In this article, we will explore how to pull cryptocurrency prices from over 100 different exchanges using the CCXT library and Pandas in Python.
Introduction
Cryptocurrencies have gained significant popularity in recent years, with thousands of digital assets being traded on various exchanges worldwide. Monitoring cryptocurrency prices across different exchanges can be a tedious task. However, with the help of Python libraries like CCXT and Pandas, we can automate this process and retrieve prices from multiple exchanges effortlessly.
CCXT and Supported Exchanges
CCXT is a popular open-source library that provides a unified API to interact with multiple cryptocurrency exchanges. It supports a wide range of exchanges, including some prominent ones like Binance, Bitstamp, and Coinbase Pro. In total, CCXT supports 117 exchanges.
Instantiating Exchanges with CCXT
To fetch prices from a specific exchange, we typically need to create an instance of that exchange using CCXT. Traditionally, we would create multiple variables for each exchange, resulting in a cumbersome process.
However, there is a more efficient way to achieve this. Python provides a built-in function called getattr that allows us to instantiate an exchange using a string value. For example, by calling getattr(ccxt, 'binance'), we can create an instance of the Binance exchange.
Retrieving Open, High, Low, Close, and Volume Data
Once we have instantiated an exchange, we can use the instance to fetch various data, such as open, high, low, close, and volume information for a specific cryptocurrency. For instance, we can fetch the last 10 rows of data for Bitcoin by calling instance.fetch_ohlcv('BTC/USDT', timeframe='1m', limit=10).
Creating a Function to Retrieve Price Data
To simplify the process of fetching price data, we can encapsulate the code into a reusable function. This function will take the exchange and symbol as arguments and return a Pandas DataFrame containing the open, high, low, close, and volume data for the specified time frame.
Transforming Unix Timestamps into Human-Readable Format
By default, the timestamps returned by CCXT are in Unix format. However, to enhance readability, we can convert these timestamps into a human-readable format. We can achieve this by using the pd.to_datetime function from the Pandas library.
Handling Errors and Failed Requests
During the process of retrieving prices from multiple exchanges, there may be instances where certain exchanges fail to respond or return data. To handle such cases, we can use a try-except block to catch any errors and store the failed exchanges in a separate list.
Iterating Over Exchanges and Retrieving Prices
To retrieve prices from multiple exchanges, we can iterate over the supported exchanges in CCXT and call our previously defined function for each exchange and cryptocurrency symbol. We can store the price data in a list for further analysis.
Future Steps and Conclusion
In this article, we have explored how to pull cryptocurrency prices from multiple exchanges using CCXT and Pandas in Python. The retrieved price data can be used for various purposes, such as analyzing price deviations and identifying potential arbitrage opportunities.
In future articles, we will delve into advanced analysis techniques, including identifying the lowest price, analyzing price deviations, and exploring arbitrage possibilities. If you’re interested in learning more about these topics, please leave a like and comment below. We appreciate your support and look forward to sharing more exciting content with you in the upcoming videos.