normalizeDeriv(src, quadraticMeanLength) Returns the smoothed hyperbolic tangent of the input series. Parameters: src: <series float> The input series (i.e., the first-order derivative for price). quadraticMeanLength: <int> The length of the quadratic mean (RMS). Returns: nDeriv <series float> The normalized derivative of the input series.
normalize(src, min, max) Rescales a source value with an unbounded range to a target range. Parameters: src: <series float> The input series min: <float> The minimum value of the unbounded range max: <float> The maximum value of the unbounded range Returns: <series float> The normalized series
rescale(src, oldMin, oldMax, newMin, newMax) Rescales a source value with a bounded range to anther bounded range Parameters: src: <series float> The input series oldMin: <float> The minimum value of the range to rescale from oldMax: <float> The maximum value of the range to rescale from newMin: <float> The minimum value of the range to rescale to newMax: <float> The maximum value of the range to rescale to Returns: <series float> The rescaled series
color_green(prediction) Assigns varying shades of the color green based on the KNN classification Parameters: prediction: Value (int|float) of the prediction Returns: color <color>
color_red(prediction) Assigns varying shades of the color red based on the KNN classification Parameters: prediction: Value of the prediction Returns: color
tanh(src) Returns the the hyperbolic tangent of the input series. The sigmoid-like hyperbolic tangent function is used to compress the input to a value between -1 and 1. Parameters: src: <series float> The input series (i.e., the normalized derivative). Returns: tanh <series float> The hyperbolic tangent of the input series.
dualPoleFilter(src, lookback) Returns the smoothed hyperbolic tangent of the input series. Parameters: src: <series float> The input series (i.e., the hyperbolic tangent). lookback: <int> The lookback window for the smoothing. Returns: filter <series float> The smoothed hyperbolic tangent of the input series.
tanhTransform(src, smoothingFrequency, quadraticMeanLength) Returns the tanh transform of the input series. Parameters: src: <series float> The input series (i.e., the result of the tanh calculation). smoothingFrequency quadraticMeanLength Returns: signal <series float> The smoothed hyperbolic tangent transform of the input series.
n_rsi(src, n1, n2) Returns the normalized RSI ideal for use in ML algorithms. Parameters: src: <series float> The input series (i.e., the result of the RSI calculation). n1: <int> The length of the RSI. n2: <int> The smoothing length of the RSI. Returns: signal <series float> The normalized RSI.
n_cci(src, n1, n2) Returns the normalized CCI ideal for use in ML algorithms. Parameters: src: <series float> The input series (i.e., the result of the CCI calculation). n1: <int> The length of the CCI. n2: <int> The smoothing length of the CCI. Returns: signal <series float> The normalized CCI.
n_wt(src, n1, n2) Returns the normalized WaveTrend Classic series ideal for use in ML algorithms. Parameters: src: <series float> The input series (i.e., the result of the WaveTrend Classic calculation). n1 n2 Returns: signal <series float> The normalized WaveTrend Classic series.
n_adx(highSrc, lowSrc, closeSrc, n1) Returns the normalized ADX ideal for use in ML algorithms. Parameters: highSrc: <series float> The input series for the high price. lowSrc: <series float> The input series for the low price. closeSrc: <series float> The input series for the close price. n1: <int> The length of the ADX.
filter_adx(src, length, adxThreshold, useAdxFilter) filter_adx Parameters: src: <series float> The source series. length: <int> The length of the ADX. adxThreshold: <int> The ADX threshold. useAdxFilter: <bool> Whether to use the ADX filter. Returns: <series float> The ADX.
filter_volatility(minLength, maxLength, useVolatilityFilter) filter_volatility Parameters: minLength: <int> The minimum length of the ATR. maxLength: <int> The maximum length of the ATR. useVolatilityFilter: <bool> Whether to use the volatility filter. Returns: <bool> Boolean indicating whether or not to let the signal pass through the filter.
backtest(high, low, open, startLongTrade, endLongTrade, startShortTrade, endShortTrade, isStopLossHit, maxBarsBackIndex, thisBarIndex) Performs a basic backtest using the specified parameters and conditions. Parameters: high: <series float> The input series for the high price. low: <series float> The input series for the low price. open: <series float> The input series for the open price. startLongTrade: <series bool> The series of conditions that indicate the start of a long trade.` endLongTrade: <series bool> The series of conditions that indicate the end of a long trade. startShortTrade: <series bool> The series of conditions that indicate the start of a short trade. endShortTrade: <series bool> The series of conditions that indicate the end of a short trade. isStopLossHit: <bool> The stop loss hit indicator. maxBarsBackIndex: <int> The maximum number of bars to go back in the backtest. thisBarIndex: <int> The current bar index. Returns: <tuple strings> A tuple containing backtest values
init_table() init_table() Returns: tbl <series table> The backtest results.
update_table(tbl, tradeStatsHeader, totalTrades, totalWins, totalLosses, winLossRatio, winrate, stopLosses) update_table(tbl, tradeStats) Parameters: tbl: <series table> The backtest results table. tradeStatsHeader: <string> The trade stats header. totalTrades: <float> The total number of trades. totalWins: <float> The total number of wins. totalLosses: <float> The total number of losses. winLossRatio: <float> The win loss ratio. winrate: <float> The winrate. stopLosses: <float> The total number of stop losses. Returns: <void> Updated backtest results table.
Notes de version
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v2
Updated: backtest(high, low, open, startLongTrade, endLongTrade, startShortTrade, endShortTrade, isEarlySignalFlip, maxBarsBackIndex, thisBarIndex, src, useWorstCase) Performs a basic backtest using the specified parameters and conditions. Parameters: high: <series float> The input series for the high price. low: <series float> The input series for the low price. open: <series float> The input series for the open price. startLongTrade: <series bool> The series of conditions that indicate the start of a long trade. endLongTrade: <series bool> The series of conditions that indicate the end of a long trade. startShortTrade: <series bool> The series of conditions that indicate the start of a short trade. endShortTrade: <series bool> The series of conditions that indicate the end of a short trade. isEarlySignalFlip: <bool> Whether or not the signal flip is early. maxBarsBackIndex: <int> The maximum number of bars to go back in the backtest. thisBarIndex: <int> The current bar index. src: <series float> The source series. useWorstCase: <bool> Whether to use the worst case scenario for the backtest. Returns: <tuple strings> A tuple containing backtest values
update_table(tbl, tradeStatsHeader, totalTrades, totalWins, totalLosses, winLossRatio, winrate, earlySignalFlips) update_table(tbl, tradeStats) Parameters: tbl: <series table> The backtest results table. tradeStatsHeader: <string> The trade stats header. totalTrades: <float> The total number of trades. totalWins: <float> The total number of wins. totalLosses: <float> The total number of losses. winLossRatio: <float> The win loss ratio. winrate: <float> The winrate. earlySignalFlips: <float> The total number of early signal flips. Returns: <void> Updated backtest results table.
Notes de version
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v3
Added: getColorShades(color) Creates an array of colors with varying shades of the input color Parameters: color (color): <color> The color to create shades of Returns: <array color> An array of colors with varying shades of the input color
getPredictionColor(prediction, neighborsCount, shadesArr) Determines the color shade based on prediction percentile Parameters: prediction (float): <float> Value of the prediction neighborsCount (int): <int> The number of neighbors used in a nearest neighbors classification shadesArr (color[]): <array color> An array of colors with varying shades of the input color Returns: shade <color> Color shade based on prediction percentile