders and investors who want to utilize mathematical techniques to develop profitable strategies through backtesting on historical data.
The primary goal is to develop profitable quantitive strategies that not only outperform the underlying asset in terms of returns but also minimize drawdown.
For instance, consider Bitcoin (BTC), which has experienced significant volatility, averaging an estimated 200% annual return over the past decade, with maximum drawdowns exceeding -80%. By employing this strategy with diverse entry and exit techniques, users can potentially seek to enhance their Compound Annual Growth Rate (CAGR) while managing risk to maintain a lower maximum drawdown.
While this strategy employs quantitative techniques, including mathematical methods such as probabilities and positive expected values, it demonstrates exceptional efficacy across all markets. It particularly excels in futures, indices, stocks, cryptocurrencies, and commodities, leveraging their inherent trending behaviors for optimized performance.
In both trending and consolidating market conditions, QuantBuilder employs a combination of multi-timeframe probabilities, expected values, directional biases, moving averages and diverse entry models to identify and capitalize on bullish market movements.
How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
1. Trading:
- Designed for traders looking to capitalize on bullish markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for both swing and intraday trading with a focus on probabilities and risk per trade approach.
2. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes pre-define percentage of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully/partially investing in the asset during bullish conditions.
How does the strategy identify market structure? What are the underlying calculations?
The strategy utilizes an efficient logic with for loops to pinpoint the first swing candle featuring a pivot of 2, establishing the point at which the break of structure begins.
What entry criteria are used in this script? What are the underlying calculations?
The script utilizes two entry models: BreakOut and fractal.
Underlying Calculations:
Breakout: The script assigns the most recent swing high to a variable. When the price closes above this level and all other conditions are met, the script executes a breakout entry (conservative approach).
Fractal: The script identifies a swing low with a period of 2. Once this condition is met, the script executes the trade (aggressive approach).
How does the script calculate probabilities? What are the underlying calculations?
The script calculates probabilities by monitoring price interactions with liquidity levels. Here’s how the underlying calculations work:
Tracking Price Hits: The script counts the number of times the price taps into each liquidity side after the EQM level is activated. This data is stored in an array for further analysis.
Sample Size Consideration: The total number of price interactions serves as the sample size for calculating probabilities.
Probability Calculation: For each liquidity side, the script calculates the probability by taking the average of the recorded hits. This allows for a dynamic assessment of the likelihood that a particular side will be hit next, based on historical performance.
Dynamic Adjustment: As new price data comes in, the probabilities are recalculated, providing real-time aduptive insights into market behavior.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
How does the script calculate expected values? What are the underlying calculations?
The script calculates expected values by leveraging the probabilities of winning and losing trades, along with their respective returns. The process involves the following steps:
This quantitative methodology provides a robust framework for assessing the expected performance of trading strategies based on historical data and backtesting results.
How is the contextual bias calculated? What are the underlying calculations?
The contextual bias in the QuantBuilder script is calculated through a structured approach that assesses market structure based on swing highs and lows. Here’s how it works:
Identification of Swing Points: The script identifies significant swing points using a defined pivot logic, focusing on the first swing high and swing low. This helps establish critical levels for determining market structure.
Break of Structure (BOS) Assessment:
Bullish BOS: The script recognizes a bullish break of structure when a candle closes above the first swing high, followed by at least one swing low.
Bearish BOS: Conversely, a bearish break of structure is identified when a candle closes below the first swing low, followed by at least one swing high.
Bias Assignment: Based on the identified break of structure, the script assigns directional biases:
A bullish bias is assigned if a bullish BOS is confirmed.
A bearish bias is assigned if a bearish BOS is confirmed.
Quantitative Evaluation: Each identified bias is quantitatively evaluated, allowing the script to assign numerical values representing the strength of each bias. This quantification aids in assessing the reliability of market sentiment across multiple timeframes.
What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
- Initial Stop-loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14)
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
3. PL Based:
This method places the stop-loss at the low of the previous candle.
If the current entry is based on the hunt entry strategy, the stop-loss will be placed at the low of the candle that wicks through the lower FRMA band.
Example:
If the previous candle's low is 100, then the stop-loss will be set at 100.
This method ensures the stop-loss is placed just below the most recent significant low, providing a logical and immediate level for risk management.
- Trailing Stop-Loss:
One of the key elements of this strategy is its ability to detect structural liquidity and structural invalidation levels across multiple timeframes to trail the stop-loss once the trade is in running profits.
By utilizing this approach, the strategy allows enough room for price to run.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
What tables are available in this script?
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Total Commission: Displays the cumulative commissions incurred from all trades executed within the selected backtesting window. This value is derived by summing the commission fees for each trade on your chart.
Average Commission: Represents the average commission per trade, calculated by dividing the Total Commission by the total number of closed trades. This metric is crucial for assessing the impact of trading costs on overall profitability.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month and year.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- UI Table: A user-friendly table that allows users to view and save the selected strategy parameters from user inputs. This table enables easy access to key settings and configurations, providing a straightforward solution for saving strategy parameters by simply taking a screenshot with Alt + S or ⌥ + S.
User-input styles and customizations:
To facilitate studying historical data, all conditions and filters can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
How to Use This Quantitive Strategy Builder to Create a Profitable Edge and System?
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker/prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 200 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
What makes this strategy original?
QuantBuilder stands out due to its unique combination of quantitative techniques and innovative algorithms that leverage historical data for real-time trading decisions. Unlike most algorithmic strategies that work based on predefined rules, this strategy adapts to real-time market probabilities and expected values, enhancing its reliability. Key features include:
Mathematical Framework: The strategy integrates advanced mathematical concepts, such as probabilities and expected values, to assess trade viability and optimize decision-making.
Multi-Timeframe Analysis: By utilizing multi-timeframe probabilities, QuantBuilder provides a comprehensive view of market conditions, enhancing the accuracy of entry and exit points.
Dynamic Market Structure Identification: The script employs a systematic approach to identify market structure changes, utilizing a blend of swing highs and lows to detect contextual/direction bias of the market.
Built-in Trailing Stop Loss: The strategy features a dynamic trailing stop loss based on multi-timeframe analysis of market structure. This allows traders to lock in profits while adapting to changing market conditions, ensuring that exits are executed at optimal levels without prematurely closing positions.
Robust Performance Metrics: With detailed performance tables and visualizations, users can easily evaluate strategy effectiveness and adjust parameters based on historical performance.
Adaptability: The strategy is designed to work across various markets and timeframes, making it versatile for different trading styles and objectives.
Suitability for Investors and Traders: QuantBuilder is ideal for both investors and traders looking to rely on mathematically proven data to create profitable strategies, ensuring that decisions are grounded in quantitative analysis.
These original elements combine to create a powerful tool that can help both traders and investors to build and refine profitable strategies based on algorithmic quantitative analysis.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
RSI & Backed-Weighted MA StrategyRSI & MA Strategy :
INTRODUCTION :
This strategy is based on two well-known indicators that work best together: the Relative Strength Index (RSI) and the Moving Average (MA). We're going to use the RSI as a trend-follower indicator, rather than a reversal indicator as most are used to. To the signals sent by the RSI, we'll add a condition on the chart's MA, filtering out irrelevant signals and considerably increasing our winning rate. This is a medium/long-term strategy. There's also a money management method enabling us to reinvest part of the profits or reduce the size of orders in the event of substantial losses.
RSI :
The RSI is one of the best-known and most widely used indicators in trading. Its purpose is to warn traders when an asset is overbought or oversold. It was designed to send reversal signals, but we're going to use it as a trend indicator by increasing its length to 20. The RSI formula is as follows :
RSI (n) = 100 - (100 / (1 + (H (n)/L (n))))
With n the length of the RSI, H(n) the average of days closing above the open and L(n) the average of days closing below the open.
MA :
The Moving Average is also widely used in technical analysis, to smooth out variations in an asset. The SMA formula is as follows :
SMA (n) = (P1 + P2 + ... + Pn) / n
where n is the length of the MA.
However, an SMA does not weight any of its terms, which means that the price 10 days ago has the same importance as the price 2 days ago or today's price... That's why in this strategy we use a RWMA, i.e. a back-weighted moving average. It weights old prices more heavily than new ones. This will enable us to limit the impact of short-term variations and focus on the trend that was dominating. The RWMA used weights :
The 4 most recent terms by : 100 / (4+(n-4)*1.30)
The other oldest terms by : weight_4_first_term*1.30
So the older terms are weighted 1.30 more than the more recent ones. The moving average thus traces a trend that accentuates past values and limits the noise of short-term variations.
PARAMETERS :
RSI Length : Lenght of RSI. Default is 20.
MA Type : Choice between a SMA or a RWMA which permits to minimize the impact of short term reversal. Default is RWMA.
MA Length : Length of the selected MA. Default is 19.
RSI Long Signal : Minimum value of RSI to send a LONG signal. Default is 60.
RSI Short signal : Maximum value of RSI to send a SHORT signal. Default is 40.
ROC MA Long Signal : Maximum value of Rate of Change MA to send a LONG signal. Default is 0.
ROC MA Short signal : Minimum value of Rate of Change MA to send a SHORT signal. Default is 0.
TP activation in multiple of ATR : Threshold value to trigger trailing stop Take Profit. This threshold is calculated as multiple of the ATR (Average True Range). Default value is 5 meaning that to trigger the trailing TP the price need to move 5*ATR in the right direction.
Trailing TP in percentage : Percentage value of trailing Take Profit. This Trailing TP follows the profit if it increases, remaining selected percentage below it, but stops if the profit decreases. Default is 3%.
Fixed Ratio : This is the amount of gain or loss at which the order quantity is changed. Default is 400, which means that for each $400 gain or loss, the order size is increased or decreased by a user-selected amount.
Increasing Order Amount : This is the amount to be added to or subtracted from orders when the fixed ratio is reached. The default is $200, which means that for every $400 gain, $200 is reinvested in the strategy. On the other hand, for every $400 loss, the order size is reduced by $200.
Initial capital : $1000
Fees : Interactive Broker fees apply to this strategy. They are set at 0.18% of the trade value.
Slippage : 3 ticks or $0.03 per trade. Corresponds to the latency time between the moment the signal is received and the moment the order is executed by the broker.
Important : A bot has been used to test the different parameters and determine which ones maximize return while limiting drawdown. This strategy is the most optimal on BITSTAMP:ETHUSD with a timeframe set to 6h. Parameters are set as follows :
MA type: RWMA
MA Length: 19
RSI Long Signal: >60
RSI Short Signal : <40
ROC MA Long Signal : <0
ROC MA Short Signal : >0
TP Activation in multiple ATR : 5
Trailing TP in percentage : 3
ENTER RULES :
The principle is very simple:
If the asset is overbought after a bear market, we are LONG.
If the asset is oversold after a bull market, we are SHORT.
We have defined a bear market as follows : Rate of Change (20) RWMA < 0
We have defined a bull market as follows : Rate of Change (20) RWMA > 0
The Rate of Change is calculated using this formula : (RWMA/RWMA(20) - 1)*100
Overbought is defined as follows : RSI > 60
Oversold is defined as follows : RSI < 40
LONG CONDITION :
RSI > 60 and (RWMA/RWMA(20) - 1)*100 < -1
SHORT CONDITION :
RSI < 40 and (RWMA/RWMA(20) - 1)*100 > 1
EXIT RULES FOR WINNING TRADE :
We have a trailing TP allowing us to exit once the price has reached the "TP Activation in multiple ATR" parameter, i.e. 5*ATR by default in the profit direction. TP trailing is triggered at this point, not limiting our gains, and securing our profits at 3% below this trigger threshold.
Remember that the True Range is : maximum(H-L, H-C(1), C-L(1))
with C : Close, H : High, L : Low
The Average True Range is therefore the average of these TRs over a length defined by default in the strategy, i.e. 20.
