dennisthiessen
437ceacfc1
Behavior-preserving cleanup (345 tests pass, ruff clean):
- scheduler: replace 62 inline logger.x(json.dumps({...})) calls with a
_log_event helper, and collapse 11 identical _job_runtime dicts into an
_idle_runtime() factory over _JOB_NAMES.
- settings: add app/services/settings_store.py (get_setting/get_value/get_map/
upsert_setting) and route ~13 hand-rolled SystemSetting queries + two
identical _settings_map helpers through it.
- scoring.get_rankings: collapse the per-ticker N+1 (3-4 queries + a commit each)
into 2 bulk reads + a single conditional commit; drop the redundant re-fetch.
Lazy recompute-on-read is preserved. Adds first tests for get_rankings.
Net ~ -245 lines across the touched modules.
751 lines
30 KiB
Python
751 lines
30 KiB
Python
"""Historical backtest (Phase 1): replay the price-derived engine over stored
|
||
OHLCV and measure how the CURRENT config would have performed.
|
||
|
||
For each ticker we step through history (weekly), and at each as-of date D we
|
||
rebuild the setup using only bars ≤ D (no lookahead), then walk the actual bars
|
||
after D to record the realized outcome. Two reports come out:
|
||
|
||
- realized hit-rate / expectancy of qualified setups (and of all setups)
|
||
- a probability calibration curve: do "60% likely" targets hit ~60% of the time?
|
||
|
||
Limitation: sentiment and fundamentals have no point-in-time history, so they're
|
||
held neutral here — this calibrates the price/S-R/probability machinery only.
|
||
"""
|
||
|
||
from __future__ import annotations
|
||
|
||
import asyncio
|
||
import json
|
||
import logging
|
||
import math
|
||
import multiprocessing
|
||
import os
|
||
from collections import defaultdict
|
||
from collections.abc import Callable
|
||
from concurrent.futures import ProcessPoolExecutor
|
||
from datetime import date, datetime, timezone
|
||
from types import SimpleNamespace
|
||
from typing import Any
|
||
|
||
from sqlalchemy import select
|
||
from sqlalchemy.ext.asyncio import AsyncSession
|
||
|
||
from app.config import settings
|
||
from app.models.ticker import Ticker
|
||
from app.services import settings_store
|
||
from app.services.admin_service import get_activation_config, update_setting
|
||
from app.services.indicator_service import _extract_ohlcv, compute_atr
|
||
from app.services.outcome_service import (
|
||
OUTCOME_AMBIGUOUS,
|
||
OUTCOME_STOP_HIT,
|
||
OUTCOME_TARGET_HIT,
|
||
Bar,
|
||
evaluate_setup_against_bars,
|
||
)
|
||
from app.services.price_service import query_ohlcv
|
||
from app.services.qualification import (
|
||
best_target_probability,
|
||
setup_qualifies,
|
||
)
|
||
from app.services.recommendation_service import (
|
||
_choose_recommended_action,
|
||
_classify_by_probability,
|
||
_risk_level_from_conflicts,
|
||
_select_primary_target,
|
||
_zone_representative_levels,
|
||
direction_analyzer,
|
||
get_recommendation_config,
|
||
probability_estimator,
|
||
signal_conflict_detector,
|
||
target_generator,
|
||
)
|
||
from app.services.scoring_service import (
|
||
compute_momentum_from_closes,
|
||
compute_technical_from_arrays,
|
||
)
|
||
from app.services.sr_service import detect_sr_levels
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
KEY_REPORT = "backtest_report"
|
||
|
||
STEP_DAYS = 5 # weekly cadence (≈ 5 trading days)
|
||
MIN_LOOKBACK = 60 # bars needed before D for indicators (EMA cross needs 51)
|
||
HORIZON = 30 # trading days to resolve an outcome (matches the evaluator)
|
||
ATR_MULTIPLIER = 1.5
|
||
|
||
_CAL_BUCKETS = [(0, 20), (20, 40), (40, 60), (60, 80), (80, 100.01)]
