F-Abschluss: wire the Python package, refresh docs and extend the test suites

Finalises the F1-F12 indicator expansion (25 -> 63 indicators).

- Python `wickra/__init__.py`: import and re-export all 63 indicators,
  grouped by family, with a matching `__all__`. The package previously
  exposed only the original 25 even though the compiled module and the
  `.pyi` stubs already carried the rest.
- Docs: `Home.md` and `README.md` indicator counts and family tables
  updated to 63; `Indicators-Overview.md` already restructured per family
  in F10-F12; `Warmup-Periods.md` gains all 38 new indicators across the
  single- and multi-output tables (and the stale two-arg `Psar::new`
  example is corrected to three args); `CHANGELOG.md` `[Unreleased]` lists
  every new indicator by family.
- Tests: `bindings/node/__tests__/indicators.test.js` covers all 63
  indicators (streaming==batch plus four new reference-value checks),
  80/80 green; new `bindings/python/tests/test_new_indicators.py` covers
  the 38 additions (streaming==batch, shapes, reference values,
  lifecycle), Python suite 105/105 green.
- `bindings/node/index.js` regenerated by `napi build`.

cargo fmt + clippy (core/wickra/data/wasm/node) clean; 454 core tests,
25 data tests, 66 doctests, 80 Node tests and 105 Python tests green;
`cargo check -p wickra-wasm --tests` green.

CHANGELOG.md

@@ -8,6 +8,19 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 
 ### Added
+- 38 new technical indicators, taking the library from 25 to 63. Each is
+  implemented once in the Rust core and wired through the Python, Node and
+  WASM bindings, with reference-value tests and a dedicated wiki page:
+  - Trend: `Smma`, `Trima`, `Zlema`, `T3`, `Vwma`.
+  - Momentum: `Mom`, `Cmo`, `Tsi`, `Pmo`, `StochRsi`, `UltimateOscillator`,
+    `Ppo`, `Dpo`, `Coppock`, `AroonOscillator`, `Vortex`, `MassIndex`.
+  - Volatility: `Natr`, `StdDev`, `UlcerIndex`, `HistoricalVolatility`,
+    `BollingerBandwidth`, `PercentB`, `SuperTrend`, `ChandelierExit`,
+    `ChandeKrollStop`, `AtrTrailingStop`.
+  - Volume: `Adl`, `VolumePriceTrend`, `ChaikinMoneyFlow`,
+    `ChaikinOscillator`, `ForceIndex`, `EaseOfMovement`.
+  - Statistics: `TypicalPrice`, `MedianPrice`, `WeightedClose`,
+    `LinearRegression`, `LinRegSlope`.
 - `TickAggregator::with_gap_fill` — opt-in mode that emits a flat placeholder
   candle for every empty bucket between two ticks, keeping the candle series
   evenly spaced for downstream indicators.

README.md

@@ -95,16 +95,17 @@ python -m benchmarks.compare_libraries
 
 ## Indicators
 
-25 streaming-first indicators across four families. Every one passes the
+63 streaming-first indicators across four families plus a statistics group.
-`batch == streaming` equivalence test, reference-value tests, and reset
+Every one passes the `batch == streaming` equivalence test, reference-value
-semantics tests.
+tests, and reset semantics tests.
 
 | Family      | Indicators |
 |-------------|-----------|
-| Trend       | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA |
+| Trend       | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA |
-| Momentum    | RSI (Wilder), MACD, Stochastic, CCI, ROC, Williams %R, ADX (+DI/-DI), MFI, TRIX, Awesome Oscillator, Aroon |
+| Momentum    | RSI (Wilder), MACD, Stochastic, CCI, ROC, Williams %R, ADX (+DI/-DI), MFI, TRIX, Awesome Oscillator, Aroon, MOM, CMO, TSI, PMO, StochRSI, Ultimate Oscillator, PPO, DPO, Coppock, Aroon Oscillator, Vortex, Mass Index |
-| Volatility  | Bollinger Bands, ATR, Keltner Channels, Donchian Channels, Parabolic SAR |
+| Volatility  | Bollinger Bands, ATR, Keltner Channels, Donchian Channels, Parabolic SAR, NATR, StdDev, Ulcer Index, Historical Volatility, Bollinger Bandwidth, %B, SuperTrend, Chandelier Exit, Chande Kroll Stop, ATR Trailing Stop |
-| Volume      | OBV, VWAP (cumulative + rolling) |
+| Volume      | OBV, VWAP (cumulative + rolling), ADL, Volume-Price Trend, Chaikin Money Flow, Chaikin Oscillator, Force Index, Ease of Movement |
+| Statistics  | Typical Price, Median Price, Weighted Close, Linear Regression, Linear Regression Slope |
 
 Adding a new indicator means implementing one trait in Rust; all four bindings
 inherit it automatically.
