GKD-BT Multi-Ticker SCSC Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker SCSC Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Multi-Ticker SCSC Backtest
The Multi-Ticker SCSC Backtest is a Solo Confirmation Super Complex backtest that allows traders to test single GKD-C Confirmation indicator filtered by both a GKD-B Multi-Ticker Baseline and GKD-V Volatility/Volume indicator across 1-10 tickers. In addition. this module adds on various other long and short signls that fall outside the normal GKD standard long and short signals. These additional signals are formed using the GKD-B Multi-Ticker Baseline, GKD-V Volatility/Volume, and GKD-C Continuation indicators. The purpose of this backtest is to enable traders to quickly evaluate a Baseline, Volatility/Volume, and Continuation indicators filtered GKD-C Confirmation 1 indicator across hundreds of tickers within 30-60 minutes.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting threshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import 1-10 tickers into the GKD-B Multi-Ticker Baseline indicator
2. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-B Multi-Ticker Baseline indicator into the GKD-BT Multi-Ticker SCSC Backtest.
3. Select the "Multi-ticker" option in the GKD-V Volatility/Volume indicator
4. Import 1-10 tickers into the GKD-V Volatility/Volume indicator
5. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-V Volatility/Volume indicator into the GKD-BT Multi-Ticker SCSC Backtest.
6. Select the "Multi-ticker" option in the GKD-C Confirmation indicator.
7. Import 1-10 tickers into the GKD-C Confirmation indicator.
8. Import the same 1-10 indicators into the GKD-BT Multi-Ticker SCSC Backtest.
9. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation indicator into the GKD-BT Multi-Ticker SCSC Backtest.
10. Import 1-10 tickers into the GKD-C Continuation indicator.
11. Import the same 1-10 indicators into the GKD-BT Multi-Ticker SCSC Backtest.
12. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Continuation indicator into the GKD-BT Multi-Ticker SCSC Backtest.
13. When importing tickers, ensure that you import the same type of tickers for all 1-10 tickers. For example, test only FX or Cryptocurrency or Stocks. Do not combine different tradable asset types.
14. Make sure that your chart is set to a ticker that corresponds to the tradable asset type. For cryptocurrency testing, set the chart to BTCUSDT. For Forex testing, set the chart to EURUSD.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Multi-Ticker Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C Confirmation" indicator.
Import GKD-C Continuation: Imports the "GKD-C Continuation" indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**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 isolate per ticker and trading side, long or short**
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
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.
█ 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: Multi-Ticker SCSC Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Transform as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve as shown on the chart above
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
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
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Multi-Ticker SCC Backtest
GKD-BT Multi-Ticker SCS Backtest
"stop loss" için komut dosyalarını ara
GKD-BT Multi-Ticker SCC Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker SCC Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
The Multi-Ticker SCC Backtest is a Solo Confirmation Complex backtest that allows traders to test single GKD-C confirmation indicator filtered by both a GKD-B Multi-Ticker Baseline and GKD-V Volatility/Volume indicator across 1-10 tickers. The purpose of this backtest is to enable traders to quickly evaluate a Baseline and Volatility/Volume filtered GKD-C Confirmation indicator across hundreds of tickers within 30-60 minutes.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting threshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import 1-10 tickers into the GKD-B Multi-Ticker Baseline indicator
2. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-B Multi-Ticker Baseline indicator into the GKD-BT Multi-Ticker SCC Backtest.
3. Select the "Multi-ticker" option in the GKD-V Volatility/Volume indicator
4. Import 1-10 tickers into the GKD-V Volatility/Volume indicator
5. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-V Volatility/Volume indicator into the GKD-BT Multi-Ticker SCC Backtest.
6. Select the "Multi-ticker" option in the GKD-C Confirmation indicator.
7. Import 1-10 tickers into the GKD-C Confirmation indicator.
8. Import the same 1-10 indicators into the GKD-BT Multi-Ticker SCC Backtest.
9. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation indicator into the GKD-BT Multi-Ticker SCC Backtest.
10. When importing tickers, ensure that you import the same type of tickers for all 1-10 tickers. For example, test only FX or Cryptocurrency or Stocks. Do not combine different tradable asset types.
11. Make sure that your chart is set to a ticker that corresponds to the tradable asset type. For cryptocurrency testing, set the chart to BTCUSDT. For Forex testing, set the chart to EURUSD.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Multi-Ticker Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C" indicator.
Activate Baseline: Activates the GKD-B Multi-Ticker Baseline.
Activate Goldie Locks Zone Minimum Threshold: Activates the inner Goldie Locks Zone from the GKD-B Multi-Ticker Baseline
Activate Goldie Locks Zone Maximum Threshold: Activates the outer Goldie Locks Zone from the GKD-B Multi-Ticker Baseline
Activate Volatility/Volume: Activates the GKD-V Volatility/Volume indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**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 isolate per ticker and trading side, long or short**
Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
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.
█ 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: Multi-Ticker SCC Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: uf2018
Continuation: Vortex
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
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
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Multi-Ticker SCS Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker SCS Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
The Multi-Ticker SCS Backtest is a Solo Confirmation Simple backtest that allows traders to test single GKD-C confirmation indicators across 1-10 tickers. The purpose of this backtest is to enable traders to quickly evaluate GKD-C across hundreds of tickers within 30-60 minutes.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting treshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Select the "Multi-ticker" option in the GKD-C Confirmation indicator.
2. Import 1-10 tickers into the GKD-C Confirmation indicator.
3. Import the same 1-10 indicators into the GKD-BT Multi-Ticker SCS Backtest.
4. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation indicator into the GKD-BT Multi-Ticker SCS Backtest.
5. When importing tickers, ensure that you import the same type of tickers for all 1-10 tickers. For example, test only FX or Cryptocurrency or Stocks. Do not combine different tradable asset types.
6. Make sure that your chart is set to a ticker that corresponds to the tradable asset type. For cryptocurrency testing, set the chart to BTCUSDT. For Forex testing, set the chart to EURUSD.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-C: Imports the "GKD-C" source, which provides signals or data for the backtest.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**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 isolate per ticker and trading side, long or short**
Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
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.
█ 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: Multi-Ticker SCS 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
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
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
Ruckard TradingLatinoThis strategy tries to mimic TradingLatino strategy.
The current implementation is beta.
Si hablas castellano o espanyol por favor consulta MENSAJE EN CASTELLANO más abajo.
It's aimed at BTCUSDT pair and 4h timeframe.
STRATEGY DEFAULT SETTINGS EXPLANATION
max_bars_back=5000 : This is a random number of bars so that the strategy test lasts for one or two years
calc_on_order_fills=false : To wait for the 4h closing is too much. Try to check if it's worth entering a position after closing one. I finally decided not to recheck if it's worth entering after an order is closed. So it is false.
calc_on_every_tick=false
pyramiding=0 : We only want one entry allowed in the same direction. And we don't want the order to scale by error.
initial_capital=1000 : These are 1000 USDT. By using 1% maximum loss per trade and 7% as a default stop loss by using 1000 USDT at 12000 USDT per BTC price you would entry with around 142 USDT which are converted into: 0.010 BTC . The maximum number of decimal for contracts on this BTCUSDT market is 3 decimals. E.g. the minimum might be: 0.001 BTC . So, this minimal 1000 amount ensures us not to entry with less than 0.001 entries which might have happened when using 100 USDT as an initial capital.
slippage=1 : Binance BTCUSDT mintick is: 0.01. Binance slippage: 0.1 % (Let's assume). TV has an integer slippage. It does not have a percentage based slippage. If we assume a 1000 initial capital, the recommended equity is 142 which at 11996 USDT per BTC price means: 0.011 BTC. The 0.1% slippage of: 0.011 BTC would be: 0.000011 . This is way smaller than the mintick. So our slippage is going to be 1. E.g. 1 (slippage) * 0.01 (mintick)
commission_type=strategy.commission.percent and commission_value=0.1 : According to: binance . com / en / fee / schedule in VIP 0 level both maker and taker fees are: 0.1 %.
BACKGROUND
Jaime Merino is a well known Youtuber focused on crypto trading
His channel TradingLatino
features monday to friday videos where he explains his strategy.
JAIME MERINO STANCE ON BOTS
Jaime Merino stance on bots (taken from memory out of a 2020 June video from him):
'~
You know. They can program you a bot and it might work.
But, there are some special situations that the bot would not be able to handle.
And, I, as a human, I would handle it. And the bot wouldn't do it.
~'
My long term target with this strategy script is add as many
special situations as I can to the script
so that it can match Jaime Merino behaviour even in non normal circumstances.
My alternate target is learn Pine script
and enjoy programming with it.
WARNING
This script might be bigger than other TradingView scripts.
However, please, do not be confused because the current status is beta.
This script has not been tested with real money.
This is NOT an official strategy from Jaime Merino.
This is NOT an official strategy from TradingLatino . net .
HOW IT WORKS
It basically uses ADX slope and LazyBear's Squeeze Momentum Indicator
to make its buy and sell decisions.
Fast paced EMA being bigger than slow paced EMA
(on higher timeframe) advices going long.
Fast paced EMA being smaller than slow paced EMA
(on higher timeframe) advices going short.
It finally add many substrats that TradingLatino uses.
SETTINGS
__ SETTINGS - Basics
____ SETTINGS - Basics - ADX
(ADX) Smoothing {14}
(ADX) DI Length {14}
(ADX) key level {23}
____ SETTINGS - Basics - LazyBear Squeeze Momentum
(SQZMOM) BB Length {20}
(SQZMOM) BB MultFactor {2.0}
(SQZMOM) KC Length {20}
(SQZMOM) KC MultFactor {1.5}
(SQZMOM) Use TrueRange (KC) {True}
____ SETTINGS - Basics - EMAs
(EMAS) EMA10 - Length {10}
(EMAS) EMA10 - Source {close}
(EMAS) EMA55 - Length {55}
(EMAS) EMA55 - Source {close}
____ SETTINGS - Volume Profile
Lowest and highest VPoC from last three days
is used to know if an entry has a support
VPVR of last 100 4h bars
is also taken into account
(VP) Use number of bars (not VP timeframe): Uses 'Number of bars {100}' setting instead of 'Volume Profile timeframe' setting for calculating session VPoC
(VP) Show tick difference from current price {False}: BETA . Might be useful for actions some day.
(VP) Number of bars {100}: If 'Use number of bars (not VP timeframe)' is turned on this setting is used to calculate session VPoC.
(VP) Volume Profile timeframe {1 day}: If 'Use number of bars (not VP timeframe)' is turned off this setting is used to calculate session VPoC.
(VP) Row width multiplier {0.6}: Adjust how the extra Volume Profile bars are shown in the chart.
(VP) Resistances prices number of decimal digits : Round Volume Profile bars label numbers so that they don't have so many decimals.
(VP) Number of bars for bottom VPOC {18}: 18 bars equals 3 days in suggested timeframe of 4 hours. It's used to calculate lowest session VPoC from previous three days. It's also used as a top VPOC for sells.
(VP) Ignore VPOC bottom advice on long {False}: If turned on it ignores bottom VPOC (or top VPOC on sells) when evaluating if a buy entry is worth it.
(VP) Number of bars for VPVR VPOC {100}: Number of bars to calculate the VPVR VPoC. We use 100 as Jaime once used. When the price bounces back to the EMA55 it might just bounce to this VPVR VPoC if its price it's lower than the EMA55 (Sells have inverse algorithm).
____ SETTINGS - ADX Slope
ADX Slope
help us to understand if ADX
has a positive slope, negative slope
or it is rather still.
(ADXSLOPE) ADX cut {23}: If ADX value is greater than this cut (23) then ADX has strength
(ADXSLOPE) ADX minimum steepness entry {45}: ADX slope needs to be 45 degrees to be considered as a positive one.
(ADXSLOPE) ADX minimum steepness exit {45}: ADX slope needs to be -45 degrees to be considered as a negative one.
(ADXSLOPE) ADX steepness periods {3}: In order to avoid false detection the slope is calculated along 3 periods.
____ SETTINGS - Next to EMA55
(NEXTEMA55) EMA10 to EMA55 bounce back percentage {80}: EMA10 might bounce back to EMA55 or maybe to 80% of its complete way to EMA55
(NEXTEMA55) Next to EMA55 percentage {15}: How much next to the EMA55 you need to be to consider it's going to bounce back upwards again.
____ SETTINGS - Stop Loss and Take Profit
You can set a default stop loss or a default take profit.
(STOPTAKE) Stop Loss % {7.0}
(STOPTAKE) Take Profit % {2.0}
____ SETTINGS - Trailing Take Profit
You can customize the default trailing take profit values
(TRAILING) Trailing Take Profit (%) {1.0}: Trailing take profit offset in percentage
(TRAILING) Trailing Take Profit Trigger (%) {2.0}: When 2.0% of benefit is reached then activate the trailing take profit.
____ SETTINGS - MAIN TURN ON/OFF OPTIONS
(EMAS) Ignore advice based on emas {false}.
(EMAS) Ignore advice based on emas (On closing long signal) {False}: Ignore advice based on emas but only when deciding to close a buy entry.
(SQZMOM) Ignore advice based on SQZMOM {false}: Ignores advice based on SQZMOM indicator.
(ADXSLOPE) Ignore advice based on ADX positive slope {false}
(ADXSLOPE) Ignore advice based on ADX cut (23) {true}
(STOPTAKE) Take Profit? {false}: Enables simple Take Profit.
(STOPTAKE) Stop Loss? {True}: Enables simple Stop Loss.
(TRAILING) Enable Trailing Take Profit (%) {True}: Enables Trailing Take Profit.
____ SETTINGS - Strategy mode
(STRAT) Type Strategy: 'Long and Short', 'Long Only' or 'Short Only'. Default: 'Long and Short'.
____ SETTINGS - Risk Management
(RISKM) Risk Management Type: 'Safe', 'Somewhat safe compound' or 'Unsafe compound'. ' Safe ': Calculations are always done with the initial capital (1000) in mind. The maximum losses per trade/day/week/month are taken into account. ' Somewhat safe compound ': Calculations are done with initial capital (1000) or a higher capital if it increases. The maximum losses per trade/day/week/month are taken into account. ' Unsafe compound ': In each order all the current capital is gambled and only the default stop loss per order is taken into account. That means that the maximum losses per trade/day/week/month are not taken into account. Default : 'Somewhat safe compound'.
(RISKM) Maximum loss per trade % {1.0}.
(RISKM) Maximum loss per day % {6.0}.
(RISKM) Maximum loss per week % {8.0}.
(RISKM) Maximum loss per month % {10.0}.
____ SETTINGS - Decimals
(DECIMAL) Maximum number of decimal for contracts {3}: How small (3 decimals means 0.001) an entry position might be in your exchange.
EXTRA 1 - PRICE IS IN RANGE indicator
(PRANGE) Print price is in range {False}: Enable a bottom label that indicates if the price is in range or not.
(PRANGE) Price range periods {5}: How many previous periods are used to calculate the medians
(PRANGE) Price range maximum desviation (%) {0.6} ( > 0 ): Maximum positive desviation for range detection
(PRANGE) Price range minimum desviation (%) {0.6} ( > 0 ): Mininum negative desviation for range detection
EXTRA 2 - SQUEEZE MOMENTUM Desviation indicator
(SQZDIVER) Show degrees {False}: Show degrees of each Squeeze Momentum Divergence lines to the x-axis.
(SQZDIVER) Show desviation labels {False}: Whether to show or not desviation labels for the Squeeze Momentum Divergences.
(SQZDIVER) Show desviation lines {False}: Whether to show or not desviation lines for the Squeeze Momentum Divergences.
EXTRA 3 - VOLUME PROFILE indicator
WARNING: This indicator works not on current bar but on previous bar. So in the worst case it might be VP from 4 hours ago. Don't worry, inside the strategy calculus the correct values are used. It's just that I cannot show the most recent one in the chart.
(VP) Print recent profile {False}: Show Volume Profile indicator
(VP) Avoid label price overlaps {False}: Avoid label prices to overlap on the chart.
EXTRA 4 - ZIGNALY SUPPORT
(ZIG) Zignaly Alert Type {Email}: 'Email', 'Webhook'. ' Email ': Prepare alert_message variable content to be compatible with zignaly expected email content format. ' Webhook ': Prepare alert_message variable content to be compatible with zignaly expected json content format.
EXTRA 5 - DEBUG
(DEBUG) Enable debug on order comments {False}: If set to true it prepares the order message to match the alert_message variable. It makes easier to debug what would have been sent by email or webhook on each of the times an order is triggered.
HOW TO USE THIS STRATEGY
BOT MODE: This is the default setting.
PROPER VOLUME PROFILE VIEWING: Click on this strategy settings. Properties tab. Make sure Recalculate 'each time the order was run' is turned off.
NEWBIE USER: (Check PROPER VOLUME PROFILE VIEWING above!) You might want to turn on the 'Print recent profile {False}' setting. Alternatively you can use my alternate realtime study: 'Resistances and supports based on simplified Volume Profile' but, be aware, it might consume one indicator.
ADVANCED USER 1: Turn on the 'Print price is in range {False}' setting and help us to debug this subindicator. Also help us to figure out how to include this value in the strategy.
ADVANCED USER 2: Turn on the all the (SQZDIVER) settings and help us to figure out how to include this value in the strategy.
ADVANCED USER 3: (Check PROPER VOLUME PROFILE VIEWING above!) Turn on the 'Print recent profile {False}' setting and report any problem with it.
JAIME MERINO: Just use the indicator as it comes by default. It should only show BUY signals, SELL signals and their associated closing signals. From time to time you might want to check 'ADVANCED USER 2' instructions to check that there's actually a divergence. Check also 'ADVANCED USER 1' instructions for your amusement.
EXTRA ADVICE
It's advised that you use this strategy in addition to these two other indicators:
* Squeeze Momentum Indicator
* ADX
so that your chart matches as close as possible to TradingLatino chart.
ZIGNALY INTEGRATION
This strategy supports Zignaly email integration by default. It also supports Zignaly Webhook integration.
ZIGNALY INTEGRATION - Email integration example
What you would write in your alert message:
||{{strategy.order.alert_message}}||key=MYSECRETKEY||
ZIGNALY INTEGRATION - Webhook integration example
What you would write in your alert message:
{ {{strategy.order.alert_message}} , "key" : "MYSECRETKEY" }
CREDITS
I have reused and adapted some code from
'Directional Movement Index + ADX & Keylevel Support' study
which it's from TradingView console user.
I have reused and adapted some code from
'3ema' study
which it's from TradingView hunganhnguyen1193 user.
I have reused and adapted some code from
'Squeeze Momentum Indicator ' study
which it's from TradingView LazyBear user.
I have reused and adapted some code from
'Strategy Tester EMA-SMA-RSI-MACD' study
which it's from TradingView fikira user.
I have reused and adapted some code from
'Support Resistance MTF' study
which it's from TradingView LonesomeTheBlue user.
I have reused and adapted some code from
'TF Segmented Linear Regression' study
which it's from TradingView alexgrover user.
I have reused and adapted some code from
"Poor man's volume profile" study
which it's from TradingView IldarAkhmetgaleev user.
FEEDBACK
Please check the strategy source code for more detailed information
where, among others, I explain all of the substrats
and if they are implemented or not.
Q1. Did I understand wrong any of the Jaime substrats (which I have implemented)?
Q2. The strategy yields quite profit when we should long (EMA10 from 1d timeframe is higher than EMA55 from 1d timeframe.
Why the strategy yields much less profit when we should short (EMA10 from 1d timeframe is lower than EMA55 from 1d timeframe)?
Any idea if you need to do something else rather than just reverse what Jaime does when longing?
FREQUENTLY ASKED QUESTIONS
FAQ1. Why are you giving this strategy for free?
TradingLatino and his fellow enthusiasts taught me this strategy. Now I'm giving back to them.
FAQ2. Seriously! Why are you giving this strategy for free?
I'm confident his strategy might be improved a lot. By keeping it to myself I would avoid other people contributions to improve it.
Now that everyone can contribute this is a win-win.
FAQ3. How can I connect this strategy to my Exchange account?
It seems that you can attach alerts to strategies.
You might want to combine it with a paying account which enable Webhook URLs to work.
I don't know how all of this works right now so I cannot give you advice on it.
You will have to do your own research on this subject. But, be careful. Automating trades, if not done properly,
might end on you automating losses.
FAQ4. I have just found that this strategy by default gives more than 3.97% of 'maximum series of losses'. That's unacceptable according to my risk management policy.
You might want to reduce default stop loss setting from 7% to something like 5% till you are ok with the 'maximum series of losses'.
FAQ5. Where can I learn more about your work on this strategy?
Check the source code. You might find unused strategies. Either because there's not a substantial increases on earnings. Or maybe because they have not been implemented yet.
FAQ6. How much leverage is applied in this strategy?
No leverage.
FAQ7. Any difference with original Jaime Merino strategy?