|
||
|
||
# Cross-sectional signal evaluation (factor IC). Each candidate signal is a
|
||
# point-in-time number computed from closes alone (sentiment/fundamentals have no
|
||
# history here), sampled one as-of per ISO week, and graded by how its rank
|
||
# correlates with the forward HORIZON-day return ACROSS the universe — i.e. does
|
||
# ranking stocks by this signal sort tomorrow's winners from losers. This is the
|
||
# test the per-setup hit-rate report can't do: it measures predictive power of a
|
||
# signal, not the outcome of a target/stop structure built on top of one.
|
||
MIN_CROSS_SECTION = 20 # min tickers present in a week to score that week
|
||
MIN_RELIABLE_PERIODS = 12 # min non-overlapping windows before a signal's IC is trusted
|
||
|
||
|
||
def _wrap_levels(level_dicts: list[dict]) -> list[Any]:
|
||
return [
|
||
SimpleNamespace(
|
||
id=i,
|
||
price_level=float(d["price_level"]),
|
||
type=d["type"],
|
||
strength=int(d["strength"]),
|
||
)
|
||
for i, d in enumerate(level_dicts)
|
||
]
|
||
|
||
|
||
def _window_setups(
|
||
window_records: list,
|
||
config: dict,
|
||
activation: dict,
|
||
) -> list[dict]:
|
||
"""Rebuild the setup(s) at the last bar of ``window_records`` (the as-of date),
|
||
using only those bars. Returns one dict per tradeable direction."""
|
||
if len(window_records) < MIN_LOOKBACK:
|
||
return []
|
||
|
||
_, highs, lows, closes, volumes = _extract_ohlcv(window_records)
|
||
entry = closes[-1]
|
||
if entry <= 0:
|
||
return []
|
||
|
||
# 12-1 month momentum (skip the last month) — the universe ranks on this.
|
||
# None until a year of history exists; such setups can't qualify on momentum.
|
||
mom_12_1 = (
|
||
closes[-22] / closes[-253] - 1.0
|
||
if len(closes) >= 253 and closes[-253] > 0
|
||
else None
|
||
)
|
||
|
||
try:
|
||
atr = compute_atr(highs, lows, closes)["atr"]
|
||
except Exception:
|
||
return []
|
||
if atr <= 0:
|
||
return []
|
||
|
||
sr_levels = _wrap_levels(detect_sr_levels(highs, lows, closes, volumes))
|
||
if not sr_levels:
|
||
return []
|
||
|
||
technical = (compute_technical_from_arrays(highs, lows, closes, volumes)[0]) or 50.0
|
||
momentum = (compute_momentum_from_closes(closes)[0]) or 50.0
|
||
dim_scores = {"technical": technical, "momentum": momentum}
|
||
|
||
conflicts = signal_conflict_detector.detect_conflicts(dim_scores, None, config)
|
||
confidences = {
|
||
"long": direction_analyzer.calculate_confidence("long", dim_scores, None, conflicts),
|
||
"short": direction_analyzer.calculate_confidence("short", dim_scores, None, conflicts),
|
||
}
|
||
|
||
# First pass: build targets per direction
|
||
per_dir: dict[str, dict] = {}
|
||
for direction in ("long", "short"):
|
||
stop = entry - atr * ATR_MULTIPLIER if direction == "long" else entry + atr * ATR_MULTIPLIER
|
||
zone_levels = _zone_representative_levels(sr_levels, entry)
|
||
targets = target_generator.generate_targets(direction, entry, stop, zone_levels, atr)
|
||
if not targets:
|
||
continue
|
||
for t in targets:
|
||
t["probability"] = probability_estimator.estimate_probability(
|
||
t, dim_scores, None, direction, config
|
||
)
|
||
t["classification"] = _classify_by_probability(t["probability"])
|
||
primary = _select_primary_target(targets)
|
||
if primary is None:
