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1991 lines
74 KiB
Python
1991 lines
74 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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import json
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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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"entry_convention": "next_open_after_reaction_close",
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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_prefers_avg_dollar_volume_20d_when_present(self):
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from libs.backtest.selector import build_candidate
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row = _make_raw_row(
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avg_dollar_volume=999_000_000.0,
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avg_dollar_volume_20d=12_345_678.0,
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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.avg_dollar_volume == 12_345_678.0
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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_sector_etf_proxy_uses_proxy_trade_fields(self):
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from libs.backtest.selector import build_candidate
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engine = StrategyEngineConfig(
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engine_id="sector_etf_proxy",
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event_types=["earnings"],
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trade_symbol_mode="sector_etf",
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)
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row = _make_raw_row(
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score=0.9,
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event_close=150.0,
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reaction_day_low=145.0,
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reaction_day_high=153.0,
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sector_etf_proxy="XLK",
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sector_etf_event_close=210.0,
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sector_etf_entry_price=211.5,
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sector_etf_reaction_day_low=206.0,
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sector_etf_reaction_day_high=212.0,
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sector_etf_avg_dollar_volume=250_000_000.0,
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sector_etf_atr_14=4.2,
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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.symbol == "XLK"
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assert c.source_symbol == "AAPL"
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assert c.trade_symbol_mode == "sector_etf"
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assert c.entry_price_est == 211.5
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assert c.avg_dollar_volume == 250_000_000.0
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assert c.atr_14 == 4.2
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assert c.features["event_close"] == 210.0
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assert c.features["reaction_day_low"] == 206.0
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assert c.features["source_event_close"] == 150.0
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def test_engine_can_force_long_direction_for_negative_reaction(self):
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from libs.backtest.selector import build_candidate
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engine = StrategyEngineConfig(
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engine_id="next_open_long_reversal_micro",
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event_types=["guidance_update"],
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timing_class="after_close",
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direction="long_only",
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forced_trade_direction_override="long",
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entry_timing_policy="next_open",
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)
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row = _make_raw_row(
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event_type="guidance_update",
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event_date="2026-01-06",
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reaction_date="2026-01-07",
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entry_date="2026-01-08",
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trade_direction="",
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reaction_day_return=-0.05,
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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.trade_direction == "long"
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def test_engine_can_override_per_trade_risk(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_day_return=0.09,
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event_direction="mixed",
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guidance_status="inline_or_maintained",
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filing_time_bucket="regular_hours",
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gap_size=0.08,
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close_location=0.66,
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volume_ratio_20d=2.7,
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event_close=150.0,
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)
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engine = StrategyEngineConfig(
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engine_id="mixed_inline_lowrisk",
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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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per_trade_risk_pct_override=0.01,
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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_per_trade_risk_pct == pytest.approx(0.01)
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assert c.engine_id == "mixed_inline_lowrisk"
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def test_engine_can_override_sector_limit(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_type="other_material_event",
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event_direction="unknown",
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guidance_status="not_provided",
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filing_time_bucket="post_market",
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reaction_day_return=0.03,
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close_location=0.78,
