ccxt-pandas 0.13.0

Library combining the power of CCXT with Pandas.

CCXT-Pandas

🚀 CCXT → Pandas DataFrames in One Line

No more JSON → DataFrame glue code. Every CCXT method returns a clean, typed pandas DataFrame.

import ccxt
from ccxt_pandas import CCXTPandasExchange

exchange = CCXTPandasExchange(exchange=ccxt.binance())
ohlcv = exchange.fetch_ohlcv("BTC/USDT", timeframe="1m", limit=1000)
plt = ohlcv.close.plot(title="BTC/USDT — 1m")
plt.show()

Why CCXT-Pandas?

CCXT-Pandas fuses the power of Pandas with the market-connectivity of CCXT. It turns CCXT’s nested JSONs into clean, typed DataFrames for analysis, backtests, or dashboards. It lets you place/cancel live orders using the same DataFrame-centric API.

1-liners, everywhere. Fetch OHLCV, tickers, trades, order books, balances, orders → all as DataFrames.

• Consistent columns & dtypes. Timestamps as UTC datetime64[ns, UTC], numeric columns as proper numerics.
• Zero boilerplate. Stop writing JSON-to-DataFrame glue for every exchange.
• CCXT-compatible. Keep your favorite CCXT params; just get DataFrames back.

Installation

CCXT-Pandas can be installed on Python 3.11~3.14:

pip install ccxt-pandas

Examples

Find all examples in the CCXT-Pandas-Example Repository

Getting Started

CCXT-Pandas works identically to CCXT. Just add exchange = CCXTPandasExchange(exchange=exchange) and the exchange methods provided by CCXT will be exposed to CCXT-Pandas. More examples can be found on Binder:

Sync

import ccxt
from ccxt_pandas import CCXTPandasExchange

# Initialize a CCXTPandasExchange object
exchange = ccxt.binance(dict(apiKey="your_api_key_here", secret="your_secret_here"))
exchange = CCXTPandasExchange(exchange=exchange)

# OHLCV
ohlcv = exchange.fetch_ohlcv("BTC/USDT", timeframe="1m", limit=100)      # -> DataFrame
# Trades
trades = exchange.fetch_trades("BTC/USDT", limit=1000)                   # -> DataFrame
# Orderbook
ob = exchange.fetch_order_book("BTC/USDT", limit=50)                 # -> DataFrame
# Tickers
tick = exchange.fetch_tickers()                               # -> DataFrame

# Fetch open orders from an exchange
open_orders = exchange.fetch_open_orders(symbol="BTC/USDT")

# Halve the amount and edit orders
open_orders["amount"] /= 2
response = exchange.edit_orders(open_orders)

# Display the transformed orders dataframe
print(response)

Async

import asyncio
import ccxt.pro as ccxtpro
from ccxt_pandas import AsyncCCXTPandasExchange

ex = AsyncCCXTPandasExchange(ccxtpro.okx())

async def main():
    while True:
        trades = await ex.watch_trades("BTC/USDT")
        print(trades)

if __name__ == "__main__":
    asyncio.run(main())

About Sigma Quantiphi

Sigma Quantiphi is a quantitative-engineering firm that builds end-to-end algorithmic-trading systems for the cryptocurrency markets. We create open-source, Python-first tools—like ccxt-pandas—and deliver turnkey execution, data, and research pipelines that emphasize simplicity, transparency, and rapid deployment.

License

This project is licensed under the Apache License. See the LICENSE file for more details.

Contributing

Contributions are welcome! If you'd like to contribute, please fork the repository, create a new branch for your feature or fix, and send a pull request.

1. Fork the repository.
2. Create your feature/fix branch: git checkout -b my-new-feature.
3. Commit your changes: git commit -am 'Add some feature'.
4. Push to the branch: git push origin my-new-feature.
5. Submit a pull request.

Support

If you encounter any issues or have questions, feel free to open an issue on the GitHub repository or contact us via email at [e-mail]. Happy trading! 🚀