|
||
continue
|
||
# Flag the primary so qualification's EV uses the primary target's
|
||
# probability (matching production's enhance_trade_setup).
|
||
for t in targets:
|
||
t["is_primary"] = t is primary
|
||
per_dir[direction] = {"stop": stop, "targets": targets, "primary": primary}
|
||
|
||
available = set(per_dir.keys())
|
||
if not available:
|
||
return []
|
||
|
||
action = _choose_recommended_action(confidences["long"], confidences["short"], config, available)
|
||
|
||
out: list[dict] = []
|
||
for direction, data in per_dir.items():
|
||
targets, primary, stop = data["targets"], data["primary"], data["stop"]
|
||
setup_conflicts = list(conflicts)
|
||
if len(targets) < 3:
|
||
setup_conflicts.append("target-availability: Fewer than 3 valid S/R targets available")
|
||
risk_level = _risk_level_from_conflicts(setup_conflicts)
|
||
rr = float(primary["rr_ratio"])
|
||
target_price = float(primary["price"])
|
||
|
||
setup_ns = SimpleNamespace(
|
||
rr_ratio=rr,
|
||
confidence_score=confidences[direction],
|
||
recommended_action=action,
|
||
risk_level=risk_level,
|
||
targets=targets,
|
||
direction=direction,
|
||
target=target_price,
|
||
stop_loss=stop,
|
||
entry_price=entry,
|
||
)
|
||
# meets_core = clears every gate EXCEPT the cross-sectional momentum
|
||
# percentile, which can only be assigned once all tickers' setups for a
|
||
# week are known. run_backtest ranks momentum and finalizes `qualified`.
|
||
core_config = {**activation, "min_momentum_percentile": 0.0}
|
||
meets_core = setup_qualifies(setup_ns, core_config)
|
||
best_prob = best_target_probability(setup_ns)
|
||
out.append({
|
||
"direction": direction,
|
||
"entry": entry,
|
||
"stop": stop,
|
||
"target": target_price,
|
||
"rr": rr,
|
||
"confidence": confidences[direction],
|
||
"primary_prob": float(primary["probability"]),
|
||
"best_prob": best_prob,
|
||
"momentum": mom_12_1,
|
||
"meets_core": meets_core,
|
||
"action": action,
|
||
"risk_level": risk_level,
|
||
})
|
||
return out
|
||
|
||
|
||
def _replay_ticker(symbol: str, records: list, config: dict, activation: dict) -> list[dict]:
|
||
"""Walk one ticker's history weekly, building setups and their realized outcomes."""
|
||
candidates: list[dict] = []
|
||
n = len(records)
|
||
if n < MIN_LOOKBACK + HORIZON:
|
||
return candidates
|
||
|
||
for i in range(MIN_LOOKBACK - 1, n - HORIZON, STEP_DAYS):
|
||
window = records[: i + 1]
|
||
forward = records[i + 1 :]
|
||
forward_bars = [Bar(date=r.date, high=r.high, low=r.low) for r in forward]
|
||
|
||
for s in _window_setups(window, config, activation):
|
||
outcome, _ = evaluate_setup_against_bars(
|
||
s["direction"], s["stop"], s["target"], forward_bars, HORIZON
|
||
)
|
||
if outcome is None:
|
||
continue
|
||
target_hit = outcome == OUTCOME_TARGET_HIT
|
||
if outcome == OUTCOME_TARGET_HIT:
|
||
realized_r = s["rr"]
|
||
elif outcome in (OUTCOME_STOP_HIT, OUTCOME_AMBIGUOUS):
|
||
realized_r = -1.0
|
||
else: # expired
|
||
realized_r = 0.0
|
||
iso = records[i].date.isocalendar()
|
||
candidates.append({
|
||
"symbol": symbol,
|
||
"date": records[i].date.isoformat(),
|
||
"iso_week": (iso[0], iso[1]),
|
||
"direction": s["direction"],
|
||
"rr": s["rr"],
|
||
"confidence": s["confidence"],
|
||
"primary_prob": s["primary_prob"],
|
||
"best_prob": s["best_prob"],
|
||
"momentum": s["momentum"],
|
||
"meets_core": s["meets_core"],
|
||
"outcome": outcome,
|
||
"target_hit": target_hit,
|
||
"realized_r": realized_r,
|
||
})
|
||
return candidates
|
||
|
||
|
||
def _bucket_stats(cands: list[dict]) -> dict:
|
||
wins = sum(1 for c in cands if c["target_hit"])
|
||
losses = sum(1 for c in cands if c["outcome"] in (OUTCOME_STOP_HIT, OUTCOME_AMBIGUOUS))
|
||
expired = sum(1 for c in cands if c["outcome"] not in (OUTCOME_TARGET_HIT, OUTCOME_STOP_HIT, OUTCOME_AMBIGUOUS))
|
||
decided = wins + losses
|
||
rs = [c["realized_r"] for c in cands]
|
||
return {
|
||
"total": len(cands),
|
||
"wins": wins,
|
||
"losses": losses,
|
||
"expired": expired,
|
||
"hit_rate": round(wins / decided * 100, 1) if decided else None,
|
||
"avg_r": round(sum(rs) / len(rs), 3) if rs else None,
|
||
"total_r": round(sum(rs), 2) if rs else None,
|
||
}
|
||
|
||
|
||
def _calibration(cands: list[dict]) -> list[dict]:
|
||
"""Predicted target probability vs realized hit rate, per probability bucket."""