@@ -174,7 +175,7 @@ A Python live-trading example using the public `websockets` package lives at
 ```
 wickra/
 ├── crates/
-│   ├── wickra-core/         core engine + all 25 indicators
+│   ├── wickra-core/         core engine + all 63 indicators
 │   ├── wickra/              top-level facade crate (publishes on crates.io)
 │   │                        + benches/ and examples/backtest.rs
 │   └── wickra-data/         CSV reader, tick aggregator, live exchange feeds

bindings/node/__tests__/indicators.test.js

@@ -1,5 +1,5 @@
 // Comprehensive tests for the Wickra Node bindings: streaming-vs-batch
-// equivalence, reference values, and lifecycle methods across all 25
+// equivalence, reference values, and lifecycle methods across all 63
 // indicators. Ported from the Python test_streaming_vs_batch / test_known_values
 // suites.
 
@@ -36,6 +36,25 @@ const scalarFactories = {
   ROC: () => new wickra.ROC(12),
   TRIX: () => new wickra.TRIX(9),
   KAMA: () => new wickra.KAMA(10, 2, 30),
+  SMMA: () => new wickra.SMMA(14),
+  TRIMA: () => new wickra.TRIMA(20),
+  ZLEMA: () => new wickra.ZLEMA(14),
+  T3: () => new wickra.T3(5, 0.7),
+  MOM: () => new wickra.MOM(10),
+  CMO: () => new wickra.CMO(14),
+  TSI: () => new wickra.TSI(25, 13),
+  PMO: () => new wickra.PMO(35, 20),
+  StochRSI: () => new wickra.StochRSI(14, 14),
+  PPO: () => new wickra.PPO(12, 26),
+  DPO: () => new wickra.DPO(20),
+  Coppock: () => new wickra.Coppock(14, 11, 10),
+  StdDev: () => new wickra.StdDev(20),
+  UlcerIndex: () => new wickra.UlcerIndex(14),
+  HistoricalVolatility: () => new wickra.HistoricalVolatility(20, 252),
+  BollingerBandwidth: () => new wickra.BollingerBandwidth(20, 2),
+  PercentB: () => new wickra.PercentB(20, 2),
+  LinearRegression: () => new wickra.LinearRegression(14),
+  LinRegSlope: () => new wickra.LinRegSlope(14),
 };
 
 for (const [name, make] of Object.entries(scalarFactories)) {
@@ -61,6 +80,21 @@ const candleScalar = {
   VWAP: { make: () => new wickra.VWAP(), step: (ind, i) => ind.update(high[i], low[i], close[i], volume[i]), batch: (ind) => ind.batch(high, low, close, volume) },
   AwesomeOscillator: { make: () => new wickra.AwesomeOscillator(5, 34), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
   OBV: { make: () => new wickra.OBV(), step: (ind, i) => ind.update(close[i], volume[i]), batch: (ind) => ind.batch(close, volume) },
+  VWMA: { make: () => new wickra.VWMA(20), step: (ind, i) => ind.update(close[i], volume[i]), batch: (ind) => ind.batch(close, volume) },
+  UltimateOscillator: { make: () => new wickra.UltimateOscillator(7, 14, 28), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  AroonOscillator: { make: () => new wickra.AroonOscillator(14), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
+  NATR: { make: () => new wickra.NATR(14), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  MassIndex: { make: () => new wickra.MassIndex(9, 25), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
+  ADL: { make: () => new wickra.ADL(), step: (ind, i) => ind.update(high[i], low[i], close[i], volume[i]), batch: (ind) => ind.batch(high, low, close, volume) },
+  VolumePriceTrend: { make: () => new wickra.VolumePriceTrend(), step: (ind, i) => ind.update(close[i], volume[i]), batch: (ind) => ind.batch(close, volume) },
+  ChaikinMoneyFlow: { make: () => new wickra.ChaikinMoneyFlow(20), step: (ind, i) => ind.update(high[i], low[i], close[i], volume[i]), batch: (ind) => ind.batch(high, low, close, volume) },
+  ChaikinOscillator: { make: () => new wickra.ChaikinOscillator(3, 10), step: (ind, i) => ind.update(high[i], low[i], close[i], volume[i]), batch: (ind) => ind.batch(high, low, close, volume) },
+  ForceIndex: { make: () => new wickra.ForceIndex(13), step: (ind, i) => ind.update(close[i], volume[i]), batch: (ind) => ind.batch(close, volume) },
+  EaseOfMovement: { make: () => new wickra.EaseOfMovement(14, 1e8), step: (ind, i) => ind.update(high[i], low[i], volume[i]), batch: (ind) => ind.batch(high, low, volume) },
+  AtrTrailingStop: { make: () => new wickra.AtrTrailingStop(14, 3), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  TypicalPrice: { make: () => new wickra.TypicalPrice(), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  MedianPrice: { make: () => new wickra.MedianPrice(), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
+  WeightedClose: { make: () => new wickra.WeightedClose(), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
 };
 
 for (const [name, d] of Object.entries(candleScalar)) {
@@ -85,6 +119,10 @@ const multi = {
   Keltner: { make: () => new wickra.Keltner(20, 10, 2), fields: ['upper', 'middle', 'lower'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
   Donchian: { make: () => new wickra.Donchian(20), fields: ['upper', 'middle', 'lower'], step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
   Aroon: { make: () => new wickra.Aroon(14), fields: ['up', 'down'], step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
+  Vortex: { make: () => new wickra.Vortex(14), fields: ['plus', 'minus'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  SuperTrend: { make: () => new wickra.SuperTrend(10, 3), fields: ['value', 'direction'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  ChandelierExit: { make: () => new wickra.ChandelierExit(22, 3), fields: ['longStop', 'shortStop'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  ChandeKrollStop: { make: () => new wickra.ChandeKrollStop(10, 1, 9), fields: ['stopLong', 'stopShort'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
 };
 
 for (const [name, d] of Object.entries(multi)) {
@@ -163,3 +201,34 @@ test('MACD histogram equals macd minus signal', () => {
   assert.ok(v);
   assert.ok(Math.abs(v.histogram - (v.macd - v.signal)) < 1e-9);
 });
+
+test('TypicalPrice reference value', () => {
+  // (high + low + close) / 3 = (12 + 6 + 9) / 3 = 9.
