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392 lines
15 KiB
Python
392 lines
15 KiB
Python
"""Unit tests for the CrossSectionalMomentum (12-1) engine.
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Like every continuous-rotation engine on this codebase, the non-negotiable bar
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is the look-ahead defense: every used bar date MUST be strictly before
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decision_date. The momentum window AND the rebalance gate AND the universe
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filters all run through `bar_provider.get_bars_before` which enforces this.
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The Phase 19 pre-commit abort criteria are documented in the engine module
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and the POC config (configs/experiments/xsmom_poc_v1.json). Tests here cover
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pure logic, lookahead defense, and the rebalance-day semantics.
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"""
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from __future__ import annotations
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import datetime as dt
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from typing import Any
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import pytest
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from libs.backtest.domain import LookaheadViolationError, StrategyEngineConfig
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from libs.backtest.cross_sectional_momentum import (
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CROSS_SECTIONAL_MOMENTUM_EVENT_TYPE,
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_SnapshotStoreBarAdapter,
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build_candidates,
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compute_12_1_momentum,
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compute_20d_volatility,
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compute_avg_dollar_volume_20d,
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is_rebalance_day,
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)
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _make_engine(**overrides: Any) -> StrategyEngineConfig:
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base: dict[str, Any] = dict(
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engine_id="xsmom_12_1_long",
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event_types=[CROSS_SECTIONAL_MOMENTUM_EVENT_TYPE],
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direction="long_only",
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timing_class="after_close",
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entry_timing_policy="next_open",
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max_holding_days=21,
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xsmom_enabled=True,
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xsmom_lookback_days=60, # smaller for tests
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xsmom_skip_days=5,
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xsmom_top_n=3,
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xsmom_holding_days=21,
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xsmom_momentum_min=0.0,
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xsmom_min_avg_dollar_volume=1_000_000.0,
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xsmom_min_price=5.0,
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xsmom_volatility_20d_max=0.10,
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xsmom_stop_pct=0.10,
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xsmom_target_pct=0.30,
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)
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base.update(overrides)
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return StrategyEngineConfig(**base)
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def _business_days(start: dt.date, count: int) -> list[dt.date]:
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out: list[dt.date] = []
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cursor = start
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while len(out) < count:
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if cursor.weekday() < 5:
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out.append(cursor)
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cursor = cursor + dt.timedelta(days=1)
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return out
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def _linear_bars(
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days: list[dt.date],
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*,
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start_close: float = 100.0,
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end_close: float = 130.0,
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volume: float = 5_000_000.0,
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) -> dict[dt.date, dict[str, Any]]:
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"""Linear close path from start_close to end_close across all bars."""
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n = len(days)
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out: dict[dt.date, dict[str, Any]] = {}
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for i, d in enumerate(days):
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c = start_close + (end_close - start_close) * (i / max(1, n - 1))
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out[d] = {
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"open": c,
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"high": c + 0.5,
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"low": c - 0.5,
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"close": c,
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"volume": volume,
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}
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return out
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# ---------------------------------------------------------------------------
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# Pure trigger computations
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# ---------------------------------------------------------------------------
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def test_12_1_momentum_basic() -> None:
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days = _business_days(dt.date(2024, 1, 2), 80) # need lookback+skip=65, plenty
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bars_dict = _linear_bars(days, start_close=100.0, end_close=200.0)
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bars = sorted(bars_dict.items(), key=lambda kv: kv[0])
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# Use full window: lookback=60, skip=5 → window is bars[-65:-5]
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momentum, used_dates = compute_12_1_momentum(bars, lookback_days=60, skip_days=5)
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assert momentum is not None
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# The window excludes the most recent 5 bars. With linear 100→200 across 80 bars,
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# start of window is bars[-65] and end is bars[-6]. Both >100, <200.
