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1480 lines
46 KiB
Python
1480 lines
46 KiB
Python
from __future__ import annotations
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from libs.intraday.domain import StrategyParams
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from libs.intraday.simulator import (
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_same_day_support_score,
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_select_momentum_sleeves,
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compute_morning_gains,
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run_simulation,
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simulate_day,
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)
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def _bars(day: str, *, open_price: float, closes: list[float], volumes: list[int]) -> list[dict]:
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times = ["09:30:00", "09:35:00", "09:40:00", "09:45:00", "09:50:00", "15:55:00"]
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bars: list[dict] = []
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prev = open_price
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for idx, close in enumerate(closes):
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ts = f"{day}T{times[idx]}-05:00"
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high = max(prev, close) + 0.2
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low = min(prev, close) - 0.2
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bars.append(
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{
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"timestamp": ts,
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"open": prev,
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"high": high,
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"low": low,
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"close": close,
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"volume": volumes[idx],
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}
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)
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prev = close
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return bars
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def test_compute_morning_gains_respects_vix_gate() -> None:
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strategy = StrategyParams(
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entry_minutes_after_open=10,
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min_morning_gain_pct=0.01,
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max_vix=25.0,
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)
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bars_by_ticker = {
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"AAA": _bars(
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"2026-01-05",
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open_price=10.0,
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closes=[10.2, 10.5, 10.7, 10.8, 10.9, 11.0],
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volumes=[100_000, 100_000, 100_000, 100_000, 100_000, 100_000],
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)
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}
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gains = compute_morning_gains(
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bars_by_ticker,
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strategy,
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"2026-01-05",
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vix_value=30.0,
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)
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assert gains == {}
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def test_simulate_day_applies_gap_regime_scaler_and_soft_day_trade_cap() -> None:
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strategy = StrategyParams(
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entry_minutes_after_open=10,
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min_morning_gain_pct=0.01,
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top_n=2,
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slippage_bps=0.0,
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daily_budget_reset=True,
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initial_capital=10_000.0,
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market_regime_gap_threshold=-0.03,
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market_regime_gap_ticker="SPY",
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regime_size_scale_low=-0.02,
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regime_size_scale_high=0.0,
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regime_size_scale_min=0.5,
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soft_day_scaler_threshold=0.8,
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soft_day_max_trades=1,
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)
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bars_by_ticker = {
