schemas.py

from pydantic import BaseModel, Field
from typing import Optional, Dict, Any, List
from datetime import datetime

class PredictRequest(BaseModel):
    asset: str = Field(..., description="Asset ticker to predict, e.g., 'BTC' or 'ETH'")
    timeframe: str = Field("5m", description="Prediction interval, currently '5m' is supported")

class PredictionResponse(BaseModel):
    id: int
    asset: str
    timestamp: datetime
    current_price: float
    polymarket_info: Optional[List[Dict[str, Any]]] = None
    kalshi_info: Optional[List[Dict[str, Any]]] = None
    technical_indicators: Optional[Dict[str, Any]] = None
    predicted_direction: str
    probability: float
    kelly_fraction: float
    bet_size: float
    status: str

    class Config:
        from_attributes = True

class MetricSummary(BaseModel):
    total_predictions: int
    resolved_predictions: int
    pending_predictions: int
    correct_predictions: int
    incorrect_predictions: int
    win_rate: float
    average_brier_score: float
    cumulative_pnl: float
    average_bet_size: float
    win_rate_by_asset: Dict[str, float]

class ResolveRequest(BaseModel):
    prediction_id: int
    actual_close_price: Optional[float] = Field(None, description="Provide a price to force-resolve, or let the feedback agent fetch it automatically")