RISK MANAGEMENT :
This strategy may incur losses. The method for limiting losses is to set a Stop Loss equal to 3*ATR. This means that if the price moves against our position and reaches three times the ATR, we exit with a loss.
Sometimes the ATR can result in a SL set below 10% of the trade value, which is not acceptable. In this case, we set the SL at 10%, limiting losses to a maximum of 10%.
MONEY MANAGEMENT :
The fixed ratio method was used to manage our gains and losses. For each gain of an amount equal to the value of the fixed ratio, we increase the order size by a value defined by the user in the "Increasing order amount" parameter. Similarly, each time we lose an amount equal to the value of the fixed ratio, we decrease the order size by the same user-defined value. This strategy increases both performance and drawdown.
Enjoy the strategy and don't forget to take the trade :)
SOFEX Strong Volatility Trend Follower + BacktestingWhat is the SOFEX Strong Volatility Trend Follower + Backtesting script?
🔬 Trading Philosophy
This script is trend-following, attempting to avoid choppy markets.
It has been developed for Bitcoin and Ethereum trading, on 1H timeframe.
The strategy does not aim to make a lot of trades, or to always remain in a position and switch from long to short. Many times there is no direction and the market is in "random walk mode", and chasing trades is futile.
Expectations of performance should be realistic.
The script focuses on a balanced take-profit to stop-loss ratio. In the default set-up of the script, that is a 2% : 2% (1:1) ratio. A relatively low stop loss and take profit build onto the idea that positions should be exited promptly. There are many options to edit these values, including enabling trailing take profit and stop loss. Traders can also completely turn off TP and SL levels, and rely on opposing signals to exit and enter new trades.
Extreme scenarios can happen on the cryptocurrency markets, and disabling stop-loss levels completely is not recommended. The position size should be monitored since all of it is at risk with no stop-loss.
⚙️ Logic of the indicator
The Strong Volatility Trend Follower indicator aims at evading ranging market conditions. It does not seek to chase volatile, yet choppy markets. It aims at aggressively following confirmed trends. The indicator works best during strong, volatile trends, however, it has the downside of entering trades at trend tops or bottoms.
This indicator also leverages proprietary adaptive moving averages to identify and follow strong trend volatility effectively. Furthermore, it uses the Average Directional Index, Awesome Oscillator, ATR and a modified version of VWAP, to categorize trends into weak or strong ones. The VWAP indicator is used to identify the monetary (volume) inflow into a given trend, further helping to avoid short-term manipulations. It also helps to distinguish choppy-market volatility with a trending market one.
📟 Parameters Menu
The script has a comprehensive parameter menu:
Preset Selection : Choose between Bitcoin or Ethereum presets to tailor the indicator to your preferred cryptocurrency market.
Indicator Sensitivity Parameter : Adjust the sensitivity to adapt the indicator, particularly to make it seek higher-strength trends.
Indicator Signal Direction : Set the signal direction as Long, Short, or Both, depending on your preference.
Exit of Signals : You have options regarding Take-Profit (TP) and Stop-Loss (SL) levels. Enable TP/SL levels to exit trades at predetermined levels, or disable them to rely on direction changes for exits. Be aware that removing stop losses can introduce additional risk, and position sizing should be carefully monitored.
By enabling Trailing TP/SL, the system switches to a trailing approach, allowing you to:
- Place an initial customizable SL.
- Specify a level (%) for the Trailing SL to become active.
- When the activation level is reached, the system moves the trailing stop by a given Offset (%).
Additionally, you can enable exit at break-even, where the system places an exit order when the trail activation level is reached, accounting for fees and slippage.
Alert Messages : Define the fields for alert messages based on specific conditions. You can set up alerts to receive email, SMS, and in-app notifications. If you use webhooks for alerts, exercise caution, as these alerts can potentially execute trades without human supervision.
Backtesting : Default backtesting parameters are set to provide realistic backtesting performance:
- 0.04% Commission per trade (for both entries and exits)
- 3 ticks Slippage (highly dependent on exchange)
- Initial capital of $1000
- Order size of $1000
While the order size is equal to the initial capital, the script employs a 2% stop-loss order to limit losses and attempts to prevent risky trades from creating big losses. The order size is a set dollar value, so that the backtesting performance is linear, instead of using % of capital which may result in unrealistic backtesting performance.
Risk Disclaimer
Please be aware that backtesting results, while valuable for statistical overview, do not guarantee future performance in any way. Cryptocurrency markets are inherently volatile and risky. Always trade responsibly and do not risk more than you can afford to lose.
SOFEX High-End Indicators + BacktestingBINANCE:BTCUSDT.P BINANCE:ETHUSDT.P
Introducing the first publicly available suite of indicators for Bitcoin and Ethereum by Sofex - the High-End Indicators & Backtesting System.
🔬 Trading Philosophy
The High-End Indicators & Backtesting system offers both trend-following and mean-reversal algorithms to provide traders with a deep insight into the highly volatile cryptocurrency markets, known for their market noise and vulnerability to manipulation.
With these factors in mind, our indicators are designed to sidestep most potentially false signals. This is facilitated further by the "middle-ground" time frame (1 Hour) we use. Our focus is on the two largest cryptocurrencies: Bitcoin and Ethereum , which provide high liquidity, necessary for reliable trading.
Therefore, we recommend using our suite on these markets.
The backtesting version of the Sofex High-End Indicators includes mainly trend-following indicators. This is because our trading vision is that volatility in cryptocurrency markets is a tool that should be used carefully, and many times avoided. Furthermore, mean-reversal trading can lead to short-term profits, but we have found it less than ideal for long-term trading.
The script does not aim to make a lot of trades, or to always remain in a position and switch from long to short. Many times there is no direction and the market is in "random walk mode", and chasing trades is futile.
Based on our experience, it is preferable if traders remain neutral the majority of the time and only enter trades that can be exited in the foreseeable future. Trading just for the sake of it ultimately leads to loss in the long-run.
Expectations of performance should be realistic.
We also focus on a balanced take-profit to stop-loss ratio. In the default set-up of the script, that is a 2% : 2% (1:1) ratio. A relatively low stop loss and take profit build onto our idea that positions should be exited promptly. There are many options to edit these values, including enabling trailing take profit and stop loss. Traders can also completely turn off TP and SL levels, and rely on opposing signals to exit and enter new trades.
Extreme scenarios can happen on the cryptocurrency markets, and disabling stop-loss levels completely is not recommended. The position size should be monitored since all of it is at risk with no stop-loss.
We take pride in presenting this comprehensive suite of trading indicators, designed for both manual and automated use. Although automated use leads to increased efficiency, traders are free to incorporate any of our indicators into their own manual trading strategy.
⚙️ Indicators
By default, all indicators are enabled for both Long and Short trades.
Extreme Trend Breakouts
The Extreme Trend Breakouts indicator seeks to follow breakouts of support and resistance levels, while also accounting for the unfortunate fact that false signals can be generated on these levels. The indicator combines trend-breakout strategies with various other volatility and direction measurements. It works best in the beginning of trends.
Underpinning this indicator are renowned Perry Kaufman's Adaptive Moving Averages (PKAMA) alongside our proprietary adaptive moving averages. These dynamic indicators adjust their parameters based on recent price movements, attempting to catch trends while maintaining consistent performance in the long run.
In addition, our modification of the TTM Squeeze indicator further enhances the Extreme Trend Breakouts indicator, making it more responsive, especially during the initial stages of trends and filtering of "flat" markets.
High-Volatility Trend Follower
The High-Volatility Trend Follower indicator is based around the logic of evading market conditions where volatility is low (choppy markets) and aggressively following confirmed trends. The indicator works best during strong trends, however, it has the downside of entering trades at trend tops or bottoms.
This indicator also leverages our proprietary adaptive moving averages to identify and follow high-volatility trends effectively. Furthermore, it uses the Average Directional Index, Aroon Oscillator, ATR and a modified version of VWAP, to categorize trends into weak or strong ones. The VWAP indicator is used to identify the monetary (volume) inflow into a given trend, further helping to avoid short-term manipulations.
Low-Volatility Reversal
The Low-Volatility Reversal aims at plugging the holes that trend-following indicators ignore. It specifically looks for choppy markets. Using proven concepts such as Relative Strength Index and volume measurements, among others, this indicator finds local tops and bottoms with good accuracy. It works best in choppy markets with low to medium volatility. It has a downside that all reversals have, losing trades at the end of choppy markets and in the beginning of big trends.
This indicator, like the others, employs PKAMA in conjunction with our proprietary adaptive moving averages, and an Average PSAR indicator to seek out "sideways" markets. Furthermore, Bollinger Bands with an adaptive basis line is used, with the idea of trading against the short-term trends by looking at big deviations in price movement. The above mentioned indicators attempt to catch local tops and bottoms in markets.
Adaptive Trend Convergence
The Adaptive Trend Convergence aims at following trends while avoiding entering positions at local bottoms and tops. It does so by comparing a number of adaptive moving averages and looking for convergence among them. Adaptive filtering techniques for avoiding choppy markets are also used.
This indicator utilizes our proprietary adaptive moving averages, and an Average Price Range indicator to identify trend convergence and divergence effectively, preventing false signals during volatile market phases. It also makes use of Bollinger Bands with an adaptive moving average basis line and price-action adjusted deviation. Contrasting to the Low-Volatility Reversal condition described above, the Bollinger Bands used here attempt to follow breakouts outside of the lower and upper bands.
Double-Filtered Channel Breakouts
The Double-Filtered Channel Breakouts indicator is made out of adaptive channel-identifying indicators. The indicator then follows trends that significantly diverge from the established channels. This aims at following extreme trends, where rapid, continuous movements in either direction occur. This indicator works best in very strong trends and follows them relentlessly. However, these strong trends can end in strong reversals, and the indicator can be stopped out on the last trade.
Our Double-Filtered Channel Breakouts indicator is built on a foundation of adaptive channel indicators. We've harnessed the power of Keltner Channels and Bollinger Band Channels, with a similar approach used in the Adaptive Trend Convergence indicator. The basis and upper/lower bands of the channels do not rely on fixed deviation parameters, rather on adaptive ones, based on price action and volatility. This combination seeks to identify and follows extreme trends.
Direction Tracker
The Direction Tracker indicator is made out of a central slower, adaptive moving average that clearly recognizes global, long-term trends. Combined with direction and range indicators, among others, this indicator excels at finding the long-term trend and ignoring temporary pullbacks in the opposite direction. It works best at the beginning and middle of long and strong trends. It can fail at the end of trends and on very strong historical resistance lines (where sharp reversals are common).
Our Direction Tracker indicator integrates an adaptive SuperTrend indicator into its core, alongside our proprietary adaptive moving averages, to accurately identify and track long-term trends while mitigating temporary pullbacks. Furthermore, it uses Average True Range, ADX and other volatility indicators to attempt to catch unusual moves on the market early-on.
📟 Parameters Menu
To offer traders flexibility, our system comes with a comprehensive parameter menu:
Preset Selection : Choose between Bitcoin or Ethereum presets to tailor the indicators to your preferred cryptocurrency market.
Global Signal Direction: Set the global signal direction as Long, Short, or Both, depending on your trading strategy.
Global Sensitivity Parameter : Adjust the system's sensitivity to adapt to different trend-following conditions, particularly beneficial during higher-strength trends.
Source of Signals : Toggle individual indicators on or off according to your preference. By default, all indicators are enabled. Customize the indicators to trade Long, Short, or Both, aligning them with your desired market exposure.
Confirmation of Signals : Set the minimum number of confirmed signals on the same bar, ensuring signals are generated only when specific confirmation criteria are met. The default value is one, and it can be adjusted for both Long and Short signals.
Exit of Signals : You have options regarding Take-Profit (TP) and Stop-Loss (SL) levels. Enable TP/SL levels to exit trades at predetermined levels, or disable them to rely on direction changes for exits. Be aware that removing stop losses can introduce additional risk, and position sizing should be carefully monitored.
