jdehorty

MLExtensions

jdehorty 已更新   
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.
發布通知:
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: The input series for the high price.
    low: The input series for the low price.
    open: The input series for the open price.
    startLongTrade: The series of conditions that indicate the start of a long trade.
    endLongTrade: The series of conditions that indicate the end of a long trade.
    startShortTrade: The series of conditions that indicate the start of a short trade.
    endShortTrade: The series of conditions that indicate the end of a short trade.
    isEarlySignalFlip: Whether or not the signal flip is early.
    maxBarsBackIndex: The maximum number of bars to go back in the backtest.
    thisBarIndex: The current bar index.
    src: The source series.
    useWorstCase: Whether to use the worst case scenario for the backtest.
  Returns: A tuple containing backtest values

update_table(tbl, tradeStatsHeader, totalTrades, totalWins, totalLosses, winLossRatio, winrate, earlySignalFlips)
  update_table(tbl, tradeStats)
  Parameters:
    tbl: The backtest results table.
    tradeStatsHeader: The trade stats header.
    totalTrades: The total number of trades.
    totalWins: The total number of wins.
    totalLosses: The total number of losses.
    winLossRatio: The win loss ratio.
    winrate: The winrate.
    earlySignalFlips: The total number of early signal flips.
  Returns: Updated backtest results table.
發布通知:
v3

Added:
getColorShades(color)
  Creates an array of colors with varying shades of the input color
  Parameters:
    color (color): The color to create shades of
  Returns: 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): Value of the prediction
    neighborsCount (int): The number of neighbors used in a nearest neighbors classification
    shadesArr (color): An array of colors with varying shades of the input color
  Returns: shade Color shade based on prediction percentile

❤️ Patreon w/ Lorentzian Beta: www.patreon.com/jdehorty

🎥 Lorentzian Classification Tutorial: youtu.be/AdINVvnJfX4

🤖 Discord w/ Deep Forecast: discord.gg/ai-edge

⏩ LinkedIn: www.linkedin.com/in/justin-dehorty
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