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.
115 lines
3.6 KiB
Python
115 lines
3.6 KiB
Python
"""Reference-value tests that pin numerical behaviour from the Python side."""
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from __future__ import annotations
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import math
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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_sma_constant_series():
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out = ta.SMA(5).batch(np.full(20, 42.0, dtype=np.float64))
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# First 4 are warmup -> NaN; rest equal 42.
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assert np.all(np.isnan(out[:4]))
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assert np.allclose(out[4:], 42.0)
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def test_sma_known_window():
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# SMA(3) of [2, 4, 6, 8, 10] -> [_, _, 4, 6, 8]
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out = ta.SMA(3).batch(np.array([2.0, 4.0, 6.0, 8.0, 10.0]))
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assert math.isnan(out[0]) and math.isnan(out[1])
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np.testing.assert_allclose(out[2:], [4.0, 6.0, 8.0])
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def test_ema_seed_equals_simple_mean_of_first_window():
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# EMA(5) seed = mean([10, 20, 30, 40, 50]) = 30
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out = ta.EMA(5).batch(np.array([10.0, 20.0, 30.0, 40.0, 50.0]))
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assert math.isnan(out[0])
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assert math.isclose(out[4], 30.0, abs_tol=1e-12)
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def test_wma_known_window():
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# WMA(4) of [1, 2, 3, 4] = (1*1 + 2*2 + 3*3 + 4*4)/10 = 3
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out = ta.WMA(4).batch(np.array([1.0, 2.0, 3.0, 4.0]))
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assert math.isnan(out[0]) and math.isnan(out[1]) and math.isnan(out[2])
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assert math.isclose(out[3], 3.0, abs_tol=1e-12)
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def test_rsi_pure_uptrend_is_100():
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out = ta.RSI(14).batch(np.arange(1.0, 21.0, dtype=np.float64))
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np.testing.assert_allclose(out[14:], 100.0)
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def test_rsi_pure_downtrend_is_0():
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out = ta.RSI(14).batch(np.arange(20.0, 0.0, -1.0))
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np.testing.assert_allclose(out[14:], 0.0)
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def test_rsi_flat_series_is_50():
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out = ta.RSI(14).batch(np.full(30, 100.0))
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np.testing.assert_allclose(out[14:], 50.0)
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def test_rsi_wilder_textbook_first_value():
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"""Wilder's original 14-period example, ~70.46 at the first emit."""
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prices = np.array(
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[
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44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10, 45.42, 45.84, 46.08,
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45.89, 46.03, 45.61, 46.28, 46.28,
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],
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dtype=np.float64,
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)
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out = ta.RSI(14).batch(prices)
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assert math.isclose(out[14], 70.464, abs_tol=0.05)
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def test_macd_constant_series_converges_to_zero():
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out = ta.MACD().batch(np.full(200, 100.0))
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# Last row's MACD and signal must be ~0.
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last = out[-1]
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assert math.isclose(last[0], 0.0, abs_tol=1e-9)
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assert math.isclose(last[1], 0.0, abs_tol=1e-9)
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assert math.isclose(last[2], 0.0, abs_tol=1e-9)
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def test_bollinger_constant_series_zero_width():
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out = ta.BollingerBands(20, 2.0).batch(np.full(50, 100.0))
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row = out[-1]
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np.testing.assert_allclose(row, [100.0, 100.0, 100.0, 0.0], atol=1e-12)
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def test_bollinger_upper_middle_lower_ordering():
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out = ta.BollingerBands(20, 2.0).batch(np.linspace(50.0, 150.0, 100))
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ready = out[~np.isnan(out[:, 0])]
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assert np.all(ready[:, 0] >= ready[:, 1])
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assert np.all(ready[:, 1] >= ready[:, 2])
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assert np.all(ready[:, 3] >= 0.0)
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def test_atr_constant_range_constant_output():
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high = np.full(30, 11.0)
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low = np.full(30, 9.0)
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close = np.full(30, 10.0)
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out = ta.ATR(14).batch(high, low, close)
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# Once seeded, ATR equals the constant TR of 2.
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np.testing.assert_allclose(out[13:], 2.0, atol=1e-12)
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def test_stochastic_extremes():
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# Close at the top of a 3-period range -> %K = 100.
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high = np.array([10.0, 11.0, 12.0])
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low = np.array([8.0, 9.0, 10.0])
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close = np.array([9.0, 10.0, 12.0])
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out = ta.Stochastic(3, 1).batch(high, low, close)
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assert math.isclose(out[2, 0], 100.0, abs_tol=1e-12)
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def test_obv_cumulative_known_sequence():
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close = np.array([10.0, 11.0, 10.5, 10.5, 12.0])
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volume = np.array([100.0, 20.0, 30.0, 40.0, 10.0])
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out = ta.OBV().batch(close, volume)
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np.testing.assert_allclose(out, [0.0, 20.0, -10.0, -10.0, 0.0])
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