dennisthiessen
402025692a
The per-setup hit-rate report can't tell whether a signal predicts returns — only how a target/stop structure built on one performs. This adds a cross-sectional factor-IC pass: each week the universe is ranked by a price-only signal and graded by its rank correlation (Spearman IC) and top-minus-bottom- quintile spread against the forward 30-day return. Candidate signals (point-in-time from price; sentiment/fundamentals have no history in the replay): 12-1/6-1/3-1 month momentum, 1-month reversal, price-vs-200d SMA, proximity to the 52-week high (George/Hwang), and 126-day realized volatility (low-vol anomaly). Reuses the existing per-ticker replay loop (no new data, no second DB pass); results land in the cached backtest_report as `signal_eval` and render as a "Signal edge" table in BacktestPanel beside the calibration curve. 330 backend tests pass (10 new in test_signal_eval); frontend build clean. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
555 lines
21 KiB
Python
555 lines
21 KiB
Python
"""Historical backtest (Phase 1): replay the price-derived engine over stored
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OHLCV and measure how the CURRENT config would have performed.
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For each ticker we step through history (weekly), and at each as-of date D we
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rebuild the setup using only bars ≤ D (no lookahead), then walk the actual bars
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after D to record the realized outcome. Two reports come out:
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- realized hit-rate / expectancy of qualified setups (and of all setups)
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- a probability calibration curve: do "60% likely" targets hit ~60% of the time?
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Limitation: sentiment and fundamentals have no point-in-time history, so they're
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held neutral here — this calibrates the price/S-R/probability machinery only.
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"""
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from __future__ import annotations
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import json
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import logging
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import math
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from collections import defaultdict
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from collections.abc import Callable
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from datetime import datetime, timezone
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from types import SimpleNamespace
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from typing import Any
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.settings import SystemSetting
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from app.models.ticker import Ticker
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from app.services.admin_service import get_activation_config, update_setting
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from app.services.indicator_service import _extract_ohlcv, compute_atr
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from app.services.outcome_service import (
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OUTCOME_AMBIGUOUS,
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OUTCOME_STOP_HIT,
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OUTCOME_TARGET_HIT,
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Bar,
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evaluate_setup_against_bars,
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)
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from app.services.price_service import query_ohlcv
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from app.services.qualification import (
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best_target_probability,
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expected_value_r,
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setup_qualifies,
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)
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from app.services.recommendation_service import (
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_choose_recommended_action,
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_classify_by_probability,
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_risk_level_from_conflicts,
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_select_primary_target,
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_zone_representative_levels,
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direction_analyzer,
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get_recommendation_config,
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probability_estimator,
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signal_conflict_detector,
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target_generator,
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)
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from app.services.scoring_service import (
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compute_momentum_from_closes,
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compute_technical_from_arrays,
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)
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from app.services.sr_service import detect_sr_levels
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logger = logging.getLogger(__name__)
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KEY_REPORT = "backtest_report"
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STEP_DAYS = 5 # weekly cadence (≈ 5 trading days)
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MIN_LOOKBACK = 60 # bars needed before D for indicators (EMA cross needs 51)
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HORIZON = 30 # trading days to resolve an outcome (matches the evaluator)
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ATR_MULTIPLIER = 1.5
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_CAL_BUCKETS = [(0, 20), (20, 40), (40, 60), (60, 80), (80, 100.01)]
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# Cross-sectional signal evaluation (factor IC). Each candidate signal is a
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# point-in-time number computed from closes alone (sentiment/fundamentals have no
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# history here), sampled one as-of per ISO week, and graded by how its rank
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# correlates with the forward HORIZON-day return ACROSS the universe — i.e. does
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# ranking stocks by this signal sort tomorrow's winners from losers. This is the
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# test the per-setup hit-rate report can't do: it measures predictive power of a
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# signal, not the outcome of a target/stop structure built on top of one.
