feat(calibration): Step 3 - Stategies preparado conceptualmente

src/web/api/v2/schemas/calibration_strategies.py

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+# src/web/api/v2/schemas/calibration_strategies.py
+
+from typing import Any, Dict, List, Literal, Optional
+from pydantic import BaseModel, Field
+
+from .calibration_risk import StopConfigSchema, RiskConfigSchema, GlobalRiskRulesSchema
+
+
+class WalkForwardConfigSchema(BaseModel):
+    train_days: int = Field(..., gt=0)
+    test_days: int = Field(..., gt=0)
+    step_days: Optional[int] = Field(None, gt=0)  # if None => step = test_days
+
+
+class OptimizationConfigSchema(BaseModel):
+    optimizer_metric: str = Field("sharpe_ratio")
+    max_combinations: int = Field(500, gt=0)
+    min_trades_train: int = Field(30, ge=0)
+    min_trades_test: int = Field(10, ge=0)
+
+
+class StrategySelectionSchema(BaseModel):
+    strategy_id: str
+    param_grid: Dict[str, List[Any]]
+
+
+class CalibrationStrategiesInspectRequest(BaseModel):
+    symbol: str
+    timeframe: str
+
+    # snapshot from Step 2 (closed)
+    stop: StopConfigSchema
+    risk: RiskConfigSchema
+    global_rules: GlobalRiskRulesSchema
+    account_equity: float = Field(..., gt=0)
+
+    strategies: List[StrategySelectionSchema]
+    wf: WalkForwardConfigSchema
+    optimization: OptimizationConfigSchema
+
+    commission: float = Field(0.001, ge=0)
+    slippage: float = Field(0.0005, ge=0)
+
+
+class WindowRowSchema(BaseModel):
+    window: int
+    train_start: str
+    train_end: str
+    test_start: str
+    test_end: str
+
+    return_pct: float
+    sharpe: float
+    max_dd_pct: float
+    trades: int
+    params: Dict[str, Any]
+
+
+class StrategyRunResultSchema(BaseModel):
+    strategy_id: str
+    status: Literal["ok", "warning", "fail"]
+    message: str
+
+    n_windows: int
+    oos_final_equity: float
+    oos_total_return_pct: float
+    oos_max_dd_worst_pct: float
+    degradation_sharpe: Optional[float] = None
+
+    windows: List[WindowRowSchema]
+
+
+class CalibrationStrategiesInspectResponse(BaseModel):
+    valid: bool
+    status: Literal["ok", "warning", "fail"]
+    checks: Dict[str, Any]
+    message: str
+
+    results: List[StrategyRunResultSchema]
+
+
+class CalibrationStrategiesValidateResponse(CalibrationStrategiesInspectResponse):
+    series: Dict[str, Any]