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>
53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
"""Pydantic schemas for trade setup endpoints."""
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from __future__ import annotations
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from datetime import date, datetime
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from pydantic import BaseModel, Field
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class TradeTargetResponse(BaseModel):
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price: float
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distance_from_entry: float
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distance_atr_multiple: float
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rr_ratio: float
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probability: float
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classification: str
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sr_level_id: int
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sr_strength: float
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is_primary: bool = False
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class RecommendationSummaryResponse(BaseModel):
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action: str
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reasoning: str | None
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risk_level: str | None
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composite_score: float
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class TradeSetupResponse(BaseModel):
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"""A single trade setup detected by the R:R scanner."""
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id: int
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symbol: str
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direction: str
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entry_price: float
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stop_loss: float
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target: float
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rr_ratio: float
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composite_score: float
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detected_at: datetime
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confidence_score: float | None = None
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targets: list[TradeTargetResponse] = Field(default_factory=list)
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conflict_flags: list[str] = Field(default_factory=list)
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recommended_action: str | None = None
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reasoning: str | None = None
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risk_level: str | None = None
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actual_outcome: str | None = None
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outcome_date: date | None = None
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evaluated_at: datetime | None = None
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current_price: float | None = None
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momentum_percentile: float | None = None
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recommendation_summary: RecommendationSummaryResponse | None = None
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