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"AAA": _bars("2026-01-05", open_price=10.0, closes=[10.2, 10.6, 10.8, 11.0, 11.1, 11.2], volumes=[120_000] * 6),
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"BBB": _bars("2026-01-05", open_price=20.0, closes=[20.3, 20.8, 21.1, 21.2, 21.3, 21.4], volumes=[120_000] * 6),
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}
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day = simulate_day(
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bars_by_ticker,
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"2026-01-05",
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strategy,
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daily_features_by_ticker={
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"AAA": {"prev_close": 9.8, "today_open": 10.0},
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"BBB": {"prev_close": 19.7, "today_open": 20.0},
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"SPY": {"prev_close": 100.0, "today_open": 99.0},
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},
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)
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assert day.regime_scaler == 0.75
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assert day.is_soft_day is True
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assert len(day.trades) == 1
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assert round(day.capital_deployed, 2) == 7500.0
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def test_simulate_day_skips_on_candidate_breadth() -> None:
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strategy = StrategyParams(
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entry_minutes_after_open=10,
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min_morning_gain_pct=0.01,
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top_n=2,
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min_candidate_breadth=0.75,
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)
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bars_by_ticker = {
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"UP": _bars("2026-01-05", open_price=10.0, closes=[10.2, 10.6, 10.8, 10.9, 11.0, 11.1], volumes=[120_000] * 6),
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"DOWN": _bars("2026-01-05", open_price=20.0, closes=[20.2, 20.6, 20.8, 20.9, 21.0, 21.1], volumes=[120_000] * 6),
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}
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day = simulate_day(
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bars_by_ticker,
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"2026-01-05",
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strategy,
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daily_features_by_ticker={
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"UP": {"prev_close": 9.5, "today_open": 10.0},
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"DOWN": {"prev_close": 21.0, "today_open": 20.0},
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},
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)
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assert day.skip_reason == "breadth"
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assert day.trades == []
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def test_simulate_day_applies_sector_concentration_scaler() -> None:
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strategy = StrategyParams(
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entry_minutes_after_open=10,
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min_morning_gain_pct=0.01,
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top_n=3,
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slippage_bps=0.0,
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daily_budget_reset=True,
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initial_capital=10_000.0,
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sector_concentration_scale_low=0.34,
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sector_concentration_scale_high=0.67,
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sector_concentration_scale_min=0.75,
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soft_day_scaler_threshold=0.8,
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use_five_sleeves=False,
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)
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bars_by_ticker = {
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"TECH_A": _bars("2026-01-05", open_price=10.0, closes=[10.2, 10.6, 10.8, 11.0, 11.1, 11.2], volumes=[120_000] * 6),
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"TECH_B": _bars("2026-01-05", open_price=20.0, closes=[20.3, 20.8, 21.0, 21.2, 21.3, 21.4], volumes=[120_000] * 6),
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"HEALTH": _bars("2026-01-05", open_price=30.0, closes=[30.3, 30.8, 31.0, 31.2, 31.3, 31.4], volumes=[120_000] * 6),
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}
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day = simulate_day(
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bars_by_ticker,
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"2026-01-05",