Most of the times Jaime defines an stop loss at the price entry. That's not the case here. The default stop loss is 7% (but, don't be confused it only means losing 1% of your investment thanks to risk management). There's also a trailing take profit that triggers at 2% profit with a 1% trailing.
FAQ8. Why this strategy return is so small?
The strategy should be improved a lot. And, well, backtesting in this platform is not guaranteed to return theoric results comparable to real-life returns. That's why I'm personally forward testing this strategy to verify it.
MENSAJE EN CASTELLANO
En primer lugar se agradece feedback para mejorar la estrategia.
Si eres un usuario avanzado y quieres colaborar en mejorar el script no dudes en comentar abajo.
Ten en cuenta que aunque toda esta descripción tenga que estar en inglés no es obligatorio que el comentario esté en inglés.
CHISTE - CASTELLANO
¡Pero Jaime!
¡400.000!
¡Tu da mun!
Multi Channel GRID & DCA LTF [trade_lexx]Multi Channel GRID & DCA LTF
Usage Guide
Part 1: The concept and general possibilities of the "Multi Channel GRID & DCA LTF" strategy
Introduction
Welcome to the guide to "Multi Channel GRID & DCA LTF", a powerful and versatile automated trading strategy for the TradingView platform. This tool was developed for traders who are looking for flexibility, control and a high degree of adaptability to various market conditions.
The strategy is based on a hybrid approach that combines two popular and time-tested techniques.:
1. GRID (grid trading): The classic method of averaging a position is by placing a grid of limit orders.
2. DCA (Dollar Cost averaging): Smart position averaging based on signals from external indicators.
However, "Multi Channel GRID & DCA LTF" goes far beyond the simple combination of these two techniques. The strategy includes a number of unique and innovative features, such as cascading MultiGRID grids for dealing with extreme volatility, Channel Mode range trading mode for profiting from sideways movement, and Low Time Frame analysis (LTF) to achieve surgical accuracy in backtesting. Deep customization options for risk management, capital, take profits, and stop losses allow you to configure a strategy for almost any trading style, asset, and timeframe.
The basic idea: How does it work?
Let's take a detailed look at each of the key concepts embedded in the logic of the strategy.
1. GRID — Automatic placement of buy and sell orders at certain price intervals.
This is a fundamental mode of operation. Its main goal is to systematically improve the average entry price for a position if the market is going against you.
* The principle of operation: After opening the base (first) order (`BO`), the strategy automatically places a series of pending limit orders (here they are called "safety orders" or "SO") at certain price intervals. For a long position, orders are placed below the entry price, and for a short position, orders are placed higher.
* Target: When the price moves against an open position, it consistently hits and executes safety orders. Each such execution adds additional volume to the position at a more favorable price, thereby shifting the overall average entry price (`position_avg_price') closer to the current market price. This means that a much smaller corrective movement will be required to gain ground.
* Flexibility: You have full control over the geometry of the grid: the number of safety orders, the percentage distance between them (`SO Step`), and you can even set a coefficient that will increase this step for each subsequent order (`SO Multiplier`), creating an expanding grid.
2. DCA (Signal Averaging) — Smart Averaging
This mode adds an additional layer of analysis to the averaging process. Instead of just buying/selling at the set price levels, the strategy waits for a confirmation signal.
* Working principle: You can connect any external indicator (for example, RSI, CCI, or even your own complex signal system) to the strategy, which outputs numerical values. As standard, 1 is used for a long signal, and -1 is used for a short signal. The strategy will place the next averaging order only at the moment when it receives the appropriate signal.
* Goal: To average a position not just during a fall (or a rise for a short), but at the moments that your main trading system considers the most favorable for this. This allows you to avoid "catching falling knives" and enter only if there are good reasons.
3. Hybrid Mode (GRID+DCA) is the best of the previous two modes
This mode is designed for maximum filtering and control. It requires two conditions to be fulfilled simultaneously.
* Working principle: The safety order will be executed only if the price has reached the calculated grid level and a confirmation signal has been received from your external indicator. If a confirmation signal is received from an external indicator, the next calculated grid level activates the limit order.
* Goal: To create the most reliable averaging system that protects against premature entries and requires double confirmation (both by price and indicator) before increasing the position size.
4. MultiGRID — Adaptation to extreme volatility
This is one of the most powerful and unique features of a strategy designed to survive and make a profit in the face of strong, protracted trends or "black swans".
* The problem it solves: The usual grid of orders has a limited depth. If the price goes beyond the last safety order, the strategy loses the opportunity to average and becomes vulnerable.
* The principle of operation: The MultiGRID function allows you to create "cascades" — several grids following one another. When all the orders of the first grid are executed, the strategy does not stop. Instead, she can activate the second, third (and so on) a grid of orders. The new grid can be activated by one of two triggers:
1. Offset: The new grid is activated when the price passes another set percentage deviation from the last executed order.
2. Signal: The new grid is activated when a signal is received from an external indicator.
* Goal: To significantly expand the working range of the strategy. This allows it to adapt to strong market movements that would "break" the usual grid, and continue to effectively average a position at a much greater depth of decline or growth.
5. Channel Mode — Trading in the range
This feature turns a standard averaging strategy into a machine for "farming" profits within a price channel that is formed during a sideways market movement.
* The problem it solves: In the standard grid strategy, after partially closing a take profit position, the volume of this part "leaves" the trade until the deal is fully closed. You are missing the opportunity to reuse this capital.
* Operating principle: When Channel Mode is enabled, the following happens. Suppose the price went against you, executed several safety orders, and then turned around and reached one of the partial take profits. At this point, the strategy is:
1. Fixes the profit, as it should be.
2. Instantly places a new limit order to buy (or sell for a short) at exactly the same price level where the last triggered safety order was executed. The volume of this order is equal to the volume of the part that was just closed for take profit.
3. If the price goes down again and executes this "repeat" order, the strategy immediately sets a corresponding take profit for it at the level where the previous profit was taken.
* Goal: To create a continuous buy-sell cycle within the local range (channel). The lower limit of the channel is the price of the last averaging, and the upper limit is the price of a partial take profit. This allows you to repeatedly profit from sideways price fluctuations, without waiting for the full closure of the main, large transaction.
6. LTF (Lower Timeframe Analysis) — Surgical precision of backtesting
This feature is critically important for obtaining reliable results during historical testing (backtesting) of grid strategies.
* The problem it solves: The standard testing mechanism in TradingView has a serious limitation. Working, for example, on a 4-hour chart, he sees only 4 candle points: Open, High, Low and Close. He does not know in what order the price moved within these 4 hours. He could have touched High first and then Low, or vice versa. For grid strategies, this is fatal — the engine can show that a take profit has been executed, although in reality the price first went down, collected the entire grid of orders and only then turned around.
* How it works: When you turn on the LTF mode, the strategy for each candle on your main chart (for example, 4H) requests and analyzes all candles from the lower timeframe you specified (for example, 1-minute). Then it virtually trades the entire price path for these minute candles, executing orders, take profits and stop losses in the sequence in which they would occur in reality. It works in the single take profit mode of the Grid strategy.
* Goal: To provide the most realistic and reliable backtest that reflects the real dynamics of the market. This allows you to avoid false expectations and accurately assess the potential performance of the strategy.
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Part 2: Detailed description of the strategy settings
This section is your main guide to all the switches and options available in the strategy. Understanding each setting is the key to unlocking the full potential of this powerful tool.
1. 🛡️ Risk Management 🛡️
This group contains fundamental parameters that determine the basic logic of risk management and the geometry of grid orders.
* Strategy type: Determines the direction of transactions.
* Long: The strategy will only open long positions (buy).
* Short: The strategy will only open short positions (sell).
* Both: The strategy will work both ways, opening long or short depending on the incoming signal.
* SO Count: Sets the maximum number of Safety (averaging) Orders (SO) that the strategy will place within the same grid. If you have MultiGRID enabled, this number applies to each individual grid.
* SO Step (%): This is the base percentage deviation from the entry price at which the first safety order will be placed. For example, at a value of 0.5, the first SO in a long trade will be placed 0.5% lower than the opening price of the base order.
* SO Multiplier: A coefficient that exponentially increases the step for each subsequent safety order. This allows you to create an expanding grid where averaging orders are placed further and further apart, which is effective with strong and accelerating price movements.
* *The step formula for the nth order*: Step(N) = (SO Step) * (SO Multiplier ^(N-1)).
* If the value is 1, all steps will be the same.
* With a value of 1.6, the step of the second SO will be 1.6 times larger than the first, the step of the third will be 1.6 times larger than the second, and so on.
* 1️⃣ TP/SL: These are simplified settings for quick configuration. They allow you to turn on/off the main take profit and stop loss and set basic percentage values for them. More detailed settings for these parameters can be found in the relevant sections below.
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2. 💰 Money Management 💰
Everything related to position size, leverage, and capital is configured here.
* Volume BO (Base Order): Determines the size of the trade's opening order.
* Volume BO: A fixed amount in the quote currency (for example, in USDT).
* USDT (check mark): Manages the information in the comments to the orders. If enabled, the volume of orders in USDT will be displayed in the comments. This is convenient for visual analysis and for sending the amount of USDT by the placeholder {{strategy.order.comment}} via webhooks when connecting the strategy to the exchange or trading terminals.
* or % of deposit: The amount calculated as a percentage of the available capital of the strategy. The check mark to the right of this field enables this mode. Important: using a percentage activates the effect of compounding (compound interest), as the amount of each new transaction will be automatically recalculated based on the current capital (initial capital + profit/loss). If enabled, the percentage of orders will be displayed in the comments. This is convenient for visual analysis and for sending percentages on the placeholder {{strategy.order.comment}} via webhooks when connecting the strategy to the stock exchange, trading terminals, or creating Copy trading.
* Martingale: The coefficient applied to the volume of orders. It increases the size of each subsequent insurance order compared to the base one.
* Volume formula for the nth SO: Volume SO (N) = (Volume BO) * (Martingale^N).
* With a value of 1.2, the volume of the first SO will be 1.2 times greater than the base, the second — 1.44 times (`1.2 * 1.2`) and so on.
* Leverage: Specify the size of your leverage. This parameter is used exclusively for calculating and displaying the approximate liquidation price. It does not affect the size of positions, but it helps to visually assess the risks.
* Liquidation: Enables or disables the calculation and display of the liquidation line on the chart.
* Margin type: Allows you to select a method for calculating the liquidation price, simulating the logic of exchanges:
* Isolated: The liquidation price is calculated based on the size and leverage of the current open position only.
* Cross: The calculation simulates using the entire available balance to maintain a position. In the strategy, the liquidation price is calculated as the level at which the loss on the current transaction is equal to the current capital.
* Commission (%): Specify the percentage of your exchange's commission per transaction. The correct value of this parameter is crucial for obtaining realistic backtest results.
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3. 🕸️ Grid Management 🕸️
This group is responsible for the logic of safety orders and advanced mechanics such as Channel Mode and MultiGRID.
* SO Type: Defines the logic of placing averaging orders.
* GRID: Classic grid. All safety orders are placed in advance as limit orders.
* DCA: Signal averaging. The strategy is waiting for a signal from an external indicator to place a market averaging order.
* GRID+DCA: Hybrid. The strategy waits for a signal, and if it arrives, places a limit order at the appropriate price level of the grid or executes a market order if the signal has arrived below the limit order level.
* Signal for SO: A data source (indicator) that will be used for signals in DCA and GRID+DCA modes.
* ↔️ Channel Mode: When this option is enabled, the strategy tries to trade in a sideways range. After partially closing a take profit position, it immediately places a limit order for re-entry at the price of the last triggered safety order. This creates a buy-sell cycle within the local channel.
* Best Price Only: This filter adds an additional condition for averaging in DCA and MultiGRID modes (when it operates on a signal). The next averaging order or a new grid will be activated only if the current price is more favorable (lower for long, higher for short) than the price of the previous entry.
* 🧩 MultiGRID ⮕ Enables cascading grid mode.
* Grid Count: The total number of grids that can be activated sequentially.
* Offset: Percentage deviation from the price of the last order of the previous grid. When this margin is reached, the following grid of orders is activated (this mode does not require a signal).
* Or signal: Allows you to use the signal from an external indicator as a trigger to activate the next grid. The checkmark on the right turns on this mode.
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4. 🎯 Entry and Stop 🎯
This group of settings allows you to fine-tune the conditions for starting a new trade and all aspects related to protective stop orders, including the complex mechanics of trailing and managing SL after partial take profits.
* 🎯 Signal: A data source (indicator) that will be used to determine when to enter a trade. The strategy expects a value of 1 for the start of a long trade and -1 for a short trade.
* Min Bars: Sets the minimum number of candles that must pass from the moment of opening the previous trade to the moment of opening the next one. A value of 0 disables this filter. This is a useful tool to prevent overly frequent entries in a "noisy" market.
* Non-stop: If this option is enabled, the strategy ignores the Entry Signal and opens a new trade immediately after closing the previous one (taking into account the Min Bars filter, if it is set). This turns the strategy into a constantly working mechanism that is always on the market.
* 🛑 SL Type: Defines the base price from which the stop loss percentage will be calculated. The stop loss in the first section must be enabled for this block of settings to work.
* From the entry point: SL is always calculated from the opening price of the very first base order. It remains static throughout the entire transaction unless it is moved by other functions.
* From breakeven line: SL is dynamically recalculated and shifted each time a safety order is executed. It always follows the average price of the position, being at a given percentage distance from it.
* From last executed SO: SL is recalculated from the price of the last executed order, whether it is a base or a safety order.
* From last SO: SL is calculated from the price of the most recent possible safety order in the grid. This is usually the most remote and conservative type of SL.
* Trailing SL Type: Defines the algorithm by which the stop loss will move after its activation.
* Standard: Classic trailing. After activation, SL will follow the price at a fixed distance.
* ATR: SL will follow the price at a distance equal to the value of the ATR indicator multiplied by the specified multiplier.
* External Source: SL will follow any selected line of the third-party indicator.
* Period and Multiplier: Common parameters for all types of trailing.
* Source: The source of the line for the trailing SL of the third-party indicator.
* Trailing SL after entry: The mode of activation of the trailing SL after entering the transaction
* SL management after TP (sections 1️⃣, 2️⃣, 3️⃣): These three blocks allow you to create a complex stop loss management logic as profits are recorded.
For each take profit level (TP1, TP2, TP3), you can configure:
* SL BE / SL TP1 / SL TP2: When the corresponding TP is reached, the stop loss will be moved to the breakeven point (for TP1), to the TP1 price level (for TP2) or to the TP2 price level (for TP3).
* Trailing SL: When the corresponding TP is reached, the trailing stop loss is activated according to the settings above.
* By ↔️ Signal: A very powerful option. If it is enabled, the above action (SL transfer or trailing activation) will occur when the opposite trading signal is received from an external indicator. This allows you to protect profits or reduce losses if the market turns sharply, even before reaching the target.
* SL Delay ⮕ Allows you to delay the activation of the stop loss.
* Number of Bars: The Stop loss will be physically placed on the market only after the specified number of candles has passed since entering the trade. This can help to avoid "taking out" the stop with a random short movement (squiz) immediately after opening a position.
* SL Block: Unique defensive mechanics for trading both ways (`Strategy Type: Both`).
* Number of SL: If the strategy receives the specified number of stop losses in a row in one direction (for example, 2 stops long), it temporarily blocks the opportunity to open new trades in that direction.
* Lock Reset mode:
* By direction: The lock is lifted if a profitable trade is closed in the allowed direction or if a stop loss is triggered in the opposite direction.
* First profit: The lock is lifted after closing any profitable transaction, regardless of its direction.
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5. ✅ Take Profit ✅
This group of settings provides comprehensive control over profit taking, from a simple take profit to a complex system of partial closures and trailing.
* ✅ TP Type: Defines the base price for calculating the percentage deviation of the take profit.
* From entry point: TP is calculated from the base order price.
* From breakeven line: TP dynamically follows the average position price.
* From last executed SO: TP is calculated from the price of the last executed order.
* Filters for closing on signal
* Only ➕: If TP is triggered by a signal, the deal will be closed only if it is in the black relative to the average price.
* Or >TP: If TP is triggered by a signal, the trade will be closed only if the closing price is better than (or equal to) the estimated price of this TP.
* TP type of trailing: Yes, take profit has a trailing too! It works differently than the SL trailing.
* Standard / ATR: After the price touches the "virtual" TP level, the trailing is activated. He does not place a stop order, but begins to move away from the price, dynamically moving the limit order to close further and further in the profitable direction, allowing him to collect the maximum from the impulse movement.
* External Source: TP will follow any selected line of the third-party indicator.
* Period and Multiplier: Parameters for calculating the trailing margin TP.
* Source: The source of the line for the trailing TP of the third-party indicator.
* TP level settings (sections 1️⃣, 2️⃣, 3️⃣, 4️⃣): The strategy supports up to four independent take profit levels, which allows for a flexible system of partial commits.
For each level, you can set:
* TP: Enable the level and set its percentage deviation from the base price.
* Size: What percentage of the current position will be closed when this level is reached. For the last active TP, this parameter is ignored, and 100% of the remaining position is closed.
* Trailing TP: Enable the above-described trailing mechanism for this particular level.
* Signal: Enable closing based on the signal from the external indicator for this level.
* Or take: If both the closing on the signal and the limit order are enabled, then whatever comes first will work.
* After SO: Activate this TP level only after the specified number of safety orders has been executed. This allows you to set closer targets for riskier (deeply averaged) positions.
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6. 🔬 GRID and MultiGrid Analysis on Lower TFs (LTF) 🔬
This group activates one of the most important functions for accurate testing of grid strategies.
* Enable LTF Calculation ⮕ The main switch of the analysis mode on the lower timeframes.
* Timeframe selection: A drop-down list where you can select a timeframe for detailed analysis. For example, if your main schedule is 1 hour, you can select 1 minute here. The strategy will emulate the trading of minute candles within each hour candle.
❗️Important: As mentioned in the first part, the use of this mode is critically necessary to obtain realistic backtest results, especially for strategies with a dense grid of orders. Without it, the results may be overly optimistic and not reflect the real dynamics of the market. It should be remembered that TradingView imposes a limit on the number of intra-bars (minor TF bars) that can be requested. This is usually about 100,000 bars.
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7. 🕘 Backtest Date Range 🕘
This group allows you to focus testing on a specific historical period.
* Limit Date Range: Enables date filtering.
* Start time: The date and time when the strategy will start analyzing and opening deals.
* End time: The date and time after which the strategy will stop opening new deals and complete testing.
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8. 🎨 Visualization 🎨
All the options responsible for the appearance and information content of the chart are collected here.
* Show PnL labels: Enables/disables the display of text labels with the result (profit/loss) after closing each trade.
* Statistics Table: Enables/disables the main dashboard with detailed statistics on the results of the backtest.
* Strategy Settings Table: Enables/disables an additional panel that summarizes all the key parameters of the current configuration.
* Monthly Profit Table: Enables/disables a table with a breakdown of percentage returns by month and year.
* Table settings: For each of the three tables, you can individually adjust the Text size and Table Position on the screen to position them as conveniently as possible.
* Decimal places: Defines how many decimal places will be displayed in numeric values in tables and on labels.
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9. ✉️ Webhook Settings ✉️
This group is intended for traders who want to automate trading on strategy signals using third-party services and exchanges (for example, 3Commas, WunderTrading, Cryptorobotics, Cryptohopper, Bitsgap, Binance, ByBit, OKX, Pionex, Bitget or proprietary solutions).
For each key event in the strategy, there is a separate switch and a text field:
* Webhook for Open: Enable and set a message for the webhook that will be sent when the base order is opened.
* Webhook for Averaging: A message sent when executing any insurance order.
* Webhook for Take Profit: A message sent when closing on take profit (including partial ones).
* Webhook for Stop-Loss: A message sent when a stop loss is closed.
You can insert a JSON code or any other message format that your service requires for automation into the text fields. The strategy supports special placeholders (for example, `{{strategy.order.alert_message}}`), which allow you to dynamically insert the necessary data into the message, such as the amount of USDT or the percentage of the deposit for entry, averaging and take profit orders.
AccumulationPro Money Flow StrategyAccumulationPro Money Flow Strategy identifies stock trading opportunities by analyzing money flow and potential long-only opportunities following periods of increased money inflow. It employs proprietary responsive indicators and oscillators to gauge the strength and momentum of the inflow relative to previous periods, detecting money inflow, buying/selling pressure, and potential continuation/reversals, while using trailing stop exits to maximize gains while minimizing losses, with careful consideration of risk management and position sizing.
Setup Instructions:
1. Configuring the Strategy Properties:
Click the "Settings" icon (the gear symbol) next to the strategy name.
Navigate to the "Properties" tab within the Settings window.
Initial Capital: This value sets the starting equity for the strategy backtesting. Keep in mind that you will need to specify your current account size in the "Inputs" settings for position sizing.
Base Currency: Leave this setting at its "Default" value.