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gap_size=0.01,
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volume_ratio_20d=1.2,
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)
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engine = StrategyEngineConfig(
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engine_id="other_material_unknown_orderly",
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event_types=["other_material_event"],
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timing_class="after_close",
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direction="long_only",
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entry_timing_policy="next_open",
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max_positions_per_sector_override=4,
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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_positions_per_sector == 4
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assert c.engine_id == "other_material_unknown_orderly"
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def test_engine_can_override_position_caps(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_type="other_material_event",
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event_direction="unknown",
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guidance_status="not_provided",
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filing_time_bucket="post_market",
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reaction_day_return=0.03,
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close_location=0.78,
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gap_size=0.01,
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volume_ratio_20d=1.2,
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)
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engine = StrategyEngineConfig(
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engine_id="other_material_unknown_capped",
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event_types=["other_material_event"],
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timing_class="after_close",
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direction="long_only",
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entry_timing_policy="next_open",
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max_position_value_pct_override=0.35,
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max_adv_fraction_override=0.015,
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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_position_value_pct == pytest.approx(0.35)
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assert c.engine_max_adv_fraction == pytest.approx(0.015)
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assert c.engine_id == "other_material_unknown_capped"
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def test_engine_can_inherit_parent_filters(self):
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from libs.backtest.selector import build_candidate
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parent = StrategyEngineConfig(
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engine_id="core_parent",
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event_types=["guidance_update"],
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guidance_statuses=["raised"],
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filing_time_buckets=["regular_hours"],
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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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close_location_min=0.55,
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)
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child = StrategyEngineConfig(
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engine_id="core_child",
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inherits_from_engine_id="core_parent",
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close_location_min=0.75,
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)
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row = _make_raw_row(
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event_type="guidance_update",
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event_direction="bullish",
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guidance_status="raised",
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filing_time_bucket="regular_hours",
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event_date="2026-01-06",
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reaction_date="2026-01-06",
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close_location=0.8,
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reaction_day_return=0.1,
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event_close=150.0,
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)
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c = build_candidate(
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row,
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strategy_engine=child,
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engine_lookup={"core_parent": parent, "core_child": child},
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)
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assert c is not None
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assert c.engine_id == "core_child"
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assert c.entry_timing_policy == "reaction_close"
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def test_engine_can_exclude_if_matches_sibling(self):
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from libs.backtest.selector import build_candidate
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cool = StrategyEngineConfig(
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engine_id="core_cool",
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event_types=["guidance_update"],
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guidance_statuses=["raised"],
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filing_time_buckets=["regular_hours"],
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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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close_location_min=0.75,
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)
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hot = StrategyEngineConfig(
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engine_id="core_hot",
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inherits_from_engine_id="core_cool",
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exclude_if_matches_engine_id="core_cool",
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close_location_min=0.55,
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)
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cool_row = _make_raw_row(
