from __future__ import annotations from libs.intraday.domain import StrategyParams from libs.intraday.simulator import _select_momentum_sleeves, compute_morning_gains, run_simulation, simulate_day def _bars(day: str, *, open_price: float, closes: list[float], volumes: list[int]) -> list[dict]: times = ["09:30:00", "09:35:00", "09:40:00", "09:45:00", "09:50:00", "15:55:00"] bars: list[dict] = [] prev = open_price for idx, close in enumerate(closes): ts = f"{day}T{times[idx]}-05:00" high = max(prev, close) + 0.2 low = min(prev, close) - 0.2 bars.append( { "timestamp": ts, "open": prev, "high": high, "low": low, "close": close, "volume": volumes[idx], } ) prev = close return bars def test_compute_morning_gains_respects_vix_gate() -> None: strategy = StrategyParams( entry_minutes_after_open=10, min_morning_gain_pct=0.01, max_vix=25.0, ) bars_by_ticker = { "AAA": _bars( "2026-01-05", open_price=10.0, closes=[10.2, 10.5, 10.7, 10.8, 10.9, 11.0], volumes=[100_000, 100_000, 100_000, 100_000, 100_000, 100_000], ) } gains = compute_morning_gains( bars_by_ticker, strategy, "2026-01-05", vix_value=30.0, ) assert gains == {} def test_simulate_day_uses_five_sleeves_and_tags_trade_sleeve() -> None: strategy = StrategyParams( entry_minutes_after_open=10, min_morning_gain_pct=0.01, top_n=3, use_five_sleeves=True, min_entry_volume=50_000, ) bars_by_ticker = { "CORE": _bars("2026-01-05", open_price=10.0, closes=[10.3, 10.8, 11.0, 11.2, 11.3, 11.5], volumes=[200_000] * 6), "GAP": _bars("2026-01-05", open_price=20.0, closes=[20.2, 20.6, 20.8, 20.9, 21.0, 21.1], volumes=[180_000] * 6), "VOL": _bars("2026-01-05", open_price=30.0, closes=[30.2, 30.7, 31.0, 31.2, 31.3, 31.4], volumes=[500_000] * 6), "ENT": _bars("2026-01-05", open_price=40.0, closes=[40.3, 40.8, 41.2, 41.4, 41.5, 41.7], volumes=[160_000] * 6), "TRND": _bars("2026-01-05", open_price=50.0, closes=[50.4, 51.0, 51.4, 51.5, 51.7, 52.0], volumes=[150_000] * 6), } daily_features = { "CORE": {"gap_pct": 0.01, "avg_daily_vol_14d": 1_000_000.0, "ret_5d": 0.02, "entropy_20d": 0.50}, "GAP": {"gap_pct": 0.07, "avg_daily_vol_14d": 1_000_000.0, "ret_5d": 0.01, "entropy_20d": 0.60}, "VOL": {"gap_pct": 0.02, "avg_daily_vol_14d": 600_000.0, "ret_5d": 0.00, "entropy_20d": 0.55}, "ENT": {"gap_pct": 0.02, "avg_daily_vol_14d": 1_000_000.0, "ret_5d": 0.03, "entropy_20d": 0.20}, "TRND": {"gap_pct": 0.03, "avg_daily_vol_14d": 1_000_000.0, "ret_5d": 0.12, "entropy_20d": 0.45}, } day = simulate_day( bars_by_ticker, "2026-01-05", strategy, daily_features_by_ticker=daily_features, ) assert len(day.trades) == 3 sleeves = {trade.trade_sleeve for trade in day.trades} assert sleeves <= {"core", "gap", "volume", "entropy", "trend", "blend"} assert all(trade.trade_sleeve is not None for trade in day.trades) def test_run_simulation_applies_entropy_filter_and_scaler() -> None: strategy = StrategyParams( entry_minutes_after_open=10, min_morning_gain_pct=0.01, top_n=2, max_entropy_20d=0.80, entropy_size_scale_low=0.40, entropy_size_scale_high=0.80, entropy_size_scale_min=0.50, ) all_intraday = { "2026-01-05": { "LOW": _bars("2026-01-05", open_price=10.0, closes=[10.2, 10.8, 11.2, 11.3, 11.4, 11.5], volumes=[120_000] * 6), "HIGH": _bars("2026-01-05", open_price=15.0, closes=[15.2, 15.8, 16.2, 16.3, 16.4, 16.5], volumes=[120_000] * 6), } } enrichment = { "LOW": {"2026-01-05": {"gap_pct": 0.02, "avg_daily_vol_14d": 