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627 lines
23 KiB
Python
627 lines
23 KiB
Python
"""Unit tests for libs/backtest/selector.py."""
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from __future__ import annotations
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import datetime as dt
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from zoneinfo import ZoneInfo
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import pytest
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from libs.backtest.domain import (
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EventTypeProfile,
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SignalConfig,
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StrategyEngineConfig,
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UniverseConfig,
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)
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_UTC = ZoneInfo("UTC")
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def _make_raw_row(**kwargs) -> dict:
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defaults = {
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"event_id": "EVT::TEST::001",
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"symbol": "AAPL",
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"issuer_id": "ISSUER::0000320193",
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"score": 0.75,
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"sector": "Technology",
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"event_type": "earnings",
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"event_timestamp": "2026-01-05T21:00:00+00:00",
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"filing_time_bucket": "post_market",
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"reaction_date": "2026-01-06",
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"entry_date": "2026-01-07",
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"entry_price": 150.0,
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"avg_dollar_volume": 5_000_000.0,
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"atr_14": 3.5,
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}
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defaults.update(kwargs)
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return defaults
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class TestBuildCandidate:
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def test_basic(self):
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from libs.backtest.selector import build_candidate
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row = _make_raw_row()
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c = build_candidate(row)
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assert c is not None
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assert c.symbol == "AAPL"
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assert c.score == 0.75
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assert c.execution_date == dt.date(2026, 1, 7)
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assert c.event_timestamp.tzinfo is not None
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assert c.timing_class == "after_close"
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def test_null_timestamp_returns_none(self):
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from libs.backtest.selector import build_candidate
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row = _make_raw_row(event_timestamp=None)
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assert build_candidate(row) is None
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def test_zero_entry_price_returns_none(self):
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from libs.backtest.selector import build_candidate
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row = _make_raw_row(entry_price=0.0)
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assert build_candidate(row) is None
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def test_missing_entry_price_returns_none(self):
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from libs.backtest.selector import build_candidate
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row = _make_raw_row()
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del row["entry_price"]
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assert build_candidate(row) is None
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def test_null_exec_date_returns_none(self):
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from libs.backtest.selector import build_candidate
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row = _make_raw_row()
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del row["entry_date"]
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assert build_candidate(row) is None
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def test_sector_defaults_to_unknown(self):
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from libs.backtest.selector import build_candidate
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row = _make_raw_row(sector=None)
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c = build_candidate(row)
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assert c is not None
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assert c.sector == "UNKNOWN"
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def test_score_bucket_classification(self):
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from libs.backtest.selector import build_candidate
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c = build_candidate(_make_raw_row(score=0.85))
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assert c.score_bucket == "high"
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c = build_candidate(_make_raw_row(score=0.65))
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assert c.score_bucket == "medium_high"
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c = build_candidate(_make_raw_row(score=0.45))
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assert c.score_bucket == "medium"
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c = build_candidate(_make_raw_row(score=0.25))
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assert c.score_bucket == "medium_low"
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c = build_candidate(_make_raw_row(score=0.10))
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assert c.score_bucket == "low"
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def test_same_day_timing_and_direction_from_reaction(self):
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from libs.backtest.selector import build_candidate
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row = _make_raw_row(
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event_date="2026-01-06",
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reaction_date="2026-01-06",
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execution_date="2026-01-07",
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trade_direction="",
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reaction_day_return=-0.12,
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)
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c = build_candidate(row)
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assert c is not None
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assert c.event_date == dt.date(2026, 1, 6)
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assert c.timing_class == "same_day"
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assert c.trade_direction == "short"
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def test_reaction_close_engine_uses_event_close(self):
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from libs.backtest.selector import build_candidate
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engine = StrategyEngineConfig(
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engine_id="earnings_same_day_long_close_v1",
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event_types=["earnings"],
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timing_class="same_day",
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direction="long_only",
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entry_timing_policy="reaction_close",
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max_holding_days=3,
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engine_risk_budget_pct=0.35,
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)
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row = _make_raw_row(
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event_date="2026-01-06",
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reaction_date="2026-01-06",
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event_close=149.5,
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entry_date="2026-01-07",
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reaction_day_return=0.11,
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)
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c = build_candidate(row, strategy_engine=engine)
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assert c is not None
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assert c.execution_date == dt.date(2026, 1, 6)
