dennisthiessen
243e369e9a
Robustness (answers 'is the edge just outliers?'):
- _bucket_stats gains median_net_r, profit_factor, and net_avg_r_ex_top5
(expectancy with the top 5% of winners removed); shown as stat tiles.
- Portfolio sim gains per-calendar-year returns, shown in the sim table.
Dynamic recommendation ('What this backtest recommends' panel):
- _build_recommendation derives advice from the report's own numbers on
every run — exit policy (target vs best hold, with sim CAGRs), which
gate floors earn their keep (ablation Hold column), best momentum
cutoff, book-vs-SPY verdict, and an outlier-dependence warning when
the trimmed expectancy goes non-positive.
Retired (conclusions reached, tables removed from report + UI):
- Take-profit sweep (no interior optimum — fixed TP is the wrong tool
for momentum), trailing sweep (converged to the hold-to-horizon exit),
probability calibration (model is display-only by decision).
- _tp_primitives slimmed to _risk_and_stop_day; trailing machinery gone.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
477 lines
18 KiB
Python
477 lines
18 KiB
Python
"""Tests for the historical backtest harness."""
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from __future__ import annotations
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import math
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from datetime import date, timedelta
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from types import SimpleNamespace
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import pytest
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from app.models.ohlcv import OHLCVRecord
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from app.models.ticker import Ticker
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from app.services import backtest_service as bt
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from app.services.outcome_service import (
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OUTCOME_EXPIRED,
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OUTCOME_STOP_HIT,
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OUTCOME_TARGET_HIT,
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)
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from tests.conftest import _test_session_factory # type: ignore
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@pytest.fixture
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async def session():
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async with _test_session_factory() as s:
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yield s
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def _cand(
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prob: float,
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outcome: str,
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rr: float,
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qualified: bool = True,
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direction: str = "long",
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risk_pct: float = 0.05,
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hold_days: int = 10,
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) -> dict:
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target_hit = outcome == OUTCOME_TARGET_HIT
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realized = rr if target_hit else (0.0 if outcome == OUTCOME_EXPIRED else -1.0)
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return {
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"primary_prob": prob,
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"outcome": outcome,
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"target_hit": target_hit,
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"rr": rr,
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"realized_r": realized,
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"qualified": qualified,
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"direction": direction,
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"risk_pct": risk_pct,
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"hold_days": hold_days,
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}
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# Round-trip cost in R for the default _cand risk_pct: 2 * 0.001 / 0.05 = 0.04R.
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_COST_R_005 = 2 * bt.COST_PER_SIDE / 0.05
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def _bar(high: float, low: float, close: float, open_: float | None = None) -> SimpleNamespace:
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"""Synthetic daily bar. ``open`` defaults to the high so a stop is pierced
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intraday (fill at the stop level); pass an explicit open beyond the stop to
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model a gap through it."""
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return SimpleNamespace(
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high=high, low=low, close=close, open=open_ if open_ is not None else high
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)
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class TestStopFillR:
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def test_intraday_fill_at_stop(self):
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assert bt._stop_fill_r("long", 100.0, 95.0, _bar(101, 94, 96)) == pytest.approx(-1.0)
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def test_gap_fill_at_open(self):
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# Opens at 92, below the 95 stop → filled at the open, worse than −1R.
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assert bt._stop_fill_r("long", 100.0, 95.0, _bar(93, 90, 91, open_=92)) == pytest.approx(-1.6)
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def test_short_gap_fill_at_open(self):
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# Short stop 105; opens at 107 above it → fill 107.
