add backtest harness (Phase 1): historical replay + hit-rate & calibration reports

Replays the price-derived engine over stored OHLCV: at each weekly as-of date,
rebuild the setup from bars <= D (no lookahead) and walk the actual forward bars
for the realized outcome. Reports realized hit-rate/expectancy of qualified
setups (and all setups, by direction) plus a probability calibration curve
(predicted target prob vs realized hit rate).

Reuses pure functions throughout; extracted compute_technical_from_arrays /
compute_momentum_from_closes from scoring_service so live and backtest stay in
sync. Runs as a weekly/triggerable 'backtest' job caching the report in a
SystemSetting; GET /backtest/report serves it. Sentiment/fundamentals held
neutral (no point-in-time history) — calibrates the price/S-R/probability machinery.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

app/services/scoring_service.py

@@ -88,11 +88,28 @@ async def _save_weights(db: AsyncSession, weights: dict[str, float]) -> None:
 async def _compute_technical_score(
     db: AsyncSession, symbol: str
 ) -> tuple[float | None, dict | None]:
-    """Compute technical dimension score from ADX, EMA, RSI, EMA Cross,
-    Volume Profile and Pivot Points.
+    """Compute technical dimension score from stored OHLCV (DB wrapper)."""
+    from app.services.indicator_service import _extract_ohlcv
+    from app.services.price_service import query_ohlcv
 
-    Returns (score, breakdown) where breakdown follows the ScoreBreakdown
-    TypedDict shape: {sub_scores, formula, unavailable}.
+    records = await query_ohlcv(db, symbol)
+    if not records:
+        return None, None
+
+    _, highs, lows, closes, volumes = _extract_ohlcv(records)
+    return compute_technical_from_arrays(highs, lows, closes, volumes)
+
+
+def compute_technical_from_arrays(
+    highs: list[float],
+    lows: list[float],
+    closes: list[float],
+    volumes: list[int],
+) -> tuple[float | None, dict | None]:
+    """Technical score from raw OHLCV arrays — ADX, EMA, RSI, EMA Cross, Volume
+    Profile, Pivot Points. Pure (no DB) so the backtest can compute it as-of-date.
+
+    Returns (score, breakdown).
     """
     from app.services.indicator_service import (
         compute_adx,
@@ -101,16 +118,11 @@ async def _compute_technical_score(
         compute_pivot_points,
         compute_rsi,
         compute_volume_profile,
-        _extract_ohlcv,
     )
-    from app.services.price_service import query_ohlcv
 
-    records = await query_ohlcv(db, symbol)
-    if not records:
+    if not closes:
         return None, None
 
-    _, highs, lows, closes, volumes = _extract_ohlcv(records)
-
     formula = (
         "Weighted average: 0.30*ADX + 0.20*EMA + 0.20*RSI + 0.15*EMA_Cross "
         "+ 0.10*Volume_Profile + 0.05*Pivot_Points, re-normalized if any "
@@ -514,13 +526,21 @@ async def _compute_momentum_score(
     """
     from app.services.price_service import query_ohlcv
 
-    formula = "Weighted average: 0.5 * ROC_5 + 0.5 * ROC_20, re-normalized if any sub-score unavailable."
-
     records = await query_ohlcv(db, symbol)
     if not records or len(records) < 6:
         return None, None
 
     closes = [float(r.close) for r in records]
+    return compute_momentum_from_closes(closes)
+
+
+def compute_momentum_from_closes(closes: list[float]) -> tuple[float | None, dict | None]:
+    """Momentum score (5- and 20-day ROC) from a close series. Pure (no DB)."""
+    formula = "Weighted average: 0.5 * ROC_5 + 0.5 * ROC_20, re-normalized if any sub-score unavailable."
+
+    if not closes or len(closes) < 6:
+        return None, None
+
     latest = closes[-1]
 
     scores: list[tuple[float, float]] = []  # (weight, score)