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.
194 lines
5.8 KiB
Python
194 lines
5.8 KiB
Python
"""Input-validation tests: malformed NumPy inputs raise ValueError, not panics."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import wickra as ta
|
|
|
|
|
|
def test_non_contiguous_array_raises_value_error():
|
|
# A strided view is not C-contiguous; batch() must reject it cleanly.
|
|
base = np.linspace(1.0, 100.0, 60)
|
|
non_contiguous = base[::2]
|
|
assert not non_contiguous.flags["C_CONTIGUOUS"]
|
|
with pytest.raises(ValueError):
|
|
ta.SMA(5).batch(non_contiguous)
|
|
|
|
|
|
def test_ascontiguousarray_recovers():
|
|
base = np.linspace(1.0, 100.0, 60)
|
|
fixed = np.ascontiguousarray(base[::2])
|
|
out = ta.SMA(5).batch(fixed)
|
|
assert out.shape == fixed.shape
|
|
|
|
|
|
def test_unequal_length_candle_batch_raises(ohlc_series):
|
|
high, low, close = ohlc_series
|
|
short = low[:-1]
|
|
with pytest.raises(ValueError):
|
|
ta.ATR(14).batch(high, short, close)
|
|
with pytest.raises(ValueError):
|
|
ta.WilliamsR(14).batch(high, short, close)
|
|
with pytest.raises(ValueError):
|
|
ta.Aroon(14).batch(high, short)
|
|
|
|
|
|
def test_pairwise_beta_rejects_bad_period():
|
|
with pytest.raises(ValueError):
|
|
ta.PairwiseBeta(0)
|
|
with pytest.raises(ValueError):
|
|
ta.PairwiseBeta(1)
|
|
|
|
|
|
def test_unequal_length_pair_batch_raises(sine_prices):
|
|
a = np.ascontiguousarray((sine_prices + 100.0).astype(np.float64))
|
|
b = a[:-1]
|
|
with pytest.raises(ValueError):
|
|
ta.PairwiseBeta(20).batch(a, b)
|
|
with pytest.raises(ValueError):
|
|
ta.PairSpreadZScore(20, 20).batch(a, b)
|
|
|
|
|
|
def test_pair_spread_zscore_rejects_bad_periods():
|
|
with pytest.raises(ValueError):
|
|
ta.PairSpreadZScore(1, 20)
|
|
with pytest.raises(ValueError):
|
|
ta.PairSpreadZScore(20, 1)
|
|
|
|
|
|
def test_lead_lag_rejects_bad_params():
|
|
with pytest.raises(ValueError):
|
|
ta.LeadLagCrossCorrelation(1, 5)
|
|
with pytest.raises(ValueError):
|
|
ta.LeadLagCrossCorrelation(10, 0)
|
|
|
|
|
|
def test_lead_lag_unequal_length_batch_raises(sine_prices):
|
|
a = np.ascontiguousarray((sine_prices + 100.0).astype(np.float64))
|
|
b = a[:-1]
|
|
with pytest.raises(ValueError):
|
|
ta.LeadLagCrossCorrelation(12, 5).batch(a, b)
|
|
|
|
|
|
def test_cointegration_rejects_too_small_period():
|
|
# period must be >= 2*adf_lags + 4.
|
|
with pytest.raises(ValueError):
|
|
ta.Cointegration(3, 0)
|
|
with pytest.raises(ValueError):
|
|
ta.Cointegration(5, 1)
|
|
|
|
|
|
def test_cointegration_unequal_length_batch_raises(sine_prices):
|
|
a = np.ascontiguousarray((sine_prices + 100.0).astype(np.float64))
|
|
b = a[:-1]
|
|
with pytest.raises(ValueError):
|
|
ta.Cointegration(20, 1).batch(a, b)
|
|
|
|
|
|
def test_relative_strength_rejects_zero_periods():
|
|
with pytest.raises(ValueError):
|
|
ta.RelativeStrengthAB(0, 14)
|
|
with pytest.raises(ValueError):
|
|
ta.RelativeStrengthAB(20, 0)
|
|
|
|
|
|
def test_relative_strength_unequal_length_batch_raises(sine_prices):
|
|
a = np.ascontiguousarray((sine_prices + 100.0).astype(np.float64))
|
|
b = a[:-1]
|
|
with pytest.raises(ValueError):
|
|
ta.RelativeStrengthAB(10, 14).batch(a, b)
|
|
|
|
|
|
def test_roc_and_trix_have_default_periods():