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start_close = bars[-65][1]["close"]
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end_close = bars[-6][1]["close"]
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expected = (end_close / start_close) - 1.0
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assert abs(momentum - expected) < 1e-9
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assert len(used_dates) == 60
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def test_12_1_momentum_insufficient_history() -> None:
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days = _business_days(dt.date(2024, 1, 2), 30)
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bars_dict = _linear_bars(days)
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bars = sorted(bars_dict.items(), key=lambda kv: kv[0])
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momentum, used_dates = compute_12_1_momentum(bars, lookback_days=60, skip_days=5)
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assert momentum is None
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assert used_dates == []
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def test_20d_volatility_basic() -> None:
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days = _business_days(dt.date(2024, 1, 2), 30)
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bars_dict = _linear_bars(days)
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bars = sorted(bars_dict.items(), key=lambda kv: kv[0])
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vol = compute_20d_volatility(bars)
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assert vol is not None
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assert vol >= 0
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def test_avg_dollar_volume_20d_basic() -> None:
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days = _business_days(dt.date(2024, 1, 2), 30)
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bars_dict = _linear_bars(days, start_close=100, end_close=100, volume=1_000_000.0)
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bars = sorted(bars_dict.items(), key=lambda kv: kv[0])
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adv = compute_avg_dollar_volume_20d(bars)
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# close ≈ 100, volume = 1M, so dollar volume ≈ 100M
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assert abs(adv - 100_000_000.0) < 1_000_000.0
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# ---------------------------------------------------------------------------
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# Rebalance-day semantics
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# ---------------------------------------------------------------------------
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def test_is_rebalance_day_first_trading_day_of_new_month() -> None:
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# Last bar is 2024-01-31 (Wed); decision_date is 2024-02-01 (Thu)
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bars = [(dt.date(2024, 1, 31), {"close": 100.0})]
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assert is_rebalance_day(bars, dt.date(2024, 2, 1)) is True
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def test_is_rebalance_day_mid_month() -> None:
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# Last bar is 2024-01-15 (Mon); decision_date 2024-01-16 (Tue) — same month
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bars = [(dt.date(2024, 1, 15), {"close": 100.0})]
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assert is_rebalance_day(bars, dt.date(2024, 1, 16)) is False
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def test_is_rebalance_day_empty_bars_returns_false() -> None:
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assert is_rebalance_day([], dt.date(2024, 1, 2)) is False
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def test_is_rebalance_day_lookahead_raises() -> None:
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bars = [(dt.date(2024, 2, 1), {"close": 100.0})]
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with pytest.raises(LookaheadViolationError):
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is_rebalance_day(bars, dt.date(2024, 2, 1))
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# ---------------------------------------------------------------------------
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# Lookahead defense: build_candidates rejects T+0 bars
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# ---------------------------------------------------------------------------
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class _LeakyProvider:
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"""Returns bars INCLUDING the decision_date — must be caught by build_candidates."""
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def __init__(self, bars_by_symbol: dict[str, dict[dt.date, dict[str, Any]]]) -> None:
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self.bars_by_symbol = bars_by_symbol
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def get_bars_before(
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self, symbol: str, as_of_date: dt.date, lookback_days: int
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) -> list[tuple[dt.date, dict[str, Any]]]:
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# INTENTIONALLY LEAKY: returns bars where date == as_of_date too.
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sym_bars = self.bars_by_symbol.get(symbol.upper(), {})
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ordered = sorted(sym_bars.items(), key=lambda kv: kv[0])
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return [(d, b) for d, b in ordered if d <= as_of_date][-lookback_days:]
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def test_build_raises_lookahead_when_provider_returns_t0_bar() -> None:
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"""Proof-by-contradiction: a leaky provider that returns T+0 must trip the assertion."""
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days = _business_days(dt.date(2024, 1, 2), 80)
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decision_date = days[-1] # final day is decision_date — leaky provider returns it
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next_date = decision_date + dt.timedelta(days=1)
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# Ensure decision_date is first trading day of new month — pick a setup that's a rebalance.
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# Easier: just patch the rebalance check by using a non-rebalance date but with a leaky T+0;
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# the leak should trip BEFORE rebalance gate via the explicit assertion.
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bars_dict = _linear_bars(days)
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leaky = _LeakyProvider({"FOO": bars_dict})
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engine = _make_engine()
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with pytest.raises(LookaheadViolationError):
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build_candidates(
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decision_date=decision_date,
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next_trading_date=next_date,
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universe_symbols=["FOO"],
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engine=engine,
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bar_provider=leaky,
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)
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def test_build_returns_empty_on_non_rebalance_day() -> None:
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"""Mid-month decision_date must yield 0 candidates (no rebalance)."""