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strategy,
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ticker_sectors={
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"TECH_A": "Technology",
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"TECH_B": "Technology",
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"HEALTH": "Healthcare",
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},
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)
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assert round(day.sector_scaler or 0.0, 2) == 0.75
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assert day.is_soft_day is True
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assert round(day.capital_deployed, 2) == 7525.25
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def test_simulate_day_uses_five_sleeves_and_tags_trade_sleeve() -> None:
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strategy = StrategyParams(
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entry_minutes_after_open=10,
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min_morning_gain_pct=0.01,
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top_n=3,
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use_five_sleeves=True,
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min_entry_volume=50_000,
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)
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bars_by_ticker = {
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"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),
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"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),
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"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),
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"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),
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"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),
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}
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daily_features = {
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"CORE": {"gap_pct": 0.01, "avg_daily_vol_14d": 1_000_000.0, "ret_5d": 0.02, "entropy_20d": 0.50},
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"GAP": {"gap_pct": 0.07, "avg_daily_vol_14d": 1_000_000.0, "ret_5d": 0.01, "entropy_20d": 0.60},
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"VOL": {"gap_pct": 0.02, "avg_daily_vol_14d": 600_000.0, "ret_5d": 0.00, "entropy_20d": 0.55},
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"ENT": {"gap_pct": 0.02, "avg_daily_vol_14d": 1_000_000.0, "ret_5d": 0.03, "entropy_20d": 0.20},
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"TRND": {"gap_pct": 0.03, "avg_daily_vol_14d": 1_000_000.0, "ret_5d": 0.12, "entropy_20d": 0.45},
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}
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day = simulate_day(
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bars_by_ticker,
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"2026-01-05",
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strategy,
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daily_features_by_ticker=daily_features,
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)
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assert len(day.trades) == 3
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sleeves = {trade.trade_sleeve for trade in day.trades}
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assert sleeves <= {"core", "gap", "volume", "entropy", "trend", "blend"}
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assert all(trade.trade_sleeve is not None for trade in day.trades)
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def test_run_simulation_applies_entropy_filter_and_scaler() -> None:
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strategy = StrategyParams(
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entry_minutes_after_open=10,
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min_morning_gain_pct=0.01,
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top_n=2,
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max_entropy_20d=0.80,
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entropy_size_scale_low=0.40,
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entropy_size_scale_high=0.80,
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entropy_size_scale_min=0.50,
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)
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all_intraday = {
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"2026-01-05": {
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"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),
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"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),
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}
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}
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enrichment = {
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"LOW": {"2026-01-05": {"gap_pct": 0.02, "avg_daily_vol_14d": 1_000_000.0, "ret_5d": 0.05, "entropy_20d": 0.40}},