+  assert.equal(new wickra.TypicalPrice().update(12, 6, 9), 9);
+});
+
+test('ChaikinMoneyFlow(2) reference value equals 0.5', () => {
+  // Bar 1 closes at the high (MFV +100); bar 2 closes mid-range (MFV 0).
+  const cmf = new wickra.ChaikinMoneyFlow(2);
+  assert.equal(cmf.update(10, 8, 10, 100), null);
+  assert.ok(Math.abs(cmf.update(12, 8, 10, 100) - 0.5) < 1e-9);
+});
+
+test('LinearRegression(3) reference values', () => {
+  // Least-squares line through [1, 2, 9] is y = 4x; endpoint 4·2 = 8.
+  const out = new wickra.LinearRegression(3).batch([1, 2, 9]);
+  assert.ok(Number.isNaN(out[0]) && Number.isNaN(out[1]));
+  assert.ok(Math.abs(out[2] - 8) < 1e-9);
+});
+
+test('SuperTrend flat market holds the lower band and an uptrend', () => {
+  // Flat candles: ATR 2, hl2 10, lower band 10 - 3·2 = 4.
+  const n = 20;
+  const out = new wickra.SuperTrend(5, 3).batch(
+    Array(n).fill(11),
+    Array(n).fill(9),
+    Array(n).fill(10),
+  );
+  assert.ok(Math.abs(out[2 * n - 2] - 4) < 1e-9); // value
+  assert.equal(out[2 * n - 1], 1); // direction
+});

bindings/node/index.js

@@ -310,7 +310,7 @@ if (!nativeBinding) {
   throw new Error(`Failed to load native binding`)
 }
 
-const { version, SMA, EMA, WMA, RSI, DEMA, TEMA, HMA, ROC, TRIX, SMMA, TRIMA, ZLEMA, T3, VWMA, MOM, CMO, TSI, PMO, StochRSI, UltimateOscillator, PPO, DPO, Coppock, AroonOscillator, Vortex, MassIndex, NATR, StdDev, UlcerIndex, HistoricalVolatility, BollingerBandwidth, PercentB, ADL, VolumePriceTrend, ChaikinMoneyFlow, ChaikinOscillator, ForceIndex, EaseOfMovement, SuperTrend, ChandelierExit, ChandeKrollStop, AtrTrailingStop, TypicalPrice, MedianPrice, WeightedClose, LinearRegression, LinRegSlope, MACD, BollingerBands, ATR, Stochastic, OBV, ADX, CCI, WilliamsR, MFI, PSAR, Keltner, Donchian, VWAP, AwesomeOscillator, Aroon, KAMA } = nativeBinding
+const { version, SMA, EMA, WMA, RSI, DEMA, TEMA, HMA, ROC, TRIX, SMMA, TRIMA, ZLEMA, MOM, CMO, DPO, StdDev, UlcerIndex, MACD, BollingerBands, ATR, Stochastic, OBV, ADX, CCI, WilliamsR, MFI, PSAR, Keltner, Donchian, VWAP, AwesomeOscillator, Aroon, KAMA, T3, TSI, PMO, ADL, VolumePriceTrend, ChaikinMoneyFlow, ChaikinOscillator, ForceIndex, EaseOfMovement, SuperTrend, ChandelierExit, ChandeKrollStop, AtrTrailingStop, TypicalPrice, MedianPrice, WeightedClose, LinearRegression, LinRegSlope, BollingerBandwidth, PercentB, NATR, HistoricalVolatility, AroonOscillator, Vortex, MassIndex, StochRSI, UltimateOscillator, PPO, Coppock, VWMA } = nativeBinding
 
 module.exports.version = version
 module.exports.SMA = SMA
@@ -325,41 +325,11 @@ module.exports.TRIX = TRIX
 module.exports.SMMA = SMMA
 module.exports.TRIMA = TRIMA
 module.exports.ZLEMA = ZLEMA
-module.exports.T3 = T3
-module.exports.VWMA = VWMA
 module.exports.MOM = MOM
 module.exports.CMO = CMO
-module.exports.TSI = TSI
-module.exports.PMO = PMO
-module.exports.StochRSI = StochRSI
-module.exports.UltimateOscillator = UltimateOscillator
-module.exports.PPO = PPO
 module.exports.DPO = DPO
-module.exports.Coppock = Coppock
-module.exports.AroonOscillator = AroonOscillator
-module.exports.Vortex = Vortex
-module.exports.MassIndex = MassIndex
-module.exports.NATR = NATR
 module.exports.StdDev = StdDev
 module.exports.UlcerIndex = UlcerIndex
-module.exports.HistoricalVolatility = HistoricalVolatility
-module.exports.BollingerBandwidth = BollingerBandwidth
-module.exports.PercentB = PercentB
-module.exports.ADL = ADL
-module.exports.VolumePriceTrend = VolumePriceTrend
-module.exports.ChaikinMoneyFlow = ChaikinMoneyFlow
-module.exports.ChaikinOscillator = ChaikinOscillator
-module.exports.ForceIndex = ForceIndex
-module.exports.EaseOfMovement = EaseOfMovement
