GKD-C Variety Stepped, Variety Filter [Loxx]Giga Kaleidoscope GKD-C Variety Stepped, Variety Filter is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ Giga Kaleidoscope Modularized Trading System
What is Loxx's "Giga Kaleidoscope Modularized Trading System"?
The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading.
What is the NNFX algorithmic trading strategy?
The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community.
The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management.
Here are the main components of the NNFX trading system:
1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines.
2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades.
3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages.
4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade.
5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level.
6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing.
Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels.
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 Stochastic Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Variety Stepped, Variety Filter as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
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
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
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
█ GKD-C Variety Stepped, Variety Filter
Variety Stepped, Variety Filter is an indicator that uses various types of stepping behavior to reduce false signals. This indicator includes 5+ volatility stepping types and 60+ moving averages.
Stepping calculations
First off, you can filter by both price and/or MA output. Both price and MA output can be filtered/stepped in their own way. You'll see two selectors in the input settings. Default is ATR ATR. Here's how stepping works in simple terms: if the price/MA output doesn't move by X deviations, then revert to the price/MA output one bar back.
ATR
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.
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 σ 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. And added to that, 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 standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
See how this compares to Standard Devaition here:
Adaptive Deviation
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.
For this indicator, I used a manual recreation of the quantile function in Pine Script. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is widely used indicator in many occasions for technical analysis . It is calculated as the RMA of true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range
See how this compares to ATR here:
ER-Adaptive ATR
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.
For Pine Coders, this is equivalent of using ta.dev()
Included Filters
Adaptive Moving Average - AMA
ADXvma - Average Directional Volatility Moving Average
Ahrens Moving Average
Alexander Moving Average - ALXMA
Deviation Scaled Moving Average - DSMA
Donchian
Double Exponential Moving Average - DEMA
Double Smoothed Exponential Moving Average - DSEMA
Double Smoothed FEMA - DSFEMA
Double Smoothed Range Weighted EMA - DSRWEMA
Double Smoothed Wilders EMA - DSWEMA
Double Weighted Moving Average - DWMA
Ehlers Optimal Tracking Filter - EOTF
Exponential Moving Average - EMA
Fast Exponential Moving Average - FEMA
Fractal Adaptive Moving Average - FRAMA
Generalized DEMA - GDEMA
Generalized Double DEMA - GDDEMA
Hull Moving Average (Type 1) - HMA1
Hull Moving Average (Type 2) - HMA2
Hull Moving Average (Type 3) - HMA3
Hull Moving Average (Type 4) - HMA4
IE /2 - Early T3 by Tim Tilson
Integral of Linear Regression Slope - ILRS
Instantaneous Trendline
Kalman Filter
Kaufman Adaptive Moving Average - KAMA
Laguerre Filter
Leader Exponential Moving Average
Linear Regression Value - LSMA ( Least Squares Moving Average )
Linear Weighted Moving Average - LWMA
McGinley Dynamic
McNicholl EMA
Non-Lag Moving Average
Ocean NMA Moving Average - ONMAMA
Parabolic Weighted Moving Average
Probability Density Function Moving Average - PDFMA
Quadratic Regression Moving Average - QRMA
Regularized EMA - REMA
Range Weighted EMA - RWEMA
Recursive Moving Trendline
Simple Decycler - SDEC
Simple Jurik Moving Average - SJMA
Simple Moving Average - SMA
Sine Weighted Moving Average
Smoothed LWMA - SLWMA
Smoothed Moving Average - SMMA
Smoother
Super Smoother
T3
Three-pole Ehlers Butterworth
Three-pole Ehlers Smoother
Triangular Moving Average - TMA
Triple Exponential Moving Average - TEMA
Two-pole Ehlers Butterworth
Two-pole Ehlers smoother
Variable Index Dynamic Average - VIDYA
Variable Moving Average - VMA
Volume Weighted EMA - VEMA
Volume Weighted Moving Average - VWMA
Zero-Lag DEMA - Zero Lag Exponential Moving Average
Zero-Lag Moving Average
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Adaptive Moving Average - AMA
Description. The Adaptive Moving Average (AMA) is a moving average that changes its sensitivity to price moves depending on the calculated volatility . It becomes more sensitive during periods when the price is moving smoothly in a certain direction and becomes less sensitive when the price is volatile.
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA , it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA .
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Deviation Scaled Moving Average - DSMA
The Deviation-Scaled Moving Average is a data smoothing technique that acts like an exponential moving average with a dynamic smoothing coefficient. The smoothing coefficient is automatically updated based on the magnitude of price changes. In the Deviation-Scaled Moving Average, the standard deviation from the mean is chosen to be the measure of this magnitude. The resulting indicator provides substantial smoothing of the data even when price changes are small while quickly adapting to these changes.
Donchian
Donchian Channels are three lines generated by moving average calculations that comprise an indicator formed by upper and lower bands around a midrange or median band. The upper band marks the highest price of a security over N periods while the lower band marks the lowest price of a security over N periods.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average ( DEMA ) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA . It's also considered a leading indicator compared to the EMA , and is best utilized whenever smoothness and speed of reaction to market changes are required.
Double Smoothed FEMA - DSFEMA
Same as the Double Exponential Moving Average ( DEMA ), but uses a faster version of EMA for its calculation.
Double Smoothed Range Weighted EMA - DSRWEMA
Range weighted exponential moving average ( EMA ) is, unlike the "regular" range weighted average calculated in a different way. Even though the basis - the range weighting - is the same, the way how it is calculated is completely different. By definition this type of EMA is calculated as a ratio of EMA of price*weight / EMA of weight. And the results are very different and the two should be considered as completely different types of averages. The higher than EMA to price changes responsiveness when the ranges increase remains in this EMA too and in those cases this EMA is clearly leading the "regular" EMA . This version includes double smoothing.
Double Smoothed Wilders EMA - DSWEMA
Welles Wilder was frequently using one "special" case of EMA ( Exponential Moving Average ) that is due to that fact (that he used it) sometimes called Wilder's EMA . This version is adding double smoothing to Wilder's EMA in order to make it "faster" (it is more responsive to market prices than the original) and is still keeping very smooth values.
Double Weighted Moving Average - DWMA
Double weighted moving average is an LWMA (Linear Weighted Moving Average ). Instead of doing one cycle for calculating the LWMA, the indicator is made to cycle the loop 2 times. That produces a smoother values than the original LWMA
Ehlers Optimal Tracking Filter - EOTF
The Elher's Optimum Tracking Filter quickly adjusts rapid shifts in the price and yet is relatively smooth when the price has a sideways action. The operation of this filter is similar to Kaufman’s Adaptive Moving
Average
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA ( Simple Moving Average ). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA .
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Generalized DEMA - GDEMA
The double exponential moving average ( DEMA ), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages.". Instead of using fixed multiplication factor in the final DEMA formula, the generalized version allows you to change it. By varying the "volume factor" form 0 to 1 you apply different multiplications and thus producing DEMA with different "speed" - the higher the volume factor is the "faster" the DEMA will be (but also the slope of it will be less smooth). The volume factor is limited in the calculation to 1 since any volume factor that is larger than 1 is increasing the overshooting to the extent that some volume factors usage makes the indicator unusable.
Generalized Double DEMA - GDDEMA
The double exponential moving average ( DEMA ), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages''. This is an extension of the Generalized DEMA using Tim Tillsons (the inventor of T3) idea, and is using GDEMA of GDEMA for calculation (which is the "middle step" of T3 calculation). Since there are no versions showing that middle step, this version covers that too. The result is smoother than Generalized DEMA , but is less smooth than T3 - one has to do some experimenting in order to find the optimal way to use it, but in any case, since it is "faster" than the T3 (Tim Tillson T3) and still smooth, it looks like a good compromise between speed and smoothness.
Hull Moving Average (Type 1) - HMA1
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMA for smoothing.
Hull Moving Average (Type 2) - HMA2
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses EMA for smoothing.
Hull Moving Average (Type 3) - HMA3
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses LWMA for smoothing.
Hull Moving Average (Type 4) - HMA4
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMMA for smoothing.
IE /2 - Early T3 by Tim Tilson and T3 new
T3 is basically an EMA on steroids, You can read about T3 here:
T3 Striped
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA ( Simple Moving Average ) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Instantaneous Trendline
The Instantaneous Trendline is created by removing the dominant cycle component from the price information which makes this Moving Average suitable for medium to long-term trading.
Kalman Filter
Kalman filter is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies. This means that the filter was originally designed to work with noisy data. Also, it is able to work with incomplete data. Another advantage is that it is designed for and applied in dynamic systems; our price chart belongs to such systems. This version is true to the original design of the trade-ready Kalman Filter where velocity is the triggering mechanism.