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MIN_CROSS_SECTION = 20 # min tickers present in a week to score that week
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def _wrap_levels(level_dicts: list[dict]) -> list[Any]:
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return [
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SimpleNamespace(
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id=i,
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price_level=float(d["price_level"]),
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type=d["type"],
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strength=int(d["strength"]),
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)
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for i, d in enumerate(level_dicts)
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]
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def _window_setups(
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window_records: list,
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config: dict,
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activation: dict,
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) -> list[dict]:
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"""Rebuild the setup(s) at the last bar of ``window_records`` (the as-of date),
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using only those bars. Returns one dict per tradeable direction."""
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if len(window_records) < MIN_LOOKBACK:
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return []
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_, highs, lows, closes, volumes = _extract_ohlcv(window_records)
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entry = closes[-1]
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if entry <= 0:
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return []
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try:
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atr = compute_atr(highs, lows, closes)["atr"]
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except Exception:
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return []
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if atr <= 0:
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return []
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sr_levels = _wrap_levels(detect_sr_levels(highs, lows, closes, volumes))
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if not sr_levels:
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return []
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technical = (compute_technical_from_arrays(highs, lows, closes, volumes)[0]) or 50.0
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momentum = (compute_momentum_from_closes(closes)[0]) or 50.0
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dim_scores = {"technical": technical, "momentum": momentum}
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conflicts = signal_conflict_detector.detect_conflicts(dim_scores, None, config)
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confidences = {
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"long": direction_analyzer.calculate_confidence("long", dim_scores, None, conflicts),
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"short": direction_analyzer.calculate_confidence("short", dim_scores, None, conflicts),
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}
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# First pass: build targets per direction
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per_dir: dict[str, dict] = {}
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for direction in ("long", "short"):
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stop = entry - atr * ATR_MULTIPLIER if direction == "long" else entry + atr * ATR_MULTIPLIER
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zone_levels = _zone_representative_levels(sr_levels, entry)
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targets = target_generator.generate_targets(direction, entry, stop, zone_levels, atr)
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if not targets:
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continue
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for t in targets:
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t["probability"] = probability_estimator.estimate_probability(
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t, dim_scores, None, direction, config
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)
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t["classification"] = _classify_by_probability(t["probability"])
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primary = _select_primary_target(targets)
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if primary is None:
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continue
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# Flag the primary so qualification's EV uses the primary target's
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# probability (matching production's enhance_trade_setup).
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for t in targets:
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t["is_primary"] = t is primary
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per_dir[direction] = {"stop": stop, "targets": targets, "primary": primary}
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available = set(per_dir.keys())
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if not available:
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return []
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action = _choose_recommended_action(confidences["long"], confidences["short"], config, available)
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out: list[dict] = []
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for direction, data in per_dir.items():
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targets, primary, stop = data["targets"], data["primary"], data["stop"]
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setup_conflicts = list(conflicts)
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if len(targets) < 3:
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setup_conflicts.append("target-availability: Fewer than 3 valid S/R targets available")
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risk_level = _risk_level_from_conflicts(setup_conflicts)
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rr = float(primary["rr_ratio"])
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target_price = float(primary["price"])
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setup_ns = SimpleNamespace(
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rr_ratio=rr,
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confidence_score=confidences[direction],
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recommended_action=action,
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risk_level=risk_level,
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targets=targets,
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direction=direction,
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target=target_price,
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stop_loss=stop,
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entry_price=entry,
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)
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# meets_core = clears every gate EXCEPT the expected-value floor, so the
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# report can sweep the min_expected_value threshold without re-replaying.
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core_config = {**activation, "min_expected_value": float("-inf")}
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meets_core = setup_qualifies(setup_ns, core_config)
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ev = expected_value_r(setup_ns)
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best_prob = best_target_probability(setup_ns)
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min_ev = float(activation.get("min_expected_value", 0.0))
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out.append({
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"direction": direction,
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"entry": entry,
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"stop": stop,
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"target": target_price,
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"rr": rr,
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"confidence": confidences[direction],
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"primary_prob": float(primary["probability"]),
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"best_prob": best_prob,
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"ev": ev,
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"meets_core": meets_core,
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"action": action,
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"risk_level": risk_level,
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"qualified": meets_core and ev is not None and ev >= min_ev,
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})
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return out
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def _replay_ticker(symbol: str, records: list, config: dict, activation: dict) -> list[dict]:
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"""Walk one ticker's history weekly, building setups and their realized outcomes."""
