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ממוצע נע MESA סגלתן (MAMA)

DominantCycleCollection of Dominant Cycle estimators. Length adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly). This collection may become encyclopaedic, so if you have any working cycle estimator, drop me a line in the comments below. Suggestions are welcome. Currently included estimators are based on the work of John F. Ehlers mamaPeriod(src, dynLow, dynHigh) MESA Adaptation - MAMA Cycle   Parameters:      src : Series to use      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length   Returns: Calculated period Based on MESA Adaptive Moving Average by John F. Ehlers Performs Hilbert Transform Homodyne Discriminator cycle measurement Unlike MAMA Alpha function (in LengthAdaptation library), this does not compute phase rate of change Introduced in the September 2001 issue of Stocks and Commodities Inspired by the @everget implementation: Inspired by the @anoojpatel implementation: paPeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Pearson Autocorrelation   Parameters:      src : Series to use      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length      preHP : Use High Pass prefilter (default)      preSS : Use Super Smoother prefilter (default)      preHP : Use Hann Windowing prefilter   Returns: Calculated period Based on Pearson Autocorrelation Periodogram by John F. Ehlers Introduced in the September 2016 issue of Stocks and Commodities Inspired by the @blackcat1402 implementation: Inspired by the @rumpypumpydumpy implementation: Corrected many errors, and made small speed optimizations, so this could be the best implementation to date (still slow, though, so may revisit in future) High Pass and Super Smoother prefilters are used in the original implementation dftPeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Discrete Fourier Transform   Parameters:      src : Series to use      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length      preHP : Use High Pass prefilter (default)      preSS : Use Super Smoother prefilter (default)      preHP : Use Hann Windowing prefilter   Returns: Calculated period Based on Spectrum from Discrete Fourier Transform by John F. Ehlers Inspired by the @blackcat1402 implementation: High Pass, Super Smoother and Hann Windowing prefilters are used in the original implementation phasePeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Phase Accumulation   Parameters:      src : Series to use      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length      preHP : Use High Pass prefilter (default)      preSS : Use Super Smoother prefilter (default)      preHP : Use Hamm Windowing prefilter   Returns: Calculated period Based on Dominant Cycle from Phase Accumulation by John F. Ehlers High Pass and Super Smoother prefilters are used in the original implementation doAdapt(type, src, len, dynLow, dynHigh, chandeSDLen, chandeSmooth, chandePower, preHP, preSS, preHP) Execute a particular Length Adaptation or Dominant Cycle Estimator from the list   Parameters:      type : Length Adaptation or Dominant Cycle Estimator type to use      src : Series to use      len : Reference lookback length      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length      chandeSDLen : Lookback length of Standard deviation for Chande's Dynamic Length      chandeSmooth : Smoothing length of Standard deviation for Chande's Dynamic Length      chandePower : Exponent of the length adaptation for Chande's Dynamic Length (lower is smaller variation)      preHP : Use High Pass prefilter for the Estimators that support it (default)      preSS : Use Super Smoother prefilter for the Estimators that support it (default)      preHP : Use Hann Windowing prefilter for the Estimators that support it   Returns: Calculated period (float, not limited) doEstimate(type, src, dynLow, dynHigh, preHP, preSS, preHP) Execute a particular Dominant Cycle Estimator from the list   Parameters:      type : Dominant Cycle Estimator type to use      src : Series to use      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length      preHP : Use High Pass prefilter for the Estimators that support it (default)      preSS : Use Super Smoother prefilter for the Estimators that support it (default)      preHP : Use Hann Windowing prefilter for the Estimators that support it   Returns: Calculated period (float, not limited)
ספריית Pine Script™
מאת ‎lastguru‎
6