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event_type="guidance_update",
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event_direction="bullish",
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guidance_status="raised",
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filing_time_bucket="regular_hours",
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event_date="2026-01-06",
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reaction_date="2026-01-06",
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close_location=0.8,
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reaction_day_return=0.1,
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event_close=150.0,
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)
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hot_only_row = _make_raw_row(
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event_type="guidance_update",
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event_direction="bullish",
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guidance_status="raised",
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filing_time_bucket="regular_hours",
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event_date="2026-01-06",
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reaction_date="2026-01-06",
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close_location=0.6,
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reaction_day_return=0.1,
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event_close=150.0,
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)
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lookup = {"core_cool": cool, "core_hot": hot}
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assert build_candidate(cool_row, strategy_engine=hot, engine_lookup=lookup) is None
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assert build_candidate(hot_only_row, strategy_engine=hot, engine_lookup=lookup) is not None
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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_r_override=3.25,
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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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early_failure_close_below_entry_and_reaction_close_override=True,
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early_failure_no_progress_days_override=1,
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early_failure_no_progress_r_override=0.2,
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early_failure_no_progress_fraction_override=1.0,
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veto_oneoff_penalty_override=0.95,
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allow_oneoff_downsizing_override=True,
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oneoff_downsize_floor_override=0.6,
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veto_parse_confidence_min_override=0.25,
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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_r == pytest.approx(3.25)
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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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assert c.engine_early_failure_close_below_entry_and_reaction_close is True
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assert c.engine_early_failure_no_progress_days == 1
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assert c.engine_early_failure_no_progress_r == pytest.approx(0.2)
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assert c.engine_early_failure_no_progress_fraction == pytest.approx(1.0)
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assert c.engine_veto_oneoff_penalty == pytest.approx(0.95)
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assert c.engine_allow_oneoff_downsizing is True
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assert c.engine_oneoff_downsize_floor == pytest.approx(0.6)
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assert c.engine_veto_parse_confidence_min == pytest.approx(0.25)
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def test_mixed_inline_execution_overrides_override_generic_engine_exit(self):
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from libs.backtest.selector import build_candidate
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engine = StrategyEngineConfig(
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engine_id="same_day_core",
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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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entry_timing_policy="reaction_close",
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early_failure_no_progress_days_override=1,
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early_failure_no_progress_r_override=0.15,
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mixed_inline_early_failure_no_progress_days_override=1,
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mixed_inline_early_failure_no_progress_r_override=0.30,
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mixed_inline_early_failure_no_progress_fraction_override=1.0,
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)
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row = _make_raw_row(
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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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event_close=149.5,
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reaction_day_return=0.11,
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event_direction="mixed",
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guidance_status="inline_or_maintained",
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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_early_failure_no_progress_days == 1
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assert c.engine_early_failure_no_progress_r == pytest.approx(0.30)
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assert c.engine_early_failure_no_progress_fraction == pytest.approx(1.0)
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def test_unknown_inline_execution_overrides_apply_only_to_weak_gap_high_close_subset(self):
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from libs.backtest.selector import build_candidate
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engine = StrategyEngineConfig(
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engine_id="same_day_core",
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event_types=["earnings_release"],
|
|
timing_class="same_day",
|
|
direction="long_only",
|
|
entry_timing_policy="reaction_close",
|
|
early_failure_no_progress_days_override=1,
|
|
early_failure_no_progress_r_override=0.15,
|
|
unknown_inline_exit_close_location_min=0.90,
|
|
unknown_inline_exit_gap_size_max=0.02,
|
|