-module.exports.SuperTrend = SuperTrend
-module.exports.ChandelierExit = ChandelierExit
-module.exports.ChandeKrollStop = ChandeKrollStop
-module.exports.AtrTrailingStop = AtrTrailingStop
-module.exports.TypicalPrice = TypicalPrice
-module.exports.MedianPrice = MedianPrice
-module.exports.WeightedClose = WeightedClose
-module.exports.LinearRegression = LinearRegression
-module.exports.LinRegSlope = LinRegSlope
 module.exports.MACD = MACD
 module.exports.BollingerBands = BollingerBands
 module.exports.ATR = ATR
@@ -376,3 +346,33 @@ module.exports.VWAP = VWAP
 module.exports.AwesomeOscillator = AwesomeOscillator
 module.exports.Aroon = Aroon
 module.exports.KAMA = KAMA
+module.exports.T3 = T3
+module.exports.TSI = TSI
+module.exports.PMO = PMO
+module.exports.ADL = ADL
+module.exports.VolumePriceTrend = VolumePriceTrend
+module.exports.ChaikinMoneyFlow = ChaikinMoneyFlow
+module.exports.ChaikinOscillator = ChaikinOscillator
+module.exports.ForceIndex = ForceIndex
+module.exports.EaseOfMovement = EaseOfMovement
+module.exports.SuperTrend = SuperTrend
+module.exports.ChandelierExit = ChandelierExit
+module.exports.ChandeKrollStop = ChandeKrollStop
+module.exports.AtrTrailingStop = AtrTrailingStop
+module.exports.TypicalPrice = TypicalPrice
+module.exports.MedianPrice = MedianPrice
+module.exports.WeightedClose = WeightedClose
+module.exports.LinearRegression = LinearRegression
+module.exports.LinRegSlope = LinRegSlope
+module.exports.BollingerBandwidth = BollingerBandwidth
+module.exports.PercentB = PercentB
+module.exports.NATR = NATR
+module.exports.HistoricalVolatility = HistoricalVolatility
+module.exports.AroonOscillator = AroonOscillator
+module.exports.Vortex = Vortex
+module.exports.MassIndex = MassIndex
+module.exports.StochRSI = StochRSI
+module.exports.UltimateOscillator = UltimateOscillator
+module.exports.PPO = PPO
+module.exports.Coppock = Coppock
+module.exports.VWMA = VWMA

bindings/python/python/wickra/__init__.py

@@ -25,58 +25,144 @@ from __future__ import annotations
 
 from ._wickra import (
     __version__,
-    ADX,
+    # Trend
-    ATR,
+    SMA,
-    Aroon,
-    AwesomeOscillator,
-    BollingerBands,
-    CCI,
-    DEMA,
-    Donchian,
     EMA,
+    WMA,
+    DEMA,
+    TEMA,
     HMA,
     KAMA,
-    Keltner,
+    SMMA,
-    MACD,
+    TRIMA,
-    MFI,
+    ZLEMA,
-    OBV,
+    T3,
-    PSAR,
+    VWMA,
-    ROC,
+    # Momentum
     RSI,
-    SMA,
+    MACD,
     Stochastic,
-    TEMA,
+    CCI,
-    TRIX,
+    ROC,
-    VWAP,
     WilliamsR,
-    WMA,
+    ADX,
+    MFI,
+    TRIX,
+    AwesomeOscillator,
+    Aroon,
+    MOM,
+    CMO,
+    TSI,
+    PMO,
+    StochRSI,
+    UltimateOscillator,
+    PPO,
+    DPO,
+    Coppock,
+    AroonOscillator,
+    Vortex,
+    MassIndex,
+    # Volatility
+    BollingerBands,
+    ATR,
+    Keltner,
+    Donchian,
+    PSAR,
+    NATR,
+    StdDev,
+    UlcerIndex,
+    HistoricalVolatility,
+    BollingerBandwidth,
+    PercentB,
+    SuperTrend,
+    ChandelierExit,
+    ChandeKrollStop,
+    AtrTrailingStop,
+    # Volume
+    OBV,
+    VWAP,
+    ADL,
+    VolumePriceTrend,
+    ChaikinMoneyFlow,
+    ChaikinOscillator,
+    ForceIndex,
+    EaseOfMovement,
+    # Statistics
+    TypicalPrice,
+    MedianPrice,
+    WeightedClose,
+    LinearRegression,
+    LinRegSlope,
 )
 
 __all__ = [
     "__version__",
+    # Trend
     "SMA",
     "EMA",
     "WMA",
-    "RSI",
-    "MACD",
-    "BollingerBands",
-    "ATR",
-    "Stochastic",
-    "OBV",
     "DEMA",
     "TEMA",
     "HMA",
     "KAMA",
+    "SMMA",
+    "TRIMA",
+    "ZLEMA",
+    "T3",
+    "VWMA",
+    # Momentum
+    "RSI",
+    "MACD",
+    "Stochastic",
     "CCI",
     "ROC",
     "WilliamsR",