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candidates: list[dict] = []
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n = len(records)
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if n < MIN_LOOKBACK + HORIZON:
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return candidates
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for i in range(MIN_LOOKBACK - 1, n - HORIZON, STEP_DAYS):
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window = records[: i + 1]
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forward = records[i + 1 :]
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forward_bars = [Bar(date=r.date, high=r.high, low=r.low) for r in forward]
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for s in _window_setups(window, config, activation):
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outcome, _ = evaluate_setup_against_bars(
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s["direction"], s["stop"], s["target"], forward_bars, HORIZON
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)
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if outcome is None:
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continue
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target_hit = outcome == OUTCOME_TARGET_HIT
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if outcome == OUTCOME_TARGET_HIT:
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realized_r = s["rr"]
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elif outcome in (OUTCOME_STOP_HIT, OUTCOME_AMBIGUOUS):
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realized_r = -1.0
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else: # expired
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realized_r = 0.0
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candidates.append({
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"symbol": symbol,
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"date": records[i].date.isoformat(),
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"direction": s["direction"],
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"rr": s["rr"],
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"confidence": s["confidence"],
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"primary_prob": s["primary_prob"],
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"best_prob": s["best_prob"],
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"ev": s["ev"],
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"meets_core": s["meets_core"],
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"qualified": s["qualified"],
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"outcome": outcome,
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"target_hit": target_hit,
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"realized_r": realized_r,
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})
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return candidates
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def _bucket_stats(cands: list[dict]) -> dict:
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wins = sum(1 for c in cands if c["target_hit"])
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losses = sum(1 for c in cands if c["outcome"] in (OUTCOME_STOP_HIT, OUTCOME_AMBIGUOUS))
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expired = sum(1 for c in cands if c["outcome"] not in (OUTCOME_TARGET_HIT, OUTCOME_STOP_HIT, OUTCOME_AMBIGUOUS))
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decided = wins + losses
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rs = [c["realized_r"] for c in cands]
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return {
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"total": len(cands),
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"wins": wins,
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"losses": losses,
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"expired": expired,
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"hit_rate": round(wins / decided * 100, 1) if decided else None,
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"avg_r": round(sum(rs) / len(rs), 3) if rs else None,
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"total_r": round(sum(rs), 2) if rs else None,
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}
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def _calibration(cands: list[dict]) -> list[dict]:
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"""Predicted target probability vs realized hit rate, per probability bucket."""
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rows: list[dict] = []
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for lo, hi in _CAL_BUCKETS:
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bucket = [c for c in cands if lo <= c["primary_prob"] < hi]
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if not bucket:
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continue
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hits = sum(1 for c in bucket if c["target_hit"])
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rows.append({
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"bucket": f"{int(lo)}-{int(min(hi, 100))}%",
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"n": len(bucket),
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"predicted_avg": round(sum(c["primary_prob"] for c in bucket) / len(bucket), 1),
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"realized_hit_rate": round(hits / len(bucket) * 100, 1),
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})
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return rows
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# ---------------------------------------------------------------------------
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# Cross-sectional signal evaluation (factor information-coefficient)
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# ---------------------------------------------------------------------------
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def _weekly_asof_indices(records: list) -> list[int]:
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"""Index of the last bar in each ISO week — the weekly rebalance as-of bars.
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Keying on the calendar week (not the raw bar index) makes every ticker's
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as-of dates line up, so the cross-section on a given week is comparable.
