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Multi-Asset Data Fetcher
Fetches historical and real-time data for multiple crypto assets.
Supports: BTC, ETH, SOL, XRP (and more)
Features:
- Parallel data fetching for efficiency
- Asset-specific metadata (volatility characteristics, typical spread)
- Unified DataFrame format across all assets
"""
import os
import pandas as pd
import numpy as np
import requests
from typing import List, Dict, Optional, Tuple
from concurrent.futures import ThreadPoolExecutor, as_completed
from dataclasses import dataclass
import logging
import time
logger = logging.getLogger(__name__)
@dataclass
class AssetConfig:
"""Configuration for a tradeable asset."""
symbol: str # e.g., "BTCUSDT"
name: str # e.g., "Bitcoin"
asset_id: int # Unique ID for embedding (0=BTC, 1=ETH, etc.)
base_volatility: float # Historical volatility multiplier relative to BTC
liquidity_score: float # 1.0 = highest (BTC), lower = less liquid
btc_correlation: float # Typical correlation with BTC
def to_features(self) -> np.ndarray:
"""Convert to feature vector for embedding."""
return np.array([
self.asset_id / 10.0, # Normalized asset ID
self.base_volatility,
self.liquidity_score,
self.btc_correlation,
])
# Predefined asset configurations
SUPPORTED_ASSETS: Dict[str, AssetConfig] = {
"BTCUSDT": AssetConfig(
symbol="BTCUSDT",
name="Bitcoin",
asset_id=0,
base_volatility=1.0,
liquidity_score=1.0,
btc_correlation=1.0,
),
"ETHUSDT": AssetConfig(
symbol="ETHUSDT",
name="Ethereum",
asset_id=1,
base_volatility=1.15, # ~15% more volatile than BTC
liquidity_score=0.9,
btc_correlation=0.85,
),
"SOLUSDT": AssetConfig(
symbol="SOLUSDT",
name="Solana",
asset_id=2,
base_volatility=1.8, # Much more volatile
liquidity_score=0.6,
btc_correlation=0.75,
),
"XRPUSDT": AssetConfig(
symbol="XRPUSDT",
name="XRP",
asset_id=3,
base_volatility=1.5,
liquidity_score=0.7,
btc_correlation=0.60,
),
"BNBUSDT": AssetConfig(
symbol="BNBUSDT",
name="BNB",
asset_id=4,
base_volatility=1.2,
liquidity_score=0.8,
btc_correlation=0.80,
),
"DOGEUSDT": AssetConfig(
symbol="DOGEUSDT",
name="Dogecoin",
asset_id=5,
base_volatility=2.5, # Very volatile, meme-driven
liquidity_score=0.5,
btc_correlation=0.50,
),
}
class MultiAssetDataFetcher:
"""
Fetches data for multiple crypto assets in parallel.
Usage:
fetcher = MultiAssetDataFetcher()
# Fetch single asset
df = fetcher.fetch_asset("BTCUSDT", "1h", days=30)
# Fetch multiple assets
data = fetcher.fetch_multiple(["BTCUSDT", "ETHUSDT"], "1h", days=30)
"""
def __init__(
self,
base_url: str = None,
max_workers: int = 4,
):
self.base_url = base_url or os.environ.get("BINANCE_FUTURES_URL", "https://data-api.binance.vision")
self.max_workers = max_workers
logger.info(f"📊 MultiAssetDataFetcher initialized for {len(SUPPORTED_ASSETS)} assets")
def get_asset_config(self, symbol: str) -> AssetConfig:
"""Get configuration for an asset."""
if symbol not in SUPPORTED_ASSETS:
raise ValueError(f"Unsupported asset: {symbol}. Supported: {list(SUPPORTED_ASSETS.keys())}")
return SUPPORTED_ASSETS[symbol]
def fetch_asset(
self,
symbol: str,
interval: str = "1h",
days: int = 30,
limit: int = 1000,
) -> pd.DataFrame:
"""
Fetch historical OHLCV data for a single asset.