|
||
rows: list[dict] = []
|
||
for lo, hi in _CAL_BUCKETS:
|
||
bucket = [c for c in cands if lo <= c["primary_prob"] < hi]
|
||
if not bucket:
|
||
continue
|
||
hits = sum(1 for c in bucket if c["target_hit"])
|
||
rows.append({
|
||
"bucket": f"{int(lo)}-{int(min(hi, 100))}%",
|
||
"n": len(bucket),
|
||
"predicted_avg": round(sum(c["primary_prob"] for c in bucket) / len(bucket), 1),
|
||
"realized_hit_rate": round(hits / len(bucket) * 100, 1),
|
||
})
|
||
return rows
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Cross-sectional signal evaluation (factor information-coefficient)
|
||
# ---------------------------------------------------------------------------
|
||
|
||
def _weekly_asof_indices(records: list) -> list[int]:
|
||
"""Index of the last bar in each ISO week — the weekly rebalance as-of bars.
|
||
|
||
Keying on the calendar week (not the raw bar index) makes every ticker's
|
||
as-of dates line up, so the cross-section on a given week is comparable.
|
||
"""
|
||
last_by_week: dict[tuple[int, int], int] = {}
|
||
for idx, r in enumerate(records):
|
||
iso = r.date.isocalendar()
|
||
last_by_week[(iso[0], iso[1])] = idx
|
||
return sorted(last_by_week.values())
|
||
|
||
|
||
def _signal_values(closes: list[float], highs: list[float], i: int) -> dict[str, float]:
|
||
"""Point-in-time candidate signals at as-of index ``i`` (price-only).
|
||
|
||
Momentum factors follow the standard "skip the last month" convention
|
||
(return up to ~1 month ago) to avoid the short-term reversal effect, which
|
||
``reversal_1m`` isolates on purpose — we expect its IC to be negative if the
|
||
universe mean-reverts. ``trend_200`` is price vs its 200-bar SMA. ``high_52w``
|
||
is closeness to the trailing 52-week high (George/Hwang anchoring effect:
|
||
higher = nearer the high, expect positive IC). ``vol_6m`` is 126-day realized
|
||
volatility (expect negative IC if the low-volatility anomaly holds).
