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Backtest EngineThis is a simple backtest engine for your trading strategies. The idea behind this script is to make testing new strategies as easy as possible. Parameters such as take profit/stop loss and time period are built into the script and are customisable by the user via the settings interface. The only coding is to set the entry and exit conditions. Users need not touch any code beyond line 30. For this post, I have used a 50/200 SMA crossover to demonstrate the ease of use for this script. The features of this script include: Backtest period start Number of days until backtest period end Take profit and stop loss % (via settings) Programmable long and short entry/exit Anti duplicate system (for entry conditions that are continuously satisfied, the engine will only make 1 trade until the is exit condition is satisfied). DISCLAIMER: The strategy in this post is only a placeholder. The TP/SL levels are set to showcase the functionality of the engine and are in no means optimal settings. Hope this helps! Feel free to ask any questions about the engine and happy coding!
Pine Script® stratejisi
panpanXBT tarafından
3
Strategy - Backtest Uber WAE - Waddah Attar Explosion [UTS]Backtest of WAE - Waddah Attar Explosion Backtest with focus win/loss profitability. Formula: profitability = win / (win+loss) Default equity 100k USD Default 2% Risk per trade Default currency USD Define backtest interval precisely by month, year, day LONG and SHORT positions Visualize SL and TP on chart ATR (len: 14, smooth: SMA) ATR based Stop-Loss, if hit trade will be closed and considered as loss ATR based Take-Profit, if hit trade will be closed and considered as win On TP or SL hit the trade is closed and marked as win/loss
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UberTradingSystems tarafından
1
Strategy - Backtest Uber Kuskus Starlight [UTS]Backtest of Uber Kuskus Starlight Backtest with focus win/loss profitability. Formula: profitability = win / (win+loss) Default equity 100k USD Default 2% Risk per trade Default currency USD Define backtest interval precisely by month, year, day LONG and SHORT positions Visualize SL and TP on chart ATR (len: 14, smooth: SMA) ATR based Stop-Loss, if hit trade will be closed and considered as loss ATR based Take-Profit, if hit trade will be closed and considered as win On TP or SL hit the trade is closed and marked as win/loss
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UberTradingSystems tarafından
2
Ichimoku Cloud [Trading Nerd]Backtesting Script that compares different way to trade the Ichimoku Cloud. With this script you can test 2*2 different Ichimoku Cloud Entry conditions, more on that down below. This script is useful to figure out what conditions work best for the applied market (so not only parameters of the Ichimoku Cloud are changeable). Strategy foundation This conditions need to be always satisfied for a valid entry: Longs: The close price must be above the (displaced) cloud: close > max(leadingspanA , leadingspanB ) The most recent cloud must be green: leadingspanA > leadingspanB Shorts: The close price must be below the (displaced) cloud: close < min(leadingspanA , leadingspanB ) The most recent cloud must be red: leadingspanA < leadingspanB Options for Conversion-/Base Line Cross: Conversion-Line cross-over Base-Line (Long), Conversion Line cross-under Base-Line (Short) Over/Und er: Conversion-Line > Base-Line (Long), Conversion-Line < Base-Line (Short) Options for Lagging Span Above/Below Price: Lagging-Span > Close Price (Long), Lagging-Span < Close Price (Short) Above/Below Cloud: Lagging-Span > Ichimoku Cloud (Long), Lagging-Span < Ichimoku Cloud (Short) Exit Conditions An optional Stoploss is available. 2 different Types: ATR: Takes a multiple (set by 'ATR multiplier for SL') of the ATR and subtract it (Long) or adds it (Short) to the close price of the previous candel (before entry candle) HH/LL: Takes the highest high/lowest low of the last X candles (set by 'Lookback Range for HH/LL SL') and sets a SL at this price none: There is no SL. The position is at latest exited at the next cross of the Conversion-Line and Base-Line! Longs: Conversion-Line cross-under Base-Line. Shorts: Conversion-Line cross-over Base-Line. The position is closed if the cross is confirmed (candle has closed). Risk Management You can set the risk percent per trade: Risk only X% of current capital (initial capital + net Profit). This requires a Stoploss-Strategy (not none). IMPORTANT: For low Timeframes and Markets with tight SL (like Forex) it requires a lower Margin Percent than default. Go to Settings->Properties and lower the required Long/Short Margin. Otherwise Trades might not be considered because of too less capital/marign. Margins can e.g. set to: 2% (Forex), 10% (Stocks), 20% (Crypto).
Pine Script® stratejisi
Trading_Nerd tarafından
3
[BACKTEST] CMYK-RMI-TRIPLEScript based on 3 RMI and the opening of long / short 'pyramiding' positions. XBT:USD on BITMEX or BTC:USD on DERIBIT Includes the calculation of the DERIBIT Index but can be used for any Crypto, the always overloaded BITMEX or even Forex, ... Best timeframe: 5 minutes. A 5-minutes chart extended to the minimum is equal to a 1H chart. ¡NO REPAINT! : Alerts 'Once per bar' It has take profit and it's so good that it doesn`t need Stop Loss Original idea by MVPMC.
Pine Script® stratejisi
UnknownUnicorn2151907 tarafından
2
GKD-M Baseline Optimizer [Loxx]Giga Kaleidoscope GKD-M Baseline Optimizer is a Metamorphosis module included in Loxx's "Giga Kaleidoscope Modularized Trading System". The Baseline Optimizer enables traders to backtest over 60 moving averages using variable period inputs. It then exports the baseline with the highest cumulative win rate per candle to any baseline-enabled GKD backtest. To perform the backtesting, the trader selects an initial period input (default is 60) and a skip value that increments the initial period input up to seven times. For instance, if a skip value of 5 is chosen, the Baseline Optimizer will run the backtest for the selected moving average on periods such as 60, 65, 70, 75, and so on, up to 90. If the user selects an initial period input of 45 and a skip value of 2, the Baseline Optimizer will conduct backtests for the chosen moving average on periods like 45, 47, 49, 51, and so forth, up to 57. The Baseline Optimizer provides a table displaying the output of the backtests for a specified date range. The table output represents the cumulative win rate for the given date range. On the Metamorphosis side of the Baseline Optimizer, a cumulative backtest is calculated for each candle within the date range. This means that each candle may exhibit a different distribution of period inputs with the highest win rate for a particular moving average. The Baseline Optimizer identifies the period input combination with the highest win rates for long and short positions and creates a win-rate adaptive long and short moving average chart. The moving average used for shorts differs from the moving average used for longs, and the moving average for each candle may vary from any other candle. This customized baseline can then be exported to all baseline-enabled GKD backtests. The backtest employed in the Baseline Optimizer is a Solo Confirmation Simple, allowing only one take profit and one stop loss to be set. Lastly, the Baseline Optimizer incorporates Goldie Locks Zone filtering, which can be utilized for signal generation in advanced GKD backtests. █ Moving Averages included in the Baseline Optimizer 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 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 Kaufman Adaptive Moving Average - KAMA 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 One More Moving Average - OMA 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 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 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 The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm. The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction. The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy. 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. 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. 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" One More Moving Average (OMA) The One More Moving Average (OMA) is a technical indicator that calculates a series of Jurik-style moving averages in order to reduce noise and provide smoother price data. It uses six exponential moving averages to generate the final value, with the length of the moving averages determined by an adaptive algorithm that adjusts to the current market conditions. The algorithm calculates the average period by comparing the signal to noise ratio and using this value to determine the length of the moving averages. The resulting values are used to generate the final value of the OMA, which can be used to identify trends and potential changes in trend direction. 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. █ Volatility Goldie Locks Zone The Goldie Locks Zone volatility filter is the standard first-pass filter used in all advanced GKD backtests (Complex, Super Complex, and Full GKd). This filter requires the price to fall within a range determined by multiples of volatility. The Goldie Locks Zone is separate from the core Baseline and utilizes its own moving average with Loxx's Exotic Source Types you can read about below. On the chart, you will find green and red dots positioned at the top, indicating whether a candle qualifies for a long or short trade respectively. Additionally, green and red triangles are located at the bottom of the chart, signifying whether the trigger has crossed up or down and qualifies within the Goldie Locks zone. The Goldie Locks zone is represented by a white color on the mean line, indicating low volatility levels that are not suitable for trading. █ Volatility Types Included in the Baseline Optimizer The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings. This module includes 17 types of volatility: Close-to-Close Parkinson Garman-Klass Rogers-Satchell Yang-Zhang Garman-Klass-Yang-Zhang Exponential Weighted Moving Average Standard Deviation of Log Returns Pseudo GARCH(2,2) Average True Range True Range Double Standard Deviation Adaptive Deviation Median Absolute Deviation Efficiency-Ratio Adaptive ATR Mean Absolute Deviation Static Percent Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user. Close-to-Close Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility. Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is. Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility. Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators. Parkinson Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day. The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility. One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator. Garman-Klass Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security. Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate. Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements. Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes. Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail. Rogers-Satchell Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero. Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending. The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions. A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail. Yang-Zhang Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error. Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator. Garman-Klass-Yang-Zhang Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation. GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator. Exponential Weighted Moving Average The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling. The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted. The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series. Standard Deviation of Log Returns This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)). Pseudo GARCH(2,2) This is calculated using a short- and long-run mean of variance multiplied by ?. ?