By enabling Trailing TP/SL, the system switches to a trailing approach, allowing you to:
- Place an initial customizable SL.
- Specify a level (%) for the Trailing SL to become active.
- When the activation level is reached, the system moves the trailing stop by a given Offset (%).
Additionally, you can enable exit at break-even, where the system places an exit order when the trail activation level is reached, accounting for fees and slippage.
Alert Messages : Define the fields for alert messages based on specific conditions. You can set up alerts to receive email, SMS, and in-app notifications. If you use webhooks for alerts, exercise caution, as these alerts can potentially execute trades without human supervision.
Backtesting : Default backtesting parameters are set to provide realistic backtesting performance:
- 0.04% Commission per trade (for both entries and exits)
- 3 ticks Slippage (highly dependent on exchange)
- Initial capital of $1000
- Order size of $1000
While the order size is equal to the initial capital, the script employs a 2% stop-loss order to limit losses and attempts to prevent risky trades from creating big losses. The order size is a set dollar value, so that the backtesting performance is linear, instead of using % of capital which may result in unrealistic backtesting performance.
Risk Disclaimer
Please be aware that backtesting results, while valuable for statistical overview, do not guarantee future performance in any way. Cryptocurrency markets are inherently volatile and risky. Always trade responsibly and do not risk more than you can afford to lose.
Multi-Timeframe MACD Strategy ver 1.0Multi-Timeframe MACD Strategy: Enhanced Trend Trading with Customizable Entry and Trailing Stop
This strategy utilizes the Moving Average Convergence Divergence (MACD) indicator across multiple timeframes to identify strong trends, generate precise entry and exit signals, and manage risk with an optional trailing stop loss. By combining the insights of both the current chart's timeframe and a user-defined higher timeframe, this strategy aims to improve trade accuracy, reduce exposure to false signals, and capture larger market moves.
Key Features:
Dual Timeframe Analysis: Calculates and analyzes the MACD on both the current chart's timeframe and a user-selected higher timeframe (e.g., Daily MACD on a 1-hour chart). This provides a broader market context, helping to confirm trends and filter out short-term noise.
Configurable MACD: Fine-tune the MACD calculation with adjustable Fast Length, Slow Length, and Signal Length parameters. Optimize the indicator's sensitivity to match your trading style and the volatility of the asset.
Flexible Entry Options: Choose between three distinct entry types:
Crossover: Enters trades when the MACD line crosses above (long) or below (short) the Signal line.
Zero Cross: Enters trades when the MACD line crosses above (long) or below (short) the zero line.
Both: Combines both Crossover and Zero Cross signals, providing more potential entry opportunities.
Independent Timeframe Control: Display and trade based on the current timeframe MACD, the higher timeframe MACD, or both. This allows you to focus on the information most relevant to your analysis.
Optional Trailing Stop Loss: Implements a configurable trailing stop loss to protect profits and limit potential losses. The trailing stop is adjusted dynamically as the price moves in your favor, based on a user-defined percentage.
No Repainting: Employs lookahead=barmerge.lookahead_off in the request.security() function to prevent data leakage and ensure accurate backtesting and real-time signals.
Clear Visual Signals (Optional): Includes optional plotting of the MACD and Signal lines for both timeframes, with distinct colors for easy visual identification. These plots are for visual confirmation and are not required for the strategy's logic.
Suitable for Various Trading Styles: Adaptable to swing trading, day trading, and trend-following strategies across diverse markets (stocks, forex, cryptocurrencies, etc.).
Fully Customizable: All parameters are adjustable, including timeframes, MACD Settings, Entry signal type and trailing stop settings.
How it Works:
MACD Calculation: The strategy calculates the MACD (using the standard formula) for both the current chart's timeframe and the specified higher timeframe.
Trend Identification: The relationship between the MACD line, Signal line, and zero line is used to determine the current trend for each timeframe.
Entry Signals: Buy/sell signals are generated based on the selected "Entry Type":
Crossover: A long signal is generated when the MACD line crosses above the Signal line, and both timeframes are in agreement (if both are enabled). A short signal is generated when the MACD line crosses below the Signal line, and both timeframes are in agreement.
Zero Cross: A long signal is generated when the MACD line crosses above the zero line, and both timeframes agree. A short signal is generated when the MACD line crosses below the zero line and both timeframes agree.
Both: Combines Crossover and Zero Cross signals.
Trailing Stop Loss (Optional): If enabled, a trailing stop loss is set at a specified percentage below (for long positions) or above (for short positions) the entry price. The stop-loss is automatically adjusted as the price moves favorably.
Exit Signals:
Without Trailing Stop: Positions are closed when the MACD signals reverse according to the selected "Entry Type" (e.g., a long position is closed when the MACD line crosses below the Signal line if using "Crossover" entries).
With Trailing Stop: Positions are closed if the price hits the trailing stop loss.
Backtesting and Optimization: The strategy automatically backtests on the chart's historical data, allowing you to assess its performance and optimize parameters for different assets and timeframes.
Example Use Cases:
Confirming Trend Strength: A trader on a 1-hour chart sees a bullish MACD crossover on the current timeframe. They check the MTF MACD strategy and see that the Daily MACD is also bullish, confirming the strength of the uptrend.
Filtering Noise: A trader using a 15-minute chart wants to avoid false signals from short-term volatility. They use the strategy with a 4-hour higher timeframe to filter out noise and only trade in the direction of the dominant trend.
Dynamic Risk Management: A trader enters a long position and enables the trailing stop loss. As the price rises, the trailing stop is automatically adjusted upwards, protecting profits. The trade is exited either when the MACD reverses or when the price hits the trailing stop.
Disclaimer:
The MACD is a lagging indicator and can produce false signals, especially in ranging markets. This strategy is for educational and informational purposes only and should not be considered financial advice. Backtest and optimize the strategy thoroughly, combine it with other technical analysis tools, and always implement sound risk management practices before using it with real capital. Past performance is not indicative of future results. Conduct your own due diligence and consider your risk tolerance before making any trading decisions.
EMA Crossover Strategy with Take Profit and Candle HighlightingStrategy Overview:
This strategy is based on the Exponential Moving Averages (EMA), specifically the EMA 20 and EMA 50. It takes advantage of EMA crossovers to identify potential trend reversals and uses multiple take-profit levels and a stop-loss for risk management.
Key Components:
EMA Crossover Signals:
Buy Signal (Uptrend): A buy signal is generated when the EMA 20 crosses above the EMA 50, signaling the start of a potential uptrend.
Sell Signal (Downtrend): A sell signal is generated when the EMA 20 crosses below the EMA 50, signaling the start of a potential downtrend.
Take Profit Levels:
Once a buy or sell signal is triggered, the strategy calculates multiple take-profit levels based on the range of the previous candle. The user can define multipliers for each take-profit level.
Take Profit 1 (TP1): 50% of the previous candle's range above or below the entry price.
Take Profit 2 (TP2): 100% of the previous candle's range above or below the entry price.
Take Profit 3 (TP3): 150% of the previous candle's range above or below the entry price.
Take Profit 4 (TP4): 200% of the previous candle's range above or below the entry price.
These levels are adjusted dynamically based on the previous candle's high and low, so they adapt to changing market conditions.
Stop Loss:
A stop-loss is set to manage risk. The default stop-loss is 3% from the entry price, but this can be adjusted in the settings. The stop-loss is triggered if the price moves against the position by this amount.
Trend Direction Highlighting:
The strategy highlights the bars (candles) with colors:
Green bars indicate an uptrend (when EMA 20 crosses above EMA 50).
Red bars indicate a downtrend (when EMA 20 crosses below EMA 50).
These visual cues help users easily identify the market direction.
Strategy Entries and Exits:
Entries: The strategy enters a long (buy) position when the EMA 20 crosses above the EMA 50 and a short (sell) position when the EMA 20 crosses below the EMA 50.
Exits: The strategy exits the positions at any of the defined take-profit levels or the stop-loss. Multiple exit levels provide opportunities to take profit progressively as the price moves in the favorable direction.
Entry and Exit Conditions in Detail:
Buy Entry Condition (Uptrend):
A buy position is opened when EMA 20 crosses above EMA 50, signaling the start of an uptrend.
The strategy calculates take-profit levels above the entry price based on the previous bar's range (high-low) and the multipliers for TP1, TP2, TP3, and TP4.
Sell Entry Condition (Downtrend):
A sell position is opened when EMA 20 crosses below EMA 50, signaling the start of a downtrend.
The strategy calculates take-profit levels below the entry price, similarly based on the previous bar's range.
Exit Conditions:
Take Profit: The strategy attempts to exit the position at one of the take-profit levels (TP1, TP2, TP3, or TP4). If the price reaches any of these levels, the position is closed.
Stop Loss: The strategy also has a stop-loss set at a default value (3% below the entry for long trades, and 3% above for short trades). The stop-loss helps to protect the position from significant losses.
Backtesting and Performance Metrics:
The strategy can be backtested using TradingView's Strategy Tester. The results will show how the strategy would have performed historically, including key metrics like:
Net Profit
Max Drawdown
Win Rate
Profit Factor
Average Trade Duration
These performance metrics can help users assess the strategy's effectiveness over historical periods and optimize the input parameters (e.g., multipliers, stop-loss level).
Customization:
The strategy allows for the adjustment of several key input values via the settings panel:
Take Profit Multipliers: Users can customize the multipliers for each take-profit level (TP1, TP2, TP3, TP4).
Stop Loss Percentage: The user can also adjust the stop-loss percentage to a custom value.
EMA Periods: The default periods for the EMA 50 and EMA 20 are fixed, but they can be adjusted for different market conditions.
Pros of the Strategy:
EMA Crossover Strategy: A classic and well-known strategy used by traders to identify the start of new trends.
Multiple Take Profit Levels: By taking profits progressively at different levels, the strategy locks in gains as the price moves in favor of the position.
Clear Trend Identification: The use of green and red bars makes it visually easier to follow the market's direction.
Risk Management: The stop-loss and take-profit features help to manage risk and optimize profit-taking.
Cons of the Strategy:
Lagging Indicators: The strategy relies on EMAs, which are lagging indicators. This means that the strategy might enter trades after the trend has already started, leading to missed opportunities or less-than-ideal entry prices.
No Confirmation Indicators: The strategy purely depends on the crossover of two EMAs and does not use other confirming indicators (e.g., RSI, MACD), which might lead to false signals in volatile markets.
How to Use in Real-Time Trading:
Use for Backtesting: Initially, use this strategy in backtest mode to understand how it would have performed historically with your preferred settings.
Paper Trading: Once comfortable, you can use paper trading to test the strategy in real-time market conditions without risking real money.
Live Trading: After testing and optimizing the strategy, you can consider using it for live trading with proper risk management in place (e.g., starting with a small position size and adjusting parameters as needed).
Summary:
This strategy is designed to identify trend reversals using EMA crossovers, with customizable take-profit levels and a stop-loss to manage risk. It's well-suited for traders looking for a systematic way to enter and exit trades based on clear market signals, while also providing flexibility to adjust for different risk profiles and trading styles.
IsAlgo - Reverse Candle Strategy► Overview:
The Reverse Candle Strategy leverages a customizable moving average to identify the start of a trend. It utilizes the highest and lowest prices to define the trend and its corrections, executing trades based on custom candlestick patterns to capitalize on the main trend's continuation.
► Description:
The Reverse Candle Strategy is designed to effectively identify and trade market trends by combining moving averages and custom candlestick patterns. The core of the strategy is a single, customizable moving average, which helps determine the trend direction. When the market price crosses above the moving average, this signifies the beginning of an uptrend. The strategy then tracks the highest price reached during the uptrend and waits for a correction. A specific custom candlestick pattern signals the end of the correction, at which point the strategy executes a long trade.