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"""
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last_by_week: dict[tuple[int, int], int] = {}
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for idx, r in enumerate(records):
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iso = r.date.isocalendar()
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last_by_week[(iso[0], iso[1])] = idx
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return sorted(last_by_week.values())
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def _signal_values(closes: list[float], highs: list[float], i: int) -> dict[str, float]:
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"""Point-in-time candidate signals at as-of index ``i`` (price-only).
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Momentum factors follow the standard "skip the last month" convention
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(return up to ~1 month ago) to avoid the short-term reversal effect, which
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``reversal_1m`` isolates on purpose — we expect its IC to be negative if the
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universe mean-reverts. ``trend_200`` is price vs its 200-bar SMA. ``high_52w``
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is closeness to the trailing 52-week high (George/Hwang anchoring effect:
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higher = nearer the high, expect positive IC). ``vol_6m`` is 126-day realized
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volatility (expect negative IC if the low-volatility anomaly holds).
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"""
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out: dict[str, float] = {}
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if i - 252 >= 0 and closes[i - 252] > 0:
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out["mom_12_1"] = closes[i - 21] / closes[i - 252] - 1.0
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if i - 126 >= 0 and closes[i - 126] > 0:
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out["mom_6_1"] = closes[i - 21] / closes[i - 126] - 1.0
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if i - 63 >= 0 and closes[i - 63] > 0:
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out["mom_3_1"] = closes[i - 21] / closes[i - 63] - 1.0
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if i - 21 >= 0 and closes[i - 21] > 0:
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out["reversal_1m"] = closes[i] / closes[i - 21] - 1.0
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if i - 199 >= 0:
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sma = sum(closes[i - 199 : i + 1]) / 200.0
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if sma > 0:
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out["trend_200"] = closes[i] / sma - 1.0
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if i - 251 >= 0:
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high_52w = max(highs[i - 251 : i + 1])
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if high_52w > 0:
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out["high_52w"] = closes[i] / high_52w
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if i - 126 >= 0:
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rets = [
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closes[k] / closes[k - 1] - 1.0
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for k in range(i - 125, i + 1)
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if closes[k - 1] > 0
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]
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if len(rets) >= 2:
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mean = sum(rets) / len(rets)
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var = sum((x - mean) ** 2 for x in rets) / (len(rets) - 1)
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out["vol_6m"] = math.sqrt(var)
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return out
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def _accumulate_signal_series(records: list, collected: dict) -> None:
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"""For each weekly as-of bar, emit (signal, forward-return) pairs keyed by ISO
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week into ``collected[name][week_key]``. Forward return is close-to-close over
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HORIZON trading days. Mutates ``collected`` (a dict of dict of list)."""
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n = len(records)
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if n < HORIZON + 21:
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return
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closes = [float(r.close) for r in records]
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highs = [float(r.high) for r in records]
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for i in _weekly_asof_indices(records):
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j = i + HORIZON
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if j >= n or closes[i] <= 0:
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continue
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fwd = closes[j] / closes[i] - 1.0
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iso = records[i].date.isocalendar()
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week_key = (iso[0], iso[1])
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for name, val in _signal_values(closes, highs, i).items():
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collected[name][week_key].append((val, fwd))
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def _rank(xs: list[float]) -> list[float]:
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"""Average (tie-corrected) ranks, 1-based."""
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order = sorted(range(len(xs)), key=lambda k: xs[k])
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ranks = [0.0] * len(xs)
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i = 0
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while i < len(xs):
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j = i
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while j + 1 < len(xs) and xs[order[j + 1]] == xs[order[i]]:
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j += 1
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avg_rank = (i + j) / 2.0 + 1.0
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for k in range(i, j + 1):
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ranks[order[k]] = avg_rank
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i = j + 1
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return ranks
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def _pearson(a: list[float], b: list[float]) -> float | None:
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n = len(a)
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if n < 3:
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return None
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ma, mb = sum(a) / n, sum(b) / n
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va = sum((x - ma) ** 2 for x in a)
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vb = sum((y - mb) ** 2 for y in b)
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if va <= 0 or vb <= 0:
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return None
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cov = sum((a[k] - ma) * (b[k] - mb) for k in range(n))
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return cov / math.sqrt(va * vb)
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def _spearman(xs: list[float], ys: list[float]) -> float | None:
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"""Rank correlation = Pearson on the ranks. None if too few/degenerate."""