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L) Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?. Average True Range The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period. The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges. True Range Double A special case of ATR that attempts to correct for volatility skew. Standard Deviation Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation. Adaptive Deviation By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility. Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting). The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions. Median Absolute Deviation The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant. Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution. For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated. Efficiency-Ratio Adaptive ATR Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range. Mean Absolute Deviation The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation. This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life. █ Loxx's Expanded Source Types Included in Baseline Optimizer This indicator allows you to select from 33 source types. They are as follows: Close Open High Low Median Typical Weighted Average Average Median Body Trend Biased Trend Biased (Extreme) HA Close HA Open HA High HA Low HA Median HA Typical HA Weighted HA Average HA Average Median Body HA Trend Biased HA Trend Biased (Extreme) HAB Close HAB Open HAB High HAB Low HAB Median HAB Typical HAB Weighted HAB Average HAB Average Median Body HAB Trend Biased HAB Trend Biased (Extreme) What are Heiken Ashi "better" candles? Heiken Ashi "better" candles are a modified version of the standard Heiken Ashi candles, which are a popular charting technique used in technical analysis. Heiken Ashi candles help traders identify trends and potential reversal points by smoothing out price data and reducing market noise. The "better formula" was proposed by Sebastian Schmidt in an article published by BNP Paribas in Warrants & Zertifikate, a German magazine, in August 2004. The aim of this formula is to further improve the smoothing of the Heiken Ashi chart and enhance its effectiveness in identifying trends and reversals. Standard Heiken Ashi candles are calculated using the following formulas: Heiken Ashi Close = (Open + High + Low + Close) / 4 Heiken Ashi Open = (Previous Heiken Ashi Open + Previous Heiken Ashi Close) / 2 Heiken Ashi High = Max (High, Heiken Ashi Open, Heiken Ashi Close) Heiken Ashi Low = Min (Low, Heiken Ashi Open, Heiken Ashi Close) The "better formula" modifies the standard Heiken Ashi calculation by incorporating additional smoothing, which can help reduce noise and make it easier to identify trends and reversals. The modified formulas for Heiken Ashi "better" candles are as follows: Better Heiken Ashi Close = (Open + High + Low + Close) / 4 Better Heiken Ashi Open = (Previous Better Heiken Ashi Open + Previous Better Heiken Ashi Close) / 2 Better Heiken Ashi High = Max (High, Better Heiken Ashi Open, Better Heiken Ashi Close) Better Heiken Ashi Low = Min (Low, Better Heiken Ashi Open, Better Heiken Ashi Close) Smoothing Factor = 2 / (N + 1), where N is the chosen period for smoothing Smoothed Better Heiken Ashi Open = (Better Heiken Ashi Open * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Open * (1 - Smoothing Factor)) Smoothed Better Heiken Ashi Close = (Better Heiken Ashi Close * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Close * (1 - Smoothing Factor)) The smoothed Better Heiken Ashi Open and Close values are then used to calculate the smoothed Better Heiken Ashi High and Low values, resulting in "better" candles that provide a clearer representation of the market trend and potential reversal points. Heiken Ashi "better" candles, as mentioned previously, provide a clearer representation of market trends and potential reversal points by reducing noise and smoothing out price data. When using these candles in conjunction with other technical analysis tools and indicators, traders can gain valuable insights into market behavior and make more informed decisions. To effectively use Heiken Ashi "better" candles in your trading strategy, consider the following tips: -Trend Identification: Heiken Ashi "better" candles can help you identify the prevailing trend in the market. When the majority of the candles are green (or another color, depending on your chart settings) and there are no or few lower wicks, it may indicate a strong uptrend. Conversely, when the majority of the candles are red (or another color) and there are no or few upper wicks, it may signal a strong downtrend. -Trend Reversals: Look for potential trend reversals when a change in the color of the candles occurs, especially when accompanied by longer wicks. For example, if a green candle with a long lower wick is followed by a red candle, it could indicate a bearish reversal. Similarly, a red candle with a long upper wick followed by a green candle may suggest a bullish reversal. -Support and Resistance: You can use Heiken Ashi "better" candles to identify potential support and resistance levels. When the candles are consistently moving in one direction and then suddenly change color with longer wicks, it could indicate the presence of a support or resistance level. -Stop-Loss and Take-Profit: Using Heiken Ashi "better" candles can help you manage risk by determining optimal stop-loss and take-profit levels. For instance, you can place your stop-loss below the low of the most recent green candle in an uptrend or above the high of the most recent red candle in a downtrend. -Confirming Signals: Heiken Ashi "better" candles should be used in conjunction with other technical indicators, such as moving averages, oscillators, or chart patterns, to confirm signals and improve the accuracy of your analysis. In this implementation, you have the choice of AMA, KAMA, or T3 smoothing. These are as follows: Kaufman Adaptive Moving Average (KAMA) The Kaufman Adaptive Moving Average (KAMA) is a type of adaptive moving average used in technical analysis to smooth out price fluctuations and identify trends. The KAMA adjusts its smoothing factor based on the market's volatility, making it more responsive in volatile markets and smoother in calm markets. The KAMA is calculated using three different efficiency ratios that determine the appropriate smoothing factor for the current market conditions. These ratios are based on the noise level of the market, the speed at which the market is moving, and the length of the moving average. The KAMA is a popular choice among traders who prefer to use adaptive indicators to identify trends and potential reversals. Adaptive Moving Average The Adaptive Moving Average (AMA) is a type of moving average that adjusts its sensitivity to price movements based on market conditions. It uses a ratio between the current price and the highest and lowest prices over a certain lookback period to determine its level of smoothing. The AMA can help reduce lag and increase responsiveness to changes in trend direction, making it useful for traders who want to follow trends while avoiding false signals. The AMA is calculated by multiplying a smoothing constant with the difference between the current price and the previous AMA value, then adding the result to the previous AMA value. T3 The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm. The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction. The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy. █ Giga Kaleidoscope Modularized Trading System Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted 8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators* *(not part of the NNFX algorithm) What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. What is an Metamorphosis indicator? The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy. The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) 6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal. That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Full GKD Backtest Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: Kase Peak Oscillator Confirmation 2: uf2018 Continuation: Vortex Exit: Rex Oscillator Metamorphosis: Baseline Optimizer as shown on the chart above Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system. █ Giga Kaleidoscope Modularized Trading System Signals Standard Entry 1. GKD-C Confirmation gives signal 2. Baseline agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Volatility/Volume agrees 1-Candle Standard Entry 1a. GKD-C Confirmation gives signal 2a. Baseline agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum Next Candle 1b. Price retraced 2b. Baseline agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Baseline Entry 1. GKD-B Basline gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Volatility/Volume agrees 7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior 1-Candle Baseline Entry 1a. GKD-B Baseline gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior Next Candle 1b. Price retraced 2b. Baseline agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Volatility/Volume Entry 1. GKD-V Volatility/Volume gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Baseline agrees 7. Confirmation 1 signal was less than 7 candles prior 1-Candle Volatility/Volume Entry 1a. GKD-V Volatility/Volume gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior Next Candle 1b. Price retraced 2b. Volatility/Volume agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Baseline agrees Confirmation 2 Entry 1. GKD-C Confirmation 2 gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Volatility/Volume agrees 6. Baseline agrees 7. Confirmation 1 signal was less than 7 candles prior 1-Candle Confirmation 2 Entry 1a. GKD-C Confirmation 2 gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior Next Candle 1b. Price retraced 2b. Confirmation 2 agrees 3b. Confirmation 1 agrees 4b. Volatility/Volume agrees 5b. Baseline agrees PullBack Entry 1a. GKD-B Baseline gives signal 2a. Confirmation 1 agrees 3a. Price is beyond 1.0x Volatility of Baseline Next Candle 1b. Price inside Goldie Locks Zone Minimum 2b. Price inside Goldie Locks Zone Maximum 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Continuation Entry 1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously 2. Baseline hasn't crossed since entry signal trigger 4. Confirmation 1 agrees 5. Baseline agrees 6. Confirmation 2 agrees █ Connecting to Backtests All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below. GKD-BT Giga Confirmation Stack Backtest: GKD-BT Giga Stacks Backtest: GKD-BT Full Giga Kaleidoscope Backtest: GKD-BT Solo Confirmation Super Complex Backtest: GKD-BT Solo Confirmation Complex Backtest: GKD-BT Solo Confirmation Simple Backtest:
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GKD-M Accuracy Alchemist [Loxx]Giga Kaleidoscope GKD-M Accuracy Alchemist is a Metamorphosis module included in Loxx's "Giga Kaleidoscope Modularized Trading System". █ GKD-M Accuracy Alchemist What is the Accuracy Alchemist? The Accuracy Alchemist is designed to process up to 10 GKD-C indicators and create a compound signal that can be utilized in a GKD-BT backtest. It achieves this by applying an individual Solo Confirmation Simple backtest to each GKD-C indicator provided. The compound signal is derived from the cumulative accuracy rate of each candle within a specified date range. To illustrate this process, consider the following scenario: The Fisher Transform indicator has a 65% win rate for long positions on the current ticker. The Vortex indicator has a 45% success rate on the current candle. Suppose a long signal is generated by the Vortex indicator. However, this signal is disregarded because its accuracy is lower than that of the Fisher Transform. Now, imagine that the subsequent candle produces a long signal from the Fisher Transform indicator. This signal will be exported to the backtest. The GKD-C indicator that exhibits the highest accuracy for the current candle is chosen to generate the signal. The dominant indicator, determined by its accuracy, will always be used to generate signals. However, it is important to note that the current dominant indicator might not retain its dominance in the future if its accuracy rate falls below that of other indicators connected within the Accuracy Alchemist indicator. The Accuracy Alchemist provides a comprehensive table that displays the dominant indicator based on accuracy, highlighted in green for the highest long accuracy rate and in red for the highest short accuracy rate. Additionally, the table presents the cumulative long and short accuracy rates for all indicators. The functionality of the Accuracy Alchemist extends to several GKD-BT backtests, allowing for seamless integration. These backtests include: -Solo Confirmation Simple -Solo Confirmation Complex -Solo Confirmation Super Complex -Full GKD (as a Confirmation 1 indicator only) -Confirmation Stack (as a Confirmation 1 indicator only) By incorporating the Accuracy Alchemist, you gain the ability to evaluate and compare GKD-C Confirmation indicators within your full GKD trading system. It serves as an ideal tool to assess the performance of different confirmation indicators and aids in the selection process for determining which indicators to incorporate into your trading strategy. Take Profit and Stoploss The GKD system utilizes volatility-based take profits and stop losses, where each take profit and stop loss is calculated as a multiple of volatility. Users have the flexibility to adjust the multiplier values in the settings to suit their preferences. Accuracy Alchemist tests the accuracy of GKD-C Confirmation indicators and therefore has only 1 take profit and 1 stoploss. You can adjust the multipliers of both in the settings Setting up Accuracy Alchemist To use this indicator, you must import GKD-C Confirmation indicators and then activate them in the Accuracy Alchemist settings. Import the value "Input into NEW GKD-BT Backtest" from a GKD-C indicator and then activate it by checking the box next to the import. See below: Volatility Types Included 17 types of volatility are included in this indicator Close-to-Close Parkinson Garman-Klass Rogers-Satchell Yang-Zhang Garman-Klass-Yang-Zhang Exponential Weighted Moving Average Standard Deviation of Log Returns Pseudo GARCH(2,2) Average True Range True Range Double Standard Deviation Adaptive Deviation Median Absolute Deviation Efficiency-Ratio Adaptive ATR Mean Absolute Deviation Static Percent Close-to-Close Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility. Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is. Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility. Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators. Parkinson Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day. The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility. One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator. Garman-Klass Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security. Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate. Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements. Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes. Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail. Rogers-Satchell Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero. Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending. The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions. A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail. Yang-Zhang Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error. Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator. Garman-Klass-Yang-Zhang Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation. GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator. Exponential Weighted Moving Average The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling. The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted. The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series. Standard Deviation of Log Returns This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)). Pseudo GARCH(2,2) This is calculated using a short- and long-run mean of variance multiplied by ?. ?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L) Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?. Average True Range The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period. The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges. True Range Double A special case of ATR that attempts to correct for volatility skew. Standard Deviation Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation. Adaptive Deviation By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility. Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting). The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions. Median Absolute Deviation The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant. Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution. Efficiency-Ratio Adaptive ATR Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range. Mean Absolute Deviation The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation. This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life. Static Percent Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss █ Giga Kaleidoscope Modularized Trading System Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted 8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators* *(not part of the NNFX algorithm) What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. What is an Metamorphosis indicator? The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy. The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) 6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal. That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Full GKD Backtest Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: Composite RSI Confirmation 2: uf2018 Continuation: Vortex Exit: Rex Oscillator Metamorphosis: Fisher Transform, Universal Oscillator, Aroon, Vortex .. combined Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system. █ Giga Kaleidoscope Modularized Trading System Signals Standard Entry 1. GKD-C Confirmation gives signal 2. Baseline agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Volatility/Volume agrees 1-Candle Standard Entry 1a. GKD-C Confirmation gives signal 2a. Baseline agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum Next Candle 1b. Price retraced 2b. Baseline agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Baseline Entry 1. GKD-B Basline gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Volatility/Volume agrees 7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior 1-Candle Baseline Entry 1a. GKD-B Baseline gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior Next Candle 1b. Price retraced 2b. Baseline agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Volatility/Volume Entry 1. GKD-V Volatility/Volume gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Confirmation 2 agrees 6. Baseline agrees 7. Confirmation 1 signal was less than 7 candles prior 1-Candle Volatility/Volume Entry 1a. GKD-V Volatility/Volume gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior Next Candle 1b. Price retraced 2b. Volatility/Volume agrees 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Baseline agrees Confirmation 2 Entry 1. GKD-C Confirmation 2 gives signal 2. Confirmation 1 agrees 3. Price inside Goldie Locks Zone Minimum 4. Price inside Goldie Locks Zone Maximum 5. Volatility/Volume agrees 6. Baseline agrees 7. Confirmation 1 signal was less than 7 candles prior 1-Candle Confirmation 2 Entry 1a. GKD-C Confirmation 2 gives signal 2a. Confirmation 1 agrees 3a. Price inside Goldie Locks Zone Minimum 4a. Price inside Goldie Locks Zone Maximum 5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior Next Candle 1b. Price retraced 2b. Confirmation 2 agrees 3b. Confirmation 1 agrees 4b. Volatility/Volume agrees 5b. Baseline agrees PullBack Entry 1a. GKD-B Baseline gives signal 2a. Confirmation 1 agrees 3a. Price is beyond 1.0x Volatility of Baseline Next Candle 1b. Price inside Goldie Locks Zone Minimum 2b. Price inside Goldie Locks Zone Maximum 3b. Confirmation 1 agrees 4b. Confirmation 2 agrees 5b. Volatility/Volume agrees Continuation Entry 1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously 2. Baseline hasn't crossed since entry signal trigger 4. Confirmation 1 agrees 5. Baseline agrees 6. Confirmation 2 agrees █ Connecting to Backtests All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below. GKD-BT Giga Confirmation Stack Backtest: GKD-BT Giga Stacks Backtest: GKD-BT Full Giga Kaleidoscope Backtest: GKD-BT Solo Confirmation Super Complex Backtest: GKD-BT Solo Confirmation Complex Backtest: GKD-BT Solo Confirmation Simple Backtest:
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Autoback Grid Lab [trade_lexx]Autoback Grid Lab: Your personal laboratory for optimizing grid strategies. Introduction First of all, it is important to understand that Autoback Grid Lab is a powerful professional tool for backtesting and optimization, created specifically for traders using both grid strategies and regular take profit with stop loss. The main purpose of this script is to save you weeks and months of manual testing and parameter selection. Instead of manually testing one combination of settings after another, Autoback Grid Lab automatically tests thousands of unique strategies on historical data, providing you with a comprehensive report on the most profitable and, more importantly, sustainable ones. If you want to find mathematically sound, most effective settings for your grid strategy on a specific asset and timeframe, then this tool was created for you. Key Features My tool has functionality that transforms the process of finding the perfect strategy from a routine into an exciting exploration. 🧪 Mass testing of thousands of combinations The script is able to systematically generate and run a huge number of unique combinations of parameters through the built-in simulator. You set the ranges, and the indicator does all the work, testing all possible options for the following grid settings: * Number of safety orders (SO Count) * Grid step (SO Step) * Step Multiplier (SO Multiplier) for building nonlinear grids * Martingale for controlling the volume of subsequent orders * Take Profit (%) * Stop Loss (%), with the possibility of calculating both from the entry point and from the dynamic breakeven line * The volume of the base order (Volume BO) as a percentage of the deposit 🏆 Unique `FinalScore` rating system Sorting strategies by net profit alone is a direct path to self—deception and choosing strategies that are "tailored" to history and will inevitably fail in real trading. To solve this problem, we have developed FinalScore, a comprehensive assessment of the sustainability and quality of the strategy. How does it work? FinalScore analyzes each combination not one by one, but by nine key performance metrics at once, including Net Profit, Drawdown, Profit Factor, WinRate, Sharpe coefficients, Sortino, Squid and Omega. Each of these indicators is normalized, that is, reduced to a single scale. Then, to test the strategy for strength, the system performs 30 iterations, each time assigning random weights to these 9 metrics. A strategy gets a high FinalScore only if it shows consistently high results under different evaluation criteria. This proves her reliability and reduces the likelihood that her success was an accident. 📈 Realistic backtesting engine The test results are meaningless if they do not take into account the actual trading conditions. Our simulator simulates real trading as accurately as possible, taking into account: * Leverage: Calculation of the required margin to open and hold positions. * Commission: A percentage commission is charged each time an order is opened and closed. * Slippage: The order execution price is adjusted by a set percentage to simulate real market conditions. * Liquidation model: This is one of the most important functions. The script continuously monitors the equity of the account (capital + unrealized P&L). If equity falls below the level of the supporting margin (calculated from the current value of the position), the simulator forcibly closes the position, as it would happen on a real exchange. This eliminates unrealistic scenarios where the strategy survives after a huge drawdown. 