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assert c.entry_price_est == pytest.approx(149.5)
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assert c.engine_id == "earnings_same_day_long_close_v1"
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assert c.entry_timing_policy == "reaction_close"
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def test_engine_execution_overrides_are_copied_to_candidate(self):
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from libs.backtest.selector import build_candidate
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engine = StrategyEngineConfig(
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engine_id="earnings_same_day_long_trend_v1",
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event_types=["earnings"],
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timing_class="same_day",
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direction="long_only",
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entry_timing_policy="reaction_close",
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max_holding_days=12,
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engine_risk_budget_pct=0.25,
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target_atr_multiplier_override=2.5,
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target_1_fraction_override=0.33,
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trailing_model_override="pct_10",
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trailing_warmup_days_override=2,
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)
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row = _make_raw_row(
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event_date="2026-01-06",
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reaction_date="2026-01-06",
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event_close=149.5,
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entry_date="2026-01-07",
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reaction_day_return=0.11,
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)
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c = build_candidate(row, strategy_engine=engine)
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assert c is not None
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assert c.engine_max_holding_days == 12
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assert c.engine_risk_budget_pct == pytest.approx(0.25)
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assert c.engine_target_atr_multiplier == pytest.approx(2.5)
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assert c.engine_target_1_fraction == pytest.approx(0.33)
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assert c.engine_trailing_model == "pct_10"
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assert c.engine_trailing_warmup_days == 2
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def test_engine_route_skips_non_matching_direction(self):
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from libs.backtest.selector import build_candidate
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engine = StrategyEngineConfig(
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engine_id="short_only_engine",
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event_types=["earnings"],
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timing_class="after_close",
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direction="short_only",
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)
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row = _make_raw_row(reaction_day_return=0.09, trade_direction="long")
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assert build_candidate(row, strategy_engine=engine) is None
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def test_select_candidates_recomputes_pead_score_for_engine_thresholds(self):
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from libs.backtest.selector import select_candidates
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rows = [
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_make_raw_row(
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symbol="LOWVOL",
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score=0.8,
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event_type="earnings_release",
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reaction_day_return=0.14,
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volume_ratio_20d=2.4,
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gap_size=0.01,
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),
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_make_raw_row(
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symbol="HIGHVOL",
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score=0.8,
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event_type="earnings_release",
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reaction_day_return=0.14,
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volume_ratio_20d=4.5,
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gap_size=0.01,
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),
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]
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universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
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signal = SignalConfig(
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scoring_model="pead",
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score_threshold=0.65,
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pead_reaction_threshold=0.10,
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pead_volume_threshold=2.0,
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max_candidates_per_day=5,
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)
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engine = StrategyEngineConfig(
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engine_id="after_close_long_quality",
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event_types=["earnings_release"],
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timing_class="after_close",
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direction="long_only",
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pead_volume_threshold_override=3.0,
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)
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selected = select_candidates(rows, universe, signal, strategy_engine=engine)
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assert [candidate.symbol for candidate in selected] == ["HIGHVOL"]
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def test_select_candidates_uses_engine_score_threshold_override(self):
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from libs.backtest.selector import select_candidates
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rows = [
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_make_raw_row(
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symbol="PASS",
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score=0.78,
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event_type="earnings_release",
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reaction_day_return=0.16,
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volume_ratio_20d=4.0,
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gap_size=0.01,
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),
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_make_raw_row(
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symbol="FAIL",
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score=0.72,
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event_type="earnings_release",
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reaction_day_return=0.14,
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volume_ratio_20d=4.0,
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gap_size=0.01,
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),
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]
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universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
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signal = SignalConfig(
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scoring_model="pead",
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score_threshold=0.65,
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pead_reaction_threshold=0.10,
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pead_volume_threshold=2.0,
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max_candidates_per_day=5,
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)
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engine = StrategyEngineConfig(
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engine_id="after_close_long_quality",
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event_types=["earnings_release"],
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timing_class="after_close",
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direction="long_only",
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score_threshold_override=0.75,
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)
