ICOptimizer

Library "ICOptimizer"
Library for IC-based parameter optimization

findOptimalParam(testParams, icValues, currentParam, smoothing)
  Find optimal parameter from array of IC values
  Parameters:
    testParams (array<float>): Array of parameter values being tested
    icValues (array<float>): Array of IC values for each parameter (same size as testParams)
    currentParam (float): Current parameter value (for smoothing)
    smoothing (simple float): Smoothing factor (0-1, e.g., 0.2 means 20% new, 80% old)
  Returns: [optimizedParam, bestIC, bestIndex] New parameter value, its IC, and array index

adaptiveParamWithStarvation(opt, testParams, icValues, smoothing, starvationThreshold, starvationJumpSize)
  Adaptive parameter selection with starvation handling
  Parameters:
    opt (ICOptimizer): ICOptimizer object
    testParams (array<float>): Array of parameter values
    icValues (array<float>): Array of IC values for each parameter
    smoothing (simple float): Normal smoothing factor
    starvationThreshold (simple int): Number of updates before triggering starvation mode
    starvationJumpSize (simple float): Jump size when in starvation (as fraction of range)
  Returns: [newParam, bestIC] Updated parameter and IC

detectAndAdjustDomination(longCount, shortCount, currentLongLevel, currentShortLevel, dominationRatio, jumpSize, minLevel, maxLevel)
  Detect signal imbalance and adjust parameters
  Parameters:
    longCount (int): Number of long signals in period
    shortCount (int): Number of short signals in period
    currentLongLevel (float): Current long threshold
    currentShortLevel (float): Current short threshold
    dominationRatio (simple int): Ratio threshold (e.g., 4 = 4:1 imbalance)
    jumpSize (simple float): Size of adjustment
    minLevel (simple float): Minimum allowed level
    maxLevel (simple float): Maximum allowed level
  Returns: [newLongLevel, newShortLevel, isDominated]

calcIC(signals, returns, lookback)
  Parameters:
    signals (float)
    returns (float)
    lookback (simple int)

classifyIC(currentIC, icWindow, goodPercentile, badPercentile)
  Parameters:
    currentIC (float)
    icWindow (simple int)
    goodPercentile (simple int)
    badPercentile (simple int)

evaluateSignal(signal, forwardReturn)
  Parameters:
    signal (float)
    forwardReturn (float)

updateOptimizerState(opt, signal, forwardReturn, currentIC, metaICPeriod)
  Parameters:
    opt (ICOptimizer)
    signal (float)
    forwardReturn (float)
    currentIC (float)
    metaICPeriod (simple int)

calcSuccessRate(successful, total)
  Parameters:
    successful (int)
    total (int)

createICStatsTable(opt, paramName, normalSuccess, normalTotal)
  Parameters:
    opt (ICOptimizer)
    paramName (string)
    normalSuccess (int)
    normalTotal (int)

initOptimizer(initialParam)
  Parameters:
    initialParam (float)

ICOptimizer
  Fields:
    currentParam (series float)
    currentIC (series float)
    metaIC (series float)
    totalSignals (series int)
    successfulSignals (series int)
    goodICSignals (series int)
    goodICSuccess (series int)
    nonBadICSignals (series int)
    nonBadICSuccess (series int)
    goodICThreshold (series float)
    badICThreshold (series float)
    updateCounter (series int)