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assert bt._stop_fill_r("short", 100.0, 105.0, _bar(110, 104, 108, open_=107)) == pytest.approx(-1.4)
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class TestRiskAndStopDay:
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def test_no_stop(self):
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risk, stop_day = bt._risk_and_stop_day("long", 100.0, 95.0, [_bar(109, 101, 108)], 30)
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assert risk == pytest.approx(0.05)
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assert stop_day is None
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def test_stop_day_is_one_based(self):
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bars = [_bar(102, 99, 101), _bar(101, 94, 96)]
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risk, stop_day = bt._risk_and_stop_day("long", 100.0, 95.0, bars, 30)
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assert risk == pytest.approx(0.05)
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assert stop_day == 2
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def test_short_direction(self):
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_, stop_day = bt._risk_and_stop_day("short", 100.0, 105.0, [_bar(106, 101, 104)], 30)
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assert stop_day == 1
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class TestTimeExits:
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def test_long_exits_at_horizon_close(self):
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bars = [_bar(103, 99, 102), _bar(105, 101, 104), _bar(107, 103, 106)]
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res = bt._time_exits("long", 100.0, 95.0, bars, (2, 5))
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assert res[2] == pytest.approx(0.8) # close 104 → +4% / 5% risk
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assert res[5] == pytest.approx(1.2) # only 3 bars → last close 106
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def test_stop_on_first_bar_loses_everywhere(self):
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res = bt._time_exits("long", 100.0, 95.0, [_bar(101, 94, 96), _bar(105, 101, 104)], (1, 5))
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assert res[1] == pytest.approx(-1.0)
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assert res[5] == pytest.approx(-1.0)
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def test_stop_after_short_horizon_only_hits_long_hold(self):
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# Day-2 close banked by the 2-day hold; the stop on day 3 only hits n=5.
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bars = [_bar(103, 99, 102), _bar(104, 100, 103), _bar(101, 94, 95)]
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res = bt._time_exits("long", 100.0, 95.0, bars, (2, 5))
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assert res[2] == pytest.approx(0.6) # close 103 → +3% / 5% risk
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assert res[5] == pytest.approx(-1.0)
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def test_short_direction(self):
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res = bt._time_exits("short", 100.0, 105.0, [_bar(101, 95, 96)], (1,))
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assert res[1] == pytest.approx(0.8) # close 96 → +4% / 5% risk
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def test_zero_risk_returns_zero(self):
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res = bt._time_exits("long", 100.0, 100.0, [_bar(103, 99, 102)], (5,))
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assert res[5] == 0.0
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def test_gap_through_stop_fills_at_open(self):
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res = bt._time_exits("long", 100.0, 95.0, [_bar(93, 90, 91, open_=92)], (5,))
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assert res[5] == pytest.approx(-1.6)
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class TestTimeExitBucket:
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def test_bucket(self):
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cands = [
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{"time_r": {5: 1.4, 21: 0.8}, "risk_pct": 0.10},
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{"time_r": {5: -1.0, 21: -1.0}, "risk_pct": 0.10},
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{"time_r": {5: 0.5, 21: 0.5}, "risk_pct": 0.10},
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]
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b = bt._time_exit_bucket(cands, 5)
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assert b["hold_days"] == 5
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assert b["total"] == 3
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assert b["wins"] == 2
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assert b["win_rate"] == pytest.approx(66.7, abs=0.1)
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assert b["avg_r"] == pytest.approx(0.3, abs=0.01)
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assert b["net_avg_r"] == pytest.approx(0.28, abs=0.01)
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assert b["best_r"] == pytest.approx(1.4)
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assert b["worst_r"] == pytest.approx(-1.0)
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# No stop_day on any candidate → every hold runs the full 5 days.
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assert b["avg_hold_days"] == 5.0
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assert b["net_r_per_day"] == pytest.approx(0.28 / 5.0, abs=0.001)
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def test_missing_hold_skipped(self):
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b = bt._time_exit_bucket([{"time_r": {5: 1.0}}], 21)
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assert b["total"] == 0
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assert b["avg_r"] is None
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def _acand(
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rr: float = 2.0,
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conf: float = 60.0,
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action: str = "LONG_MODERATE",
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mp: float | None = 90.0,
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direction: str = "long",
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) -> dict:
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"""Ablation candidate: meets_core mirrors the default floors (min_rr 1.2,
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min_confidence 55, exclude_neutral on)."""