Kalman Filter is a more accurate smoothing/prediction algorithm than the moving average because it is adaptive: it accounts for estimation errors and tries to adjust its predictions from the information it learned in the previous stage. Theoretically, Kalman Filter consists of measurement and transition components.
Kaufman Adaptive Moving Average - KAMA
Developed by Perry Kaufman, Kaufman's Adaptive Moving Average ( KAMA ) is a moving average designed to account for market noise or volatility . KAMA will closely follow prices when the price swings are relatively small and the noise is low.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and its smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA ( Least Squares Moving Average )
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA . Although it's similar to the Simple Moving Average , the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track prices better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non-lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Ocean NMA Moving Average - ONMAMA
Created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility without being programmed to do so. For more info, read his guide "Ocean Theory, an Introduction"
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average . The Linear Weighted Moving Average calculates the average by assigning different weights to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Probability Density Function Moving Average - PDFMA
Probability density function based MA is a sort of weighted moving average that uses probability density function to calculate the weights. By its nature it is similar to a lot of digital filters.
Quadratic Regression Moving Average - QRMA
A quadratic regression is the process of finding the equation of the parabola that best fits a set of data. This moving average is an obscure concept that was posted to Forex forums in around 2008.
Regularized EMA - REMA
The regularized exponential moving average (REMA) by Chris Satchwell is a variation on the EMA (see Exponential Moving Average ) designed to be smoother but not introduce too much extra lag.
Range Weighted EMA - RWEMA
This indicator is a variation of the range weighted EMA . The variation comes from a possible need to make that indicator a bit less "noisy" when it comes to slope changes. The method used for calculating this variation is the method described by Lee Leibfarth in his article "Trading With An Adaptive Price Zone".
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrow's price.
Simple Decycler - SDEC
The Ehlers Simple Decycler study is a virtually zero-lag technical indicator proposed by John F. Ehlers . The original idea behind this study (and several others created by John F. Ehlers ) is that market data can be considered a continuum of cycle periods with different cycle amplitudes. Thus, trending periods can be considered segments of longer cycles, or, in other words, low-frequency segments. Applying the right filter might help identify these segments.
Simple Loxx Moving Average - SLMA
A three stage moving average combining an adaptive EMA , a Kalman Filter, and a Kauffman adaptive filter.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA .
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed LWMA - SLWMA
A smoothed version of the LWMA
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average ( SMA ), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen as an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA ( Smoothed Moving Average ). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a Two pole Butterworth filter combined with a 2-bar SMA ( Simple Moving Average ) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three-pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA . They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three-pole Ehlers Smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, its signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two-pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two-pole Ehlers Smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers .
Variable Index Dynamic Average - VIDYA
Variable Index Dynamic Average Technical Indicator ( VIDYA ) was developed by Tushar Chande. It is an original method of calculating the Exponential Moving Average ( EMA ) with the dynamically changing period of averaging.
Variable Moving Average - VMA
The Variable Moving Average (VMA) is a study that uses an Exponential Moving Average being able to automatically adjust its smoothing factor according to the market volatility .
Volume Weighted EMA - VEMA
An EMA that uses a volume and price weighted calculation instead of the standard price input.
Volume Weighted Moving Average - VWMA
A Volume Weighted Moving Average is a moving average where more weight is given to bars with heavy volume than with light volume . Thus the value of the moving average will be closer to where most trading actually happened than it otherwise would be without being volume weighted.
Zero-Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero-Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers , as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero-Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA , this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
Requirements
Inputs
Confirmation 1 and Solo Confirmation: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Outputs
Confirmation 2 and Solo Confirmation Complex: GKD-E Exit indicator
Confirmation 1: GKD-C Confirmation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest strategy
Additional features will be added in future releases.
Cerca negli script per "track"
PATIThis indicator is part of our educational suite focused on teaching price structure, momentum, and mean reversion trading strategies for intraday trading. Our team has selected this set of tools and metrics, which define our trading style and serve as the foundation for our teaching, to be included in this indicator. We are displaying each component in a way we believe is helpful to their understanding which also provides a clean, comprehensive look.
This indicator is for Intraday Trading
Our Traders most commonly use this indicator on the 1,3 or 5 minute chart.
Components of this Indicator:
Multiple VWAP Levels: monthly, weekly, standard (anchored to the right of price)
Dynamically Anchored VWAP Cloud (trend tool)
13 EMA (trend tool)
Structural Orderblocks
Multi-Timeframe Fair Value Gap detection
Key Daily Price Levels (anchored to the right of price)
Customizable Opening Range (anchored to the right of price)
15 minute “Golden Zone” (shows the .5-.618 zone of the previous 15m candle)
ADR (Average Daily Range)
A4R (Average 4hr Range)
These tools are used in conjunction with the education we provide to help our users determine their optimal trade plan to utilize their edge.
Specific Functionalities and Uses:
Monthly-VWAP & Weekly-VWAP (M-VWAP/W-VWAP):
VWAP = “Volume Weighted Average Price”
These levels provide probable zones where price may mean revert and risk should be taken off/ put on. We have anchored these to the right-hand side of your chart by default to minimize the noise on your chart.
Average Daily Range (ADR): The Average Daily Range is a technical indicator used to measure the volatility of an asset. It displays how much an instrument can move on average during a given day. The significance is that each market has a unique range that is likely to be covered on any given day.
Average 4hr Range (A4R): The Average 4hr Range is a technical indicator used to measure the volatility of an asset twice in a single session. It displays how much an instrument can move on average during a session and is measured twice in a day. Calculating a smaller volatility range may seem strange at first but can be a huge advantage by analyzing the volatility of the intraday action, giving you average price targets based on more recent market data.
Tip: When used in conjunction with key support and resistance levels, ADR & A4R can be a huge edge to traders to determine where to push/pull risk.
Opening Range: The open often establishes the trend and sentiment for the day, but there is also statistical significance to the open that is overlooked. Statistically, on average, the open is near the high or low of the day and offers plenty of opportunities to build trading strategies. The chart below provides some potential trades that could be taken once the opening range has been established.
Dynamically Anchored VWAP Cloud: Our dynamically anchored VWAP cloud tracks the most recent impulsive move and re-anchors to show you potential bounce points in a trend. We re-anchor at each structural shift to give the most probable targets for buyers/sellers to defend their positions to continue the current trend push.
By utilizing the re-anchoring at each significant structural inflection point, we can establish a much less lagging trend following technique.
We have also included the feature to substitute this cloud for a 34/55 EMA cloud for the traders already familiar with that system.
The chart below provides potential trades that could be taken using the VWAP cloud system.
FVGS (Fair Value Gaps/ Imbalances): These areas represent potential buy/sell side liquidity imbalances where price is pushed aggressively, sweeping the orderbook and will likely return to “fix” the structure before continuing. Below is an example of 3 possible trade paths we look for inside these structural imbalances.
Structural Orderblocks:
These areas are based on structural pivots that have been pushed out of with aggression determined by subsequent structural breaks to confirm their validity. Because of this, when price returns to these areas we can anticipate this area to be defended.
The blue boxes track Orderblocks. These highlight instances of past participation which create areas likely to be defended again when retested.
Swing High/Low/Previous:
We use swing high and lows as points of short-term support and resistance, a break of these levels can signify a shift in market sentiment.
-The dashed green line shows the previous structural swing high or low pivot point.
-The solid green lines show the high and low in our current trading structure.
Note: Displaying the previous swing can provide us with context of the current market trend, and will assist us make better decisions.
15 Minute Golden Zone:
Displayed as a gray box, it tracks the .5-.618 of the previous 15m candle and gives us an area where we look for short-term resistance/support on smaller time frame price action. This area can be viewed as an equilibrium of the current range. If the price can hold this area, it can show a likely support area for continuation.
13 EMA:
This is the choice length ema of our traders, they use this ema to confirm (short-term) trend direction and reference it for a common bounce point for re-entries. Our traders consider this as a crucial point to speculate reversals and break of short-term trends.
Note: Typically in a trend we see the price hold to one side of this ema, by looking for this characteristic, it brings confidence to staying in trades.
Please check the Author Instructions Below for how to gain access to our indicators.
[astropark] Trend Skywalker V2 [alarms]Dear Followers,
today I'm glad to present you Trend Skywalker V2 , the evolution of Trend Skywalker V1 indicator that you can see here below:
This indicator works on every timeframe and market, it's quite responsive to market movements, so it's especially good on volatile markets.
