dennisthiessen
80b4113280
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>
46 lines
2.1 KiB
Python
46 lines
2.1 KiB
Python
from datetime import datetime
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from sqlalchemy import DateTime, Float, ForeignKey, String, Text
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from app.database import Base
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class SignalContextSnapshot(Base):
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"""Point-in-time context captured when a trade setup is generated.
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This stores the discretionary overlay inputs (scores, sentiment,
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fundamentals) as they looked at detection time, so future analysis can test
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whether human filtering improved or hurt the qualified-list strategy.
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"""
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__tablename__ = "signal_context_snapshots"
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id: Mapped[int] = mapped_column(primary_key=True)
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trade_setup_id: Mapped[int] = mapped_column(
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ForeignKey("trade_setups.id", ondelete="CASCADE"), nullable=False, unique=True
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)
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ticker_id: Mapped[int] = mapped_column(
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ForeignKey("tickers.id", ondelete="CASCADE"), nullable=False
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)
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detected_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
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strategy_version: Mapped[str] = mapped_column(String(80), nullable=False)
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direction: Mapped[str] = mapped_column(String(10), nullable=False)
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entry_price: Mapped[float] = mapped_column(Float, nullable=False)
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stop_loss: Mapped[float] = mapped_column(Float, nullable=False)
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target: Mapped[float] = mapped_column(Float, nullable=False)
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rr_ratio: Mapped[float] = mapped_column(Float, nullable=False)
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confidence_score: Mapped[float | None] = mapped_column(Float, nullable=True)
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recommended_action: Mapped[str | None] = mapped_column(String(20), nullable=True)
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risk_level: Mapped[str | None] = mapped_column(String(10), nullable=True)
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momentum_percentile: Mapped[float | None] = mapped_column(Float, nullable=True)
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score_context_json: Mapped[str] = mapped_column(Text, nullable=False, default="{}")
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sentiment_context_json: Mapped[str] = mapped_column(Text, nullable=False, default="{}")
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fundamental_context_json: Mapped[str] = mapped_column(Text, nullable=False, default="{}")
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trade_setup = relationship("TradeSetup")
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ticker = relationship("Ticker")
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