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"HIGH": {"2026-01-05": {"gap_pct": 0.02, "avg_daily_vol_14d": 1_000_000.0, "ret_5d": 0.05, "entropy_20d": 0.90}},
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}
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results = run_simulation(
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all_intraday,
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["2026-01-05"],
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strategy,
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daily_enrichment=enrichment,
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)
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assert len(results) == 1
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assert [trade.ticker for trade in results[0].trades] == ["LOW"]
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def test_simulate_day_reports_portfolio_return_after_sparse_scaling() -> None:
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strategy = StrategyParams(
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entry_minutes_after_open=10,
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min_morning_gain_pct=0.01,
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top_n=1,
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slippage_bps=0.0,
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daily_budget_reset=True,
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initial_capital=10_000.0,
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full_size_positions_threshold=2,
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sparse_day_size_floor=0.5,
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)
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bars_by_ticker = {
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"AAA": _bars(
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"2026-01-05",
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open_price=10.0,
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closes=[10.2, 10.5, 10.8, 11.0, 11.1, 11.2],
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volumes=[120_000] * 6,
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)
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}
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day = simulate_day(
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bars_by_ticker,
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"2026-01-05",
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strategy,
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)
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assert len(day.trades) == 1
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assert day.capital_deployed == 5_000.0
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assert round(day.daily_pnl, 2) == 185.19
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assert round(day.daily_return_pct, 4) == 0.0185
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def test_run_simulation_applies_rolling_loss_pause() -> None:
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strategy = StrategyParams(
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entry_minutes_after_open=10,
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min_morning_gain_pct=0.01,
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top_n=1,
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stop_loss_pct=None,
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slippage_bps=0.0,
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daily_budget_reset=True,
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initial_capital=10_000.0,
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rolling_loss_days=1,
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rolling_loss_threshold=-0.05,
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)
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all_intraday = {
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"2026-01-05": {
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"LOSER": _bars(
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"2026-01-05",
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open_price=10.0,
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closes=[10.2, 10.6, 10.8, 9.8, 9.2, 9.0],
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volumes=[120_000] * 6,
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),
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},
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"2026-01-06": {
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"WINNER": _bars(
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"2026-01-06",
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open_price=10.0,
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closes=[10.2, 10.6, 10.9, 11.0, 11.1, 11.2],
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volumes=[120_000] * 6,
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),
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},
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}
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results = run_simulation(all_intraday, ["2026-01-05", "2026-01-06"], strategy)