In this new version you have 3 trend clouds available :
a short-term one (yellow)
a mid-term one (green)
a long-term one (blue)
You can also enable an option to show all trend clouds as one, the result will be similar to a special bollinger bands tool.
Of course you can edit trend clouds analysis period and color, also you can turn on or off the cloud that you prefer.
The indicator can run 4 different kinds of strategy : one for each trend cloud individually or a mixed one.
Also the indicator tracks for you a peak profit from entry: this tracker is a suggestion for you to take profits while price goes up!
All red-green circles you see in the chart is a reminder that a peak profit label was there in the past: what does this tell you?
if price starts losing the short-term trend and you had a lot of TP suggestions, maybe trend ended and you should start consider closing your trade before you give back all your profit.
This indicator will let you set alerts on each buy/sell/close/tp label.
For backtesting, you can use the indicator here below:
This is a premium indicator , so send me a private message in order to get access to this script.
[astropark] Trend Skywalker V2 [strategy]Dear Followers,
today I'm glad to present you Trend Skywalker V2 , the evolution of Trend Skywalker V1 indicator that you can see here below:
This indicator works on every timeframe and market, it's quite responsive to market movements, so it's especially good on volatile markets.
In this new version you have 3 trend clouds available :
a short-term one (yellow)
a mid-term one (green)
a long-term one (blue)
You can also enable an option to show all trend clouds as one, the result will be similar to a special bollinger bands tool.
Of course you can edit trend clouds analysis period and color, also you can turn on or off the cloud that you prefer.
The indicator can run 4 different kinds of strategy : one for each trend cloud individually or a mixed one.
Also the indicator tracks for you a peak profit from entry: this tracker is a suggestion for you to take profits while price goes up!
All red-green circles you see in the chart is a reminder that a peak profit label was there in the past: what does this tell you?
if price starts losing the short-term trend and you had a lot of TP suggestions, maybe trend ended and you should start consider closing your trade before you give back all your profit.
On backtesting you can you test long and short setups individually or both, as well as performance in a specific time window.
This is a premium indicator , so send me a private message in order to get access to this script.
Self-Optimising MACD (Experimental)Hi guys, just thought I'd share a small part of an idea i've been working on.
One of the biggest problems with algo trading is optimisation and finding a way to constantly adapt to the market conditions as time unfolds.
First of all... You should NEVER EVER trade just using a MACD, including this study, and I only produced this script in a small amount of time, so make sure you backtest it properly before using it. When backtesting, it is my advice that your sample size should be at least 5000 trades, but I recommend 10000 in order to get sufficient statistical significance.
Also, I am not a financial advisor, and any trading based decisions are your sole responsibility.
Anyways...
This script is simple... it simply uses 4 different MACD's and tracks their profit/loss and automatically uses the one with the most historical profit at any given time to execute a trade. The type of MACD will obviously change as market states fluctuate.
Included are : Hull MACD, Ema MACD, Sma MACD and VWMA Macd.
You can adjust all four of their settings to your desire.
The trade execution is simple and definitely flawed... it simply tracks the MACD when it has a crossover for long, and then the opposite for short.
The green line represents the performance of the top MACD for Longs at any given time. This line refreshes once a year, and where it is in relation to price, reflects how profitable it has been I.e - the higher it is the better.
The Red line represents the performance on the Short side, and again, it reflects profit/loss, but this time the LOWER the line is in relation to price the better.
There is no exit strategy in place! This is why I do NOT recommend trading off this script alone, but to use it as a tool to help optimise your choice of MACD.
However, your exit strategy could change your optimal choice of MACD, so keep that in mind.
The lookback period represents how far the script will track the performance at any given time. This will change your results. The longer the period, the more it will show long term success and vice versa.
This optimisation process could be done with different indicators, moving averages, or even multiple strategies to find the most statistically viable option at any given time... if you wish to have this process coded into your strategies or indicators, message me.
Enjoy.
Session High and Session LowI have heard many people ask for a script that will identify the high and low of a specific session. So, I made one.
Important Note: This indicator has to be set up properly or you will get an error. Important things to note are the length of the range and the session definition. The idea is that you would set it up for what's relevant to your trading. Going too far back in the chart history will cause errors. Setting the session for a time that is not on the chart can cause errors. If you set it to look farther back than there are bars to display, you may get an error. What I've found is that if you get an error, you just need to change the settings to reflect available data and it will be able to compile the script. At the time of its publishing, the default range start is set to 10/01/2020. If you're looking at this years later, you'll probably have to set the range to something more recent.
Features:
Plot or Lines:
Using Plot (displayed), the indicator will track the high/low from the end of the session into the next session. Then at the start of the next session, it will start tracking the high/low of that session until its end, then track that high/low until the start of the next session then reset.
Using lines, it will extend horizontal lines to the right indefinitely. The number of sessions back that the lines apply to is a user-defined number of sessions. There are limits to the number of lines that can be cast on a chart (roughly 40-50). So, the maximum number of sessions you can apply the lines to is the last 21 sessions (42 lines total). That gets really noisy though so I can't imagine that is a limiting factor.
Colors:
You can change the background color and its transparency, as well as turn the background color on or off.
You can change the highs and lows colors
You can adjust the line width to your preference
Session Length:
You can use a continuous session covering any user-defined period (provided its not tooooo many candles back)
You can define the session length for intraday
You can exclude weekends
Display Options:
You can adjust the colors, transparency, and linewidth
You can display the plotline or horizontal lines
You can show/hide the background color.
You can change how many sessions back the horizontal lines will track
Let me know if there's anything this script is missing or if you run into any issues that I might be able to help resolve.
Here's what it looks like with Lines for the last 5 sessions and different background color.
™TʀᴀᴅᴇCʜᴀʀᴛɪsᴛ Tʀᴇɴᴅsᴇᴛᴛᴇʀ™TradeChartist Trendsetter is an elegantly designed functional indicator that helps spot price trends based on user input and volatility to generate high probability BUY and SELL signals.
1. What does ™TradeChartist Trendsetter do?
Plots high probability BUY/SELL signals based on user input and price volatility.
Plots recommended Stop Loss and SOS signals.
Plots regular RSI divergences based on user input.
Plots Linear Regression trend lines based on user input.
Displays Trendsetter Dashboard with useful trade information.
Displays real time gains tracker.
Tracks another symbol on Dashboard based on user input.
Alerts when BUY and SELL signals are generated.
2. What markets can this indicator be used on?
Forex
Stocks - Signal prices calculated taking gaps into account.
Commodities
Cryptocurrencies
and almost any asset on Trading View.
Works really well when there is good volume, volatility or both in the asset traded/observed.
3. Do the indicator signals repaint?
No. Once the BUY and SELL signals are generated with entry price (open price of signal candle), there is no repainting.
This can be verified using Trading View Bar Replay to check if the signals stay in the same candle in real-time as the Bar Replay.
4. Does the indicator send alerts when a signal is generated?
Yes. Traders can get alerts by setting up Trading View alerts for BUY/SELL signals. For confirmed BUY/SELL alerts, 'Once Per Bar' must be used as there is no need to wait for the candle close.
Example Charts
GBP-USD 1hr chart with indicator plots description
GOLD 4hr chart using Daily HTF resolution from indicator settings.
SPX 15m chart using Daily HTF resolution with RSI divergences.
Note: Default settings work really well for most assets and time frames. Change HTF resolution (default 4hr) from indicator settings and make sure it is higher time frame than the chart resolution.
PpSignal Multi-Day VWAPThank to @mortdiggiddy
original script:
Chart the multi-day Volume Weighted Average Price ( VWAP ). Normally, the VWAP is tracked for the current day, from the first bar of the day (regular or extended session). The VWAP shows the current value of:
-> sum(hlc3 * volume , barsForDay) / sum( volume , barsForDay),
-> where 'barsForDay' is the total number bars that have elapsed during the day for the chart interval.
The multi-day version tracks the VWAP for N days back, by averaging the previous N - 1 day bars VWAP and the current VWAP for the current bar (chart interval).
This is very different that simply using a volume weighted moving average , since the closing VWAP values are used for the historical day bars. The results are interesting for intraday trades... especially for values of 1, 2, 3, 4, 5 ....to 21 days.
GrowingVip-MME=5x1EMAS strategy to define trends, inputs and outputs correctly
1 ° EMA 5 serves to define aggressive entry or exit to the market.
at the time of crossing EMA 5 with EMA 12 up, or vice versa ...
Scalping tracking in short T 15 Min. 5 Min
2nd EMA 12 confirms entry in the short term when crossing with the fast EMA 36.
EMA 12 is indicating the price tracking. Both for Entry or Exit. Combination EMA 12/36.
