dennisthiessenandClaude Fable 5
565484de87
The run-id marker proved which scan wrote last, but the shadow book still selected setups by detected_at >= scan_start. An overlapping manual scan could insert rows in that same window; if the pipeline's scan wrote the marker last its id matched and the shadow book proceeded, then swept in -- or ranked highest -- a manual-scan row. The identity check gated entry but selection did not. Carry the run id onto the rows. Migration 025 adds an indexed trade_setups.scan_run_id. scan_all_tickers computes one id per run (pipeline's when a step, else fresh), passes it to scan_ticker which stamps every row after enhancement, and writes the same id to the completion marker. The shadow book selects WHERE scan_run_id == the matched id, so a concurrent scan's rows are excluded by identity regardless of their detected_at. The now-unused STARTED marker is dropped; COMPLETED (freshness) and RUN_ID (identity) remain. Decisive test: the pipeline's id matches, but a same-window manual row with a higher rank is present and is excluded -- only the pipeline's own row is traded. A time-window select would have swept it in and ranked it first. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
77 lines
3.4 KiB
Python
77 lines
3.4 KiB
Python
from datetime import date, datetime
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import json
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from sqlalchemy import Date, DateTime, Float, ForeignKey, Index, 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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__table_args__ = (Index("ix_trade_setups_ticker_rr", "ticker_id", "rr_ratio"),)
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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 activation momentum percentile across the universe at detection
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# time. Since July 2026 this is residual 12-1 momentum when benchmark data is
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# available, with raw 12-1 as a fallback.
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momentum_percentile: Mapped[float | None] = mapped_column(Float, nullable=True)
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# Production ordering score. July 2026 promotion: residual momentum remains
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# the gate, while this rank blends residual momentum with realized volatility.
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strategy_rank: Mapped[float | None] = mapped_column(Float, nullable=True)
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volatility_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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# Identity of the scan run that produced this row. The shadow book selects
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# its batch by this id, not by a detected_at window, so a concurrent manual
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# scan writing rows in the same time window is excluded by identity. Null on
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# rows predating the column and on any non-scan creator.
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scan_run_id: Mapped[str | None] = mapped_column(String(32), 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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