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assert len(results) == 2
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assert results[0].daily_return_pct < -0.05
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assert results[1].skip_reason == "rolling_loss"
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assert results[1].trades == []
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def test_select_momentum_sleeves_respects_weights_and_force_count() -> None:
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strategy = StrategyParams(
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top_n=2,
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use_five_sleeves=True,
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five_sleeve_force_count=1,
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five_sleeve_core_weight=0.05,
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five_sleeve_gap_weight=0.0,
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five_sleeve_volume_weight=0.0,
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five_sleeve_entropy_weight=0.90,
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five_sleeve_trend_weight=0.05,
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)
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morning_gains = {
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"CORE": {
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"gain_pct": 0.09,
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"entry_volume": 150_000,
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"volume_ratio_14d": 0.03,
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"gap_pct": 0.01,
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"ret_5d": 0.02,
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"entropy_20d": 0.60,
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},
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"ENT": {
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"gain_pct": 0.05,
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"entry_volume": 140_000,
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"volume_ratio_14d": 0.02,
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"gap_pct": 0.01,
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"ret_5d": 0.03,
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"entropy_20d": 0.10,
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},
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"TRND": {
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"gain_pct": 0.04,
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"entry_volume": 130_000,
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"volume_ratio_14d": 0.02,
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"gap_pct": 0.01,
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"ret_5d": 0.15,
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"entropy_20d": 0.55,
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},
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}
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picks = _select_momentum_sleeves(morning_gains, strategy)
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assert picks[0] == ("ENT", "entropy")
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assert len(picks) == 2
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def test_select_momentum_sleeves_respects_sector_cap() -> None:
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strategy = StrategyParams(
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top_n=3,
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use_five_sleeves=False,
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max_positions_per_sector=1,
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)
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morning_gains = {
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"TECH_A": {"gain_pct": 0.10, "entry_volume": 300_000},
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"TECH_B": {"gain_pct": 0.09, "entry_volume": 280_000},
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"HEALTH": {"gain_pct": 0.08, "entry_volume": 260_000},
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"FIN": {"gain_pct": 0.07, "entry_volume": 250_000},
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}
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picks = _select_momentum_sleeves(
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morning_gains,
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strategy,
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ticker_sectors={
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"TECH_A": "Technology",
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"TECH_B": "Technology",
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"HEALTH": "Healthcare",
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"FIN": "Financial Services",
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},
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)
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assert [ticker for ticker, _ in picks] == ["TECH_A", "HEALTH", "FIN"]
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|