3 ° EMA 36 defines as the basis or support of the price action.
Sitting EMA 12 on the EMA 36 ..
For more information, ask us.
Multi-Day VWAP V2Updated from V1.
Chart the multi-day Volume Weighted Average Price ( VWAP ). Normally, the VWAP is tracked for the current day, from the first bar of the day (regular or extended session). The VWAP shows the current value of:
-> sum(hlc3 * volume , barsForDay) / sum( volume , barsForDay),
-> where 'barsForDay' is the total number bars that have elapsed during the day for the chart interval.
The multi-day version tracks the VWAP for N days back, by averaging the previous N - 1 day bars VWAP and the current VWAP for the current bar (chart interval).
This is very different that simply using a volume weighted moving average , since the closing VWAP values are used for the historical day bars. The results are interesting for intraday trades... especially for values of 1, 2, 3, 4, and 5 days.
Version 2 includes the closing VWAP for the previous day. There are enough instances where the price chooses to bounce from the previous day's closing VWAP value that it is worth discussing. Usually this value is at or near the daily pivot, but sometimes not. Circled in the chart are some areas of recent SPY bounces on the previous day's closing VWAP.
It seems that when the 5-Day VWAP and normal VWAP have "enough" percentage separation, that there can be good intraday swing opportunities using bounces off VWAP indicators. This is similar to waiting for Hourly/Daily/Weekly/Monthly/etc pivots to have "enough" separation to allow for swing setups. When pivots are "closely" spaced, odds are the price is range bound for the time period (daily range in the case of day pivots, etc).
Previous closing VWAPs can be plotted for all 5 of the original. As with my other scripts, I welcome all comments to spark new ideas that we can all benefit from.
Enjoy.
Multi-Day VWAP
Chart the multi-day Volume Weighted Average Price ( VWAP ). Normally, the VWAP is tracked for the current day, from the first bar of the day (regular or extended session). The VWAP shows the current value of:
-> sum(hlc3 * volume , barsForDay) / sum( volume , barsForDay),
-> where 'barsForDay' is the total number bars that have elapsed during the day for the chart interval.
The multi-day version tracks the VWAP for N days back, by averaging the previous N - 1 day bars VWAP and the current VWAP for the current bar (chart interval).
This is very different that simply using a volume weighted moving average , since the closing VWAP values are used for the historical day bars. The results are interesting for intraday trades... especially for values of 1, 2, 3, 4, and 5 days.
Enjoy.
Highs and Lows🔍 Highs and Lows – Liquidity Zone Tracker
This script automatically detects and highlights key swing highs and lows on your chart using a pivot-based algorithm. These zones are dynamically plotted as visual rectangles that help identify unmitigated liquidity pools commonly used in Smart Money and institutional trading models.
Each level is marked as “fresh” when first plotted, meaning it hasn't been interacted with by price. When price touches a zone (via wick or full body), the script automatically de-emphasizes that zone to help you focus on actionable, untested levels.
📌 Key Features:
Pivot-Based Detection: Highs and lows are derived from confirmed swing points using a user-defined lookback period (default: 25 bars).
Freshness Logic:
Fresh zones are visually emphasized.
Touched zones fade automatically once price interacts, reducing chart clutter and drawing focus to relevant liquidity.
Customizable Visuals:
Individual styling for high and low zones (border color, fill color, style, width).
Adjustable max number of zones shown (default: 4 per side).
Touch & Break Detection:
Uses both wick interaction and full-body candle cross to determine freshness.
Real-Time Alerts:
Optional alerts for when price touches fresh high or low levels, ideal for breakout, mitigation, or reaction-based strategies.
📈 Practical Use Cases:
Identify untapped liquidity pools for entries/exits.
Visualize institutional interest areas in line with ICT/Smart Money models.
Use as entry confirmation zones in confluence with FVGs, BOS/CHOCH, and displacement tools.
Highlight stop-hunt or inducement zones before market expansion.
⚙️ How It Works:
High/low levels are detected using ta.pivothigh and ta.pivotlow.
Detected zones are boxed from the swing candle’s high/low to its close.
Price interaction logic:
Wick touch sets a box to "unfresh".
Full-body cross can reset a box as “fresh”.
Arrays are used to manage both box objects and their freshness states.
Max zone limits keep the chart clean and focused.
🛑 This script is closed-source to protect unique zone-tracking and visual management logic, but all key functionality and use cases are fully described above.
XRP Whale Accumulation Sniper v2 by Team UndergroundXRP Whale Sniper v2 by Team Underground
The XRP Whale Sniper v2 is a precision tool developed by Team Underground to identify large-scale accumulation and distribution events by whales in the XRP/USDT market on the daily chart. It combines historical on-chain behaviour patterns, momentum shifts, and smart money accumulation models into one clear visual system.
Key Features:
Green Line (Whale Activity Tracker): Smoothed oscillator-like overlay tracking potential whale accumulation (bottoming) phases.
Yellow triangle (Buy Signal): Indicates accumulation or whale entry zones, historically correlating with strong price bounces or trend reversals.
Adaptive Behaviour: The indicator adapts dynamically to volatility and trend strength, filtering out noise to highlight only high-probability zones.
Ideal Use:
Swing traders and long-term holders looking to ride whale moves.
Confirmation tool alongside your existing momentum, volume, or trend indicators.
Works best on daily timeframes for strategic entries and exits.
Not for financial advice. Provided for Coin Theory.
🔄 QuantSignals AI Reversal Pro🔄 QuantSignals AI Reversal Pro — 78%+ Win Rate Reversal Detection
🚀 Catch Market Tops & Bottoms with AI-Powered Precision!
This powerful script brings you professional-grade reversal signals—built on cutting-edge AI, smart confluence logic, and rigorous backtesting.
Whether you’re swing trading, scalping, or position trading, this tool is your new edge.
🎯 Why Traders Love QS AI Reversal Pro:
✅ 78%+ Win Rate on major timeframes (tested on S&P 500, tech stocks, crypto)
🔄 AI-powered oversold/overbought reversal detection
📊 Built-in divergence detection engine (RSI, price, volume)
⚖️ Mean reversion zones + VWAP extremes + Bollinger Band signals
💎 High-Probability Mode: Filters only A+ setups for premium entries
🧠 Confluence Engine: Assigns quality scores to each reversal
🔔 Smart Alerts: Reversal alerts + divergence + premium triggers
🏆 Live Win Rate Tracker on your chart with quality % dashboard
🧠 Powered by QuantSignals AI Engine
This is a limited free version of our proprietary 85%+ win rate reversal algorithm—join our Discord to unlock:
🔐 Institutional-level AI reversal strategy
📈 Real-time confluence dashboards across timeframes
🎯 Custom reversal alerts with entry/exit/stop targets
💬 Live strategy signals, backtests & expert community
💥 Perfect For:
🔁 Reversal traders (crypto, stocks, futures)
⏱ Scalpers & 15m–4H swing traders
📊 Mean reversion systems
🧪 Traders who want data-driven signal confidence
📌 How It Works:
Every signal is based on multi-layer confluence:
Oversold/Overbought + Divergence + VWAP/BB + Volume Surge
Optional: Only show signals with minimum Risk:Reward (e.g. 1:2.5)
Each signal is scored, and you’ll see real-time win rate on-screen
Reversal zones highlighted via color-coded backgrounds
📺 On-Chart Display:
🔄 BUY / SELL Reversal Labels (color-coded for high-probability)
📉 Divergence Lines (bullish & bearish)
🧮 Signal Quality % + Live Win Rate
⚠️ Alerts on all major events (standard + high-prob + divergence)
🔓 Upgrade to Premium (in Discord):
✅ Access full 85%+ win rate reversal model
🧠 AI pattern recognition engine
🔍 Multi-timeframe signal agreement
💡 Institutional order-flow reversal tracking
📊 Backtests + optimization support
🚨 Advanced alerts + automation-ready signals
📲 Join Our 1,800+ Member Community:
🌐 Website: quantsignals.xyz
💬 Discord: discord.gg/quantsignals
🎓 Learn the reversal strategies that top traders use
🔄 Start catching market reversals like a pro—install QS AI Reversal Pro today!
Note: This script is a visual indicator and not a strategy tester. For full backtest-ready premium strategy, please contact us on Discord.
Apex Edge - RSI Trend LinesThe Apex Edge - RSI Trend Lines indicator is a precision tool that automatically draws real-time trendlines on the RSI oscillator using confirmed pivot highs and lows. These dynamic trendlines track RSI structure in motion, helping you anticipate breakout zones, reversals, and hidden divergences.