unknown_inline_early_failure_no_progress_days_override=1,
|
|
unknown_inline_early_failure_no_progress_r_override=0.30,
|
|
unknown_inline_early_failure_no_progress_fraction_override=1.0,
|
|
)
|
|
weak_hot_row = _make_raw_row(
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=149.5,
|
|
reaction_day_return=0.11,
|
|
event_direction="unknown",
|
|
guidance_status="inline_or_maintained",
|
|
close_location=0.93,
|
|
gap_size=0.01,
|
|
)
|
|
c = build_candidate(weak_hot_row, strategy_engine=engine)
|
|
assert c is not None
|
|
assert c.engine_early_failure_no_progress_days == 1
|
|
assert c.engine_early_failure_no_progress_r == pytest.approx(0.30)
|
|
assert c.engine_early_failure_no_progress_fraction == pytest.approx(1.0)
|
|
|
|
stronger_gap_row = _make_raw_row(
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=149.5,
|
|
reaction_day_return=0.11,
|
|
event_direction="unknown",
|
|
guidance_status="inline_or_maintained",
|
|
close_location=0.93,
|
|
gap_size=0.04,
|
|
)
|
|
c2 = build_candidate(stronger_gap_row, strategy_engine=engine)
|
|
assert c2 is not None
|
|
assert c2.engine_early_failure_no_progress_days == 1
|
|
assert c2.engine_early_failure_no_progress_r == pytest.approx(0.15)
|
|
|
|
def test_engine_route_skips_non_matching_direction(self):
|
|
from libs.backtest.selector import build_candidate
|
|
|
|
engine = StrategyEngineConfig(
|
|
engine_id="short_only_engine",
|
|
event_types=["earnings"],
|
|
timing_class="after_close",
|
|
direction="short_only",
|
|
)
|
|
row = _make_raw_row(reaction_day_return=0.09, trade_direction="long")
|
|
assert build_candidate(row, strategy_engine=engine) is None
|
|
|
|
def test_engine_route_respects_event_direction_filter(self):
|
|
from libs.backtest.selector import build_candidate
|
|
|
|
engine = StrategyEngineConfig(
|
|
engine_id="bullish_only_engine",
|
|
event_types=["earnings"],
|
|
event_directions=["bullish"],
|
|
timing_class="after_close",
|
|
direction="long_only",
|
|
)
|
|
row = _make_raw_row(
|
|
reaction_day_return=0.09,
|
|
trade_direction="long",
|
|
event_direction="mixed",
|
|
)
|
|
assert build_candidate(row, strategy_engine=engine) is None
|
|
|
|
def test_engine_route_respects_guidance_status_filter(self):
|
|
from libs.backtest.selector import build_candidate
|
|
|
|
engine = StrategyEngineConfig(
|
|
engine_id="raised_only_engine",
|
|
event_types=["earnings"],
|
|
guidance_statuses=["raised"],
|
|
timing_class="after_close",
|
|
direction="long_only",
|
|
)
|
|
row = _make_raw_row(
|
|
reaction_day_return=0.09,
|
|
trade_direction="long",
|
|
guidance_status="inline_or_maintained",
|
|
)
|
|
assert build_candidate(row, strategy_engine=engine) is None
|
|
|
|
def test_engine_route_respects_generic_feature_ranges(self):
|
|
from libs.backtest.selector import build_candidate
|
|
|
|
engine = StrategyEngineConfig(
|
|
engine_id="mixed_largecap_priority",
|
|
event_types=["earnings_release"],
|
|
event_directions=["mixed"],
|
|
guidance_statuses=["inline_or_maintained"],
|
|
timing_class="same_day",
|
|
direction="long_only",
|
|
entry_timing_policy="reaction_close",
|
|
min_market_cap_proxy=8_000_000_000.0,
|
|
max_market_cap_proxy=25_000_000_000.0,
|
|
document_quality_score_max=0.71,
|
|
signal_strength_score_max=0.2,
|
|
parse_confidence_overall_max=0.70,
|
|
)
|
|
allowed = _make_raw_row(
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=149.5,
|
|
reaction_day_return=0.11,
|
|
event_direction="mixed",
|
|
guidance_status="inline_or_maintained",
|
|
market_cap_proxy=12_000_000_000.0,
|
|
document_quality_score=0.68,
|
|
signal_strength_score=0.2,
|
|
parse_confidence_overall=0.65,
|
|
)
|
|
assert build_candidate(allowed, strategy_engine=engine) is not None
|
|
|
|
too_small = _make_raw_row(
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=149.5,
|
|
reaction_day_return=0.11,
|
|
event_direction="mixed",
|
|
guidance_status="inline_or_maintained",
|
|
market_cap_proxy=5_000_000_000.0,
|
|
document_quality_score=0.68,
|
|
signal_strength_score=0.2,
|
|
parse_confidence_overall=0.65,
|
|
)
|
|
assert build_candidate(too_small, strategy_engine=engine) is None
|
|
|
|
too_clean = _make_raw_row(
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=149.5,
|
|
reaction_day_return=0.11,
|
|
event_direction="mixed",
|
|
guidance_status="inline_or_maintained",
|
|
market_cap_proxy=12_000_000_000.0,
|
|
document_quality_score=0.75,
|
|
signal_strength_score=0.2,
|
|
parse_confidence_overall=0.65,
|
|
)
|
|
assert build_candidate(too_clean, strategy_engine=engine) is None
|
|
|
|
def test_engine_route_respects_filing_exchange_and_bar_shape_filters(self):
|
|
from libs.backtest.selector import build_candidate
|
|
|
|
engine = StrategyEngineConfig(
|
|
engine_id="regular_hours_nyse_gap_conflict",
|
|
event_types=["earnings_release"],
|
|
timing_class="same_day",
|
|
direction="long_only",
|
|
filing_time_buckets=["regular_hours"],
|
|
allowed_exchanges=["NYSE"],
|
|
avg_dollar_volume_max=150_000_000.0,
|
|
reaction_day_range_pct_max=0.09,
|
|
upper_wick_pct_max=0.03,
|
|
)
|
|
allowed = _make_raw_row(
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
filing_time_bucket="regular_hours",
|
|
exchange_proxy="NYSE",
|
|
event_close=100.0,
|
|
reaction_day_low=94.0,
|
|
reaction_day_high=102.0,
|
|
reaction_day_return=0.08,
|
|
avg_dollar_volume_20d=120_000_000.0,
|
|
)
|
|
assert build_candidate(allowed, strategy_engine=engine) is not None
|
|
|
|
wrong_bucket = dict(allowed, filing_time_bucket="pre_market")
|
|
assert build_candidate(wrong_bucket, strategy_engine=engine) is None
|
|
|
|
wrong_exchange = dict(allowed, exchange_proxy="NASDAQ")
|
|
assert build_candidate(wrong_exchange, strategy_engine=engine) is None
|
|
|
|
too_wide = dict(allowed, reaction_day_low=90.0, reaction_day_high=102.0)
|
|
assert build_candidate(too_wide, strategy_engine=engine) is None
|
|
|
|
too_wicky = dict(allowed, reaction_day_low=97.0, reaction_day_high=104.5)
|
|
assert build_candidate(too_wicky, strategy_engine=engine) is None
|
|
|
|
too_liquid = dict(allowed, avg_dollar_volume_20d=220_000_000.0)
|
|
assert build_candidate(too_liquid, strategy_engine=engine) is None
|
|
|
|
def test_select_candidates_recomputes_pead_score_for_engine_thresholds(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="LOWVOL",
|
|
score=0.8,
|
|
event_type="earnings_release",
|
|
reaction_day_return=0.14,
|
|
volume_ratio_20d=2.4,
|
|
gap_size=0.01,
|
|
),
|
|
_make_raw_row(
|
|
symbol="HIGHVOL",
|
|
score=0.8,
|
|
event_type="earnings_release",
|
|
reaction_day_return=0.14,
|
|
volume_ratio_20d=4.5,
|
|
gap_size=0.01,
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="pead",
|
|
score_threshold=0.65,
|
|
pead_reaction_threshold=0.10,
|
|
pead_volume_threshold=2.0,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="after_close_long_quality",
|
|
event_types=["earnings_release"],
|
|
timing_class="after_close",
|
|
direction="long_only",
|
|
pead_volume_threshold_override=3.0,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["HIGHVOL"]
|
|
|
|
