feat: trading feedback engine — periodic performance reports with AI summarization

- Migration 038: trading_reports table + report-summarizer agent seed
- 6 reporting modules: models, collector, sections, validator, summarizer, generator
- API endpoints: GET /api/reports (paginated, filterable), GET /api/reports/{id}
- Frontend hooks: useReports, useReport with TanStack Query
- Scheduler: daily (after 16:30 ET) and weekly (Saturday) report triggers
- Redis queue consumer for async report generation with retry/dedup
- 5 property-based tests (chunking, serialization, validation, accuracy, deltas)
- 109 unit/integration tests across all modules
- 6 frontend hook tests with MSW mocks

services/reporting/models.py

@@ -0,0 +1,104 @@
+from __future__ import annotations
+
+from datetime import date, datetime
+from enum import Enum
+
+from pydantic import BaseModel, Field
+
+
+class ReportType(str, Enum):
+    DAILY = "daily"
+    WEEKLY = "weekly"
+
+
+class ValidationStatus(str, Enum):
+    PASSED = "passed"
+    WARNINGS = "warnings"
+
+
+class ValidationWarning(BaseModel):
+    field_name: str
+    computed_value: float
+    snapshot_value: float
+    pct_difference: float
+
+
+class PLSection(BaseModel):
+    realized_pnl: float
+    unrealized_pnl: float
+    daily_return: float
+    cumulative_return: float
+    win_count: int
+    loss_count: int
+    win_rate: float
+    profit_factor: float
+    sharpe_ratio: float
+    summary: str = ""
+    validation_warnings: list[ValidationWarning] = Field(default_factory=list)
+
+
+class RecommendationAccuracySection(BaseModel):
+    total_evaluated: int
+    act_count: int
+    skip_count: int
+    acted_win_rate: float
+    avg_confidence_acted: float
+    avg_confidence_skipped: float
+    summary: str = ""
+    validation_warnings: list[ValidationWarning] = Field(default_factory=list)
+
+
+class PositionDetail(BaseModel):
+    ticker: str
+    entry_price: float
+    current_or_exit_price: float
+    pnl: float
+    pnl_pct: float
+    hold_duration_hours: float
+    status: str  # "open" or "closed"
+
+
+class PositionPerformanceSection(BaseModel):
+    positions: list[PositionDetail] = Field(default_factory=list)
+    summary: str = ""
+
+
+class RiskMetricsSection(BaseModel):
+    current_risk_tier: str
+    portfolio_heat: float
+    max_drawdown: float
+    current_drawdown_pct: float
+    reserve_pool_balance: float
+    circuit_breaker_event_count: int
+    summary: str = ""
+
+
+class ModelQualityWindow(BaseModel):
+    lookback: str
+    win_rate: float | None
+    directional_accuracy: float | None
+    information_coefficient: float | None
+    calibration_error: float | None
+    brier_score: float | None
+
+
+class ModelQualitySection(BaseModel):
+    windows: list[ModelQualityWindow] = Field(default_factory=list)
+    summary: str = ""
+    validation_warnings: list[ValidationWarning] = Field(default_factory=list)
+
+
+class ReportData(BaseModel):
+    """Top-level report structure stored as JSONB."""
+
+    pnl: PLSection
+    recommendation_accuracy: RecommendationAccuracySection
+    position_performance: PositionPerformanceSection
+    risk_metrics: RiskMetricsSection
+    model_quality: ModelQualitySection
+    executive_summary: str = ""
+    validation_status: ValidationStatus = ValidationStatus.PASSED
+    generated_at: datetime
+    period_start: date
+    period_end: date
+    report_type: ReportType