dennisthiessen
605f95098c
Part 1 — long-only. The momentum edge is long top-momentum; the gate was
qualifying shorts on high-momentum names (fighting the trend), which showed as
the -0.13R Short(qual.) drag. While the gate is active, shorts no longer qualify
(backend qualification, backtest _momentum_qualifies, and the frontend mirror).
Part 2 — production wiring. Live setups now carry a real momentum rank, so the
dashboard, the Track Record's qualified stats, and outcome evaluation all gate on
the same value instead of deferring to floors:
- new momentum_service.compute_momentum_percentiles: 12-1 momentum per ticker,
ranked across the universe into a {symbol: percentile} map.
- the daily R:R scan ranks the universe up front and stores each setup's
percentile (new trade_setups.momentum_percentile column, migration 010).
- enhance_trade_setup mutates the same row, so the percentile is preserved;
_trade_setup_to_dict + TradeSetupResponse expose it to the API.
Until a fresh scan runs, pre-existing setups have a null percentile and the gate
falls back to floors for them (longs) / excludes them (shorts) — they fill in on
the next scan. 341 backend tests pass; frontend build clean.
Needs the alembic upgrade (migration 010) on deploy.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
66 lines
2.6 KiB
Python
66 lines
2.6 KiB
Python
from datetime import date, datetime
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import json
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from sqlalchemy import Date, 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 TradeSetup(Base):
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__tablename__ = "trade_setups"
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id: Mapped[int] = mapped_column(primary_key=True)
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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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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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composite_score: Mapped[float] = mapped_column(Float, nullable=False)
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detected_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), nullable=False
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)
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confidence_score: Mapped[float | None] = mapped_column(Float, nullable=True)
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# Ticker's 12-1 momentum percentile across the universe at detection time
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# (0–100, 100 = strongest). Drives the activation gate's core selection.
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momentum_percentile: Mapped[float | None] = mapped_column(Float, nullable=True)
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targets_json: Mapped[str | None] = mapped_column(Text, nullable=True)
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conflict_flags_json: Mapped[str | None] = mapped_column(Text, nullable=True)
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recommended_action: Mapped[str | None] = mapped_column(String(20), nullable=True)
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reasoning: Mapped[str | None] = mapped_column(Text, nullable=True)
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risk_level: Mapped[str | None] = mapped_column(String(10), nullable=True)
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actual_outcome: Mapped[str | None] = mapped_column(String(20), nullable=True)
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evaluated_at: Mapped[datetime | None] = mapped_column(
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DateTime(timezone=True), nullable=True
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)
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outcome_date: Mapped[date | None] = mapped_column(Date, nullable=True)
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ticker = relationship("Ticker", back_populates="trade_setups")
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@property
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def targets(self) -> list[dict]:
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if not self.targets_json:
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return []
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try:
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parsed = json.loads(self.targets_json)
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except (TypeError, ValueError):
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return []
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return parsed if isinstance(parsed, list) else []
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@property
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def conflict_flags(self) -> list[str]:
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if not self.conflict_flags_json:
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return []
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try:
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parsed = json.loads(self.conflict_flags_json)
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except (TypeError, ValueError):
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return []
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if not isinstance(parsed, list):
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return []
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return [str(item) for item in parsed]
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