def test_select_candidates_uses_engine_score_threshold_override(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="PASS",
|
|
score=0.78,
|
|
event_type="earnings_release",
|
|
reaction_day_return=0.16,
|
|
volume_ratio_20d=4.0,
|
|
gap_size=0.01,
|
|
),
|
|
_make_raw_row(
|
|
symbol="FAIL",
|
|
score=0.72,
|
|
event_type="earnings_release",
|
|
reaction_day_return=0.14,
|
|
volume_ratio_20d=4.0,
|
|
gap_size=0.01,
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="pead",
|
|
score_threshold=0.65,
|
|
pead_reaction_threshold=0.10,
|
|
pead_volume_threshold=2.0,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="after_close_long_quality",
|
|
event_types=["earnings_release"],
|
|
timing_class="after_close",
|
|
direction="long_only",
|
|
score_threshold_override=0.75,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["PASS"]
|
|
|
|
def test_select_candidates_filters_weak_reaction_gap_chase(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="FILTERED",
|
|
score=0.80,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=151.0,
|
|
reaction_day_return=0.018,
|
|
close_location=0.85,
|
|
volume_ratio_20d=1.4,
|
|
gap_size=0.03,
|
|
),
|
|
_make_raw_row(
|
|
symbol="KEPT",
|
|
score=0.80,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=151.0,
|
|
reaction_day_return=0.018,
|
|
close_location=0.85,
|
|
volume_ratio_20d=1.4,
|
|
gap_size=0.01,
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="return_max_long_v2",
|
|
score_threshold=0.45,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="same_day_long_core",
|
|
event_types=["earnings_release"],
|
|
timing_class="same_day",
|
|
direction="long_only",
|
|
entry_timing_policy="reaction_close",
|
|
weak_reaction_threshold=0.03,
|
|
weak_reaction_gap_max=0.02,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["KEPT"]
|
|
|
|
def test_select_candidates_filters_weak_unknown_direction_reactions(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="FILTERED",
|
|
score=0.80,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=151.0,
|
|
reaction_day_return=0.021,
|
|
close_location=0.85,
|
|
volume_ratio_20d=1.4,
|
|
event_direction="unknown",
|
|
),
|
|
_make_raw_row(
|
|
symbol="KEPT",
|
|
score=0.80,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=151.0,
|
|
reaction_day_return=0.051,
|
|
close_location=0.85,
|
|
volume_ratio_20d=1.4,
|
|
event_direction="unknown",
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="return_max_long_v2",
|
|
score_threshold=0.45,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="same_day_long_core",
|
|
event_types=["earnings_release"],
|
|
timing_class="same_day",
|
|
direction="long_only",
|
|
entry_timing_policy="reaction_close",
|
|
unknown_direction_reaction_min=0.04,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["KEPT"]
|
|
|
|
def test_select_candidates_filters_unknown_direction_low_close_location(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="FILTERED",
|
|
score=0.80,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=151.0,
|
|
reaction_day_return=0.061,
|
|
close_location=0.58,
|
|
volume_ratio_20d=1.4,
|
|
event_direction="unknown",
|
|
),
|
|
_make_raw_row(
|
|
symbol="KEPT",
|
|
score=0.80,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=151.0,
|
|
reaction_day_return=0.061,
|
|
close_location=0.78,
|
|
volume_ratio_20d=1.4,
|
|
event_direction="unknown",
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="return_max_long_v2",
|
|
score_threshold=0.45,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="same_day_long_core",
|
|
event_types=["earnings_release"],
|
|
timing_class="same_day",
|
|
direction="long_only",
|
|
entry_timing_policy="reaction_close",
|
|
unknown_direction_close_location_min=0.70,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["KEPT"]
|
|
|
|
def test_select_candidates_filters_unknown_direction_high_close_location(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="FILTERED",
|
|
score=0.80,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=151.0,
|
|
reaction_day_return=0.061,
|
|
close_location=0.94,
|
|
volume_ratio_20d=1.6,
|
|
event_direction="unknown",
|
|
),
|
|
_make_raw_row(
|
|
symbol="KEPT",
|
|
score=0.80,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=151.0,
|
|
reaction_day_return=0.061,
|
|
close_location=0.84,
|
|
volume_ratio_20d=1.6,
|
|
event_direction="unknown",
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="return_max_long_v2",
|
|
score_threshold=0.45,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="same_day_long_core",
|
|
event_types=["earnings_release"],
|
|
timing_class="same_day",
|
|
direction="long_only",
|
|
entry_timing_policy="reaction_close",
|
|
unknown_direction_close_location_max=0.90,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["KEPT"]
|
|
|
|
def test_select_candidates_filters_unknown_direction_low_gap_size(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="FILTERED",
|
|
score=0.80,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=151.0,
|
|
reaction_day_return=0.061,
|
|
close_location=0.78,
|
|
gap_size=0.01,
|
|
volume_ratio_20d=1.6,
|
|
event_direction="unknown",
|
|
),
|
|
_make_raw_row(
|
|
symbol="KEPT",
|
|
score=0.80,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
event_close=151.0,
|
|
reaction_day_return=0.061,
|
|
close_location=0.78,
|
|
gap_size=0.03,
|
|
volume_ratio_20d=1.6,
|
|
event_direction="unknown",
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="return_max_long_v2",
|
|
score_threshold=0.45,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="same_day_long_core",
|
|
event_types=["earnings_release"],
|
|
timing_class="same_day",
|
|
direction="long_only",
|
|
entry_timing_policy="reaction_close",
|
|
unknown_direction_gap_size_min=0.02,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["KEPT"]
|
|
|
|
def test_select_candidates_respects_engine_reaction_day_return_bounds(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="CRASH",
|
|
score=0.85,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
reaction_day_return=-0.52,
|
|
volume_ratio_20d=8.0,
|
|
trade_direction="short",
|
|
),
|
|
_make_raw_row(
|
|
symbol="NORMAL",
|
|
score=0.82,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
reaction_day_return=-0.18,
|
|
volume_ratio_20d=5.0,
|
|
trade_direction="short",
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="pead",
|
|
score_threshold=0.65,
|
|
pead_reaction_threshold=0.10,
|
|
pead_volume_threshold=2.0,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="same_day_short_filtered",
|
|
event_types=["earnings_release"],
|
|
timing_class="same_day",
|
|
direction="short_only",
|
|
reaction_day_return_min=-0.45,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["NORMAL"]
|
|
|
|
def test_select_candidates_caps_mixed_inline_close_location(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="TOO_WEAK",
|
|
score=0.78,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
reaction_day_return=0.11,
|
|
event_close=151.0,
|
|
close_location=0.55,
|
|
gap_size=0.03,
|
|
volume_ratio_20d=2.4,
|
|
event_direction="mixed",
