kingchenc
3be267cb03
A multi-language technical analysis library: 25 indicators across trend,
momentum, volatility, and volume families, every one a state machine with
O(1) per-tick updates. Batch evaluation is provided by a blanket extension
trait over the streaming primitive, so live trading bots and historical
backtests run the same code path.
What ships in this initial drop:
crates/wickra-core - 25 indicators, Indicator/BatchExt/Chain traits,
OHLCV types with validation; 171 unit tests,
property tests, Wilder/Bollinger textbook tests.
crates/wickra - top-level facade + criterion benches for every
indicator at 1K/10K/100K series sizes.
crates/wickra-data - streaming CSV reader, tick-to-candle aggregator,
multi-timeframe resampler, Binance Spot kline
WebSocket adapter behind feature live-binance;
11 unit + 1 doctest.
bindings/python - PyO3 + maturin, NumPy I/O, type stubs (.pyi),
56 pytest tests including streaming==batch
equivalence, Wilder reference values, lifecycle.
bindings/node - napi-rs native module, TypeScript .d.ts
auto-generated, 7 node --test cases.
bindings/wasm - wasm-bindgen ES module for browser/bundler/Node;
interactive HTML demo at examples/index.html.
examples/ - Python and Rust scripts: backtest, live trading,
parallel multi-asset, multi-timeframe, Binance.
benchmarks/ - cross-library comparison against TA-Lib,
pandas-ta, finta, talipp; Wickra wins every
category by 11-1030x (batch) and 17x+ streaming.
.github/workflows/ - CI matrix (Rust + Python + Node + WASM on
Linux/macOS/Windows), release pipeline for
PyPI wheels and npm.
Indicators (25):
Trend SMA EMA WMA DEMA TEMA HMA KAMA
Momentum RSI MACD Stochastic CCI ROC WilliamsR ADX MFI TRIX
AwesomeOscillator Aroon
Volatility BollingerBands ATR Keltner Donchian PSAR
Volume OBV VWAP (cumulative + rolling)
cargo clippy --workspace --all-targets -D warnings is clean. License: Apache-2.0.
118 lines
3.5 KiB
Python
118 lines
3.5 KiB
Python
"""For every indicator, batch(prices) must equal repeated update(price).
|
|
|
|
This is the central correctness contract of Wickra: the two APIs share one
|
|
implementation, so they cannot disagree. These tests verify it from Python
|
|
across the entire warmup → steady-state transition.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import math
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import wickra as ta
|
|
|
|
|
|
def _equal_with_nan(a: np.ndarray, b: np.ndarray, tol: float = 1e-9) -> bool:
|
|
"""NumPy ``==`` treats NaN as not-equal; emulate ``equal_nan`` for floats."""
|
|
if a.shape != b.shape:
|
|
return False
|
|
both_nan = np.isnan(a) & np.isnan(b)
|
|
diff_ok = np.where(both_nan, 0.0, np.abs(a - b))
|
|
return bool(np.all(diff_ok <= tol))
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"cls, args",
|
|
[
|
|
(ta.SMA, (14,)),
|
|
(ta.EMA, (14,)),
|
|
(ta.WMA, (14,)),
|
|
(ta.RSI, (14,)),
|
|
],
|
|
)
|
|
def test_scalar_streaming_matches_batch(cls, args, sine_prices):
|
|
batch = cls(*args).batch(sine_prices)
|
|
|
|
streamer = cls(*args)
|
|
streamed = np.array(
|
|
[streamer.update(float(p)) if streamer is not None else None for p in sine_prices],
|
|
dtype=object,
|
|
)
|
|
# Map None -> NaN to compare against batch.
|
|
streamed = np.array(
|
|
[math.nan if v is None else float(v) for v in streamed], dtype=np.float64
|
|
)
|
|
|
|
assert _equal_with_nan(batch, streamed)
|
|
|
|
|
|
def test_macd_streaming_matches_batch(sine_prices):
|
|
batch = ta.MACD().batch(sine_prices)
|
|
|
|
streamer = ta.MACD()
|
|
rows = []
|
|
for p in sine_prices:
|
|
v = streamer.update(float(p))
|
|
if v is None:
|
|
rows.append([math.nan, math.nan, math.nan])
|
|
else:
|
|
rows.append(list(v))
|
|
streamed = np.array(rows, dtype=np.float64)
|
|
assert _equal_with_nan(batch, streamed)
|
|
|
|
|
|
def test_bollinger_streaming_matches_batch(sine_prices):
|
|
batch = ta.BollingerBands().batch(sine_prices)
|
|
|
|
streamer = ta.BollingerBands()
|
|
rows = []
|
|
for p in sine_prices:
|
|
v = streamer.update(float(p))
|
|
if v is None:
|
|
rows.append([math.nan, math.nan, math.nan, math.nan])
|
|
else:
|
|
rows.append(list(v))
|
|
streamed = np.array(rows, dtype=np.float64)
|
|
assert _equal_with_nan(batch, streamed)
|
|
|
|
|
|
def test_atr_streaming_matches_batch(ohlc_series):
|
|
high, low, close = ohlc_series
|
|
batch = ta.ATR(14).batch(high, low, close)
|
|
|
|
streamer = ta.ATR(14)
|
|
rows = []
|
|
for h, l, c in zip(high, low, close):
|
|
rows.append(streamer.update((float(c), float(h), float(l), float(c), 0.0, 0)))
|
|
streamed = np.array([math.nan if v is None else v for v in rows], dtype=np.float64)
|
|
assert _equal_with_nan(batch, streamed)
|
|
|
|
|
|
def test_stochastic_streaming_matches_batch(ohlc_series):
|
|
high, low, close = ohlc_series
|
|
batch = ta.Stochastic(14, 3).batch(high, low, close)
|
|
|
|
streamer = ta.Stochastic(14, 3)
|
|
rows = []
|
|
for h, l, c in zip(high, low, close):
|
|
v = streamer.update((float(c), float(h), float(l), float(c), 0.0, 0))
|
|
rows.append([math.nan, math.nan] if v is None else list(v))
|
|
streamed = np.array(rows, dtype=np.float64)
|
|
assert _equal_with_nan(batch, streamed)
|
|
|
|
|
|
def test_obv_streaming_matches_batch(ohlc_series):
|
|
_, _, close = ohlc_series
|
|
volume = np.ones_like(close)
|
|
batch = ta.OBV().batch(close, volume)
|
|
|
|
streamer = ta.OBV()
|
|
rows = []
|
|
for c, v in zip(close, volume):
|
|
rows.append(streamer.update((float(c), float(c), float(c), float(c), float(v), 0)))
|
|
streamed = np.array([math.nan if x is None else x for x in rows], dtype=np.float64)
|
|
assert _equal_with_nan(batch, streamed)
|