Order Size: This setting, which determines the capital used for each trade during backtesting, is automatically calculated and updated by the script. You should leave it set to "1 Contract" and the script will calculate the appropriate number of contracts based on your risk per trade, account size, and stop-loss placement.
Pyramiding: Set this setting at 1 order to prevent the strategy from adding to existing positions.
Commission: Enter your broker's commission fee per trade as a percentage, some brokers might offer commission free trading. Verify Price for limit orders: Keep this value as 0 ticks.
Slippage: This value depends on the instrument you are trading, If you are trading liquid stocks on a 1D chart slippage might be neglected. You can Keep this value as 1 ticks if you want to be conservative.
Margin for long positions/short positions: Set both of these to 100% since this strategy does not employ leverage or margin trading.
Recalculate:
Select the "After order is filled" option.
Select the "On every tick" option.
Fill Orders: Keep “Using bar magnifier” unselected.
Select "On bar close". Select "Using standard OHLC"
2. Configuring the Strategy Inputs:
Click the "Inputs" tab in the Settings window.
From/Thru (Date Range): To effectively backtest the strategy, define a substantial period that includes various bullish and bearish cycles. This ensures the testing window captures a range of market conditions and provides an adequate number of trades. It is usually favorable to use a minimum of 8 years for backtesting. Ensure the "Show Date Range" box is checked.
Account Size: This is your actual current Account Size used in the position sizing table calculations.
Risk on Capital %: This setting allows you to specify the percentage of your capital you are willing to risk on each trade. A common value is 0.5%.
3. Configuring Strategy Style:
Select the "Style" tab.
Select the checkbox for “Stop Loss” and “Stop Loss Final” to display the black/red Average True Range Stop Loss step-lines
Make sure the checkboxes for "Upper Channel", "Middle Line", and "Lower Channel" are selected.
Select the "Plots Background" checkboxes for "Color 0" and "Color 1" so that the potential entry and exit zones become color-coded.
Having the checkbox for "Tables" selected allows you to see position sizing and other useful information within the chart.
Have the checkboxes for "Trades on chart" and "Signal Labels" selected for viewing entry and exit point labels and positions.
Uncheck* the "Quantity" checkbox.
Precision: select “Default”.
Check “Labels on price scale”
Check “Values in status line”
Strategy Application Guidelines:
Entry Conditions:
The strategy identifies long entry opportunities based on substantial money inflow, as detected by our proprietary indicators and oscillators. This assessment considers the strength and momentum of the inflow relative to previous periods, in conjunction with strong price momentum (indicated by our modified, less-lagging MACD) and/or a potential price reversal (indicated by our modified, less-noisy Stochastic). Additional confirmation criteria related to price action are also incorporated. Potential entry and exit zones are visually represented by bands on the chart.
A blue upward-pointing arrow, accompanied by the label 'Long' and green band fills, signifies a long entry opportunity. Conversely, a magenta downward-pointing arrow, labeled 'Close entry(s) order Long' with yellow band fills, indicates a potential exit.
Take Profit:
The strategy employs trailing stops, rather than fixed take-profit levels, to maximize gains while minimizing losses. Trailing stops adjust the stop-loss level as the stock price moves in a favorable direction. The strategy utilizes two types of trailing stop mechanisms: one based on the Average True Range (ATR), and another based on price action, which attempts to identify shifts in price momentum.
Stop Loss:
The strategy uses an Average True Range (ATR)-based stop-loss, represented by two lines on the chart. The black line indicates the primary ATR-based stop-loss level, set upon trade entry. The red line represents a secondary ATR stop-loss buffer, used in the position sizing calculation to account for potential slippage or price gaps.
To potentially reduce the risk of stop-hunting, discretionary traders might consider using a market sell order within the final 30 to 60 minutes of the main session, instead of automated stop-loss orders.
Order Types:
Market Orders are intended for use with this strategy, specifically when the candle and signal on the chart stabilize within the final 30 to 60 minutes of the main trading session.
Position Sizing:
A key aspect of this strategy is that its position size is calculated and displayed in a table on the chart. The position size is calculated based on stop-loss placement, including the stop-loss buffer, and the capital at risk per trade which is commonly set around 0.5% Risk on Capital per Trade.
Backtesting:
The backtesting results presented below the chart are for informational purposes only and are not intended to predict future performance. Instead, they serve as a tool for identifying instruments with which the strategy has historically performed well.
It's important to note that the backtester utilizes a tiny portion of the capital for each trade while our strategy relies on a diversified portfolio of multiple stocks or instruments being traded at once.
Important Considerations:
Volume data is crucial; the strategy will not load or function correctly without it. Ensure that your charts include volume data, preferably from a centralized exchange.
Our system is designed for trading a portfolio. Therefore, if you intend to use our system, you should employ appropriate position sizing, without leverage or margin, and seek out a variety of long opportunities, rather than opening a single trade with an excessively large position size.
If you are trading without automated signals, always allow the chart to stabilize. Refrain from taking action until the final 1 hour to 30 minutes before the end of the main trading session to minimize the risk of acting on false signals.
To align with the strategy's design, it's generally preferable to enter a trade during the same session that the signal appears, rather than waiting for a later session.
Disclaimer:
Trading in financial markets involves a substantial degree of risk. You should be aware of the potential for significant financial losses. It is imperative that you trade responsibly and avoid overtrading, as this can amplify losses. Remember that market conditions can change rapidly, and past performance is not indicative of future results. You could lose some or all of your initial investment. It is strongly recommended that you fully understand the risks involved in trading and seek independent financial advice from a qualified professional before using this strategy.
Pro Volume By TradeINskiOverview
The Pro Volume By TradeINski indicator is a comprehensive trading tool designed to enhance volume analysis, position sizing, and trend identification. It integrates multiple trading metrics into a single dashboard, helping traders make informed decisions based on volume dynamics, momentum bursts, trend intensity, and risk management.
Key Features
1. Position Size Calculator
Helps traders determine optimal position sizes based on risk parameters:
Capital & Risk Amount: Set account size and risk per trade.
Lot Size Adjustments: Automatically calculates nearest lot size for futures trading.
Stop Loss-Based Quantity: Computes position size based on distance from stop-loss levels (LOD or mid-price).
Standard Stop Losses: Predefined stop-loss levels (1%, 1.25%, 1.5%, 1.75%) for quick risk assessment.
Reverse Pyramiding: Enhances position sizing with adjustable risk multipliers (25%, 50%).
Closing Range & Range Expansion: Measures price strength and volatility expansion.
2. Volume Analysis & Bar Coloring
Default Bar Colors: Green for bullish bars, red for bearish bars.
Dry Volume Detection: Highlights low-volume bars (below 20-period SMA) in gray.
3. Momentum Burst (MB)
Identifies high-momentum moves:
Bullish Momentum: Volume surge + price rise ≥ user-defined threshold (default: 4%).
Bearish Momentum: Volume surge + price drop ≥ user-defined threshold (default: -4%).
4. Trend Intensity (TI)
Measures trend strength using moving averages:
Fast MA (7) vs. Slow MA (65): Highlights strong bullish/bearish trends when deviation exceeds sensitivity threshold (default: 5%).
5. Anticipation (ANTS)
Detects consolidation before potential breakouts:
Price Change Range: Filters minor price fluctuations (default: -0.4% to +0.4%).
Trend Confirmation: Requires TI_65 sensitivity (default: 5%) for validation.
6. Episodic Pivot (EP)
Flags unusually high-volume bars (default: 9M+ volume) as potential trend reversal or continuation signals.
7. Data Metrics Table
Displays key trading metrics:
Trend Intensity (TI): 21-period SMA comparison.
Industry & Sector: Stock classification.
Market Cap & Free Float: Fundamental liquidity metrics.
Volume × Price (VP): Monetary value of traded volume.
Relative Volume (RV): Today’s volume vs. previous day.
Persistent Intensity (PI): Count of consecutive up closes (default: 21-period).
Use Cases for Traders
✅ Day Traders: Identify momentum bursts and high-volume breakouts.
✅ Swing Traders: Use trend intensity and episodic pivots to confirm trends.
✅ Position Traders: Optimize risk with dynamic position sizing.
✅ Risk Managers: Set stop-loss levels and reverse pyramiding for controlled exposure.
Settings & Customization Overview
The Pro Volume By TradeINski indicator is a comprehensive trading tool designed to enhance volume analysis, position sizing, and trend identification. It integrates multiple trading metrics into a single dashboard, helping traders make informed decisions based on volume dynamics, momentum bursts, trend intensity, and risk management.
Key Features
1. Position Size Calculator
Helps traders determine optimal position sizes based on risk parameters:
Capital & Risk Amount: Set account size and risk per trade.
Lot Size Adjustments: Automatically calculates nearest lot size for futures trading.
Stop Loss-Based Quantity: Computes position size based on distance from stop-loss levels (LOD or mid-price).
Standard Stop Losses: Predefined stop-loss levels (1%, 1.25%, 1.5%, 1.75%) for quick risk assessment.
Reverse Pyramiding: Enhances position sizing with adjustable risk multipliers (25%, 50%).
Closing Range & Range Expansion: Measures price strength and volatility expansion.
2. Volume Analysis & Bar Coloring
Default Bar Colors: Green for bullish bars, red for bearish bars.
Dry Volume Detection: Highlights low-volume bars (below 20-period SMA) in gray.
3. Momentum Burst (MB)
Identifies high-momentum moves:
Bullish Momentum: Volume surge + price rise ≥ user-defined threshold (default: 4%).
Bearish Momentum: Volume surge + price drop ≥ user-defined threshold (default: -4%).
4. Trend Intensity (TI)
Measures trend strength using moving averages:
Fast MA (7) vs. Slow MA (65): Highlights strong bullish/bearish trends when deviation exceeds sensitivity threshold (default: 5%).
5. Anticipation (ANTS)
Detects consolidation before potential breakouts:
Price Change Range: Filters minor price fluctuations (default: -0.4% to +0.4%).
Trend Confirmation: Requires TI_65 sensitivity (default: 5%) for validation.
6. Episodic Pivot (EP)
Flags unusually high-volume bars (default: 9M+ volume) as potential trend reversal or continuation signals.
7. Data Metrics Table
Displays key trading metrics:
Trend Intensity (TI): 21-period SMA comparison.
Industry & Sector: Stock classification.
Market Cap & Free Float: Fundamental liquidity metrics.
Volume × Price (VP): Monetary value of traded volume.
Relative Volume (RV): Today’s volume vs. previous day.
Persistent Intensity (PI): Count of consecutive up closes (default: 21-period).
Use Cases for Traders
✅ Day Traders: Identify momentum bursts and high-volume breakouts.
✅ Swing Traders: Use trend intensity and episodic pivots to confirm trends.
✅ Position Traders: Optimize risk with dynamic position sizing.
✅ Risk Managers: Set stop-loss levels and reverse pyramiding for controlled exposure.
Settings & Customization
Trade Direction: Long, Short, or Both (auto-detects based on % change).
Table Positioning: Adjust location (Top/Middle/Bottom, Left/Center/Right).
Color Customization: Modify bar colors, table lines, and background.
Trade Direction: Long, Short, or Both (auto-detects based on % change).
Table Positioning: Adjust location (Top/Middle/Bottom, Left/Center/Right).
Color Customization: Modify bar colors, table lines, and background.
Multi-Timeframe MACD Strategy ver 1.0Multi-Timeframe MACD Strategy: Enhanced Trend Trading with Customizable Entry and Trailing Stop
This strategy utilizes the Moving Average Convergence Divergence (MACD) indicator across multiple timeframes to identify strong trends, generate precise entry and exit signals, and manage risk with an optional trailing stop loss. By combining the insights of both the current chart's timeframe and a user-defined higher timeframe, this strategy aims to improve trade accuracy, reduce exposure to false signals, and capture larger market moves.
Key Features:
Dual Timeframe Analysis: Calculates and analyzes the MACD on both the current chart's timeframe and a user-selected higher timeframe (e.g., Daily MACD on a 1-hour chart). This provides a broader market context, helping to confirm trends and filter out short-term noise.
Configurable MACD: Fine-tune the MACD calculation with adjustable Fast Length, Slow Length, and Signal Length parameters. Optimize the indicator's sensitivity to match your trading style and the volatility of the asset.
Flexible Entry Options: Choose between three distinct entry types:
Crossover: Enters trades when the MACD line crosses above (long) or below (short) the Signal line.
Zero Cross: Enters trades when the MACD line crosses above (long) or below (short) the zero line.
Both: Combines both Crossover and Zero Cross signals, providing more potential entry opportunities.
Independent Timeframe Control: Display and trade based on the current timeframe MACD, the higher timeframe MACD, or both. This allows you to focus on the information most relevant to your analysis.
Optional Trailing Stop Loss: Implements a configurable trailing stop loss to protect profits and limit potential losses. The trailing stop is adjusted dynamically as the price moves in your favor, based on a user-defined percentage.
No Repainting: Employs lookahead=barmerge.lookahead_off in the request.security() function to prevent data leakage and ensure accurate backtesting and real-time signals.
Clear Visual Signals (Optional): Includes optional plotting of the MACD and Signal lines for both timeframes, with distinct colors for easy visual identification. These plots are for visual confirmation and are not required for the strategy's logic.
Suitable for Various Trading Styles: Adaptable to swing trading, day trading, and trend-following strategies across diverse markets (stocks, forex, cryptocurrencies, etc.).
Fully Customizable: All parameters are adjustable, including timeframes, MACD Settings, Entry signal type and trailing stop settings.
How it Works:
MACD Calculation: The strategy calculates the MACD (using the standard formula) for both the current chart's timeframe and the specified higher timeframe.
Trend Identification: The relationship between the MACD line, Signal line, and zero line is used to determine the current trend for each timeframe.
Entry Signals: Buy/sell signals are generated based on the selected "Entry Type":
Crossover: A long signal is generated when the MACD line crosses above the Signal line, and both timeframes are in agreement (if both are enabled). A short signal is generated when the MACD line crosses below the Signal line, and both timeframes are in agreement.
Zero Cross: A long signal is generated when the MACD line crosses above the zero line, and both timeframes agree. A short signal is generated when the MACD line crosses below the zero line and both timeframes agree.
Both: Combines Crossover and Zero Cross signals.
Trailing Stop Loss (Optional): If enabled, a trailing stop loss is set at a specified percentage below (for long positions) or above (for short positions) the entry price. The stop-loss is automatically adjusted as the price moves favorably.
Exit Signals:
Without Trailing Stop: Positions are closed when the MACD signals reverse according to the selected "Entry Type" (e.g., a long position is closed when the MACD line crosses below the Signal line if using "Crossover" entries).
With Trailing Stop: Positions are closed if the price hits the trailing stop loss.
Backtesting and Optimization: The strategy automatically backtests on the chart's historical data, allowing you to assess its performance and optimize parameters for different assets and timeframes.
Example Use Cases:
Confirming Trend Strength: A trader on a 1-hour chart sees a bullish MACD crossover on the current timeframe. They check the MTF MACD strategy and see that the Daily MACD is also bullish, confirming the strength of the uptrend.
Filtering Noise: A trader using a 15-minute chart wants to avoid false signals from short-term volatility. They use the strategy with a 4-hour higher timeframe to filter out noise and only trade in the direction of the dominant trend.
Dynamic Risk Management: A trader enters a long position and enables the trailing stop loss. As the price rises, the trailing stop is automatically adjusted upwards, protecting profits. The trade is exited either when the MACD reverses or when the price hits the trailing stop.
Disclaimer:
The MACD is a lagging indicator and can produce false signals, especially in ranging markets. This strategy is for educational and informational purposes only and should not be considered financial advice. Backtest and optimize the strategy thoroughly, combine it with other technical analysis tools, and always implement sound risk management practices before using it with real capital. Past performance is not indicative of future results. Conduct your own due diligence and consider your risk tolerance before making any trading decisions.
Custom Strategy: ETH Martingale 2.0Strategic characteristics
ETH Little Martin 2.0 is a self-developed trading strategy based on the Martingale strategy, mainly used for trading ETH (Ethereum). The core idea of this strategy is to place orders in the same direction at a fixed price interval, and then use Martin's multiple investment principle to reduce losses, but this is also the main source of losses.
Parameter description:
1 Interval： The minimum spacing for taking profit, stop loss, and opening/closing of orders. Different targets have different spacing. Taking ETH as an example, it is generally recommended to have a spacing of 2% for fluctuations in the target.
2 Base Price： This is the price at which you triggered the first order. Similarly, I am using ETH as an example. If you have other targets, I suggest using the initial value of a price that can be backtesting. The Base Price is only an initial order price and has no impact on subsequent orders.
3 Initial Order Amount： Users can set an initial order amount to control the risk of each transaction. If the stop loss is reached, we will double the amount based on this value. This refers to the value of the position held, not the number of positions held.
4 Loss Multiplier： The strategy will increase the next order amount based on the set multiple after the stop loss, in order to make up for the previous losses through a larger position. Note that after taking profit, it will be reset to 1 times the Initial Order Amount.
5. Long Short Operation: The first order of the strategy is a multiple entry, and in subsequent orders, if the stop loss is reached, a reverse order will be opened. The position value of a one-way order is based on the Loss Multiplier multiple investment, so it is generally recommended that the Loss Multiplier default to 2.
Improvement direction
Although this strategy already has a certain trading logic, there are still some improvement directions that can be considered:
1. Dynamic adjustment of spacing: Currently, the spacing is fixed, and it can be considered to dynamically adjust the spacing based on market volatility to improve the adaptability of the strategy. Try using dynamic spacing, which may be more suitable for the actual market situation.
2. Filtering criteria: Orders and no orders can be optimized separately. The biggest problem with this strategy is that it will result in continuous losses during fluctuations, and eventually increase the investment amount. You can consider filtering out some fluctuations or only focusing on trend trends.
3. Risk management: Add more risk management measures, such as setting a maximum loss limit to avoid huge losses caused by continuous stop loss.
4. Optimize the stop loss multiple: Currently, the stop loss multiple is fixed, and it can be considered to dynamically adjust the multiple according to market conditions to reduce risk.
Atlas Trend Position TableAtlas Trend Position Table
This script provides an easy-to-understand position overview for traders, including key metrics such as entry price, potential profit, potential loss, and current profit/loss (PnL). It’s designed to help traders manage their open positions effectively, especially when using leverage.
Inputs:
Order Size ($): The total amount of capital used for the trade.
Entry Price: The price at which the trade was entered.
Stop Loss: The price level at which the trade will be exited to prevent further losses.
Take Profit: The price level where the trader aims to take profits.
Leverage: The multiplier for leveraged trading.
Commission (%): The commission fee applied to each trade.
Key Features:
Position Value Calculation: The script calculates the total position value by taking into account the leverage used in the trade.
Potential Profit and Loss:
Potential profit is calculated based on the difference between the take profit and the entry price, adjusted for commission.
Potential loss is calculated similarly, using the stop loss, and includes the effect of commission.
Real-Time Profit/Loss: The script also calculates real-time profit or loss using the current market price, factoring in leverage and commission.
Dynamic Background Colors:
The PnL background color dynamically adjusts: green when in profit, red when in loss. This provides a quick visual cue to assess the current trade status.
Table Display:
The output is shown in a table positioned on the right side of the chart. It contains the following information:
Entry Price: Displays the trade’s entry price.
Order Size ($): Shows the total leveraged position value.
Potential Profit: The potential profit from the trade based on the take profit level.
Potential Loss: The potential loss from the trade based on the stop loss level.
Current PnL: Displays the current profit or loss based on the live market price.
How to Use:
Input your trade details in the settings menu, including your entry price, stop loss, take profit, and leverage.
The script will automatically calculate and display the potential outcomes and live PnL.
Use the visual indicators to monitor the status of your open position and adjust your strategy accordingly.
This tool is designed to be simple, effective, and user-friendly, providing traders with the essential data they need for better risk management and decision-making.
QuantBuilder | FractalystWhat's the strategy's purpose and functionality?
QuantBuilder is designed for both traders and investors who want to utilize mathematical techniques to develop profitable strategies through backtesting on historical data.
The primary goal is to develop profitable quantitive strategies that not only outperform the underlying asset in terms of returns but also minimize drawdown.
For instance, consider Bitcoin (BTC), which has experienced significant volatility, averaging an estimated 200% annual return over the past decade, with maximum drawdowns exceeding -80%. By employing this strategy with diverse entry and exit techniques, users can potentially seek to enhance their Compound Annual Growth Rate (CAGR) while managing risk to maintain a lower maximum drawdown.
While this strategy employs quantitative techniques, including mathematical methods such as probabilities and positive expected values, it demonstrates exceptional efficacy across all markets. It particularly excels in futures, indices, stocks, cryptocurrencies, and commodities, leveraging their inherent trending behaviors for optimized performance.
In both trending and consolidating market conditions, QuantBuilder employs a combination of multi-timeframe probabilities, expected values, directional biases, moving averages and diverse entry models to identify and capitalize on bullish market movements.