🔌 Integration with external signals The indicator operates in two modes: 1. `No Signal': Standard mode. The trading cycle starts immediately as soon as the previous one has been closed. Ideal for testing the "pure" mechanics of the grid. 2. `External Signal`: In this mode, a new trading cycle will start only when a signal is received from an external source. You can connect any other indicator (such as the RSI, MACD, or your own strategy) to the script and use it as a trigger to log in. This allows you to combine the power of a grid strategy with your own entry points. 📊 Interactive and informative results panel Upon completion of the calculations, a detailed table with the TOP N best strategies appears on the screen, sorted according to your chosen criterion. For each strategy in the rating, you will see not only the key metrics (Profit, Drawdown, duration of transactions), but also all the parameters that led to this result. You can immediately take these settings and apply them in your trading. Application Options: How To Solve Your Problems Autoback Grid Lab is a flexible tool that can be adapted to solve various tasks, from complete grid optimization to fine—tuning existing strategies. Here are some key scenarios for its use: 1. Complete Optimization Of The Grid Strategy This is the basic and most powerful mode of use. You can find the most efficient grid configuration for any asset from scratch. * How to use: Set wide ranges for all key grid parameters ('SO Count`, SO Step, SO Multiplier, Martingale, TP, etc.). * In the `No Signal` mode: You will find the most stable grid configuration that works as an independent, constantly active strategy, regardless of which-or entrance indicators. * In the `External Signal` mode: You can connect your favorite indicator for input (for example, RSI, MACD or a complex author's script) and find the optimal grid parameters that best complement your input signals. This allows you to turn a simple signaling strategy into a full-fledged grid system. 2. Selecting the Optimal Take Profit and Stop Loss for Your Strategy Do you already have an entry strategy, but you are not sure where it is best to put Take Profit and Stop Loss? Autoback Grid Lab can solve this problem as well. * How to use: 1. Disable optimization of all grid parameters (uncheck SO Count, SO Step, Martingale, etc.). Set the Min value for SO Count to 0. 2. Set the ranges for iteration only for 'Take Profit` and `Stop Loss'. 3. Turn on the External Signal mode and connect your indicator with input signals. * Result: The script will run your historical entry signals with hundreds of different TP and SL combinations and show you which stop order levels bring maximum profit with minimal risk specifically for your entry points. 3. Building a Secure Network with Risk Management Many traders are afraid of grid strategies because of the risk of large drawdowns. With the help of the optimizer, you can purposefully find the parameters for such a grid, which includes mandatory risk management through Stop Loss. * How to use: Enable and set the range for Stop Loss, along with other grid parameters. Don't forget to test both types of SL calculations (`From entry point` and `From breakeven line`) to determine which one works more efficiently. * Result: You will find balanced strategies in which the grid parameters (number of orders, martingale) and the Stop Loss level are selected in such a way as to maximize profits without going beyond the acceptable risk level for you. How To Use The Indicator (Step-By-Step Guide) Working with the Autoback Grid Lab is a sequential process consisting of four main steps: from initial setup to analysis of the finished results. Follow this guide to get the most out of the tool. Step 1: Initial Setup 1. Add the indicator to the chart of your chosen asset and timeframe. 2. Open the script settings. The first thing you should pay attention to is the ⚙️ Optimization Settings ⚙️ group. 3. Set the `Bars Count'. This parameter determines how much historical data will be used for testing. * Important: The more bars you specify, the more statistically reliable the backtest results will be. We recommend using the maximum available value (25,000) to test strategies at different market phases. * Consider: The indicator performs all calculations on the last historical bar. After applying the TradingView settings, it will take some time to load all the specified bars. The results table will appear only after the data is fully loaded. Don't worry if it doesn't appear instantly. And if an error occurs, simply switch the number of combinations to 990 and back to 1000 until the table appears. Step 2: Optimization Configuration At this stage, you define the "universe" of parameters that our algorithm will explore. 1. Set the search ranges (🛠 Optimization Parameters 🛠 group). For each grid parameter that you want to optimize (for example, SO Count or `Take Profit'), you must specify three values: * Min: The minimum value of the range. * Max: The maximum value of the range. * Step: The step with which the values from Min to Max will be traversed. *Example:* If you set Min=5, Max=10, and Step=1 for SO Count, the script will test strategies with 5, 6, 7, 8, 9, and 10 safety orders. * Tip for users: To get the first results quickly, start with a larger step (for example, TP from 0.5% to 2.5% in 0.5 increments instead of 0.1). After you identify the most promising areas, you can perform a deeper analysis by expanding the ranges around these values. 2. Set Up Money Management (Group `💰 Money Management Settings 💰`). Fill in these fields with the values that best match your actual trading conditions. This is critically important for obtaining reliable results. * Capital: Your initial deposit. * Leverage: Leverage. * Commission (%): Your trading commission as a percentage. * Slippage (%): Expected slippage. * Liquidation Level (%): The level of the supporting margin (MMR in %). For example, for Binance Futures, this value is usually between 0.4% and 2.5%, depending on the asset and position size. Specify this value for your exchange. 3. Select the Sorting Criterion and the Direction (Group `⚙️ Optimization Settings ⚙️'). * `Sort by': Specify the main criteria by which the best strategies will be selected and sorted. I strongly recommend using finalScore to find the most balanced and sustainable strategies. * `Direction': Choose which trades to test: Long, Short or Both. Step 3: Start Testing and Work with "Parts" The total number of unique combinations generated based on your ranges can reach tens of millions. TradingView has technical limitations on the number of calculations that the script can perform at a time. To get around this, I implemented a "Parts" system. 1. What are `Part` and `Combinations in Part'? * `Combinations in Part': This is the number of backtests that the script performs in one run (1000 by default). * `Part`: This is the number of the "portion" of combinations that you want to test. 2. How does it work in practice? * After you have everything set up, leave Part:1 and wait for the results table to appear. You will see the TOP N best strategies from the first thousand tested. * Analyze them. Then, to check the next thousand combinations, just change the Part to 2 in the settings and click OK. The script will run a test for the next batch. * Repeat this process by increasing the Part number (`3`, 4, 5...), until you reach the last available part. * Where can I see the total number of parts? In the information row below the results table, you will find Total parts. This will help you figure out how many more tests are left to run. Step 4: Analyze the Results in the Table The results table is your main decision—making tool. It displays the best strategies found, sorted by the criteria you have chosen. 1. Study the performance metrics: * Rating: Position in the rating. * Profit %: Net profit as a percentage of the initial capital. * Drawdown%: The maximum drawdown of the deposit for the entire test period. * Max Length: The maximum duration of one transaction in days, hours and minutes. * Trades: The total number of completed trades. 2. Examine the winning parameters: * To the right of the performance metrics are columns showing the exact settings that led to this result ('SO Count`, SO Step, TP (%), etc.). 3. How to choose the best strategy? * Don't chase after the maximum profit! The strategy with the highest profit often has the highest drawdown, which makes it extremely risky. * Seek a balance. The ideal strategy is a compromise between high profitability, low drawdown (Drawdown) and the maximum length of trades acceptable to you (Max Length). * finalScore was created to find this balance. Trust him — he often highlights not the most profitable, but the most stable and reliable options. Detailed Description Of The Settings This section serves as a complete reference for each parameter available in the script settings. The parameters are grouped in the same way as in the indicator interface for your convenience. Group: ⚙️ Optimization Settings ⚙️ The main parameters governing the testing process are collected here. * `Enable Optimizer': The main switch. Activates or deactivates all backtesting functionality. * `Direction': Determines which way trades will be opened during the simulation. * Long: Shopping only. * Short: Sales only. * Both: Testing in both directions. Important: This mode only works in conjunction with an External Signal, as the script needs an external signal to determine the direction for each specific transaction. * `Signal Mode`: Controls the conditions for starting a new trading cycle (opening a base order). * No Signal: A new cycle starts immediately after the previous one is completed. This mode is used to test "pure" grid mechanics without reference to market conditions. * External Signal: A new cycle begins only when a signal is received from an external indicator connected via the Signal field. * `Signal': A field for connecting an external signal source (works only in the `External Signal` mode). You can select any other indicator on the chart. * For Long** trades, the signal is considered received if the value of the external indicator ** is greater than 0. * For Short** trades, the signal is considered received if the value of the external indicator ** is less than 0. * `Bars Count': Sets the depth of the history in the bars for the backtest. The maximum value (25000) provides the most reliable results. * `Sort by`: A key criterion for selecting and ranking the best strategies in the final table. * FinalScore: Recommended mode. A comprehensive assessment that takes into account 9 metrics to find the most balanced and sustainable strategies. * Profit: Sort by net profit. * Drawdown: Sort by minimum drawdown. * Max Length: Sort by the minimum length of the longest transaction. * `Combinations Count': Indicates how many of the best strategies (from 1 to 50) will be displayed in the results table. * `Close last trade`: If this option is enabled, any active trade will be forcibly closed at the closing price of the last historical bar. For grid strategies, it is recommended to always enable this option in order to get the correct calculation of the final profit and eliminate grid strategies that have been stuck for a long time. Group: 💰 Money Management Settings 💰 The parameters in this group determine the financial conditions of the simulation. Specify values that are as close as possible to your actual values in order to get reliable results. * `Capital': The initial deposit amount for the simulation. * `Leverage`: The leverage used to calculate the margin. * `Slippage` (%): Simulates the