PINE LIBRARY
MLExtensions
Updated
Library "MLExtensions"
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.
regime_filter(src, threshold, useRegimeFilter)
Parameters:
src
threshold
useRegimeFilter
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.
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.
regime_filter(src, threshold, useRegimeFilter)
Parameters:
src
threshold
useRegimeFilter
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.
Release Notes
v2Updated:
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.
Release Notes
v3Added:
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
Pine library
In true TradingView spirit, the author has published this Pine code as an open-source library so that other Pine programmers from our community can reuse it. Cheers to the author! You may use this library privately or in other open-source publications, but reuse of this code in a publication is governed by House rules.
❤️ Patreon w/ Lorentzian 2.0: patreon.com/jdehorty
🎥 Lorentzian Classification Tutorial: youtu.be/AdINVvnJfX4
🤖 Discord: discord.gg/ai-edge-107567337
⏩ LinkedIn: linkedin.com/in/justin-dehorty
🎥 Lorentzian Classification Tutorial: youtu.be/AdINVvnJfX4
🤖 Discord: discord.gg/ai-edge-107567337
⏩ LinkedIn: linkedin.com/in/justin-dehorty
Disclaimer
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.