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meets = rr >= 1.2 and conf >= 55.0 and action != "NEUTRAL"
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return {
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"rr": rr,
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"confidence": conf,
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"action": action,
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"momentum_percentile": mp,
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"direction": direction,
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"meets_core": meets,
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"risk_level": "Low",
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"target_hit": True,
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"outcome": OUTCOME_TARGET_HIT,
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"realized_r": rr,
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"risk_pct": 0.05,
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"time_r": {d: 0.5 for d in bt.TIME_EXIT_DAYS},
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}
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class TestGateAblation:
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ACTIVATION = {
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"min_rr": 1.2,
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"min_confidence": 55.0,
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"exclude_neutral": True,
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"require_high_conviction": False,
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"exclude_conflicts": False,
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}
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def test_variant_counts(self):
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cands = [
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_acand(), # clears everything
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_acand(conf=40.0), # fails confidence floor
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_acand(rr=1.0), # fails R:R floor
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_acand(action="NEUTRAL"), # fails NEUTRAL exclusion
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_acand(mp=50.0), # fails the momentum cutoff
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_acand(direction="short", mp=95.0), # short — gated out
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]
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rows = {r["variant"]: r for r in bt._gate_ablation(cands, self.ACTIVATION, 80.0)}
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assert rows["all_floors"]["total"] == 1
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assert rows["no_confidence_floor"]["total"] == 2
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assert rows["no_rr_floor"]["total"] == 2
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assert rows["no_neutral_exclusion"]["total"] == 2
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assert rows["momentum_only"]["total"] == 4
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assert rows["all_floors"]["net_avg_r"] is not None
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# Every variant is also graded under the hold-to-horizon exit.
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assert rows["all_floors"]["hold_days"] == max(bt.TIME_EXIT_DAYS)
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assert rows["all_floors"]["hold_avg_r"] == pytest.approx(0.5)
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assert rows["all_floors"]["hold_net_avg_r"] is not None
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assert rows["momentum_only"]["hold_total_r"] == pytest.approx(4 * 0.5, abs=0.01)
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def test_threshold_zero_disables_momentum_gate(self):
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# Floors only: the short and the low-momentum long both pass all_floors.
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cands = [_acand(mp=50.0), _acand(direction="short", mp=None)]
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rows = {r["variant"]: r for r in bt._gate_ablation(cands, self.ACTIVATION, 0.0)}
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assert rows["all_floors"]["total"] == 2
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def _sim_prices(start_ord: int, closes: list[float]) -> tuple:
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"""Column arrays for consecutive daily bars: open = close (no gaps),
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high/low = close ± 1."""
