Building an algorithmic trading system is like solving a complex puzzle — every piece matters. Indicators play a central role in it and having a robust method to integrate them into the backtesting and trading workflows is of paramount importance.

To backtest any strategy we need a large amount of data. In this post I show how to prepare the code base to store data and develop the necessary methods in the Galgoz class to fetch data, convert it into a DataFrame and store it for later use.

A simple way to open and close trades with the OANDA API and the galgoz algorithmic trading system.

The objective of this story is to set the foundation to build a an algorithmic trading system using python and the OANDA API. To get the project started I will start the Galgoz python class with a few basic features that will allow me to connect to OANDA and test the connection by fetching some data.

I recently wrote a story about how I failed at algorithmic trading and all the sideways benefits I got from it. Many responded positively, offering unexpected advice and encouragement. Which motivated me to try again. So, thank you!