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days = _business_days(dt.date(2024, 1, 2), 80)
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bars_dict = _linear_bars(days)
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bars_by_symbol = {f"SYM{i}": bars_dict for i in range(5)}
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adapter = _SnapshotStoreBarAdapter(bars_by_symbol=bars_by_symbol)
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engine = _make_engine()
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# Use a mid-month date (where last_bar.month == decision_date.month).
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decision_date = days[40] # somewhere mid-period
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next_date = decision_date + dt.timedelta(days=1)
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# Adjust: ensure last_bar (strictly before decision_date) is in same month
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# The adapter strictly-before guarantees last_bar = days[39]. days[39] and days[40] same month.
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candidates = build_candidates(
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decision_date=decision_date,
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next_trading_date=next_date,
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universe_symbols=list(bars_by_symbol.keys()),
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engine=engine,
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bar_provider=adapter,
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)
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assert candidates == []
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# ---------------------------------------------------------------------------
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# Build candidates — happy path with multiple symbols + top-N selection
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# ---------------------------------------------------------------------------
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def test_build_emits_top_n_on_rebalance_day() -> None:
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"""Construct 5 symbols with distinct momentum and verify top-3 emission."""
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days = _business_days(dt.date(2024, 1, 2), 200)
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# decision_date should be first trading day of a new month AND have
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# >= lookback+skip bars (65) of history strictly before it.
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rebalance_idx = None
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for i in range(70, len(days)):
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if days[i].month != days[i - 1].month:
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rebalance_idx = i
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break
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assert rebalance_idx is not None
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decision_date = days[rebalance_idx]
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next_date = decision_date + dt.timedelta(days=1)
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used_days = days[: rebalance_idx + 1] # bar_provider returns strictly-before, so up to days[rebalance_idx-1] are eligible
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bars_by_symbol: dict[str, dict[dt.date, dict[str, Any]]] = {}
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# 5 symbols with end_close in increasing order → momentum ranks ascending
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end_closes = [110.0, 120.0, 130.0, 140.0, 150.0]
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for i, end_c in enumerate(end_closes):
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sym = f"SYM{i}"
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bars_by_symbol[sym] = _linear_bars(used_days, start_close=100.0, end_close=end_c)
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adapter = _SnapshotStoreBarAdapter(bars_by_symbol=bars_by_symbol)
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engine = _make_engine(xsmom_top_n=3)
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candidates = build_candidates(
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decision_date=decision_date,
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next_trading_date=next_date,
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universe_symbols=list(bars_by_symbol.keys()),
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engine=engine,
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bar_provider=adapter,
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)
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assert len(candidates) == 3
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# Highest momentum (SYM4) should be top-ranked
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symbols_emitted = [c.symbol for c in candidates]
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assert "SYM4" in symbols_emitted
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assert "SYM3" in symbols_emitted
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assert "SYM2" in symbols_emitted
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assert "SYM0" not in symbols_emitted
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assert "SYM1" not in symbols_emitted
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def test_build_filters_negative_momentum() -> None:
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"""Symbols with momentum < momentum_min must be dropped."""
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days = _business_days(dt.date(2024, 1, 2), 200)
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rebalance_idx = None
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for i in range(70, len(days)):
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if days[i].month != days[i - 1].month:
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rebalance_idx = i
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break
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assert rebalance_idx is not None
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decision_date = days[rebalance_idx]
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next_date = decision_date + dt.timedelta(days=1)
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used_days = days[: rebalance_idx + 1]
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# 3 symbols with end_close BELOW start → negative momentum
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bars_by_symbol = {
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f"NEG{i}": _linear_bars(used_days, start_close=100.0, end_close=80.0 + i)
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for i in range(3)
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}
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# 2 with positive
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bars_by_symbol["POS1"] = _linear_bars(used_days, start_close=100.0, end_close=120.0)
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bars_by_symbol["POS2"] = _linear_bars(used_days, start_close=100.0, end_close=140.0)
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adapter = _SnapshotStoreBarAdapter(bars_by_symbol=bars_by_symbol)
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engine = _make_engine(xsmom_top_n=10, xsmom_momentum_min=0.0)
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candidates = build_candidates(
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decision_date=decision_date,
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next_trading_date=next_date,
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universe_symbols=list(bars_by_symbol.keys()),
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engine=engine,
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bar_provider=adapter,
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)
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symbols_emitted = {c.symbol for c in candidates}
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assert symbols_emitted == {"POS1", "POS2"}
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def test_build_filters_volatility_max() -> None:
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"""Symbols with 20d vol above the max must be dropped."""