Every time a new pivot forms, the indicator automatically re-draws the RSI trendline between the two most recent pivots — giving you an always-current view of momentum structure. You’ll instantly see when RSI begins compressing or expanding, long before price reacts.
Key Features: • Dynamic RSI trendlines drawn from the last 2 pivots
• Auto re-draws in real-time as new pivots form
• Optional "Full Extend" or "Pivot Only" modes
• Slope color-coded: green = support, red = resistance
• Built-in dotted RSI levels (30/70 default)
• Alert conditions for RSI trendline breakout signals
• Ideal for spotting divergence, compression, and early SMC confluence
This is not your average RSI — it’s a fully reactive momentum edge overlay designed to give you clarity, structure, and timing from within the oscillator itself. Perfect for traders using Smart Money Concepts, divergence setups, or algorithmic trend tracking.
⚔️ Built for precision. Built for edge. Built for Apex.
Fair Value Gap Profiles [AlgoAlpha]🟠 OVERVIEW
This script draws and manages Fair Value Gap (FVG) zones by detecting unfilled gaps in price action and then augmenting them with intra-gap volume profiles from a lower timeframe. It is designed to help traders find potential areas where price may return to fill liquidity voids, and to provide extra detail about volume distribution inside each gap to assess strength and likely mitigation. The script automatically tracks each gap, updates its state over time, and can show which gaps are still unfilled or have been mitigated.
🟠 CONCEPTS
A Fair Value Gap is a zone between candles where no trades occurred, often seen as an inefficiency that price later revisits. The script checks each bar to see if a bullish (low above 2-bars-ago high) or bearish (high below 2-bars-ago low) gap has formed, and measures whether the gap’s size exceeds a threshold defined by a volatility-adjusted multiplier of past gap widths (to only detect significantly large gaps). Once a qualified gap is found, it gets recorded and visualized with a box that can stretch forward in time until filled. To add more context, a mini volume profile is built from a lower timeframe’s price and volume data, showing how volume is distributed inside the gap. The lowest-volume subzone is also highlighted using a sliding window scan method to visualise the true gap (area with least trading activity)
🟠 FEATURES
Visual gap boxes that appear automatically when bullish or bearish fair value gaps are detected on the chart.
Color-coded zones showing bullish gaps in one color and bearish gaps in another so you can easily see which side the gap favors.
Volume profile histograms plotted inside each gap using data from a lower timeframe, helping you see where volume concentrated inside the gap area.
Highlight of the lowest-volume subzone within each gap so you can spot areas price may target when filling the gap.
Dynamic extension of the gap boxes across the chart until price comes back and fills them, marking them as mitigated.
Customizable colors and transparency settings for gap boxes, profiles, and low-volume highlights to match your chart style.
Alerts that notify you when a new gap is created or when price fills an existing gap.
🟠 USAGE
This indicator helps you find and track unfilled price gaps that often act as magnets for price to revisit. You can use it to spot areas where liquidity may rest and plan entries or exits around these zones.
The colored gap boxes show you exactly where a fair value gap starts and ends, so you can anticipate potential pullbacks or continuations when price approaches them.
The intra-gap volume profile lets you gauge whether the gap was created on strong or thin participation, which can help judge how likely it is to be filled. The highlighted lowest-volume subzone shows where price might accelerate once inside the gap.
Traders often look for entries when price returns to a gap, aiming for a reaction or reversal in that area. You can also combine the mitigation alerts with your trade management to track when gaps have been closed and adjust your bias accordingly. Overall, the tool gives a clear visual reference for imbalance zones that can help structure trades around supply and demand dynamics.
Logistic Regression ICT FVG🚀 OVERVIEW
Welcome to the Logistic Regression Fair Value Gap (FVG) System — a next-gen trading tool that blends precision gap detection with machine learning intelligence.
Unlike traditional FVG indicators, this one evolves with each bar of price action, scoring and filtering gaps based on real market behavior.
🔧 CORE FEATURES
✨ Smart Gap Detection
Automatically identifies bullish and bearish Fair Value Gaps using volatility-aware candle logic.
📊 Probability-Based Filtering
Uses logistic regression to assign each gap a confidence score (0 to 1), showing only high-probability setups.
🔁 Real-Time Retest Tracking
Continuously watches how price interacts with each gap to determine if it deserves respect.
📈 Multi-Factor Assessment
Evaluates RSI, MACD, and body size at gap formation to build a full context snapshot.
🧠 Self-Learning Engine
The logistic regression model updates on each bar using gradient descent, refining its predictions over time.
📢 Built-In Alerts
Get instant alerts when a gap forms, gets retested, or breaks.
🎨 Custom Display Options
Control the color of bullish/bearish zones, and toggle on/off probability labels for cleaner charts.
🚩 WHAT MAKES IT DIFFERENT
This isn’t just another box-drawing indicator.
While others mark every imbalance, this system thinks before it draws — using statistical modeling to filter out noise and prioritize high-impact zones.
By learning from how price behaves around gaps (not just how they form), it helps you trade only what matters — not what clutters.
⚙️ HOW IT WORKS
1️⃣ Detection
FVGs are identified using ATR-based thresholds and sharp wick imbalances.
2️⃣ Behavior Monitoring
Every gap is tracked — and if respected enough times, it becomes part of the elite training set.
3️⃣ Context Capture
Each new FVG logs RSI, MACD, and body size to provide a feature-rich context for prediction.
4️⃣ Prediction (Logistic Regression)
The model predicts how likely the gap is to be respected and assigns it a probability score.
5️⃣ Classification & Alerts
Gaps above the threshold are plotted with score labels, and alerts trigger for entry/respect/break.
⚙️ CONFIGURATION PANEL
🔧 System Inputs
• Max Retests – How many times a gap must be respected to train the model
• Prediction Threshold – Minimum score to show a gap on the chart
• Learning Rate – Controls how fast the model adapts (default: 0.009)
• Max FVG Lifetime – Expiration duration for unused gaps
• Show Historic Gaps – Show/hide expired or invalidated gaps
🎨 Visual Options
• Bullish/Bearish Colors – Set gap colors to fit your chart style
• Confidence Labels – Show probability scores next to FVGs
• Alert Toggles – Enable alerts for:
– New FVG detected
– FVG respected (entry)
– FVG invalidated (break)
💡 WHY LOGISTIC REGRESSION?
Traditional FVG tools rely on candle shapes.
This system relies on probability — by training on RSI, MACD, and price behavior, it predicts whether a gap will act as a true liquidity zone.
Logistic regression lets the system continuously adapt using new data, making it more accurate the longer it runs.
That means smarter signals, fewer false positives, and a clearer view of where real opportunities lie.
Multi-Position DashMulti-Position Dash — Risk Dashboard for Forex, Stocks & Indices
Overview:
The Multi-Position Dash is a highly customizable trading dashboard designed to help active traders manage up to 8 simultaneous positions across Forex, Stocks, and Indices. Whether you're trading single entries, layering positions, using DCA (Dollar Cost Averaging), or running complex hedging setups, this tool provides essential, real-time risk and P&L insights—directly on your chart.
Key Features:
✔️ Supports Forex, Stocks, Indices — with automatic pip and contract conversions
✔️ Track up to 8 manual positions, each with customizable direction, lot size or contracts, entry price, Take Profit, and Stop Loss
✔️ Full GBP-based P&L and risk calculation, including automatic USD-to-GBP conversion for non-FX assets
✔️ Real-time display of:
Total potential Take Profit (GBP)
Total potential Stop Loss (GBP)
Risk % relative to account balance
Live P&L (GBP) based on current price
✔️ Breakeven price calculation, even across mixed-direction positions (DCA & hedging aware)
✔️ Visual breakeven line, live P&L arrows, and entry price markers
✔️ Shared Stop Loss option for all positions — perfect for DCA traders
✔️ Easy export strings for logging trades to external tools like spreadsheets
Ideal For:
✅ Forex traders using lot-based risk models
✅ Stock & Index traders wanting simplified contract-based position tracking
✅ Traders managing multiple active positions, with or without hedging
✅ Anyone needing at-a-glance P&L and risk monitoring, independent of broker platforms
Notes & Usage:
This is a manual tracking tool—you enter your positions, TP, SL levels, etc., and the dashboard calculates the rest. It does not place or manage live orders.
Supports both Long and Short positions.
All calculations are based on your inputs and market price—accuracy depends on maintaining your inputs properly.
Shared Stop Loss feature applies a single, unified stop across all active positions for simplified risk control in DCA setups.
GBP is used as the account currency—USD-to-GBP conversion is applied to stocks and indices as needed.