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def test_select_momentum_sleeves_prunes_weak_tail_with_quality_floor() -> None:
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strategy = StrategyParams(
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top_n=3,
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use_five_sleeves=True,
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five_sleeve_force_count=0,
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five_sleeve_core_weight=1.0,
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five_sleeve_gap_weight=0.0,
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five_sleeve_volume_weight=0.0,
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five_sleeve_entropy_weight=0.0,
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five_sleeve_trend_weight=0.0,
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basket_quality_relative_floor=0.5,
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basket_quality_min_count=2,
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)
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morning_gains = {
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"STRONG": {
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"gain_pct": 0.10,
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"entry_volume": 300_000,
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"volume_ratio_14d": 0.08,
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"gap_pct": 0.03,
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"ret_5d": 0.12,
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"entropy_20d": 0.30,
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},
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"MID": {
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"gain_pct": 0.06,
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"entry_volume": 250_000,
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"volume_ratio_14d": 0.05,
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"gap_pct": 0.02,
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"ret_5d": 0.08,
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"entropy_20d": 0.40,
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},
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"WEAK": {
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"gain_pct": 0.025,
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"entry_volume": 200_000,
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"volume_ratio_14d": 0.02,
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"gap_pct": 0.005,
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"ret_5d": 0.01,
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"entropy_20d": 0.70,
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},
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}
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picks = _select_momentum_sleeves(morning_gains, strategy)
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assert [ticker for ticker, _ in picks] == ["STRONG", "MID"]
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def test_select_momentum_sleeves_prunes_only_blend_tail_when_configured() -> None:
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strategy = StrategyParams(
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top_n=3,
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use_five_sleeves=True,
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five_sleeve_force_count=1,
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five_sleeve_core_weight=1.0,
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five_sleeve_gap_weight=0.0,
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five_sleeve_volume_weight=0.0,
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five_sleeve_entropy_weight=0.0,
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five_sleeve_trend_weight=0.0,
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basket_quality_relative_floor=0.5,
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basket_quality_min_count=2,
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basket_quality_prune_blend_only=True,
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)
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morning_gains = {
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"FORCED": {
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"gain_pct": 0.10,
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"entry_volume": 300_000,
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"volume_ratio_14d": 0.08,
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"gap_pct": 0.03,
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"ret_5d": 0.12,
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"entropy_20d": 0.30,
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},
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"MID": {
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"gain_pct": 0.06,
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"entry_volume": 250_000,
|
|
"volume_ratio_14d": 0.05,
|
|
"gap_pct": 0.02,
|
|
"ret_5d": 0.08,
|
|
"entropy_20d": 0.40,
|
|
},
|
|
"WEAK": {
|
|
"gain_pct": 0.025,
|
|