In the case of a downtrend, the market price crossing below the moving average marks the trend’s start. The strategy monitors the lowest price during the downtrend and awaits a correction. The end of this correction is identified by another custom candlestick pattern, prompting the strategy to execute a short trade. This combination of a moving average with precise candlestick patterns ensures that trades are made at optimal moments, improving the likelihood of successful trades.
The integration of the moving average and candlestick patterns is critical. The moving average smooths out price data to highlight the trend direction, while the custom candlestick patterns provide specific entry signals after a correction, ensuring the trend’s resumption is genuine. This synergy enhances the strategy’s ability to filter out false signals and improve trade accuracy.
↑ Long Entry Example:
When the price is moving above the moving average and the highest price has been detected, the strategy will wait for the entry candle to execute the long trade.
↓ Short Entry Example:
When the price is moving below the moving average and the lowest price has been detected, the strategy will wait for the entry candle to execute the short trade.
✕ Exit Conditions:
To manage risk effectively, the strategy provides multiple stop-loss options. Traders can set stop-loss levels using fixed pips, ATR-based calculations, or the higher/lower price of past candles. Additionally, trades can be closed if a candle moves against the trade direction. Up to three take-profit levels can be set using fixed pips, ATR, or risk-to-reward ratios, allowing traders to secure profits at different stages. The trailing stop feature adjusts the stop loss as the trade moves into profit, locking in gains while allowing for continued potential upside. Furthermore, a break-even feature moves the stop loss to the entry price once a certain profit level is reached, protecting against losses. Trades can also be closed when the price crosses the moving average.
► Features & Settings:
⚙︎ Moving Average: Users can choose between various types of moving averages (e.g., SMA, EMA) to confirm the trend direction.
⚙︎ Trend & Corrections: Set minimum and maximum pips for trends and corrections, with an option to define correction percentages relative to the trend.
⚙︎ Entry Candle: Define the entry candle by specifying the minimum and maximum size of the candle's body and the ratio of the body to the entire candle size, ensuring significant breakouts trigger trades.
⚙︎ Trading Session: This feature allows users to define specific trading hours during which the strategy should operate, ensuring trades are executed only during preferred market periods.
⚙︎ Trading Days: Users can specify which days the strategy should be active, offering the flexibility to avoid trading on specific days of the week.
⚙︎ Backtesting: Enables a backtesting period during which the strategy can be tested over a selected start and end date. This feature can be deactivated if not needed.
⚙︎ Trades: Configure trade direction (long, short, or both), position sizing (fixed or percentage-based), maximum number of open trades, and daily trade limits.
⚙︎ Trades Exit: Various exit methods, such as setting profit or loss limits, trade duration, or closing trades on moving average crossings.
⚙︎ Stop Loss: Various stop-loss methods are available, including a fixed number of pips, ATR-based, or using the highest or lowest price points within a specified number of previous candles. Additionally, trades can be closed after a specific number of candles move in the opposite direction of the trade.
⚙︎ Break Even: This feature adjusts the stop loss to a break-even point once certain conditions are met, such as reaching predefined profit levels, to protect gains.
⚙︎ Trailing Stop: The trailing stop feature adjusts the stop loss as the trade moves into profit, securing gains while potentially capturing further upside.
⚙︎ Take Profit: up to three take-profit levels using fixed pips, ATR, or risk-to-reward ratios based on the stop loss. Alternatively, specify a set number of candles moving in the trade direction.
⚙︎ Alerts: The strategy includes a comprehensive alert system that informs the user of all significant actions, such as trade openings and closings. It supports placeholders for dynamic values like take-profit levels and stop-loss prices.
⚙︎ Dashboard: Visual display providing detailed information about ongoing and past trades on the chart, helping users monitor performance and make informed decisions.
► Backtesting Details:
Timeframe: 30-minute NAS100 chart
Initial Balance: $10,000
Order Size: 5 Units
Commission: $0.5 per contract
Slippage: 5 ticks
Stop Loss: MA Crossing or by break even
IsAlgo - Ultra Trend Strategy► Overview:
The Ultra Trend strategy is designed to identify trend lines based on average price movement and execute trades when the price crosses the middle line, confirmed by an entry candle. This strategy combines ATR, Moving Averages, and customizable candlestick patterns to provide a versatile and robust trading approach.
► Description:
The Ultra Trend strategy employs a multi-faceted approach to accurately gauge market trends and execute trades. It combines the Average True Range (ATR) with trendline analysis and Moving Averages, providing a comprehensive view of market conditions. The strategy uses ATR to measure market volatility and the average price movement, helping to set dynamic thresholds for trend detection and adapting to changing market conditions. The slope of the trend is calculated based on the angle of price movement, which aids in identifying the strength and direction of the trend.
Additionally, a Moving Average is used to filter trades, ensuring alignment with the broader market direction and reducing false signals, thereby enhancing trade accuracy.
Traders can configure the strategy to enter trades in the direction of the trend, against the trend, or both. This feature enhances the adaptability of the Ultra Trend strategy, making it suitable for various trading styles and market environments.
↑ Long Entry:
A long trade is executed when the entry candle crosses and closes above the trend line. This indicates a bullish market condition, signaling an opportunity to enter a buy position.
↓ Short Entry:
A short trade is executed when the entry candle crosses and closes below the trend line. This indicates a bearish market condition, signaling an opportunity to enter a sell position.
✕ Exit Conditions:
The strategy offers multiple stop-loss options to manage risk effectively. Traders can set stop-loss levels using fixed pips, ATR-based calculations, the higher/lower price of past candles, or close a trade if a candle moves against the trade direction.
Up to three take profit levels can be set using methods such as fixed pips, ATR, and risk-to-reward ratios. This allows traders to secure profits at various stages of the trade.
A trailing stop feature adjusts the stop loss as the trade moves into profit, locking in gains while allowing the trade to continue capturing potential upside. Additionally, a break-even feature moves the stop loss to the entry price once a certain profit level is reached, protecting against losses.
Trades can also be closed when a trend change is detected or when a candle closes outside a predefined channel, ensuring that positions are exited promptly in response to changing market conditions.
► Features and Settings:
⚙︎ Trend: Users can configure the trend direction, length, factor, and slope, allowing for precise control over how trends are identified and followed.
⚙︎ Moving Average: An Exponential Moving Average (EMA) can be employed to confirm the trend direction indicated by the trend lines. This provides further assurance that the trend line breakout is not a false signal. The EMA can be enabled or disabled based on user preference.
⚙︎ Entry Candle: The entry candle is the candle that breaks the trend line, signaling an entry opportunity. Users can specify the minimum and maximum size of the candle's body and the ratio of the body to the entire candle size. This ensures that only significant breakouts trigger trades.
⚙︎ Trading Session: This feature allows users to define specific trading hours during which the strategy should operate, ensuring trades are executed only during preferred market periods.
⚙︎ Trading Days: Users can specify which days the strategy should be active, offering the flexibility to avoid trading on specific days of the week.
⚙︎ Backtesting: Enables a backtesting period during which the strategy can be tested over a selected start and end date. This feature can be deactivated if not needed.
⚙︎ Trades: This includes configuring the direction of trades (long, short, or both), position sizing (fixed or percentage-based), the maximum number of open trades, and limitations on the number of trades per day or based on trend.
⚙︎ Trades Exit: The strategy offers various exit methods, such as setting profit or loss limits, specifying the duration a trade should remain open, or closing trades based on trend reversal.
⚙︎ Stop Loss: Various stop-loss methods are available, including a fixed number of pips, ATR-based, or using the highest or lowest price points within a specified number of previous candles. Additionally, trades can be closed after a specific number of candles move in the opposite direction of the trade.
⚙︎ Break Even: This feature adjusts the stop loss to a break-even point once certain conditions are met, such as reaching predefined profit levels, to protect gains.
⚙︎ Trailing Stop: The trailing stop feature adjusts the stop loss as the trade moves into profit, securing gains while potentially capturing further upside.
⚙︎ Take Profit: Up to three take-profit levels can be set using various methods, such as a fixed amount of pips, ATR, or risk-to-reward ratios based on the stop loss. Alternatively, users can specify a set number of candles moving in the direction of the trade.
⚙︎ Alerts: The strategy includes a comprehensive alert system that informs the user of all significant actions, such as trade openings and closings. It supports placeholders for dynamic values like take-profit levels and stop-loss prices.
⚙︎ Dashboard: A visual display provides detailed information about ongoing and past trades on the chart, helping users monitor the strategy's performance and make informed decisions.
► Backtesting Details:
Timeframe: 5-minute US30 chart
Initial Balance: $10,000
Order Size: 4% of equity per trade
Commission: $0.05 per contract
Slippage: 5 ticks
Stop Loss: ATR-based
IsAlgo - AI Trend Strategy► Overview:
The AI Trend Strategy employs a combination of technical indicators to guide trading decisions across various markets and timeframes. It uses a custom Super Trend indicator and an Exponential Moving Average (EMA) to analyze market trends and executes trades based on specific candlestick patterns. This strategy includes options for setting stop losses, take profit levels, and features an alert system for trade notifications.
► Description:
This strategy focuses on identifying the optimal "entry candle," which signals either a potential correction within the ongoing trend or the emergence of a new trend. The entry criteria for this candle are highly customizable, allowing traders to specify dimensions such as the candle's minimum and maximum size and body ratio. Additional settings include whether this candle should be the highest or lowest compared to recent candles and if a confirmation candle is necessary to validate the entry.
The Super Trend indicator is central to the strategy’s operation, dictating the direction of trades by identifying bullish or bearish trends. Traders have the option to configure trades to align with the direction of the trend identified by this indicator, or alternatively, to take positions counter to the trend for potential reversal strategies. This flexibility can be crucial during varying market conditions.
Additionally, the strategy incorporates an EMA alongside the Super Trend indicator to further analyze trend directions. This combined approach aims to reduce the occurrence of false signals and improve the strategy's overall trend analysis.
The learning algorithm is a standout feature of the AI Trend Strategy. After accumulating data from a predefined number of trades (e.g., after the first 100 trades), the algorithm begins to analyze past performances to identify patterns in wins and losses. It considers variables such as the distance from the current price to the trend line, the range between the highest and lowest prices during the trend, and the duration of the trend. This data informs the algorithm's predictions for future trades, aiming to improve accuracy and reduce losses by adapting to the evolving market conditions.
► Examples of Trade Execution:
1. In an Uptrend: The strategy might detect a suitable entry candle during a correction phase, which aligns with the continuing uptrend for a potential long trade.
2. In a Downtrend: Alternatively, the strategy might identify an entry candle at the end of a downtrend, suggesting a potential reversal or correction where a long trade could be initiated.
3. In an Uptrend: The strategy may also spot an entry candle at the end of an uptrend and execute a short trade, anticipating a reversal or significant pullback.
4. In a Downtrend: The strategy might find a suitable entry candle during a correction phase, indicating a continuation of the downtrend for a potential short trade.
These examples illustrate how the strategy identifies potential trading opportunities based on trend behavior and candlestick patterns.
► Features and Settings:
⚙︎ Trend: Utilizes a custom Super Trend indicator to identify the direction of the market trend. Users can configure the strategy to execute trades in alignment with this trend, take positions contrary to the trend, or completely ignore the trend information for their trading decisions.
⚙︎ Moving average: Employs an Exponential Moving Average (EMA) to further confirm the trend direction indicated by the Super Trend indicator. This setting can be used in conjunction with the Super Trend or disabled if preferred.
⚙︎ Entry candle: Defines the criteria for the candle that triggers a trade. Users can customize aspects such as the candle's size, body, and its relative position to previous candles to ensure it meets specific trading requirements before initiating a trade.
⚙︎ Learning algorithm: This component uses historical trade data to refine the strategy. It assesses various aspects of past trades, such as price trends and market conditions, to make more informed trading decisions in the future.
