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.
153 lines
4.0 KiB
Python
153 lines
4.0 KiB
Python
"""Tests for the indicator lifecycle methods: reset, is_ready, warmup_period, repr."""
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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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SCALAR_INDICATORS = [
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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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(ta.MACD, ()),
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(ta.BollingerBands, ()),
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]
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@pytest.mark.parametrize("cls, args", SCALAR_INDICATORS)
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def test_is_ready_transitions_after_warmup(cls, args):
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ind = cls(*args)
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assert not ind.is_ready()
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series = np.linspace(1.0, 200.0, 200)
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ind.batch(series)
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assert ind.is_ready()
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@pytest.mark.parametrize("cls, args", SCALAR_INDICATORS)
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def test_reset_returns_to_initial_state(cls, args):
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ind = cls(*args)
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ind.batch(np.linspace(1.0, 200.0, 200))
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assert ind.is_ready()
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ind.reset()
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assert not ind.is_ready()
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@pytest.mark.parametrize(
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"cls, args, period",
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[
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(ta.SMA, (14,), 14),
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(ta.EMA, (14,), 14),
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(ta.WMA, (14,), 14),
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(ta.RSI, (14,), 15),
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(ta.BollingerBands, (20, 2.0), 20),
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],
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)
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def test_warmup_period(cls, args, period):
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assert cls(*args).warmup_period() == period
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def test_repr_contains_class_and_parameters():
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assert "SMA" in repr(ta.SMA(14))
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assert "14" in repr(ta.SMA(14))
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assert "BollingerBands" in repr(ta.BollingerBands(20, 2.0))
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def test_constructor_rejects_zero_period():
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with pytest.raises(ValueError):
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ta.SMA(0)
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with pytest.raises(ValueError):
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ta.RSI(0)
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def test_macd_rejects_fast_geq_slow():
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with pytest.raises(ValueError):
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ta.MACD(fast=26, slow=12, signal=9)
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def test_bollinger_rejects_non_positive_multiplier():
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with pytest.raises(ValueError):
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ta.BollingerBands(20, 0.0)
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with pytest.raises(ValueError):
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ta.BollingerBands(20, -1.0)
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def test_candle_dict_input_supported():
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atr = ta.ATR(2)
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atr.update({"open": 10.0, "high": 11.0, "low": 9.0, "close": 10.5, "volume": 1.0})
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v = atr.update({"open": 10.5, "high": 12.0, "low": 10.0, "close": 11.0, "volume": 1.0})
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assert v is not None
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def test_candle_tuple_input_supported():
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atr = ta.ATR(2)
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atr.update((10.0, 11.0, 9.0, 10.5, 1.0, 0))
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v = atr.update((10.5, 12.0, 10.0, 11.0, 1.0, 1))
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assert v is not None
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def test_initial_balance_reset_unlocks():
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ib = ta.InitialBalance(2)
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assert not ib.is_ready()
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ib.update((101.0, 102.0, 100.0, 101.0, 0.0, 0))
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ib.update((102.0, 103.0, 101.0, 102.0, 0.0, 1))
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assert ib.is_ready()
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assert ib.is_locked()
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ib.reset()
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assert not ib.is_ready()
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assert not ib.is_locked()
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def test_opening_range_reset_unlocks():
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or_ind = ta.OpeningRange(2)
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or_ind.update((101.0, 102.0, 100.0, 101.0, 0.0, 0))
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or_ind.update((102.0, 103.0, 101.0, 102.0, 0.0, 1))
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assert or_ind.is_locked()
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or_ind.reset()
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assert not or_ind.is_locked()
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def test_value_area_warmup_equals_period():
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assert ta.ValueArea(20, 50, 0.70).warmup_period() == 20
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assert ta.ValueArea(10, 30, 0.80).warmup_period() == 10
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def test_ehlers_indicators_lifecycle():
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# Spot-check a few Family-10 entries beyond what test_new_indicators covers.
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series = np.linspace(1.0, 200.0, 200) + np.sin(np.arange(200) * 0.3) * 5.0
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for ind in [
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ta.SuperSmoother(10),
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ta.FisherTransform(10),
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ta.MAMA(),
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ta.HilbertDominantCycle(),
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ta.SineWave(),
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]:
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assert not ind.is_ready()
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ind.batch(series)
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assert ind.is_ready()
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ind.reset()
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assert not ind.is_ready()
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def test_orderbook_lifecycle():
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snapshot = ([100.0], [1.0], [101.0], [1.0])
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for ind in [
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ta.OrderBookImbalanceTop1(),
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ta.OrderBookImbalanceTopN(3),
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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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assert ind.warmup_period() == 1
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assert not ind.is_ready()
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ind.update(*snapshot)
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assert ind.is_ready()
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ind.reset()
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assert not ind.is_ready()
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def test_orderbook_topn_repr():
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assert repr(ta.OrderBookImbalanceTopN(5)) == "OrderBookImbalanceTopN(levels=5)"
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