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selected = select_candidates(rows, universe, signal, strategy_engine=engine)
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assert [candidate.symbol for candidate in selected] == ["PASS"]
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def test_select_candidates_respects_engine_reaction_day_return_bounds(self):
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from libs.backtest.selector import select_candidates
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rows = [
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_make_raw_row(
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symbol="CRASH",
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score=0.85,
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event_type="earnings_release",
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event_date="2026-01-06",
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reaction_date="2026-01-06",
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reaction_day_return=-0.52,
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volume_ratio_20d=8.0,
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trade_direction="short",
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),
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_make_raw_row(
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symbol="NORMAL",
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score=0.82,
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event_type="earnings_release",
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event_date="2026-01-06",
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reaction_date="2026-01-06",
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reaction_day_return=-0.18,
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volume_ratio_20d=5.0,
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trade_direction="short",
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),
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]
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universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
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signal = SignalConfig(
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scoring_model="pead",
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score_threshold=0.65,
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pead_reaction_threshold=0.10,
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pead_volume_threshold=2.0,
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max_candidates_per_day=5,
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)
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engine = StrategyEngineConfig(
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engine_id="same_day_short_filtered",
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event_types=["earnings_release"],
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timing_class="same_day",
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direction="short_only",
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reaction_day_return_min=-0.45,
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)
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selected = select_candidates(rows, universe, signal, strategy_engine=engine)
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assert [candidate.symbol for candidate in selected] == ["NORMAL"]
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def test_select_candidates_respects_engine_reaction_day_return_upper_bound(self):
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from libs.backtest.selector import select_candidates
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rows = [
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_make_raw_row(
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symbol="TOO_HOT",
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score=0.90,
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event_type="earnings_release",
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event_date="2026-01-06",
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reaction_date="2026-01-06",
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reaction_day_return=0.42,
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volume_ratio_20d=6.0,
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trade_direction="long",
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),
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_make_raw_row(
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symbol="OK",
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score=0.80,
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event_type="earnings_release",
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event_date="2026-01-06",
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reaction_date="2026-01-06",
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reaction_day_return=0.18,
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volume_ratio_20d=4.0,
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trade_direction="long",
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),
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]
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universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
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signal = SignalConfig(
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scoring_model="pead",
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score_threshold=0.65,
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pead_reaction_threshold=0.10,
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pead_volume_threshold=2.0,
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max_candidates_per_day=5,
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)
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engine = StrategyEngineConfig(
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engine_id="same_day_long_capped",
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event_types=["earnings_release"],
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timing_class="same_day",
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direction="long_only",
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reaction_day_return_max=0.30,
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)
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selected = select_candidates(rows, universe, signal, strategy_engine=engine)
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assert [candidate.symbol for candidate in selected] == ["OK"]
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def test_select_candidates_respects_engine_gap_size_bounds(self):
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from libs.backtest.selector import select_candidates
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rows = [
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_make_raw_row(
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symbol="TIGHT",
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score=0.82,
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event_type="earnings_release",
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event_date="2026-01-06",
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reaction_date="2026-01-06",
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reaction_day_return=0.18,
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volume_ratio_20d=4.0,
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gap_size=0.04,
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trade_direction="long",
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),
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_make_raw_row(
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symbol="WIDE",
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score=0.84,
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event_type="earnings_release",
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event_date="2026-01-06",
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reaction_date="2026-01-06",
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reaction_day_return=0.18,
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volume_ratio_20d=4.0,
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gap_size=0.16,
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trade_direction="long",
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),
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_make_raw_row(
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symbol="TOO_WIDE",
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score=0.86,
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event_type="earnings_release",
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event_date="2026-01-06",
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reaction_date="2026-01-06",
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reaction_day_return=0.18,
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volume_ratio_20d=4.0,
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gap_size=0.34,
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trade_direction="long",
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),
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]