|
||
"""
|
||
out: dict[str, float] = {}
|
||
if i - 252 >= 0 and closes[i - 252] > 0:
|
||
out["mom_12_1"] = closes[i - 21] / closes[i - 252] - 1.0
|
||
if i - 126 >= 0 and closes[i - 126] > 0:
|
||
out["mom_6_1"] = closes[i - 21] / closes[i - 126] - 1.0
|
||
if i - 63 >= 0 and closes[i - 63] > 0:
|
||
out["mom_3_1"] = closes[i - 21] / closes[i - 63] - 1.0
|
||
if i - 21 >= 0 and closes[i - 21] > 0:
|
||
out["reversal_1m"] = closes[i] / closes[i - 21] - 1.0
|
||
if i - 199 >= 0:
|
||
sma = sum(closes[i - 199 : i + 1]) / 200.0
|
||
if sma > 0:
|
||
out["trend_200"] = closes[i] / sma - 1.0
|
||
if i - 251 >= 0:
|
||
high_52w = max(highs[i - 251 : i + 1])
|
||
if high_52w > 0:
|
||
out["high_52w"] = closes[i] / high_52w
|
||
if i - 126 >= 0:
|
||
rets = [
|
||
closes[k] / closes[k - 1] - 1.0
|
||
for k in range(i - 125, i + 1)
|
||
if closes[k - 1] > 0
|
||
]
|
||
if len(rets) >= 2:
|
||
mean = sum(rets) / len(rets)
|
||
var = sum((x - mean) ** 2 for x in rets) / (len(rets) - 1)
|
||
out["vol_6m"] = math.sqrt(var)
|
||
return out
|
||
|
||
|
||
def _accumulate_signal_series(records: list, collected: dict) -> None:
|
||
"""For each weekly as-of bar, emit (signal, forward-return) pairs keyed by ISO
|
||
week into ``collected[name][week_key]``. Forward return is close-to-close over
|
||
HORIZON trading days. Mutates ``collected`` (a dict of dict of list)."""
|
||
n = len(records)
|
||
if n < HORIZON + 21:
|
||
return
|
||
closes = [float(r.close) for r in records]
|
||
highs = [float(r.high) for r in records]
|
||
for i in _weekly_asof_indices(records):
|
||
j = i + HORIZON
|
||
if j >= n or closes[i] <= 0:
|
||
continue
|
||
fwd = closes[j] / closes[i] - 1.0
|
||
iso = records[i].date.isocalendar()
|
||
week_key = (iso[0], iso[1])
|
||
for name, val in _signal_values(closes, highs, i).items():
|
||
collected[name][week_key].append((val, fwd))
|
||
|
||
|
||
def _rank(xs: list[float]) -> list[float]:
|
||
"""Average (tie-corrected) ranks, 1-based."""
|
||
order = sorted(range(len(xs)), key=lambda k: xs[k])
|
||
ranks = [0.0] * len(xs)
|
||
i = 0
|
||
while i < len(xs):
|
||
j = i
|
||
while j + 1 < len(xs) and xs[order[j + 1]] == xs[order[i]]:
|
||
j += 1
|
||
avg_rank = (i + j) / 2.0 + 1.0
|
||
for k in range(i, j + 1):
|
||
ranks[order[k]] = avg_rank
|
||
i = j + 1
|
||
return ranks
|
||
|
||
|
||
def _pearson(a: list[float], b: list[float]) -> float | None:
|
||
n = len(a)
|
||
if n < 3:
|
||
return None
|
||
ma, mb = sum(a) / n, sum(b) / n
|
||
va = sum((x - ma) ** 2 for x in a)
|
||
vb = sum((y - mb) ** 2 for y in b)
|
||
if va <= 0 or vb <= 0:
|
||
return None
|
||
cov = sum((a[k] - ma) * (b[k] - mb) for k in range(n))
|
||
return cov / math.sqrt(va * vb)
|
||
|
||
|
||
def _spearman(xs: list[float], ys: list[float]) -> float | None:
|
||
"""Rank correlation = Pearson on the ranks. None if too few/degenerate."""
|
||
if len(xs) < 3:
|
||
return None
|
||
return _pearson(_rank(xs), _rank(ys))
|
||
|
||
|
||
def _quintile_spread(pairs: list[tuple[float, float]]) -> float | None:
|
||
"""Mean forward return of the top signal-quintile minus the bottom quintile."""
|
||
n = len(pairs)
|
||
if n < 10:
|
||
return None
|
||
ordered = sorted(pairs, key=lambda p: p[0])
|
||
k = n // 5
|
||
top = ordered[-k:]
|
||
bottom = ordered[:k]
|
||
return sum(p[1] for p in top) / k - sum(p[1] for p in bottom) / k
|
||
|
||
|
||
def _week_ordinal(week_key: tuple[int, int]) -> int:
|
||
"""Monotonic absolute week number from an (ISO year, ISO week) key."""