LengthAdaptationCollection of dynamic length adaptation algorithms. Mostly from various Adaptive Moving Averages (they are usually just EMA otherwise). Now you can combine Adaptations with any other Moving Averages or Oscillators (see my other libraries), to get something like Deviation Scaled RSI or Fractal Adaptive VWMA. This collection is not encyclopaedic. Suggestions are welcome. chande(src, len, sdlen, smooth, power) Chande's Dynamic Length   Parameters:      src : Series to use      len : Reference lookback length      sdlen : Lookback length of Standard deviation      smooth : Smoothing length of Standard deviation      power : Exponent of the length adaptation (lower is smaller variation)   Returns: Calculated period Taken from Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length Original default power value is 1, but I use 0.5 A variant of this algorithm is also included, where volume is used instead of price vidya(src, len, dynLow) Variable Index Dynamic Average Indicator (VIDYA)   Parameters:      src : Series to use      len : Reference lookback length      dynLow : Lower bound for the dynamic length   Returns: Calculated period Standard VIDYA algorithm. The period oscillates from the Lower Bound up (slow) I took the adaptation part, as it is just an EMA otherwise vidyaRS(src, len, dynHigh) Relative Strength Dynamic Length - VIDYA RS   Parameters:      src : Series to use      len : Reference lookback length      dynHigh : Upper bound for the dynamic length   Returns: Calculated period Based on Vitali Apirine's modification (Stocks and Commodities, January 2022) of VIDYA algorithm. The period oscillates from the Upper Bound down (fast) I took the adaptation part, as it is just an EMA otherwise kaufman(src, len, dynLow, dynHigh) Kaufman Efficiency Scaling   Parameters:      src : Series to use      len : Reference lookback length      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length   Returns: Calculated period Based on Efficiency Ratio calculation orifinally used in Kaufman Adaptive Moving Average developed by Perry J. Kaufman I took the adaptation part, as it is just an EMA otherwise ds(src, len) Deviation Scaling   Parameters:      src : Series to use      len : Reference lookback length   Returns: Calculated period Based on Derivation Scaled Super Smoother (DSSS) by John F. Ehlers Originally used with Super Smoother RMS originally has 50 bar lookback. Changed to 4x length for better flexibility. Could be wrong. maa(src, len, threshold) Median Average Adaptation   Parameters:      src : Series to use      len : Reference lookback length      threshold : Adjustment threshold (lower is smaller length, default: 0.002, min: 0.0001)   Returns: Calculated period Based on Median Average Adaptive Filter by John F. Ehlers Discovered and implemented by @cheatcountry: I took the adaptation part, as it is just an EMA otherwise fra(len, fc, sc) Fractal Adaptation   Parameters:      len : Reference lookback length      fc : Fast constant (default: 1)      sc : Slow constant (default: 200)   Returns: Calculated period Based on FRAMA by John F. Ehlers Modified to allow lower and upper bounds by an unknown author I took the adaptation part, as it is just an EMA otherwise mama(src, dynLow, dynHigh) MESA Adaptation - MAMA Alpha   Parameters:      src : Series to use      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length   Returns: Calculated period Based on MESA Adaptive Moving Average by John F. Ehlers Introduced in the September 2001 issue of Stocks and Commodities Inspired by the @everget implementation: I took the adaptation part, as it is just an EMA otherwise doAdapt(type, src, len, dynLow, dynHigh, chandeSDLen, chandeSmooth, chandePower) Execute a particular Length Adaptation from the list   Parameters:      type : Length Adaptation type to use      src : Series to use      len : Reference lookback length      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length      chandeSDLen : Lookback length of Standard deviation for Chande's Dynamic Length      chandeSmooth : Smoothing length of Standard deviation for Chande's Dynamic Length      chandePower : Exponent of the length adaptation for Chande's Dynamic Length (lower is smaller variation)   Returns: Calculated period (float, not limited) doMA(type, src, len) MA wrapper on wrapper: if DSSS is selected, calculate it here   Parameters:      type : MA type to use      src : Series to use      len : Filtering length   Returns: Filtered series Demonstration of a combined indicator: Deviation Scaled Super Smoother
ספריית Pine Script™
מאת ‎lastguru‎
2