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universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
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signal = SignalConfig(
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scoring_model="pead",
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score_threshold=0.65,
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pead_reaction_threshold=0.10,
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pead_volume_threshold=2.0,
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max_candidates_per_day=5,
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)
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engine = StrategyEngineConfig(
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engine_id="same_day_long_gapped",
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event_types=["earnings_release"],
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timing_class="same_day",
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direction="long_only",
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gap_size_min=0.10,
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gap_size_max=0.30,
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)
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selected = select_candidates(rows, universe, signal, strategy_engine=engine)
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assert [candidate.symbol for candidate in selected] == ["WIDE"]
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class TestRankCandidates:
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def test_sorted_by_score_desc(self):
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from libs.backtest.selector import build_candidate, rank_candidates
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rows = [
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_make_raw_row(symbol="A", score=0.5, avg_dollar_volume=1e6),
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_make_raw_row(symbol="B", score=0.8, avg_dollar_volume=1e6),
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_make_raw_row(symbol="C", score=0.6, avg_dollar_volume=1e6),
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]
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candidates = [build_candidate(r) for r in rows]
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ranked = rank_candidates([c for c in candidates if c])
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assert ranked[0].symbol == "B"
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assert ranked[1].symbol == "C"
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assert ranked[2].symbol == "A"
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def test_tiebreak_by_avg_dollar_volume(self):
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from libs.backtest.selector import build_candidate, rank_candidates
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rows = [
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_make_raw_row(symbol="A", score=0.7, avg_dollar_volume=1e6),
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_make_raw_row(symbol="B", score=0.7, avg_dollar_volume=5e6),
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]
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candidates = [build_candidate(r) for r in rows]
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ranked = rank_candidates([c for c in candidates if c])
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assert ranked[0].symbol == "B" # higher avg_dollar_volume
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def test_tiebreak_by_symbol_asc(self):
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from libs.backtest.selector import build_candidate, rank_candidates
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rows = [
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_make_raw_row(symbol="Z", score=0.7, avg_dollar_volume=1e6),
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_make_raw_row(symbol="A", score=0.7, avg_dollar_volume=1e6),
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]
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candidates = [build_candidate(r) for r in rows]
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ranked = rank_candidates([c for c in candidates if c])
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assert ranked[0].symbol == "A"
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def test_deterministic(self):
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from libs.backtest.selector import build_candidate, rank_candidates
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rows = [
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_make_raw_row(symbol="C", score=0.9),
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_make_raw_row(symbol="A", score=0.7),
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_make_raw_row(symbol="B", score=0.8),
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]
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candidates = [build_candidate(r) for r in rows]
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r1 = rank_candidates([c for c in candidates if c])
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r2 = rank_candidates([c for c in candidates if c])
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assert [c.symbol for c in r1] == [c.symbol for c in r2]
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class TestFilterCandidates:
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def test_score_threshold(self):
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from libs.backtest.selector import build_candidate, filter_by_score
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rows = [
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_make_raw_row(symbol="A", score=0.3),
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_make_raw_row(symbol="B", score=0.7),
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_make_raw_row(symbol="C", score=0.5),
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]
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candidates = [build_candidate(r) for r in rows if build_candidate(r)]
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filtered = filter_by_score(candidates, score_threshold=0.5)
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assert len(filtered) == 2
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assert all(c.score >= 0.5 for c in filtered)
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def test_min_price_filter(self):
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from libs.backtest.selector import build_candidate, filter_by_universe
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u = UniverseConfig(min_price=100.0, min_avg_dollar_volume=0)
|
|
rows = [
|
|
_make_raw_row(symbol="CHEAP", entry_price=50.0),
|
|
_make_raw_row(symbol="OK", entry_price=150.0),
|
|
]
|
|
candidates = [build_candidate(r) for r in rows if build_candidate(r)]
|
|
filtered = filter_by_universe(candidates, u)
|
|
assert len(filtered) == 1
|
|
assert filtered[0].symbol == "OK"
|
|
|
|
def test_min_adv_filter(self):
|
|
from libs.backtest.selector import build_candidate, filter_by_universe
|
|
|
|
u = UniverseConfig(min_price=0, min_avg_dollar_volume=2_000_000)
|
|
rows = [
|
|
_make_raw_row(symbol="ILLIQUID", avg_dollar_volume=500_000),
|
|
_make_raw_row(symbol="LIQUID", avg_dollar_volume=5_000_000),
|
|
]
|
|
candidates = [build_candidate(r) for r in rows if build_candidate(r)]
|
|
filtered = filter_by_universe(candidates, u)
|
|
assert len(filtered) == 1
|
|
assert filtered[0].symbol == "LIQUID"
|
|
|
|
def test_truncate(self):
|
|
from libs.backtest.selector import build_candidate, rank_candidates, truncate_candidates
|
|
|
|
rows = [_make_raw_row(symbol=s, score=0.9 - i * 0.1) for i, s in enumerate("ABCDE")]
|
|
candidates = rank_candidates([build_candidate(r) for r in rows if build_candidate(r)])
|
|
truncated = truncate_candidates(candidates, max_per_day=3)
|
|
assert len(truncated) == 3
|
|
|
|
|
|
class TestFilterByEventType:
|
|
def test_disabled_event_type_filtered(self):
|
|
from libs.backtest.selector import build_candidate, filter_by_event_type
|
|
|
|
rows = [
|
|
_make_raw_row(symbol="A", event_type="earnings_release"),
|
|
_make_raw_row(symbol="B", event_type="management_change"),
|
|
]
|
|
candidates = [build_candidate(r) for r in rows if build_candidate(r)]
|
|
profiles = {
|
|
"earnings_release": EventTypeProfile(enabled=True),
|
|
"management_change": EventTypeProfile(enabled=False),
|
|
}
|
|
filtered = filter_by_event_type(candidates, profiles)
|
|
assert len(filtered) == 1
|
|
assert filtered[0].symbol == "A"
|
|
|
|
def test_per_type_score_threshold(self):
|
|
from libs.backtest.selector import build_candidate, filter_by_event_type
|
|
|
|
rows = [
|
|
_make_raw_row(symbol="A", event_type="earnings_release", score=0.55),
|
|
_make_raw_row(symbol="B", event_type="earnings_release", score=0.75),
|
|
]
|
|
candidates = [build_candidate(r) for r in rows if build_candidate(r)]
|
|
profiles = {
|
|
"earnings_release": EventTypeProfile(score_threshold_override=0.6),
|
|
}
|
|
filtered = filter_by_event_type(candidates, profiles)
|
|
assert len(filtered) == 1
|
|
assert filtered[0].symbol == "B"
|
|
|
|
def test_unknown_event_type_blocked(self):
|
|
"""Event types not in profiles dict are blocked (default deny)."""