|
|
guidance_status="inline_or_maintained",
|
|
trade_direction="long",
|
|
),
|
|
_make_raw_row(
|
|
symbol="TOO_HOT",
|
|
score=0.78,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
reaction_day_return=0.11,
|
|
event_close=151.0,
|
|
close_location=0.94,
|
|
gap_size=0.03,
|
|
volume_ratio_20d=2.4,
|
|
event_direction="mixed",
|
|
guidance_status="inline_or_maintained",
|
|
trade_direction="long",
|
|
),
|
|
_make_raw_row(
|
|
symbol="KEPT",
|
|
score=0.77,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
reaction_day_return=0.10,
|
|
event_close=151.0,
|
|
close_location=0.84,
|
|
gap_size=0.03,
|
|
volume_ratio_20d=2.2,
|
|
event_direction="mixed",
|
|
guidance_status="inline_or_maintained",
|
|
trade_direction="long",
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="return_max_long_v2",
|
|
score_threshold=0.45,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="same_day_long_core",
|
|
event_types=["earnings_release"],
|
|
timing_class="same_day",
|
|
direction="long_only",
|
|
entry_timing_policy="reaction_close",
|
|
mixed_inline_close_location_min=0.60,
|
|
mixed_inline_close_location_max=0.90,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["KEPT"]
|
|
|
|
def test_select_candidates_caps_mixed_inline_gap_size(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="TOO_GAPPY",
|
|
score=0.78,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
reaction_day_return=0.11,
|
|
event_close=151.0,
|
|
close_location=0.81,
|
|
gap_size=0.10,
|
|
volume_ratio_20d=2.4,
|
|
event_direction="mixed",
|
|
guidance_status="inline_or_maintained",
|
|
trade_direction="long",
|
|
),
|
|
_make_raw_row(
|
|
symbol="KEPT",
|
|
score=0.77,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
reaction_day_return=0.10,
|
|
event_close=151.0,
|
|
close_location=0.82,
|
|
gap_size=0.04,
|
|
volume_ratio_20d=2.2,
|
|
event_direction="mixed",
|
|
guidance_status="inline_or_maintained",
|
|
trade_direction="long",
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="return_max_long_v2",
|
|
score_threshold=0.45,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="same_day_long_core",
|
|
event_types=["earnings_release"],
|
|
timing_class="same_day",
|
|
direction="long_only",
|
|
entry_timing_policy="reaction_close",
|
|
mixed_inline_gap_size_max=0.05,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["KEPT"]
|
|
|
|
def test_select_candidates_respects_raw_macro_vix_bounds(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="LOWVIX",
|
|
score=0.78,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-07",
|
|
event_direction="bullish",
|
|
guidance_status="raised",
|
|
reaction_day_return=0.08,
|
|
close_location=0.82,
|
|
gap_size=0.02,
|
|
volume_ratio_20d=2.0,
|
|
macro_vix=17.5,
|
|
),
|
|
_make_raw_row(
|
|
symbol="MIDVIX",
|
|
score=0.77,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-07",
|
|
event_direction="bullish",
|
|
guidance_status="raised",
|
|
reaction_day_return=0.08,
|
|
close_location=0.82,
|
|
gap_size=0.02,
|
|
volume_ratio_20d=2.0,
|
|
macro_vix=19.5,
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="return_max_long_v2",
|
|
score_threshold=0.45,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="after_close_vix_gate",
|
|
event_types=["earnings_release"],
|
|
timing_class="after_close",
|
|
direction="long_only",
|
|
entry_timing_policy="next_open",
|
|
macro_vix_min=18.0,
|
|
macro_vix_max=22.0,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["MIDVIX"]
|
|
|
|
def test_select_candidates_respects_pre_event_noise_feature_bounds(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="FILTERED",
|
|
score=0.78,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-07",
|
|
event_direction="bullish",
|
|
guidance_status="raised",
|
|
reaction_day_return=0.08,
|
|
close_location=0.82,
|
|
gap_size=0.02,
|
|
volume_ratio_20d=2.0,
|
|
pre_event_hurst_60d=0.78,
|
|
pre_event_entropy_60d=2.04,
|
|
pre_event_market_temperature=1.42,
|
|
),
|
|
_make_raw_row(
|
|
symbol="KEPT",
|
|
score=0.77,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-07",
|
|
event_direction="bullish",
|
|
guidance_status="raised",
|
|
reaction_day_return=0.08,
|
|
close_location=0.82,
|
|
gap_size=0.02,
|
|
volume_ratio_20d=2.0,
|
|
pre_event_hurst_60d=0.58,
|
|
pre_event_entropy_60d=1.78,
|
|
pre_event_market_temperature=0.92,
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="return_max_long_v2",
|
|
score_threshold=0.45,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="after_close_noise_gate",
|
|
event_types=["earnings_release"],
|
|
timing_class="after_close",
|
|
direction="long_only",
|
|
entry_timing_policy="next_open",
|
|
pre_event_hurst_60d_max=0.70,
|
|
pre_event_entropy_60d_max=1.90,
|
|
pre_event_market_temperature_max=1.20,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["KEPT"]
|
|
|
|
def test_select_candidates_respects_pre_event_bb_position_bounds(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="FILTERED",
|
|
score=0.78,
|
|
event_type="other_material_event",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-07",
|
|
event_direction="mixed",
|
|
guidance_status="not_provided",
|
|
reaction_day_return=0.02,
|
|
close_location=0.84,
|
|
gap_size=0.01,
|
|
volume_ratio_20d=0.9,
|
|
pre_event_bb_position=0.18,
|
|
),
|
|
_make_raw_row(
|
|
symbol="KEPT",
|
|
score=0.77,
|
|
event_type="other_material_event",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-07",
|
|
event_direction="mixed",
|
|
guidance_status="not_provided",
|
|
reaction_day_return=0.02,
|
|
close_location=0.84,
|
|
gap_size=0.01,
|
|
volume_ratio_20d=0.9,
|
|
pre_event_bb_position=0.42,
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="return_max_long_v2",
|
|
score_threshold=0.45,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="mixed_ome_bb_gate",
|
|
event_types=["other_material_event"],
|
|
timing_class="after_close",
|
|
direction="long_only",
|
|
entry_timing_policy="next_open",
|
|
event_directions=["mixed"],
|
|
guidance_statuses=["not_provided"],
|
|
pre_event_bb_position_min=0.25,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["KEPT"]
|
|
|
|
def test_select_candidates_respects_pre_event_gravitational_pull_max(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="FILTERED",
|
|
score=0.77,
|
|
event_type="other_material_event",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-07",
|
|
event_direction="unknown",
|
|
guidance_status="not_provided",
|
|
reaction_day_return=0.02,
|
|
close_location=0.84,
|
|
gap_size=0.01,
|
|
volume_ratio_20d=1.1,
|
|
pre_event_gravitational_pull=3.4,
|
|
),
|
|
_make_raw_row(
|
|
symbol="KEPT",
|
|
score=0.76,
|
|
event_type="other_material_event",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-07",
|
|
event_direction="unknown",
|
|
guidance_status="not_provided",
|
|
reaction_day_return=0.02,
|
|
close_location=0.84,
|
|
gap_size=0.01,
|
|
volume_ratio_20d=1.1,
|
|
pre_event_gravitational_pull=2.4,
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="return_max_long_v2",
|
|
