dennisthiessenandClaude Fable 5
ba2df8b9fd
The manual paper book only contains trades taken by hand, inside a 20
minute window, on days someone was available. The backtest that validated
this strategy auto-takes the top-ranked qualified setups up to capacity
every session. The forward record was therefore measuring strategy plus
discretion plus availability -- and degrading silently on busy days.
The shadow book closes that gap: it mirrors the backtest's selection rule
(top strategy_rank qualified, up to capacity, 1% fixed-fractional risk)
and shares the manual book's exit policy, so the only difference between
the two books is which setups get taken. Selection ordering reuses the
strategy_rank the scanner already stores rather than recomputing it, so
the two cannot drift apart. It runs as a near-close pipeline step right
after the scan, marking entries at the same prices a human would see.
Gate-reset re-entry state is now scoped per book -- the books diverge as
soon as their entries differ, and each must see only its own stops.
Performance view rewritten around the comparison:
- three series (shadow, manual, SPY) from a new endpoint
- SPY changes from a per-trade cost-basis counterfactual to plain
buy-and-hold %, since one line has to serve two books
- headline stats are R-multiples, not currency: the books size
differently, so only R compares across them
- configurable start date, because the strategy has been revised
repeatedly and pre-cutover trades ran under rules that no longer
exist
Migration 024 also repairs the numeric weekday crons written by 023,
rewriting only rows still holding the broken form so hand-corrected
settings survive. Its literals are inlined because bound parameters
render as NULL under 'alembic upgrade --sql'.
The shadow book is opt-in and writes nothing until enabled. Verify its
first selections match a backtest of that day's cross-section before
trusting any point on the curve.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
61 lines
3.0 KiB
Python
61 lines
3.0 KiB
Python
from datetime import datetime
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from sqlalchemy import DateTime, Float, ForeignKey, String
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from sqlalchemy.orm import Mapped, mapped_column
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from app.database import Base
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class PaperTrade(Base):
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"""A simulated ('taken') trade for paper trading.
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Captured from a setup at the moment the user marks it taken: direction,
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entry, size, stop and target. Open trades are marked-to-market against the
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latest close; closing records the exit price and time.
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"""
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__tablename__ = "paper_trades"
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id: Mapped[int] = mapped_column(primary_key=True)
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user_id: Mapped[int] = mapped_column(
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ForeignKey("users.id", ondelete="CASCADE"), nullable=False
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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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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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shares: 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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status: Mapped[str] = mapped_column(String(10), nullable=False, default="open")
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opened_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), default=datetime.utcnow, nullable=False
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)
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close_price: Mapped[float | None] = mapped_column(Float, nullable=True)
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closed_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
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# How the trade was closed: "time" | "trailing" | "stop" | "target" | "manual".
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close_reason: Mapped[str | None] = mapped_column(String(10), nullable=True)
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# A trade stopped at its initial stop starts a re-entry gate-reset episode.
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# The daily full-universe scanner records both state transitions: the first
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# failed gate observation and a later fresh qualification. Re-entry remains
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# non-actionable until both timestamps exist.
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reentry_gate_failed_at: Mapped[datetime | None] = mapped_column(
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DateTime(timezone=True), nullable=True
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)
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reentry_gate_requalified_at: Mapped[datetime | None] = mapped_column(
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DateTime(timezone=True), nullable=True
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)
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# Execution era for forward vs backtest comparison:
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# null/legacy = pre-cutover morning-scan, "near_close" = post near-close cutover.
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fill_mode: Mapped[str | None] = mapped_column(String(20), nullable=True)
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# Which book this trade belongs to:
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# "manual" — discretionary, opened by the user from a qualified setup
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# "shadow" — opened automatically by the validated strategy (top-ranked
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# qualified up to capacity, 1% risk). The shadow book is the
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# faithful live twin of the backtest; the two books share the
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# same exit policy so the only difference is *selection*.
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# Gate-reset re-entry state is tracked per book — the books diverge as soon
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# as their entries differ, and each must see its own trade history.
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book: Mapped[str] = mapped_column(String(10), nullable=False, default="manual")
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