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.
66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
from typing import Dict
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from app.ml.rl_battery.agent import QLearningAgent
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from app.ml.rl_battery.environment import BatteryEnvironment
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from app.utils.logger import get_logger
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logger = get_logger(__name__)
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class BatteryPolicy:
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def __init__(self, policy_path: str = "models/rl_battery"):
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self.policy_path = policy_path
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self.agent: QLearningAgent = None
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self.env: BatteryEnvironment = None
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self._load_policy()
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def _load_policy(self):
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from pathlib import Path
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filepath = Path(self.policy_path) / "battery_policy.pkl"
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if filepath.exists():
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self.agent = QLearningAgent.load(filepath)
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self.env = BatteryEnvironment()
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logger.info(f"Loaded policy from {filepath}")
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def get_action(
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self,
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charge_level: float,
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current_price: float,
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price_forecast_1m: float = 0,
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price_forecast_5m: float = 0,
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price_forecast_15m: float = 0,
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hour: int = 0,
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) -> Dict:
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if self.agent is None:
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return {
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"action": "hold",
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"q_values": [0.0, 0.0, 0.0],
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"confidence": 0.0,
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}
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self.env.charge_level = charge_level
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self.env.current_price = current_price
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self.env.time_step = hour * 60
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state = self.env._get_state()
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action_idx = self.agent.get_action(state, training=False)
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actions = ["charge", "hold", "discharge"]
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action_name = actions[action_idx]
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state_idx = self.agent._discretize_state(state)
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q_values = self.agent.q_table[state_idx].tolist() if self.agent.q_table is not None else [0.0, 0.0, 0.0]
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max_q = max(q_values) if q_values else 0.0
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confidence = (max_q - min(q_values)) / (max_q + 0.001) if q_values else 0.0
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return {
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"action": action_name,
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"q_values": q_values,
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"confidence": min(confidence, 1.0),
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}
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__all__ = ["BatteryPolicy"]
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