"entry_volume": 200_000,
|
|
"volume_ratio_14d": 0.02,
|
|
"gap_pct": 0.005,
|
|
"ret_5d": 0.01,
|
|
"entropy_20d": 0.70,
|
|
},
|
|
}
|
|
|
|
picks = _select_momentum_sleeves(morning_gains, strategy)
|
|
|
|
assert picks[0] == ("FORCED", "core")
|
|
assert [ticker for ticker, _ in picks] == ["FORCED", "MID"]
|
|
|
|
|
|
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_compute_morning_gains_allows_moderate_gap_liquid_followthrough() -> None:
|
|
strategy = StrategyParams(
|
|
entry_minutes_after_open=10,
|
|
confirmation_minutes_after_entry=5,
|
|
min_confirmation_return_pct=0.01,
|
|
min_morning_gain_pct=0.04,
|
|
use_moderate_gap_liquid_sleeve=True,
|
|
moderate_gap_liquid_weight=0.2,
|
|
moderate_gap_liquid_min_gap_pct=0.005,
|
|
moderate_gap_liquid_max_gap_pct=0.025,
|
|
moderate_gap_liquid_min_gain_pct=0.015,
|
|
moderate_gap_liquid_max_gain_pct=0.04,
|
|
moderate_gap_liquid_min_confirmation_return_pct=0.005,
|
|
moderate_gap_liquid_min_entry_dollar_volume=40_000_000,
|
|
moderate_gap_liquid_min_avg_dollar_vol_30d=250_000_000,
|
|
moderate_gap_liquid_max_avg_dollar_vol_30d=2_000_000_000,
|
|
moderate_gap_liquid_max_entropy_20d=0.86,
|
|
)
|
|
bars_by_ticker = {
|
|
"TER": _bars(
|
|
"2026-01-13",
|
|
open_price=100.0,
|
|
closes=[100.4, 100.8, 101.2, 101.9, 102.0, 102.5],
|
|
volumes=[150_000, 150_000, 150_000, 150_000, 150_000, 150_000],
|
|
),
|
|
}
|
|
|
|
gains = compute_morning_gains(
|
|
bars_by_ticker,
|
|
strategy,
|
|
"2026-01-13",
|
|
daily_features_by_ticker={
|
|
"TER": {
|
|
"gap_pct": 0.012,
|
|
"avg_daily_vol_14d": 8_000_000.0,
|
|
"avg_dollar_vol_30d": 750_000_000.0,
|
|
"entropy_20d": 0.79,
|
|
"atr_14": 5.0,
|
|
}
|
|
},
|
|
)
|
|
|
|
assert list(gains) == ["TER"]
|
|
assert gains["TER"]["is_moderate_gap_liquid"] is True
|
|
assert gains["TER"]["gain_pct"] < strategy.min_morning_gain_pct
|
|
|
|
|
|
def test_select_momentum_sleeves_can_force_moderate_gap_liquid_pick() -> None:
|
|
strategy = StrategyParams(
|
|
top_n=1,
|
|
use_five_sleeves=True,
|
|
use_moderate_gap_liquid_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,
|
|
moderate_gap_liquid_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": 0.04,
|
|
"entropy_20d": 0.50,
|
|
"is_moderate_gap_liquid": False,
|
|
},
|
|
"TER": {
|
|
"gain_pct": 0.019,
|
|
"entry_volume": 500_000,
|
|
"entry_dollar_volume": 55_000_000.0,
|
|
"avg_dollar_vol_30d": 750_000_000.0,
|
|
"confirmation_return_pct": 0.011,
|
|
"volume_ratio_14d": 0.08,
|
|
"gap_pct": 0.012,
|
|
"entropy_20d": 0.79,
|
|
"is_moderate_gap_liquid": True,
|
|
},
|
|
}
|
|
|
|
picks = _select_momentum_sleeves(morning_gains, strategy)
|
|
|
|
assert picks == [("TER", "moderate_gap_liquid")]
|
|
|
|
|
|
def test_simulate_day_can_enable_event_sleeve_only_on_soft_days() -> None:
|
|
strategy = StrategyParams(
|
|
entry_minutes_after_open=10,
|
|
min_morning_gain_pct=0.01,
|
|
top_n=1,
|
|
use_five_sleeves=True,
|
|
use_event_sleeve=True,
|
|
event_weight=1.0,
|
|
event_min_score=1.0,
|
|
event_sleeve_soft_day_only=True,
|
|
five_sleeve_core_weight=0.2,
|
|
five_sleeve_gap_weight=0.0,
|
|
five_sleeve_volume_weight=0.0,
|
|
five_sleeve_entropy_weight=0.0,
|
|
five_sleeve_trend_weight=0.0,
|
|
five_sleeve_force_count=1,
|
|
min_entry_volume=50_000,
|
|
market_regime_gap_threshold=-0.03,
|
|
market_regime_gap_ticker="SPY",
|
|
regime_size_scale_low=-0.02,
|
|
regime_size_scale_high=0.0,
|
|
regime_size_scale_min=0.5,
|
|
soft_day_scaler_threshold=0.8,
|
|
)
|
|
bars_by_ticker = {
|
|
"FAST": _bars(
|
|
"2026-01-16",
|
|
open_price=10.0,
|
|
closes=[10.2, 10.5, 10.8, 10.9, 11.0, 11.1],
|
|
volumes=[120_000] * 6,
|
|
),
|
|
"CAT": _bars(
|
|
"2026-01-16",
|
|
open_price=20.0,
|
|
closes=[20.15, 20.35, 20.45, 20.55, 20.6, 20.7],
|
|
volumes=[120_000] * 6,
|
|
),
|
|
}
|
|
day = simulate_day(
|
|
bars_by_ticker,
|
|
"2026-01-16",
|
|
strategy,
|
|
daily_features_by_ticker={
|
|
"FAST": {"gap_pct": 0.01, "avg_daily_vol_14d": 1_000_000.0, "ret_5d": 0.01, "entropy_20d": 0.45},
|
|
"CAT": {
|
|
"gap_pct": 0.01,
|
|
"avg_daily_vol_14d": 1_000_000.0,
|
|
"ret_5d": 0.01,
|
|
"entropy_20d": 0.45,
|
|
"event_flag": True,
|
|
"event_score": 1.5,
|
|
},
|
|
"SPY": {"prev_close": 100.0, "today_open": 99.0},
|
|
},
|
|
)
|
|
|
|
assert day.is_soft_day is True
|
|
assert [trade.ticker for trade in day.trades] == ["CAT"]
|
|
assert [trade.trade_sleeve for trade in day.trades] == ["event"]
|
|
|
|
|
|
def test_simulate_day_can_keep_event_sleeve_off_when_soft_day_basket_is_still_strong() -> None:
|
|
strategy = StrategyParams(
|
|
entry_minutes_after_open=10,
|
|
min_morning_gain_pct=0.01,
|
|
top_n=1,
|
|
use_five_sleeves=True,
|
|
use_event_sleeve=True,
|
|
event_weight=1.0,
|
|
event_min_score=1.0,
|
|
event_sleeve_soft_day_only=True,
|
|
event_sleeve_soft_day_max_avg_quality=0.01,
|
|
five_sleeve_core_weight=0.2,
|
|
five_sleeve_gap_weight=0.0,
|
|
five_sleeve_volume_weight=0.0,
|
|
five_sleeve_entropy_weight=0.0,
|
|
five_sleeve_trend_weight=0.0,
|
|
five_sleeve_force_count=1,
|
|
min_entry_volume=50_000,
|
|
market_regime_gap_threshold=-0.03,
|
|
market_regime_gap_ticker="SPY",
|
|
regime_size_scale_low=-0.02,
|
|
regime_size_scale_high=0.0,
|
|
regime_size_scale_min=0.5,
|
|
soft_day_scaler_threshold=0.8,
|
|
)
|
|
bars_by_ticker = {
|
|
"FAST": _bars(
|
|
