Introduction

Utilizing server-side computing can be particularly beneficial when processing indicators or backtesting with large datasets that can't fit on a user's disk or be loaded into RAM with conventional tools like pandas. This approach minimizes compute time on standard computers. With full access to public data tables in the database, users can not only retrieve market data but also execute computations directly on the server, harnessing the speed of our Clickhouse instance

Example: Computing the sum, the average and the standard deviation of the quantity or ALL the orders done on BTC-USD on Coinbase from 2014.

This computation took 26s on our server and it's easy to see that this is handier than have to download a 835 millions rows long CSV file.

The users have limits for how many rows their query can read depending on their tier, this information is provided to you by Koinju and an error will be thrown by the database if you run queries that would go above the limit.

In the next chapter we will show how to run a backtest for a simple SMA strategy in seconds direcly inside the database.