How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
1. Trading:
- Designed for traders looking to capitalize on bullish markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for both swing and intraday trading with a focus on probabilities and risk per trade approach.
2. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes pre-define percentage of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully/partially investing in the asset during bullish conditions.
How does the strategy identify market structure? What are the underlying calculations?
The strategy utilizes an efficient logic with for loops to pinpoint the first swing candle featuring a pivot of 2, establishing the point at which the break of structure begins.
What entry criteria are used in this script? What are the underlying calculations?
The script utilizes two entry models: BreakOut and fractal.
Underlying Calculations:
Breakout: The script assigns the most recent swing high to a variable. When the price closes above this level and all other conditions are met, the script executes a breakout entry (conservative approach).
Fractal: The script identifies a swing low with a period of 2. Once this condition is met, the script executes the trade (aggressive approach).
How does the script calculate probabilities? What are the underlying calculations?
The script calculates probabilities by monitoring price interactions with liquidity levels. Here’s how the underlying calculations work:
Tracking Price Hits: The script counts the number of times the price taps into each liquidity side after the EQM level is activated. This data is stored in an array for further analysis.
Sample Size Consideration: The total number of price interactions serves as the sample size for calculating probabilities.
Probability Calculation: For each liquidity side, the script calculates the probability by taking the average of the recorded hits. This allows for a dynamic assessment of the likelihood that a particular side will be hit next, based on historical performance.
Dynamic Adjustment: As new price data comes in, the probabilities are recalculated, providing real-time aduptive insights into market behavior.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
How does the script calculate expected values? What are the underlying calculations?
The script calculates expected values by leveraging the probabilities of winning and losing trades, along with their respective returns. The process involves the following steps:
This quantitative methodology provides a robust framework for assessing the expected performance of trading strategies based on historical data and backtesting results.
How is the contextual bias calculated? What are the underlying calculations?
The contextual bias in the QuantBuilder script is calculated through a structured approach that assesses market structure based on swing highs and lows. Here’s how it works:
Identification of Swing Points: The script identifies significant swing points using a defined pivot logic, focusing on the first swing high and swing low. This helps establish critical levels for determining market structure.
Break of Structure (BOS) Assessment:
Bullish BOS: The script recognizes a bullish break of structure when a candle closes above the first swing high, followed by at least one swing low.
Bearish BOS: Conversely, a bearish break of structure is identified when a candle closes below the first swing low, followed by at least one swing high.
Bias Assignment: Based on the identified break of structure, the script assigns directional biases:
A bullish bias is assigned if a bullish BOS is confirmed.
A bearish bias is assigned if a bearish BOS is confirmed.
Quantitative Evaluation: Each identified bias is quantitatively evaluated, allowing the script to assign numerical values representing the strength of each bias. This quantification aids in assessing the reliability of market sentiment across multiple timeframes.
What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
- Initial Stop-loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14)
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
3. PL Based:
This method places the stop-loss at the low of the previous candle.
If the current entry is based on the hunt entry strategy, the stop-loss will be placed at the low of the candle that wicks through the lower FRMA band.
Example:
If the previous candle's low is 100, then the stop-loss will be set at 100.
This method ensures the stop-loss is placed just below the most recent significant low, providing a logical and immediate level for risk management.
- Trailing Stop-Loss:
One of the key elements of this strategy is its ability to detect structural liquidity and structural invalidation levels across multiple timeframes to trail the stop-loss once the trade is in running profits.
By utilizing this approach, the strategy allows enough room for price to run.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
What tables are available in this script?
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Total Commission: Displays the cumulative commissions incurred from all trades executed within the selected backtesting window. This value is derived by summing the commission fees for each trade on your chart.
Average Commission: Represents the average commission per trade, calculated by dividing the Total Commission by the total number of closed trades. This metric is crucial for assessing the impact of trading costs on overall profitability.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month and year.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- UI Table: A user-friendly table that allows users to view and save the selected strategy parameters from user inputs. This table enables easy access to key settings and configurations, providing a straightforward solution for saving strategy parameters by simply taking a screenshot with Alt + S or ⌥ + S.
User-input styles and customizations:
To facilitate studying historical data, all conditions and filters can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
How to Use This Quantitive Strategy Builder to Create a Profitable Edge and System?
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker/prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 200 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
What makes this strategy original?
QuantBuilder stands out due to its unique combination of quantitative techniques and innovative algorithms that leverage historical data for real-time trading decisions. Unlike most algorithmic strategies that work based on predefined rules, this strategy adapts to real-time market probabilities and expected values, enhancing its reliability. Key features include:
Mathematical Framework: The strategy integrates advanced mathematical concepts, such as probabilities and expected values, to assess trade viability and optimize decision-making.
Multi-Timeframe Analysis: By utilizing multi-timeframe probabilities, QuantBuilder provides a comprehensive view of market conditions, enhancing the accuracy of entry and exit points.
Dynamic Market Structure Identification: The script employs a systematic approach to identify market structure changes, utilizing a blend of swing highs and lows to detect contextual/direction bias of the market.
Built-in Trailing Stop Loss: The strategy features a dynamic trailing stop loss based on multi-timeframe analysis of market structure. This allows traders to lock in profits while adapting to changing market conditions, ensuring that exits are executed at optimal levels without prematurely closing positions.
Robust Performance Metrics: With detailed performance tables and visualizations, users can easily evaluate strategy effectiveness and adjust parameters based on historical performance.
Adaptability: The strategy is designed to work across various markets and timeframes, making it versatile for different trading styles and objectives.
Suitability for Investors and Traders: QuantBuilder is ideal for both investors and traders looking to rely on mathematically proven data to create profitable strategies, ensuring that decisions are grounded in quantitative analysis.
These original elements combine to create a powerful tool that can help both traders and investors to build and refine profitable strategies based on algorithmic quantitative analysis.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
IsAlgo - AI Trend Strategy► Overview:
The AI Trend Strategy employs a combination of technical indicators to guide trading decisions across various markets and timeframes. It uses a custom Super Trend indicator and an Exponential Moving Average (EMA) to analyze market trends and executes trades based on specific candlestick patterns. This strategy includes options for setting stop losses, take profit levels, and features an alert system for trade notifications.
► Description:
This strategy focuses on identifying the optimal "entry candle," which signals either a potential correction within the ongoing trend or the emergence of a new trend. The entry criteria for this candle are highly customizable, allowing traders to specify dimensions such as the candle's minimum and maximum size and body ratio. Additional settings include whether this candle should be the highest or lowest compared to recent candles and if a confirmation candle is necessary to validate the entry.
The Super Trend indicator is central to the strategy’s operation, dictating the direction of trades by identifying bullish or bearish trends. Traders have the option to configure trades to align with the direction of the trend identified by this indicator, or alternatively, to take positions counter to the trend for potential reversal strategies. This flexibility can be crucial during varying market conditions.
Additionally, the strategy incorporates an EMA alongside the Super Trend indicator to further analyze trend directions. This combined approach aims to reduce the occurrence of false signals and improve the strategy's overall trend analysis.
The learning algorithm is a standout feature of the AI Trend Strategy. After accumulating data from a predefined number of trades (e.g., after the first 100 trades), the algorithm begins to analyze past performances to identify patterns in wins and losses. It considers variables such as the distance from the current price to the trend line, the range between the highest and lowest prices during the trend, and the duration of the trend. This data informs the algorithm's predictions for future trades, aiming to improve accuracy and reduce losses by adapting to the evolving market conditions.
► Examples of Trade Execution:
1. In an Uptrend: The strategy might detect a suitable entry candle during a correction phase, which aligns with the continuing uptrend for a potential long trade.
2. In a Downtrend: Alternatively, the strategy might identify an entry candle at the end of a downtrend, suggesting a potential reversal or correction where a long trade could be initiated.
3. In an Uptrend: The strategy may also spot an entry candle at the end of an uptrend and execute a short trade, anticipating a reversal or significant pullback.
4. In a Downtrend: The strategy might find a suitable entry candle during a correction phase, indicating a continuation of the downtrend for a potential short trade.
These examples illustrate how the strategy identifies potential trading opportunities based on trend behavior and candlestick patterns.
► Features and Settings:
⚙︎ Trend: Utilizes a custom Super Trend indicator to identify the direction of the market trend. Users can configure the strategy to execute trades in alignment with this trend, take positions contrary to the trend, or completely ignore the trend information for their trading decisions.
⚙︎ Moving average: Employs an Exponential Moving Average (EMA) to further confirm the trend direction indicated by the Super Trend indicator. This setting can be used in conjunction with the Super Trend or disabled if preferred.
⚙︎ Entry candle: Defines the criteria for the candle that triggers a trade. Users can customize aspects such as the candle's size, body, and its relative position to previous candles to ensure it meets specific trading requirements before initiating a trade.
⚙︎ Learning algorithm: This component uses historical trade data to refine the strategy. It assesses various aspects of past trades, such as price trends and market conditions, to make more informed trading decisions in the future.
⚙︎ Trading session: Users can define specific trading hours during which the strategy should operate, allowing trades to be executed only during preferred market periods.
⚙︎ Trading days: This option enables users to specify which days the strategy should be active, providing the flexibility to avoid trading on certain days of the week if desired.
⚙︎ Backtesting: Enables a period during which the strategy can be tested over a selected start and end date, with an option to deactivate this feature if not needed.
⚙︎ Trades: Detailed configuration options include the direction of trades (long, short, or both), position sizing (fixed or percentage-based), the maximum number of open trades, and limitations on the number of trades per day or based on trend changes.
⚙︎ Trades Exit: Offers various strategies for exiting trades, such as setting limits on profits or losses, specifying the duration a trade should remain open, or closing trades based on trend reversal signals.
⚙︎ Stop loss: Various methods for setting stop losses are available, including fixed pips, based on Average True Range (ATR), or utilizing the highest or lowest price points within a designated number of previous candles. Another option allows for closing the trade after a specific number of candles moving in the opposite direction.
⚙︎ Break even: This feature adjusts the stop loss to a break-even point under certain conditions, such as reaching predefined profit levels, to protect gains.
⚙︎ Trailing stop: The trailing stop feature adjusts the stop loss as the trade moves into profit, aiming to secure gains while potentially capturing further upside.
⚙︎ Take profit: Up to three take profit levels can be established using various methods, such as a fixed amount of pips, risk-to-reward ratios based on the stop loss, ATR, or after a set number of candles that move in the direction of the trade.
⚙︎ Alerts: Includes a comprehensive alert system that informs the user of all significant actions taken by the strategy, such as trade openings and closings. It supports placeholders for dynamic values like take profit levels, stop loss prices, and more.
⚙︎ Dashboard: Provides a visual display of detailed information about ongoing and past trades on the chart, helping users monitor the strategy’s performance and make informed decisions.
► Backtesting Details:
Timeframe: 15-minute BTCUSD chart.
Initial Balance: $10,000.
Order Size: 4% of equity per trade.
Commission: 0.01%.
Slippage: 5 ticks.
Risk Management: Strategic stop loss settings are applied based on the most extreme price points within the last 18 candles.
Donchian Quest Research// =================================
Trend following strategy.
// =================================
Strategy uses two channels. One channel - for opening trades. Second channel - for closing.
Channel is similar to Donchian channel, but uses Close prices (not High/Low). That helps don't react to wicks of volatile candles (“stop hunting”). In most cases openings occur earlier than in Donchian channel. Closings occur only for real breakout.
// =================================
Strategy waits for beginning of trend - when price breakout of channel. Default length of both channels = 50 candles.
Conditions of trading:
- Open Long: If last Close = max Close for 50 closes.
- Close Long: If last Close = min Close for 50 closes.
- Open Short: If last Close = min Close for 50 closes.
- Close Short: If last Close = max Close for 50 closes.
// =================================
Color of lines:
- black - channel for opening trade.
- red - channel for closing trade.
- yellow - entry price.
- fuchsia - stoploss and breakeven.
- vertical green - go Long.
- vertical red - go Short.
- vertical gray - close in end, don't trade anymore.
// =================================
Order size calculated with ATR and volatility.
You can't trade 1 contract in BTC and 1 contract in XRP - for example. They have different price and volatility, so 1 contract BTC not equal 1 contract XRP.
Script uses universal calculation for every market. It is based on:
- Risk - USD sum you ready to loss in one trade. It calculated as percent of Equity.
- ATR indicator - measurement of volatility.
With default setting your stoploss = 0.5 percent of equity:
- If initial capital is 1000 USD and used parameter "Permit stop" - loss will be 5 USD (0.5 % of equity).
- If your Equity rises to 2000 USD and used parameter "Permit stop"- loss will be 10 USD (0.5 % of Equity).
// =================================
This Risk works only if you enable “Permit stop” parameter in Settings.
If this parameter disabled - strategy works as reversal strategy:
⁃ If close Long - channel border works as stoploss and momentarily go Short.
⁃ If close Short - channel border works as stoploss and momentarily go Long.
Channel borders changed dynamically. So sometime your loss will be greater than ‘Risk %’. Sometime - less than ‘Risk %’.
If this parameter enabled - maximum loss always equal to 'Risk %'. This parameter also include breakeven: if profit % = Risk %, then move stoploss to entry price.
// =================================
Like all trend following strategies - it works only in trend conditions. If no trend - slowly bleeding. There is no special additional indicator to filter trend/notrend. You need to trade every signal of strategy.
Strategy gives many losses:
⁃ 30 % of trades will close with profit.
⁃ 70 % of trades will close with loss.
⁃ But profit from 30% will be much greater than loss from 70 %.
Your task - patiently wait for it and don't use risky setting for position sizing.
// =================================
Recommended timeframe - Daily.
// =================================
Trend can vary in lengths. Selecting length of channels determine which trend you will be hunting:
⁃ 20/10 - from several days to several weeks.
⁃ 20/20 or 50/20 - from several weeks to several months.
⁃ 50/50 or 100/50 or 100/100 - from several months to several years.
// =================================
Inputs (Settings):
- Length: length of channel for trade opening/closing. You can choose 20/10, 20/20, 50/20, 50/50, 100/50, 100/100. Default value: 50/50.
- Permit Long / Permit short: Longs are most profitable for this strategy. You can disable Shorts and enable Longs only. Default value: permit all directions.
- Risk % of Equity: for position sizing used Equity percent. Don't use values greater than 5 % - it's risky. Default value: 0.5%.
⁃ ATR multiplier: this multiplier moves stoploss up or down. Big multiplier = small size of order, small profit, stoploss far from entry, low chance of stoploss. Small multiplier = big size of order, big profit, stop near entry, high chance of stoploss. Default value: 2.
- ATR length: number of candles to calculate ATR indicator. It used for order size and stoploss. Default value: 20.
- Close in end - to close active trade in the end (and don't trade anymore) or leave it open. You can see difference in Strategy Tester. Default value: don’t close.
- Permit stop: use stop or go reversal. Default value: without stop, reversal strategy.
// =================================
Properties (Settings):
- Initial capital - 1000 USD.
- Script don't uses 'Order size' - you need to change 'Risk %' in Inputs instead.
- Script don't uses 'Pyramiding'.
- 'Commission' 0.055 % and 'Slippage' 0 - this parameters are for crypto exchanges with perpetual contracts (for example Bybit). If use on other markets - set it accordingly to your exchange parameters.
// =================================
Big dataset used for chart - 'BITCOIN ALL TIME HISTORY INDEX'. It gives enough trades to understand logic of script. It have several good trends.
// =================================
SOFEX Strong Volatility Trend Follower + BacktestingWhat is the SOFEX Strong Volatility Trend Follower + Backtesting script?
🔬 Trading Philosophy
This script is trend-following, attempting to avoid choppy markets.
It has been developed for Bitcoin and Ethereum trading, on 1H timeframe.
The strategy does not aim to make a lot of trades, or to always remain in a position and switch from long to short. Many times there is no direction and the market is in "random walk mode", and chasing trades is futile.
Expectations of performance should be realistic.
The script focuses on a balanced take-profit to stop-loss ratio. In the default set-up of the script, that is a 2% : 2% (1:1) ratio. A relatively low stop loss and take profit build onto the idea that positions should be exited promptly. There are many options to edit these values, including enabling trailing take profit and stop loss. Traders can also completely turn off TP and SL levels, and rely on opposing signals to exit and enter new trades.
Extreme scenarios can happen on the cryptocurrency markets, and disabling stop-loss levels completely is not recommended. The position size should be monitored since all of it is at risk with no stop-loss.
⚙️ Logic of the indicator
The Strong Volatility Trend Follower indicator aims at evading ranging market conditions. It does not seek to chase volatile, yet choppy markets. It aims at aggressively following confirmed trends. The indicator works best during strong, volatile trends, however, it has the downside of entering trades at trend tops or bottoms.
This indicator also leverages proprietary adaptive moving averages to identify and follow strong trend volatility effectively. Furthermore, it uses the Average Directional Index, Awesome Oscillator, ATR and a modified version of VWAP, to categorize trends into weak or strong ones. The VWAP indicator is used to identify the monetary (volume) inflow into a given trend, further helping to avoid short-term manipulations. It also helps to distinguish choppy-market volatility with a trending market one.
📟 Parameters Menu
The script has a comprehensive parameter menu:
Preset Selection : Choose between Bitcoin or Ethereum presets to tailor the indicator to your preferred cryptocurrency market.
Indicator Sensitivity Parameter : Adjust the sensitivity to adapt the indicator, particularly to make it seek higher-strength trends.
Indicator Signal Direction : Set the signal direction as Long, Short, or Both, depending on your preference.
Exit of Signals : You have options regarding Take-Profit (TP) and Stop-Loss (SL) levels. Enable TP/SL levels to exit trades at predetermined levels, or disable them to rely on direction changes for exits. Be aware that removing stop losses can introduce additional risk, and position sizing should be carefully monitored.
By enabling Trailing TP/SL, the system switches to a trailing approach, allowing you to:
- Place an initial customizable SL.
- Specify a level (%) for the Trailing SL to become active.
- When the activation level is reached, the system moves the trailing stop by a given Offset (%).
Additionally, you can enable exit at break-even, where the system places an exit order when the trail activation level is reached, accounting for fees and slippage.
Alert Messages : Define the fields for alert messages based on specific conditions. You can set up alerts to receive email, SMS, and in-app notifications. If you use webhooks for alerts, exercise caution, as these alerts can potentially execute trades without human supervision.
Backtesting : Default backtesting parameters are set to provide realistic backtesting performance:
- 0.04% Commission per trade (for both entries and exits)
- 3 ticks Slippage (highly dependent on exchange)
- Initial capital of $1000
- Order size of $1000
While the order size is equal to the initial capital, the script employs a 2% stop-loss order to limit losses and attempts to prevent risky trades from creating big losses. The order size is a set dollar value, so that the backtesting performance is linear, instead of using % of capital which may result in unrealistic backtesting performance.
Risk Disclaimer
Please be aware that backtesting results, while valuable for statistical overview, do not guarantee future performance in any way. Cryptocurrency markets are inherently volatile and risky. Always trade responsibly and do not risk more than you can afford to lose.
Strategy Developer ToolSolar Strategies: Strategy Developer Tool Complete Guide
This guide provides full explanation of the intended purpose of our script along with individual explanation of each input and the logic behind them coupled with general knowledge which we find useful in using our tool regarding elements of risk and strategy. Use this information wisely and understand we are not providing financial advise, this is a learning tool meant to help advance traders knowledge of the markets and their strategies which are formed as such.
Basics
Before getting into the specifics of how to use our strategy developer tool, it's important to understand a few basic fundamental things about it. The purpose of the tool is to allow the user to optimize a strategy through back testing with our strategy tracker and 50+ user inputs. The way you optimize your strategy depends on a couple things:
The state of the current and recent previous market.
The timeframe you trade on.
The types of trades you prefer. (swings, scalps, etc.)
How much risk you are willing to take on.
Risk Basics:
Going off the last bullet point on the list above, risk plays a huge part in how you optimize your strategy, with that being said here are a few general rules of risk as they relate to trades:
The more trades you take on, the more risk you are opening your strategy up to.
If done correctly, more trades will often result in more profit with slightly lower accuracy, and more risk.
The less trades you take on, the easier it is to have higher accuracy because ideally by rooting out the losing trades, you are left with fewer overall trades but mostly winning trades.
Less trades with higher accuracy often result in less profit but will 100% be less risky than the opposite. (More trades, less accurate, more profit, MORE RISK)
Input Basics:
More trades, less trades, more risk, less risk, what does this all mean as it relates to our tool?
The 50+ user inputs that allow you to optimize and create your strategy all effect when the script takes a trade.
Many of the inputs are essentially conditions. By changing these inputs, what you are doing is changing how specific the conditions need to be in order to take a trade.
This is how the inputs tie into the bullet point list above regarding risk and the number of trades you take on. By raising or lowering certain inputs, you are making the conditions more or less specific on when to trade.
Making conditions more specific will allow for less trades to be taken and will often result in a higher win rate, and less associated risk.