"2026-01-16",
|
|
open_price=10.0,
|
|
closes=[10.2, 10.5, 10.8, 10.9, 11.0, 11.1],
|
|
volumes=[120_000] * 6,
|
|
),
|
|
"CAT": _bars(
|
|
"2026-01-16",
|
|
open_price=20.0,
|
|
closes=[20.15, 20.35, 20.45, 20.55, 20.6, 20.7],
|
|
volumes=[120_000] * 6,
|
|
),
|
|
}
|
|
day = simulate_day(
|
|
bars_by_ticker,
|
|
"2026-01-16",
|
|
strategy,
|
|
daily_features_by_ticker={
|
|
"FAST": {"gap_pct": 0.01, "avg_daily_vol_14d": 1_000_000.0, "ret_5d": 0.01, "entropy_20d": 0.45},
|
|
"CAT": {
|
|
"gap_pct": 0.01,
|
|
"avg_daily_vol_14d": 1_000_000.0,
|
|
"ret_5d": 0.01,
|
|
"entropy_20d": 0.45,
|
|
"event_flag": True,
|
|
"event_score": 1.5,
|
|
},
|
|
"SPY": {"prev_close": 100.0, "today_open": 99.0},
|
|
},
|
|
)
|
|
|
|
assert day.is_soft_day is True
|
|
assert [trade.ticker for trade in day.trades] == ["FAST"]
|
|
assert [trade.trade_sleeve for trade in day.trades] == ["core"]
|
|
|
|
|
|
def test_simulate_day_can_apply_tail_risk_day_scaler() -> None:
|
|
strategy = StrategyParams(
|
|
entry_minutes_after_open=10,
|
|
min_morning_gain_pct=0.01,
|
|
top_n=1,
|
|
slippage_bps=0.0,
|
|
daily_budget_reset=True,
|
|
initial_capital=10_000.0,
|
|
tail_risk_day_max_trades=1,
|
|
tail_risk_day_min_max_gain_pct=0.04,
|
|
tail_risk_day_max_avg_quality=5.0,
|
|
tail_risk_day_require_no_event=True,
|
|
tail_risk_day_exempt_largecap=True,
|
|
tail_risk_day_scale=0.5,
|
|
)
|
|
bars_by_ticker = {
|
|
"TAIL": _bars(
|
|
"2026-01-21",
|
|
open_price=10.0,
|
|
closes=[10.1, 10.35, 10.5, 10.55, 10.45, 10.4],
|
|
volumes=[120_000] * 6,
|
|
),
|
|
}
|
|
|
|
day = simulate_day(
|
|
bars_by_ticker,
|
|
"2026-01-21",
|
|
strategy,
|
|
daily_features_by_ticker={
|
|
"TAIL": {
|
|
"gap_pct": 0.01,
|
|
"avg_daily_vol_14d": 1_000_000.0,
|
|
"avg_dollar_vol_30d": 100_000_000.0,
|
|
"ret_5d": 0.01,
|
|
"entropy_20d": 0.45,
|
|
},
|
|
},
|
|
)
|
|
|
|
assert day.tail_risk_scaler == 0.5
|
|
assert round(day.capital_deployed, 2) == 5000.0
|
|
assert len(day.trades) == 1
|
|
|
|
|
|
def test_same_day_support_score_prefers_prior_and_entry_liquidity_over_weak_spike() -> None:
|
|
strong_liquidity = _same_day_support_score(
|
|
{
|
|
"avg_dollar_vol_30d": 90_000_000.0,
|
|
"entry_dollar_volume": 12_000_000.0,
|
|
"attention_wiki_spike_10d": 0.0,
|
|
"attention_article_count_3d": 0,
|
|
"attention_us_article_count_3d": 0,
|
|
"event_score": 0.0,
|
|
}
|
|
)
|
|
weak_single_name = _same_day_support_score(
|
|
{
|
|
"avg_dollar_vol_30d": 50_000_000.0,
|
|
"entry_dollar_volume": 18_000_000.0,
|
|
"attention_wiki_spike_10d": 0.8,
|
|
"attention_article_count_3d": 0,
|
|
"attention_us_article_count_3d": 0,
|
|
"event_score": 0.0,
|
|
}
|
|
)
|
|
|
|
assert round(strong_liquidity, 3) == 0.38
|
|
assert round(weak_single_name, 3) == 0.222
|
|
assert strong_liquidity > weak_single_name
|
|
|
|
|
|
def test_simulate_day_can_apply_tail_risk_scaler_on_weak_support_single_name() -> None:
|
|
strategy = StrategyParams(
|
|
entry_minutes_after_open=15,
|
|
confirmation_minutes_after_entry=5,
|
|
min_morning_gain_pct=0.01,
|
|
top_n=1,
|
|
slippage_bps=0.0,
|
|
daily_budget_reset=True,
|
|
initial_capital=10_000.0,
|
|
tail_risk_day_max_trades=1,
|
|
tail_risk_day_min_max_gain_pct=0.03,
|
|
tail_risk_day_max_support_score=0.25,
|
|
tail_risk_day_min_max_entropy_20d=0.80,
|
|
tail_risk_day_min_max_confirmation_return_pct=0.01,
|
|
tail_risk_day_exempt_largecap=True,
|
|
tail_risk_day_scale=0.6,
|
|
)
|
|
|
|
day = simulate_day(
|
|
{
|
|
"TAIL": _bars(
|
|
"2026-01-13",
|
|
open_price=72.64,
|
|
closes=[73.75, 73.02, 73.52, 76.235, 77.1, 70.965],
|
|
volumes=[56_506, 51_911, 16_479, 120_666, 78_459, 33_740],
|
|
),
|
|
},
|
|
"2026-01-13",
|
|
strategy,
|
|
daily_features_by_ticker={
|
|
"TAIL": {
|
|
"gap_pct": 0.0308,
|
|
"avg_daily_vol_14d": 853_044.0,
|
|
"avg_dollar_vol_30d": 51_257_180.0,
|
|
"ret_5d": -0.0023,
|
|
"entropy_20d": 0.8333,
|
|
"attention_wiki_spike_10d": 0.806,
|
|
},
|
|
},
|
|
)
|
|
|
|
assert day.tail_risk_scaler == 0.6
|
|
assert round(day.capital_deployed, 2) == 6000.0
|
|
assert len(day.trades) == 1
|
|
|
|
|
|
def test_simulate_day_keeps_full_size_for_supported_single_name() -> None:
|
|
strategy = StrategyParams(
|
|
entry_minutes_after_open=15,
|
|
confirmation_minutes_after_entry=5,
|
|
min_morning_gain_pct=0.01,
|
|
top_n=1,
|
|
slippage_bps=0.0,
|
|
daily_budget_reset=True,
|
|
initial_capital=10_000.0,
|
|
tail_risk_day_max_trades=1,
|
|
tail_risk_day_min_max_gain_pct=0.03,
|
|
tail_risk_day_max_support_score=0.25,
|
|
tail_risk_day_min_max_entropy_20d=0.80,
|
|
tail_risk_day_min_max_confirmation_return_pct=0.01,
|
|
tail_risk_day_scale=0.6,
|
|
)
|
|
|
|
day = simulate_day(
|
|
{
|
|
"FLY": _bars(
|
|
"2026-01-27",
|
|
open_price=25.61,
|
|
closes=[25.59, 25.673, 26.4162, 26.65, 26.5381, 29.455],
|
|
volumes=[98_085, 69_040, 108_146, 159_489, 167_075, 60_723],
|
|
),
|
|
},
|
|
"2026-01-27",
|
|
strategy,
|
|
daily_features_by_ticker={
|
|
"FLY": {
|
|
"gap_pct": 0.0163,
|
|
"avg_daily_vol_14d": 3_455_439.0,
|
|
"avg_dollar_vol_30d": 86_573_309.1,
|
|
"ret_5d": -0.1696,
|
|
"entropy_20d": 0.7976,
|
|
},
|
|
},
|
|
)
|
|
|
|
assert day.tail_risk_scaler == 1.0
|
|
assert round(day.capital_deployed, 2) == 10000.0
|
|
assert len(day.trades) == 1
|
|
|
|
|
|
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"
|