Args:
symbol: Trading pair (e.g., "BTCUSDT")
interval: Candle interval (1m, 5m, 15m, 1h, 4h, 1d)
days: Number of days to fetch
limit: Max candles per request (Binance max: 1000)
Returns:
DataFrame with OHLCV data + asset metadata
"""
all_data = []
# Calculate timestamps
end_time = int(time.time() * 1000)
# Map interval to milliseconds
interval_ms = {
"1m": 60 * 1000,
"5m": 5 * 60 * 1000,
"15m": 15 * 60 * 1000,
"30m": 30 * 60 * 1000,
"1h": 60 * 60 * 1000,
"4h": 4 * 60 * 60 * 1000,
"1d": 24 * 60 * 60 * 1000,
}.get(interval, 60 * 60 * 1000)
candles_needed = int(days * 24 * 60 * 60 * 1000 / interval_ms)
# Fetch data in chunks
current_end = end_time
remaining = candles_needed
while remaining > 0:
chunk_size = min(remaining, limit)
try:
url = f"{self.base_url}/api/v3/klines"
params = {
"symbol": symbol,
"interval": interval,
"limit": chunk_size,
"endTime": current_end,
}
response = requests.get(url, params=params, timeout=30)
response.raise_for_status()
data = response.json()
if not data:
break
all_data.extend(data)
# Move to earlier time
current_end = int(data[0][0]) - 1
remaining -= len(data)
except Exception as e:
logger.error(f"Error fetching {symbol}: {e}")
break
if not all_data:
return pd.DataFrame()
# Convert to DataFrame
df = pd.DataFrame(all_data, columns=[
'timestamp', 'open', 'high', 'low', 'close', 'volume',
'close_time', 'quote_volume', 'trades', 'taker_buy_base',
'taker_buy_quote', 'ignore'
])
# Process data
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
df = df.sort_values('timestamp').reset_index(drop=True)
for col in ['open', 'high', 'low', 'close', 'volume', 'quote_volume']:
df[col] = df[col].astype(float)
# Add asset metadata
config = self.get_asset_config(symbol)
df['symbol'] = symbol
df['asset_id'] = config.asset_id
df['base_volatility'] = config.base_volatility
df['liquidity_score'] = config.liquidity_score
df['btc_correlation'] = config.btc_correlation
# Keep essential columns
df = df[[
'timestamp', 'open', 'high', 'low', 'close', 'volume', 'quote_volume',
'symbol', 'asset_id', 'base_volatility', 'liquidity_score', 'btc_correlation'
]]
logger.info(f"📊 Fetched {len(df)} candles for {symbol} ({interval})")
return df
def fetch_multiple(
self,
symbols: List[str],
interval: str = "1h",
days: int = 30,
) -> Dict[str, pd.DataFrame]:
"""
Fetch data for multiple assets in parallel.
Args:
symbols: List of trading pairs
interval: Candle interval
days: Number of days
Returns:
Dictionary mapping symbol to DataFrame
"""
results = {}
with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
future_to_symbol = {
executor.submit(self.fetch_asset, symbol, interval, days): symbol
for symbol in symbols
}
for future in as_completed(future_to_symbol):
symbol = future_to_symbol[future]
try:
df = future.result()
results[symbol] = df
except Exception as e:
logger.error(f"Failed to fetch {symbol}: {e}")
results[symbol] = pd.DataFrame()
return results
def fetch_all_supported(
self,
interval: str = "1h",
days: int = 30,
) -> Dict[str, pd.DataFrame]:
"""Fetch data for all supported assets."""
return self.fetch_multiple(list(SUPPORTED_ASSETS.keys()), interval, days)
def create_combined_dataset(
self,
symbols: List[str],
interval: str = "1h",
days: int = 30,
) -> pd.DataFrame:
"""
Create a combined dataset with all assets for multi-asset training.
Returns:
Single DataFrame with all assets, marked by symbol/asset_id
"""
data = self.fetch_multiple(symbols, interval, days)
dfs = []
for symbol, df in data.items():
if not df.empty:
dfs.append(df)
if not dfs:
return pd.DataFrame()
combined = pd.concat(dfs, ignore_index=True)
combined = combined.sort_values(['symbol', 'timestamp']).reset_index(drop=True)
logger.info(f"📊 Combined dataset: {len(combined)} rows across {len(dfs)} assets")
return combined
# Convenience functions
def get_asset_embedding(symbol: str) -> np.ndarray:
"""Get the feature embedding for an asset."""
if symbol not in SUPPORTED_ASSETS:
raise ValueError(f"Unknown asset: {symbol}")
return SUPPORTED_ASSETS[symbol].to_features()
def get_all_supported_symbols() -> List[str]:
"""Get list of all supported trading symbols."""
return list(SUPPORTED_ASSETS.keys())
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
fetcher = MultiAssetDataFetcher()
# Test single asset
df = fetcher.fetch_asset("BTCUSDT", "1h", days=7)
print(f"BTC: {len(df)} candles")
# Test multiple assets
data = fetcher.fetch_multiple(["BTCUSDT", "ETHUSDT", "SOLUSDT"], "1h", days=7)
for symbol, df in data.items():
print(f"{symbol}: {len(df)} candles")
# Test combined dataset
combined = fetcher.create_combined_dataset(["BTCUSDT", "ETHUSDT"], "1h", days=7)
print(f"Combined: {len(combined)} total rows")
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