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ords = list(range(start_ord, start_ord + len(closes)))
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return (
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ords,
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list(closes),
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[c + 1.0 for c in closes],
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[c - 1.0 for c in closes],
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list(closes),
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[1_000_000] * len(closes),
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)
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def _sim_cand(
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sym: str, day_ord: int, entry: float, stop: float, target: float, mp: float = 90.0
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) -> dict:
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return {
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"qualified": True,
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"direction": "long",
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"symbol": sym,
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"date": date.fromordinal(day_ord).isoformat(),
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"entry": entry,
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"stop": stop,
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"target": target,
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"momentum_percentile": mp,
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}
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class TestSimulatePortfolio:
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ORD = date(2025, 1, 6).toordinal()
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def test_hold_policy_accounting(self):
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closes = [100.0, 102.0, 104.0, 106.0, 108.0, 110.0]
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prices = {"AAA": _sim_prices(self.ORD, closes)}
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cand = _sim_cand("AAA", self.ORD, entry=100.0, stop=95.0, target=130.0)
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sim = bt._simulate_portfolio([cand], prices, None, "hold", 3)
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assert sim is not None
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assert sim["trades"] == 1
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# 20 shares (1% risk / $5 stop distance), exit at the day-3 close 106:
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# pnl = 2120 − 2000 − 2.00 entry cost − 2.12 exit cost = 115.88
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assert sim["final_equity"] == pytest.approx(10_115.88, abs=0.01)
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assert sim["win_rate"] == 100.0
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assert sim["best_trade_r"] == pytest.approx(1.2)
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assert sim["avg_hold_days"] == 3.0
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assert sim["max_drawdown_pct"] == 0.0
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assert sim["cagr_pct"] is None # window far too short to annualize
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assert sim["spy_return_pct"] is None
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assert sim["yearly_returns"] == [
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{"year": 2025, "return_pct": pytest.approx(1.2, abs=0.05)}
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]
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def test_target_policy_exits_at_target(self):
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closes = [100.0, 102.0, 104.0, 106.0, 108.0, 110.0]
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prices = {"AAA": _sim_prices(self.ORD, closes)}
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cand = _sim_cand("AAA", self.ORD, entry=100.0, stop=95.0, target=105.0)
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sim = bt._simulate_portfolio([cand], prices, None, "target", 30)
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assert sim is not None
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assert sim["trades"] == 1
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assert sim["best_trade_r"] == pytest.approx(1.0) # filled exactly at 105
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def test_stop_gap_fills_at_open(self):
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# Day-1 bar gaps to a 90 open, below the 95 stop → fill at the open.
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ords = list(range(self.ORD, self.ORD + 2))
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prices = {"AAA": (ords, [100.0, 90.0], [101.0, 92.0], [99.0, 88.0], [100.0, 91.0], [1, 1])}
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cand = _sim_cand("AAA", self.ORD, entry=100.0, stop=95.0, target=120.0)
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sim = bt._simulate_portfolio([cand], prices, None, "hold", 30)
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assert sim is not None
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assert sim["trades"] == 1
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assert sim["worst_trade_r"] == pytest.approx(-2.0) # (90 − 100) / 5
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def test_nothing_qualified_returns_none(self):
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assert bt._simulate_portfolio([], {}, None, "hold", 30) is None
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def test_bucket_stats_counts_and_expectancy():
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cands = [
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_cand(70, OUTCOME_TARGET_HIT, 3.0), # +3R win
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_cand(60, OUTCOME_TARGET_HIT, 2.0), # +2R win
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_cand(40, OUTCOME_STOP_HIT, 3.0), # -1R loss
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_cand(30, OUTCOME_EXPIRED, 3.0), # 0R expired
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]
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s = bt._bucket_stats(cands)
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assert s["total"] == 4
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assert s["wins"] == 2
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assert s["losses"] == 1
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assert s["expired"] == 1
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# hit rate is over decided (wins+losses) only
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assert s["hit_rate"] == round(2 / 3 * 100, 1)
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# avg R = (3 + 2 - 1 + 0) / 4 = 1.0
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assert s["avg_r"] == 1.0
