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fix(docs): correct .md documentation across errors, dynamics, filters, forecasts, momentum, numerics, oscillators, reversals, statistics, trends, volatility, volume

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Deep review of all indicator categories verified .md headers against .cs WarmupPeriod, parameters, inputs, and outputs. Fixes include warmup corrections, parameter documentation, output type accuracy, and Pine Script alignment.
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Miha Kralj
2026-03-10 18:38:23 -07:00
parent 8906c62dcf
commit 35a6702b06
178 changed files with 2579 additions and 998 deletions
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lib/forecasts/afirma/Afirma.md
+8 -8
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@@ -137,18 +137,18 @@ $$
### Operation Count (Streaming Mode)
AFIRMA chains AR model estimation with IIR/FIR filtering — O(p) per bar where p = AR order.
Windowed FIR convolution — O(P) per bar where P = period (window length).
| Operation | Count | Cost (cycles) | Subtotal |
| :--- | :---: | :---: | :---: |
| AR coefficient estimation (p terms) | p | 4 cy | ~4p cy |
| FIR forward pass (p multiplies) | p | 1 cy | ~p cy |
| IIR feedback pass (p multiplies) | p | 1 cy | ~p cy |
| Output computation via FMA | 1 | 1 cy | ~1 cy |
| NaN guard + state update | 1 | 2 cy | ~2 cy |
| **Total (p=8)** | **O(p)** | — | **~49 cy** |
| RingBuffer write | 1 | ~2 cy | ~2 cy |
| FIR convolution (P FMA ops) | P | ~1 cy | ~P cy |
| Weight normalization | 1 | ~1 cy | ~1 cy |
| LS regression (if enabled) | n | ~2 cy | ~2n cy |
| NaN guard + state update | 1 | ~2 cy | ~2 cy |
| **Total (P=20)** | **O(P)** | — | **~25 cy** |
O(p) per bar where p = AR order. AR coefficient estimation dominates; FMA-fused filter passes are cheap. Streaming mode maintains p state variables.
O(P) per bar where P = window length. FMA-fused dot product dominates; LS regression adds O(n) where n = min(⌊(P−1)/2⌋, 50).
| Metric | Value | Notes |
| :--- | :---: | :--- |
lib/forecasts/afirma/afirma.pine
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@@ -7,7 +7,7 @@ indicator("Autoregressive FIR Moving Average (AFIRMA)", "AFIRMA", overlay=true)
//@param source Series to calculate AFIRMA from
//@param period Lookback period - window size
//@param windowType Window function type (1:Hanning, 2:Hamming, 3:Blackman, 4:Blackman-Harris)
//@param leastSquares Apply least squares cubic polynomial fitting for autoregressive prediction
//@param leastSquares Apply least squares linear regression fitting for autoregressive prediction
//@returns AFIRMA value, calculates from first bar using available data
//@optimized Uses windowing functions with O(n) complexity; least squares adds O(n) for polynomial fitting
afirma(series float src, simple int period, simple int windowType=4, simple bool leastSquares=false) =>
@@ -111,7 +111,7 @@ afirma(series float src, simple int period, simple int windowType=4, simple bool
i_period = input.int(20, "Period", minval=1)
i_source = input.source(close, "Source")
i_windowType = input.int(4, "Window Function", minval=1, maxval=4, tooltip="1:Hanning, 2:Hamming, 3:Blackman, 4:Blackman-Harris")
i_leastSquares = input.bool(false, "Least Squares Method", tooltip="Enable cubic polynomial fitting for autoregressive prediction")
i_leastSquares = input.bool(false, "Least Squares Method", tooltip="Enable linear regression fitting for autoregressive prediction")
// Calculation
afirma_value = afirma(i_source, i_period, i_windowType, i_leastSquares)