1_000_000.0, "ret_5d": 0.05, "entropy_20d": 0.40}}, "HIGH": {"2026-01-05": {"gap_pct": 0.02, "avg_daily_vol_14d": 1_000_000.0, "ret_5d": 0.05, "entropy_20d": 0.90}}, } results = run_simulation( all_intraday, ["2026-01-05"], strategy, daily_enrichment=enrichment, ) assert len(results) == 1 assert [trade.ticker for trade in results[0].trades] == ["LOW"] def test_select_momentum_sleeves_respects_weights_and_force_count() -> None: strategy = StrategyParams( top_n=2, use_five_sleeves=True, five_sleeve_force_count=1, five_sleeve_core_weight=0.05, five_sleeve_gap_weight=0.0, five_sleeve_volume_weight=0.0, five_sleeve_entropy_weight=0.90, five_sleeve_trend_weight=0.05, ) morning_gains = { "CORE": { "gain_pct": 0.09, "entry_volume": 150_000, "volume_ratio_14d": 0.03, "gap_pct": 0.01, "ret_5d": 0.02, "entropy_20d": 0.60, }, "ENT": { "gain_pct": 0.05, "entry_volume": 140_000, "volume_ratio_14d": 0.02, "gap_pct": 0.01, "ret_5d": 0.03, "entropy_20d": 0.10, }, "TRND": { "gain_pct": 0.04, "entry_volume": 130_000, "volume_ratio_14d": 0.02, "gap_pct": 0.01, "ret_5d": 0.15, "entropy_20d": 0.55, }, } picks = _select_momentum_sleeves(morning_gains, strategy) assert picks[0] == ("ENT", "entropy") assert len(picks) == 2 def test_select_momentum_sleeves_respects_sector_cap() -> None: strategy = StrategyParams( top_n=3, use_five_sleeves=False, max_positions_per_sector=1, ) morning_gains = { "TECH_A": {"gain_pct": 0.10, "entry_volume": 300_000}, "TECH_B": {"gain_pct": 0.09, "entry_volume": 280_000}, "HEALTH": {"gain_pct": 0.08, "entry_volume": 260_000}, "FIN": {"gain_pct": 0.07, "entry_volume": 250_000}, } picks = _select_momentum_sleeves( morning_gains, strategy, ticker_sectors={ "TECH_A": "Technology", "TECH_B": "Technology", "HEALTH": "Healthcare", "FIN": "Financial Services", }, ) assert [ticker for ticker, _ in picks] == ["TECH_A", "HEALTH", "FIN"] def test_compute_morning_gains_applies_confirmation_filter_and_later_entry() -> None: strategy = StrategyParams( entry_minutes_after_open=10, confirmation_minutes_after_entry=5, min_confirmation_return_pct=0.0, min_morning_gain_pct=0.01, ) bars_by_ticker = { "KEEP": _bars( "2026-01-13", open_price=10.0, closes=[10.2, 10.6, 10.8, 11.0, 11.1, 11.2], volumes=[100_000] * 6, ), "DROP": _bars( "2026-01-13", open_price=10.0, closes=[10.2, 10.6, 10.8, 10.7, 10.6, 10.5], volumes=[100_000] * 6, ), } gains = compute_morning_gains( bars_by_ticker, strategy, "2026-01-13", ) assert list(gains) == ["KEEP"] assert gains["KEEP"]["entry_bar"]["timestamp"].endswith("09:45:00-05:00") def test_compute_morning_gains_allows_slow_ignite_below_primary_gain_floor() -> None: strategy = StrategyParams( entry_minutes_after_open=10, confirmation_minutes_after_entry=5, min_confirmation_return_pct=0.0005, min_morning_gain_pct=0.015, use_slow_ignite_sleeve=True, slow_ignite_weight=0.08, slow_ignite_min_gain_pct=0.005, slow_ignite_max_gain_pct=0.015, slow_ignite_min_entry_dollar_volume=50_000_000, slow_ignite_min_volume_ratio_14d=0.08, slow_ignite_min_ret_5d=0.03, slow_ignite_max_entropy_20d=0.80, ) bars_by_ticker = { "SLOW": _bars( "2026-01-13", open_price=100.0, closes=[100.2, 100.4, 100.6, 101.2, 101.3, 101.5], volumes=[250_000, 250_000, 250_000, 250_000, 250_000, 250_000], ), } gains = compute_morning_gains( bars_by_ticker, strategy, "2026-01-13", daily_features_by_ticker={ "SLOW": { "avg_daily_vol_14d": 