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assert s["total_r"] == 4.0
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# net = gross minus a 0.04R round trip per candidate (risk_pct 0.05)
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assert s["net_avg_r"] == pytest.approx(1.0 - _COST_R_005, abs=0.001)
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assert s["net_total_r"] == pytest.approx(4.0 - 4 * _COST_R_005, abs=0.01)
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assert s["best_r"] == 3.0
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assert s["worst_r"] == -1.0
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assert s["avg_hold_days"] == 10.0
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assert s["net_r_per_day"] == pytest.approx((1.0 - _COST_R_005) / 10.0, abs=0.001)
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# robustness: net rs are [2.96, 1.96, -1.04, -0.04]
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assert s["median_net_r"] == pytest.approx(0.96, abs=0.001)
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assert s["profit_factor"] == pytest.approx(4.92 / 1.08, abs=0.01)
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# ex-top-5%: ceil(4 * 0.05) = 1 winner trimmed → mean of the remaining three
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assert s["net_avg_r_ex_top5"] == pytest.approx((1.96 - 1.04 - 0.04) / 3, abs=0.001)
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def test_bucket_stats_empty():
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s = bt._bucket_stats([])
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assert s["total"] == 0
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assert s["hit_rate"] is None
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assert s["avg_r"] is None
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assert s["net_avg_r"] is None
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def test_bucket_stats_no_risk_pct_means_no_cost():
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c = _cand(50, OUTCOME_TARGET_HIT, 2.0)
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del c["risk_pct"]
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s = bt._bucket_stats([c])
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assert s["net_avg_r"] == s["avg_r"]
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assert s["net_total_r"] == s["total_r"]
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def test_build_recommendation_reads_the_report():
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report = {
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"overall_qualified": {"net_avg_r": 0.13, "net_avg_r_ex_top5": 0.05},
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"time_exit_sweep": [
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{"hold_days": 21, "net_avg_r": 0.38},
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{"hold_days": 30, "net_avg_r": 0.50},
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],
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"gate_ablation": [
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{"variant": "all_floors", "total": 100, "hold_net_avg_r": 0.50},
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{"variant": "no_confidence_floor", "total": 130, "hold_net_avg_r": 0.49},
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{"variant": "no_rr_floor", "total": 400, "hold_net_avg_r": 0.34},
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{"variant": "no_neutral_exclusion", "total": 120, "hold_net_avg_r": 0.46},
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],
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"sweep": [
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{"min_momentum_percentile": 80.0, "net_avg_r": 0.13, "total": 100},
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{"min_momentum_percentile": 60.0, "net_avg_r": 0.05, "total": 300},
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{"min_momentum_percentile": 0.0, "net_avg_r": -0.12, "total": 1000},
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],
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"portfolio_sim": {"policies": [
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{"policy": "target", "cagr_pct": 23.7, "total_return_pct": 134.8,
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"spy_return_pct": 95.9, "max_drawdown_pct": 20.7},
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{"policy": "hold", "cagr_pct": 31.9, "total_return_pct": 203.6,
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"spy_return_pct": 95.9, "max_drawdown_pct": 21.2},
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]},
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}
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rec = bt._build_recommendation(report)
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by_topic: dict[str, list[str]] = {}
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for item in rec["items"]:
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by_topic.setdefault(item["topic"], []).append(item["text"])
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assert rec["headline"] is not None and "hold 30" in rec["headline"]
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assert any("hold 30 trading days" in t for t in by_topic["exit"])
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gate_texts = " | ".join(by_topic["gate"])
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assert "confidence floor adds nothing" in gate_texts
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assert "keep the R:R floor" in gate_texts
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assert "keep the NEUTRAL exclusion" in gate_texts
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assert "80" in by_topic["cutoff"][0]
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assert "beats" in by_topic["benchmark"][0]
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assert any("not a handful of outliers" in t for t in by_topic["robustness"])
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def test_build_recommendation_flags_outlier_dependence():
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rec = bt._build_recommendation({
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"overall_qualified": {"net_avg_r": 0.13, "net_avg_r_ex_top5": -0.02},
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})
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robustness = [i["text"] for i in rec["items"] if i["topic"] == "robustness"]
|
||
assert robustness and "WARNING" in robustness[0]
|
||
|
||
|
||
def test_window_setups_too_short_returns_empty():
|
||
assert bt._window_setups([], {}, {}) == []
|
||
|
||
|
||
def test_replay_ticker_candidates_carry_gate_fields():
|
||
"""The ablation recomputes floors from candidate fields — a candidate missing
|
||
action/risk_level silently zeroes the ablation rows (July 2026 regression)."""
