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.
58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
"""Smoke tests: every public class can be constructed and emits the right shape."""
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from __future__ import annotations
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import numpy as np
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import pytest
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import wickra as ta
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def test_version_is_a_nonempty_string():
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assert isinstance(ta.__version__, str)
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assert ta.__version__
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@pytest.mark.parametrize(
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"cls, args",
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[
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(ta.SMA, (14,)),
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(ta.EMA, (14,)),
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(ta.WMA, (14,)),
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(ta.RSI, (14,)),
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],
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)
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def test_scalar_batch_returns_same_length(cls, args, sine_prices):
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out = cls(*args).batch(sine_prices)
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assert out.shape == sine_prices.shape
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assert out.dtype == np.float64
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def test_macd_batch_returns_n_by_3(sine_prices):
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out = ta.MACD().batch(sine_prices)
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assert out.shape == (sine_prices.size, 3)
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def test_bollinger_batch_returns_n_by_4(sine_prices):
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out = ta.BollingerBands().batch(sine_prices)
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assert out.shape == (sine_prices.size, 4)
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def test_atr_batch_shape(ohlc_series):
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high, low, close = ohlc_series
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out = ta.ATR(14).batch(high, low, close)
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assert out.shape == close.shape
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def test_stochastic_batch_shape(ohlc_series):
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high, low, close = ohlc_series
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out = ta.Stochastic(14, 3).batch(high, low, close)
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assert out.shape == (close.size, 2)
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def test_obv_batch_shape(ohlc_series):
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_, _, close = ohlc_series
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volume = np.ones_like(close)
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out = ta.OBV().batch(close, volume)
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assert out.shape == close.shape
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