kbt-devops
fe76bc7629
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.
146 lines
3.4 KiB
Python
146 lines
3.4 KiB
Python
from datetime import datetime
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from typing import Optional, List, Dict, Any
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from pydantic import BaseModel, Field
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from app.models.enums import (
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RegionEnum,
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StrategyEnum,
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TradeTypeEnum,
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BacktestStatusEnum,
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ModelType,
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AlertTypeEnum,
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TrainingStatusEnum,
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)
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class PriceData(BaseModel):
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timestamp: datetime
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region: RegionEnum
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day_ahead_price: float
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real_time_price: float
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volume_mw: float
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class BatteryState(BaseModel):
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timestamp: datetime
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battery_id: str
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capacity_mwh: float
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charge_level_mwh: float
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charge_rate_mw: float
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discharge_rate_mw: float
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efficiency: float
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charge_level_pct: float = Field(default_factory=lambda: 0.0)
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class BacktestConfig(BaseModel):
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start_date: str
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end_date: str
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strategies: List[StrategyEnum] = Field(default_factory=list)
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use_ml: bool = True
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battery_min_reserve: Optional[float] = None
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battery_max_charge: Optional[float] = None
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arbitrage_min_spread: Optional[float] = None
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class BacktestMetrics(BaseModel):
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total_revenue: float
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arbitrage_profit: float
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battery_revenue: float
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mining_profit: float
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battery_utilization: float
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price_capture_rate: float
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win_rate: float
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sharpe_ratio: float
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max_drawdown: float
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total_trades: int
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class TrainingRequest(BaseModel):
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model_type: ModelType
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horizon: Optional[int] = None
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start_date: str
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end_date: str
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hyperparameters: Dict[str, Any] = Field(default_factory=dict)
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class PredictionResponse(BaseModel):
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model_id: str
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timestamp: datetime
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prediction: float
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confidence: Optional[float] = None
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features_used: List[str] = Field(default_factory=list)
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class ModelInfo(BaseModel):
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model_id: str
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model_type: ModelType
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version: str
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created_at: datetime
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metrics: Dict[str, float] = Field(default_factory=dict)
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hyperparameters: Dict[str, Any] = Field(default_factory=dict)
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class TrainingStatus(BaseModel):
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training_id: str
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status: TrainingStatusEnum
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progress: float = 0.0
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current_epoch: Optional[int] = None
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total_epochs: Optional[int] = None
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metrics: Dict[str, float] = Field(default_factory=dict)
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error_message: Optional[str] = None
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started_at: Optional[datetime] = None
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completed_at: Optional[datetime] = None
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class ArbitrageOpportunity(BaseModel):
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timestamp: datetime
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buy_region: RegionEnum
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sell_region: RegionEnum
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buy_price: float
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sell_price: float
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spread: float
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volume_mw: float
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class DashboardSummary(BaseModel):
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latest_timestamp: datetime
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total_volume_mw: float
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avg_realtime_price: float
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arbitrage_count: int
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battery_count: int
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avg_battery_charge: float
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class Trade(BaseModel):
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timestamp: datetime
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backtest_id: str
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trade_type: TradeTypeEnum
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region: Optional[RegionEnum] = None
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price: float
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volume_mw: float
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revenue: float
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battery_id: Optional[str] = None
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class StrategyStatus(BaseModel):
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strategy: StrategyEnum
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enabled: bool
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last_execution: Optional[datetime] = None
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total_trades: int = 0
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profit_loss: float = 0.0
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class Alert(BaseModel):
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alert_id: str
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alert_type: AlertTypeEnum
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timestamp: datetime
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message: str
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data: Dict[str, Any] = Field(default_factory=dict)
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acknowledged: bool = False
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class AppSettings(BaseModel):
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battery_min_reserve: float
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battery_max_charge: float
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arbitrage_min_spread: float
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mining_margin_threshold: float
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