|
|
# ROC/TRIX gained constructor defaults matching the TA-Lib convention.
|
|
assert ta.ROC().period == 10
|
|
assert ta.TRIX() is not None
|
|
|
|
|
|
def test_value_area_rejects_zero_period():
|
|
with pytest.raises(ValueError):
|
|
ta.ValueArea(0, 50, 0.7)
|
|
with pytest.raises(ValueError):
|
|
ta.ValueArea(20, 0, 0.7)
|
|
|
|
|
|
def test_value_area_rejects_invalid_pct():
|
|
with pytest.raises(ValueError):
|
|
ta.ValueArea(20, 50, 0.0)
|
|
with pytest.raises(ValueError):
|
|
ta.ValueArea(20, 50, 1.5)
|
|
|
|
|
|
def test_initial_balance_rejects_zero_period():
|
|
with pytest.raises(ValueError):
|
|
ta.InitialBalance(0)
|
|
|
|
|
|
def test_opening_range_rejects_zero_period():
|
|
with pytest.raises(ValueError):
|
|
ta.OpeningRange(0)
|
|
|
|
|
|
def test_value_area_unequal_length_raises():
|
|
high = np.array([1.0, 2.0, 3.0])
|
|
low = np.array([0.5, 1.5])
|
|
volume = np.array([10.0, 10.0, 10.0])
|
|
with pytest.raises(ValueError):
|
|
ta.ValueArea(2, 10, 0.7).batch(high, low, volume)
|
|
|
|
|
|
def test_ichimoku_rejects_zero_and_non_increasing_periods():
|
|
with pytest.raises(ValueError):
|
|
ta.Ichimoku(0, 26, 52, 26)
|
|
with pytest.raises(ValueError):
|
|
ta.Ichimoku(9, 26, 52, 0)
|
|
# Periods must satisfy tenkan < kijun < senkou_b.
|
|
with pytest.raises(ValueError):
|
|
ta.Ichimoku(26, 9, 52, 26)
|
|
with pytest.raises(ValueError):
|
|
ta.Ichimoku(9, 52, 52, 26)
|
|
|
|
|
|
def test_family_10_ehlers_rejects_invalid_parameters():
|
|
with pytest.raises(ValueError):
|
|
ta.SuperSmoother(0)
|
|
with pytest.raises(ValueError):
|
|
ta.FisherTransform(0)
|
|
with pytest.raises(ValueError):
|
|
ta.InverseFisherTransform(0.0)
|
|
with pytest.raises(ValueError):
|
|
ta.DecyclerOscillator(30, 10)
|
|
with pytest.raises(ValueError):
|
|
ta.RoofingFilter(48, 10)
|
|
with pytest.raises(ValueError):
|
|
ta.MAMA(0.05, 0.5)
|
|
with pytest.raises(ValueError):
|
|
ta.EmpiricalModeDecomposition(20, 0.0)
|
|
|
|
|
|
def test_orderbook_topn_zero_levels_raises():
|
|
with pytest.raises(ValueError):
|
|
ta.OrderBookImbalanceTopN(0)
|
|
|
|
|
|
def test_orderbook_unequal_price_size_lengths_raise():
|
|
# bid_px has 2 entries but bid_sz has 1 -> mismatched -> ValueError.
|
|
with pytest.raises(ValueError):
|
|
ta.OrderBookImbalanceTop1().update([100.0, 99.0], [1.0], [101.0], [1.0])
|
|
with pytest.raises(ValueError):
|
|
ta.Microprice().update([100.0], [1.0], [101.0, 102.0], [1.0])
|
|
|
|
|
|
def test_orderbook_crossed_book_raises():
|
|
# best_bid (102) >= best_ask (101) is a crossed book -> rejected.
|
|
with pytest.raises(ValueError):
|
|
ta.QuotedSpread().update([102.0], [1.0], [101.0], [1.0])
|
|
|
|
|
|
def test_orderbook_misordered_levels_raise():
|
|
# Bids must be strictly descending in price.
|
|
with pytest.raises(ValueError):
|
|
ta.OrderBookImbalanceFull().update([99.0, 100.0], [1.0, 1.0], [101.0], [1.0])
|