difference between the expected and actual order execution price. The specified percentage will be applied to each transaction. * `Commission` (%): The trading commission of your exchange as a percentage. It is charged at the execution of each order (both at opening and closing). * `Liquidation Level' (%): Maintenance Margin Ratio. This is a critical parameter for a realistic test. Liquidation in the simulator occurs if the Equity of the account (Capital + Unrealized P&L) falls below the level of the supporting margin. Group: 🛠 Optimization Parameters 🛠 This is the "heart" of the optimizer, where you set ranges for iterating through the grid parameters. * `Part`: The portion number of the combinations to be tested. Start with 1, and then increment (`2`, 3, ...) sequentially to check all generated strategies. * `Combinations in Part': The number of backtests performed at a time (in one "Part"). Increasing the value may speed up the process, but it may cause the script to error due to platform limitations. If an error occurs, it is recommended to switch to the step below and back. Three fields are available for each of the following parameters (`SO Count`, SO Step, SO Multiplier, etc.): * `Min`: Minimum value for testing. * `Max': The maximum value for testing. * `Step`: The step with which the values in the range from Min to Max will be iterated over. There is also a checkbox for each parameter. If it is enabled, the parameter will be optimized in the specified range. If disabled, only one value specified in the Min field will be used for all tests. * 'Stop Loss': In addition to the standard settings Min, Max, Step, it has an additional parameter: * `Type`: Defines how the stop loss price is calculated. * From entry point: The SL level is calculated once from the entry price (base order price). * From breakeven line: The SL level is dynamically recalculated from the average position price after each new safety order is executed. Group: ⚡️Filters⚡️ Filters allow you to filter out those results from the final table that do not meet your minimum requirements. For each filter (`Max Profit`, Min Drawdown, `Min Trade Length`), you can: 1. Turn it on or off using the checkbox. 2. Select the comparison condition: Greater (More) or Less (Less). 3. Set a threshold value. *Example:* If you set Less and 20 for the Min Drawdown filter, only those strategies with a maximum drawdown of less than 20% will be included in the final table. Group: 🎨 Visual Settings 🎨 Here you can customize the appearance of the results table. * `Position': Selects the position of the table on the screen (for example, Bottom Left — bottom left). * `Font Size': The size of the text in the table. * `Header Background / Data Background`: Background colors for the header and data cells. * `Header Font Color / Data Font Color`: Text colors for the header and data cells. Important Notes and Limitations So that you can use the Autoback Grid Lab as efficiently and consciously as possible, please familiarize yourself with the following key features of its work. 1. It is a Tool for Analysis, not for Signals It is extremely important to understand that this script does not generate trading signals in real time. Its sole purpose is to conduct in—depth research (**backtesting**) on historical data. * The results you see in the table are a report on how a particular strategy would have worked in the past. * The script does not provide alerts and does not draw entry/exit points on the chart for the current market situation. * Your task is to take the best sets of parameters found during optimization and use them in your real trading, for example, when setting up a trading bot or in a manual trading system. 2. Features Of Calculations (This is not a "Repainting") You will notice that the results table appears and is updated only once — when all historical bars on the chart are loaded. It does not change in real time with each tick of the price. This is correct and intentional behavior.: * To test thousands, and sometimes millions of combinations, the script needs to perform a huge amount of calculations. In the Pine Script™ environment, it is technically possible to do this only once, at the very last bar in history. * The script does not show false historical signals, which then disappear or change. It provides a static report on the results of the simulation, which remains unchanged for a specific historical period. 3. Past Results do not Guarantee Future Results. This is the golden rule of trading, and it fully applies to the results of backtesting. Successful strategy performance in the past is not a guarantee that it will be as profitable in the future. Market conditions, volatility and trends are constantly changing. My tool, especially when sorting by finalScore, is aimed at finding statistically stable and reliable strategies to increase the likelihood of their success in the future. However, it is a tool for managing probabilities, not a crystal ball for predicting the future. Always use proper risk management. 4. Dependence on the Quality and Depth of the Story The reliability of the results directly depends on the quantity and quality of the historical data on which the test was conducted. * Always strive to use the maximum number of bars available (`Bars Count: 25,000`) so that your strategy is tested on different market cycles (rise, fall, flat). * The results obtained on data for one month may differ dramatically from the results obtained on data for two years. The longer the testing period, the higher the confidence in the parameters found. Conclusion The Autoback Grid Lab is your personal research laboratory, designed to replace intuitive guesses and endless manual selection of settings with a systematic, data—driven approach. Experiment with different assets, timeframes, and settings ranges to find the unique combinations that best suit your trading style.
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GKD-M Stepped Baseline Optimizer [Loxx]The Giga Kaleidoscope GKD-M Stepped Baseline Optimizer is a Metamorphosis module included in the "Giga Kaleidoscope Modularized Trading System." █ Introduction The GKD-M Stepped Baseline Optimizer is an advanced component of the Giga Kaleidoscope Modularized Trading System (GKD), designed to enhance trading strategy development by dynamically optimizing Baseline moving averages. This tool allows traders to evaluate over 65 moving averages, adjusting them across multiple periods to identify which settings yield the highest win rates for their trading strategies. The optimizer systematically tests these moving averages across specified timeframes and intervals, offering insights into net profit, total closed trades, win percentages, and other critical metrics for both long and short positions. Traders can define the initial period and incrementally adjust this value to explore a wide range of periods, thus fine-tuning their strategies with precision. What sets the GKD-M Stepped Baseline Optimizer apart is its unique capability to adapt the baseline moving average according to the highest win rates identified during backtesting, at each trading candle. This win-rate adaptive approach ensures that the trading system is always aligned with the most effective period settings for the selected moving average, enhancing the system's overall performance. Moreover, the 'stepped' aspect of this optimizer introduces a filtering process based ons, significantly reducing market noise and ensuring that identified trends are both significant and reliable. This feature is critical for traders looking to mitigate the risks associated with volatile market conditions and to capitalize on genuine market movements.In essence, the GKD-M Stepped Baseline Optimizer is tailored for traders who utilize the GKD trading system, offering a sophisticated tool to refine their baseline indicators dynamically, ensuring that their trading strategies are continuously optimized for maximum efficacy. **the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolated per ticker and trading side, long or short** █ Core Features Stepped Baseline for Noise Reduction One of the hallmark features of the GKD-M Stepped Baseline Optimizer is its stepped baseline capability. This advanced functionality employs volatility filters to refine the selection of moving averages, significantly reducing market noise. The optimizer ensures that only substantial and reliable trends are considered, eliminating the false signals often caused by minor price fluctuations. This stepped approach to baseline optimization is critical for traders aiming to develop strategies that are both resilient and responsive to genuine market movements. Dynamic Win Rate Adaptive Capability Another cornerstone feature is the optimizer’s dynamic win rate adaptive capability. This unique aspect allows the optimizer to adjust the moving average period settings in real-time, based on the highest win rates derived from backtesting over a predefined range. At every trading candle, the optimizer evaluates a comprehensive set of backtesting data to ascertain the optimal period settings for the moving average in use. To perform the backtesting, the trader selects an initial period input (default is 60) and a skip value that increments the initial period input up to seven times. For instance, if a skip value of 5 is chosen, the Baseline Optimizer will run the backtest for the selected moving average on periods such as 60, 65, 70, 75, and so on, up to 90. If the user selects an initial period input of 45 and a skip value of 2, the Baseline Optimizer will conduct backtests for the chosen moving average on periods like 45, 47, 49, 51, and so forth, up to 57. The GKD-M Stepped Baseline Optimizer then exports the baseline with the highest cumulative win rate per candle to any baseline-enabled GKD backtest. This ensures that the baseline indicator remains continually aligned with the most efficacious parameters, dynamically adapting to changing market conditions. Comprehensive Moving Averages Evaluation The optimizer’s ability to test over 65 different moving averages across multiple periods stands as a testament to its comprehensive analytical capability. Traders have the flexibility to explore a wide array of moving averages, from traditional ones like the Simple Moving Average (SMA) and Exponential Moving Average (EMA) to more complex types such as the Hull Moving Average (HMA) and Adaptive Moving Average (AMA). This extensive evaluation allows traders to pinpoint the moving average that best aligns with their trading strategy and market conditions, further enhancing the system’s adaptability and effectiveness. Volatility Filtering and Ticker Volatility Types Incorporating a wide range of volatility types, including the option to utilize external volatility tickers like the VIX for filtering, adds another layer of sophistication to the optimizer. This feature allows traders to calibrate their baseline according to externals, providing an additional dimension of customization. Whether using standard deviation, ATR, or external volatility indices, traders can fine-tune their strategies to be responsive to the broader market sentiment and volatility trends. █ Key Inputs Baseline Settings • Baseline Source: Determines the price data (Open, High, Low, Close) used for moving average calculations. • Baseline Period: The starting period for moving average calculation. • Backtest Skip: Incremental steps for period adjustments in the optimization process. • Baseline Filter Type: Selection from over 65 moving averages for baseline calculation. Volatility and Filter Settings • Price Filter Type & Moving Average Filter Type: Defines thement applied to the price or the moving average, enhancing filter specificity. • Filter Options: Allows users to select the application area of the volatility filter (price, moving average, or both). • Filter Multiplier & Period: Configures the intensity and temporal scope of the filter, fine-tuning sensitivity to market volatility. Backtest Configuration • Window Period: Specifies the length of