Disclaimer:
This tool is for educational and planning purposes only. It does not place or manage live trades, and is not a substitute for broker risk management tools. Always double-check your own position sizing and risk before placing live orders.
Langlands-Operadic Möbius Vortex (LOMV)Langlands-Operadic Möbius Vortex (LOMV)
Where Pure Mathematics Meets Market Reality
A Revolutionary Synthesis of Number Theory, Category Theory, and Market Dynamics
🎓 THEORETICAL FOUNDATION
The Langlands-Operadic Möbius Vortex represents a groundbreaking fusion of three profound mathematical frameworks that have never before been combined for market analysis:
The Langlands Program: Harmonic Analysis in Markets
Developed by Robert Langlands (Fields Medal recipient), the Langlands Program creates bridges between number theory, algebraic geometry, and harmonic analysis. In our indicator:
L-Function Implementation:
- Utilizes the Möbius function μ(n) for weighted price analysis
- Applies Riemann zeta function convergence principles
- Calculates quantum harmonic resonance between -2 and +2
- Measures deep mathematical patterns invisible to traditional analysis
The L-Function core calculation employs:
L_sum = Σ(return_val × μ(n) × n^(-s))
Where s is the critical strip parameter (0.5-2.5), controlling mathematical precision and signal smoothness.
Operadic Composition Theory: Multi-Strategy Democracy
Category theory and operads provide the mathematical framework for composing multiple trading strategies into a unified signal. This isn't simple averaging - it's mathematical composition using:
Strategy Composition Arity (2-5 strategies):
- Momentum analysis via RSI transformation
- Mean reversion through Bollinger Band mathematics
- Order Flow Polarity Index (revolutionary T3-smoothed volume analysis)
- Trend detection using Directional Movement
- Higher timeframe momentum confirmation
Agreement Threshold System: Democratic voting where strategies must reach consensus before signal generation. This prevents false signals during market uncertainty.
Möbius Function: Number Theory in Action
The Möbius function μ(n) forms the mathematical backbone:
- μ(n) = 1 if n is a square-free positive integer with even number of prime factors
- μ(n) = -1 if n is a square-free positive integer with odd number of prime factors
- μ(n) = 0 if n has a squared prime factor
This creates oscillating weights that reveal hidden market periodicities and harmonic structures.
🔧 COMPREHENSIVE INPUT SYSTEM
Langlands Program Parameters
Modular Level N (5-50, default 30):
Primary lookback for quantum harmonic analysis. Optimized by timeframe:
- Scalping (1-5min): 15-25
- Day Trading (15min-1H): 25-35
- Swing Trading (4H-1D): 35-50
- Asset-specific: Crypto 15-25, Stocks 30-40, Forex 35-45
L-Function Critical Strip (0.5-2.5, default 1.5):
Controls Riemann zeta convergence precision:
- Higher values: More stable, smoother signals
- Lower values: More reactive, catches quick moves
- High frequency: 0.8-1.2, Medium: 1.3-1.7, Low: 1.8-2.3
Frobenius Trace Period (5-50, default 21):
Galois representation lookback for price-volume correlation:
- Measures harmonic relationships in market flows
- Scalping: 8-15, Day Trading: 18-25, Swing: 25-40
HTF Multi-Scale Analysis:
Higher timeframe context prevents trading against major trends:
- Provides market bias and filters signals
- Improves win rates by 15-25% through trend alignment
Operadic Composition Parameters
Strategy Composition Arity (2-5, default 4):
Number of algorithms composed for final signal:
- Conservative: 4-5 strategies (higher confidence)
- Moderate: 3-4 strategies (balanced approach)
- Aggressive: 2-3 strategies (more frequent signals)
Category Agreement Threshold (2-5, default 3):
Democratic voting minimum for signal generation:
- Higher agreement: Fewer but higher quality signals
- Lower agreement: More signals, potential false positives
Swiss-Cheese Mixing (0.1-0.5, default 0.382):
Golden ratio φ⁻¹ based blending of trend factors:
- 0.382 is φ⁻¹, optimal for natural market fractals
- Higher values: Stronger trend following
- Lower values: More contrarian signals
OFPI Configuration:
- OFPI Length (5-30, default 14): Order Flow calculation period
- T3 Smoothing (3-10, default 5): Advanced exponential smoothing
- T3 Volume Factor (0.5-1.0, default 0.7): Smoothing aggressiveness control
Unified Scoring System
Component Weights (sum ≈ 1.0):
- L-Function Weight (0.1-0.5, default 0.3): Mathematical harmony emphasis
- Galois Rank Weight (0.1-0.5, default 0.2): Market structure complexity
- Operadic Weight (0.1-0.5, default 0.3): Multi-strategy consensus
- Correspondence Weight (0.1-0.5, default 0.2): Theory-practice alignment
Signal Threshold (0.5-10.0, default 5.0):
Quality filter producing:
- 8.0+: EXCEPTIONAL signals only
- 6.0-7.9: STRONG signals
- 4.0-5.9: MODERATE signals
- 2.0-3.9: WEAK signals
🎨 ADVANCED VISUAL SYSTEM
Multi-Dimensional Quantum Aura Bands
Five-layer resonance field showing market energy:
- Colors: Theme-matched gradients (Quantum purple, Holographic cyan, etc.)
- Expansion: Dynamic based on score intensity and volatility
- Function: Multi-timeframe support/resistance zones
Morphism Flow Portals
Category theory visualization showing market topology:
- Green/Cyan Portals: Bullish mathematical flow
- Red/Orange Portals: Bearish mathematical flow
- Size/Intensity: Proportional to signal strength
- Recursion Depth (1-8): Nested patterns for flow evolution
Fractal Grid System
Dynamic support/resistance with projected L-Scores:
- Multiple Timeframes: 10, 20, 30, 40, 50-period highs/lows
- Smart Spacing: Prevents level overlap using ATR-based minimum distance
- Projections: Estimated signal scores when price reaches levels
- Usage: Precise entry/exit timing with mathematical confirmation
Wick Pressure Analysis
Rejection level prediction using candle mathematics:
- Upper Wicks: Selling pressure zones (purple/red lines)
- Lower Wicks: Buying pressure zones (purple/green lines)
- Glow Intensity (1-8): Visual emphasis and line reach
- Application: Confluence with fractal grid creates high-probability zones
Regime Intensity Heatmap
Background coloring showing market energy:
- Black/Dark: Low activity, range-bound markets
- Purple Glow: Building momentum and trend development
- Bright Purple: High activity, strong directional moves
- Calculation: Combines trend, momentum, volatility, and score intensity
Six Professional Themes
- Quantum: Purple/violet for general trading and mathematical focus
- Holographic: Cyan/magenta optimized for cryptocurrency markets
- Crystalline: Blue/turquoise for conservative, stability-focused trading
- Plasma: Gold/magenta for high-energy volatility trading
- Cosmic Neon: Bright neon colors for maximum visibility and aggressive trading
📊 INSTITUTIONAL-GRADE DASHBOARD
Unified AI Score Section
- Total Score (-10 to +10): Primary decision metric
- >5: Strong bullish signals
- <-5: Strong bearish signals
- Quality ratings: EXCEPTIONAL > STRONG > MODERATE > WEAK
- Component Analysis: Individual L-Function, Galois, Operadic, and Correspondence contributions
Order Flow Analysis
Revolutionary OFPI integration:
- OFPI Value (-100% to +100%): Real buying vs selling pressure
- Visual Gauge: Horizontal bar chart showing flow intensity
- Momentum Status: SHIFTING, ACCELERATING, STRONG, MODERATE, or WEAK
- Trading Application: Flow shifts often precede major moves
Signal Performance Tracking
- Win Rate Monitoring: Real-time success percentage with emoji indicators
- Signal Count: Total signals generated for frequency analysis
- Current Position: LONG, SHORT, or NONE with P&L tracking
- Volatility Regime: HIGH, MEDIUM, or LOW classification
Market Structure Analysis
- Möbius Field Strength: Mathematical field oscillation intensity
- CHAOTIC: High complexity, use wider stops
- STRONG: Active field, normal position sizing
- MODERATE: Balanced conditions
- WEAK: Low activity, consider smaller positions
- HTF Trend: Higher timeframe bias (BULL/BEAR/NEUTRAL)
- Strategy Agreement: Multi-algorithm consensus level
Position Management
When in trades, displays:
- Entry Price: Original signal price
- Current P&L: Real-time percentage with risk level assessment
- Duration: Bars in trade for timing analysis
- Risk Level: HIGH/MEDIUM/LOW based on current exposure
🚀 SIGNAL GENERATION LOGIC
Balanced Long/Short Architecture
The indicator generates signals through multiple convergent pathways:
Long Entry Conditions:
- Score threshold breach with algorithmic agreement
- Strong bullish order flow (OFPI > 0.15) with positive composite signal
- Bullish pattern recognition with mathematical confirmation
- HTF trend alignment with momentum shifting
- Extreme bullish OFPI (>0.3) with any positive score
Short Entry Conditions:
- Score threshold breach with bearish agreement
- Strong bearish order flow (OFPI < -0.15) with negative composite signal
