Add FastAPI backend for energy trading system

Implements FastAPI backend with ML model support for energy trading,
including price prediction models and RL-based battery trading policy.
Features dashboard, trading, backtest, and settings API routes with
WebSocket support for real-time updates.

backend/app/models/schemas.py

@@ -0,0 +1,145 @@
+from datetime import datetime
+from typing import Optional, List, Dict, Any
+from pydantic import BaseModel, Field
+from app.models.enums import (
+    RegionEnum,
+    StrategyEnum,
+    TradeTypeEnum,
+    BacktestStatusEnum,
+    ModelType,
+    AlertTypeEnum,
+    TrainingStatusEnum,
+)
+
+
+class PriceData(BaseModel):
+    timestamp: datetime
+    region: RegionEnum
+    day_ahead_price: float
+    real_time_price: float
+    volume_mw: float
+
+
+class BatteryState(BaseModel):
+    timestamp: datetime
+    battery_id: str
+    capacity_mwh: float
+    charge_level_mwh: float
+    charge_rate_mw: float
+    discharge_rate_mw: float
+    efficiency: float
+    charge_level_pct: float = Field(default_factory=lambda: 0.0)
+
+
+class BacktestConfig(BaseModel):
+    start_date: str
+    end_date: str
+    strategies: List[StrategyEnum] = Field(default_factory=list)
+    use_ml: bool = True
+    battery_min_reserve: Optional[float] = None
+    battery_max_charge: Optional[float] = None
+    arbitrage_min_spread: Optional[float] = None
+
+
+class BacktestMetrics(BaseModel):
+    total_revenue: float
+    arbitrage_profit: float
+    battery_revenue: float
+    mining_profit: float
+    battery_utilization: float
+    price_capture_rate: float
+    win_rate: float
+    sharpe_ratio: float
+    max_drawdown: float
+    total_trades: int
+
+
+class TrainingRequest(BaseModel):
+    model_type: ModelType
+    horizon: Optional[int] = None
+    start_date: str
+    end_date: str
+    hyperparameters: Dict[str, Any] = Field(default_factory=dict)
+
+
+class PredictionResponse(BaseModel):
+    model_id: str
+    timestamp: datetime
+    prediction: float
+    confidence: Optional[float] = None
+    features_used: List[str] = Field(default_factory=list)
+
+
+class ModelInfo(BaseModel):
+    model_id: str
+    model_type: ModelType
+    version: str
+    created_at: datetime
+    metrics: Dict[str, float] = Field(default_factory=dict)
+    hyperparameters: Dict[str, Any] = Field(default_factory=dict)
+
+
+class TrainingStatus(BaseModel):
+    training_id: str
+    status: TrainingStatusEnum
+    progress: float = 0.0
+    current_epoch: Optional[int] = None
+    total_epochs: Optional[int] = None
+    metrics: Dict[str, float] = Field(default_factory=dict)
+    error_message: Optional[str] = None
+    started_at: Optional[datetime] = None
+    completed_at: Optional[datetime] = None
+
+
+class ArbitrageOpportunity(BaseModel):
+    timestamp: datetime
+    buy_region: RegionEnum
+    sell_region: RegionEnum
+    buy_price: float
+    sell_price: float
+    spread: float
+    volume_mw: float
+
+
+class DashboardSummary(BaseModel):
+    latest_timestamp: datetime
+    total_volume_mw: float
+    avg_realtime_price: float
+    arbitrage_count: int
+    battery_count: int
+    avg_battery_charge: float
+
+
+class Trade(BaseModel):
+    timestamp: datetime
+    backtest_id: str
+    trade_type: TradeTypeEnum
+    region: Optional[RegionEnum] = None
+    price: float
+    volume_mw: float
+    revenue: float
+    battery_id: Optional[str] = None
+
+
+class StrategyStatus(BaseModel):
+    strategy: StrategyEnum
+    enabled: bool
+    last_execution: Optional[datetime] = None
+    total_trades: int = 0
+    profit_loss: float = 0.0
+
+
+class Alert(BaseModel):
+    alert_id: str
+    alert_type: AlertTypeEnum
+    timestamp: datetime
+    message: str
+    data: Dict[str, Any] = Field(default_factory=dict)
+    acknowledged: bool = False
+
+
+class AppSettings(BaseModel):
+    battery_min_reserve: float
+    battery_max_charge: float
+    arbitrage_min_spread: float
+    mining_margin_threshold: float