kingchenc
5867f71450
* feat(core): add 3 trade-flow microstructure indicators SignedVolume (per-trade size signed by aggressor), CumulativeVolumeDelta (running signed-volume total), and TradeImbalance (rolling buy/sell volume imbalance over a trade window). All consume the Trade type, with full unit coverage. Extends the Microstructure family. * feat(bindings): expose trade-flow microstructure indicators Python, Node and WASM bindings for SignedVolume, CumulativeVolumeDelta and TradeImbalance. Each takes a trade via update(price, size, is_buy); Python and Node expose a batch over three parallel arrays, WASM exposes per-trade update. Regenerates node index.d.ts/.js. * test(bindings,fuzz,bench): cover trade-flow microstructure indicators Python and Node: reference values, streaming-vs-batch, lifecycle/repr and input validation (zero window, negative size, non-positive price, mismatched batch lengths). New indicator_update_trade fuzz target. Synthetic trade-tape benches (signed_volume cheapest, trade_imbalance windowed/expensive). * docs: add trade-flow indicators + bump counter to 227 README Microstructure family row gains signed volume / CVD / trade imbalance and the counter goes 224 -> 227; CHANGELOG records the trade-flow indicators.
234 lines
7.6 KiB
Python
234 lines
7.6 KiB
Python
"""For every indicator, batch(prices) must equal repeated update(price).
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This is the central correctness contract of Wickra: the two APIs share one
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implementation, so they cannot disagree. These tests verify it from Python
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across the entire warmup → steady-state transition.
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"""
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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 _equal_with_nan(a: np.ndarray, b: np.ndarray, tol: float = 1e-9) -> bool:
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"""NumPy ``==`` treats NaN as not-equal; emulate ``equal_nan`` for floats."""
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if a.shape != b.shape:
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return False
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both_nan = np.isnan(a) & np.isnan(b)
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diff_ok = np.where(both_nan, 0.0, np.abs(a - b))
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return bool(np.all(diff_ok <= tol))
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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_streaming_matches_batch(cls, args, sine_prices):
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batch = cls(*args).batch(sine_prices)
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streamer = cls(*args)
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streamed = np.array(
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[streamer.update(float(p)) if streamer is not None else None for p in sine_prices],
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dtype=object,
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)
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# Map None -> NaN to compare against batch.
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streamed = np.array(
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[math.nan if v is None else float(v) for v in streamed], dtype=np.float64
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)
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assert _equal_with_nan(batch, streamed)
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def test_macd_streaming_matches_batch(sine_prices):
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batch = ta.MACD().batch(sine_prices)
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streamer = ta.MACD()
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rows = []
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for p in sine_prices:
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v = streamer.update(float(p))
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if v is None:
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rows.append([math.nan, math.nan, math.nan])
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else:
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rows.append(list(v))
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streamed = np.array(rows, dtype=np.float64)
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assert _equal_with_nan(batch, streamed)
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def test_bollinger_streaming_matches_batch(sine_prices):
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batch = ta.BollingerBands().batch(sine_prices)
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streamer = ta.BollingerBands()
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rows = []
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for p in sine_prices:
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v = streamer.update(float(p))
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if v is None:
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rows.append([math.nan, math.nan, math.nan, math.nan])
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else:
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rows.append(list(v))
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streamed = np.array(rows, dtype=np.float64)
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assert _equal_with_nan(batch, streamed)
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def test_atr_streaming_matches_batch(ohlc_series):
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high, low, close = ohlc_series
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batch = ta.ATR(14).batch(high, low, close)
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streamer = ta.ATR(14)
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rows = []
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for h, l, c in zip(high, low, close):
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rows.append(streamer.update((float(c), float(h), float(l), float(c), 0.0, 0)))
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streamed = np.array([math.nan if v is None else v for v in rows], dtype=np.float64)
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assert _equal_with_nan(batch, streamed)
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def test_stochastic_streaming_matches_batch(ohlc_series):
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high, low, close = ohlc_series
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batch = ta.Stochastic(14, 3).batch(high, low, close)
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streamer = ta.Stochastic(14, 3)
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rows = []
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for h, l, c in zip(high, low, close):
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v = streamer.update((float(c), float(h), float(l), float(c), 0.0, 0))
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rows.append([math.nan, math.nan] if v is None else list(v))
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streamed = np.array(rows, dtype=np.float64)
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assert _equal_with_nan(batch, streamed)
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def test_obv_streaming_matches_batch(ohlc_series):
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_, _, close = ohlc_series
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volume = np.ones_like(close)
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batch = ta.OBV().batch(close, volume)
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streamer = ta.OBV()
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rows = []
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for c, v in zip(close, volume):
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rows.append(streamer.update((float(c), float(c), float(c), float(c), float(v), 0)))
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streamed = np.array([math.nan if x is None else x for x in rows], dtype=np.float64)
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assert _equal_with_nan(batch, streamed)
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def test_mama_streaming_matches_batch(sine_prices):
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batch = ta.MAMA().batch(sine_prices)
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streamer = ta.MAMA()
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rows = []
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for p in sine_prices:
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v = streamer.update(float(p))
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if v is None:
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rows.append([math.nan, math.nan])
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else:
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rows.append(list(v))
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streamed = np.array(rows, dtype=np.float64)
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assert _equal_with_nan(batch, streamed)
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def test_super_smoother_streaming_matches_batch(sine_prices):
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batch = ta.SuperSmoother(10).batch(sine_prices)
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streamer = ta.SuperSmoother(10)
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streamed = np.array(
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[math.nan if (v := streamer.update(float(p))) is None else float(v) for p in sine_prices],
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dtype=np.float64,
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)
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assert _equal_with_nan(batch, streamed)
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def test_rolling_vwap_streaming_matches_batch(ohlc_series):
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# RollingVWAP(20) on the shared OHLC series. Provides finite-memory VWAP
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# parity coverage now that the indicator is exposed across all bindings.
