"""Unit tests for the pure calendar<->EPS-history alignment. Anchored on the research script's exact constants (SKIP costs = 45, typical lag = 30, session penalty = 3, windows 90/14) so a silently changed constant fails here rather than quietly corrupting surprise-history pairing. """ from __future__ import annotations from datetime import date from app.services import earnings_alignment as ea def test_normalise_symbol(): assert ea.normalise_symbol("bf.b ") == "BF-B" assert ea.normalise_symbol(" aapl") == "AAPL" assert ea.normalise_symbol(None) == "" def test_normalise_session(): assert ea.normalise_session("Before market open") == "bmo" assert ea.normalise_session("After market close") == "amc" assert ea.normalise_session("During market hours") == "unknown" assert ea.normalise_session(None) == "unknown" assert ea.normalise_session("") == "unknown" def test_safe_number(): assert ea.safe_number("1.5") == 1.5 assert ea.safe_number("") is None assert ea.safe_number("not-a-number") is None assert ea.safe_number("nan") is None # non-finite rejected def test_constants_pinned(): assert ea.SKIP_EVENT_COST == 45.0 assert ea.SKIP_PERIOD_COST == 45.0 assert ea._TYPICAL_ANNOUNCE_LAG_DAYS == 30 assert ea._MISSING_SESSION_PENALTY == 3.0 def test_match_cost_uses_pinned_lag_and_penalty(): period = {"period_end_date": date(2026, 3, 31)} # delta == 30 (typical lag) → base cost 0; known session → no penalty e_known = {"announce_date": date(2026, 4, 30), "session": "amc"} assert ea.match_cost(e_known, period) == 0.0 # unknown session adds the penalty e_unknown = {"announce_date": date(2026, 4, 30), "session": "unknown"} assert ea.match_cost(e_unknown, period) == 3.0 # delta 45 → |45-30| == 15 e_far = {"announce_date": date(2026, 5, 15), "session": "amc"} assert ea.match_cost(e_far, period) == 15.0 def test_dedup_calendar_prefers_known_session(): rows = [ {"symbol": "AAPL", "announce_date": date(2026, 5, 1), "session": "unknown"}, {"symbol": "AAPL", "announce_date": date(2026, 5, 1), "session": "amc"}, ] grouped, stats = ea.dedup_calendar(rows) assert stats["duplicate_rows"] == 1 assert grouped["AAPL"][0]["session"] == "amc" def test_dedup_history_prefers_fuller_row(): rows = [ {"symbol": "AAPL", "period_end_date": date(2026, 3, 31), "eps_actual": 2.0, "eps_estimate": None}, {"symbol": "AAPL", "period_end_date": date(2026, 3, 31), "eps_actual": 2.0, "eps_estimate": 1.9}, ] grouped, stats = ea.dedup_history(rows) assert stats["duplicate_rows"] == 1 kept = grouped["AAPL"][0] assert kept["eps_actual"] == 2.0 and kept["eps_estimate"] == 1.9 def _events(*days): return [{"announce_date": d, "session": "amc"} for d in days] def _periods(*days): return [{"period_end_date": d, "eps_actual": 1.0, "eps_estimate": 0.9} for d in days] def test_align_matches_monotonic_pairs(): # two announcements ~30d after two quarter ends events = _events(date(2026, 4, 30), date(2026, 7, 30)) periods = _periods(date(2026, 3, 31), date(2026, 6, 30)) matches, um_events, um_periods = ea.align_symbol( events, periods, max_lag_days=90, max_lead_days=14 ) assert matches == [(0, 0), (1, 1)] assert um_events == [] and um_periods == [] def test_align_leaves_out_of_window_unmatched(): # announcement 200 days after the only period end → outside the 90d window events = _events(date(2026, 10, 17)) periods = _periods(date(2026, 3, 31)) matches, um_events, um_periods = ea.align_symbol( events, periods, max_lag_days=90, max_lead_days=14 ) assert matches == [] assert um_events == [0] and um_periods == [0]