the backtesting window in days. • Backtest Type: Chooses between a fixed window or cumulative data approach for backtesting. • Initial Capital, Order Size, & Type: Sets the financial parameters for backtesting, including starting equity and the scale of trades. • Commission per Order: Accounts for trading costs within backtest profitability calculations. Date and Time Range • From/Thru Year/Month/Day: Defines the historical period for strategy testing. • Entry Time: Specifies the time frame during which trades can be initiated, crucial for strategies sensitive to market timing. Volatility Measurements for Goldie Locks Volatility Qualifiers • Mean Type & Period: Chooses the moving average type and period for volatility assessment. • Inner/Outer Volatility Qualifier Multipliers: Adjusts the boundaries for volatility-based trade qualification. • Activate Qualifier Boundaries: Enables or disables the upper and lower volatility qualifiers. Advanced Volatility Inputs • Volatility Ticker Selection & Trading Days: Incorporates external volatility indices (e.g., VIX) into the strategy, adjusting for market volatility. • Static Percent, MAD Internal Filter Period, etc.: Provides fixed or adaptive volatility thresholds for filtering. UI Customization • Baseline Width & Table Display Options: Customizes the visual representation of the baseline and the display of optimization results. • Table Header/Content Color & Text Size: Enhances readability and user interface aesthetics. Export Options • Export Data: Selects the specific metric to be exported from the script, such as net profit or average profit per trade. Moving Average Specific Parameters Specific inputs tailored to the characteristics of selected moving averages (e.g., Fractal Adjusted (FRAMA), Least Squares Moving Average (LSMA), T3, etc.), allowing users to fine-tune the behavior of these averages based on unique formula requirements. █ Indicator UI • Long and Short Baselines: The optimizer differentiates trends through two distinct baselines: a green line for long (uptrend) baselines and a red line for short (downtrend) baselines. These baselines alternate activation based on the current trend direction as determined by the moving average plus length combination for the candle in view. Ambiguity in market direction, when an uptrend and downtrend are concurrently indicated, is visually represented by yellow lines. • Stepping Mechanism for Trend Visualization: Adjusting the source input and the moving average output based on volatility, the indicator exhibits a stepped appearance on the chart. This mechanism ensures that only substantial market movements, surpassing a specified volatility threshold, are recognized as trend changes. Stepping Activated • Goldilocks Zone: Beyond the long and short baselines, the Goldilocks zone introduces a distinct moving average that closely follows the selected price or source input, aiming to strike the perfect balance between not too much and not too little market movement for trading. The upper limit of the Goldilocks zone indicates a market stretch too far for advantageous trading (overextension), while the lower limit suggests inadequate market movement for entry (underextension). Trading within the Goldilocks zone is deemed optimal, as it signifies sufficient but not excessive volatility for entering trades, aligning with either the long or short baseline recommendations. Moreover, the mean of the Goldilocks zone serves as a critical indicator, offering a median reference point that aligns closely with the market's current state. This mean is pivotal for traders, as it represents a 'just right' condition for market entry, embodying the essence of the Goldilocks principle in financial trading strategies. • Signal Indicators and Entry Points: The chart includes with green or red dots to signify valid price points within the Goldilocks zone, indicative of conducive trading conditions. Furthermore, small directional arrows at the chart's bottom highlight potential long or short entry points, validated by the Goldilocks zone's parameters. • Data Table: A table presenting real-time statistics from the current candle backward through the chosen range offers insights into win rates and other relevant data, aiding in informed decision-making. This table adapts with each new candle, highlighting the most favorable win rates for both long and short positions. █ Optimizing Strategy with Backtesting Optimizing a trading strategy with backtesting involves rigorously testing the strategy on historical data to evaluate its performance and robustness before applying it in live markets. The GKD-M Stepped Baseline Optimizer incorporates advanced backtesting capabilities, offering both cumulative and rolling window types of backtests. Here's how each backtest type operates and the insights they provide for refining trading strategies: Cumulative Backtest • Overview: A cumulative backtest evaluates a strategy's performance over a continuous period without resetting the strategy parameters or the simulated trading capital at the beginning of each new period. • Utility: This type is useful for understanding a strategy's long-term viability, assessing how it adapts to different market conditions over an extended timeframe. • Interpreting Statistics: Cumulative backtest results often focus on overall return, drawdowns, win rate, and the Sharpe ratio. A strategy with consistent returns, manageable drawdowns, a high win rate, and a favorable Sharpe ratio is considered robust. Rolling Window Backtest • Overview: Unlike the cumulative approach, a rolling window backtest divides the historical data into smaller, overlapping or non-overlapping periods (windows), running the strategy separately on each. After each window, the strategy parameters and simulated trading capital are reset. • Utility: This method is invaluable for assessing a strategy's consistency and adaptability to various market phases. It helps identify if the strategy's performance is dependent on specific market conditions. • Interpreting Statistics: For rolling window backtests, consistency is key. Look for similar performance metrics (returns, drawdowns, win rate) across different windows. Variability in performance indicates sensitivity to market conditions, suggesting the need for strategy adjustments. Strategy Refinement Through Backtest Statistics • Net Profit and Loss: Measures the strategy’s overall effectiveness. Consistent profitability across different market conditions is a positive indicator. • Win Rate and Profit Factor: High win rates and profit factors indicate a strategy's efficiency in capturing gains over losses. • Average Profit per Trade: Understanding the strategy's ability to generate profit on a per-trade basis can highlight its operational efficiency. • Average Number of Bars in Trade: This metric helps understand the strategy's market exposure and timing efficiency. █ Exporting Data and Integration with GKD Backtests The GKD-M Stepped Baseline Optimizer seamlessly integrates with the broader GKD trading system, allowing traders to export the optimization data and leverage it within the various GKD backtest modules. This feature allows users to forward the GKD-M Stepped Baseline Optimizer adaptive signals to a GKD backtest to be used as a Baseline component in a GKD trading system. █ Moving Averages included in the Stepped Baseline Optimizer The GKD-M Stepped Baseline Optimizer incorporates an extensive array of over 65 moving averages, each with unique characteristics and implications for trading strategy development. This comprehensive suite enables traders to conduct nuanced analysis and optimization, ensuring the selection of the most effective moving average for Baseline input into their GKD trading system. Adaptive Moving Average - AMA ADXvma - Average Directional Volatility Moving Average Ahrens Moving Average Alexander Moving Average - ALXMA Coral 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 Geometric Mean Moving Average 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 One More Moving Average - OMA Parabolic Weighted Moving Average Probability Density Function Moving Average - PDFMA Quadratic Regression Moving Average - QRMA Range Filter Range Weighted EMA - RWEMA Recursive Moving Trendline Regularized EMA - REMA Simple Decycler - SDEC Simple Loxx Moving Average - SLMA Simple Moving Average - SMA Sine Weighted Moving Average Smoothed LWMA - SLWMA Smoothed Moving Average - SMMA Smoother Super Smoother T3 Tether Lines Three-pole Ehlers Butterworth Three-pole Ehlers Smoother Triangular Moving Average - TMA Triangle Moving Average Generalized Triple Exponential Moving Average - TEMA Two-pole Ehlers Butterworth Two-pole Ehlers smoother Ultimate Smoother Variable Index Dynamic Average - VIDYA Variable Moving Average - VMA Volume Weighted EMA - VEMA Volume Weighted Moving Average - VWMA Zero-Lag DEMA - Zero Lag Double Exponential Moving Average Zero-Lag Moving Average Zero Lag TEMA - Zero Lag Triple Exponential Moving Average █ Volatility Types and Filtering The GKD-M Stepped Baseline Optimizer features a comprehensive selection of over 15 volatility types, each tailored to capture different aspects of market behavior and risk. Volatility Ticker Selection: Enables direct incorporation of external volatility indicators like VIX and EUVIX into the script for market sentiment analysis, signal filtering enhancement, and real-time risk management adjustments. Standard Deviation of Logarithmic Returns: Quantifies asset volatility using the standard deviation applied to logarithmic returns, capturing symmetric price movements and financial returns' compound nature. Exponential Weighted Moving Average (EWMA) for Volatility: Focuses on recent market information by applying exponentially decreasing weights to squared logarithmic returns, offering a dynamic view of market volatility. Roger-Satchell Volatility Measure: Estimates asset volatility by analyzing the high, low, open, and close prices, providing a nuanced view of intraday volatility and market dynamics. Close-to-Close Volatility Measure: Calculates volatility based on the closing prices of stocks, offering a streamlined but limited perspective on market behavior. Parkinson Volatility Measure: Enhances volatility estimation by including high and low prices of the trading day, capturing a more accurate reflection of intraday market movements. Garman-Klass Volatility Measure: Incorporates open, high, low, and close prices for a comprehensive daily volatility measure, capturing significant price movements and market activity. Yang-Zhang Volatility Measure: Offers an efficient estimation of stock market volatility by combining overnight and intraday price movements, capturing opening jumps and overall market dynamics. Garman-Klass-Yang-Zhang Volatility Measure: Merges the benefits of Garman-Klass and Yang-Zhang measures, providing a fuller picture of market volatility including opening market reactions. Pseudo GARCH(2,2) Volatility Model: Mimics a GARCH(2,2) process using exponential moving averages of squared returns, highlighting volatility shocks and their future impact. ER-Adaptive Average True Range (ATR): Adjusts the ATR period length based on market efficiency, offering a volatility measure that adapts to changing market conditions. Adaptive Deviation: Dynamically adjusts its calculation period to offer a nuanced measure of volatility that responds to the market's intrinsic rhythms. Median Absolute Deviation (MAD): Provides a robust measure of statistical variability, focusing on deviations from the median price, offering resilience against outliers. Mean Absolute Deviation (MAD): Measures the average magnitude of deviations from the mean price, facilitating a straightforward understanding of volatility. ATR (Average True Range): Finds the average of true ranges over a specified period, indicating the expected price movement and market volatility. True Range Double (TRD): Offers a nuanced view of volatility by considering a broader range of price movements, identifying significant market sentiment shifts.