- Bearish pattern recognition with mathematical confirmation
- HTF trend alignment with momentum shifting
- Extreme bearish OFPI (<-0.3) with any negative score
Exit Logic:
- Score deterioration below continuation threshold
- Signal quality degradation
- Opposing order flow acceleration
- 10-bar minimum between signals prevents overtrading
⚙️ OPTIMIZATION GUIDELINES
Asset-Specific Settings
Cryptocurrency Trading:
- Modular Level: 15-25 (capture volatility)
- L-Function Precision: 0.8-1.3 (reactive to price swings)
- OFPI Length: 10-20 (fast correlation shifts)
- Cascade Levels: 5-7, Theme: Holographic
Stock Index Trading:
- Modular Level: 25-35 (balanced trending)
- L-Function Precision: 1.5-1.8 (stable patterns)
- OFPI Length: 14-20 (standard correlation)
- Cascade Levels: 4-5, Theme: Quantum
Forex Trading:
- Modular Level: 35-45 (smooth trends)
- L-Function Precision: 1.6-2.1 (high smoothing)
- OFPI Length: 18-25 (disable volume amplification)
- Cascade Levels: 3-4, Theme: Crystalline
Timeframe Optimization
Scalping (1-5 minute charts):
- Reduce all lookback parameters by 30-40%
- Increase L-Function precision for noise reduction
- Enable all visual elements for maximum information
- Use Small dashboard to save screen space
Day Trading (15 minute - 1 hour):
- Use default parameters as starting point
- Adjust based on market volatility
- Normal dashboard provides optimal information density
- Focus on OFPI momentum shifts for entries
Swing Trading (4 hour - Daily):
- Increase lookback parameters by 30-50%
- Higher L-Function precision for stability
- Large dashboard for comprehensive analysis
- Emphasize HTF trend alignment
🏆 ADVANCED TRADING STRATEGIES
The Mathematical Confluence Method
1. Wait for Fractal Grid level approach
2. Confirm with projected L-Score > threshold
3. Verify OFPI alignment with direction
4. Enter on portal signal with quality ≥ STRONG
5. Exit on score deterioration or opposing flow
The Regime Trading System
1. Monitor Aether Flow background intensity
2. Trade aggressively during bright purple periods
3. Reduce position size during dark periods
4. Use Möbius Field strength for stop placement
5. Align with HTF trend for maximum probability
The OFPI Momentum Strategy
1. Watch for momentum shifting detection
2. Confirm with accelerating flow in direction
3. Enter on immediate portal signal
4. Scale out at Fibonacci levels
5. Exit on flow deceleration or reversal
⚠️ RISK MANAGEMENT INTEGRATION
Mathematical Position Sizing
- Use Galois Rank for volatility-adjusted sizing
- Möbius Field strength determines stop width
- Fractal Dimension guides maximum exposure
- OFPI momentum affects entry timing
Signal Quality Filtering
- Trade only STRONG or EXCEPTIONAL quality signals
- Increase position size with higher agreement levels
- Reduce risk during CHAOTIC Möbius field periods
- Respect HTF trend alignment for directional bias
🔬 DEVELOPMENT JOURNEY
Creating the LOMV was an extraordinary mathematical undertaking that pushed the boundaries of what's possible in technical analysis. This indicator almost didn't happen. The theoretical complexity nearly proved insurmountable.
The Mathematical Challenge
Implementing the Langlands Program required deep research into:
- Number theory and the Möbius function
- Riemann zeta function convergence properties
- L-function analytical continuation
- Galois representations in finite fields
The mathematical literature spans decades of pure mathematics research, requiring translation from abstract theory to practical market application.
The Computational Complexity
Operadic composition theory demanded:
- Category theory implementation in Pine Script
- Multi-dimensional array management for strategy composition
- Real-time democratic voting algorithms
- Performance optimization for complex calculations
The Integration Breakthrough
Bringing together three disparate mathematical frameworks required:
- Novel approaches to signal weighting and combination
- Revolutionary Order Flow Polarity Index development
- Advanced T3 smoothing implementation
- Balanced signal generation preventing directional bias
Months of intensive research culminated in breakthrough moments when the mathematics finally aligned with market reality. The result is an indicator that reveals market structure invisible to conventional analysis while maintaining practical trading utility.
🎯 PRACTICAL IMPLEMENTATION
Getting Started
1. Apply indicator with default settings
2. Select appropriate theme for your markets
3. Observe dashboard metrics during different market conditions
4. Practice signal identification without trading
5. Gradually adjust parameters based on observations
Signal Confirmation Process
- Never trade on score alone - verify quality rating
- Confirm OFPI alignment with intended direction
- Check fractal grid level proximity for timing
- Ensure Möbius field strength supports position size
- Validate against HTF trend for bias confirmation
Performance Monitoring
- Track win rate in dashboard for strategy assessment
- Monitor component contributions for optimization
- Adjust threshold based on desired signal frequency
- Document performance across different market regimes
🌟 UNIQUE INNOVATIONS
1. First Integration of Langlands Program mathematics with practical trading
2. Revolutionary OFPI with T3 smoothing and momentum detection
3. Operadic Composition using category theory for signal democracy
4. Dynamic Fractal Grid with projected L-Score calculations
5. Multi-Dimensional Visualization through morphism flow portals
6. Regime-Adaptive Background showing market energy intensity
7. Balanced Signal Generation preventing directional bias
8. Professional Dashboard with institutional-grade metrics
📚 EDUCATIONAL VALUE
The LOMV serves as both a practical trading tool and an educational gateway to advanced mathematics. Traders gain exposure to:
- Pure mathematics applications in markets
- Category theory and operadic composition
- Number theory through Möbius function implementation
- Harmonic analysis via L-function calculations
- Advanced signal processing through T3 smoothing
⚖️ RESPONSIBLE USAGE
This indicator represents advanced mathematical research applied to market analysis. While the underlying mathematics are rigorously implemented, markets remain inherently unpredictable.
Key Principles:
- Use as part of comprehensive trading strategy
- Implement proper risk management at all times
- Backtest thoroughly before live implementation
- Understand that past performance does not guarantee future results
- Never risk more than you can afford to lose
The mathematics reveal deep market structure, but successful trading requires discipline, patience, and sound risk management beyond any indicator.
🔮 CONCLUSION
The Langlands-Operadic Möbius Vortex represents a quantum leap forward in technical analysis, bringing PhD-level pure mathematics to practical trading while maintaining visual elegance and usability.
From the harmonic analysis of the Langlands Program to the democratic composition of operadic theory, from the number-theoretic precision of the Möbius function to the revolutionary Order Flow Polarity Index, every component works in mathematical harmony to reveal the hidden order within market chaos.
This is more than an indicator - it's a mathematical lens that transforms how you see and understand market structure.
Trade with mathematical precision. Trade with the LOMV.
*"Mathematics is the language with which God has written the universe." - Galileo Galilei*
*In markets, as in nature, profound mathematical beauty underlies apparent chaos. The LOMV reveals this hidden order.*
— Dskyz, Trade with insight. Trade with anticipation.
McGinley Dynamic debugged🔍 McGinley Dynamic Debugged (Adaptive Moving Average)
This indicator plots the McGinley Dynamic, a mathematically adaptive moving average designed to reduce lag and better track price action during both trends and consolidations.
✅ Key Features:
Adaptive smoothing: The McGinley Dynamic adjusts itself based on the speed of price changes.
Lag reduction: Compared to traditional moving averages like EMA or SMA, McGinley provides smoother yet responsive tracking.
Stability fix: This version includes a robust fix for rare recursive calculation issues, particularly on low-priced historical assets (e.g., Wipro pre-2000).
⚙️ What’s Different in This Debugged Version?
Implements manual clamping on the source / previous value ratio to prevent mathematical spikes that could cause flattening or distortion in the plotted line.
Ensures more stable behavior across all instruments and timeframes, especially those with historically low price points or volatile early data.
💡 Use Case:
Ideal for:
Trend confirmation
Entry filtering
Adaptive support/resistance visualization
Improving signal precision in low-volatility or high-noise environments
⚠️ Notes:
Works best when combined with volume filters or other trend indicators for validation.
