PINE LIBRARY
MLExtensions
Atualizado
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.
Notas de Lançamento
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.
Notas de Lançamento
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
Biblioteca do Pine
De acordo com o verdadeiro espírito do TradingView, o autor publicou esse código Pine como uma biblioteca de código aberto para que outros programadores Pine de nossa comunidade possam reutilizá-lo. Parabéns ao autor! Você pode usar essa biblioteca de forma privada ou em outras publicações de código aberto, mas a reutilização desse código em uma publicação é regida pelas Regras da Casa.
❤️ 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
Aviso legal
As informações e publicações não devem ser e não constituem conselhos ou recomendações financeiras, de investimento, de negociação ou de qualquer outro tipo, fornecidas ou endossadas pela TradingView. Leia mais em Termos de uso.