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.
122 lines
3.3 KiB
Python
122 lines
3.3 KiB
Python
"""Smoke tests: every public class can be constructed and emits the right shape."""
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from __future__ import annotations
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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_version_is_a_nonempty_string():
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assert isinstance(ta.__version__, str)
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assert ta.__version__
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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_batch_returns_same_length(cls, args, sine_prices):
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out = cls(*args).batch(sine_prices)
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assert out.shape == sine_prices.shape
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assert out.dtype == np.float64
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def test_macd_batch_returns_n_by_3(sine_prices):
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out = ta.MACD().batch(sine_prices)
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assert out.shape == (sine_prices.size, 3)
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def test_bollinger_batch_returns_n_by_4(sine_prices):
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out = ta.BollingerBands().batch(sine_prices)
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assert out.shape == (sine_prices.size, 4)
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def test_atr_batch_shape(ohlc_series):
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high, low, close = ohlc_series
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out = ta.ATR(14).batch(high, low, close)
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assert out.shape == close.shape
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def test_stochastic_batch_shape(ohlc_series):
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high, low, close = ohlc_series
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out = ta.Stochastic(14, 3).batch(high, low, close)
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assert out.shape == (close.size, 2)
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def test_obv_batch_shape(ohlc_series):
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_, _, close = ohlc_series
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volume = np.ones_like(close)
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out = ta.OBV().batch(close, volume)
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assert out.shape == close.shape
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def test_value_area_batch_shape(ohlc_series):
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high, low, close = ohlc_series
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volume = np.ones_like(close)
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out = ta.ValueArea(20, 50, 0.70).batch(high, low, volume)
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assert out.shape == (close.size, 3)
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def test_initial_balance_batch_shape(ohlc_series):
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high, low, _close = ohlc_series
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out = ta.InitialBalance(12).batch(high, low)
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assert out.shape == (high.size, 2)
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def test_opening_range_batch_shape(ohlc_series):
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high, low, close = ohlc_series
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out = ta.OpeningRange(6).batch(high, low, close)
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assert out.shape == (close.size, 3)
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def test_ichimoku_batch_returns_n_by_5(ohlc_series):
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high, low, close = ohlc_series
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out = ta.Ichimoku().batch(high, low, close)
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assert out.shape == (close.size, 5)
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def test_heikin_ashi_batch_returns_n_by_4(ohlc_series):
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high, low, close = ohlc_series
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open_ = (high + low) / 2.0
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out = ta.HeikinAshi().batch(open_, high, low, close)
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assert out.shape == (close.size, 4)
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def test_ehlers_super_smoother_batch_shape(sine_prices):
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out = ta.SuperSmoother(10).batch(sine_prices)
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assert out.shape == sine_prices.shape
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def test_mama_batch_shape(sine_prices):
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out = ta.MAMA().batch(sine_prices)
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assert out.shape == (sine_prices.size, 2)
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def test_orderbook_indicators_construct_and_emit():
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# All five order-book indicators accept a four-array snapshot and emit a float.
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snapshot = ([100.0, 99.0], [2.0, 1.0], [101.0, 102.0], [1.0, 1.0])
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indicators = [
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ta.OrderBookImbalanceTop1(),
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ta.OrderBookImbalanceTopN(2),
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ta.OrderBookImbalanceFull(),
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ta.Microprice(),
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ta.QuotedSpread(),
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]
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for ind in indicators:
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out = ind.update(*snapshot)
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assert isinstance(out, float)
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def test_orderbook_batch_returns_one_value_per_snapshot():
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snapshots = [([100.0], [3.0], [101.0], [1.0])] * 5
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out = ta.OrderBookImbalanceTop1().batch(snapshots)
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assert out.shape == (5,)
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assert out.dtype == np.float64
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