⚙︎ Trading session: Users can define specific trading hours during which the strategy should operate, allowing trades to be executed only during preferred market periods.
⚙︎ Trading days: This option enables users to specify which days the strategy should be active, providing the flexibility to avoid trading on certain days of the week if desired.
⚙︎ Backtesting: Enables a period during which the strategy can be tested over a selected start and end date, with an option to deactivate this feature if not needed.
⚙︎ Trades: Detailed configuration options include the direction of trades (long, short, or both), position sizing (fixed or percentage-based), the maximum number of open trades, and limitations on the number of trades per day or based on trend changes.
⚙︎ Trades Exit: Offers various strategies for exiting trades, such as setting limits on profits or losses, specifying the duration a trade should remain open, or closing trades based on trend reversal signals.
⚙︎ Stop loss: Various methods for setting stop losses are available, including fixed pips, based on Average True Range (ATR), or utilizing the highest or lowest price points within a designated number of previous candles. Another option allows for closing the trade after a specific number of candles moving in the opposite direction.
⚙︎ Break even: This feature adjusts the stop loss to a break-even point under certain conditions, such as reaching predefined profit levels, to protect gains.
⚙︎ Trailing stop: The trailing stop feature adjusts the stop loss as the trade moves into profit, aiming to secure gains while potentially capturing further upside.
⚙︎ Take profit: Up to three take profit levels can be established using various methods, such as a fixed amount of pips, risk-to-reward ratios based on the stop loss, ATR, or after a set number of candles that move in the direction of the trade.
⚙︎ Alerts: Includes a comprehensive alert system that informs the user of all significant actions taken by the strategy, such as trade openings and closings. It supports placeholders for dynamic values like take profit levels, stop loss prices, and more.
⚙︎ Dashboard: Provides a visual display of detailed information about ongoing and past trades on the chart, helping users monitor the strategy’s performance and make informed decisions.
► Backtesting Details:
Timeframe: 15-minute BTCUSD chart.
Initial Balance: $10,000.
Order Size: 4% of equity per trade.
Commission: 0.01%.
Slippage: 5 ticks.
Risk Management: Strategic stop loss settings are applied based on the most extreme price points within the last 18 candles.
Donchian Quest Research// =================================
Trend following strategy.
// =================================
Strategy uses two channels. One channel - for opening trades. Second channel - for closing.
Channel is similar to Donchian channel, but uses Close prices (not High/Low). That helps don't react to wicks of volatile candles (“stop hunting”). In most cases openings occur earlier than in Donchian channel. Closings occur only for real breakout.
// =================================
Strategy waits for beginning of trend - when price breakout of channel. Default length of both channels = 50 candles.
Conditions of trading:
- Open Long: If last Close = max Close for 50 closes.
- Close Long: If last Close = min Close for 50 closes.
- Open Short: If last Close = min Close for 50 closes.
- Close Short: If last Close = max Close for 50 closes.
// =================================
Color of lines:
- black - channel for opening trade.
- red - channel for closing trade.
- yellow - entry price.
- fuchsia - stoploss and breakeven.
- vertical green - go Long.
- vertical red - go Short.
- vertical gray - close in end, don't trade anymore.
// =================================
Order size calculated with ATR and volatility.
You can't trade 1 contract in BTC and 1 contract in XRP - for example. They have different price and volatility, so 1 contract BTC not equal 1 contract XRP.
Script uses universal calculation for every market. It is based on:
- Risk - USD sum you ready to loss in one trade. It calculated as percent of Equity.
- ATR indicator - measurement of volatility.
With default setting your stoploss = 0.5 percent of equity:
- If initial capital is 1000 USD and used parameter "Permit stop" - loss will be 5 USD (0.5 % of equity).
- If your Equity rises to 2000 USD and used parameter "Permit stop"- loss will be 10 USD (0.5 % of Equity).
// =================================
This Risk works only if you enable “Permit stop” parameter in Settings.
If this parameter disabled - strategy works as reversal strategy:
⁃ If close Long - channel border works as stoploss and momentarily go Short.
⁃ If close Short - channel border works as stoploss and momentarily go Long.
Channel borders changed dynamically. So sometime your loss will be greater than ‘Risk %’. Sometime - less than ‘Risk %’.
If this parameter enabled - maximum loss always equal to 'Risk %'. This parameter also include breakeven: if profit % = Risk %, then move stoploss to entry price.
// =================================
Like all trend following strategies - it works only in trend conditions. If no trend - slowly bleeding. There is no special additional indicator to filter trend/notrend. You need to trade every signal of strategy.
Strategy gives many losses:
⁃ 30 % of trades will close with profit.
⁃ 70 % of trades will close with loss.
⁃ But profit from 30% will be much greater than loss from 70 %.
Your task - patiently wait for it and don't use risky setting for position sizing.
// =================================
Recommended timeframe - Daily.
// =================================
Trend can vary in lengths. Selecting length of channels determine which trend you will be hunting:
⁃ 20/10 - from several days to several weeks.
⁃ 20/20 or 50/20 - from several weeks to several months.
⁃ 50/50 or 100/50 or 100/100 - from several months to several years.
// =================================
Inputs (Settings):
- Length: length of channel for trade opening/closing. You can choose 20/10, 20/20, 50/20, 50/50, 100/50, 100/100. Default value: 50/50.
- Permit Long / Permit short: Longs are most profitable for this strategy. You can disable Shorts and enable Longs only. Default value: permit all directions.
- Risk % of Equity: for position sizing used Equity percent. Don't use values greater than 5 % - it's risky. Default value: 0.5%.
⁃ ATR multiplier: this multiplier moves stoploss up or down. Big multiplier = small size of order, small profit, stoploss far from entry, low chance of stoploss. Small multiplier = big size of order, big profit, stop near entry, high chance of stoploss. Default value: 2.
- ATR length: number of candles to calculate ATR indicator. It used for order size and stoploss. Default value: 20.
- Close in end - to close active trade in the end (and don't trade anymore) or leave it open. You can see difference in Strategy Tester. Default value: don’t close.
- Permit stop: use stop or go reversal. Default value: without stop, reversal strategy.
// =================================
Properties (Settings):
- Initial capital - 1000 USD.
- Script don't uses 'Order size' - you need to change 'Risk %' in Inputs instead.
- Script don't uses 'Pyramiding'.
- 'Commission' 0.055 % and 'Slippage' 0 - this parameters are for crypto exchanges with perpetual contracts (for example Bybit). If use on other markets - set it accordingly to your exchange parameters.
// =================================
Big dataset used for chart - 'BITCOIN ALL TIME HISTORY INDEX'. It gives enough trades to understand logic of script. It have several good trends.
// =================================
Cracking Cryptocurrency - Bottom Feeder Strategy TesterBottom Feeder - Strategy Tester
The Bottom Feeder is designed to algorithmically detect significantly oversold conditions in price that represent profitable buying opportunities. Combining this with it’s unique Stop and Target System, the Bottom Feeder is designed to return consistent return with minimal draw down. Whether used as a Market Bottom Detector or as a system for executing safe, profitable mean reversion trades, the Bottom Feeder is a powerful tool in any trader’s arsenal.
Bottom Feeder was designed to be used on BTCUSD, however it is also effective on other USD/USDT pairs. One will have to check the individual pair they wish to trade with the Strategy Tester to simulate performance.
Strategy displayed is from 2018-2021 on **Conservative Mode** with Percent of Equity (30%) enabled.
Options
Let’s go through the input options one by one, so that you are able to comfortably navigate all that this indicator has to offer. The link below will display a picture of the layout of the settings for your convenience.
For the sake of simplicity, let’s note now that all settings marked **Conservative Mode** will not work in Aggressive Mode.
Mode : Determines how aggressively Bottom Feeder generates a buy signal. In Conservative Mode, trades can only be opened once per candle and the stop and target will update as new signals appear. In Aggressive Mode, a separate trade is opened each time Bottom Feeder signals, which may be multiple times within one Daily candle.
Position Sizing Strategy : Determines what Risk Management system you will deploy when trading Bottom Feeder. Your options are “Percent of Equity” and “Distance to Stop Loss”. If Percent of Equity is selected, a trade size will be equal to a percentage of your equity, pursuant to the value in the ‘Percent of Equity’ box. If Distance to Stop Loss is selected, then your Position Size will be determined based off the distance to your stop loss and the value in the ‘Risk Percentage’ box.
Percent Of Equity : Determines what percentage of your equity will be allocated to each trade when ‘Position Sizing Strategy’ is enabled.
Risk Percentage : Determines the size of each trade if ‘Distance to Stop Loss’ strategy is enabled. This value reflects what percent of your account you will lose per trade if the trade hits your stop loss.
Plot Target and Stop Loss : Toggles on/off the visualized take profit and stop losses on the chart.
**Conservative Mode** TP Multiplier : This is an input box, it requires a float value. That is, it can accept either a whole number integer or a number with a decimal. This number will determine your Take Profit target. It will take whatever number is entered into this box and multiply the Average True Range against it to determine your Take Profit.
**Conservative Mode** SL Multiplier : See above - this will modify your Stop Loss Value.
**Conservative Mode** Average or Median True Range : This is a drop-down option, the two options are Average True Range or Median True Range. If Average True Range is selected, then this indicator will use the Average True Range calculation, that is, the average of a historical set of True Range values to determine the Average True Range value for Target and Stop Loss calculation. If Median True Range is selected, it will not take an average and will instead take the Median value of your historical look back period.
**Conservative Mode** True Range Length : This is an input that requires an integer. This will represent your historical lookback period for Average/Median True Range calculation.
**Conservative Mode** True Range Smoothing : This is a drop-down with the following options: Exponential Moving Average ( EMA ), Simple Moving Average ( SMA ), Weighted Moving Average ( WMA ), Relative Moving Average (RMA). This will determine the smoothing type for calculating the Average True Range if it is selected. Note: if Median True Range is selected above, this option will not have any effect as there is no smoothing for a Median value.
**Conservative Mode** Custom True Range Value? : This is a true/false option that is false by default. If enabled, it will override the Average/Median True Range calculation in favor of a users custom True Range value to be input below.
**Conservative Mode** Custom True Range Value : This is an input box that requires a float value. If Custom True Range is enabled this is where a user will input their desired custom True Range value for Target and Stop Loss calculation.
From Month/Day/Year to Month/Day/Year : This sets the Time Frame of your backtest for the Bottom Feeder Strategy. It will run FROM the date selected TO the date selected.
Stop and Target Description
Because Bottom Feeder is designed only to scalp the various market bottoms that can appear over time in the market and not to identify trends or to trade ranges, it’s imperative that the indicator notify us not just to when to enter our trades, but when to exit! In the service of that, CC Bottom Feeder has a built in Stop and Target system that tracks and displays the stop loss and take profit levels of each individual open trade, whether in Aggressive or Conservative Mode.
Conservative Mode Targeting: In Conservative Mode, Bottom Feeder signals are aggregated into a compound trade. The signal will appear as a green label pointing up below a candle, and will appear upon a candle close. If Bottom Feeder then generates another signal the stop loss and target price will be updated. The process will continue until the aggregated trade completes in either direction. On a trade with multiple signals, a larger position is slowly entered into upon each buy signal.
Aggressive Mode Targeting: In Aggressive Mode, Bottom Feeder signals are individually displayed as they are generated, regardless of how many signals are generated on any single candle. If Bottom Feeder continues to signal, each individual open trade will have their own stop loss and target that will be displayed on the chart until the individual trade completes in either direction. As opposed to a large compound position, aggressive mode represents a higher number of independent signals with their own stop and target levels.
Stop losses and targets are designed to be hard, not soft. That is, they are intended to be stop market orders, not mental stop losses. If price wicks through the target or stop, it will activate.
Turtle Trading Strategy@lihexieThe full implementation of the Turtle Trading Rules (as distinct from the various truncated versions circulating within the community) is now ready.