Making conditions less specific will allow for more trades to be taken and depending on the state of the market, could result in more profit being realized, but at the same time opens you up to more risk because you are stating a more general set of conditions in order to take a trade.
How does it work?
Our strategy developer tool is based on two simple factors in order to identify specific areas in the market deemed good for trade. They are as follows:
Directional momentum to identify when a move might happen.
A confirmation of the desired move.
Indicators:
The tool gets its information on these two factors from two custom built indicators which are hard coded into the script. These two indicators and the inputs which affect them can be found labeled with Indicator 1 or Indicator 2 in the tool's settings.
When the conditions are met based on the factors of both indicators, it then decides your stop losses and take profits using pivot points.
Indicator 1 is the momentum indicator.
Indicator 2 looks for confirmation of the move.
Hedges:
Since nothing is ever certain when trading, our tool also aims to minimize potential loss before it can happen by incorporating hedges when a signal prints in the opposite direction of the trade you are currently in.
To identify when to hedge, the candles will appear with the opposite color of your original trade. Candles, while in a long trade, appear as green and candles while in a short trade appear as red. While in a long trade the only time red candles will appear is when a hedge occurs and vice versa for shorts.
Example: If you just took a long trade based on a long signal that the script gave off, but a short signal prints off while you are in the long, you are directed to sell half your long position and enter that half into a short position. Since there is now more uncertainty in the long because of the short signal, minimizing your position size and having a smaller position in the opposite direction allows you to cover your bases if the trade moves against you. If it doesn’t move against you and ends up going long as originally intended, you are not to lose any money, likely a small profit or break even when all is said and done.
In order to give the hedges a greater change of hitting, the take profits are smaller than a normal trade, this way even if your hedge wasn’t necessary and the original trade does not move against you, it's likely that your hedge will still win, and you can just consider it a small scalp to further your profits on the original trade.
Doubles:
Besides minimizing loss, we also aim to maximize the potential gain. When a second signal prints off in the direction of the trade you are currently already in, the tool directs you to double your position size.
The signal for doubling is a label with “2x” written inside.
The logic here is similar to hedging but in the opposite way. Just as a signal in the opposite direction creates uncertainty, a signal in the same direction indicates more certainty hence doubling your position size.
Example: If you are currently in a long position and you get a second long signal, you would then double your existing position since two long signals printing off before the first one has a chance to play out indicates a stronger chance of movement in the intended direction of your trade.
User Inputs
Upon opening the tools settings tab, you will find all the user inputs which can then be modified to fit your desired strategy. In this section of our guides, you will find individual explanations and use cases for each input so you can correctly use them to your best advantage.
Strategy Tracker Table:
By ticking this input on, the strategy tracker table will be visible to the user. (Default is on)
Indicator 1 Greater Than: Long:
By ticking this input on, you are adding a condition the script will then look for in order to take a long. (Default is on)
This condition is that an average of indicator 1, which searches for momentum, must fall above a certain level, which is determined in the next input.
The purpose of this is to ensure that the average momentum is not too low because this would indicate prolonged downwards movement on the timeframe of the market being observed, making a long position riskier.
Indicator 1 Greater Than Input: Long:
This input correlates to the previous input directly above.
If Indicator 1 Greater Than: Long is ticked on, then one of the conditions in order to take a long position will be that the average of indicator 1 must fall above the level which you set in this input.
max level 100, min level 0
Indicator 1 Less Than: Long
By ticking this input on, you are adding a condition the script will then look for in order to take a long position. (Default is on)
This condition is that an average of indicator 1, which searches for momentum, must fall below a certain level, which is determined in the next input.
The purpose of this is to ensure that the average momentum is not too high, because this would indicate a prior significant upwards movement or trend on the timeframe of the market being observed.
Taking a long position while the average momentum is at higher levels exposes the risk of longing as the market has started to pull back from a peak or when the market has just reached a peak.
Indicator 1 Less Than Input: Long
This input correlates to the previous input directly above.
If Indicator 1 Less Than: Long is ticked on, then one of the conditions in order to take a long position will be that the average of indicator 1 must fall below the level which you set in this input.
max level 100, min level 0
Indicator 1 Greater Than: Short
By ticking this input on, you are adding a condition the script will then look for in order to take a short. (Default is on)
This condition is that an average of indicator 1, which searches for momentum, must fall above a certain level, which is determined in the next input.
The purpose of this is to ensure that the average momentum is not too low because this would indicate prolonged downwards movement or trend on the timeframe of the market being observed.
Taking a short position while the average momentum is at lower levels exposes the risk of shorting as the market has started to recover from a bottom or when the market has just reached a bottom.
Indicator 1 Greater Than Input: Short
This input correlates to the previous input directly above.
If Indicator 1 Greater Than: Short is ticked on, then one of the conditions in order to take a short position will be that the average of indicator 1 must fall above the level which you set in this input.
max level 100, min level 0
Indicator 1 Less Than: Short
By ticking this input on, you are adding a condition the script will then look for in order to take a short position. (Default is on)
This condition is that an average of indicator 1, which searches for momentum, must fall below a certain level, which is determined in the next input.
The purpose of this is to ensure that the average momentum is not too high, because this would indicate a prior significant upwards movement or trend on the timeframe of the market being observed.
Taking a short position while the average momentum is at higher levels exposes the risk of shorting as the market is currently in a strong uptrend.
Indicator 1 Less Than: Short
This input correlates to the previous input directly above.
If Indicator 1 Less Than: Short is ticked on, then one of the conditions in order to take a short position will be that the average of indicator 1 must fall below the level which you set in this input.
max level 100, min level 0
Summary of Input Group: Indicator 1 Greater/Less Than Long/Short
This grouping of inputs is best used as a filter of sorts, much like many of the other inputs which are also essentially filters of the market to find areas ripe for trade. Specifically, however, this group of inputs is especially powerful because if used correctly, it can specify a range for the average momentum to fall in when looking for either long or short trades. Think of it like a sweet spot where the average is not too high nor too low. In combination with the numerous other inputs which will shortly be explained, this sweet spot can be a great indication. Keep in mind that once you find a working range, this will not last forever. Conditions in the market are ever changing and as such your inputs, in this case the range the average momentum must fall in, will also need to change with the market conditions.
Bars Since Crossover:
This input simply describes a crossover of the momentum indicator (indicator 1) and its average.
In the category How does it work? Two main factors are discussed, the first being directional momentum to determine when an upwards move might happen. The crossover correlated to this input is the directional momentum as mentioned earlier.
As also mentioned in How does it work? The second factor is a confirmation of the desired upwards move. This confirmation is a crossover of the current price and indicator 2 which will be further addressed later on.
What's important to understand about the two key factors at play in regard to Bars Since Crossover is that this input is determining a condition which looks for a certain number of bars prior to the confirmation of indicator 2 which the crossover of momentum and its average has happened on indicator 1.
Example: Bars Since Crossover input is set to 10. This means that the crossover of momentum and its average from indicator 1 must be within 10 bars prior to the confirmation from indicator 2. If this happens then this condition is met for a long position.
Bars Since Crossunder:
This input simply describes a crossunder of the momentum indicator (indicator 1) and its average.
In the category How does it work? Two main factors are discussed, the first being directional momentum to determine when a downwards move might happen. The crossunder correlated to this input is the directional momentum as mentioned earlier.
As also mentioned in How does it work? The second factor is a confirmation of the desired downwards move. This confirmation is a crossunder of the current price and indicator 2 which will be further addressed later on.
What's important to understand about the two key factors at play in regard to Bars Since Crossunder is that this input is determining a condition which looks for a certain number of bars prior to the confirmation of indicator 2 which the crossunder of momentum and its average has happened on indicator 1.
Example: Bars Since Crossunder input is set to 10. This means that the crossunder of momentum and its average from indicator 1 must be within 10 bars prior to the confirmation from indicator 2. If this happens then this condition is met for a short position.
Summary of Input Group: Bars Since Crossover/Crossunder
These two inputs can have a large effect on the types of trades being taken and the risk which your strategy opens up to. The idea is that in order for the two key factors described in How does it work? to be correlated and therefore indicate a strong directional move, the two events must happen within a somewhat small period of time. If the period of time between the two events taking place is too large, then it's riskier for your strategy due to a delay in directional momentum and the necessary confirmation. It's important to note that this “small period of time” is relative to the security you're trading and the timeframe its being trades on. Small could mean 5 bars in some cases or 20 bars in others, this is why our custom back tester exists. So that the process of optimization on different securities and different timeframes is smooth and only requires adjustments to inputs then your own analysis of the back test results.
Indicator 1 Input Long
Defines how strong the upwards momentum needs to be in order to take a long position.
When optimizing your strategy, this input is likely to have some of the most effect on when the script takes a long position.
The reasoning for this is because the level you set for this input is the level which indicator 1 must close above following the crossover of its average.
Example: Indicator 1 Input Long set to 50, this means that when the momentum crosses over its average from indicator 1, upon the close of this crossover the momentum must be above the level 50 in order for this condition to be met to take a long position.
The higher the level, the stronger the upwards momentum must be, and therefore by using higher levels for this input, the script will search for stronger directional moves leaving less chance for the trade to move against you.
Indicator 1 Input Short
Defines how strong the downwards momentum needs to be in order to take a short position.
When optimizing your strategy, this input is likely to have some of the most effect on when the script takes a short position.
The reasoning for this is because the level you set for this input is the level which indicator 1 must close below following the crossunder of its average.
Example: Indicator 1 Input Short set to 40, this means that when the momentum crosses under its average from indicator 1, upon the close of this crossunder the momentum must be below the level 40 in order for this condition to be met to take a short position.
The lower the level, the stronger the downwards momentum must be, and therefore by using lower levels for this input, the script will search for stronger directional moves leaving less chance for the trade to move against you.
Summary of Input Group: Indicator 1 Input Long/Short
These two inputs are so important to your strategy because at the end of the day no matter how you set it up, it's still a momentum-based strategy. With that being said the level of momentum or the strength needed in order to take trades is of course going to be a key decider in the successfulness of the strategy. When optimizing these two inputs make sure to take into account what the overall market conditions are, meaning if it’s a bull market maybe make the momentum needed to take a long slightly less comparatively to the amount needed to take a short, in other words make long conditions less specific and short conditions more specific. Slight variations of this input can have very big effects, even changing it by 1 or 2 can make a major difference. In might even be good to consider starting optimization with these inputs and then work the rest of the strategy out from there. A lot could be said about these inputs and more docs will be added in order to further explain more strategy approaches revolving around them, for now don’t hesitate to ask any questions.
Indicator 2 Red
This input is used as a sort of chop filter at its base level, however if used correctly it can be a much broader filter for what areas of the market you want to trade in.
Indicator 2 shows as either red or green and is used as a confirmation when price crosses over it following the crossover of momentum and its average from indicator 1 to take a long position.
If ticked on, Indicator 2 Red states a condition in order for the script to take a long position. (Default is on)
The condition is that upon the crossover of the current price and Indicator 2, 10 bars ago indicator 2 must have been red.
The reason for this input is because the current color of indicator 2 upon the crossover must also be red. However, this condition is hard coded in and cannot be changed by any input.
This is because the type of trade being targeted is that of a type of reversal or continuation.
If indicator 2 showed green 10 bars ago and is currently red this would indicate that a top was just reached, and price is reversing downwards making this not a good area to take a long.
Another scenario if indicator 2 showed green 10 bars ago and is currently red is that there is currently a sideways trend going on or otherwise known as chop, also not an ideal area to take a long
However, if 10 bars ago indicator 2 was red and it's currently red this would indicate a more prolonged pullback.
If all conditions are met and we know that price has been pulling back, now we can enter a long with more knowledge pointing to price reversing upwards from a downwards trend, or continuing its upwards trend after a pullback.
Indicator 2 Green
This input is used as a sort of chop filter at its base level, however if used correctly it can be a much broader filter for what areas of the market you want to trade in.
Indicator 2 shows as either red or green and is used as a confirmation when price crosses under it following the crossunder of momentum and its average from indicator 1 to take a short position.
If ticked on, Indicator 2 Green states a condition in order for the script to take a short position. (Default is on)
The condition is that upon the crossunder of the current price and Indicator 2, 10 bars ago indicator 2 must have been green.
The reason for this input is because the current color of indicator 2 upon the crossunder must also be green. However, this condition is hard coded in and cannot be changed by any input.
This is because the type of trade being targeted is that of a type of reversal or continuation.
If indicator 2 showed red 10 bars ago and is currently green this would indicate that a bottom was just reached, and price is reversing upwards making this not a good area to take a short.
Another scenario if indicator 2 showed red 10 bars ago and is currently green is that there is currently a sideways trend going on or otherwise known as chop, also not an ideal area to take a short.
However, if 10 bars ago indicator 2 was green and it's currently green this would indicate a more prolonged upwards movement.
If all conditions are met and we know that price has been moving up, now we can enter a short with more knowledge pointing to price reversing downwards from an upwards trend, or continuing its downwards trend after a bounce up.
Summary of Input Group: Indicator 2 Red/Green
Similar to Indicator 1 Greater/Less Than Long/Short, the goal of these inputs is to try to get a picture of what the previous recent market has been doing. By getting this picture it's easier to find different areas of the market more ideal for trades. Different from Indicator 1 Greater/Less Than Long/Short though, Indicator 2 Red/Green is directly correlated to the price action in the market rather than the momentum. By switching these on or off you are setting more or less specific conditions for taking trades. Some markets require this extra condition to lower your risk in your strategy, however others may not.
Pivot Low
This input is used to define the number of bars the script will look back to grab a pivot low when taking a long position.
This pivot low is then used to set the stop loss when entering a long position.
This input is very important and optimizing it correctly can be extremely crucial to your strategies success.
The Strategy Developer tool uses a 1:1 risk to reward ratio when setting your first take profit point, so when the script looks back to get a pivot low based on the input you set, it will then set your first take profit at an equal ratio to the stop loss found from the pivot low.
The goal in optimizing this input is to give enough lookback to find real pivot points where price has reversed off of, but not to give too much lookback where its grabbing previous pivot points unrelated to the current move of momentum the script is giving a long signal from.
Consider the type of trades you're looking for in your strategy and what timeframe you are trying to trade on.
Longer swing trades which aim to catch bigger moves in the market, possibly on higher time frames, may require a further lookback in order to get your take profits in the correct positioning to catch the desired move, and not exit early before the trade has fully played out.
Shorter scalp trades may aim to catch smaller moves and therefore you don’t want to allow for too much risk by having a large stop loss and large take profits as a result.
Pivot Low 2
Pivot low 2 can be thought of as a backup lookback in order to get the correct pivot low.
In an input which will be discussed shortly called Pivot Low Minimum, you can set a minimum percentage for your pivot low to be, if the pivot low does not meet the minimum then the script will look to Pivot Low 2’s input to use as a bar lookback in order to get the correct pivot low.
This input is used because you might find a Pivot Low input that works well for the majority of the trades in your back tested strategy, however, there will always be outliers and when this Pivot Low input falls short of getting the correct level to put your stop losses at, Pivot Low 2 is used.
Pivot Low 2’s input should always be higher than Pivot Low’s input, that way you can allow the script to look back further in time to find the correct level when the minimum is not met.
Pivot High
This input is used to define the number of bars the script will look back to grab a pivot high when taking a short position.
This pivot high is then used to set the stop loss when entering a short position.
This input is very important and optimizing it correctly can be extremely crucial to your strategies success.
The Strategy Developer tool uses a 1:1 risk to reward ratio when setting your first take profit point, so when the script looks back to get a pivot high based on the input you set, it will then set your first take profit at an equal ratio to the stop loss found from the pivot high.
The goal in optimizing this input is to give enough lookback to find real pivot points where price has reversed off of, but not to give too much lookback where its grabbing previous pivot points unrelated to the current move of momentum the script is giving a short signal from.
Consider the type of trades you're looking for in your strategy and what timeframe you are trying to trade on.
Longer swing trades which aim to catch bigger moves in the market, possibly on higher time frames, may require a further lookback in order to get your take profits in the correct positioning to catch the desired move, and not exit early before the trade has fully played out.
Shorter scalp trades may aim to catch smaller moves and therefore you don’t want to allow for too much risk by having a large stop loss and large take profits as a result.
Pivot High 2
Pivot high 2 can be thought of as a backup lookback in order to get the correct pivot high.
In an input which will be discussed shortly called Pivot High Minimum, you can set a minimum percentage for your pivot high to be, if the pivot high does not meet the minimum then the script will look to Pivot High 2’s input to use as a bar lookback in order to get the correct pivot high.
This input is used because you might find a Pivot High input that works well for the majority of the trades in your back tested strategy, however, there will always be outliers and when this Pivot High input falls short of getting the correct level to put your stop losses at, Pivot High 2 is used.
Pivot High 2’s input should always be higher than Pivot High’s input, that way you can allow the script to look back further in time to find the correct level when the minimum is not met.
Pivot Low Risk Tolerance
This input is very important in managing the risk associated with your strategy.
Pivot Low Risk Tolerance is defining a maximum percentage the pivot low can be away from your entry.
Since the pivot low that’s found is assigned to your stop loss and directly affects the placement of your take profits when taking a long position, making sure the pivot low isn’t too far down is crucial.
Depending on the types of trades you're aiming to take, the timeframe you choose to trade on, and the leverage you use in your strategy, you may want to assign a higher risk tolerance or a lower one.
Example: Pivot Low Risk Tolerance input set to 3, this means that when all other conditions are met in order to take a long position, when searching for the pivot low in order to set a stop loss, if the script finds the pivot low is greater than 3% away from the entry point, it will not take the trade.
Pivot High Risk Tolerance
This input is very important in managing the risk associated with your strategy.
Pivot High Risk Tolerance is defining a maximum percentage the pivot high can be away from your entry.
Since the pivot high that’s found is assigned to your stop loss and directly affects the placement of your take profits when taking a short position, making sure the pivot high isn’t too far up is crucial.
Depending on the types of trades you're aiming to take, the timeframe you choose to trade on, and the leverage you use in your strategy, you may want to assign a higher risk tolerance or a lower one.
Example: Pivot High Risk Tolerance input set to 3, this means that when all other conditions are met in order to take a short position, when searching for the pivot high in order to set a stop loss, if the script finds the pivot high is greater than 3% away from the entry point, it will not take the trade.
Pivot Low Minimum
Sometimes when searching for the pivot low, the script's defined lookback may not be enough to find the proper pivot point.
This can cause improper placement of stop losses and take profits and may cause trades to be exited early before they can fully play out in your favor.
Pivot Low Minimum is an input used to combat this problem, when the script finds a pivot low that does not meet the minimum percentage away from the entry point, it will then turn to Pivot Low 2 input in order to gain a further lookback and grab the correct pivot point to set your stop loss and take profits with.
When reading and setting this input, understand that setting it to 1 means there is no minimum, setting it to 0.9 would mean the minimum is a 10% difference between the pivot low and your entry point.
Think of it in terms of decimals and their equivalent percentage, 0.1 is equal to 10%, 0.01 is equal to 1%.
Whatever percentage you want to set for a minimum, convert it to a decimal, then simply subtract it from 1.
Example: Say you desire a 1.5% minimum pivot low and as a result an equivalent stop loss of 1.5% below your long entry and furthermore a take profit 1.5% above your long entry since the script uses a 1:1 ratio. Converting 1.5% to a decimal would give you 0.015, then subtracting it from 1 would give you 0.985, this would be the input assigned to Pivot Low Minimum.
Pivot High Minimum
Sometimes when searching for the pivot high, the script's defined lookback may not be enough to find the proper pivot point.
This can cause improper placement of stop losses and take profits and may cause trades to be exited early before they can fully play out in your favor.
Pivot High Minimum is an input used to combat this problem, when the script finds a pivot high that does not meet the minimum percentage away from the entry point, it will then turn to Pivot High 2 input in order to gain a further lookback and grab the correct pivot point to set your stop loss and take profits with.
When reading and setting this input, understand that setting it to 1 means there is no minimum, setting it to 0.9 would mean the minimum is a 10% difference between the pivot high and your entry point.
Think of it in terms of decimals and their equivalent percentage, 0.1 is equal to 10%, 0.01 is equal to 1%.
Whatever percentage you want to set for a minimum, convert it to a decimal, then simply subtract it from 1.
Example: Say you desire a 1.5% minimum pivot high and as a result an equivalent stop loss of 1.5% above your short entry and furthermore a take profit 1.5% below your short entry since the script uses a 1:1 ratio. Converting 1.5% to a decimal would give you 0.015, then subtracting it from 1 would give you 0.985, this would be the input assigned to Pivot High Minimum.