score_threshold=0.45,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="unknown_ome_pull_gate",
|
|
event_types=["other_material_event"],
|
|
timing_class="after_close",
|
|
direction="long_only",
|
|
entry_timing_policy="next_open",
|
|
event_directions=["unknown"],
|
|
guidance_statuses=["not_provided"],
|
|
pre_event_gravitational_pull_max=2.7,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["KEPT"]
|
|
|
|
def test_select_candidates_respects_engine_reaction_day_return_upper_bound(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="TOO_HOT",
|
|
score=0.90,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
reaction_day_return=0.42,
|
|
volume_ratio_20d=6.0,
|
|
trade_direction="long",
|
|
),
|
|
_make_raw_row(
|
|
symbol="OK",
|
|
score=0.80,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
reaction_day_return=0.18,
|
|
volume_ratio_20d=4.0,
|
|
trade_direction="long",
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="pead",
|
|
score_threshold=0.65,
|
|
pead_reaction_threshold=0.10,
|
|
pead_volume_threshold=2.0,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="same_day_long_capped",
|
|
event_types=["earnings_release"],
|
|
timing_class="same_day",
|
|
direction="long_only",
|
|
reaction_day_return_max=0.30,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["OK"]
|
|
|
|
def test_select_candidates_filters_by_entry_convention(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="EVENTDAY",
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
entry_convention="next_open_after_reaction_close",
|
|
reaction_day_return=0.18,
|
|
volume_ratio_20d=4.0,
|
|
trade_direction="long",
|
|
),
|
|
_make_raw_row(
|
|
symbol="CONTINUATION",
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
entry_convention="next_open_after_continuation_signal",
|
|
reaction_day_return=0.18,
|
|
volume_ratio_20d=4.0,
|
|
trade_direction="long",
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="pead",
|
|
score_threshold=0.65,
|
|
pead_reaction_threshold=0.10,
|
|
pead_volume_threshold=2.0,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="next_open_continuation_only",
|
|
event_types=["earnings_release"],
|
|
entry_conventions=["next_open_after_continuation_signal"],
|
|
timing_class="same_day",
|
|
direction="long_only",
|
|
reaction_day_return_max=0.30,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["CONTINUATION"]
|
|
|
|
def test_select_candidates_respects_engine_gap_size_bounds(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="TIGHT",
|
|
score=0.82,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
reaction_day_return=0.18,
|
|
volume_ratio_20d=4.0,
|
|
gap_size=0.04,
|
|
trade_direction="long",
|
|
),
|
|
_make_raw_row(
|
|
symbol="WIDE",
|
|
score=0.84,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
reaction_day_return=0.18,
|
|
volume_ratio_20d=4.0,
|
|
gap_size=0.16,
|
|
trade_direction="long",
|
|
),
|
|
_make_raw_row(
|
|
symbol="TOO_WIDE",
|
|
score=0.86,
|
|
event_type="earnings_release",
|
|
event_date="2026-01-06",
|
|
reaction_date="2026-01-06",
|
|
reaction_day_return=0.18,
|
|
volume_ratio_20d=4.0,
|
|
gap_size=0.34,
|
|
trade_direction="long",
|
|
),
|
|
]
|
|
universe = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0.0)
|
|
signal = SignalConfig(
|
|
scoring_model="pead",
|
|
score_threshold=0.65,
|
|
pead_reaction_threshold=0.10,
|
|
pead_volume_threshold=2.0,
|
|
max_candidates_per_day=5,
|
|
)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="same_day_long_gapped",
|
|
event_types=["earnings_release"],
|
|
timing_class="same_day",
|
|
direction="long_only",
|
|
gap_size_min=0.10,
|
|
gap_size_max=0.30,
|
|
)
|
|
|
|
selected = select_candidates(rows, universe, signal, strategy_engine=engine)
|
|
assert [candidate.symbol for candidate in selected] == ["WIDE"]
|
|
|
|
|
|
class TestRankCandidates:
|
|
def test_sorted_by_score_desc(self):
|
|
from libs.backtest.selector import build_candidate, rank_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(symbol="A", score=0.5, avg_dollar_volume=1e6),
|
|
_make_raw_row(symbol="B", score=0.8, avg_dollar_volume=1e6),
|
|
_make_raw_row(symbol="C", score=0.6, avg_dollar_volume=1e6),
|
|
]
|
|
candidates = [build_candidate(r) for r in rows]
|
|
ranked = rank_candidates([c for c in candidates if c])
|
|
assert ranked[0].symbol == "B"
|
|
assert ranked[1].symbol == "C"
|
|
assert ranked[2].symbol == "A"
|
|
|
|
def test_tiebreak_by_avg_dollar_volume(self):
|
|
from libs.backtest.selector import build_candidate, rank_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(symbol="A", score=0.7, avg_dollar_volume=1e6),
|
|
_make_raw_row(symbol="B", score=0.7, avg_dollar_volume=5e6),
|
|
]
|
|
candidates = [build_candidate(r) for r in rows]
|
|
ranked = rank_candidates([c for c in candidates if c])
|
|
assert ranked[0].symbol == "B" # higher avg_dollar_volume
|
|
|
|
def test_tiebreak_by_symbol_asc(self):
|
|
from libs.backtest.selector import build_candidate, rank_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(symbol="Z", score=0.7, avg_dollar_volume=1e6),
|
|
_make_raw_row(symbol="A", score=0.7, avg_dollar_volume=1e6),
|
|
]
|
|
candidates = [build_candidate(r) for r in rows]
|
|
ranked = rank_candidates([c for c in candidates if c])
|
|
assert ranked[0].symbol == "A"
|
|
|
|
def test_deterministic(self):
|
|
from libs.backtest.selector import build_candidate, rank_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(symbol="C", score=0.9),
|
|
_make_raw_row(symbol="A", score=0.7),
|
|
_make_raw_row(symbol="B", score=0.8),
|
|
]
|
|
candidates = [build_candidate(r) for r in rows]
|
|
r1 = rank_candidates([c for c in candidates if c])
|
|
r2 = rank_candidates([c for c in candidates if c])
|
|
assert [c.symbol for c in r1] == [c.symbol for c in r2]
|
|
|
|
def test_custom_score_band_tiebreak_can_prefer_positive_prior(self):
|
|
from libs.backtest.selector import build_candidate, rank_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="A",
|
|
score=0.621,
|
|
avg_dollar_volume=1e6,
|
|
prior_event_fwd5d=0.05,
|
|
),
|
|
_make_raw_row(
|
|
symbol="B",
|
|
score=0.629,
|
|
avg_dollar_volume=1e6,
|
|
prior_event_fwd5d=-0.04,
|
|
),
|
|
]
|
|
candidates = [build_candidate(r) for r in rows]
|
|
ranked = rank_candidates(
|
|
[c for c in candidates if c],
|
|
["-score_band_2dp", "-prior_positive_flag", "-score"],
|
|
)
|
|
assert [c.symbol for c in ranked] == ["A", "B"]
|
|
|
|
def test_custom_score_band_tiebreak_can_prefer_macro_favorable(self):
|
|
from libs.backtest.selector import build_candidate, rank_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="A",
|
|
score=0.621,
|
|
avg_dollar_volume=1e6,
|
|
macro_vix=20.0,
|
|
macro_hy_spread=3.4,
|
|
),
|
|
_make_raw_row(
|
|
symbol="B",
|
|
score=0.629,
|
|
avg_dollar_volume=1e6,
|
|
macro_vix=14.0,
|
|
macro_hy_spread=2.8,
|
|
),
|
|
]
|
|
candidates = [build_candidate(r) for r in rows]
|
|
ranked = rank_candidates(
|
|
[c for c in candidates if c],
|
|
["-score_band_2dp", "-macro_favorable_flag", "-score"],
|
|
)
|
|
assert [c.symbol for c in ranked] == ["A", "B"]
|
|
|
|
