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Data Loader
Fetches and caches historical OHLCV data from Binance public API.
"""
import os
import pandas as pd
import requests
from datetime import datetime, timedelta
from pathlib import Path
from typing import Optional, List
import logging
import time
logger = logging.getLogger(__name__)
class BinanceHistoricalDataFetcher:
"""
Fetches historical klines data from Binance public API.
No authentication required.
"""
BASE_URL = os.environ.get("BINANCE_API_URL", "https://data-api.binance.vision/api/v3/klines")
# Mapping from our timeframe format to Binance's interval format
TIMEFRAME_MAP = {
'1m': '1m',
'3m': '3m',
'5m': '5m',
'15m': '15m',
'30m': '30m',
'1h': '1h',
'2h': '2h',
'4h': '4h',
'6h': '6h',
'8h': '8h',
'12h': '12h',
'1d': '1d',
'3d': '3d',
'1w': '1w',
'1M': '1M',
}
def __init__(self, max_retries: int = 3, retry_delay: float = 1.0):
"""
Initialize the Binance historical data fetcher.
Args:
max_retries: Maximum number of retries for failed requests
retry_delay: Delay between retries in seconds
"""
self.max_retries = max_retries
self.retry_delay = retry_delay
self.session = requests.Session()
def fetch_klines(
self,
symbol: str,
interval: str,
start_time: Optional[int] = None,
end_time: Optional[int] = None,
limit: int = 1000,
) -> List[List]:
"""
Fetch klines (candlestick) data from Binance.
Args:
symbol: Trading pair (e.g., 'BTCUSDT')
interval: Kline interval (e.g., '1h', '4h', '1d')
start_time: Start time in milliseconds
end_time: End time in milliseconds
limit: Maximum number of klines to return (max 1000)
Returns:
List of klines data
"""
params = {
'symbol': symbol,
'interval': interval,
'limit': min(limit, 1000),
}
if start_time:
params['startTime'] = start_time
if end_time:
params['endTime'] = end_time
for attempt in range(self.max_retries):
try:
response = self.session.get(self.BASE_URL, params=params, timeout=30)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
logger.warning(f"Request failed (attempt {attempt + 1}/{self.max_retries}): {e}")
if attempt < self.max_retries - 1:
time.sleep(self.retry_delay * (attempt + 1))
else:
raise
def fetch_historical_data(
self,
symbol: str,
timeframe: str,
start_date: datetime,
end_date: datetime,
) -> pd.DataFrame:
"""
Fetch historical OHLCV data for a date range.
Args:
symbol: Trading pair (e.g., 'BTC/USDT' or 'BTCUSDT')
timeframe: Candle timeframe (e.g., '1h', '4h', '1d')
start_date: Start datetime
end_date: End datetime
Returns:
DataFrame with OHLCV data
"""
# Convert symbol format (BTC/USDT -> BTCUSDT)
binance_symbol = symbol.replace('/', '')
# Get Binance interval
interval = self.TIMEFRAME_MAP.get(timeframe, '1h')
# Convert dates to milliseconds
start_ms = int(start_date.timestamp() * 1000)
end_ms = int(end_date.timestamp() * 1000)
all_klines = []
current_start = start_ms
logger.info(f"Fetching historical data for {binance_symbol} from {start_date} to {end_date}")
while current_start < end_ms:
klines = self.fetch_klines(
symbol=binance_symbol,
interval=interval,
start_time=current_start,
end_time=end_ms,
limit=1000,
)
if not klines:
break
all_klines.extend(klines)
# Update start time for next batch
# Last kline's close time + 1ms
current_start = klines[-1][6] + 1
# Rate limiting - Binance allows 1200 requests/minute
time.sleep(0.1)
logger.debug(f"Fetched {len(klines)} klines, total: {len(all_klines)}")
if not all_klines:
logger.warning("No data fetched from Binance")
return pd.DataFrame()
# Convert to DataFrame
df = pd.DataFrame(all_klines, columns=[
'open_time', 'open', 'high', 'low', 'close', 'volume',
'close_time', 'quote_volume', 'trades', 'taker_buy_base',
'taker_buy_quote', 'ignore'
])
# Convert types
df['timestamp'] = pd.to_datetime(df['open_time'], unit='ms')
df = df.set_index('timestamp')
# Select and convert numeric columns
df = df[['open', 'high', 'low', 'close', 'volume']].astype(float)
logger.info(f"Fetched {len(df)} klines from Binance")
return df
class DataLoader:
"""
Loads and caches historical OHLCV data.
Supports:
- Fetching from Binance public API (real data)
- Loading from local cache
- Generating synthetic data for testing (fallback)
"""
def __init__(
self,
cache_dir: str = "./data/historical",
connector: Optional['BinanceConnector'] = None,
):
"""
Initialize the data loader.
Args:
cache_dir: Directory for cached data files
connector: Optional BinanceConnector (not used for historical data)
"""
self.cache_dir = Path(cache_dir)
self.cache_dir.mkdir(parents=True, exist_ok=True)
self.connector = connector
self.binance_fetcher = BinanceHistoricalDataFetcher()
def load(
self,
symbol: str = 'BTC/USDT',
timeframe: str = '1h',
start_date: Optional[str] = None,
end_date: Optional[str] = None,
use_cache: bool = True,
force_download: bool = False,
) -> pd.DataFrame:
"""
Load OHLCV data for the specified parameters.