10_000_000.0, "ret_5d": 0.06, "entropy_20d": 0.70, "gap_pct": 0.01, } }, ) assert list(gains) == ["SLOW"] assert gains["SLOW"]["is_slow_ignite"] is True assert gains["SLOW"]["gain_pct"] < strategy.min_morning_gain_pct def test_select_momentum_sleeves_can_force_slow_ignite_pick() -> None: strategy = StrategyParams( top_n=2, use_five_sleeves=True, use_slow_ignite_sleeve=True, five_sleeve_core_weight=0.0, five_sleeve_gap_weight=0.0, five_sleeve_volume_weight=0.0, five_sleeve_entropy_weight=0.0, five_sleeve_trend_weight=0.0, slow_ignite_weight=1.0, five_sleeve_force_count=1, ) morning_gains = { "FAST": { "gain_pct": 0.06, "entry_volume": 300_000, "entry_dollar_volume": 20_000_000.0, "volume_ratio_14d": 0.03, "gap_pct": 0.01, "ret_5d": 0.02, "entropy_20d": 0.55, "confirmation_return_pct": 0.001, "is_slow_ignite": False, }, "SLOW": { "gain_pct": 0.009, "entry_volume": 1_000_000, "entry_dollar_volume": 150_000_000.0, "volume_ratio_14d": 0.12, "gap_pct": 0.008, "ret_5d": 0.08, "entropy_20d": 0.70, "confirmation_return_pct": 0.006, "is_slow_ignite": True, }, } picks = _select_momentum_sleeves(morning_gains, strategy) assert ("SLOW", "slow_ignite") in picks def test_compute_morning_gains_allows_gap_reclaim_candidates() -> None: strategy = StrategyParams( entry_minutes_after_open=10, confirmation_minutes_after_entry=5, min_morning_gain_pct=0.015, min_confirmation_return_pct=0.005, max_gap_pct=0.055, use_gap_reclaim_sleeve=True, gap_reclaim_weight=0.05, gap_reclaim_min_gap_pct=0.10, gap_reclaim_min_gain_pct=-0.03, gap_reclaim_max_gain_pct=0.003, gap_reclaim_min_confirmation_return_pct=0.0015, gap_reclaim_min_entry_dollar_volume=100_000_000, gap_reclaim_min_recovery_from_opening_low_pct=0.005, ) bars_by_ticker = { "RECLAIM": _bars( "2026-01-13", open_price=100.0, closes=[96.0, 95.0, 97.8, 98.0, 98.5, 101.0], volumes=[400_000, 400_000, 400_000, 400_000, 400_000, 400_000], ), } gains = compute_morning_gains( bars_by_ticker, strategy, "2026-01-13", daily_features_by_ticker={ "RECLAIM": { "avg_daily_vol_14d": 5_000_000.0, "avg_dollar_vol_30d": 800_000_000.0, "ret_5d": 0.02, "entropy_20d": None, "gap_pct": 0.18, } }, ) assert list(gains) == ["RECLAIM"] assert gains["RECLAIM"]["is_gap_reclaim"] is True assert gains["RECLAIM"]["gain_pct"] < 0.0 assert gains["RECLAIM"]["recovery_from_opening_low_pct"] > 0.005 def test_select_momentum_sleeves_can_force_gap_reclaim_pick() -> None: strategy = StrategyParams( top_n=1, use_five_sleeves=True, use_gap_reclaim_sleeve=True, five_sleeve_core_weight=0.0, five_sleeve_gap_weight=0.0, five_sleeve_volume_weight=0.0, five_sleeve_entropy_weight=0.0, five_sleeve_trend_weight=0.0, gap_reclaim_weight=1.0, five_sleeve_force_count=1, ) morning_gains = { "FAST": { "gain_pct": 0.05, "entry_volume": 200_000, "entry_dollar_volume": 10_000_000.0, "gap_pct": 0.02, "confirmation_return_pct": 0.002, "recovery_from_opening_low_pct": 0.002, "is_gap_reclaim": False, }, "RECLAIM": { "gain_pct": -0.015, "entry_volume": 900_000, "entry_dollar_volume": 220_000_000.0, "gap_pct": 0.21, "confirmation_return_pct": 0.003, "recovery_from_opening_low_pct": 0.01, "is_gap_reclaim": True, }, } picks = _select_momentum_sleeves(morning_gains, strategy) assert ("RECLAIM", "gap_reclaim") in picks def test_compute_morning_gains_allows_liquid_largecap_candidates() -> None: strategy = StrategyParams( entry_minutes_after_open=10, confirmation_minutes_after_entry=5, min_morning_gain_pct=0.015, min_confirmation_return_pct=0.0005, use_liquid_largecap_sleeve=True, liquid_largecap_weight=0.3, liquid_largecap_min_gain_pct=0.004, liquid_largecap_max_gain_pct=0.02, liquid_largecap_min_confirmation_return_pct=0.0005, liquid_largecap_min_entry_dollar_volume=50_000_000, liquid_largecap_min_avg_dollar_vol_30d=500_000_000, liquid_largecap_max_entropy_20d=0.90, ) bars_by_ticker = { "LQ": _bars( "2026-01-13", open_price=100.0, closes=[100.2, 100.5, 100.8, 101.4, 101.6, 101.8], volumes=[500_000, 500_000, 500_000, 500_000, 500_000, 500_000], ), } gains = compute_morning_gains( bars_by_ticker, strategy, "2026-01-13", daily_features_by_ticker={ "LQ": { "avg_daily_vol_14d": 10_000_000.0, "avg_dollar_vol_30d": 1_000_000_000.0, "ret_5d": 0.01, "entropy_20d": 0.88, "gap_pct": 0.01, } }, ) assert list(gains) == ["LQ"] assert gains["LQ"]["is_liquid_largecap"] is True def test_compute_morning_gains_can_relax_global_entropy_for_liquid_largecap() -> None: strategy = StrategyParams( entry_minutes_after_open=10, confirmation_minutes_after_entry=5, min_morning_gain_pct=0.015, min_confirmation_return_pct=0.0005, max_entropy_20d=0.86, use_liquid_largecap_sleeve=True, liquid_largecap_weight=0.3, liquid_largecap_min_gain_pct=0.004, liquid_largecap_max_gain_pct=0.02, liquid_largecap_min_confirmation_return_pct=0.0005, liquid_largecap_min_entry_dollar_volume=50_000_000, liquid_largecap_min_avg_dollar_vol_30d=500_000_000, liquid_largecap_max_entropy_20d=0.87, ) bars_by_ticker = { "LQ": _bars( "2026-01-13", open_price=100.0, closes=[100.2, 100.5, 100.8, 101.4, 101.6, 101.8], volumes=[500_000, 500_000, 500_000, 500_000, 500_000, 500_000], ), } gains = compute_morning_gains( bars_by_ticker, strategy, "2026-01-13", daily_features_by_ticker={ "LQ": { "avg_daily_vol_14d": 10_000_000.0, "avg_dollar_vol_30d": 1_000_000_000.0, "ret_5d": 0.01, "entropy_20d": 0.865, "gap_pct": 0.01, } }, ) assert list(gains) == ["LQ"] assert gains["LQ"]["is_liquid_largecap"] is True def test_select_momentum_sleeves_can_force_liquid_largecap_pick() -> None: strategy = StrategyParams( top_n=1, use_five_sleeves=True, use_liquid_largecap_sleeve=True, five_sleeve_core_weight=0.0, five_sleeve_gap_weight=0.0, five_sleeve_volume_weight=0.0, five_sleeve_entropy_weight=0.0, five_sleeve_trend_weight=0.0, liquid_largecap_weight=1.0, five_sleeve_force_count=1, ) morning_gains = { "FAST": { "gain_pct": 0.06, "entry_volume": 150_000, "entry_dollar_volume": 5_000_000.0, "avg_dollar_vol_30d": 100_000_000.0, "confirmation_return_pct": 0.003, "volume_ratio_14d": 0.03, "gap_pct": 0.01, "ret_5d": 0.02, "entropy_20d": 0.50, "is_liquid_largecap": False, }, "LQ": { "gain_pct": 0.008, "entry_volume": 500_000, "entry_dollar_volume": 80_000_000.0, "avg_dollar_vol_30d": 1_500_000_000.0, "confirmation_return_pct": 0.001, "volume_ratio_14d": 0.12, "gap_pct": 0.01, "ret_5d": 0.01, "entropy_20d": 0.85, "is_liquid_largecap": True, }, } picks = _select_momentum_sleeves(morning_gains, strategy) assert ("LQ", "liquid_largecap") in picks def test_select_momentum_sleeves_can_add_liquid_largecap_fallback_when_sparse() -> None: strategy = StrategyParams( top_n=3, use_five_sleeves=True, five_sleeve_force_count=0, five_sleeve_core_weight=1.0, five_sleeve_gap_weight=0.0, five_sleeve_volume_weight=0.0, five_sleeve_entropy_weight=0.0, five_sleeve_trend_weight=0.0, fallback_liquid_largecap_slots=1, fallback_liquid_largecap_trigger_below=2, ) morning_gains = { "FAST": { "gain_pct": 0.06, "entry_volume": 150_000, "entry_dollar_volume": 5_000_000.0, "avg_dollar_vol_30d": 100_000_000.0, "confirmation_return_pct": 0.003, "volume_ratio_14d": 0.03, "gap_pct": 0.01, "ret_5d": 0.02, "entropy_20d": 0.50, "is_liquid_largecap": False, }, "LQ": { "gain_pct": 0.008, "entry_volume": 500_000, "entry_dollar_volume": 80_000_000.0, "avg_dollar_vol_30d": 1_500_000_000.0, "confirmation_return_pct": 0.005, "volume_ratio_14d": 0.12, "gap_pct": 0.01, "ret_5d": 0.01, "entropy_20d": 0.85, "is_liquid_largecap": True, }, } picks = _select_momentum_sleeves(morning_gains, strategy) assert ("FAST", "blend") in picks assert ("LQ", "liquid_largecap_fallback") in picks def test_compute_morning_gains_applies_entry_dollar_volume_filter() -> None: strategy = StrategyParams( entry_minutes_after_open=10, min_morning_gain_pct=0.01, min_entry_volume=100_000, min_entry_dollar_volume=2_000_000, ) bars_by_ticker = { "CHEAP": _bars( "2026-01-14", open_price=5.0, closes=[5.1, 5.3, 5.4, 5.5, 5.6, 5.7], volumes=[60_000, 60_000, 60_000, 60_000, 60_000, 60_000], ), "RICH": _bars( "2026-01-14", open_price=20.0, closes=[20.2, 20.8, 21.0, 21.2, 21.3, 21.4], volumes=[60_000, 60_000, 60_000, 60_000, 60_000, 60_000], ), } gains = compute_morning_gains( bars_by_ticker, strategy, "2026-01-14", ) assert list(gains) == ["RICH"] def test_compute_morning_gains_allows_liquid_largecap_candidates_for_fallback() -> None: strategy = StrategyParams( entry_minutes_after_open=10, confirmation_minutes_after_entry=5, min_morning_gain_pct=0.015, min_confirmation_return_pct=0.005, fallback_liquid_largecap_slots=1, fallback_liquid_largecap_trigger_below=2, liquid_largecap_min_gain_pct=0.004, liquid_largecap_max_gain_pct=0.02, liquid_largecap_min_confirmation_return_pct=0.0005, liquid_largecap_min_entry_dollar_volume=50_000_000, liquid_largecap_min_avg_dollar_vol_30d=500_000_000, liquid_largecap_max_entropy_20d=0.90, ) bars_by_ticker = { "LQ": _bars( "2026-01-13", open_price=100.0, closes=[100.2, 100.5, 100.8, 101.4, 101.6, 101.8], volumes=[500_000, 500_000, 500_000, 500_000, 500_000, 500_000], ), } gains = compute_morning_gains( bars_by_ticker, strategy, "2026-01-13", daily_features_by_ticker={ "LQ": { "avg_daily_vol_14d": 10_000_000.0, "avg_dollar_vol_30d": 1_000_000_000.0, "ret_5d": 0.01, "entropy_20d": 0.88, "gap_pct": 0.01, } }, ) assert list(gains) == ["LQ"] assert gains["LQ"]["is_liquid_largecap"] is True def test_simulate_day_skips_sparse_baskets_when_min_positions_required() -> None: strategy = StrategyParams( entry_minutes_after_open=10, min_morning_gain_pct=0.01, top_n=3, min_positions_to_trade=2, ) bars_by_ticker = { "ONLY": _bars( "2026-01-15", open_price=10.0, closes=[10.2, 10.7, 10.9, 11.0, 11.1, 11.2], volumes=[120_000] * 6, ), } day = simulate_day( bars_by_ticker, "2026-01-15", strategy, ) assert day.candidates_found == 1 assert day.trades == [] assert day.daily_pnl == 0.0 def test_simulate_day_scales_sparse_days_when_threshold_set() -> None: base = StrategyParams( entry_minutes_after_open=10, min_morning_gain_pct=0.01, top_n=3, initial_capital=9_000.0, exit_minutes_before_close=5, ) scaled = StrategyParams( entry_minutes_after_open=10, min_morning_gain_pct=0.01, top_n=3, initial_capital=9_000.0, exit_minutes_before_close=5, full_size_positions_threshold=4, sparse_day_size_floor=0.5, ) bars_by_ticker = { "ONLY": _bars( "2026-01-16", open_price=10.0, closes=[10.2, 10.8, 11.0, 11.1, 11.2, 11.4], volumes=[120_000] * 6, ), } base_day = simulate_day(bars_by_ticker, "2026-01-16", base) scaled_day = simulate_day(bars_by_ticker, "2026-01-16", scaled) assert len(base_day.trades) == 1 assert len(scaled_day.trades) == 1 assert round(scaled_day.daily_pnl, 2) == round(base_day.daily_pnl * 0.5, 2) def test_simulate_day_tightens_trailing_for_overextended_leaders() -> None: base = StrategyParams( entry_minutes_after_open=10, min_morning_gain_pct=0.01, trailing_stop_pct=-0.075, exit_minutes_before_close=5, ) tightened = StrategyParams( entry_minutes_after_open=10, min_morning_gain_pct=0.01, trailing_stop_pct=-0.075, overextended_trailing_gain_pct=0.05, overextended_trailing_stop_pct=-0.065, exit_minutes_before_close=5, ) bars_by_ticker = { "HOT": _bars( "2026-01-20", open_price=10.0, closes=[10.3, 10.8, 10.9, 10.4, 10.3, 10.2], volumes=[120_000] * 6, ), } base_day = simulate_day(bars_by_ticker, "2026-01-20", base) tightened_day = simulate_day(bars_by_ticker, "2026-01-20", tightened) assert len(base_day.trades) == 1 assert len(tightened_day.trades) == 1 assert tightened_day.trades[0].exit_reason == "trailing_stop" assert base_day.trades[0].exit_reason == "trailing_stop" assert tightened_day.trades[0].exit_price > base_day.trades[0].exit_price assert tightened_day.daily_pnl > base_day.daily_pnl def test_simulate_day_can_use_atr_catastrophic_stop_without_trailing() -> None: strategy = StrategyParams( entry_minutes_after_open=10, min_morning_gain_pct=0.01, exit_minutes_before_close=5, atr_stop_multiplier=0.5, trailing_stop_pct=None, ) bars_by_ticker = { "ATR": _bars( "2026-01-21", open_price=10.0, closes=[10.2, 10.8, 11.0, 10.6, 10.5, 10.4], volumes=[120_000] * 6, ), } day = simulate_day( bars_by_ticker, "2026-01-21", strategy, daily_features_by_ticker={ "ATR": { "gap_pct": 0.01, "avg_daily_vol_14d": 1_000_000.0, "atr_14": 1.0, } }, ) assert len(day.trades) == 1 assert day.trades[0].exit_reason == "stop_loss" def test_simulate_day_delays_trailing_until_gain_threshold() -> None: strategy = StrategyParams( entry_minutes_after_open=10, min_morning_gain_pct=0.01, exit_minutes_before_close=5, atr_stop_multiplier=0.5, trailing_stop_pct=-0.05, trailing_activation_gain_pct=0.03, ) bars_by_ticker = { "TRAIL": _bars( "2026-01-22", open_price=10.0, closes=[10.2, 10.8, 11.0, 11.3, 11.0, 10.9], volumes=[120_000] * 6, ), } day = simulate_day( bars_by_ticker, "2026-01-22", strategy, daily_features_by_ticker={ "TRAIL": { "gap_pct": 0.01, "avg_daily_vol_14d": 1_000_000.0, "atr_14": 1.0, } }, ) assert len(day.trades) == 1 assert day.trades[0].exit_reason == "trailing_stop" def test_simulate_day_can_use_opening_range_catastrophic_stop() -> None: strategy = StrategyParams( entry_minutes_after_open=10, min_morning_gain_pct=0.01, exit_minutes_before_close=5, opening_range_stop_multiplier=1.0, trailing_stop_pct=None, ) bars_by_ticker = { "OR": _bars( "2026-01-23", open_price=10.0, closes=[10.15, 10.25, 10.3, 9.6, 9.5, 9.4], volumes=[120_000] * 6, ), } day = simulate_day( bars_by_ticker, "2026-01-23", strategy, daily_features_by_ticker={ "OR": { "gap_pct": 0.01, "avg_daily_vol_14d": 1_000_000.0, } }, ) assert len(day.trades) == 1 assert day.trades[0].exit_reason == "stop_loss"