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loxx tarafından
Smart DCA StrategyINSPIRATION While Dollar Cost Averaging (DCA) is a popular and stress-free investment approach, I noticed an opportunity for enhancement. Standard DCA involves buying consistently, regardless of market conditions, which can sometimes mean missing out on optimal investment opportunities. This led me to develop the Smart DCA Strategy – a 'set and forget' method like traditional DCA, but with an intelligent twist to boost its effectiveness. The goal was to build something more profitable than a standard DCA strategy so it was equally important that this indicator could backtest its own results in an A/B test manner against the regular DCA strategy. WHY IS IT SMART? The key to this strategy is its dynamic approach: buying aggressively when the market shows signs of being oversold, and sitting on the sidelines when it's not. This approach aims to optimize entry points, enhancing the potential for better returns while maintaining the simplicity and low stress of DCA. WHAT THIS STRATEGY IS, AND IS NOT This is an investment style strategy. It is designed to improve upon the common standard DCA investment strategy. It is therefore NOT a day trading strategy. Feel free to experiment with various timeframes, but it was designed to be used on a daily timeframe and that's how I recommend it to be used. You may also go months without any buy signals during bull markets, but remember that is exactly the point of the strategy - to keep your buying power on the sidelines until the markets have significantly pulled back. You need to be patient and trust in the historical backtesting you have performed. HOW IT WORKS The Smart DCA Strategy leverages a creative approach to using Moving Averages to identify the most opportune moments to buy. A trigger occurs when a daily candle, in its entirety including the high wick, closes below the threshold line or box plotted on the chart. The indicator is designed to facilitate both backtesting and live trading. HOW TO USE Settings: The input parameters for tuning have been intentionally simplified in an effort to prevent users falling into the overfitting trap. The main control is the Buying strictness scale setting. Setting this to a lower value will provide more buying days (less strict) while higher values mean less buying days (more strict). In my testing I've found level 9 to provide good all round results. Validation days is a setting to prevent triggering entries until the asset has spent a given number of days (candles) in the overbought state. Increasing this makes entries stricter. I've found 0 to give the best results across most assets. In the backtest settings you can also configure how much to buy for each day an entry triggers. Blind buy size is the amount you would buy every day in a standard DCA strategy. Smart buy size is the amount you would buy each day a Smart DCA entry is triggered. You can also experiment with backtesting your strategy over different historical datasets by using the Start date and End date settings. The results table will not calculate for any trades outside what you've set in the date range settings. Backtesting: When backtesting you should use the results table on the top right to tune and optimise the results of your strategy. As with all backtests, be careful to avoid overfitting the parameters. It's better to have a setup which works well across many currencies and historical periods than a setup which is excellent on one dataset but bad on most others. This gives a much higher probability that it will be effective when you move to live trading. The results table provides a clear visual representation as to which strategy, standard or smart, is more profitable for the given dataset. You will notice the columns are dynamically coloured red and green. Their colour changes based on which strategy is more profitable in the A/B style backtest - green wins, red loses. The key metrics to focus on are GOA (Gain on Account) and Avg Cost . Live Trading: After you've finished backtesting you can proceed with configuring your alerts for live trading. But first, you need to estimate the amount you should buy on each Smart DCA entry. We can use the Total invested row in the results table to calculate this. Assuming we're looking to trade on BITSTAMP:BTCUSD Decide how much USD you would spend each day to buy BTC if you were using a standard DCA strategy. Lets say that is $5 per day Enter that USD amount in the Blind buy size settings box Check the Blind Buy column in the results table. If we set the backtest date range to the last 10 years, we would expect the amount spent on blind buys over 10 years to be $18,250 given $5 each day Next we need to tweak the value of the Smart buy size parameter in setting to get it as close as we can to the Total Invested amount for Blind Buy By following this approach it means we will invest roughly the same amount into our Smart DCA strategy as we would have into a standard DCA strategy over any given time period. After you have calculated the Smart buy size , you can go ahead and set up alerts on Smart DCA buy triggers. BOT AUTOMATION In an effort to maintain the 'set and forget' stress-free benefits of a standard DCA strategy, I have set my personal Smart DCA Strategy up to be automated. The bot runs on AWS and I have a fully functional project for the bot on my GitHub account. Just reach out if you would like me to point you towards it. You can also hook this into any other 3rd party trade automation system of your choice using the pre-configured alerts within the indicator. PLANNED FUTURE DEVELOPMENTS Currently this is purely an accumulation strategy. It does not have any sell signals right now but I have ideas on how I will build upon it to incorporate an algorithm for selling. The strategy should gradually offload profits in bull markets which generates more USD which gives more buying power to rinse and repeat the same process in the next cycle only with a bigger starting capital. Watch this space! MARKETS Crypto: This strategy has been specifically built to work on the crypto markets. It has been developed, backtested and tuned against crypto markets and I personally only run it on crypto markets to accumulate more of the coins I believe in for the long term. In the section below I will provide some backtest results from some of the top crypto assets. Stocks: I've found it is generally more profitable than a standard DCA strategy on the majority of stocks, however the results proved to be a lot more impressive on crypto. This is mainly due to the volatility and cycles found in crypto markets. The strategy makes its profits from capitalising on pullbacks in price. Good stocks on the other hand tend to move up and to the right with less significant pullbacks, therefore giving this strategy less opportunity to flourish. Forex: As this is an accumulation style investment strategy, I do not recommend that you use it to trade Forex. STRATEGY IN ACTION Here you see the indicator running on the BITSTAMP:BTCUSD pair. You can read the indicator as follows: Vertical green bands on historical candles represents where buy signals triggered in the past Table on the top right represents the results of the A/B backtest against a standard DCA strategy Green Smart Buy column shows that Smart DCA was more profitable than standard DCA on this backtest. That is shown by the percentage GOA (Gain on Account) and the Avg Cost Smart Buy Zone label marks the threshold which the entire candle must be below to trigger a buy signal (line can be changed to a box under plotting settings) Green color of Smart Buy Zone label represents that the open candle is still valid for a buy signal. A signal will only be generated if the candle closes while this label is still green Below is the same BITSTAMP:BTCUSD chart a couple of days later. Notice how the threshold has been broken and the Smart Buy Zone label has turned from green to red. No buy signal can be triggered for this day - even if the candle retraced and closed below the threshold before daily candle close. Notice how the green vertical bands tend to be present after significant pullbacks in price. This is the reason the strategy works! Below is the same BITSTAMP:BTCUSD chart, but this time zoomed out to present a clearer picture of the times it would invest vs times it would sit out of the market. You will notice it invests heavily in bear markets and significant pullbacks, and does not buy anything during bull markets. Finally, to visually demonstrate the indicator on an asset other than BTC, here is an example on CRYPTO:ETHUSD . In this case the current daily high has not touched the threshold so it is still possible for this to be a valid buy trigger on daily candle close. The vertical green band will not print until the buy trigger is confirmed. BACKTEST RESULTS Now for some backtest results to demonstrate the improved performance over a standard DCA strategy using all non-stablecoin assets in the top 30 cryptos by marketcap. I've used the TradingView ticker (exchange name denoted as CRYPTO in the symbol search) for every symbol tested with the exception of BTCUSD because there was some dodgy data at the beginning of the TradingView BTCUSD chart which overinflated the effectiveness of the Smart DCA strategy on that ticker. For BTCUSD I've used the BITSTAMP exchange data. The symbol links below will take you to the correct chart and exchange used for the test. I'm using the GOA (Gain on Account) values to present how each strategy performed. The value on the left side is the standard DCA result and the right is the Smart DCA result. ✅ means Smart DCA strategy outperformed the standard DCA strategy ❌ means standard DCA strategy outperformed the Smart DCA strategy To avoid overfitting, and to prove that this strategy does not suffer from overfitting, I've used the exact same input parameters for every symbol tested below. The settings used in these backtests are: Buying strictness scale: 9 Validation days: 0 You can absolutely tweak the values per symbol to further improve the results of each, however I think using identical settings on every pair tested demonstrates a higher likelihood that the results will be similar in the live markets. I'm presenting results for two time periods: First price data available for trading pair -> closing candle on Friday 26th Jan 2024 (ALL TIME) Opening candle on Sunday 1st Jan 2023 -> closing candle on Friday 26th Jan 2024 (JAN 2023 -> JAN 2024) ALL TIME: BITSTAMP:BTCUSD 80,884% / 133,582% ✅ CRYPTO:ETHUSD 17,231% / 36,146% ✅ CRYPTO:BNBUSD 5,314% / 2,702% ❌ CRYPTO:SOLUSD 1,745% / 1,171% ❌ CRYPTO:XRPUSD 2,585% / 4,544% ✅ CRYPTO:ADAUSD 338% / 353% ✅ CRYPTO:AVAXUSD 130% / 160% ✅ CRYPTO:DOGEUSD 13,690% / 16,432% ✅ CRYPTO:TRXUSD 414% / 466% ✅ CRYPTO:DOTUSD -16% / -7% ✅ CRYPTO:LINKUSD 1,161% / 2,164% ✅ CRYPTO:TONUSD 25% / 47% ✅ CRYPTO:MATICUSD 1,769% / 1,587% ❌ CRYPTO:ICPUSD 70% / 50% ❌ CRYPTO:SHIBUSD -20% / -19% ✅ CRYPTO:LTCUSD 486% / 718% ✅ CRYPTO:BCHUSD -4% / 3% ✅ CRYPTO:LEOUSD 102% / 151% ✅ CRYPTO:ATOMUSD 46% / 91% ✅ CRYPTO:UNIUSD -16% / 1% ✅ CRYPTO:ETCUSD 283% / 414% ✅ CRYPTO:OKBUSD 1,286% / 1,935% ✅ CRYPTO:XLMUSD 1,471% / 1,592% ✅ CRYPTO:INJUSD 830% / 1,035% ✅ CRYPTO:OPUSD 138% / 195% ✅ CRYPTO:NEARUSD 23% / 44% ✅ Backtest result analysis: Assuming we have an initial investment amount of $10,000 spread evenly across each asset since the creation of each asset, it would have provided the following results. Standard DCA Strategy results: Average percent return: 4,998.65% Profit: $499,865 Closing balance: $509,865 Smart DCA Strategy results: Average percent return: 7,906.03% Profit: $790,603 Closing balance: $800,603 JAN 2023 -> JAN 2024: BITSTAMP:BTCUSD 47% / 66% ✅ CRYPTO:ETHUSD 26% / 33% ✅ CRYPTO:BNBUSD 15% / 17% ✅ CRYPTO:SOLUSD 272% / 394% ✅ CRYPTO:XRPUSD 7% / 12% ✅ CRYPTO:ADAUSD 43% / 59% ✅ CRYPTO:AVAXUSD 116% / 151% ✅ CRYPTO:DOGEUSD 8% / 14% ✅ CRYPTO:TRXUSD 48% / 65% ✅ CRYPTO:DOTUSD 24% / 35% ✅ CRYPTO:LINKUSD 83% / 124% ✅ CRYPTO:TONUSD 7% / 21% ✅ CRYPTO:MATICUSD -3% / 7% ✅ CRYPTO:ICPUSD 161% / 196% ✅ CRYPTO:SHIBUSD 1% / 8% ✅ CRYPTO:LTCUSD -15% / -7% ✅ CRYPTO:BCHUSD 47% / 68% ✅ CRYPTO:LEOUSD 9% / 11% ✅ CRYPTO:ATOMUSD 1% / 15% ✅ CRYPTO:UNIUSD 9% / 23% ✅ CRYPTO:ETCUSD 27% / 40% ✅ CRYPTO:OKBUSD 21% / 30% ✅ CRYPTO:XLMUSD 11% / 19% ✅ CRYPTO:INJUSD 477% / 446% ❌ CRYPTO:OPUSD 77% / 91% ✅ CRYPTO:NEARUSD 78% / 95% ✅ Backtest result analysis: Assuming we have an initial investment amount of $10,000 spread evenly across each asset for the duration of 2023, it would have provided the following results. Standard DCA Strategy results: Average percent return: 61.42% Profit: $6,142 Closing balance: $16,142 Smart DCA Strategy results: Average percent return: 78.19% Profit: $7,819 Closing balance: $17,819
