[PLUS] Easily Fetch All Historical Trade Ticks from Gemini API using Python
Historical trade ticks are often necessary for training machine learning and other high frequency trading models - the general rule of thumb is: "the more data, the better". Each trade "tick" represents one transaction between a buyer and a seller at a point in time for a price and amount. We will demonstrate how to use the public Gemini API to pull every trade print for any of their available tickers and save the results to a Sqlite database. The reason we use a database in this example instead of Excel is due to the sheer size of the data... easily could be millions of records. For BTC/USD, this will capture trade print data all the way back to October 2015!
As an aside, Sqlite also provides a great user interface to interact directly with the database (https://sqlitebrowser.org/) -- Every line of our Python code is commented so you know exactly what is going on. We will use 3 main functions and less than 110 lines of code to include everything! One function is used to create the database and data structure to store the trade print data. One function is specific to retrieving what is the ID of the last transaction stored in our database. And the last function will query the Gemini rest API and store results 500 at a time into the sqlite database! If you run the code and it fails, it will start capturing from where it left off in the database.
This is a premium post. Create Plus+ Account to view the live, working codebase for this article.
Notice: Information contained herein is not and should not be construed as an offer, solicitation, or recommendation to buy or sell securities. The information has been obtained from sources we
believe to be reliable; however no guarantee is made or implied with respect to its accuracy, timeliness, or completeness. Author does not own the any crypto currency discussed. The information
and content are subject to change without notice. CryptoDataDownload and its affiliates do not provide investment, tax, legal or accounting advice.
This material has been prepared for informational purposes only and is the opinion of the author, and is not intended to provide, and should not be relied on for, investment, tax, legal,
accounting advice. You should consult your own investment, tax, legal and accounting advisors before engaging in any transaction. All content published by CryptoDataDownload is not an