     "ADX",
     "MFI",
     "TRIX",
-    "PSAR",
-    "Keltner",
-    "Donchian",
-    "VWAP",
     "AwesomeOscillator",
     "Aroon",
+    "MOM",
+    "CMO",
+    "TSI",
+    "PMO",
+    "StochRSI",
+    "UltimateOscillator",
+    "PPO",
+    "DPO",
+    "Coppock",
+    "AroonOscillator",
+    "Vortex",
+    "MassIndex",
+    # Volatility
+    "BollingerBands",
+    "ATR",
+    "Keltner",
+    "Donchian",
+    "PSAR",
+    "NATR",
+    "StdDev",
+    "UlcerIndex",
+    "HistoricalVolatility",
+    "BollingerBandwidth",
+    "PercentB",
+    "SuperTrend",
+    "ChandelierExit",
+    "ChandeKrollStop",
+    "AtrTrailingStop",
+    # Volume
+    "OBV",
+    "VWAP",
+    "ADL",
+    "VolumePriceTrend",
+    "ChaikinMoneyFlow",
+    "ChaikinOscillator",
+    "ForceIndex",
+    "EaseOfMovement",
+    # Statistics
+    "TypicalPrice",
+    "MedianPrice",
+    "WeightedClose",
+    "LinearRegression",
+    "LinRegSlope",
 ]

bindings/python/tests/test_new_indicators.py

@@ -0,0 +1,253 @@
+"""Streaming-vs-batch, shape and reference-value tests for the F1-F12 families.
+
+Every indicator added since the original 25 is exercised here. The central
+contract is the same as the rest of the suite: ``batch(...)`` must equal
+repeated streaming ``update(...)`` across the whole warmup -> steady-state
+transition, and batch shapes must match the input length.
+"""
+
+from __future__ import annotations
+
+import math
+
+import numpy as np
+import pytest
+
+import wickra as ta
+
+
+def _eq_nan(a: np.ndarray, b: np.ndarray, tol: float = 1e-9) -> bool:
+    """Compare two float arrays treating NaN positions as equal."""
+    a = np.asarray(a, dtype=np.float64)
+    b = np.asarray(b, dtype=np.float64)
+    if a.shape != b.shape:
+        return False
+    both_nan = np.isnan(a) & np.isnan(b)
+    return bool(np.all(np.where(both_nan, 0.0, np.abs(a - b)) <= tol))
+
+
+@pytest.fixture
+def ohlcv() -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Synthetic high / low / close / volume series, 200 bars."""
+    t = np.arange(200, dtype=np.float64)
+    close = 100.0 + np.sin(t * 0.15) * 8.0 + np.cos(t * 0.32) * 3.0
+    spread = 0.5 + np.abs(np.sin(t * 0.07))
+    high = close + spread
+    low = close - spread
+    volume = 1000.0 + (t % 7) * 50.0
+    return high, low, close, volume
+
+
+# --- Scalar (f64 -> f64) indicators ---------------------------------------
+
+SCALAR = [
+    (ta.SMMA, (14,)),
+    (ta.TRIMA, (20,)),
+    (ta.ZLEMA, (14,)),
+    (ta.T3, (5, 0.7)),
+    (ta.MOM, (10,)),
+    (ta.CMO, (14,)),
+    (ta.TSI, (25, 13)),
+    (ta.PMO, (35, 20)),
+    (ta.StochRSI, (14, 14)),
+    (ta.PPO, (12, 26)),
+    (ta.DPO, (20,)),
+    (ta.Coppock, (14, 11, 10)),
+    (ta.StdDev, (20,)),
+    (ta.UlcerIndex, (14,)),
+    (ta.HistoricalVolatility, (20, 252)),
+    (ta.BollingerBandwidth, (20, 2.0)),
+    (ta.PercentB, (20, 2.0)),
+    (ta.LinearRegression, (14,)),
+    (ta.LinRegSlope, (14,)),
+]
+
+
+@pytest.mark.parametrize("cls, args", SCALAR, ids=[c.__name__ for c, _ in SCALAR])
+def test_scalar_streaming_matches_batch(cls, args, sine_prices):
+    batch = cls(*args).batch(sine_prices)
+    assert batch.shape == sine_prices.shape
+    assert batch.dtype == np.float64
+
+    streamer = cls(*args)
+    streamed = []
+    for p in sine_prices:
+        v = streamer.update(float(p))
+        streamed.append(math.nan if v is None else float(v))
+    assert _eq_nan(batch, np.array(streamed, dtype=np.float64))
+
+
+# --- Candle-input, single-output indicators -------------------------------
+#
+# Each entry is (factory, batch-call). Streaming always feeds the full
+# 6-tuple candle; the batch helper takes only the columns it needs.
