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.
51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
from typing import Dict
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from datetime import datetime
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from app.utils.logger import get_logger
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from app.models.schemas import TrainingRequest, TrainingStatusEnum
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import uuid
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logger = get_logger(__name__)
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async def train_model_task(training_id: str, request: TrainingRequest):
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logger.info(f"Training model: {request.model_type.value}, horizon: {request.horizon}")
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try:
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if request.model_type.value == "price_prediction":
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from app.ml.price_prediction.trainer import PricePredictionTrainer
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trainer = PricePredictionTrainer()
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results = trainer.train_all(horizons=[request.horizon] if request.horizon else None)
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trainer.save_models()
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return {
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"training_id": training_id,
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"status": TrainingStatusEnum.COMPLETED,
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"results": results,
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"completed_at": datetime.utcnow().isoformat(),
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}
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elif request.model_type.value == "rl_battery":
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from app.ml.rl_battery.trainer import BatteryRLTrainer
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trainer = BatteryRLTrainer()
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results = trainer.train(n_episodes=500)
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trainer.save()
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return {
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"training_id": training_id,
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"status": TrainingStatusEnum.COMPLETED,
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"results": results,
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"completed_at": datetime.utcnow().isoformat(),
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}
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else:
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raise ValueError(f"Unknown model type: {request.model_type}")
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except Exception as e:
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logger.error(f"Training failed: {e}")
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raise
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__all__ = ["train_model_task"]
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