dennisthiessen
21ed83c56c
Closes the feedback loop on R:R scanner signals: - Nightly outcome_evaluator job replays unresolved setups against daily OHLCV bars: target_hit / stop_hit / ambiguous (same-bar, counted as loss) / expired after OUTCOME_EVALUATION_MAX_BARS (default 30) - Migration 004: evaluated_at + outcome_date on trade_setups - GET /trades/performance: hit rate, expectancy (avg R), total R with breakdowns by direction, recommended action, and confidence bucket - New Performance page (stat cards, breakdown tables, Evaluate Now, methodology disclosure) wired into sidebar and mobile nav - 17 new unit tests for evaluation logic and stats aggregation Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
50 lines
1.3 KiB
Python
50 lines
1.3 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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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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recommendation_summary: RecommendationSummaryResponse | None = None
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