kingchenc
9b8e1346ed
* feat(family-16): add ValueArea + InitialBalance + OpeningRange Opens family #16 (Market Profile) with the three OHLCV-compatible scalar / multi-output indicators: - ValueArea(period, bin_count, value_area_pct) -> {poc, vah, val}. Rolling bin-approximation volume profile over the last `period` candles. Each candle's volume is spread uniformly across [low, high]; POC is the bin with highest cumulative volume; the value area expands symmetrically from POC and always absorbs the higher-volume neighbour next, until `value_area_pct` (default 0.70) of total volume is enclosed. Defaults (20, 50, 0.70). - InitialBalance(period) -> {high, low}. Tracks session-opening high and low over the first `period` bars, then locks. Default period = 12 (one-hour IB on 5-minute bars for US equities). Callers MUST invoke reset() at every session boundary, otherwise IB stays fixed for the lifetime of the instance. - OpeningRange(period) -> {high, low, breakout_distance}. Same lock-after-N-bars semantics as IB with a shorter default period (6 = 30 min on 5-minute bars) and a third output that tracks close - or_mid (positive above the range mid, negative below). Histogram-output Market Profile variants (Volume Profile, VPVR, Composite Profile) are deferred because they need a new histogram output API layer rather than fixed-arity scalars. Tick-data-only variants (TPO Profile, Single Print, Order Flow Delta, Cumulative Delta, Volume-Weighted Open) are out of scope because `wickra-data` does not currently expose tick / L2 data. All four bindings (Rust core, Python, Node, WASM) ship the new indicators with parity tests; benches added; fuzz target extended. Counter 71 -> 74 across 8 -> 9 families. cargo check --workspace --all-features green. * fix(family-16): cover cold paths in InitialBalance + ValueArea InitialBalance::value() public getter had no test covering the post-update Some(...) branch — extended accessors_and_metadata to call value() after one update. ValueArea single-print bar path (c.high == c.low) was unreachable in existing tests since the only single-print test used a uniform 100-price window which exits early via the span == 0 guard; added a mixed-window test that triggers the c.high <= c.low branch directly. The (None, None) arm of the expansion match was by-construction unreachable (the loop condition already requires at least one neighbour) and has been folded into an if/else.
204 lines
6.7 KiB
Python
204 lines
6.7 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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