Summary of Input Group: Pivot Low/High - Pivot Low/High 2 – Pivot Low/High Risk Tolerance – Pivot Low/High Minimum
The first key takeaway from all these inputs is that your stop losses and take profits will be directly affected through optimizing any of them. The second key takeaway is that these inputs are crucial in managing the risk in your strategy, and while this has been said many times throughout the guide for various inputs, when it comes to stop losses and take profits it is especially true. Having a stop loss which is too high opens up the possibility for much bigger losses, and as a result your take profits will also be too high, minimizing the chance of any of them being hit. Having a stop loss which is too low increases the chance that your trade will get stopped out preemptively, before the trade can mature and move in your favor because remember that trades will not always move immediately in the intended direction, a good amount of patience is often involved in creating consistent successful trades and a successful strategy as such. On the same note, too low of a stop loss could also mean you are missing out on unrealized profit since your take profits are a direct result of the stop loss which is found. When optimizing your pivot low/high risk tolerance, think not about how much you are willing to lose on a single trade, but how much your portfolio can actually afford to lose not just on a single trade but multiple trades, sometimes even in a row. Obviously, the goal in creating a strategy is that you avoid losing trades and especially multiple in a row, however, there are many things that can’t be accounted for. The only way to manage this unaccounted risk is to use proper risk management and not open yourself up to big losses even in the worst most unlikely scenarios. Even if you don’t lose multiple trades in a row, ask yourself, could I afford to lose multiple trades with the risk tolerance I have set if everything were to go to $hit, (hopefully it would not), but in the off chance it did, instead of beating yourself up over what you did wrong, you’ll be patting yourself on the back for what you did right.
TP2-4 Long Placement
The first thing to understand about the take profit placement is that our system of stop losses and take profits uses a 1:1 risk to reward ratio for the first stop loss and first take profit.
This means that if your stop loss falls 2% below your long entry, your first take profit will be 2% above your long entry, hence 1:1.
As for take profits 2-4, they are just extensions of that ratio. This means that if TP2 Long Placement is set to 1.5, the ratio for your second take profit is 1:1.5.
Using the same percentage from the second bullet point being 2%, we can now gather that with a 1:1.5 ratio our second take profit would be at 3% above our long entry.
The same applies for the rest of the take profits, meaning whatever the take profit is set at regardless of which one, apply that number to the second placeholder of the ratio.
Example: First stop loss falls 2% below long entry. TP2 Long Placement input set to 1.5; risk to reward ratio is 1:1.5; corresponding percentage would be a 3% gain. TP3 Long Placement input set to 2; risk to reward ratio is 1:2; corresponding percentage would be a 4% gain. TP4 Long Placement input set to 2.5; risk to reward ratio is 1:2.5; corresponding percentage would be a 5% gain.
The next key thing to understand about the trailing take profits system is the position size being sold at each take profit and therefore how the strategy tracker calculates your strategy's profit.
At the first take profit, 50% of your position is being calculated as sold, locking in good profits off the bat.
At TP2, 20% of your position is being calculated as sold, leaving a remaining 30% open to gain more profit.
At TP3, another 20% of your position is being calculated as sold, leaving 10% to collect any additional possible gains.
At TP4 the remaining 10% of your position is sold and the trade will be fully closed out.
SL2-4 Long Placement
Our system of trailing stop losses is completely similar to that of our trailing take profits.
Just like the trailing take profits, the inputs for stop losses 2-4 are also used as the second placeholders in the risk to reward ratio.
This may be confusing since generally stop losses are associated with a loss on your position, however, the only stop loss which results in a loss on your position is the first one, not stop losses 2-4.
This is because once your first take profit is hit on your long, your stop loss will automatically move up to the price equivalent to the ratio which you set using these inputs that lies in profit.
Example: Since your first take profit will always be at a 1:1 risk to reward ratio with your stop loss, your second take profit could be at a 1:0.8 ratio. So, to clarify, once your first take profit is hit at a 1:1, your original first stop loss will now be moved up in profits to just below your first take profit at a 1:0.8 risk to reward ratio. This only happens AFTER the first take profit is hit.
For stop losses 3 and 4, the same logic is true, once TP2 is hit, your second stop loss will now be moved up to the placement of SL3 which will fall somewhere below TP2. Once TP3 is hit, your third stop loss will now be moved up to the placement of SL4 which will fall somewhere below TP3. If stop loss 4 does not get hit, then the only thing left to happen is for TP4 to hit and the trade will fully close out.
The one major difference between our system of trailing stop losses and take profits is that no matter what stop loss is hit, the entire remainder of your position will be calculated as sold.
So, if your first take profit hits and sells 50% of your long position, but the trade does not continue upwards and moves down to your second stop loss, the remaining 50% of your position will be calculated as sold.
The same applies to SL3 and SL4, so at SL3 the remaining 30% of your position will be calculated as sold, and at SL4 the remaining 10% will be calculated as sold.
Your trailing stop loss placement is dependent on what types of trades you desire. For shorter scalps on smaller timeframes, it's recommended to place each stop loss just below each corresponding take profit for long trades.
This way you leave just enough room for the trade to continue upwards if there is enough momentum, but you don’t open yourself up to losing your unrealized profit if it does not make this continuation.
If you desire longer swing trades on higher timeframes, it might be a good idea to leave more room in between the take profit and corresponding stop loss.
This way you leave more room for the trade to mature and move in your favor since when trading longer moves, often they will not shoot straight up but rather have a series of small pullbacks throughout the more general upwards trend.
Note that when a long trade is first entered the only stop loss and take profit in play are your original stop loss found by the pivot low which would result in a loss, and the first take profit at a 1:1 risk to reward ratio from that pivot low.
TP2-4 Short Placement
The first thing to understand about the take profit placement is that our system of stop losses and take profits uses a 1:1 risk to reward ratio for the first stop loss and first take profit.
This means that if your stop loss falls 2% above your short entry, your first take profit will be 2% below your short entry, hence, 1:1.
As for take profits 2-4, they are just extensions of that ratio. This means that if TP2 Short Placement is set to 1.5, the ratio for your second take profit is 1:1.5.
Using the same percentage from the second bullet point being 2%, we can now gather that with a 1:1.5 ratio our second take profit would be at 3% below our short entry.
The same applies for the rest of the take profits, meaning whatever the take profit is set at regardless of which one, apply that number to the second placeholder of the ratio.
Example: First stop loss falls 2% above short entry. TP2 Short Placement input set to 1.5; risk to reward ratio is 1:1.5; corresponding percentage would be a 3% gain. TP3 Short Placement input set to 2; risk to reward ratio is 1:2; corresponding percentage would be a 4% gain. TP4 Short Placement input set to 2.5; risk to reward ratio is 1:2.5; corresponding percentage would be a 5% gain.
The next key thing to understand about the trailing take profits system is the position size being sold at each take profit and therefore how the strategy tracker calculates your strategy's profit.
At the first take profit, 50% of your position is being calculated as sold, locking in good profits off the bat.
At TP2, 20% of your position is being calculated as sold, leaving a remaining 30% open to gain more profit.
At TP3, another 20% of your position is being calculated as sold, leaving 10% to collect any additional possible gains.
At TP4 the remaining 10% of your position is sold and the trade will be fully closed out.
SL2-4 Short Placement
Our system of trailing stop losses is completely similar to that of our trailing take profits.
Just like the trailing take profits, the inputs for stop losses 2-4 are also used as the second placeholders in the risk to reward ratio.
This may be confusing since generally stop losses are associated with a loss on your position, however, the only stop loss which results in a loss on your position is the first one, not stop losses 2-4.
This is because once your first take profit is hit on your short, your stop loss will automatically move down to the price equivalent to the ratio which you set using these inputs that lies in profit.
Example: Since your first take profit will always be at a 1:1 risk to reward ratio with your stop loss, your second take profit could be at a 1:0.8 ratio. So, to clarify, once your first take profit is hit at a 1:1, your original first stop loss will now be moved down in profits to just below your first take profit at a 1:0.8 risk to reward ratio. This only happens AFTER the first take profit is hit.
For stop losses 3 and 4, the same logic is true, once TP2 is hit, your second stop loss will now be moved down to the placement of SL3 which will fall somewhere above TP2. Once TP3 is hit, your third stop loss will now be moved down to the placement of SL4 which will fall somewhere above TP3. If stop loss 4 does not get hit, then the only thing left to happen is for TP4 to hit and the trade will fully close out.
The one major difference between our system of trailing stop losses and take profits is that no matter what stop loss is hit, the entire remainder of your position will be calculated as sold.
So, if your first take profit hits and sells 50% of your short position, but the trade does not continue downwards and moves up to your second stop loss, the remaining 50% of your position will be calculated as sold.
The same applies to SL3 and SL4, so at SL3 the remaining 30% of your position will be calculated as sold, and at SL4 the remaining 10% will be calculated as sold.
Your trailing stop loss placement is dependent on what types of trades you desire. For shorter scalps on smaller timeframes, it's recommended to place each stop loss just above each corresponding take profit for short trades.
This way you leave just enough room for the trade to continue downwards if there is enough momentum, but you don’t open yourself up to losing your unrealized profit if it does not make this continuation.
If you desire longer swing trades on higher timeframes, it might be a good idea to leave more room in between the take profit and corresponding stop loss.
This way you leave more room for the trade to mature and move in your favor since when trading longer moves, often they will not shoot straight down but rather have a series of small bounces throughout the more general downwards trend.
Note that when a short trade is first entered the only stop loss and take profit in play are your original stop loss found by the pivot high which would result in a loss, and the first take profit at a 1:1 risk to reward ratio from that pivot high.
Summary of Take Profit/Stop Loss Placement:
Correctly placed take profits and stop losses are essential in having a successful strategy and proper risk management. With that being said there are also many ways in which to use this system. Deciding how to set them up is really just a matter of determining the trading style you aim to succeed with. Once this has been determined, the placement of take profits and stop losses should be easier to configure. However, if there is any confusion on either of these topics as the ratios and corresponding TP/SL can get confusing, please do not hesitate to ask further questions in our discord!
Leverage Long
Leverage Long input simply defines the leverage used in your long positions, and is used in calculating the profit in Strategy Tracker
A rundown of risk associated with using leverage will not be given here since it should assume that if you're using leverage, you should already understand the risks.
If you are not using any leverage, then set Leverage Long input to 1.
Long Position Size
This input defines the position size you are using in your long trades.
This input is also used in calculating profit in Strategy Tracker.
Long Hedge Position Size
This input is used to define the position size of long hedge positions.
This input is also used in calculating profit in Strategy Tracker.
Important: Your Long Hedge Position Size should always be half of your Long Position Size for accurate profit calculation.
Double Long Position Size
This input is used to define the position size when in a double long.
This input is also used in calculating profit in Strategy Tracker
Important: Your Double Long Position Size should always be double your Long Position Size for accurate profit calculation.
Short Position Size
This input defines the position size you are using in your short trades.
This input is also used in calculating profit in Strategy Tracker.
Short Hedge Position Size
This input is used to define the position size of short hedge positions.
This input is also used in calculating profit in Strategy Tracker.
Important: Your Short Hedge Position Size should always be half of your Short Position Size for accurate profit calculation.
Double Short Position Size
This input is used to define the position size when in a double short.
This input is also used in calculating profit in Strategy Tracker
Important: Your Double Short Position Size should always be double your Short Position Size for accurate profit calculation.
A Message From the Developer PLEASE READ!!!
If you have made it this far in the guide, I applaud you and thank you for sticking with it as I know there is a lot of information here! This is not an exaggeration when I say there are hundreds of millions of possible variations that could be applied throughout all the inputs which is why I much prefer to call this a tool rather than an algorithm. Algorithm is a loaded word in my opinion as it comes with an implication of guarantee in the trades being made. This is not meant to discourage anybody from taking trades based off the tool which is also why I provided the option for automated alerts which through third party software can turn into automated trades; if you have the confidence in your strategy by all means I encourage you to trade it, automated or not. Just please understand that it's highly recommended to also apply your own knowledge and analysis before taking a trade as historical back testing data has its limitations and cannot always account for current market conditions. The real applicability does not fall in what the back tester window is saying you would have made or how accurate your strategy would have been, it's within the sheer number of markets and scenarios this tool can be used in and the information you can get which a human just can’t comprehend all at once; its literally endless. I urge all of you to be creative and think outside the box about what you can do with such a powerful tool at your fingertips. After all this is the reason why so many inputs were provided. Another main goal of this project was to give users a better understanding of risk management. It can be hard to manage your risk when it’s all kept in your head, but when you can modify your strategy to better manage your risk by simply optimizing a few inputs, it’s a lot easier to comprehend and actually apply when trading. The last thing I want to say is have fun working through the possible learning curve in using this tool, it may be a process but enjoy it because the one thing I can guarantee is that you will come out the other side a better trader than before!
Cyatophilum Swing Trader [ALERTSETUP]This is an indicator for swing trading which allows you to build your own strategies, backtest and alert. This version is the alertsetup which allows to create automated alerts hosted on TradingView servers that will trigger in form of emails, SMS, webhooks, notifications, and more. The backtest version can be found in my profile scripts page.
The particularity of this indicator is that it contains several indicators, including a custom one, that you can choose in a drop down list, as well as a trailing stop loss and take profit system.
The current indicators are :
CYATO AI: a custom indicator inspired by Donchian Channels that will catch each big trend and important reversal points .
The indicator has two major "bands" or channels and two minor bands. The major bands are bigger and are always displayed.
When price reaches a major band, acting as a support/resistance, it will either bounce on it or break through it. This is how "tops" and "bottoms", and breakouts are caught.
The minor bands are used to catch smaller moves inside the major bands. A combination of volume, momentum and price action is used to calculate the signals.
Advantages of this indicator: it should catch top and bottoms better than other swing trade indicators.
Cons of this indicator: Some minor moves might be ignored. Sometimes the script will catch a fakeout due to the Bands design.
Best timeframes to use it : 2H~4H
Sample:
Other indicators available:
SARMA: A combination of Parabolic Stop and Reverse and Exponential Moving Average (20 and 40) .
SAR: Regular Parabolic Stop and Reverse .
QQE: An indicator based on Quantitative Qualitative Estimation .
SUPERTREND: A reversal indicator based on Average True Range .
CHANNELS: The classic Donchian Channels .
More indicators might be added in the future.
About the signals: each entry (long & short) is calculated at bar close to avoid repainting. Exits (SL & TP) can either be intra-bar or at bar close using the Exit alert type parameter.
STOP LOSS SYSTEM
The base indicators listed above can be used with or without TP/SL.
TP and SL can be both turned on and off and configured for both directions.
The system can be configured with 3 parameters as follows:
Stop Loss Base % Price: Starting Value for LONG/SHORT stop loss
Trailing Stop % Price to Trigger First parameter related to the trailing stop loss. Percentage of price movement in the right direction required to make the stop loss line move.
Trailing Stop % Price Movement: Second parameter related to the trailing stop loss. Percentage for the stop loss trailing movement.
Another option is the "Reverse order on Stop Loss". Use this if you want the strategy to trigger a reverse order when a stop loss is hit.
TAKE PROFIT SYSTEM
The system can be configured with 2 parameters as follows:
Take Profit %: Take profit value in percentage of price.
Trailing Profit Deviation %: Percent deviation for the trailing take profit.
Combining indicators and Take Profit/Stop Loss
One thing to note is that if a reversal signal triggers during a trade, the trade will be closed before SL or TP is reached.
Indeed, the base indicators are reversal indicators, they will trigger long/short signals to follow the trend.
It is possible to use a takeprofit without stop loss, like in this example, knowing that the signal will reverse if the trade goes badly.
The base indicators settings can be changed in the "Advanced Parameters" section.
Configuration used for this snapshot:
ALERTS DEFINITION
Each alert correspond to the labels on chart.
01. LONG ENTRY (BUY) : Long alert
02. LONG STOP LOSS : Long stop loss event
03. LONG TAKE PROFIT : Long take profit event
04. SHORT ENTRY (SELL) : Short alert
05. SHORT STOP LOSS : Short stop loss event
06. SHORT TAKE PROFIT : Short take profit event
07. LONG EXIT : Long exit alert. Triggers on both Stop loss and Take Profit
08. SHORT EXIT : Short exit alert. Triggers on both Stop loss and Take Profit
09. ALL TAKE PROFITS : Long and Short Take Profits. Both directions.
10. ALL STOP LOSSES : Long and Short Stop Losses. Both directions.
11. ALL EXITS : Long and Short exits alert. Stop Loss and Take Profit both Long and Short.
Use the link below to obtain access to this indicator.
Game Theory Trading StrategyGame Theory Trading Strategy: Explanation and Working Logic
This Pine Script (version 5) code implements a trading strategy named "Game Theory Trading Strategy" in TradingView. Unlike the previous indicator, this is a full-fledged strategy with automated entry/exit rules, risk management, and backtesting capabilities. It uses Game Theory principles to analyze market behavior, focusing on herd behavior, institutional flows, liquidity traps, and Nash equilibrium to generate buy (long) and sell (short) signals. Below, I'll explain the strategy's purpose, working logic, key components, and usage tips in detail.
1. General Description
Purpose: The strategy identifies high-probability trading opportunities by combining Game Theory concepts (herd behavior, contrarian signals, Nash equilibrium) with technical analysis (RSI, volume, momentum). It aims to exploit market inefficiencies caused by retail herd behavior, institutional flows, and liquidity traps. The strategy is designed for automated trading with defined risk management (stop-loss/take-profit) and position sizing based on market conditions.
Key Features:
Herd Behavior Detection: Identifies retail panic buying/selling using RSI and volume spikes.
Liquidity Traps: Detects stop-loss hunting zones where price breaks recent highs/lows but reverses.
Institutional Flow Analysis: Tracks high-volume institutional activity via Accumulation/Distribution and volume spikes.
Nash Equilibrium: Uses statistical price bands to assess whether the market is in equilibrium or deviated (overbought/oversold).
Risk Management: Configurable stop-loss (SL) and take-profit (TP) percentages, dynamic position sizing based on Game Theory (minimax principle).
Visualization: Displays Nash bands, signals, background colors, and two tables (Game Theory status and backtest results).
Backtesting: Tracks performance metrics like win rate, profit factor, max drawdown, and Sharpe ratio.
Strategy Settings:
Initial capital: $10,000.
Pyramiding: Up to 3 positions.
Position size: 10% of equity (default_qty_value=10).
Configurable inputs for RSI, volume, liquidity, institutional flow, Nash equilibrium, and risk management.
Warning: This is a strategy, not just an indicator. It executes trades automatically in TradingView's Strategy Tester. Always backtest thoroughly and use proper risk management before live trading.
2. Working Logic (Step by Step)
The strategy processes each bar (candle) to generate signals, manage positions, and update performance metrics. Here's how it works:
a. Input Parameters
The inputs are grouped for clarity:
Herd Behavior (🐑):
RSI Period (14): For overbought/oversold detection.
Volume MA Period (20): To calculate average volume for spike detection.
Herd Threshold (2.0): Volume multiplier for detecting herd activity.
Liquidity Analysis (💧):
Liquidity Lookback (50): Bars to check for recent highs/lows.
Liquidity Sensitivity (1.5): Volume multiplier for trap detection.
Institutional Flow (🏦):
Institutional Volume Multiplier (2.5): For detecting large volume spikes.
Institutional MA Period (21): For Accumulation/Distribution smoothing.
Nash Equilibrium (⚖️):
Nash Period (100): For calculating price mean and standard deviation.
Nash Deviation (0.02): Multiplier for equilibrium bands.
Risk Management (🛡️):
Use Stop-Loss (true): Enables SL at 2% below/above entry price.
Use Take-Profit (true): Enables TP at 5% above/below entry price.
b. Herd Behavior Detection
RSI (14): Checks for extreme conditions:
Overbought: RSI > 70 (potential herd buying).
Oversold: RSI < 30 (potential herd selling).
Volume Spike: Volume > SMA(20) x 2.0 (herd_threshold).
Momentum: Price change over 10 bars (close - close ) compared to its SMA(20).
Herd Signals:
Herd Buying: RSI > 70 + volume spike + positive momentum = Retail buying frenzy (red background).
Herd Selling: RSI < 30 + volume spike + negative momentum = Retail selling panic (green background).
c. Liquidity Trap Detection
Recent Highs/Lows: Calculated over 50 bars (liquidity_lookback).
Psychological Levels: Nearest round numbers (e.g., $100, $110) as potential stop-loss zones.
Trap Conditions:
Up Trap: Price breaks recent high, closes below it, with a volume spike (volume > SMA x 1.5).
Down Trap: Price breaks recent low, closes above it, with a volume spike.
Visualization: Traps are marked with small red/green crosses above/below bars.
d. Institutional Flow Analysis
Volume Check: Volume > SMA(20) x 2.5 (inst_volume_mult) = Institutional activity.
Accumulation/Distribution (AD):
Formula: ((close - low) - (high - close)) / (high - low) * volume, cumulated over time.
Smoothed with SMA(21) (inst_ma_length).
Accumulation: AD > MA + high volume = Institutions buying.
Distribution: AD < MA + high volume = Institutions selling.
Smart Money Index: (close - open) / (high - low) * volume, smoothed with SMA(20). Positive = Smart money buying.
e. Nash Equilibrium
Calculation:
Price mean: SMA(100) (nash_period).