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days = _business_days(dt.date(2024, 1, 2), 200)
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rebalance_idx = None
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for i in range(70, len(days)):
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if days[i].month != days[i - 1].month:
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rebalance_idx = i
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break
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assert rebalance_idx is not None
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decision_date = days[rebalance_idx]
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next_date = decision_date + dt.timedelta(days=1)
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used_days = days[: rebalance_idx + 1]
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# SMOOTH: linear path → low vol
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smooth_bars = _linear_bars(used_days, start_close=100.0, end_close=130.0)
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# CHOPPY: alternating up/down by 20% from base, end at 130
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choppy_bars: dict[dt.date, dict[str, Any]] = {}
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for i, d in enumerate(used_days):
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base = 100 + 30 * (i / max(1, len(used_days) - 1))
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# Alternate ±20% on consecutive days
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c = base * (1.2 if i % 2 == 0 else 0.8)
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choppy_bars[d] = {"open": c, "high": c + 0.5, "low": c - 0.5, "close": c, "volume": 5_000_000.0}
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bars_by_symbol = {"SMOOTH": smooth_bars, "CHOPPY": choppy_bars}
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adapter = _SnapshotStoreBarAdapter(bars_by_symbol=bars_by_symbol)
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engine = _make_engine(xsmom_top_n=10, xsmom_volatility_20d_max=0.05) # ~5% vol cap
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candidates = build_candidates(
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decision_date=decision_date,
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next_trading_date=next_date,
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universe_symbols=list(bars_by_symbol.keys()),
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engine=engine,
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bar_provider=adapter,
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)
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symbols_emitted = {c.symbol for c in candidates}
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assert "CHOPPY" not in symbols_emitted
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# SMOOTH may or may not survive depending on momentum_min — but it must NOT be the choppy one
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# ---------------------------------------------------------------------------
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# Engine disabled / no universe / etc — defensive
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# ---------------------------------------------------------------------------
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def test_build_returns_empty_when_engine_disabled() -> None:
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engine = _make_engine(xsmom_enabled=False)
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adapter = _SnapshotStoreBarAdapter(bars_by_symbol={})
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candidates = build_candidates(
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decision_date=dt.date(2024, 2, 1),
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next_trading_date=dt.date(2024, 2, 2),
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universe_symbols=["FOO"],
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engine=engine,
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bar_provider=adapter,
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)
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assert candidates == []
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def test_build_raises_when_next_date_not_after_decision() -> None:
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engine = _make_engine()
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adapter = _SnapshotStoreBarAdapter(bars_by_symbol={})
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with pytest.raises(LookaheadViolationError):
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build_candidates(
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decision_date=dt.date(2024, 2, 2),
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next_trading_date=dt.date(2024, 2, 1), # before
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universe_symbols=["FOO"],
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engine=engine,
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bar_provider=adapter,
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)
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def test_snapshot_store_adapter_returns_only_strictly_before() -> None:
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"""The cached adapter must NEVER return bars where date >= as_of_date."""
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days = [dt.date(2024, 1, d) for d in (2, 3, 4, 5, 8)] # mix of weekdays
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bars = {d: {"close": 100.0 + i, "high": 101.0, "low": 99.0, "volume": 1e6} for i, d in enumerate(days)}
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adapter = _SnapshotStoreBarAdapter(bars_by_symbol={"FOO": bars})
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# as_of_date = days[3]; should return only days[:3]
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out = adapter.get_bars_before("FOO", days[3], lookback_days=10)
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out_dates = [d for d, _ in out]
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assert all(d < days[3] for d in out_dates)
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assert out_dates == days[:3]
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