kingchenc
2be21df803
* feat(core): add microstructure input types (OrderBook, Trade, TradeQuote) New non-OHLCV value types for the order-book / trade-flow indicator family: Level, OrderBook (sorted, uncrossed depth snapshot), Side, Trade (with aggressor side), and TradeQuote (trade paired with prevailing mid). Each has a validating constructor plus a new_unchecked hot-path constructor, with full unit coverage. Adds InvalidOrderBook / InvalidTrade error variants. * feat(core): add 5 order-book microstructure indicators OrderBookImbalanceTop1/TopN/Full (signed depth imbalance), Microprice (size-weighted fair value), and QuotedSpread (top-of-book spread in bps). All consume the OrderBook snapshot type, emit f64, are stateless and ready after the first snapshot, with full unit coverage. Registers a new Microstructure family in the taxonomy. * feat(bindings): expose order-book microstructure indicators Python, Node, and WASM bindings for OrderBookImbalanceTop1/TopN/Full, Microprice and QuotedSpread. Each takes a depth snapshot via four equal-length (bid_px, bid_sz, ask_px, ask_sz) arrays. Python and Node expose a batch over a list of snapshots; WASM exposes per-snapshot update (the streaming model that fits a browser book feed). Regenerates node index.d.ts/.js and registers the new InvalidOrderBook/InvalidTrade arms in the Python error mapping. * test(bindings,fuzz): cover order-book microstructure indicators Python: smoke, reference values, streaming-vs-batch, lifecycle/repr and input validation (mismatched lengths, crossed book, misordered levels, zero levels) for all five order-book indicators. Node: reference values, streaming-vs-batch, and rejection cases. Adds an indicator_update_orderbook fuzz target driving every order-book indicator over arbitrary (incl. degenerate) snapshots. * bench(microstructure): synthetic order-book benchmarks Add a bench_orderbook_input harness and synthesise a five-level book around each candle close (no order-book dataset ships with the repo). Benches the cheapest (top-of-book imbalance) and most-expensive (full-depth imbalance) plus microprice, matching the curated cheapest/expensive-per-family approach. * docs: add Microstructure family + bump indicator counter to 224 README gains the Microstructure family row (order-book imbalance, microprice, quoted spread) and the indicator counter goes 219 -> 224 across seventeen families; CHANGELOG records the new order-book indicators and value types.
220 lines
7.1 KiB
Python
220 lines
7.1 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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