|
||
from app.services.admin_service import ACTIVATION_DEFAULTS
|
||
from app.services.recommendation_service import DEFAULT_RECOMMENDATION_CONFIG
|
||
|
||
base = date(2025, 1, 1)
|
||
bars = []
|
||
for i in range(160):
|
||
close = 100.0 + 8.0 * math.sin(i / 6.0)
|
||
bars.append(SimpleNamespace(
|
||
date=base + timedelta(days=i),
|
||
open=close,
|
||
high=close + 1.5,
|
||
low=close - 1.5,
|
||
close=close,
|
||
volume=1_000_000 + (i % 5) * 1000,
|
||
))
|
||
cands = bt._replay_ticker(
|
||
"OSC", bars, dict(DEFAULT_RECOMMENDATION_CONFIG), dict(ACTIVATION_DEFAULTS)
|
||
)
|
||
assert cands, "expected the oscillating series to produce candidates"
|
||
for c in cands:
|
||
assert c.get("action") is not None
|
||
assert "risk_level" in c
|
||
|
||
|
||
async def _seed_oscillating_ticker(session, symbol: str, n: int = 160) -> None:
|
||
t = Ticker(symbol=symbol)
|
||
session.add(t)
|
||
await session.flush()
|
||
base = date(2025, 1, 1)
|
||
for i in range(n):
|
||
close = 100.0 + 8.0 * math.sin(i / 6.0)
|
||
session.add(OHLCVRecord(
|
||
ticker_id=t.id,
|
||
date=base + timedelta(days=i),
|
||
open=close,
|
||
high=close + 1.5,
|
||
low=close - 1.5,
|
||
close=close,
|
||
volume=1_000_000 + (i % 5) * 1000,
|
||
))
|
||
await session.commit()
|
||
|
||
|
||
async def test_run_backtest_smoke(session):
|
||
await _seed_oscillating_ticker(session, "OSC")
|
||
report = await bt.run_backtest(session)
|
||
|
||
# well-formed report
|
||
assert report["tickers"] == 1
|
||
assert isinstance(report["candidates"], int)
|
||
for key in (
|
||
"overall_qualified", "overall_all", "by_direction", "sweep",
|
||
"gate_ablation", "time_exit_sweep", "portfolio_sim", "recommendation",
|
||
):
|
||
assert key in report
|
||
# the oscillating series should yield at least some resolved setups
|
||
assert report["candidates"] >= 1
|
||
|
||
# cost assumption is reported, and every bucket carries net numbers
|
||
assert report["params"]["cost_per_side_pct"] == pytest.approx(bt.COST_PER_SIDE * 100)
|
||
assert "net_avg_r" in report["overall_all"]
|
||
|
||
# ablation baseline reproduces the qualified set exactly, and every row
|
||
# carries the hold-to-horizon grading alongside the target model
|
||
ablation = {r["variant"]: r for r in report["gate_ablation"]}
|
||
assert ablation["all_floors"]["total"] == report["overall_qualified"]["total"]
|
||
for row in report["gate_ablation"]:
|
||
assert "hold_net_avg_r" in row
|
||
|
||
# time-exit sweep covers the configured hold lengths
|
||
assert [r["hold_days"] for r in report["time_exit_sweep"]] == list(bt.TIME_EXIT_DAYS)
|
||
|
||
# portfolio simulation section is always present (policies may be empty
|
||
# when nothing qualifies)
|
||
assert "portfolio_sim" in report
|
||
assert isinstance(report["portfolio_sim"]["policies"], list)
|
||
assert report["portfolio_sim"]["params"]["max_positions"] == bt.SIM_MAX_POSITIONS
|
||
|
||
# sweep: lowering the momentum-percentile cutoff can only add qualifiers
|
||
sweep = sorted(report["sweep"], key=lambda r: r["min_momentum_percentile"], reverse=True)
|
||
counts = [r["total"] for r in sweep]
|
||
assert counts == sorted(counts) # ascending as threshold descends
|