Sigma Quantiphi builds algorithmic trading solutions for the cryptocurrency markets — both for proprietary use and third-party clients.
In crypto, accessing reliable exchange data can feel like parsing chaos in real time. APIs are inconsistent, documentation is thin, and custom data pipelines are fragile. That’s exactly why Crypto-Pandas was created: to make structured, exchange-grade data as accessible as a Pandas DataFrame.
What started as a productivity tool for quant developers has grown into something much more impactful. From accounting and compliance to risk management and treasury, Crypto-Pandas is helping cross-functional teams make sense of crypto markets without needing to become API experts.
At its core, Crypto-Pandas is a lightweight Python library that wraps the CCXT and Pandas interfaces. It delivers exchange data in clean, typed Pandas DataFrames, eliminating the need for pagination logic, JSON parsing, or custom format converters.
from crypto_pandas import CCXTPandasExchange
import ccxt
exchange = CCXTPandasExchange(exchange=ccxt.binance())
df = exchange.fetch_trades(“BTC/USDT”)
df.head()
Use Cases Across Teams
While Crypto-Pandas was initially designed to serve quantitative traders and algorithm developers, it’s quickly proving itself as a multi-functional tool across the crypto stack.
Financial controllers and auditors can use Crypto-Pandas to:
df = exchange.fetch_deposits()
df.to_excel(“deposits.xlsx”)
Why it matters: No more downloading CSVs from clunky dashboards, every transaction is timestamped and normalized, making audits and tax filings far easier.
Crypto-Pandas helps risk teams monitor:
Example: Visualize ETH Order Book Instantly
df = exchange.watch_order_book(“ETH/USDT”)
df.plot(x=”price”, y=”qty”)
Why it matters: With direct access to structured order book data, analysts can build alerts, dashboards, or internal risk models without having to decode each exchange’s API quirks.
Operations and compliance teams need reliable access to:
symbols = [“BTC/USDT”, “ETH/USDT”]
df = exchange.fetch_ohlcv_for_symbols(symbols, timeframe=”1h”)
df.to_sql(“ohlcv_data”, con=your_sql_connection)
Why it matters: Back office workflows can now be automated and standardized, removing manual steps and improving audit trails.
You can:
Crypto-Pandas lets you write once and run anywhere. Instead of learning each exchange’s API, and rewriting your data logic every time, the library gives you one consistent interface.
You can:
Crypto-Pandas is an open-source and production-grade software with regular maintenance. Hence, it is an ideal choice for developers, teams, and analysts wanting to automate their crypto data pipelines.
You don’t require an elaborate setup or fancy infrastructure, just follow this step:
Install via pip, with one command:
pip install crypto-pandas
Let your DataFrame do the hard work. Start with Crypto-Pandas today with us.
Sigma Quantiphi builds algorithmic trading solutions for the cryptocurrency markets — both for proprietary use and third-party clients.
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