+
+CANDLE_SCALAR = {
+    "VWMA": (lambda: ta.VWMA(20), lambda ind, h, l, c, v: ind.batch(c, v)),
+    "UltimateOscillator": (
+        lambda: ta.UltimateOscillator(7, 14, 28),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+    ),
+    "AroonOscillator": (
+        lambda: ta.AroonOscillator(14),
+        lambda ind, h, l, c, v: ind.batch(h, l),
+    ),
+    "NATR": (lambda: ta.NATR(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
+    "MassIndex": (lambda: ta.MassIndex(9, 25), lambda ind, h, l, c, v: ind.batch(h, l)),
+    "ADL": (lambda: ta.ADL(), lambda ind, h, l, c, v: ind.batch(h, l, c, v)),
+    "VolumePriceTrend": (
+        lambda: ta.VolumePriceTrend(),
+        lambda ind, h, l, c, v: ind.batch(c, v),
+    ),
+    "ChaikinMoneyFlow": (
+        lambda: ta.ChaikinMoneyFlow(20),
+        lambda ind, h, l, c, v: ind.batch(h, l, c, v),
+    ),
+    "ChaikinOscillator": (
+        lambda: ta.ChaikinOscillator(3, 10),
+        lambda ind, h, l, c, v: ind.batch(h, l, c, v),
+    ),
+    "ForceIndex": (
+        lambda: ta.ForceIndex(13),
+        lambda ind, h, l, c, v: ind.batch(c, v),
+    ),
+    "EaseOfMovement": (
+        lambda: ta.EaseOfMovement(14),
+        lambda ind, h, l, c, v: ind.batch(h, l, v),
+    ),
+    "AtrTrailingStop": (
+        lambda: ta.AtrTrailingStop(14, 3.0),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+    ),
+    "TypicalPrice": (
+        lambda: ta.TypicalPrice(),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+    ),
+    "MedianPrice": (
+        lambda: ta.MedianPrice(),
+        lambda ind, h, l, c, v: ind.batch(h, l),
+    ),
+    "WeightedClose": (
+        lambda: ta.WeightedClose(),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+    ),
+}
+
+
+@pytest.mark.parametrize("name", list(CANDLE_SCALAR))
+def test_candle_scalar_streaming_matches_batch(name, ohlcv):
+    high, low, close, volume = ohlcv
+    make, batch_call = CANDLE_SCALAR[name]
+
+    batch = batch_call(make(), high, low, close, volume)
+    assert batch.shape == close.shape
+
+    streamer = make()
+    streamed = []
+    for i in range(close.size):
+        candle = (
+            float(close[i]),
+            float(high[i]),
+            float(low[i]),
+            float(close[i]),
+            float(volume[i]),
+            i,
+        )
+        v = streamer.update(candle)
+        streamed.append(math.nan if v is None else float(v))
+    assert _eq_nan(batch, np.array(streamed, dtype=np.float64)), f"{name} mismatch"
+
+
+# --- Candle-input, multi-output indicators --------------------------------
+
+MULTI = {
+    "Vortex": (lambda: ta.Vortex(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
+    "SuperTrend": (
+        lambda: ta.SuperTrend(10, 3.0),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+    ),
+    "ChandelierExit": (
+        lambda: ta.ChandelierExit(22, 3.0),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+    ),
+    "ChandeKrollStop": (
+        lambda: ta.ChandeKrollStop(10, 1.0, 9),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+    ),
+}
+
+
+@pytest.mark.parametrize("name", list(MULTI))
+def test_multi_streaming_matches_batch(name, ohlcv):
+    high, low, close, volume = ohlcv
+    make, batch_call = MULTI[name]
+
+    batch = batch_call(make(), high, low, close, volume)
+    assert batch.shape == (close.size, 2)
+
+    streamer = make()
+    rows = []
+    for i in range(close.size):
+        candle = (
+            float(close[i]),
+            float(high[i]),
+            float(low[i]),
+            float(close[i]),
+            float(volume[i]),
+            i,
+        )
+        v = streamer.update(candle)
+        rows.append([math.nan, math.nan] if v is None else list(v))
+    assert _eq_nan(batch, np.array(rows, dtype=np.float64)), f"{name} mismatch"
+
+
+# --- Reference values -----------------------------------------------------
+
+
+def test_typical_price_reference():
+    # (high + low + close) / 3 = (12 + 6 + 9) / 3 = 9.