|
|
from libs.backtest.selector import build_candidate, filter_by_event_type
|
|
|
|
rows = [
|
|
_make_raw_row(symbol="A", event_type="earnings_release"),
|
|
_make_raw_row(symbol="B", event_type="unknown_type"),
|
|
]
|
|
candidates = [build_candidate(r) for r in rows if build_candidate(r)]
|
|
profiles = {
|
|
"earnings_release": EventTypeProfile(enabled=True),
|
|
}
|
|
filtered = filter_by_event_type(candidates, profiles)
|
|
assert len(filtered) == 1
|
|
assert filtered[0].symbol == "A"
|
|
|
|
def test_unknown_event_type_passes_when_in_profiles(self):
|
|
"""Event type 'unknown' passes through when explicitly enabled in profiles."""
|
|
from libs.backtest.selector import build_candidate, filter_by_event_type
|
|
|
|
rows = [
|
|
_make_raw_row(symbol="A", event_type="earnings_release"),
|
|
_make_raw_row(symbol="B", event_type="unknown"),
|
|
]
|
|
candidates = [build_candidate(r) for r in rows if build_candidate(r)]
|
|
profiles = {
|
|
"earnings_release": EventTypeProfile(enabled=True),
|
|
"unknown": EventTypeProfile(enabled=True, direction_filter="any"),
|
|
}
|
|
filtered = filter_by_event_type(candidates, profiles)
|
|
assert len(filtered) == 2
|
|
symbols = [c.symbol for c in filtered]
|
|
assert "A" in symbols
|
|
assert "B" in symbols
|
|
|
|
def test_no_profiles_passthrough(self):
|
|
from libs.backtest.selector import build_candidate, filter_by_event_type
|
|
|
|
rows = [_make_raw_row(symbol="A")]
|
|
candidates = [build_candidate(r) for r in rows if build_candidate(r)]
|
|
assert filter_by_event_type(candidates, {}) == candidates
|
|
|
|
|
|
class TestSelectCandidates:
|
|
def test_full_pipeline(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(symbol="A", score=0.9, avg_dollar_volume=5e6, entry_price=100.0),
|
|
_make_raw_row(symbol="B", score=0.3, avg_dollar_volume=5e6, entry_price=100.0), # below threshold
|
|
_make_raw_row(symbol="C", score=0.8, avg_dollar_volume=1e4, entry_price=100.0), # low ADV
|
|
_make_raw_row(symbol="D", score=0.7, avg_dollar_volume=5e6, entry_price=2.0), # below min_price
|
|
]
|
|
u = UniverseConfig(min_price=5.0, min_avg_dollar_volume=1_000_000)
|
|
s = SignalConfig(score_threshold=0.5, max_candidates_per_day=10)
|
|
result = select_candidates(rows, u, s)
|
|
symbols = [c.symbol for c in result]
|
|
assert "A" in symbols
|
|
assert "B" not in symbols # below threshold
|
|
assert "C" not in symbols # low ADV
|
|
assert "D" not in symbols # below min_price
|
|
|
|
def test_pipeline_with_event_type_profiles(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(symbol="A", score=0.9, avg_dollar_volume=5e6, entry_price=100.0, event_type="earnings_release"),
|
|
_make_raw_row(symbol="B", score=0.7, avg_dollar_volume=5e6, entry_price=100.0, event_type="management_change"),
|
|
]
|
|
u = UniverseConfig(min_price=5.0, min_avg_dollar_volume=1_000_000)
|
|
s = SignalConfig(score_threshold=0.5, max_candidates_per_day=10)
|
|
profiles = {
|
|
"earnings_release": EventTypeProfile(enabled=True),
|
|
"management_change": EventTypeProfile(enabled=False),
|
|
}
|
|
result = select_candidates(rows, u, s, event_type_profiles=profiles)
|
|
assert len(result) == 1
|
|
assert result[0].symbol == "A"
|