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

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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.
This commit is contained in:
kingchenc
2026-05-22 20:04:13 +02:00
parent 2d0ee926c5
commit 2f3b5cc3be
8 changed files with 534 additions and 73 deletions
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CHANGELOG.md
+13
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@@ -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
+9 -8
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@@ -95,16 +95,17 @@ python -m benchmarks.compare_libraries
## Indicators
25 streaming-first indicators across four families. Every one passes the
`batch == streaming` equivalence test, reference-value tests, and reset
semantics tests.
63 streaming-first indicators across four families plus a statistics group.
Every one passes the `batch == streaming` equivalence test, reference-value
tests, and reset semantics tests.
| Family | Indicators |
|-------------|-----------|
| Trend | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA |
| Momentum | RSI (Wilder), MACD, Stochastic, CCI, ROC, Williams %R, ADX (+DI/-DI), MFI, TRIX, Awesome Oscillator, Aroon |
| Volatility | Bollinger Bands, ATR, Keltner Channels, Donchian Channels, Parabolic SAR |
| Volume | OBV, VWAP (cumulative + rolling) |
| 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, 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, NATR, StdDev, Ulcer Index, Historical Volatility, Bollinger Bandwidth, %B, SuperTrend, Chandelier Exit, Chande Kroll Stop, ATR Trailing Stop |
| 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
+70 -1
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@@ -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
+31 -31
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@@ -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
+115 -29
View File
@@ -25,58 +25,144 @@ from __future__ import annotations
from ._wickra import (
__version__,
ADX,
ATR,
Aroon,
AwesomeOscillator,
BollingerBands,
CCI,
DEMA,
Donchian,
# Trend
SMA,
EMA,
WMA,
DEMA,
TEMA,
HMA,
KAMA,
Keltner,
MACD,
MFI,
OBV,
PSAR,
ROC,
SMMA,
TRIMA,
ZLEMA,
T3,
VWMA,
# Momentum
RSI,
SMA,
MACD,
Stochastic,
TEMA,
TRIX,
VWAP,
CCI,
ROC,
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
+253
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@@ -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
+4 -3
View File
@@ -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,
momentum, volatility, volume) and a small set of supporting types (`Candle`,
`Tick`, `Chain`). The Rust core forbids `unsafe`, so every binding inherits a
The project ships 63 indicators across the four classical families (trend,
momentum, volatility, volume) plus a statistics group, and a small set of
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
+39 -1
View File
@@ -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