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if len(xs) < 3:
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return None
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return _pearson(_rank(xs), _rank(ys))
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def _quintile_spread(pairs: list[tuple[float, float]]) -> float | None:
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"""Mean forward return of the top signal-quintile minus the bottom quintile."""
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n = len(pairs)
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if n < 10:
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return None
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ordered = sorted(pairs, key=lambda p: p[0])
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k = n // 5
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top = ordered[-k:]
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bottom = ordered[:k]
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return sum(p[1] for p in top) / k - sum(p[1] for p in bottom) / k
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def _signal_evaluation(collected: dict) -> list[dict]:
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"""Per-signal factor diagnostics, one row per candidate signal:
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mean_ic average weekly rank-IC (Spearman of signal vs fwd ret)
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ic_t_stat mean_ic / stderr — is the IC reliably non-zero?
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ic_positive_pct share of weeks the IC is positive (consistency)
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mean_quintile_spread avg top-minus-bottom-quintile forward return
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A signal with no edge lands near IC 0 and spread 0. Caveat: weekly rebalances
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with a HORIZON-day forward window overlap, so the t-stat overstates
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significance — read it as directional, alongside ic_positive_pct.
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"""
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rows: list[dict] = []
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for name in sorted(collected):
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ics: list[float] = []
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spreads: list[float] = []
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sizes: list[int] = []
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for recs in collected[name].values():
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if len(recs) < MIN_CROSS_SECTION:
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continue
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ic = _spearman([r[0] for r in recs], [r[1] for r in recs])
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if ic is not None:
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ics.append(ic)
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spread = _quintile_spread(recs)
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if spread is not None:
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spreads.append(spread)
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sizes.append(len(recs))
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if not ics:
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continue
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mean_ic = sum(ics) / len(ics)
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if len(ics) > 1:
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std = math.sqrt(sum((x - mean_ic) ** 2 for x in ics) / (len(ics) - 1))
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|
else:
|
|
std = 0.0
|
|
t_stat = mean_ic / std * math.sqrt(len(ics)) if std > 0 else None
|
|
rows.append({
|
|
"signal": name,
|
|
"weeks": len(ics),
|
|
"avg_cross_section": round(sum(sizes) / len(sizes), 1) if sizes else None,
|
|
"mean_ic": round(mean_ic, 4),
|
|
"ic_t_stat": round(t_stat, 2) if t_stat is not None else None,
|
|
"ic_positive_pct": round(sum(1 for x in ics if x > 0) / len(ics) * 100, 1),
|
|
"mean_quintile_spread": round(sum(spreads) / len(spreads), 4) if spreads else None,
|
|
})
|
|
rows.sort(key=lambda r: r["mean_ic"], reverse=True)
|
|
return rows
|
|
|
|
|
|
async def run_backtest(
|
|
db: AsyncSession,
|
|
progress_cb: Callable[[int, int, str], None] | None = None,
|
|
) -> dict:
|
|
"""Replay every ticker and aggregate the Phase-1 reports for the current config."""