Pine Script® göstergesi
kevinmck100 tarafından
3Commas Bot DCA Backtester & Signals FREEThis is a DCA Strategy backtester + signals, built to emulate the 3Commas DCA bots. It uses your choice of 4 different buy signals, 2 of which can be adjusted in the settings. Everything is customizable so you can backtest specific settings with different buy signals and find the best performing strategy for your risk tolerance and capital. It can be used to backtest strategies on stocks as well, but just make sure your base order is larger than the share price for the entire backtesting range or it will not calculate properly. You can use this template to code your own buy signals and then backtest them as a DCA strategy if you know some basic pine script. The indicator shows all of your backtesting orders on the chart. The red line is your take profit level, the blue line is your average price level, the white line is your first order and the green lines are your average down orders. If you enable a stop loss in the settings your stop loss will be shown as an orange line once all of your average down orders have been hit, it will not be set until price has dipped below your covered trading range. These levels update when things change during backtesting so you can visualize your strategy and how it would perform as well as see if your percentage deviation is large enough to cover dips. When backtesting trades are taken, the chart will show where they were taken(in backtesting) along with info on those trades such as the number each order is, the size of that order and the percentage deviation that order is from the initial buy. SENDING SIGNALS TO 3COMMAS Tradingview cannot sync this backtester to 3Commas and with the way alerts are setup for strategies on Tradingview, the best option for you to give signals to your bot would be to use this backtester to figure out what trigger you want to use and then setup that indicator separately to send alerts to your bot. All of the indicators used for signals in this backtester are available for free and can be configured to match this backtester and send alerts to 3Commas for you. Just make sure you set your alerts to once per bar close and don’t use less than a 15 second timeframe because then you could trigger the Tradingview threshold for alerts and get your alerts shut off. You can also use this backtester with your own buy triggers if you know a little pine script. Just make copy of the script and code in your own buy signals and see how it backtests. INFO PANEL FOR ANALYZING YOUR STRATEGY The right hand side of the screen will show an info panel that shows a lot of different information so you can quickly see your bot settings and how it performed right on the screen. In the top right corner you will see in purple your bot settings. These include your stoploss % if turned on, take profit %, average down order %, average down order % multiplier, volume multiplier, max number of orders allowed and size of your base order. The top section of the first column “Current Trade” shows these stats: the open trade’s average price, the open trade’s take profit price, the open trade’s PNL, how far price is from your open tarde’s take profit level in percentage, your open position size and number of open orders. The bottom section of the first column “Overall Performance” shows these stats: total number of trades taken during backtesting range, the largest amount of trades that were open at one time during backtesting, the max drawdown, the average number of bars per trade, gross profit, net profit, percent profit from your initial capital, current portfolio value and your initial capital. CUSTOMIZABLE OPTIONS TO FIND THE PERFECT STRATEGY Stoploss On/Off This will turn your stoploss on or off. By default it is set to off and will not affect anything unless turned on. Stoploss Percentage This is the percentage below your final average down order price that will be set as a stoploss to keep your account from going too far in the red on big dips. Take Profit Percentage - This is the percentage of profit you want the trade to hit before taking profit on your entire DCA trade. This level updates everytime you average down. Average Down Percentage - This is the percentage that price has to drop from your initial order to initiate your first safety order. If the Average Down Percent Multiplier is set to 1 then this percentage will be the same for every average down order. Average Down Percentage Multiplier - This multiplies your Average Down Percentage so each safety order needs a larger percentage deviation than the previous one. This keeps your buys closer together at the beginning and further apart when you hit more orders so you can extend your trading range but still be aggressive when price is going sideways. Volume Multiplier Per New Order - This multiplies the size of each trade based on your base order. If you set it to a 2x multiplier then each average down order will be 2 times the size of the last one. So for example, a $100 base order with a 2x multiplier would have these values for the first 3 average down orders: 200, 400, 800. Size Of Base Order - This is the size of your first position entry and will be used as a starting point for the volume multiplier. If your base order is $100 then it will buy $100 worth of whatever crypto you are backtesting this on. If you are looking at stock charts, you need to make sure your base order is higher than the share price across the entire backtesting range or it will not perform correctly. Max Number Of Orders - This is the maximum number of orders the bot can take, including your base order. Adjust this to suit the amount of capital you are willing to allocate to your bot based on how much money it will require to run according to your bot settings. TIPS ON HOW TO USE FOR BEST RESULTS If you don’t have a lot of capital to work with, then use longer timeframes with a reasonable take profit percentage so that you don’t need a lot of average down orders. You can also try keeping the volume multiplier close to 1. You can use the 3Commas dca bot settings page to see how much capital you will need for your strategy if you match it to the settings you have on this indicator. You can also check to see how much of a percentage deviation your bot is covering to make sure you have a reasonable range to trade in and orders to cover big dips. You can also check your coverage by seeing how far down the chart the green lines cover, which are your average down orders. Make sure the initial capital in the properties tab of the settings has enough to cover all of your orders otherwise you will get unrealistic backtesting results. Also, make sure you leave the order size in the properties tab on contracts so it calculates your trades correctly. The only settings you need to touch in the properties tab is the initial capital. Unless you are trading somewhere that has lower commission fees, then you can change that to match, but leave all the other settings as is for it to function properly. Increasing the volume multiplier will make your average price and take profit target follow the price action a lot closer as price falls, but it can also lead to having very large orders very quickly once you get into the 1.5-3x multiple range. Try using a high volume multiplier with less safety orders and you will get better results, however you need to have money on the sidelines to add on major dips to keep your bot turning a profit. Be very careful with this as greed and impatience will hurt your overall performance. This bot is meant to make money with lots of small wins so don’t get greedy and make sure you have enough money to cover large dips. If you are being aggressive with your bot, then I recommend only using 25% or less of your portfolio to trade aggressively and then use the smart trade feature on 3commas to add chunks of funds to your trades when price dips below your last safety order. Or if you want it to run without any supervision, then use lower volume multipliers and have lots of safety orders that can cover entire bear markets and still keep buying lower. It’s a good idea to have some capital on the sidelines that you can add in when price dips quickly. This will help lower your average price and allow your bot to get out in profit quicker. 3Commas bot has a smart trade feature that will allow you to track your average price when adding extra funds and it will automatically update your other orders which is very convenient. Look at the longer timeframes when price dips and only add chunks at major areas where price is very likely to bounce. Or you can be aggressive when trading and add to your position when price dips and is at a likely bounce zone to maximize profits. Only trade coins that have a good amount of liquidity as the larger your orders get, the harder it will be to sell if there isn’t much liquidity. Also, beware of how large your first order is as it will usually be a market order and can move the market if there is not much liquidity. Since this bot takes a lot of trades and performs best when taking small profits consistently, you will need to factor in exchange fees. The bot is set to .5% commission(you can change this) on the buy and sell orders as most exchanges charge that amount. Some exchanges offer no fee trading on certain coins so be sure to look around for those so you can keep the commissions and maximize profits. I strongly encourage you to try out a lot of different setting combinations across multiple different coins and do it across a few months to see how it would have performed under various market conditions. This will help you get a better idea of how much of a percentage deviation you’ll need to be able to cover to keep your bot running and making constant profits. You can also use the deep backtesting feature of the strategy panel to see how it would have done, but just beware that the info panel of the indicator will not reflect deep backtesting results, only the normal backtesting range. MARKETS This backtester can be used on any market including crypto, stocks, forex & futures. You just need to make sure your base order is larger than the share price when using this on things besides crypto. TIMEFRAMES This backtester can be used on all timeframes.
Pine Script® stratejisi
FriendOfTheTrend tarafından
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