+    assert ta.TypicalPrice().update((9.0, 12.0, 6.0, 9.0, 1.0, 0)) == pytest.approx(9.0)
+
+
+def test_median_price_reference():
+    # (high + low) / 2 = (12 + 8) / 2 = 10.
+    assert ta.MedianPrice().update((10.0, 12.0, 8.0, 11.0, 1.0, 0)) == pytest.approx(10.0)
+
+
+def test_weighted_close_reference():
+    # (high + low + 2*close) / 4 = (12 + 8 + 22) / 4 = 10.5.
+    assert ta.WeightedClose().update((10.0, 12.0, 8.0, 11.0, 1.0, 0)) == pytest.approx(
+        10.5
+    )
+
+
+def test_chaikin_money_flow_reference():
+    cmf = ta.ChaikinMoneyFlow(2)
+    assert cmf.update((8.0, 10.0, 8.0, 10.0, 100.0, 0)) is None
+    assert cmf.update((10.0, 12.0, 8.0, 10.0, 100.0, 1)) == pytest.approx(0.5)
+
+
+def test_linear_regression_reference():
+    out = ta.LinearRegression(3).batch(np.array([1.0, 2.0, 9.0]))
+    assert math.isnan(out[0]) and math.isnan(out[1])
+    assert out[2] == pytest.approx(8.0)
+
+
+def test_linreg_slope_reference():
+    out = ta.LinRegSlope(3).batch(np.array([1.0, 2.0, 9.0]))
+    assert math.isnan(out[0]) and math.isnan(out[1])
+    assert out[2] == pytest.approx(4.0)
+
+
+# --- Lifecycle ------------------------------------------------------------
+
+
+def test_new_indicators_expose_lifecycle():
+    instances = [make() for make, _ in CANDLE_SCALAR.values()]
+    instances += [make() for make, _ in MULTI.values()]
+    instances += [cls(*args) for cls, args in SCALAR]
+    for ind in instances:
+        assert ind.is_ready() is False
+        assert ind.warmup_period() >= 1
+        ind.reset()
+        assert ind.is_ready() is False

docs/wiki/Home.md

@@ -7,9 +7,10 @@ Node.js, WebAssembly, and Rust itself. The same `update` call you write inside
 a live trading loop also drives the historical backtest of that same
 strategy — there is no second code path that drifts behind the streaming one.
 
-The project ships 25 indicators across the four classical families (trend,
+The project ships 63 indicators across the four classical families (trend,
-momentum, volatility, volume) and a small set of supporting types (`Candle`,
+momentum, volatility, volume) plus a statistics group, and a small set of
-`Tick`, `Chain`). The Rust core forbids `unsafe`, so every binding inherits a
+supporting types (`Candle`, `Tick`, `Chain`). The Rust core forbids `unsafe`,
+so every binding inherits a
 memory-safe implementation. Install is one command on every supported
 platform: `pip install wickra`, `cargo add wickra`, `npm install wickra` — no
 system compilers, no C dependencies, no headers.

docs/wiki/Warmup-Periods.md

@@ -38,10 +38,44 @@ index" in 0-indexed terms is `warmup_period − 1`.
 | `Trix`          | `Trix::new(15)`                              | `3 * period - 1`                 | 44                               | 44th                     |
 | `AwesomeOscillator` | `AwesomeOscillator::new(5, 34)`          | `slow_period`                    | 34                               | 34th                     |
 | `Atr`           | `Atr::new(14)`                               | `period`                         | 14                               | 14th                     |
-| `Psar`          | `Psar::new(0.02, 0.20)`                      | constant `2`                     | 2                                | 2nd                      |
+| `Psar`          | `Psar::new(0.02, 0.02, 0.20)`                | constant `2`                     | 2                                | 2nd                      |
 | `Obv`           | `Obv::new()`                                 | constant `1`                     | 1                                | 1st                      |
 | `Vwap`          | `Vwap::new()`                                | constant `1`                     | 1                                | 1st                      |
 | `RollingVwap`   | `RollingVwap::new(20)`                       | `period`                         | 20                               | 20th                     |
+| `Smma`          | `Smma::new(14)`                              | `period`                         | 14                               | 14th                     |
+| `Trima`         | `Trima::new(20)`                             | `period`                         | 20                               | 20th                     |
+| `Zlema`         | `Zlema::new(14)`                             | `lag + period` (`lag = (period − 1) / 2`) | 20                      | 20th                     |
+| `T3`            | `T3::new(5, 0.7)`                            | `6 * period - 5`                 | 25                               | 25th                     |
+| `Vwma`          | `Vwma::new(20)`                              | `period`                         | 20                               | 20th                     |
+| `Mom`           | `Mom::new(10)`                               | `period + 1`                     | 11                               | 11th                     |
+| `Cmo`           | `Cmo::new(14)`                               | `period + 1`                     | 15                               | 15th                     |
+| `Tsi`           | `Tsi::new(25, 13)`                           | `long + short`                   | 38                               | 38th                     |
+| `Pmo`           | `Pmo::new(35, 20)`                           | constant `2`                     | 2                                | 2nd                      |
+| `StochRsi`      | `StochRsi::new(14, 14)`                      | `rsi_period + stoch_period`      | 28                               | 28th                     |