|
|
config = await get_recommendation_config(db)
|
|
activation = await get_activation_config(db)
|
|
|
|
result = await db.execute(select(Ticker).order_by(Ticker.symbol))
|
|
tickers = list(result.scalars().all())
|
|
total = len(tickers)
|
|
|
|
candidates: list[dict] = []
|
|
# collected[signal_name][iso_week] -> list of (signal_value, forward_return)
|
|
collected: dict = defaultdict(lambda: defaultdict(list))
|
|
for index, ticker in enumerate(tickers):
|
|
if progress_cb is not None:
|
|
progress_cb(index, total, ticker.symbol)
|
|
try:
|
|
records = await query_ohlcv(db, ticker.symbol)
|
|
candidates.extend(_replay_ticker(ticker.symbol, records, config, activation))
|
|
_accumulate_signal_series(records, collected)
|
|
except Exception:
|
|
logger.exception("Backtest replay failed for %s", ticker.symbol)
|
|
|
|
if progress_cb is not None and total:
|
|
progress_cb(total, total, "")
|
|
|
|
qualified = [c for c in candidates if c["qualified"]]
|
|
longs = [c for c in qualified if c["direction"] == "long"]
|
|
shorts = [c for c in qualified if c["direction"] == "short"]
|
|
|
|
# Threshold sweep: re-apply the gate at several min_expected_value values
|
|
# (holding the other conditions fixed) so the trade-off between how many
|
|
# setups qualify and their expectancy is visible without re-replaying.
|
|
current_min_ev = float(activation.get("min_expected_value", 0.15))
|
|
sweep = []
|
|
for threshold in (0.4, 0.3, 0.25, 0.2, 0.15, 0.1, 0.05, 0.0):
|
|
cands = [
|
|
c for c in candidates
|
|
if c["meets_core"] and c["ev"] is not None and c["ev"] >= threshold
|
|
]
|
|
sweep.append({"min_expected_value": threshold, **_bucket_stats(cands)})
|
|
|
|
return {
|
|
"generated_at": datetime.now(timezone.utc).isoformat(),
|
|
"tickers": total,
|
|
"candidates": len(candidates),
|
|
"qualified": len(qualified),
|
|
"params": {"step_days": STEP_DAYS, "horizon_days": HORIZON, "min_lookback": MIN_LOOKBACK},
|
|
"activation": activation,
|
|
"overall_qualified": _bucket_stats(qualified),
|
|
"overall_all": _bucket_stats(candidates),
|
|
"by_direction": {
|
|
"long": _bucket_stats(longs),
|
|
"short": _bucket_stats(shorts),
|
|
},
|
|
"min_expected_value": current_min_ev,
|
|
"sweep": sweep,
|
|
"calibration": _calibration(candidates),
|
|
"signal_eval": _signal_evaluation(collected),
|
|
"signal_eval_note": (
|
|
"Cross-sectional rank-IC of price-only signals vs the forward "
|
|
f"{HORIZON}-day return (weekly rebalance, min {MIN_CROSS_SECTION} "
|
|
"names/week). |IC| ≳ 0.03 with a consistent sign is a real (if small) "
|
|
"edge; near 0 means ranking on it sorts nothing. Momentum factors and "
|
|
"high_52w are expected positive; reversal_1m and vol_6m are expected "
|
|
"negative (mean-reversion / low-vol anomaly). Overlapping windows inflate "
|
|
"the t-stat — read directionally."
|
|
),
|
|
"note": (
|
|
"Sentiment & fundamentals held neutral (no point-in-time history). "
|
|
"~6 months ≈ one market regime — treat as directional, not gospel."
|
|
),
|
|
}
|
|
|
|
|
|
async def run_and_store(
|
|
db: AsyncSession,
|
|
progress_cb: Callable[[int, int, str], None] | None = None,
|
|
) -> dict:
|
|
"""Run the backtest and cache the report in a SystemSetting. Job entrypoint."""
|
|
report = await run_backtest(db, progress_cb)
|
|
await update_setting(db, KEY_REPORT, json.dumps(report))
|
|
return report
|
|
|
|
|
|
async def get_backtest_report(db: AsyncSession) -> dict | None:
|
|
"""Return the last cached backtest report, or None if never run."""
|
|
result = await db.execute(select(SystemSetting).where(SystemSetting.key == KEY_REPORT))
|
|
setting = result.scalar_one_or_none()
|
|
if setting is None:
|
|
return None
|
|
try:
|
|
return json.loads(setting.value)
|
|
except (TypeError, ValueError):
|
|
return None
|