deploy: 2026-03-20 07:49

2026-03-20 07:49:42 +09:00
commit d14a8bab04
73 changed files with 76534 additions and 0 deletions
--- a/backtest/data_loader.py
+++ b/backtest/data_loader.py
@@ -0,0 +1,114 @@
+"""Historical data loader for backtesting.
+
+Fetches OHLCV data from exchanges or loads from CSV files.
+"""
+
+from __future__ import annotations
+
+import asyncio
+from pathlib import Path
+from typing import Optional
+
+import pandas as pd
+from loguru import logger
+
+from execution.exchange_client import ExchangeClient
+
+
+class DataLoader:
+    """Load historical OHLCV data for backtesting."""
+
+    def __init__(self, exchange_client: ExchangeClient | None = None):
+        self._client = exchange_client
+
+    async def fetch_from_exchange(
+        self,
+        symbol: str,
+        timeframe: str = "1h",
+        since: str | None = None,
+        limit: int = 1000,
+    ) -> pd.DataFrame:
+        """Fetch historical data from an exchange.
+
+        Args:
+            symbol: Trading pair (e.g., "BTC/USDT").
+            timeframe: Candle timeframe.
+            since: ISO date string for start (e.g., "2025-01-01").
+            limit: Max candles to fetch.
+        """
+        if self._client is None:
+            raise RuntimeError("ExchangeClient required for live data fetch")
+
+        if not await self._client.is_connected():
+            await self._client.connect()
+
+        since_ts = None
+        if since:
+            since_ts = int(pd.Timestamp(since).timestamp() * 1000)
+
+        # Fetch in chunks if needed
+        all_data: list = []
+        remaining = limit
+        current_since = since_ts
+
+        while remaining > 0:
+            batch_limit = min(remaining, 500)
+            df = await self._client.fetch_ohlcv(
+                symbol, timeframe, since=current_since, limit=batch_limit
+            )
+            if df.empty:
+                break
+            all_data.append(df)
+            remaining -= len(df)
+            # Move since to after the last candle
+            current_since = int(df.index[-1].timestamp() * 1000) + 1
+
+            if len(df) < batch_limit:
+                break
+
+        if not all_data:
+            return pd.DataFrame()
+
+        result = pd.concat(all_data)
+        result = result[~result.index.duplicated(keep="last")]
+        logger.info("Loaded {} candles for {} {}", len(result), symbol, timeframe)
+        return result.sort_index()
+
+    @staticmethod
+    def load_from_csv(file_path: str) -> pd.DataFrame:
+        """Load OHLCV data from a CSV file.
+
+        Expected columns: timestamp (or date), open, high, low, close, volume
+        """
+        path = Path(file_path)
+        if not path.exists():
+            raise FileNotFoundError(f"CSV file not found: {file_path}")
+
+        df = pd.read_csv(file_path)
+
+        # Detect timestamp column
+        ts_col = None
+        for col in ["timestamp", "date", "datetime", "time"]:
+            if col in df.columns:
+                ts_col = col
+                break
+
+        if ts_col:
+            df[ts_col] = pd.to_datetime(df[ts_col])
+            df.set_index(ts_col, inplace=True)
+
+        required = {"open", "high", "low", "close", "volume"}
+        missing = required - set(df.columns)
+        if missing:
+            raise ValueError(f"CSV missing required columns: {missing}")
+
+        logger.info("Loaded {} candles from {}", len(df), file_path)
+        return df
+
+    @staticmethod
+    def save_to_csv(df: pd.DataFrame, file_path: str) -> None:
+        """Save OHLCV DataFrame to CSV."""
+        path = Path(file_path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        df.to_csv(file_path)
+        logger.info("Saved {} candles to {}", len(df), file_path)