Standard deviation: stdev(100).
Upper Nash: Mean + StdDev x 0.02 (nash_deviation).
Lower Nash: Mean - StdDev x 0.02.
Conditions:
Near Equilibrium: Price between upper and lower Nash bands (stable market).
Above Nash: Price > upper band (overbought, sell potential).
Below Nash: Price < lower band (oversold, buy potential).
Visualization: Orange line (mean), red/green lines (upper/lower bands).
f. Game Theory Signals
The strategy generates three types of signals, combined into long/short triggers:
Contrarian Signals:
Buy: Herd selling + (accumulation or down trap) = Go against retail panic.
Sell: Herd buying + (distribution or up trap).
Momentum Signals:
Buy: Below Nash + positive smart money + no herd buying.
Sell: Above Nash + negative smart money + no herd selling.
Nash Reversion Signals:
Buy: Below Nash + rising close (close > close ) + volume > MA.
Sell: Above Nash + falling close + volume > MA.
Final Signals:
Long Signal: Contrarian buy OR momentum buy OR Nash reversion buy.
Short Signal: Contrarian sell OR momentum sell OR Nash reversion sell.
g. Position Management
Position Sizing (Minimax Principle):
Default: 1.0 (10% of equity).
In Nash equilibrium: Reduced to 0.5 (conservative).
During institutional volume: Increased to 1.5 (aggressive).
Entries:
Long: If long_signal is true and no existing long position (strategy.position_size <= 0).
Short: If short_signal is true and no existing short position (strategy.position_size >= 0).
Exits:
Stop-Loss: If use_sl=true, set at 2% below/above entry price.
Take-Profit: If use_tp=true, set at 5% above/below entry price.
Pyramiding: Up to 3 concurrent positions allowed.
h. Visualization
Nash Bands: Orange (mean), red (upper), green (lower).
Background Colors:
Herd buying: Red (90% transparency).
Herd selling: Green.
Institutional volume: Blue.
Signals:
Contrarian buy/sell: Green/red triangles below/above bars.
Liquidity traps: Red/green crosses above/below bars.
Tables:
Game Theory Table (Top-Right):
Herd Behavior: Buying frenzy, selling panic, or normal.
Institutional Flow: Accumulation, distribution, or neutral.
Nash Equilibrium: In equilibrium, above, or below.
Liquidity Status: Trap detected or safe.
Position Suggestion: Long (green), Short (red), or Wait (gray).
Backtest Table (Bottom-Right):
Total Trades: Number of closed trades.
Win Rate: Percentage of winning trades.
Net Profit/Loss: In USD, colored green/red.
Profit Factor: Gross profit / gross loss.
Max Drawdown: Peak-to-trough equity drop (%).
Win/Loss Trades: Number of winning/losing trades.
Risk/Reward Ratio: Simplified Sharpe ratio (returns / drawdown).
Avg Win/Loss Ratio: Average win per trade / average loss per trade.
Last Update: Current time.
i. Backtesting Metrics
Tracks:
Total trades, winning/losing trades.
Win rate (%).
Net profit ($).
Profit factor (gross profit / gross loss).
Max drawdown (%).
Simplified Sharpe ratio (returns / drawdown).
Average win/loss ratio.
Updates metrics on each closed trade.
Displays a label on the last bar with backtest period, total trades, win rate, and net profit.
j. Alerts
No explicit alertconditions defined, but you can add them for long_signal and short_signal (e.g., alertcondition(long_signal, "GT Long Entry", "Long Signal Detected!")).
Use TradingView's alert system with Strategy Tester outputs.
3. Usage Tips
Timeframe: Best for H1-D1 timeframes. Shorter frames (M1-M15) may produce noisy signals.
Settings:
Risk Management: Adjust sl_percent (e.g., 1% for volatile markets) and tp_percent (e.g., 3% for scalping).
Herd Threshold: Increase to 2.5 for stricter herd detection in choppy markets.
Liquidity Lookback: Reduce to 20 for faster markets (e.g., crypto).
Nash Period: Increase to 200 for longer-term analysis.
Backtesting:
Use TradingView's Strategy Tester to evaluate performance.
Check win rate (>50%), profit factor (>1.5), and max drawdown (<20%) for viability.
Test on different assets/timeframes to ensure robustness.
Live Trading:
Start with a demo account.
Combine with other indicators (e.g., EMAs, support/resistance) for confirmation.
Monitor liquidity traps and institutional flow for context.
Risk Management:
Always use SL/TP to limit losses.
Adjust position_size for risk tolerance (e.g., 5% of equity for conservative trading).
Avoid over-leveraging (pyramiding=3 can amplify risk).
Troubleshooting:
If no trades are executed, check signal conditions (e.g., lower herd_threshold or liquidity_sensitivity).
Ensure sufficient historical data for Nash and liquidity calculations.
If tables overlap, adjust position.top_right/bottom_right coordinates.
4. Key Differences from the Previous Indicator
Indicator vs. Strategy: The previous code was an indicator (VP + Game Theory Integrated Strategy) focused on visualization and alerts. This is a strategy with automated entries/exits and backtesting.
Volume Profile: Absent in this strategy, making it lighter but less focused on high-volume zones.
Wick Analysis: Not included here, unlike the previous indicator's heavy reliance on wick patterns.
Backtesting: This strategy includes detailed performance metrics and a backtest table, absent in the indicator.
Simpler Signals: Focuses on Game Theory signals (contrarian, momentum, Nash reversion) without the "Power/Ultra Power" hierarchy.
Risk Management: Explicit SL/TP and dynamic position sizing, not present in the indicator.
5. Conclusion
The "Game Theory Trading Strategy" is a sophisticated system leveraging herd behavior, institutional flows, liquidity traps, and Nash equilibrium to trade market inefficiencies. It’s designed for traders who understand Game Theory principles and want automated execution with robust risk management. However, it requires thorough backtesting and parameter optimization for specific markets (e.g., forex, crypto, stocks). The backtest table and visual aids make it easy to monitor performance, but always combine with other analysis tools and proper capital management.
If you need help with backtesting, adding alerts, or optimizing parameters, let me know!
Stochastic RSI with MTF TableShort Description of the Script
The provided Pine Script indicator, titled "Stochastic RSI with MTF Table," calculates and displays the Stochastic RSI for the current timeframe and multiple other timeframes (5m, 15m, 30m, 60m, 240m, and daily). The Stochastic RSI is a momentum indicator that blends the Relative Strength Index (RSI) and Stochastic Oscillator to identify overbought and oversold conditions, as well as potential trend reversals via K and D line crossovers.
Key features of the script include:
Inputs: Customizable parameters such as K smoothing (default 3), D smoothing (default 3), RSI length (default 14), Stochastic length (default 14), source price (default close), and overbought/oversold levels (default 80/20).
MTF Table: A table displays the Stochastic RSI status for each timeframe:
"OB" (overbought) if K > 80, "OS" (oversold) if K < 20, or "N" (neutral) otherwise.
Crossovers: "K↑D" for bullish (K crosses above D) and "K↓D" for bearish (K crosses below D).
Visualization: Plots the K and D lines for the current timeframe, with horizontal lines at 80 (overbought), 50 (middle), and 20 (oversold), plus a background fill for clarity.
Table Position: Configurable to appear in one of four chart corners (default: top-right).
This indicator helps traders assess momentum across multiple timeframes simultaneously, aiding in the identification of trend strength and potential entry/exit points.
Trading Strategy with 50EMA and 200EMA for Highest Winning Rate
To create a strategy with the best probability of a high winning rate using the Stochastic RSI MTF indicator alongside the 50-period Exponential Moving Average (50EMA) and 200-period Exponential Moving Average (200EMA), we can combine trend identification with momentum-based entry timing. The 50EMA and 200EMA are widely used to determine medium- and long-term trends, while the Stochastic RSI MTF table provides multi-timeframe momentum signals. Here’s the strategy:
1. Determine the Overall Trend
Bullish Trend: The 50EMA is above the 200EMA on the current timeframe (e.g., daily or 60m chart). This suggests an uptrend, often associated with a "Golden Cross."
Bearish Trend: The 50EMA is below the 200EMA on the current timeframe. This indicates a downtrend, often linked to a "Death Cross."
Implementation: Plot the 50EMA and 200EMA on your chart and visually confirm their relative positions.
2. Identify Entry Signals Using the Stochastic RSI MTF Table
In a Bullish Trend (50EMA > 200EMA):
Look for timeframes in the MTF table showing:
Oversold (OS): K < 20, indicating a potential pullback in the uptrend where price may rebound.
Bullish Crossover (K↑D): K crosses above D, signaling rising momentum and a potential entry point.
Example: If the 60m and 240m timeframes show "OS" or "K↑D," this could be a buy signal.
In a Bearish Trend (50EMA < 200EMA):
Look for timeframes in the MTF table showing:
Overbought (OB): K > 80, suggesting a rally in the downtrend where price may reverse downward.
Bearish Crossover (K↓D): K crosses below D, indicating declining momentum and a potential short entry.
Example: If the 30m and daily timeframes show "OB" or "K↓D," this could be a sell/short signal.
Current Timeframe Check: Use the plotted K and D lines on your trading timeframe for precise entry timing (e.g., confirm a K↑D crossover on a 60m chart for a long trade).
3. Confirm Signals Across Multiple Timeframes
Strengthen the Signal: A higher winning rate is more likely when multiple timeframes align with the trend and signal. For instance:
Bullish trend + "OS" or "K↑D" on 60m, 240m, and daily = strong buy signal.
Bearish trend + "OB" or "K↓D" on 15m, 60m, and 240m = strong sell signal.
Prioritize Higher Timeframes: Signals from the 240m or daily timeframe carry more weight due to their indication of broader trends, increasing reliability.
4. Set Stop-Loss and Take-Profit Levels
Long Trades (Bullish):
Stop-Loss: Place below the most recent swing low or below the 50EMA, whichever is closer, to protect against trend reversals.
Take-Profit: Target a key resistance level or use a risk-reward ratio (e.g., 2:1 or 3:1) based on the stop-loss distance.
Short Trades (Bearish):
Stop-Loss: Place above the most recent swing high or above the 50EMA, whichever is closer.
Take-Profit: Target a key support level or apply a similar risk-reward ratio.
Trailing Stop Option: As the trend progresses, trail the stop below the 50EMA (for longs) or above it (for shorts) to lock in profits.
5. Risk Management
Position Sizing: Risk no more than 1-2% of your trading capital per trade to minimize losses from false signals.
Volatility Consideration: Adjust stop-loss distances and position sizes based on the asset’s volatility (e.g., wider stops for volatile stocks or crypto).
Avoid Overtrading: Wait for clear alignment between the EMA trend and MTF signals to avoid low-probability setups.
Example Scenario
Chart: 60-minute timeframe.
Trend: 50EMA > 200EMA (bullish).
MTF Table: 60m shows "OS," 240m shows "K↑D," and daily is "N."
Action: Enter a long position when the 60m K line crosses above D, confirming the table signal.
Stop-Loss: Below the recent 60m swing low (e.g., 2% below entry).
Take-Profit: At the next resistance level or a 3:1 reward-to-risk ratio.
Outcome: High probability of success due to trend alignment and multi-timeframe confirmation.
Why This Strategy Works
Trend Following: Trading in the direction of the 50EMA/200EMA trend reduces the risk of fighting the market’s momentum.
Momentum Timing: The Stochastic RSI MTF table pinpoints pullbacks or reversals within the trend, improving entry timing.
Multi-Timeframe Confirmation: Alignment across timeframes filters out noise, increasing the win rate.
Risk Control: Defined stop-loss and position sizing protect against inevitable losses.
Caveats
No strategy guarantees a 100% win rate; false signals can occur, especially in choppy markets.
Test this strategy on historical data or a demo account to verify its effectiveness for your asset and timeframe.
This approach leverages the strengths of both trend-following (EMA) and momentum (Stochastic RSI) tools, aiming for a high-probability, disciplined trading system.
EMA Crossover Strategy with Take Profit and Candle HighlightingStrategy Overview:
This strategy is based on the Exponential Moving Averages (EMA), specifically the EMA 20 and EMA 50. It takes advantage of EMA crossovers to identify potential trend reversals and uses multiple take-profit levels and a stop-loss for risk management.
Key Components:
EMA Crossover Signals:
Buy Signal (Uptrend): A buy signal is generated when the EMA 20 crosses above the EMA 50, signaling the start of a potential uptrend.
Sell Signal (Downtrend): A sell signal is generated when the EMA 20 crosses below the EMA 50, signaling the start of a potential downtrend.
Take Profit Levels:
Once a buy or sell signal is triggered, the strategy calculates multiple take-profit levels based on the range of the previous candle. The user can define multipliers for each take-profit level.
Take Profit 1 (TP1): 50% of the previous candle's range above or below the entry price.
Take Profit 2 (TP2): 100% of the previous candle's range above or below the entry price.
Take Profit 3 (TP3): 150% of the previous candle's range above or below the entry price.
Take Profit 4 (TP4): 200% of the previous candle's range above or below the entry price.
These levels are adjusted dynamically based on the previous candle's high and low, so they adapt to changing market conditions.
Stop Loss:
A stop-loss is set to manage risk. The default stop-loss is 3% from the entry price, but this can be adjusted in the settings. The stop-loss is triggered if the price moves against the position by this amount.
Trend Direction Highlighting:
The strategy highlights the bars (candles) with colors:
Green bars indicate an uptrend (when EMA 20 crosses above EMA 50).
Red bars indicate a downtrend (when EMA 20 crosses below EMA 50).
These visual cues help users easily identify the market direction.
Strategy Entries and Exits:
Entries: The strategy enters a long (buy) position when the EMA 20 crosses above the EMA 50 and a short (sell) position when the EMA 20 crosses below the EMA 50.
Exits: The strategy exits the positions at any of the defined take-profit levels or the stop-loss. Multiple exit levels provide opportunities to take profit progressively as the price moves in the favorable direction.
Entry and Exit Conditions in Detail:
Buy Entry Condition (Uptrend):
A buy position is opened when EMA 20 crosses above EMA 50, signaling the start of an uptrend.
The strategy calculates take-profit levels above the entry price based on the previous bar's range (high-low) and the multipliers for TP1, TP2, TP3, and TP4.
Sell Entry Condition (Downtrend):
A sell position is opened when EMA 20 crosses below EMA 50, signaling the start of a downtrend.
The strategy calculates take-profit levels below the entry price, similarly based on the previous bar's range.
Exit Conditions:
Take Profit: The strategy attempts to exit the position at one of the take-profit levels (TP1, TP2, TP3, or TP4). If the price reaches any of these levels, the position is closed.
Stop Loss: The strategy also has a stop-loss set at a default value (3% below the entry for long trades, and 3% above for short trades). The stop-loss helps to protect the position from significant losses.
Backtesting and Performance Metrics:
The strategy can be backtested using TradingView's Strategy Tester. The results will show how the strategy would have performed historically, including key metrics like:
Net Profit
Max Drawdown
Win Rate
Profit Factor
Average Trade Duration
These performance metrics can help users assess the strategy's effectiveness over historical periods and optimize the input parameters (e.g., multipliers, stop-loss level).
Customization:
The strategy allows for the adjustment of several key input values via the settings panel:
Take Profit Multipliers: Users can customize the multipliers for each take-profit level (TP1, TP2, TP3, TP4).
Stop Loss Percentage: The user can also adjust the stop-loss percentage to a custom value.
EMA Periods: The default periods for the EMA 50 and EMA 20 are fixed, but they can be adjusted for different market conditions.
Pros of the Strategy:
EMA Crossover Strategy: A classic and well-known strategy used by traders to identify the start of new trends.
Multiple Take Profit Levels: By taking profits progressively at different levels, the strategy locks in gains as the price moves in favor of the position.
Clear Trend Identification: The use of green and red bars makes it visually easier to follow the market's direction.
Risk Management: The stop-loss and take-profit features help to manage risk and optimize profit-taking.
Cons of the Strategy:
Lagging Indicators: The strategy relies on EMAs, which are lagging indicators. This means that the strategy might enter trades after the trend has already started, leading to missed opportunities or less-than-ideal entry prices.
No Confirmation Indicators: The strategy purely depends on the crossover of two EMAs and does not use other confirming indicators (e.g., RSI, MACD), which might lead to false signals in volatile markets.
How to Use in Real-Time Trading:
Use for Backtesting: Initially, use this strategy in backtest mode to understand how it would have performed historically with your preferred settings.
Paper Trading: Once comfortable, you can use paper trading to test the strategy in real-time market conditions without risking real money.
Live Trading: After testing and optimizing the strategy, you can consider using it for live trading with proper risk management in place (e.g., starting with a small position size and adjusting parameters as needed).
Summary:
This strategy is designed to identify trend reversals using EMA crossovers, with customizable take-profit levels and a stop-loss to manage risk. It's well-suited for traders looking for a systematic way to enter and exit trades based on clear market signals, while also providing flexibility to adjust for different risk profiles and trading styles.
Filtered MACD with Backtest [UAlgo]The "Filtered MACD with Backtest " indicator is an advanced trading tool designed for the TradingView platform. It combines the Moving Average Convergence Divergence (MACD) with additional filters such as Moving Average (MA) and Average Directional Index (ADX) to enhance trading signals. This indicator aims to provide more reliable entry and exit points by filtering out noise and confirming trends. Additionally, it includes a comprehensive backtesting module to simulate trading strategies and assess their performance based on historical data. The visual backtest module allows traders to see potential trades directly on the chart, making it easier to evaluate the effectiveness of the strategy.
🔶 Customizable Parameters :
Price Source Selection: Users can choose their preferred price source for calculations, providing flexibility in analysis.
Filter Parameters:
MA Filter: Option to use a Moving Average filter with types such as EMA, SMA, WMA, RMA, and VWMA, and a customizable length.
ADX Filter: Option to use an ADX filter with adjustable length and threshold to determine trend strength.
MACD Parameters: Customizable fast length, slow length, and signal smoothing for the MACD indicator.
Backtest Module:
Entry Type: Supports "Buy and Sell", "Buy", and "Sell" strategies.
Stop Loss Types: Choose from ATR-based, fixed point, or X bar high/low stop loss methods.
Reward to Risk Ratio: Set the desired take profit level relative to the stop loss.
Backtest Visuals: Display entry, stop loss, and take profit levels directly on the chart with
colored backgrounds.
Alerts: Configurable alerts for buy and sell signals.
🔶 Filtered MACD : Understanding How Filters Work with ADX and MA
ADX Filter:
The Average Directional Index (ADX) measures the strength of a trend. The script calculates ADX using the user-defined length and applies a threshold value.
Trading Signals with ADX Filter:
Buy Signal: A regular MACD buy signal (crossover of MACD line above the signal line) is only considered valid if the ADX is above the set threshold. This suggests a stronger uptrend to potentially capitalize on.
Sell Signal: Conversely, a regular MACD sell signal (crossunder of MACD line below the signal line) is only considered valid if the ADX is above the threshold, indicating a stronger downtrend for potential shorting opportunities.
Benefits: The ADX filter helps avoid whipsaws or false signals that might occur during choppy market conditions with weak trends.
MA Filter:
You can choose from various Moving Average (MA) types (EMA, SMA, WMA, RMA, VWMA) for the filter. The script calculates the chosen MA based on the user-defined length.
Trading Signals with MA Filter:
Buy Signal: A regular MACD buy signal is only considered valid if the closing price is above the MA value. This suggests a potential uptrend confirmed by the price action staying above the moving average.
Sell Signal: Conversely, a regular MACD sell signal is only considered valid if the closing price is below the MA value. This suggests a potential downtrend confirmed by the price action staying below the moving average.
Benefits: The MA filter helps identify potential trend continuation opportunities by ensuring the price aligns with the chosen moving average direction.
Combining Filters:
You can choose to use either the ADX filter, the MA filter, or both depending on your strategy preference. Using both filters adds an extra layer of confirmation for your signals.
🔶 Backtesting Module
The backtesting module in this script allows you to visually assess how the filtered MACD strategy would have performed on historical data. Here's a deeper dive into its features:
Backtesting Type: You can choose to backtest for buy signals only, sell signals only, or both. This allows you to analyze the strategy's effectiveness in different market conditions.
Stop-Loss Types: You can define how stop-loss orders are placed:
ATR (Average True Range): This uses a volatility measure (ATR) multiplied by a user-defined factor to set the stop-loss level.
Fixed Point: This allows you to specify a fixed dollar amount or percentage value as the stop-loss.
X bar High/Low: This sets the stop-loss at a certain number of bars (defined by the user) above/below the bar's high (for long positions) or low (for short positions).
Reward-to-Risk Ratio: Define the desired ratio between your potential profit and potential loss on each trade. The backtesting module will calculate take-profit levels based on this ratio and the stop-loss placement.
