feat: add strategy variant lab and signal context snapshots

Backtest report now includes research-only hold-to-horizon portfolio variants comparing raw vs residual 12-1 momentum, cutoff 80 vs 90, max 10 vs 15 positions, and SPY-200 risk scaling. A dynamic research recommendation panel flags residual momentum, cutoff 90, or regime scaling only when transparent promotion rules pass. Adds signal_context_snapshots with migration 016 and captures one point-in-time context row per newly generated TradeSetup: setup fields, composite/dimensions, latest sentiment, latest fundamentals, and strategy_version=momentum_12_1_rr_time_v1. This is forward-only; no historical sentiment/fundamental backfill is attempted. No live gate, paper-trade exit, or production ranking behavior changes. Verification: 458 backend tests pass, ruff check app/ clean, frontend npm run build clean. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-02 16:25:04 +02:00
parent 13374087db
commit 80b4113280
10 changed files with 885 additions and 28 deletions
@@ -0,0 +1,45 @@
+from datetime import datetime
+
+from sqlalchemy import DateTime, Float, ForeignKey, String, Text
+from sqlalchemy.orm import Mapped, mapped_column, relationship
+
+from app.database import Base
+
+
+class SignalContextSnapshot(Base):
+    """Point-in-time context captured when a trade setup is generated.
+
+    This stores the discretionary overlay inputs (scores, sentiment,
+    fundamentals) as they looked at detection time, so future analysis can test
+    whether human filtering improved or hurt the qualified-list strategy.
+    """
+
+    __tablename__ = "signal_context_snapshots"
+
+    id: Mapped[int] = mapped_column(primary_key=True)
+    trade_setup_id: Mapped[int] = mapped_column(
+        ForeignKey("trade_setups.id", ondelete="CASCADE"), nullable=False, unique=True
+    )
+    ticker_id: Mapped[int] = mapped_column(
+        ForeignKey("tickers.id", ondelete="CASCADE"), nullable=False
+    )
+    detected_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
+    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
+    strategy_version: Mapped[str] = mapped_column(String(80), nullable=False)
+
+    direction: Mapped[str] = mapped_column(String(10), nullable=False)
+    entry_price: Mapped[float] = mapped_column(Float, nullable=False)
+    stop_loss: Mapped[float] = mapped_column(Float, nullable=False)
+    target: Mapped[float] = mapped_column(Float, nullable=False)
+    rr_ratio: Mapped[float] = mapped_column(Float, nullable=False)
+    confidence_score: Mapped[float | None] = mapped_column(Float, nullable=True)
+    recommended_action: Mapped[str | None] = mapped_column(String(20), nullable=True)
+    risk_level: Mapped[str | None] = mapped_column(String(10), nullable=True)
+    momentum_percentile: Mapped[float | None] = mapped_column(Float, nullable=True)
+
+    score_context_json: Mapped[str] = mapped_column(Text, nullable=False, default="{}")
+    sentiment_context_json: Mapped[str] = mapped_column(Text, nullable=False, default="{}")
+    fundamental_context_json: Mapped[str] = mapped_column(Text, nullable=False, default="{}")
+
+    trade_setup = relationship("TradeSetup")
+    ticker = relationship("Ticker")