This trading strategy script distinguishes itself from all currently publicly available Turtle trading systems on Tradingview by comprehensively embodying the rules for entries, exits, position management, and profit and loss controls.
Market Selection:
Trade in highly liquid markets such as forex, commodity futures, and stock index futures.
Entry Strategies:
Model 1: Buy when the price breaks above the highest point of the last 20 trading days; Sell when the price drops below the lowest point of the last 20 trading days. When an entry opportunity arises, if the previous trade was profitable, skip the current breakout opportunity and refrain from entering.
Model 2: Buy when the price breaks above the highest point of the last 55 trading days; Sell when the price drops below the lowest point of the last 55 trading days.
Position Sizing:
Determine the size of each position based on the price volatility (ATR) to ensure that the risk of each trade does not exceed 2% of the account balance.
Exit Strategies:
1. Use a fixed stop-loss point to limit losses: Close long positions when the price falls below the lowest point of the last 10 trading days.
2. Trailing stop-loss: Once a position is profitable, adjust the stop-loss point to protect profits.
Pyramiding Rules:
Unit Doubling: Increase position size by one unit every time the price moves forward by n (default is 0.5) units of ATR, up to a maximum of 4 units, while also raising the stop-loss point to below the ATR value at the level of additional entries.
海龟交易法则的完整实现（区别于当前社区各种有阉割海龟交易系统代码）
本策略脚本区别于Tradingview目前公开的所有的海龟交易系统，完整的实现了海龟交易法则中入场、出场、仓位管理，止盈止损的规则。
市场选择：
选择流动性高的市场进行交易，如外汇、商品期货和股指期货等。
入市策略：
模式1：当价格突破过去20个交易日的高点时，买入；当价格跌破过去20个交易日的低点时，卖出。当出现入场机会时，如果上一笔交易是盈利的，那么跳过当前突破的机会，不进行入场。
模式2：当价格突破过去55个交易日的高点时，买入；当价格跌破过去55个交易日的低点时，卖出。
头寸规模：
根据价格波动性（ATR）来确定每个头寸的大小, 使每笔交易的风险不超过账户余额的2%。
退出策略：
1. 使用一个固定的止损点来限制损失：当多头头寸的价格跌破过去10个交易日的低点时，平仓止损。
2. 跟踪止损：一旦头寸盈利，移动止损点以保护利润。
加仓规则：
单位加倍：每当价格向前n（默认是0.5）个单位的ATR移动时，就增加一个单位的头寸大小（默认最大头寸数量是4个），同时将止损点提升至加仓点位的ATR值以下。
Price Action Pattern Breakout Strategy: Wedge,Triangle,ChannelIntroducing the Price Action Pattern Breakout Strategy: Wedge,Triangle,Channel 💹🚀
The "Price Action Pattern Breakout Strategy: Wedge, Triangle, Channel" is a dynamic and automated trading strategy that excels in recognizing and capitalizing on breakout opportunities within the realm of powerful price action patterns. It is finely tuned to achieve exceptional precision in detecting three distinct pattern types: Wedge, Triangle, and Channel. This diversity equips you to confidently navigate a wide range of market scenarios and opportunities.
This strategy automates trade entries and exits upon confirmed pattern breakouts, this eliminates human errors in correctly recognizing patterns and prevents emotional decisions. This strategy is designed to work across different time frames, making it suitable for both short-term and long-term traders. Whether you're a day trader, swing trader, or investor, this strategy provides the flexibility you need to thrive in diverse market conditions.
💎 How it Works:
▶️ In this strategy, three price action patterns have been utilized, one of which is the "Wedge" pattern. The Wedge pattern has consistently demonstrated a high level of credibility, typically resulting in sharp and rapid price movements following a confirmed breakout from this pattern. This characteristic makes the Wedge pattern highly noteworthy in our strategy. The second pattern is the "Triangle" pattern, which, depending on its formation, whether ascending or descending, can indicate a strong continuation or reversal of the trend. The last pattern is the "Channel" pattern. The reason for using the Channel pattern is its versatility in various market conditions and its tendency to produce reliable results.
In the snapshot below, you can observe the types of patterns that this strategy is capable of identifying at a glance:
▶️ This strategy employs two types of targeting systems: Fixed Targets and Trailing Targets.
Fixed Targets is the default targeting system of the strategy, incorporating two primary targets: TP1 (Target Point 1) and TP2 (Target Point 2). These targets are thoughtfully adjusted in alignment with specific rules for each pattern. With Fixed Targets, you have the flexibility to designate the position size percentage for your exits at TP1 and TP2. For instance, should you opt to allocate 60% of your position size to TP1, as soon as the price triggers the first take profit level, 60% of your initial position is gracefully closed, leaving the remaining 40% to exit the trade upon reaching TP2.
Trailing Targets represent the strategy's alternative targeting system. With this system, the trailing stop becomes active once the price reaches the specified trigger point. The strategy then exits the trade based on the defined offset percentage and price retracement from the trailing limit.
▶️ This strategy relies on a single type of stop loss, determined by previous pivot points and adjusted based on the trade's direction, whether long or short, placing the stop loss above or below the prior pivot. This stop loss approach has demonstrated reliability when used alongside price action patterns.
In addition to this fixed stop loss, you can specify a percentage buffer, offering protection against potential stop hunting due to market fluctuations. This buffer helps protect your positions from sudden price swings. For example, selecting a 1% buffer means your stop loss will be positioned 1% higher or lower concerning the last pivot, depending on your trade's direction. This added layer of security ensures your trades remain resilient and less vulnerable to market volatility.
▶️ A practical feature of this strategy is the "Risk-Free" option. Once activated, it continuously monitors price movements, and as soon as the price progresses in the trade's direction and surpasses the designated Risk-Free Trigger Point in percentage, the stop loss is dynamically shifted from its initial position to the entry price, effectively making the trade "risk-free." This means that if the trade doesn't go as expected, we exit at the entry point, incurring neither profit nor loss from the trade.
Additionally, you have the flexibility to fine-tune the modified stop loss, positioning it slightly above or below the entry price through the configuration of a specified percentage. This allows for effective consideration of commission fees in your trading strategy.
▶️ Risk management is a crucial concept in trading, playing a significant role in a trader's long-term success. This strategy introduces a unique feature called "Fixed Loss Position Sizing", where upon activation, you can limit the risk exposure to a specified percentage of your capital per trade. Set your preferred risk percentage along with the intended leverage. The strategy independently considers your available capital and designated leverage, determining the position size before executing any trade.
In the case of a stop loss, your loss is limited to the specified risk percentage. For instance, with a $1000 account and a 1% risk set, the strategy adjusts each trade's size to ensure a maximum loss of $10 if the stop loss is triggered. Enabling this feature will ensure disciplined risk management, aligning potential losses precisely with your predetermined risk percentage, contingent upon your total available capital.
▶️ Another feature of this strategy is a sophisticated mechanism called "Loss Compensation". When enabled, Loss Compensation dynamically adjusts the position size after a loss, aiming to recover from previous losses in subsequent trades. This adaptive mechanism continually modifies the position size to mitigate the impact of consecutive losses until reaching a user-defined limit for consecutive loss compensations.
The feature's configurability allows users to set the maximum number of consecutive losses to compensate for and also includes an option to factor in trading fees from prior trades into the compensation calculation. Loss Compensation operates in conjunction with the 'Fixed Loss Position Sizing' setting, ensuring that once losses are sufficiently compensated, subsequent entries revert to the predefined configurations within the 'Fixed Loss Position Sizing' settings.
This advanced tool ensures a stable risk management approach by changing trade sizes dynamically according to past results during consecutive loss periods.
▶️ This strategy incorporates a feature known as the "Counter-Pattern Breakout", altering its approach to wedge, triangle, and channel pattern breakouts. Normally, the strategy relies on standard pattern signals to determine whether to enter long or short positions based on breakout directions.
For example, in an ascending channel or a rising wedge pattern, the strategy typically seeks a short position opportunity upon a confirmed breakout in the lower line, and breakouts from the upper line are disregarded by the strategy. But with this feature enabled, strategy disregards the conventional pattern signals, seizing breakouts from upper or lower lines to open corresponding positions. For instance, in the ascending channel or the rising wedge pattern example, the strategy might enter a long position if the upper line breaks or a short position if the lower line breaks.
This introduces a more adaptive and opportunistic trading style, allowing you to capitalize on price movements, irrespective of the typical signal direction indicated by the pattern.
▶️ This strategy is fully compatible with third-party trading bots, allowing for easy connectivity to popular trading platforms. By leveraging the TradingView webhook functionality, you can effortlessly link the strategy to your preferred bot and receive accurate signals for position entry and exit. The strategy provides all the necessary alert message fields, ensuring a smooth and user-friendly trading experience. With this integration, you can automate the execution of trades, saving time and effort while enjoying the benefits of this powerful strategy.
⚙️ How to Use & Configure User Settings:
To fully utilize the "Price Action Pattern Breakout Strategy: Wedge, Triangle, Channel," it's essential to consider and comprehend the following steps. They play a crucial role in enhancing its functionality and achieving its utmost potential outcomes:
1. General Strategy Settings:
Enable Dark Mode if using a dark TradingView theme for improved chart visibility.
Select the Strategy's Trade Direction: Long, Short, or Both.
Choose Pattern Recognition Accuracy: High for precise recognition but fewer positions, Low for more positions with slightly less accuracy.
Enable 'Prevent New Entry on Opposite Signal While In Position' to avoid new trades if the opposite signal occurs.
Switch to Indicator Mode if solely using the strategy as an indicator or in combination with other strategies.
2. Pattern and Pivot Configuration:
Consider configuring the Number of Patterns and Pivot Lookback Lengths. Here, you can personalize the pivot lookback lengths for wedge, triangle, and channel patterns across eight different settings on your chart. For lower time frames, consider larger lengths to reduce chart noise. Alternatively, to maintain clarity on your chart, you can disable multiple patterns with different lengths while ensuring at least one pattern remains enabled.
Note that enabling more patterns doesn't always equate to increased potential profit. Sometimes, fewer patterns result in greater profit potential, and vice versa. Experiment with lengths and the number of patterns to determine the most profitable and optimal outcome for your trading symbol and timeframe.
3. Targeting System Selection:
Choose between 'Fixed Targets' or 'Trailing Targets' for your targeting system.
'Fixed Targets' is the default setting, operational when 'Trailing Targets' are turned off.
Set the TP1 Position Size as a percentage, defining the size for TP1, and the rest exits at TP2.
Optionally activate 'Skip Entry if TP1 is Passed' to bypass entering positions if the price has exceeded TP1.
Alternatively, opt for the 'Trailing Target' for dynamic exits based on trigger points and offsets. Note that this option disables fixed targets.
4. Stop Loss Configuration:
Determine the number of candles to consider for stop loss placement based on the last pivot.
Optionally add a percentage to the stop loss to create a buffer against market fluctuations, guarding your positions from sudden price swings.
5. Risk Management Configuration:
You can activate the 'Risk-Free' feature, making your trades risk-free by moving the stop loss to the entry price upon reaching a specified trigger point.
You have the possibility to enable 'Fixed Loss Position Sizing' to limit risk to a percentage of total capital per trade, ensuring prudent risk management.
You can employ 'Use Real-Time Balance for Each Entry' to precisely calculate fixed loss position sizing according to the real-time balance for every entry.
The 'Loss Compensation' feature can be activated to automatically adjust trade sizes during consecutive losses and compensate for prior incurred losses.
Loss compensation continues adjusting trade sizes until it reaches the defined limit of consecutive losses specified in the 'Maximum Consecutive Losses To Compensate' field.
You can factor in commission fees by specifying a percentage in the 'Include Trading Fees in Compensation (%)' field, providing an option for more accurate loss compensation calculations.
You have the option to enable 'Limit Compensation to Real-Time Balance' to prevent consecutive loss compensation from exceeding your current real-time account balance.