This version is optimized for visual use—for signal generation, consider pairing it with additional logic or thresholds.
Crypto Long RSI Entry with AveragingIndicator Name:
04 - Crypto Long RSI Entry with Averaging + Info Table + Lines (03 style lines)
Description:
This indicator is designed for crypto trading on the long side only, using RSI-based entry signals combined with a multi-step averaging strategy and a visual information panel. It aims to capture price rebounds from oversold RSI levels and manage position entries with two staged averaging points, optimizing the average entry price and take-profit targets.
Key Features:
RSI-Based Entry: Enters a long position when the RSI crosses above a defined oversold level (default 25), with an optional faster entry if RSI crosses above 20 after being below it.
Two-Stage Averaging: Allows up to two averaging entries at user-defined price drop percentages (default 5% and 14%), increasing position size to improve average entry price.
Dynamic Take Profit: Adjusts take profit targets after each averaging stage, with customizable percentage levels.
Visual Signals: Marks entries, averaging points, and exits on the chart using colored labels and lines for easy tracking.
Info Table: Displays current trade status, averaging stages, total profit, number of wins, and maximum drawdown percentage in a table on the chart.
Graphical Lines: Shows horizontal lines for entry price, take profit, and averaging prices to visually track trade management.
Breaker Blocks & Unicorns (with Deviations) by RiseBreaker Block and Unicorns (with Deviations) - The Highest Probability ICT Pattern
This advanced indicator identifies and tracks ICT Breaker Blocks, while incorporating powerful supplementary features including Unicorn patterns and customizable deviation levels.
These patterns develop through a precise market structure sequence culminating in structural breaks. Following Breaker Block confirmation, users can optionally enable highly customizable deviation levels. Additionally, the indicator can scan active Breaker Blocks for overlapping Fair Value Gaps (FVGs) and Inverted Fair Value Gaps (IFVGs)-(also known as "Unicorns") that represent high-probability trading opportunities, highly regarded in the ICT community.
This comprehensive tool provides unmatched functionality for traders and analysts seeking to track, backtest, and execute Breaker Block strategies. With its extensive feature set and granular customization options, it delivers capabilities that surpass existing alternatives in the market.
What is an ICT Breaker Block?
To explain this, we must understand the ABC sequence that form this pattern. It consists of:
Initial range (from A -> B)
First break point, commonly called "Manipulation" (C)
Second break, which is when the pattern is formed.
Each of these "points" consist of pivot levels, with an adjustable strength.
Breaker Blocks are invalidated and made inactive if price breaks the "C point", or manipulation.
Unicorns
Unicorns are Fair Value Gaps or Inverted Fair Value Gaps that overlap a Breaker Block. Breakers have their associated Unicorn, which is updated until price retraces into said gap.
Standard Deviations
This indicator has options to display deviations based on Breaker Blocks:
Breaker Deviations -> using the initial range (A -> B).
Manipulation Deviations -> using the manipulation (B -> C).
Input Settings:
This tool offers a lot of customizable options, which could be overwhelming to some users. Below you will find an in-depth definition of every input's purpose, to complement the tooltips that can be found directly in the indicator's settings.
Mode ⚙️
Default -> Displays every Breaker Block pattern found.
Bullish -> Displays every Bullish Breaker Block found.
Bearish -> Displays every Bearish Breaker Block found.
Reversals -> Displays alternate Breaker Blocks (Bearish -> Bullish -> Bearish and so on).
This is paired with a Historical input, to select the amount of previous Breakers to display.
Extend 📏
Last -> This option will extend the most recent Breaker's drawings.
Specified -> Extend Breakers a preset amount of bars.
All -> Extend all active Breakers to the current bar.
None -> Never extend Breaker Blocks.
Each object has it's specific " offset " parameter, which defines the amount of bars to extend drawings past the current bar.
Parameters
This section defines the main parameters used to define the Breaker Block pattern.
Time Filter -> Optional session to filter Breakers based on time of day.
Pivot Strength -> Determines how many consecutive bars to the left of a pivot must be lower (for highs) or higher (for lows) to confirm it as a point.
Range Lookback -> Amount of ranges that the indicator will keep track for each direction.
Breaker Type -> Defines how a Breaker Block is displayed:
Range -> Entire initial range.
Consecutive -> Last consecutive onside candles (upclose for bullish, downclose for bearish).
Last -> Last onside candle.
Breaker Offset -> Amount of bars to extend Breaker Blocks past the current bar.
Use Candle Bodies? -> Use bar open to close rather than high to low.
Require Candle Close? -> Use bar close to form Breaker Blocks.
Remove After Invalidation? -> Remove drawings for invalidated Breakers.
Style
Breaker Block boxes styling based on directions.
Optional Middle Line and styling.
Optional Signals for Breaker Block formation:
Triangle label with adjustable sizing on the formation bar.
Line with custom styling at breakout point to the formation bar.
Unicorn Fair Value Gaps
Checkbox to display Unicorns with adjustable "FVGs", "IFVGs", or "Both" types.
Overlap Threshold -> Distance away from Breaker to still consider an "overlap".
Unicorn Offset -> Amount of bars to extend unicorn gaps past the current bar.
Lines styling.
Optional Middle Line and styling.
Include Volume Imbalances? -> Include adjacent VIs as part of Fair Value Gaps.
Extend until Reached? -> Extend Unicorn drawings until price reaches them.
Deviations
Checkbox to display Standard Deviations with adjustable types and levels.
Lines styling.
Text size and positioning.
Extend until Reached? -> Extend deviation lines until price reaches them.
Text
Label contents:
Default -> "+/- Breaker".
Abbreviation -> "+/- BB".
None -> No text.
Size .
Font (Default or Monospace) and Format (None, Italic or Bold).
Align -> vertical and horizontal positioning.
This indicator is for educational and informational purposes only. Past performance and historical patterns do not guarantee future results. Trading involves substantial risk of loss and is not suitable for all investors. Always conduct your own analysis and consider your financial situation before making any trading decisions. The identification of patterns does not constitute trading advice.
For any additional questions and/or feedback related to this indicator, users can comment below!
[Stoxello] Linear Regression Chop Zone Indicator📊 Linear Regression Chop Zone Indicator – Description
The Stoxello Linear Regression Chop Zone Indicator is a custom-built, multi-functional visual tool for identifying market trend direction, strength, and potential entry/exit signals using a combination of linear regression, EMA slope angles, and volatility-adjusted smoothing.
🧠 Core Features:
🔶 1. Chop Zone Color Coding (Trend Strength via EMA Angle)
The script calculates the angle of a 34-period EMA, representing momentum and trend steepness.
This angle is then translated into color-coded bars on the chart to help traders visually identify chop zones and trend strength.
Turquoise / Dark Green / Pale Green = Increasing bullish trend.
Lime / Yellow = Neutral or low momentum (choppy zones).
Orange / Red / Dark Red = Increasing bearish trend.
🔶 2. Linear Regression Deviation Channels (Trend Path)
A custom linear regression line is drawn with +/- deviation bands above and below it.
These lines track the expected price path and visually define upper/lower zones, similar to regression channels.
The correlation (R) and determination (R²) values are displayed as labels on the chart, measuring the strength and reliability of the linear fit.
🔶 3. Linear Regression-Adjusted EMA (Smoothing with Volatility)
A novel volatility-adaptive EMA is computed by combining a traditional EMA with distance from a linear regression line.
The result is a dynamic EMA that becomes more reactive in volatile conditions and smoother in stable ones.
Two lines are plotted:
Primary EMA (Yellow)
Trigger Line (Lagged by 2 bars, Fuchsia)
The fill color between these two helps visualize short-term bullish or bearish pressure.
🔶 4. Buy/Sell Signal Logic with De-Duplication
Buy signals are triggered when:
The adjusted EMA crosses above its previous value (bullish inflection).
Or when the EMA angle exceeds +5° (strong trend detected).
Sell signals occur when:
The adjusted EMA crosses below its previous value.
Each signal is deduplicated by tracking the last signal using var string lastSignal:
No repeat buys after a buy, or sells after a sell.
Signals are marked on the chart using clean text labels:
Buy: "•Entry• = Price"
Sell: "•Exit• = Price"
🔶 5. Alerts
Two alertconditions are included for:
BUY signals (long_signal)
SELL signals (short_signal)
Can be used with webhooks, email, or app notifications to automate or monitor trades.
🔍 Ideal Use Cases:
Traders who want a clear visual aid for market chop vs. trend.
Swing or intraday traders looking for adaptive entry/exit points.
Anyone combining regression analysis and momentum tracking into one indicator.