Args:
symbol: Trading pair (e.g., 'BTC/USDT')
timeframe: Candle timeframe (e.g., '1h', '4h', '1d')
start_date: Start date string (YYYY-MM-DD)
end_date: End date string (YYYY-MM-DD)
use_cache: Whether to use cached data
force_download: Force download even if cache exists
Returns:
DataFrame with OHLCV data
"""
# Parse dates
start = datetime.strptime(start_date, '%Y-%m-%d') if start_date else datetime.now() - timedelta(days=365)
end = datetime.strptime(end_date, '%Y-%m-%d') if end_date else datetime.now()
# Check cache first
cache_file = self._get_cache_path(symbol, timeframe, start, end)
if use_cache and cache_file.exists() and not force_download:
logger.info(f"Loading from cache: {cache_file}")
return self._load_from_cache(cache_file)
# Fetch from Binance public API
try:
logger.info(f"Downloading from Binance: {symbol} {timeframe}")
df = self.binance_fetcher.fetch_historical_data(
symbol=symbol,
timeframe=timeframe,
start_date=start,
end_date=end,
)
if len(df) > 0:
# Cache the data
self._save_to_cache(df, cache_file)
return df
else:
logger.warning("No data received from Binance, falling back to synthetic data")
return self._generate_synthetic_data(start, end, timeframe)
except Exception as e:
logger.error(f"Failed to fetch from Binance: {e}")
logger.warning("Falling back to synthetic data")
return self._generate_synthetic_data(start, end, timeframe)
def _get_cache_path(
self,
symbol: str,
timeframe: str,
start: datetime,
end: datetime,
) -> Path:
"""Generate cache file path."""
symbol_clean = symbol.replace('/', '_')
filename = f"{symbol_clean}_{timeframe}_{start.strftime('%Y%m%d')}_{end.strftime('%Y%m%d')}.csv"
return self.cache_dir / filename
def _load_from_cache(self, path: Path) -> pd.DataFrame:
"""Load data from cached CSV file."""
df = pd.read_csv(path, parse_dates=['timestamp'], index_col='timestamp')
return df
def _save_to_cache(self, df: pd.DataFrame, path: Path):
"""Save data to cache."""
df.to_csv(path)
logger.info(f"Data cached to: {path}")
def _generate_synthetic_data(
self,
start: datetime,
end: datetime,
timeframe: str,
) -> pd.DataFrame:
"""
Generate synthetic OHLCV data for testing.
Uses geometric Brownian motion to simulate price movement.
"""
import numpy as np
# Parse timeframe to minutes
tf_minutes = {
'1m': 1,
'5m': 5,
'15m': 15,
'30m': 30,
'1h': 60,
'4h': 240,
'1d': 1440,
}.get(timeframe, 60)
# Generate timestamps
n_candles = int((end - start).total_seconds() / (tf_minutes * 60))
timestamps = pd.date_range(start=start, periods=n_candles, freq=f'{tf_minutes}min')
# Generate prices using GBM
initial_price = 40000 # Starting price (BTC-like)
mu = 0.0001 # Drift
sigma = 0.02 # Volatility
# Generate log returns
returns = np.random.normal(mu, sigma, n_candles)
log_prices = np.log(initial_price) + np.cumsum(returns)
close_prices = np.exp(log_prices)
# Generate OHLC from close
high_mult = 1 + np.abs(np.random.normal(0, 0.005, n_candles))
low_mult = 1 - np.abs(np.random.normal(0, 0.005, n_candles))
open_mult = 1 + np.random.normal(0, 0.002, n_candles)
df = pd.DataFrame({
'open': close_prices * open_mult,
'high': close_prices * high_mult,
'low': close_prices * low_mult,
'close': close_prices,
'volume': np.random.uniform(100, 10000, n_candles),
}, index=timestamps)
df.index.name = 'timestamp'
# Ensure high is highest, low is lowest
df['high'] = df[['open', 'high', 'low', 'close']].max(axis=1)
df['low'] = df[['open', 'high', 'low', 'close']].min(axis=1)
logger.info(f"Generated {len(df)} synthetic candles")
return df
def clear_cache(self):
"""Clear all cached data."""
import shutil
if self.cache_dir.exists():
shutil.rmtree(self.cache_dir)
self.cache_dir.mkdir(parents=True, exist_ok=True)
logger.info("Cache cleared")
def download_binance_data(
symbol: str = 'BTC/USDT',
timeframe: str = '1h',
days: int = 365,
output_dir: str = './data/historical',
) -> pd.DataFrame:
"""
Convenience function to download Binance historical data.
Args:
symbol: Trading pair (e.g., 'BTC/USDT')
timeframe: Candle timeframe (e.g., '1h', '4h', '1d')
days: Number of days of historical data
output_dir: Directory to save data
Returns:
DataFrame with OHLCV data
"""
loader = DataLoader(cache_dir=output_dir)
end_date = datetime.now()
start_date = end_date - timedelta(days=days)
return loader.load(
symbol=symbol,
timeframe=timeframe,
start_date=start_date.strftime('%Y-%m-%d'),
end_date=end_date.strftime('%Y-%m-%d'),
force_download=True,
)
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