🔶 Disclaimer:
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
IsAlgo - Reverse Candle Strategy► Overview:
The Reverse Candle Strategy leverages a customizable moving average to identify the start of a trend. It utilizes the highest and lowest prices to define the trend and its corrections, executing trades based on custom candlestick patterns to capitalize on the main trend's continuation.
► Description:
The Reverse Candle Strategy is designed to effectively identify and trade market trends by combining moving averages and custom candlestick patterns. The core of the strategy is a single, customizable moving average, which helps determine the trend direction. When the market price crosses above the moving average, this signifies the beginning of an uptrend. The strategy then tracks the highest price reached during the uptrend and waits for a correction. A specific custom candlestick pattern signals the end of the correction, at which point the strategy executes a long trade.
In the case of a downtrend, the market price crossing below the moving average marks the trend’s start. The strategy monitors the lowest price during the downtrend and awaits a correction. The end of this correction is identified by another custom candlestick pattern, prompting the strategy to execute a short trade. This combination of a moving average with precise candlestick patterns ensures that trades are made at optimal moments, improving the likelihood of successful trades.
The integration of the moving average and candlestick patterns is critical. The moving average smooths out price data to highlight the trend direction, while the custom candlestick patterns provide specific entry signals after a correction, ensuring the trend’s resumption is genuine. This synergy enhances the strategy’s ability to filter out false signals and improve trade accuracy.
↑ Long Entry Example:
When the price is moving above the moving average and the highest price has been detected, the strategy will wait for the entry candle to execute the long trade.
↓ Short Entry Example:
When the price is moving below the moving average and the lowest price has been detected, the strategy will wait for the entry candle to execute the short trade.
✕ Exit Conditions:
To manage risk effectively, the strategy provides multiple stop-loss options. Traders can set stop-loss levels using fixed pips, ATR-based calculations, or the higher/lower price of past candles. Additionally, trades can be closed if a candle moves against the trade direction. Up to three take-profit levels can be set using fixed pips, ATR, or risk-to-reward ratios, allowing traders to secure profits at different stages. The trailing stop feature adjusts the stop loss as the trade moves into profit, locking in gains while allowing for continued potential upside. Furthermore, a break-even feature moves the stop loss to the entry price once a certain profit level is reached, protecting against losses. Trades can also be closed when the price crosses the moving average.
► Features & Settings:
⚙︎ Moving Average: Users can choose between various types of moving averages (e.g., SMA, EMA) to confirm the trend direction.
⚙︎ Trend & Corrections: Set minimum and maximum pips for trends and corrections, with an option to define correction percentages relative to the trend.
⚙︎ Entry Candle: Define the entry candle by specifying the minimum and maximum size of the candle's body and the ratio of the body to the entire candle size, ensuring significant breakouts trigger trades.
⚙︎ Trading Session: This feature allows users to define specific trading hours during which the strategy should operate, ensuring trades are executed only during preferred market periods.
⚙︎ Trading Days: Users can specify which days the strategy should be active, offering the flexibility to avoid trading on specific days of the week.
⚙︎ Backtesting: Enables a backtesting period during which the strategy can be tested over a selected start and end date. This feature can be deactivated if not needed.
⚙︎ Trades: Configure trade direction (long, short, or both), position sizing (fixed or percentage-based), maximum number of open trades, and daily trade limits.
⚙︎ Trades Exit: Various exit methods, such as setting profit or loss limits, trade duration, or closing trades on moving average crossings.
⚙︎ Stop Loss: Various stop-loss methods are available, including a fixed number of pips, ATR-based, or using the highest or lowest price points within a specified number of previous candles. Additionally, trades can be closed after a specific number of candles move in the opposite direction of the trade.
⚙︎ Break Even: This feature adjusts the stop loss to a break-even point once certain conditions are met, such as reaching predefined profit levels, to protect gains.
⚙︎ Trailing Stop: The trailing stop feature adjusts the stop loss as the trade moves into profit, securing gains while potentially capturing further upside.
⚙︎ Take Profit: up to three take-profit levels using fixed pips, ATR, or risk-to-reward ratios based on the stop loss. Alternatively, specify a set number of candles moving in the trade direction.
⚙︎ Alerts: The strategy includes a comprehensive alert system that informs the user of all significant actions, such as trade openings and closings. It supports placeholders for dynamic values like take-profit levels and stop-loss prices.
⚙︎ Dashboard: Visual display providing detailed information about ongoing and past trades on the chart, helping users monitor performance and make informed decisions.
► Backtesting Details:
Timeframe: 30-minute NAS100 chart
Initial Balance: $10,000
Order Size: 5 Units
Commission: $0.5 per contract
Slippage: 5 ticks
Stop Loss: MA Crossing or by break even
IsAlgo - Ultra Trend Strategy► Overview:
The Ultra Trend strategy is designed to identify trend lines based on average price movement and execute trades when the price crosses the middle line, confirmed by an entry candle. This strategy combines ATR, Moving Averages, and customizable candlestick patterns to provide a versatile and robust trading approach.
► Description:
The Ultra Trend strategy employs a multi-faceted approach to accurately gauge market trends and execute trades. It combines the Average True Range (ATR) with trendline analysis and Moving Averages, providing a comprehensive view of market conditions. The strategy uses ATR to measure market volatility and the average price movement, helping to set dynamic thresholds for trend detection and adapting to changing market conditions. The slope of the trend is calculated based on the angle of price movement, which aids in identifying the strength and direction of the trend.
Additionally, a Moving Average is used to filter trades, ensuring alignment with the broader market direction and reducing false signals, thereby enhancing trade accuracy.
Traders can configure the strategy to enter trades in the direction of the trend, against the trend, or both. This feature enhances the adaptability of the Ultra Trend strategy, making it suitable for various trading styles and market environments.
↑ Long Entry:
A long trade is executed when the entry candle crosses and closes above the trend line. This indicates a bullish market condition, signaling an opportunity to enter a buy position.
↓ Short Entry:
A short trade is executed when the entry candle crosses and closes below the trend line. This indicates a bearish market condition, signaling an opportunity to enter a sell position.
✕ Exit Conditions:
The strategy offers multiple stop-loss options to manage risk effectively. Traders can set stop-loss levels using fixed pips, ATR-based calculations, the higher/lower price of past candles, or close a trade if a candle moves against the trade direction.
Up to three take profit levels can be set using methods such as fixed pips, ATR, and risk-to-reward ratios. This allows traders to secure profits at various stages of the trade.
A trailing stop feature adjusts the stop loss as the trade moves into profit, locking in gains while allowing the trade to continue capturing potential upside. Additionally, a break-even feature moves the stop loss to the entry price once a certain profit level is reached, protecting against losses.
Trades can also be closed when a trend change is detected or when a candle closes outside a predefined channel, ensuring that positions are exited promptly in response to changing market conditions.
► Features and Settings:
⚙︎ Trend: Users can configure the trend direction, length, factor, and slope, allowing for precise control over how trends are identified and followed.
⚙︎ Moving Average: An Exponential Moving Average (EMA) can be employed to confirm the trend direction indicated by the trend lines. This provides further assurance that the trend line breakout is not a false signal. The EMA can be enabled or disabled based on user preference.
⚙︎ Entry Candle: The entry candle is the candle that breaks the trend line, signaling an entry opportunity. Users can specify the minimum and maximum size of the candle's body and the ratio of the body to the entire candle size. This ensures that only significant breakouts trigger trades.
⚙︎ Trading Session: This feature allows users to define specific trading hours during which the strategy should operate, ensuring trades are executed only during preferred market periods.
⚙︎ Trading Days: Users can specify which days the strategy should be active, offering the flexibility to avoid trading on specific days of the week.
⚙︎ Backtesting: Enables a backtesting period during which the strategy can be tested over a selected start and end date. This feature can be deactivated if not needed.
⚙︎ Trades: This includes configuring the direction of trades (long, short, or both), position sizing (fixed or percentage-based), the maximum number of open trades, and limitations on the number of trades per day or based on trend.
⚙︎ Trades Exit: The strategy offers various exit methods, such as setting profit or loss limits, specifying the duration a trade should remain open, or closing trades based on trend reversal.
⚙︎ Stop Loss: Various stop-loss methods are available, including a fixed number of pips, ATR-based, or using the highest or lowest price points within a specified number of previous candles. Additionally, trades can be closed after a specific number of candles move in the opposite direction of the trade.
⚙︎ Break Even: This feature adjusts the stop loss to a break-even point once certain conditions are met, such as reaching predefined profit levels, to protect gains.
⚙︎ Trailing Stop: The trailing stop feature adjusts the stop loss as the trade moves into profit, securing gains while potentially capturing further upside.
⚙︎ Take Profit: Up to three take-profit levels can be set using various methods, such as a fixed amount of pips, ATR, or risk-to-reward ratios based on the stop loss. Alternatively, users can specify a set number of candles moving in the direction of the trade.
⚙︎ Alerts: The strategy includes a comprehensive alert system that informs the user of all significant actions, such as trade openings and closings. It supports placeholders for dynamic values like take-profit levels and stop-loss prices.
⚙︎ Dashboard: A visual display provides detailed information about ongoing and past trades on the chart, helping users monitor the strategy's performance and make informed decisions.
► Backtesting Details:
Timeframe: 5-minute US30 chart
Initial Balance: $10,000
Order Size: 4% of equity per trade
Commission: $0.05 per contract
Slippage: 5 ticks
Stop Loss: ATR-based
Price Action Pattern Breakout Strategy: Wedge,Triangle,ChannelIntroducing the Price Action Pattern Breakout Strategy: Wedge,Triangle,Channel 💹🚀
The "Price Action Pattern Breakout Strategy: Wedge, Triangle, Channel" is a dynamic and automated trading strategy that excels in recognizing and capitalizing on breakout opportunities within the realm of powerful price action patterns. It is finely tuned to achieve exceptional precision in detecting three distinct pattern types: Wedge, Triangle, and Channel. This diversity equips you to confidently navigate a wide range of market scenarios and opportunities.
This strategy automates trade entries and exits upon confirmed pattern breakouts, this eliminates human errors in correctly recognizing patterns and prevents emotional decisions. This strategy is designed to work across different time frames, making it suitable for both short-term and long-term traders. Whether you're a day trader, swing trader, or investor, this strategy provides the flexibility you need to thrive in diverse market conditions.
💎 How it Works:
▶️ In this strategy, three price action patterns have been utilized, one of which is the "Wedge" pattern. The Wedge pattern has consistently demonstrated a high level of credibility, typically resulting in sharp and rapid price movements following a confirmed breakout from this pattern. This characteristic makes the Wedge pattern highly noteworthy in our strategy. The second pattern is the "Triangle" pattern, which, depending on its formation, whether ascending or descending, can indicate a strong continuation or reversal of the trend. The last pattern is the "Channel" pattern. The reason for using the Channel pattern is its versatility in various market conditions and its tendency to produce reliable results.
In the snapshot below, you can observe the types of patterns that this strategy is capable of identifying at a glance:
▶️ This strategy employs two types of targeting systems: Fixed Targets and Trailing Targets.
Fixed Targets is the default targeting system of the strategy, incorporating two primary targets: TP1 (Target Point 1) and TP2 (Target Point 2). These targets are thoughtfully adjusted in alignment with specific rules for each pattern. With Fixed Targets, you have the flexibility to designate the position size percentage for your exits at TP1 and TP2. For instance, should you opt to allocate 60% of your position size to TP1, as soon as the price triggers the first take profit level, 60% of your initial position is gracefully closed, leaving the remaining 40% to exit the trade upon reaching TP2.
Trailing Targets represent the strategy's alternative targeting system. With this system, the trailing stop becomes active once the price reaches the specified trigger point. The strategy then exits the trade based on the defined offset percentage and price retracement from the trailing limit.
▶️ This strategy relies on a single type of stop loss, determined by previous pivot points and adjusted based on the trade's direction, whether long or short, placing the stop loss above or below the prior pivot. This stop loss approach has demonstrated reliability when used alongside price action patterns.
In addition to this fixed stop loss, you can specify a percentage buffer, offering protection against potential stop hunting due to market fluctuations. This buffer helps protect your positions from sudden price swings. For example, selecting a 1% buffer means your stop loss will be positioned 1% higher or lower concerning the last pivot, depending on your trade's direction. This added layer of security ensures your trades remain resilient and less vulnerable to market volatility.
▶️ A practical feature of this strategy is the "Risk-Free" option. Once activated, it continuously monitors price movements, and as soon as the price progresses in the trade's direction and surpasses the designated Risk-Free Trigger Point in percentage, the stop loss is dynamically shifted from its initial position to the entry price, effectively making the trade "risk-free." This means that if the trade doesn't go as expected, we exit at the entry point, incurring neither profit nor loss from the trade.
Additionally, you have the flexibility to fine-tune the modified stop loss, positioning it slightly above or below the entry price through the configuration of a specified percentage. This allows for effective consideration of commission fees in your trading strategy.
▶️ Risk management is a crucial concept in trading, playing a significant role in a trader's long-term success. This strategy introduces a unique feature called "Fixed Loss Position Sizing", where upon activation, you can limit the risk exposure to a specified percentage of your capital per trade. Set your preferred risk percentage along with the intended leverage. The strategy independently considers your available capital and designated leverage, determining the position size before executing any trade.
In the case of a stop loss, your loss is limited to the specified risk percentage. For instance, with a $1000 account and a 1% risk set, the strategy adjusts each trade's size to ensure a maximum loss of $10 if the stop loss is triggered. Enabling this feature will ensure disciplined risk management, aligning potential losses precisely with your predetermined risk percentage, contingent upon your total available capital.
▶️ Another feature of this strategy is a sophisticated mechanism called "Loss Compensation". When enabled, Loss Compensation dynamically adjusts the position size after a loss, aiming to recover from previous losses in subsequent trades. This adaptive mechanism continually modifies the position size to mitigate the impact of consecutive losses until reaching a user-defined limit for consecutive loss compensations.
The feature's configurability allows users to set the maximum number of consecutive losses to compensate for and also includes an option to factor in trading fees from prior trades into the compensation calculation. Loss Compensation operates in conjunction with the 'Fixed Loss Position Sizing' setting, ensuring that once losses are sufficiently compensated, subsequent entries revert to the predefined configurations within the 'Fixed Loss Position Sizing' settings.
This advanced tool ensures a stable risk management approach by changing trade sizes dynamically according to past results during consecutive loss periods.
▶️ This strategy incorporates a feature known as the "Counter-Pattern Breakout", altering its approach to wedge, triangle, and channel pattern breakouts. Normally, the strategy relies on standard pattern signals to determine whether to enter long or short positions based on breakout directions.
For example, in an ascending channel or a rising wedge pattern, the strategy typically seeks a short position opportunity upon a confirmed breakout in the lower line, and breakouts from the upper line are disregarded by the strategy. But with this feature enabled, strategy disregards the conventional pattern signals, seizing breakouts from upper or lower lines to open corresponding positions. For instance, in the ascending channel or the rising wedge pattern example, the strategy might enter a long position if the upper line breaks or a short position if the lower line breaks.
This introduces a more adaptive and opportunistic trading style, allowing you to capitalize on price movements, irrespective of the typical signal direction indicated by the pattern.
▶️ This strategy is fully compatible with third-party trading bots, allowing for easy connectivity to popular trading platforms. By leveraging the TradingView webhook functionality, you can effortlessly link the strategy to your preferred bot and receive accurate signals for position entry and exit. The strategy provides all the necessary alert message fields, ensuring a smooth and user-friendly trading experience. With this integration, you can automate the execution of trades, saving time and effort while enjoying the benefits of this powerful strategy.
⚙️ How to Use & Configure User Settings:
To fully utilize the "Price Action Pattern Breakout Strategy: Wedge, Triangle, Channel," it's essential to consider and comprehend the following steps. They play a crucial role in enhancing its functionality and achieving its utmost potential outcomes:
1. General Strategy Settings:
Enable Dark Mode if using a dark TradingView theme for improved chart visibility.
Select the Strategy's Trade Direction: Long, Short, or Both.
Choose Pattern Recognition Accuracy: High for precise recognition but fewer positions, Low for more positions with slightly less accuracy.
Enable 'Prevent New Entry on Opposite Signal While In Position' to avoid new trades if the opposite signal occurs.
Switch to Indicator Mode if solely using the strategy as an indicator or in combination with other strategies.
2. Pattern and Pivot Configuration:
Consider configuring the Number of Patterns and Pivot Lookback Lengths. Here, you can personalize the pivot lookback lengths for wedge, triangle, and channel patterns across eight different settings on your chart. For lower time frames, consider larger lengths to reduce chart noise. Alternatively, to maintain clarity on your chart, you can disable multiple patterns with different lengths while ensuring at least one pattern remains enabled.
Note that enabling more patterns doesn't always equate to increased potential profit. Sometimes, fewer patterns result in greater profit potential, and vice versa. Experiment with lengths and the number of patterns to determine the most profitable and optimal outcome for your trading symbol and timeframe.
3. Targeting System Selection:
Choose between 'Fixed Targets' or 'Trailing Targets' for your targeting system.
'Fixed Targets' is the default setting, operational when 'Trailing Targets' are turned off.
Set the TP1 Position Size as a percentage, defining the size for TP1, and the rest exits at TP2.
Optionally activate 'Skip Entry if TP1 is Passed' to bypass entering positions if the price has exceeded TP1.
Alternatively, opt for the 'Trailing Target' for dynamic exits based on trigger points and offsets. Note that this option disables fixed targets.
4. Stop Loss Configuration:
Determine the number of candles to consider for stop loss placement based on the last pivot.
Optionally add a percentage to the stop loss to create a buffer against market fluctuations, guarding your positions from sudden price swings.
5. Risk Management Configuration:
You can activate the 'Risk-Free' feature, making your trades risk-free by moving the stop loss to the entry price upon reaching a specified trigger point.
You have the possibility to enable 'Fixed Loss Position Sizing' to limit risk to a percentage of total capital per trade, ensuring prudent risk management.
You can employ 'Use Real-Time Balance for Each Entry' to precisely calculate fixed loss position sizing according to the real-time balance for every entry.
The 'Loss Compensation' feature can be activated to automatically adjust trade sizes during consecutive losses and compensate for prior incurred losses.
Loss compensation continues adjusting trade sizes until it reaches the defined limit of consecutive losses specified in the 'Maximum Consecutive Losses To Compensate' field.
You can factor in commission fees by specifying a percentage in the 'Include Trading Fees in Compensation (%)' field, providing an option for more accurate loss compensation calculations.
You have the option to enable 'Limit Compensation to Real-Time Balance' to prevent consecutive loss compensation from exceeding your current real-time account balance.
It's important to note that for the 'Loss Compensation' feature to operate, the 'Fixed Loss Position Sizing' must be enabled.
6. Counter-Pattern Breakout Configuration:
In this section you have the option to enable the "Counter-Pattern Breakout" feature to adjust the strategy's approach to wedge, triangle, and channel pattern breakouts. Once enabled, the strategy disregards traditional pattern signals and capitalizes on breakouts from either the upper or lower lines, initiating corresponding positions accordingly.
Choose between 'Fixed Target' or 'Trailing Target' for your targeting system. If you opt for the 'Fixed Target', set a specific target point as a percentage, serving as the default target for counter-pattern breakouts. Alternatively, choose the 'Trailing Target' for dynamic exits based on trigger points and offsets. Do keep in mind that selecting the 'Trailing Target' option disables the fixed target setting.
Keep in mind that for standard, non-counter-pattern breakouts, the target point settings in their respective sections remain applicable, distinct from the settings configured for targeting within this section.
Note that the stop loss configurations are shared across standard pattern and counter-pattern breakouts and can be adjusted within the stop loss section.
7. Info Tables:
In the info tables section, you can show or hide different tables on the charts. This includes the backtest table, the current balance table displaying available funds, and a table showcasing Maximum Consecutive Wins or Losses. Choose which to display according to your preferences and specific needs.
8.Date & Time Range Filter:
Utilize the Date & Time Range filter feature to precisely select a start and end date, including time, to filter data within the chosen range.
When connecting this strategy to a trading bot for automated trades, ensure to set the start date and time to the intended initiation moment to avoid undesired outcomes as this directly affects the real-time balance calculations of the strategy.
8. Integration with Third-Party Bots:
To automate trading, leverage the strategy's compatibility with third-party trading bots. Seamlessly integrate the strategy into well-known trading platforms by using alert message fields to input commands from third-party trading bots, enabling automated trade execution for both long and short positions.
By furnishing these adjustable settings, the strategy empowers you to personalize it according to your unique requirements, thereby bolstering the adaptability and efficacy of your trading approach.
🔐 Source Code Protection:
The 'Price Action Pattern Breakout Strategy: Wedge, Triangle, Channel' source code is engineered for precision, reliability, and effectiveness. Its original and innovative design warrants protection and restricted access, preserving the strategy's exclusivity. Safeguarding the code maintains the strategy's integrity and distinctiveness, providing users with a competitive advantage in their trading endeavors.