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high, low, close = ohlc_series
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volume = np.linspace(100.0, 200.0, num=close.size, dtype=np.float64)
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batch = ta.RollingVWAP(20).batch(high, low, close, volume)
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streamer = ta.RollingVWAP(20)
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rows = []
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for h, l, c, v in zip(high, low, close, volume):
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rows.append(streamer.update((float(c), float(h), float(l), float(c), float(v), 0)))
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streamed = np.array([math.nan if x is None else x for x in rows], dtype=np.float64)
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assert _equal_with_nan(batch, streamed)
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assert streamer.period == 20
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assert streamer.warmup_period() == 20
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assert streamer.is_ready()
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streamer.reset()
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assert not streamer.is_ready()
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def test_value_area_streaming_matches_batch(ohlc_series):
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high, low, close = ohlc_series
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volume = np.linspace(100.0, 200.0, num=close.size, dtype=np.float64)
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batch = ta.ValueArea(20, 50, 0.70).batch(high, low, volume)
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streamer = ta.ValueArea(20, 50, 0.70)
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rows = []
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for h, l, v in zip(high, low, volume):
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mid = float((h + l) / 2.0)
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out = streamer.update((mid, float(h), float(l), mid, float(v), 0))
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rows.append([math.nan, math.nan, math.nan] if out is None else list(out))
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streamed = np.array(rows, dtype=np.float64)
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assert _equal_with_nan(batch, streamed)
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def test_initial_balance_streaming_matches_batch(ohlc_series):
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high, low, _close = ohlc_series
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batch = ta.InitialBalance(12).batch(high, low)
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streamer = ta.InitialBalance(12)
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rows = []
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for h, l in zip(high, low):
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mid = float((h + l) / 2.0)
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out = streamer.update((mid, float(h), float(l), mid, 0.0, 0))
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rows.append([math.nan, math.nan] if out is None else list(out))
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streamed = np.array(rows, dtype=np.float64)
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assert _equal_with_nan(batch, streamed)
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def test_opening_range_streaming_matches_batch(ohlc_series):
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high, low, close = ohlc_series
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batch = ta.OpeningRange(6).batch(high, low, close)
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streamer = ta.OpeningRange(6)
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rows = []
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for h, l, c in zip(high, low, close):
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out = streamer.update((float(c), float(h), float(l), float(c), 0.0, 0))
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rows.append([math.nan, math.nan, math.nan] if out is None else list(out))
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streamed = np.array(rows, dtype=np.float64)
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assert _equal_with_nan(batch, streamed)
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def test_orderbook_streaming_matches_batch():
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snaps = [
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(
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[100.0, 99.0],
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[1.0 + (i % 5), 1.0],
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[101.0, 102.0],
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[1.0 + ((i + 1) % 3), 1.0],
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)
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for i in range(30)
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]
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batch = ta.Microprice().batch(snaps)
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streamer = ta.Microprice()
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streamed = np.array([streamer.update(*snap) for snap in snaps], dtype=np.float64)
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assert _equal_with_nan(batch, streamed)
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def test_tradeflow_streaming_matches_batch():
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n = 30
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price = np.full(n, 100.0)
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size = np.array([1.0 + (i % 4) for i in range(n)], dtype=np.float64)
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is_buy = [i % 3 != 0 for i in range(n)]
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batch = ta.CumulativeVolumeDelta().batch(price, size, is_buy)
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streamer = ta.CumulativeVolumeDelta()
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streamed = np.array(
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[streamer.update(price[i], size[i], is_buy[i]) for i in range(n)],
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dtype=np.float64,
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)
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assert _equal_with_nan(batch, streamed)
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