+| `UltimateOscillator` | `UltimateOscillator::new(7, 14, 28)`    | `max(short, mid, long) + 1`      | 29                               | 29th                     |
+| `Ppo`           | `Ppo::new(12, 26)`                           | `slow`                           | 26                               | 26th                     |
+| `Dpo`           | `Dpo::new(20)`                               | `max(period, period / 2 + 2)`    | 20                               | 20th                     |
+| `Coppock`       | `Coppock::new(14, 11, 10)`                   | `max(roc_long, roc_short) + wma_period` | 24                        | 24th                     |
+| `AroonOscillator` | `AroonOscillator::new(14)`                 | `period + 1`                     | 15                               | 15th                     |
+| `MassIndex`     | `MassIndex::new(9, 25)`                      | `2 * ema_period + sum_period - 2` | 41                              | 41st                     |
+| `Natr`          | `Natr::new(14)`                              | `period`                         | 14                               | 14th                     |
+| `StdDev`        | `StdDev::new(20)`                            | `period`                         | 20                               | 20th                     |
+| `UlcerIndex`    | `UlcerIndex::new(14)`                        | `2 * period - 1`                 | 27                               | 27th                     |
+| `HistoricalVolatility` | `HistoricalVolatility::new(20, 252)`  | `period + 1`                     | 21                               | 21st                     |
+| `BollingerBandwidth` | `BollingerBandwidth::new(20, 2.0)`      | `period`                         | 20                               | 20th                     |
+| `PercentB`      | `PercentB::new(20, 2.0)`                     | `period`                         | 20                               | 20th                     |
+| `AtrTrailingStop` | `AtrTrailingStop::new(14, 3.0)`            | `atr_period`                     | 14                               | 14th                     |
+| `Adl`           | `Adl::new()`                                 | constant `1`                     | 1                                | 1st                      |
+| `VolumePriceTrend` | `VolumePriceTrend::new()`                 | constant `1`                     | 1                                | 1st                      |
+| `ChaikinMoneyFlow` | `ChaikinMoneyFlow::new(20)`               | `period`                         | 20                               | 20th                     |
+| `ChaikinOscillator` | `ChaikinOscillator::new(3, 10)`          | `slow`                           | 10                               | 10th                     |
+| `ForceIndex`    | `ForceIndex::new(13)`                        | `period + 1`                     | 14                               | 14th                     |
+| `EaseOfMovement` | `EaseOfMovement::new(14)`                   | `period + 1`                     | 15                               | 15th                     |
+| `TypicalPrice`  | `TypicalPrice::new()`                        | constant `1`                     | 1                                | 1st                      |
+| `MedianPrice`   | `MedianPrice::new()`                         | constant `1`                     | 1                                | 1st                      |
+| `WeightedClose` | `WeightedClose::new()`                       | constant `1`                     | 1                                | 1st                      |
+| `LinearRegression` | `LinearRegression::new(14)`               | `period`                         | 14                               | 14th                     |
+| `LinRegSlope`   | `LinRegSlope::new(14)`                       | `period`                         | 14                               | 14th                     |
 
 ## Multi-output indicators
 
@@ -59,6 +93,10 @@ ready" to "ready" together — there are no rows that have a `signal` but no
 | `Aroon`           | `Aroon::new(14)`                     | `period + 1`                             | 15                               | 15th                     | `up`, `down`                                           |
 | `Keltner`         | `Keltner::new(20, 10, 2.0)`          | `ema_period.max(atr_period)`             | 20                               | 20th                     | `upper`, `middle`, `lower`                             |
 | `Donchian`        | `Donchian::new(20)`                  | `period`                                 | 20                               | 20th                     | `upper`, `middle`, `lower`                             |
+| `Vortex`          | `Vortex::new(14)`                    | `period + 1`                             | 15                               | 15th                     | `plus`, `minus`                                        |
+| `SuperTrend`      | `SuperTrend::new(10, 3.0)`           | `atr_period`                             | 10                               | 10th                     | `value`, `direction`                                   |
+| `ChandelierExit`  | `ChandelierExit::new(22, 3.0)`       | `period`                                 | 22                               | 22nd                     | `long_stop`, `short_stop`                              |
+| `ChandeKrollStop` | `ChandeKrollStop::new(10, 1.0, 9)`   | `atr_period + stop_period - 1`           | 18                               | 18th                     | `stop_long`, `stop_short`                              |
 
 ## "Off-by-one" cases worth memorising