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.
63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
from fastapi import WebSocket
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from typing import List
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from app.utils.logger import get_logger
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logger = get_logger(__name__)
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class ConnectionManager:
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def __init__(self):
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self.active_connections: List[WebSocket] = []
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async def connect(self, websocket: WebSocket):
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await websocket.accept()
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self.active_connections.append(websocket)
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logger.info(f"WebSocket connected. Total connections: {len(self.active_connections)}")
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def disconnect(self, websocket: WebSocket):
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if websocket in self.active_connections:
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self.active_connections.remove(websocket)
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logger.info(f"WebSocket disconnected. Total connections: {len(self.active_connections)}")
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async def broadcast(self, event_type: str, data: dict):
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message = {"event_type": event_type, "data": data, "timestamp": None}
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disconnected = []
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for connection in self.active_connections:
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try:
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await connection.send_json(message)
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except Exception:
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disconnected.append(connection)
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for conn in disconnected:
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self.disconnect(conn)
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async def broadcast_price_update(self, region: str, price_data: dict):
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await self.broadcast("price_update", {"region": region, "price_data": price_data})
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async def broadcast_battery_update(self, battery_id: str, battery_state: dict):
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await self.broadcast("battery_update", {"battery_id": battery_id, "battery_state": battery_state})
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async def broadcast_trade(self, trade: dict):
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await self.broadcast("trade_executed", trade)
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async def broadcast_alert(self, alert: dict):
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await self.broadcast("alert_triggered", alert)
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async def broadcast_backtest_progress(self, backtest_id: str, progress: float, status: str):
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await self.broadcast(
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"backtest_progress",
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{"backtest_id": backtest_id, "progress": progress, "status": status},
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)
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async def broadcast_model_training_progress(
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self, model_id: str, progress: float, epoch: int = None, metrics: dict = None
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):
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await self.broadcast(
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"model_training_progress",
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{"model_id": model_id, "progress": progress, "epoch": epoch, "metrics": metrics},
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)
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manager = ConnectionManager()
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