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.
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174
backend/app/services/data_service.py
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174
backend/app/services/data_service.py
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from typing import Dict, List, Optional
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from pathlib import Path
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import pandas as pd
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from datetime import datetime
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from app.config import settings
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from app.utils.logger import get_logger
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logger = get_logger(__name__)
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class DataService:
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def __init__(self):
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self.data_path: Path = settings.DATA_PATH_RESOLVED
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self._price_data: Dict[str, pd.DataFrame] = {}
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self._battery_data: Optional[pd.DataFrame] = None
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self._loaded: bool = False
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async def initialize(self):
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logger.info(f"Loading data from {self.data_path}")
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self._load_price_data()
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self._load_battery_data()
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self._loaded = True
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logger.info("Data loaded successfully")
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def _load_price_data(self):
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if not self.data_path.exists():
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logger.warning(f"Data path {self.data_path} does not exist")
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return
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prices_file = self.data_path / "electricity_prices.parquet"
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if prices_file.exists():
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df = pd.read_parquet(prices_file)
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logger.info(f"Loaded price data: {len(df)} total rows from {prices_file}")
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if "region" in df.columns:
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for region in ["FR", "BE", "DE", "NL", "UK"]:
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region_df = df[df["region"] == region].copy()
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if len(region_df) > 0:
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self._price_data[region] = region_df
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logger.info(f"Loaded {region} price data: {len(region_df)} rows")
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else:
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logger.warning("Price data file does not contain 'region' column")
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else:
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logger.warning(f"Price data file not found: {prices_file}")
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def _load_battery_data(self):
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battery_path = self.data_path / "battery_capacity.parquet"
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if battery_path.exists():
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self._battery_data = pd.read_parquet(battery_path)
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logger.info(f"Loaded battery data: {len(self._battery_data)} rows")
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else:
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logger.warning(f"Battery data file not found: {battery_path}")
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def get_latest_prices(self) -> Dict[str, Dict]:
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result = {}
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for region, df in self._price_data.items():
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if len(df) > 0:
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latest = df.iloc[-1].to_dict()
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result[region] = {
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"timestamp": latest.get("timestamp"),
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"day_ahead_price": latest.get("day_ahead_price", 0),
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"real_time_price": latest.get("real_time_price", 0),
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"volume_mw": latest.get("volume_mw", 0),
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}
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return result
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def get_price_history(
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self, region: str, start: Optional[str] = None, end: Optional[str] = None, limit: int = 1000
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) -> List[Dict]:
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if region not in self._price_data:
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return []
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df = self._price_data[region].copy()
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if "timestamp" in df.columns:
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df = df.sort_values("timestamp")
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if start:
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df = df[df["timestamp"] >= start]
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if end:
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df = df[df["timestamp"] <= end]
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df = df.tail(limit)
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return df.to_dict("records")
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def get_battery_states(self) -> List[Dict]:
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if self._battery_data is None or len(self._battery_data) == 0:
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return []
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latest_by_battery = self._battery_data.groupby("battery_id").last().reset_index()
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result = []
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for _, row in latest_by_battery.iterrows():
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result.append(
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{
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"timestamp": row.get("timestamp"),
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"battery_id": row.get("battery_id"),
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"capacity_mwh": row.get("capacity_mwh", 0),
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"charge_level_mwh": row.get("charge_level_mwh", 0),
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"charge_rate_mw": row.get("charge_rate_mw", 0),
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"discharge_rate_mw": row.get("discharge_rate_mw", 0),
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"efficiency": row.get("efficiency", 0.9),
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}
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)
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return result
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def get_arbitrage_opportunities(self, min_spread: Optional[float] = None) -> List[Dict]:
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if min_spread is None:
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min_spread = settings.ARBITRAGE_MIN_SPREAD
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opportunities = []
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latest_prices = self.get_latest_prices()
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regions = list(latest_prices.keys())
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for i in range(len(regions)):
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for j in range(i + 1, len(regions)):
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region_a = regions[i]
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region_b = regions[j]
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price_a = latest_prices[region_a].get("real_time_price", 0)
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price_b = latest_prices[region_b].get("real_time_price", 0)
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if price_a > 0 and price_b > 0:
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spread = abs(price_a - price_b)
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if spread >= min_spread:
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if price_a < price_b:
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buy_region, sell_region = region_a, region_b
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buy_price, sell_price = price_a, price_b
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else:
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buy_region, sell_region = region_b, region_a
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buy_price, sell_price = price_b, price_a
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opportunities.append(
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{
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"timestamp": datetime.utcnow(),
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"buy_region": buy_region,
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"sell_region": sell_region,
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"buy_price": buy_price,
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"sell_price": sell_price,
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"spread": spread,
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"volume_mw": 100,
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}
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)
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return opportunities
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def get_dashboard_summary(self) -> Dict:
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latest_prices = self.get_latest_prices()
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total_volume = sum(p.get("volume_mw", 0) for p in latest_prices.values())
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avg_price = (
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sum(p.get("real_time_price", 0) for p in latest_prices.values()) / len(latest_prices)
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if latest_prices
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else 0
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)
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arbitrage = self.get_arbitrage_opportunities()
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battery_states = self.get_battery_states()
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avg_battery_charge = 0
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if battery_states:
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avg_battery_charge = sum(
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b.get("charge_level_mwh", 0) / b.get("capacity_mwh", 1) for b in battery_states
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) / len(battery_states)
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return {
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"latest_timestamp": datetime.utcnow(),
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"total_volume_mw": total_volume,
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"avg_realtime_price": avg_price,
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"arbitrage_count": len(arbitrage),
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"battery_count": len(battery_states),
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"avg_battery_charge": avg_battery_charge,
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}
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