It's important to note that for the 'Loss Compensation' feature to operate, the 'Fixed Loss Position Sizing' must be enabled.
6. Counter-Pattern Breakout Configuration:
In this section you have the option to enable the "Counter-Pattern Breakout" feature to adjust the strategy's approach to wedge, triangle, and channel pattern breakouts. Once enabled, the strategy disregards traditional pattern signals and capitalizes on breakouts from either the upper or lower lines, initiating corresponding positions accordingly.
Choose between 'Fixed Target' or 'Trailing Target' for your targeting system. If you opt for the 'Fixed Target', set a specific target point as a percentage, serving as the default target for counter-pattern breakouts. Alternatively, choose the 'Trailing Target' for dynamic exits based on trigger points and offsets. Do keep in mind that selecting the 'Trailing Target' option disables the fixed target setting.
Keep in mind that for standard, non-counter-pattern breakouts, the target point settings in their respective sections remain applicable, distinct from the settings configured for targeting within this section.
Note that the stop loss configurations are shared across standard pattern and counter-pattern breakouts and can be adjusted within the stop loss section.
7. Info Tables:
In the info tables section, you can show or hide different tables on the charts. This includes the backtest table, the current balance table displaying available funds, and a table showcasing Maximum Consecutive Wins or Losses. Choose which to display according to your preferences and specific needs.
8.Date & Time Range Filter:
Utilize the Date & Time Range filter feature to precisely select a start and end date, including time, to filter data within the chosen range.
When connecting this strategy to a trading bot for automated trades, ensure to set the start date and time to the intended initiation moment to avoid undesired outcomes as this directly affects the real-time balance calculations of the strategy.
8. Integration with Third-Party Bots:
To automate trading, leverage the strategy's compatibility with third-party trading bots. Seamlessly integrate the strategy into well-known trading platforms by using alert message fields to input commands from third-party trading bots, enabling automated trade execution for both long and short positions.
By furnishing these adjustable settings, the strategy empowers you to personalize it according to your unique requirements, thereby bolstering the adaptability and efficacy of your trading approach.
🔐 Source Code Protection:
The 'Price Action Pattern Breakout Strategy: Wedge, Triangle, Channel' source code is engineered for precision, reliability, and effectiveness. Its original and innovative design warrants protection and restricted access, preserving the strategy's exclusivity. Safeguarding the code maintains the strategy's integrity and distinctiveness, providing users with a competitive advantage in their trading endeavors.
Rate of Change StrategyRate of Change Strategy :
INTRODUCTION :
This strategy is based on the Rate of Change indicator. It compares the current price with that of a user-defined period of time ago. This makes it easy to spot trends and even speculative bubbles. The strategy is long term and very risky, which is why we've added a Stop Loss. There's also a money management method that allows you to reinvest part of your profits or reduce the size of your orders in the event of substantial losses.
RATE OF CHANGE (ROC) :
As explained above, the ROC is used to situate the current price compared to that of a certain period of time ago. The formula for calculating ROC in relation to the previous year is as follows :
ROC (365) = (close/close (365) - 1) * 100
With this formula we can find out how many percent the change in the current price is compared with 365 days ago, and thus assess the trend.
PARAMETERS :
ROC Length : Length of the ROC to be calculated. The current price is compared with that of the selected length ago.
ROC Bubble Signal : ROC value indicating that we are in a bubble. This value varies enormously depending on the financial product. For example, in the equity market, a bubble exists when ROC = 40, whereas in cryptocurrencies, a bubble exists when ROC = 150.
Stop Loss (in %) : Stop Loss value in percentage. This is the maximum trade value percentage that can be lost in a single trade.
Fixed Ratio : This is the amount of gain or loss at which the order quantity is changed. The default is 400, which means that for each $400 gain or loss, the order size is increased or decreased by an amount chosen by the user.
Increasing Order Amount : This is the amount to be added to or subtracted from orders when the fixed ratio is reached. The default is $200, which means that for every $400 gain, $200 is reinvested in the strategy. On the other hand, for every $400 loss, the order size is reduced by $200.
Initial capital : $1000
Fees : Interactive Broker fees apply to this strategy. They are set at 0.18% of the trade value.
Slippage : 3 ticks or $0.03 per trade. Corresponds to the latency time between the moment the signal is received and the moment the order is executed by the broker.
Important : A bot has been used to test the different parameters and determine which ones maximize return while limiting drawdown. This strategy is the most optimal on BITSTAMP:BTCUSD in 1D timeframe with the following parameters :
ROC Length = 365
ROC Bubble Signal = 180
Stop Loss (in %) = 6
LONG CONDITION :
We are in a LONG position if ROC (365) > 0 for at least two days. This allows us to limit noise and irrelevant signals to ensure that the ROC remains positive.
SHORT CONDITION :
We are in a SHORT position if ROC (365) < 0 for at least two days. We also open a SHORT position when the speculative bubble is about to burst. If ROC (365) > 180, we're in a bubble. If the bubble has been in existence for at least a week and the ROC falls back below this threshold, we can expect the asset to return to reasonable prices, and thus a downward trend. So we're opening a SHORT position to take advantage of this upcoming decline.
EXIT RULES FOR WINNING TRADE :
The strategy is self-regulating. We don't exit a LONG trade until a SHORT signal has arrived, and vice versa. So, to exit a winning position, you have to wait for the entry signal of the opposite position.
RISK MANAGEMENT :
This strategy is very risky, and we can easily end up on the wrong side of the trade. That's why we're going to manage our risk with a Stop Loss, limiting our losses as a percentage of the trade's value. By default, this percentage is set at 6%. Each trade will therefore take a maximum loss of 6%.
If the SL has been triggered, it probably means we were on the wrong side. This is why we change the direction of the trade when a SL is triggered. For example, if we were SHORT and lost 6% of the trade value, the strategy will close this losing trade and open a long position without taking into account the ROC value. This allows us to be in position all the time and not miss the best opportunities.
MONEY MANAGEMENT :
The fixed ratio method was used to manage our gains and losses. For each gain of an amount equal to the value of the fixed ratio, we increase the order size by a value defined by the user in the "Increasing order amount" parameter. Similarly, each time we lose an amount equal to the value of the fixed ratio, we decrease the order size by the same user-defined value. This strategy increases both performance and drawdown.
NOTE :
Please note that the strategy is backtested from 2017-01-01. As the timeframe is 1D, this strategy is a medium/long-term strategy. That's why only 34 trades were closed. Be careful, as the test sample is small and performance may not necessarily reflect what may happen in the future.
Enjoy the strategy and don't forget to take the trade :)
Martingale with MACD+KDJ opening conditionsStrategy Overview:
This strategy is based on a Martingale trading approach, incorporating MACD and KDJ indicators. It features pyramiding, trailing stops, and dynamic profit-taking mechanisms, suitable for both long and short trades. The strategy increases position size progressively using a Multiplier, a key feature of Martingale systems.
Key Concepts:
Martingale Strategy: A trading system where positions are doubled or increased after a loss to recover previous losses with a single successful trade. In this script, the position size is incremented using a Multiplier for each addition.
Pyramiding: Allows adding to existing trades when market conditions are favorable, enhancing profitability during trends.
Settings:
Basic Inputs:
Initial Order: Defines the starting size of the position.
Default: 150.0
MACD Settings: Customize the fast, slow, and signal smoothing lengths.
Default: Fast Length: 9, Slow Length: 26, Signal Smoothing: 9
KDJ Settings: Customize the length and smoothing parameters for KDJ.
Default: Length: 14, Smooth K: 3, Smooth D: 3
Max Additions: Sets the number of additional positions (pyramiding).
Default: 5 (Min: 1, Max: 10)
Position Sizing: Percent to add to positions on favorable conditions.
Default: 1.0%
Martingale Multiplier:
Add Multiplier: This value controls the scaling of additional positions according to the Martingale principle. After each loss, a new position is added, and its size is increased by the Multiplier factor. For example, with a multiplier of 2, each new addition will be twice as large as the previous one, accelerating recovery if the price moves favorably.
Default: 1.0 (no multiplication)
Can be adjusted up to 10x to aggressively increase position size after losses.
Trade Execution:
Long Trades:
Entry Condition: A long position is opened when the MACD line crosses over the signal line, and the KDJ’s %K crosses above %D.
Additions (Martingale): After the initial long position, new positions are added if the price drops by the defined percentage, and each new addition is increased using the Multiplier. This continues up to the set Max Additions.
Short Trades:
Entry Condition: A short position is opened when the MACD line crosses under the signal line, and the KDJ’s %K crosses below %D.
Additions (Martingale): After the initial short position, new positions are added if the price rises by the defined percentage, and each new addition is increased using the Multiplier.
Exit Conditions:
Take Profit: Exits are triggered when the price reaches the take-profit threshold.
Stop Loss: If the price moves unfavorably, the position will be closed at the set stop-loss level.
Trailing Stop: Adjusts dynamically as the price moves in favor of the trade to lock in profits.
On-Chart Visuals:
Long Signals: Blue triangles below the bars indicate long entries, and green triangles mark additional long positions.
Short Signals: Red triangles above the bars indicate short entries, and orange triangles mark additional short positions.
Information Table:
The strategy displays a table with key metrics:
Open Price: The entry price of the trade.
Average Price: The average price of the current position.
Additions: The number of additional positions taken.
Next Add Price: The price level for the next position.
Take Profit: The price at which profits will be taken.
Stop Loss: The stop-loss level to minimize risk.
Usage Instructions:
Adjust the parameters to your trading style using the input settings.
The Multiplier amplifies your position size after each addition, so use it cautiously, especially in volatile markets.
Monitor the signals and table on the chart for entry/exit decisions and trade management.
Larry Connors %b Strategy (Bollinger Band)Larry Connors’ %b Strategy is a mean-reversion trading approach that uses Bollinger Bands to identify buy and sell signals based on the %b indicator. This strategy was developed by Larry Connors, a renowned trader and author known for his systematic, data-driven trading methods, particularly those focusing on short-term mean reversion.
The %b indicator measures the position of the current price relative to the Bollinger Bands, which are volatility bands placed above and below a moving average. The strategy specifically targets times when prices are oversold within a long-term uptrend and aims to capture rebounds by buying at relatively low points and selling at relatively high points.
Strategy Rules
The basic rules of the %b Strategy are:
1. Trend Confirmation: The closing price must be above the 200-day moving average. This filter ensures that trades are made in alignment with a longer-term uptrend, thereby avoiding trades against the primary market trend.
2. Oversold Conditions: The %b indicator must be below 0.2 for three consecutive days. The %b value below 0.2 indicates that the price is near the lower Bollinger Band, suggesting an oversold condition.
3. Entry Signal: Enter a long position at the close when conditions 1 and 2 are met.
4. Exit Signal: Exit the position when the %b value closes above 0.8, signaling an overbought condition where the price is near the upper Bollinger Band.
How the Strategy Works
This strategy operates on the premise of mean reversion, which suggests that extreme price movements will revert to the mean over time. By entering positions when the %b value indicates an oversold condition (below 0.2) in a confirmed uptrend, the strategy attempts to capture short-term price rebounds. The exit rule (when %b is above 0.8) aims to lock in profits once the price reaches an overbought condition, often near the upper Bollinger Band.
Who Was Larry Connors?
Larry Connors is a well-known figure in the world of financial markets and trading. He co-authored several influential trading books, including “Short-Term Trading Strategies That Work” and “High Probability ETF Trading.” Connors is recognized for his quantitative approach, focusing on systematic, rules-based strategies that leverage historical data to validate trading edges.
His work primarily revolves around short-term trading strategies, often using technical indicators like RSI (Relative Strength Index), Bollinger Bands, and moving averages. Connors’ methodologies have been widely adopted by traders seeking structured approaches to exploit short-term inefficiencies in the market.
