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.
37 lines
1019 B
Python
37 lines
1019 B
Python
"""Shared pytest fixtures for the Wickra Python test suite."""
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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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@pytest.fixture
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def linear_prices() -> np.ndarray:
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"""Strictly increasing prices: 1, 2, 3, ..., 50."""
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return np.arange(1.0, 51.0, dtype=np.float64)
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@pytest.fixture
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def constant_prices() -> np.ndarray:
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"""50 prices of 100.0."""
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return np.full(50, 100.0, dtype=np.float64)
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@pytest.fixture
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def sine_prices() -> np.ndarray:
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"""Smooth sine-wave prices used to stress the indicators a little."""
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t = np.arange(200, dtype=np.float64)
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return 50.0 + 10.0 * np.sin(t * 0.13) + 4.0 * np.cos(t * 0.41)
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@pytest.fixture
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def ohlc_series() -> tuple[np.ndarray, np.ndarray, np.ndarray]:
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"""Synthetic high / low / close triple."""
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t = np.arange(200, dtype=np.float64)
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close = 100.0 + np.sin(t * 0.15) * 8.0 + np.cos(t * 0.32) * 3.0
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spread = 0.5 + np.abs(np.sin(t * 0.07))
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high = close + spread
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low = close - spread
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return high, low, close
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