|
||
year, week = week_key
|
||
return year * 53 + week
|
||
|
||
|
||
def _nonoverlapping_weeks(
|
||
week_keys: list[tuple[int, int]], stride: int
|
||
) -> list[tuple[int, int]]:
|
||
"""Thin to weeks at least ``stride`` apart so their forward windows don't
|
||
overlap — greedy earliest-first. Removes the autocorrelation that would
|
||
otherwise inflate the IC t-stat across adjacent weekly rebalances."""
|
||
kept: list[tuple[int, int]] = []
|
||
last: int | None = None
|
||
for wk in sorted(week_keys, key=_week_ordinal):
|
||
o = _week_ordinal(wk)
|
||
if last is None or o - last >= stride:
|
||
kept.append(wk)
|
||
last = o
|
||
return kept
|
||
|
||
|
||
def _signal_evaluation(collected: dict) -> list[dict]:
|
||
"""Per-signal factor diagnostics, one row per candidate signal:
|
||
|
||
mean_ic average rank-IC (Spearman of signal vs fwd ret)
|
||
ic_t_stat mean_ic / stderr — is the IC reliably non-zero?
|
||
ic_positive_pct share of windows the IC is positive (consistency)
|
||
mean_quintile_spread avg top-minus-bottom-quintile forward return
|
||
reliable True once there are >= MIN_RELIABLE_PERIODS windows
|
||
|
||
IC is measured on NON-OVERLAPPING forward windows (weeks thinned to ~HORIZON
|
||
apart) so the t-stat isn't inflated by autocorrelation. A signal with no edge
|
||
lands near IC 0 / spread 0; one with too few independent windows is flagged
|
||
unreliable rather than trusted on a lucky handful.
|
||
"""
|
||
stride = max(1, round(HORIZON / 5)) # ISO weeks spanned by the forward window
|
||
rows: list[dict] = []
|
||
for name in sorted(collected):
|
||
weeks_map = collected[name]
|
||
usable = [wk for wk, recs in weeks_map.items() if len(recs) >= MIN_CROSS_SECTION]
|
||
kept = _nonoverlapping_weeks(usable, stride)
|
||
ics: list[float] = []
|
||
spreads: list[float] = []
|
||
sizes: list[int] = []
|
||
for wk in kept:
|
||
recs = weeks_map[wk]
|
||
ic = _spearman([r[0] for r in recs], [r[1] for r in recs])
|
||
if ic is not None:
|
||
ics.append(ic)
|
||
spread = _quintile_spread(recs)
|
||
if spread is not None:
|
||
spreads.append(spread)
|
||
sizes.append(len(recs))
|
||
if not ics:
|
||
continue
|
||
mean_ic = sum(ics) / len(ics)
|
||
if len(ics) > 1:
|
||
std = math.sqrt(sum((x - mean_ic) ** 2 for x in ics) / (len(ics) - 1))
|
||
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,
|
||
"reliable": len(ics) >= MIN_RELIABLE_PERIODS,
|
||
})
|
||
rows.sort(key=lambda r: r["mean_ic"], reverse=True)
|
||
return rows
|
||
|
||
|
||
def _signal_series(records: list) -> dict:
|
||
"""Per-ticker signal/forward-return series as a PLAIN (picklable) nested dict
|
||
— no defaultdict/lambda — so it can cross a process boundary."""
|
||
tmp: dict = defaultdict(lambda: defaultdict(list))
|
||
_accumulate_signal_series(records, tmp)
|
||
return {name: dict(weeks) for name, weeks in tmp.items()}
|
||
|
||
|
||
def _replay_and_signals(
|
||
symbol: str,
|
||
columns: tuple,
|
||
config: dict,
|
||
activation: dict,
|
||
) -> tuple[list[dict], dict]:
|
||
"""The CPU-bound per-ticker work, as a top-level (picklable) function so it can
|
||
run in a worker process. Takes primitive column arrays (cheap to pickle),
|
||
rebuilds bar objects, and returns (candidates, signal_series)."""
