narf 0.1.1

Market data loader library for cryptocurrency exchanges

Narf

Narf is a simple Python library for downloading historical cryptocurrency market data from Binance. Get klines (candlestick data), trades, and aggregated trades for spot and futures markets with just a few lines of code.

Installation

pip install narf

Quick Start

from datetime import datetime
from narf.data import binance

# Load 1-minute klines for BTC/USDT futures (USD-M) from January 2023 to November 2025
df = binance.futures.um.klines.load("BTCUSDT", datetime(2023, 1, 1), datetime(2025, 11, 1))

# Load spot market aggregated trades for ETH/USDT with custom interval
df = binance.spot.aggTrades.load("ETHUSDT", datetime(2024, 1, 1), datetime(2024, 12, 31), interval="1h")

# Load data up to now (end date is optional)
df = binance.futures.um.klines.load("BTCUSDT", datetime(2024, 1, 1))

Features

• Simple API: Intuitive interface for accessing Binance historical data
• Date Range Support: Load data for any date range with automatic month-by-month fetching
• Automatic Caching: Downloaded data is cached locally to avoid re-downloading
• Pandas Integration: Returns pandas DataFrames ready for analysis
• Multiple Markets: Support for spot, futures USD-M (UM), and futures Coin-M (CM)
• Multiple Data Types: Klines (candlestick), trades, and aggregated trades

Available Markets

Spot Market

binance.spot.klines.load(symbol, start, end=None, interval="1m")
binance.spot.trades.load(symbol, start, end=None, interval="1m")
binance.spot.aggTrades.load(symbol, start, end=None, interval="1m")

Futures Market - USD-M (UM)

binance.futures.um.klines.load(symbol, start, end=None, interval="1m")
binance.futures.um.trades.load(symbol, start, end=None, interval="1m")

Futures Market - Coin-M (CM)

binance.futures.cm.klines.load(symbol, start, end=None, interval="1m")
binance.futures.cm.trades.load(symbol, start, end=None, interval="1m")

Parameters

• symbol: Trading pair symbol (e.g., "BTCUSDT", "ETHUSDT")
• start: Start date as a datetime object (e.g., datetime(2023, 1, 1))
• end: End date as a datetime object (optional, defaults to current date)
• interval: Time interval for klines (default: "1m"). Examples: "1m", "5m", "1h", "1d"

Supported Intervals

Common intervals include: 1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d, 3d, 1w, 1M

Data Format

All functions return pandas DataFrames with time-indexed data:

• Klines: Indexed by open_time with OHLCV (Open, High, Low, Close, Volume) columns
• Trades: Indexed by timestamp with trade details
• Aggregated Trades: Indexed by timestamp with aggregated trade information

Examples

Load multiple years of Bitcoin futures data

from datetime import datetime
from narf.data import binance

# Load data from January 2023 to November 2025
df = binance.futures.um.klines.load("BTCUSDT", datetime(2023, 1, 1), datetime(2025, 11, 1))

print(df.head())
print(f"Total records: {len(df)}")

Compare spot and futures prices

from datetime import datetime
from narf.data import binance

start = datetime(2024, 1, 1)
end = datetime(2024, 12, 31)

spot = binance.spot.klines.load("BTCUSDT", start, end, interval="1d")
futures = binance.futures.um.klines.load("BTCUSDT", start, end, interval="1d")

# Compare closing prices
print(spot['close'].head())
print(futures['close'].head())

Load recent data up to now

from datetime import datetime
from narf.data import binance

# Load all data from January 2024 to now
df = binance.futures.um.klines.load("BTCUSDT", datetime(2024, 1, 1))
print(df.tail())

Load aggregated trades for analysis

from datetime import datetime
from narf.data import binance

# Load aggregated trades for a specific period
agg_trades = binance.spot.aggTrades.load(
    "ETHUSDT", 
    datetime(2024, 1, 1), 
    datetime(2024, 1, 31),
    interval="1h"
)
print(agg_trades.head())

Caching

Narf automatically caches downloaded data in a local cache/ directory. This means:

• First download: Data is fetched from Binance and saved
• Subsequent requests: Data is loaded from cache (much faster)

To clear the cache, simply delete the cache/ directory.

Requirements

• Python 3.12+
• pandas
• requests

License

See the repository for license information.

Links

• GitHub Repository
• Report Issues