Risks of the Strategy
While the %b Strategy can be effective, particularly in mean-reverting markets, it is not without risks:
1. Mean Reversion Assumption: The strategy is based on the assumption that prices will revert to the mean. In trending or sharply falling markets, this reversion may not occur, leading to sustained losses.
2. False Signals in Choppy Markets: In volatile or sideways markets, the strategy may generate multiple false signals, resulting in whipsaw trades that can erode capital through frequent small losses.
3. No Stop Loss: The basic implementation of the strategy does not include a stop loss, which increases the risk of holding losing trades longer than intended, especially if the market continues to move against the position.
4. Performance During Market Crashes: During major market downturns, the strategy’s buy signals could be triggered frequently as prices decline, compounding losses without the presence of a risk management mechanism.
Scientific References and Theoretical Basis
The %b Strategy relies on the concept of mean reversion, which has been extensively studied in finance literature. Studies by Avellaneda and Lee (2010) and Bouchaud et al. (2018) have demonstrated that mean-reverting strategies can be profitable in specific market environments, particularly when combined with volatility filters like Bollinger Bands. However, the same studies caution that such strategies are highly sensitive to market conditions and often perform poorly during periods of prolonged trends.
Bollinger Bands themselves were popularized by John Bollinger and are widely used to assess price volatility and detect potential overbought and oversold conditions. The %b value is a critical part of this analysis, as it standardizes the position of price relative to the bands, making it easier to compare conditions across different securities and time frames.
Conclusion
Larry Connors’ %b Strategy is a well-known mean-reversion technique that leverages Bollinger Bands to identify buying opportunities in uptrending markets when prices are temporarily oversold. While the strategy can be effective under the right conditions, traders should be aware of its limitations and risks, particularly in trending or highly volatile markets. Incorporating risk management techniques, such as stop losses, could help mitigate some of these risks, making the strategy more robust against adverse market conditions.
MACD + RSI + ADX Strategy (ChatGPT-powered) by TradeSmartThis is a trading strategy made by TradeSmart, using the recommendations given by ChatGPT . As an experiment, we asked ChatGPT on which indicators are the most popular for trading. We used all of the recommendations given, and added more. We ended up with a strategy that performs surprisingly well on many crypto and forex assets. See below for exact details on what logic was implemented and how you can change the parameters of the strategy.
The strategy is a Christmas special , this is how we would like to thank the support of our followers.
The strategy has performed well on Forex, tested on 43 1-hour pairs and turned a profit in 21 cases. Also it has been tested on 51 crypto pairs using the 1-hour timeframe, and turned a profit in 45 cases with a Profit Factor over 1.4 in the top-5 cases. Tests were conducted without commission or slippage, unlike the presented result which uses 0.01% commission and 5 tick slippage.
Some of the top performers were:
SNXUSDT
SOLUSDT
CAKEUSDT
LINKUSDT
EGLDUSDT
GBPJPY
TRYJPY
USDJPY
The strategy was implemented using the following logic:
Entry strategy:
Long entry:
Price should be above the Simple Moving Average (SMA)
There should be a cross up on the MACD (indicated by the color switch on the histogram, red to green)
RSI should be above the 50 level
Volume is above the selected volume-based Exponential Moving Average (EMA)
ADX should also agree to this position: below 50 and over 20, and above the Regularized Moving Average (REMA)
Short entry:
Price should be under the Simple Moving Average (SMA)
There should be a cross down on the MACD (indicated by the color switch on the histogram, red to green)
RSI should be below the 50 level
Volume is above the selected volume-based Exponential Moving Average (EMA)
ADX should also agree to this position: below 50 and over 20, and above the Regularized Moving Average (REMA)
Exit strategy:
Stop Loss will be placed based on ATR value (with 1.5 Risk)
Take profit level will be placed with a 2.5 Risk/Reward Ratio
Open positions will be closed early based on the Squeeze Momentum (Long: change to red, Short: change to green)
NOTE! : The position sizes used in the example is with 'Risk Percentage (current)', according which the position size will be determined such
that the potential loss is equal to % of the current available capital. This means that in most of the cases, the positions are calculated using leverage.
Parameters of every indicator used in the strategy can be tuned in the strategy settings as follows:
Plot settings:
Plot Signals: true by default, Show all Long and Short signals on the signal candle
Allow early TP/SL plots: false by default, Checking this option will result in the TP and SL lines to be plotted also on the signal candle rather than just the entry candle. Consider this only when manual trading, since backtest entries does not happen on the signal candle.
Entry Signal:
Fast Length: 12 by default
Slow Length: 26 by default
Source: hlcc4 by default
Signal Smoothing: 9 by default
Oscillator MA Type: EMA by default
Signal Line MA Type: EMA by default
Exit Strategy:
ATR Based Stop Loss: true by default
ATR Length (of the SL): 14 by default
ATR Smoothing (of the SL): EMA by default
Candle Low/High Based Stop Loss: false by default, recent lowest or highest point (depending on long/short position) will be used to calculate stop loss value. Set 'Base Risk Multiplier' to 1 if you would like to use the calculated value as is. Setting it to a different value will count as an additional multiplier. Please select only one active stop loss. Default value (if nothing or multiple stop losses are selected) is the 'ATR Based Stop Loss'.
Candle Lookback (of the SL): 10 by default
Base Risk Multiplier: 1.5 by default, the stop loss will be placed at this risk level (meaning in case of ATR SL that the ATR value will be multiplied by this factor and the SL will be placed that value away from the entry level)
Risk to Reward Ratio: 2.5 by default, the take profit level will be placed such as this Risk/Reward ratio is met
Force Exit based on Squeeze Momentum: true by default, a Long position will be closed when Squeeze Momentum turns red inside an open position and a Short position will be closed when Squeeze Momentum turns green inside an open position
BB Length: 20 by default
BB Mult Factor: 1.0 by default
KC Length: 20 by default
KC Mult Factor: 1.5 by default
Use True Range (KC): Yes by default
Base Setups:
Allow Long Entries: true by default
Allow Short Entries: true by default
Order Size: 1.5 by default
Order Type: Risk Percentage (current) by default, allows adjustment on how the position size is calculated: Cash: only the set cash ammount will be used for each trade Contract(s): the adjusted number of contracts will be used for each trade Capital Percentage: a % of the current available capital will be used for each trade Risk Percentage (current): position size will be determined such that the potential loss is equal to % of the current available capital Risk Percentage (initial): position size will be determined such that the potential loss is equal to % of the initial capital
Trend Filter:
Use long trend filter: true by default, only enter long if price is above Long MA
Show long trend filter: true by default, plot the selected MA on the chart
MA Type (Long): SMA by default
MA Length (Long): 100 by default
MA Source (Long): close by default
Use short trend filter: true by default, only enter long if price is under Short MA
Show short trend filter: false by default, plot the selected MA on the chart
MA Type (Short): SMA by default
MA Length (Short): 100 by default
MA Source (Short): close by default
Simple RSI Limiter:
Limit using Simple RSI: true by default, if set to 'Normal', only enter long when Simple RSI is lower then Long Boundary, and only enter short when Simple RSI is higher then Short Boundary. If set to 'Reverse', only enter long when Simple RSI is higher then Long Boundary, and only enter short when Simple RSI is lower then Short Boundary.
Simple RSI Limiter Type:
RSI Length: 14 by default
RSI Source: hl2 by default
Simple RSI Long Boundary: 50 by default
Simple RSI Short Boundary: 50 by default
ADX Limiter:
Use ADX Limiter: true by default, only enter into any position (long/short) if ADX value is higher than the Low Boundary and lower than the High Boundary.
ADX Length: 5 by default
DI Length: 5 by default
High Boundary: 50 by default
Low Boundary: 20 by default
Use MA based calculation: Yes by default, if 'Yes', only enter into position (long/short) if ADX value is higher than MA (ADX as source).
MA Type: REMA by default
MA Length: 5 by default
Volume Filter:
Only enter trades where volume is higher then the volume-based MA: true by default, a set type of MA will be calculated with the volume as source, and set length
MA Type: EMA by default
MA Length: 10 by default
Session Limiter:
Show session plots: false by default, show crypto market sessions on chart: Sidney (red), Tokyo (orange), London (yellow), New York (green)
Use session limiter: false by default, if enabled, trades will only happen in the ticked sessions below.
Sidney session: false by default, session between: 15:00 - 00:00 (EST)
Tokyo session: false by default, session between: 19:00 - 04:00 (EST)
London session: false by default, session between: 03:00 - 11:00 (EST)
New York session: false by default, session between: 08:00 - 17:00 (EST)
Date Range:
Limit Between Dates: false by default
Start Date: Jul 01 2021 00:00:00 by default
End Date: Dec 31 2022 00:00:00 by default
Trading Time:
Limit Trading Time: false by default, tick this together with the options below to enable limiting based on day and time
Valid Trading Days Global: 1234567 by default, if the Limit Trading Time is on, trades will only happen on days that are present in this field. If any of the not global Valid Trading Days is used, this field will be neglected. Values represent days: Sunday (1), Monday (2), ..., Friday (6), Saturday(7) To trade on all days use: 123457
(1) Valid Trading Days: false, 1234567 by default, values represent days: Sunday (1), Monday (2), ..., Friday (6), Saturday(7) The script will trade on days that are present in this field. Please make sure that this field and also (1) Valid Trading Hours Between is checked
(1) Valid Trading Hours Between: false, 0930-1600 by default, hours between which the trades can happen. The time is always in the exchange's timezone
Fine-tuning is highly recommended when using other asset/timeframe combinations.
SMA + RSI + Volume + ATR StrategySMA + RSI + Volume + ATR Strategy
1. Indicators Used:
SMA (Simple Moving Average): This is a trend-following indicator that calculates the average price of a security over a specified period (50 periods in this case). It's used to identify the overall trend of the market.
RSI (Relative Strength Index): This measures the speed and change of price movements. It tells us if the market is overbought (too high) or oversold (too low). Overbought is above 70 and oversold is below 30.
Volume: This is the amount of trading activity. A higher volume often indicates strong interest in a particular price move.
ATR (Average True Range): This measures volatility, or how much the price is moving in a given period. It helps us adjust stop losses and take profits based on market volatility.
2. Conditions for Entering Trades:
Buy Signal (Green Up Arrow):
Price is above the 50-period SMA (indicating an uptrend).
RSI is below 30 (indicating the market might be oversold or undervalued, signaling a potential reversal).
Current volume is higher than average volume (indicating strong interest in the move).
ATR is increasing (indicating higher volatility, suggesting that the market might be ready for a move).
Sell Signal (Red Down Arrow):
Price is below the 50-period SMA (indicating a downtrend).
RSI is above 70 (indicating the market might be overbought or overvalued, signaling a potential reversal).
Current volume is higher than average volume (indicating strong interest in the move).
ATR is increasing (indicating higher volatility, suggesting that the market might be ready for a move).
3. Take Profit & Stop Loss:
Take Profit: When a trade is made, the strategy will set a target price at a certain percentage above or below the entry price (1.5% in this case) to automatically exit the trade once that target is hit.
Stop Loss: If the price goes against the position, a stop loss is set at a percentage below or above the entry price (0.5% in this case) to limit losses.
4. Execution of Trades:
When the buy condition is met, the strategy will enter a long position (buying).
When the sell condition is met, the strategy will enter a short position (selling).
5. Visual Representation:
Green Up Arrow: Appears on the chart when the buy condition is met.
Red Down Arrow: Appears on the chart when the sell condition is met.
These arrows help you see at a glance when the strategy suggests you should buy or sell.
In Summary:
This strategy uses a combination of trend-following (SMA), momentum (RSI), volume, and volatility (ATR) to decide when to buy or sell a stock. It looks for opportunities when the market is either oversold (buy signal) or overbought (sell signal) and makes sure there’s enough volume and volatility to back up the move. It also includes take-profit and stop-loss levels to manage risk.