|
|
class TestFilterCandidates:
|
|
def test_score_threshold(self):
|
|
from libs.backtest.selector import build_candidate, filter_by_score
|
|
|
|
rows = [
|
|
_make_raw_row(symbol="A", score=0.3),
|
|
_make_raw_row(symbol="B", score=0.7),
|
|
_make_raw_row(symbol="C", score=0.5),
|
|
]
|
|
candidates = [build_candidate(r) for r in rows if build_candidate(r)]
|
|
filtered = filter_by_score(candidates, score_threshold=0.5)
|
|
assert len(filtered) == 2
|
|
assert all(c.score >= 0.5 for c in filtered)
|
|
|
|
def test_min_price_filter(self):
|
|
from libs.backtest.selector import build_candidate, filter_by_universe
|
|
|
|
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_engine_min_price_override_bypasses_global_floor(self):
|
|
from libs.backtest.selector import build_candidate, filter_by_universe
|
|
|
|
u = UniverseConfig(min_price=15.0, min_avg_dollar_volume=0)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="low_price_pocket",
|
|
min_entry_price_override=5.0,
|
|
)
|
|
rows = [
|
|
_make_raw_row(symbol="LOW", entry_price=9.62),
|
|
_make_raw_row(symbol="HIGH", entry_price=20.0),
|
|
]
|
|
candidates = [build_candidate(r, strategy_engine=engine) for r in rows]
|
|
filtered = filter_by_universe([c for c in candidates if c], u)
|
|
assert {c.symbol for c in filtered} == {"LOW", "HIGH"}
|
|
|
|
def test_engine_max_price_override_filters_high_names(self):
|
|
from libs.backtest.selector import build_candidate, filter_by_universe
|
|
|
|
u = UniverseConfig(min_price=5.0, min_avg_dollar_volume=0)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="low_price_only",
|
|
max_entry_price_override=15.0,
|
|
)
|
|
rows = [
|
|
_make_raw_row(symbol="LOW", entry_price=9.62),
|
|
_make_raw_row(symbol="HIGH", entry_price=20.0),
|
|
]
|
|
candidates = [build_candidate(r, strategy_engine=engine) for r in rows]
|
|
filtered = filter_by_universe([c for c in candidates if c], u)
|
|
assert {c.symbol for c in filtered} == {"LOW"}
|
|
|
|
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_sector_etf_proxy_dedupes_by_trade_symbol(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
event_id="EVT::TEST::001",
|
|
symbol="AAPL",
|
|
score=0.9,
|
|
sector="Technology",
|
|
sector_etf_proxy="XLK",
|
|
sector_etf_event_close=210.0,
|
|
sector_etf_entry_price=211.0,
|
|
sector_etf_avg_dollar_volume=250_000_000.0,
|
|
sector_etf_atr_14=4.0,
|
|
),
|
|
_make_raw_row(
|
|
event_id="EVT::TEST::002",
|
|
symbol="MSFT",
|
|
score=0.8,
|
|
sector="Technology",
|
|
sector_etf_proxy="XLK",
|
|
sector_etf_event_close=210.0,
|
|
sector_etf_entry_price=211.0,
|
|
sector_etf_avg_dollar_volume=250_000_000.0,
|
|
sector_etf_atr_14=4.0,
|
|
),
|
|
]
|
|
u = UniverseConfig(min_price=5.0, min_avg_dollar_volume=1_000_000)
|
|
s = SignalConfig(score_threshold=0.1, max_candidates_per_day=5)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="sector_etf_proxy",
|
|
event_types=["earnings"],
|
|
trade_symbol_mode="sector_etf",
|
|
)
|
|
|
|
result = select_candidates(rows, u, s, strategy_engine=engine)
|
|
assert len(result) == 1
|
|
assert result[0].symbol == "XLK"
|
|
assert result[0].source_symbol == "AAPL"
|
|
|
|
def test_sector_etf_proxy_respects_excluded_trade_symbol(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
event_id="EVT::TEST::001",
|
|
symbol="AAPL",
|
|
score=0.9,
|
|
sector="Technology",
|
|
sector_etf_proxy="XLK",
|
|
sector_etf_event_close=210.0,
|
|
sector_etf_entry_price=211.0,
|
|
sector_etf_avg_dollar_volume=250_000_000.0,
|
|
sector_etf_atr_14=4.0,
|
|
),
|
|
]
|
|
u = UniverseConfig(min_price=5.0, min_avg_dollar_volume=1_000_000)
|
|
s = SignalConfig(score_threshold=0.1, max_candidates_per_day=5)
|
|
engine = StrategyEngineConfig(
|
|
engine_id="sector_etf_proxy",
|
|
event_types=["earnings"],
|
|
trade_symbol_mode="sector_etf",
|
|
)
|
|
|
|
result = select_candidates(
|
|
rows,
|
|
u,
|
|
s,
|
|
strategy_engine=engine,
|
|
excluded_symbols={"XLK"},
|
|
)
|
|
assert result == []
|
|
|
|
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"
|
|
|
|
def test_pipeline_honors_custom_ranking_fields(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="WIDEGAP",
|
|
score=0.8,
|
|
avg_dollar_volume=5e6,
|
|
entry_price=100.0,
|
|
event_type="earnings_release",
|
|
gap_size=0.06,
|
|
close_location=0.85,
|
|
),
|
|
_make_raw_row(
|
|
symbol="TIGHTGAP",
|
|
score=0.8,
|
|
avg_dollar_volume=5e6,
|
|
entry_price=100.0,
|
|
event_type="earnings_release",
|
|
gap_size=0.01,
|
|
close_location=0.70,
|
|
),
|
|
]
|
|
u = UniverseConfig(min_price=5.0, min_avg_dollar_volume=1_000_000)
|
|
s = SignalConfig(
|
|
score_threshold=0.5,
|
|
max_candidates_per_day=10,
|
|
ranking_fields=["gap_size", "-close_location"],
|
|
)
|
|
result = select_candidates(rows, u, s)
|
|
assert [candidate.symbol for candidate in result] == ["TIGHTGAP", "WIDEGAP"]
|
|
|
|
def test_pipeline_honors_guidance_rank_flags(self):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="INLINE",
|
|
score=0.8,
|
|
avg_dollar_volume=5e6,
|
|
entry_price=100.0,
|
|
event_type="earnings_release",
|
|
guidance_status="inline_or_maintained",
|
|
close_location=0.90,
|
|
),
|
|
_make_raw_row(
|
|
symbol="RAISED",
|
|
score=0.8,
|
|
avg_dollar_volume=5e6,
|
|
entry_price=100.0,
|
|
event_type="earnings_release",
|
|
guidance_status="raised",
|
|
close_location=0.70,
|
|
),
|
|
]
|
|
u = UniverseConfig(min_price=5.0, min_avg_dollar_volume=1_000_000)
|
|
s = SignalConfig(
|
|
score_threshold=0.5,
|
|
max_candidates_per_day=10,
|
|
ranking_fields=["-guidance_raised_flag", "-close_location"],
|
|
)
|
|
result = select_candidates(rows, u, s)
|
|
assert [candidate.symbol for candidate in result] == ["RAISED", "INLINE"]
|
|
|
|
def test_pipeline_honors_learned_ranking_model(self, tmp_path):
|
|
from libs.backtest.selector import select_candidates
|
|
|
|
model_path = tmp_path / "ranker.json"
|
|
model_path.write_text(json.dumps({
|
|
"model_type": "bucket_blend_v1",
|
|
"global_mean": 0.01,
|
|
"features": [
|
|
{
|
|
"name": "direction_guidance_combo",
|
|
"weight": 1.0,
|
|
"values": {
|
|
"bullish|raised": 0.08,
|
|
"unknown|inline_or_maintained": 0.02,
|
|
},
|
|
},
|
|
],
|
|
}))
|
|
|
|
rows = [
|
|
_make_raw_row(
|
|
symbol="UNKNOWN",
|
|
score=0.95,
|
|
avg_dollar_volume=5e6,
|
|
entry_price=100.0,
|
|
event_type="earnings_release",
|
|
event_direction="unknown",
|
|
guidance_status="inline_or_maintained",
|
|
),
|
|
_make_raw_row(
|
|
symbol="RAISED",
|
|
score=0.70,
|
|
avg_dollar_volume=5e6,
|
|
entry_price=100.0,
|
|
event_type="earnings_release",
|
|
event_direction="bullish",
|
|
guidance_status="raised",
|
|
),
|
|
]
|
|
u = UniverseConfig(min_price=5.0, min_avg_dollar_volume=1_000_000)
|
|
s = SignalConfig(
|
|
score_threshold=0.5,
|
|
max_candidates_per_day=10,
|
|
ranking_model_path=str(model_path),
|
|
ranking_fields=["-ranking_model_score", "-score"],
|
|
)
|
|
result = select_candidates(rows, u, s)
|
|
assert [candidate.symbol for candidate in result] == ["RAISED", "UNKNOWN"]
|
|
assert result[0].features["ranking_model_score"] == pytest.approx(0.08)
|