|
||
date_ords, opens, highs, lows, closes, volumes = columns
|
||
bars = [
|
||
SimpleNamespace(
|
||
date=date.fromordinal(o), open=op, high=hi, low=lo, close=cl, volume=vo
|
||
)
|
||
for o, op, hi, lo, cl, vo in zip(date_ords, opens, highs, lows, closes, volumes)
|
||
]
|
||
return _replay_ticker(symbol, bars, config, activation), _signal_series(bars)
|
||
|
||
|
||
def _backtest_worker_count() -> int:
|
||
"""How many worker processes to replay tickers across. Capped to cpu_count-1
|
||
so a core stays free for the web server; 1 means sequential."""
|
||
configured = int(getattr(settings, "backtest_workers", 4))
|
||
if configured <= 1:
|
||
return 1
|
||
cpu = os.cpu_count() or 1
|
||
return max(1, min(configured, cpu - 1))
|
||
|
||
|
||
def _mp_context():
|
||
"""A start method safe to use from the threaded asyncio server: ``forkserver``
|
||
(workers forked from a clean, single-threaded server — avoids the
|
||
fork-with-threads deadlock) when available, else ``fork``. Returns None on
|
||
spawn-only platforms (Windows), where the caller falls back to a thread."""
|
||
methods = multiprocessing.get_all_start_methods()
|
||
for method in ("forkserver", "fork"):
|
||
if method in methods:
|
||
return multiprocessing.get_context(method)
|
||
return None
|
||
|
||
|
||
async def _fetch_columns(db: AsyncSession, symbol: str) -> tuple | None:
|
||
"""Read one ticker's OHLCV and detach it to primitive column arrays in the
|
||
event loop (safe ORM access), ready to hand to a worker. None if no data."""
|
||
records = await query_ohlcv(db, symbol)
|
||
if not records:
|
||
return None
|
||
return (
|
||
[r.date.toordinal() for r in records],
|
||
[float(r.open) for r in records],
|
||
[float(r.high) for r in records],
|
||
[float(r.low) for r in records],
|
||
[float(r.close) for r in records],
|
||
[int(r.volume) for r in records],
|
||
)
|
||
|
||
|
||
def _assign_momentum_percentiles(candidates: list[dict]) -> None:
|
||
"""Per ISO week, rank candidates by their ticker's 12-1 momentum and attach a
|
||
0–100 ``momentum_percentile`` (100 = highest momentum in the universe that
|
||
week). Candidates whose momentum is unknown (insufficient lookback) get None
|
||
and therefore can't clear a momentum gate. Mutates ``candidates``."""
|
||
by_week: dict = defaultdict(list)
|
||
for c in candidates:
|
||
if c.get("momentum") is not None:
|
||
by_week[c["iso_week"]].append(c)
|
||
for group in by_week.values():
|
||
ordered = sorted(group, key=lambda c: c["momentum"])
|
||
n = len(ordered)
|
||
for rank, c in enumerate(ordered):
|
||
c["momentum_percentile"] = (rank / (n - 1) * 100.0) if n > 1 else 100.0
|
||
for c in candidates:
|
||
c.setdefault("momentum_percentile", None)
|
||
|
||
|
||
def _momentum_qualifies(cand: dict, threshold: float) -> bool:
|
||
"""Whether a candidate clears the floors (meets_core) and the momentum gate.
|
||
Threshold 0 disables the momentum gate (floors only). The gate is long-only:
|
||
while it's active, shorts (fighting the trend) never qualify."""
|
||
if not cand["meets_core"]:
|
||
return False
|
||
if threshold <= 0:
|
||
return True
|
||
if cand["direction"] == "short":
|
||
return False
|
||
mp = cand.get("momentum_percentile")
|
||
return mp is not None and mp >= threshold
|
||
|
||
|
||
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))
|
||
|
||
def _merge(result: tuple[list[dict], dict]) -> None:
|
||
cands, series = result
|
||
candidates.extend(cands)
|
||
for name, weeks in series.items():
|
||
for wk, pairs in weeks.items():
|
||
collected[name][wk].extend(pairs)
|
||
|
||
workers = _backtest_worker_count()
|
||
ctx = _mp_context() if (workers > 1 and total > 1) else None
|
||
loop = asyncio.get_running_loop()
|
||
|
||
pool = None
|
||
if ctx is not None:
|
||
try:
|
||
pool = ProcessPoolExecutor(max_workers=workers, mp_context=ctx)
|
||
except Exception:
|
||
logger.exception("Backtest process pool unavailable; falling back to sequential")
|
||
|
||
if pool is not None:
|
||
# Parallel: replay tickers across worker processes — true multi-core, since
|
||
# the GIL only serializes work within a single process. Bars are fetched in
|
||
# the event loop (ORM-safe) and a bounded batch is fanned out to the pool.
|
||
logger.info(json.dumps({
|
||
"event": "backtest_parallel", "workers": workers,
|
||
"start_method": ctx.get_start_method(),
|
||
}))
|
||
chunk = workers * 2
|
||
done = 0
|
||
with pool:
|
||
for start in range(0, total, chunk):
|
||
batch = tickers[start : start + chunk]
|
||
futures = []
|
||
for ticker in batch:
|
||
try:
|
||
columns = await _fetch_columns(db, ticker.symbol)
|
||
except Exception:
|
||
logger.exception("Backtest fetch failed for %s", ticker.symbol)
|
||
continue
|
||
if columns is not None:
|
||
futures.append(loop.run_in_executor(
|
||
pool, _replay_and_signals, ticker.symbol, columns, config, activation
|
||
))
|
||
for result in await asyncio.gather(*futures, return_exceptions=True):
|
||
if isinstance(result, Exception):
|
||
logger.error(json.dumps({"event": "backtest_worker_error", "message": str(result)}))
|
||
else:
|
||
_merge(result)
|
||
done += len(batch)
|
||
if progress_cb is not None:
|
||
progress_cb(min(done, total), total, "")
|
||
else:
|
||
# Sequential fallback (Windows / 1 worker): run each replay in a worker
|
||
# thread so the event loop — and the API server — stays responsive.
|
||
for index, ticker in enumerate(tickers):
|
||
if progress_cb is not None:
|
||
progress_cb(index, total, ticker.symbol)
|
||
try:
|
||
columns = await _fetch_columns(db, ticker.symbol)
|
||
if columns is not None:
|
||
_merge(await asyncio.to_thread(
|
||
_replay_and_signals, ticker.symbol, columns, config, activation
|
||
))
|
||
except Exception:
|
||
logger.exception("Backtest replay failed for %s", ticker.symbol)
|
||
|
||
if progress_cb is not None and total:
|
||
progress_cb(total, total, "")
|
||
|
||
# Cross-sectional momentum: rank every week's universe, then "qualified" means
|
||
# floors + top ``min_momentum_percentile`` by 12-1 momentum.
|
||
_assign_momentum_percentiles(candidates)
|
||
current_min_pct = float(activation.get("min_momentum_percentile", 80.0))
|
||
for c in candidates:
|
||
c["qualified"] = _momentum_qualifies(c, current_min_pct)
|
||
|
||
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 momentum gate at several percentile cutoffs
|
||
# (floors held fixed) so the trade-off between how many setups qualify and
|
||
# their expectancy is visible without re-replaying. 0 = floors only.
|
||
sweep = []
|
||
for threshold in (90.0, 80.0, 70.0, 60.0, 50.0, 0.0):
|
||
cands = [c for c in candidates if _momentum_qualifies(c, threshold)]
|
||
sweep.append({"min_momentum_percentile": 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_momentum_percentile": current_min_pct,
|
||
"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 (min {MIN_CROSS_SECTION} names/window). |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 expected negative (mean-reversion / "
|
||
"low-vol anomaly). IC is measured on non-overlapping windows; signals "
|
||
f"with fewer than {MIN_RELIABLE_PERIODS} independent windows are flagged "
|
||
"unreliable (too few regimes — deepen history with the Data Backfill job)."
|
||
),
|
||
"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."""
|
||
setting = await settings_store.get_setting(db, KEY_REPORT)
|
||
if setting is None:
|
||
return None
|
||
try:
|
||
return json.loads(setting.value)
|
||
except (TypeError, ValueError):
|
||
return None
|