Indicators

10 dic 2024

Library "Indicators"

cmf(lookback, n_to_smooth)
  Calculates the Chaikin's Money Flow.
  Parameters:
    lookback (simple int)
    n_to_smooth (simple int)
  Returns: float The Money Flow value.

cmma(lookback, atr_length)
  Calculates the CMMA (Close Minus Moving Average) indicator.
  Parameters:
    lookback (simple int)
    atr_length (simple int)
  Returns: float The CMMA value.

macd(fast_length, slow_length, n_to_smooth)
  Calculates the normalized and scaled MACD.
  Parameters:
    fast_length (simple int)
    slow_length (simple int)
    n_to_smooth (simple int)
  Returns: [float, float, float] A tuple containing [macd_n, macd_s, macd_diff].

stochK(length, n_to_smooth)
  Calculates a simplified Stochastic Oscillator.
Uses: 100 * ta.sma((close - lowest_low) / (highest_high - lowest_low), n_to_smooth)
  Parameters:
    length (simple int)
    n_to_smooth (simple int)
  Returns: float The Stochastic %K value.

williamsR(length)
  Calculates the Williams %R using the stochK function.
Uses: -1 * (100 - stoch(length, 1))
  Parameters:
    length (simple int)
  Returns: float The Williams %R value.

10 dic 2024

Notas de prensa

v2

Updated:
cmf(lookback, n_to_smooth)
  Calculates the Chaikin's Money Flow.
  Parameters:
    lookback (simple int)
    n_to_smooth (simple int)
  Returns: float The Money Flow value.

macd(fast_length, slow_length, n_to_smooth)
  Calculates the normalized and scaled MACD.
  Parameters:
    fast_length (simple int)
    slow_length (simple int)
    n_to_smooth (simple int)
  Returns: [float, float, float] A tuple containing [macd_n, macd_s, macd_diff].

williamsR(length)
  Calculates the Williams %R using the stochK function.
Uses: -1 * (100 - stoch(length, 1))
  Parameters:
    length (simple int)
  Returns: float The Williams %R value.

10 dic 2024

Notas de prensa

v3

Added:
volatility_risk(atr_length, lookback)
  Calculates a volatility-based risk measure.
  Parameters:
    atr_length (simple int)
    lookback (simple int)
  Returns: [float, float] Returns [NDlr, NDlr_diff]:
NDlr: Normalized Daily Loss Range (ATR * pointvalue)
NDlr_diff: Difference between NDlr and NDlr_threshold (NDlr - (NDlr_avg + NDlr_std))
A value <= 0 indicates NDlr <= NDlr_threshold