Last week I spent some time learning the basics of QuantConnect Lean, the engine that our group is using to create our stock market trading algorithms. I have no experience with algorithmic trading, so it took a good amount of effort to get comfortable with running basic algorithms using the engine. To complicate things further, there are also two options for backtesting algorithms: one is to clone the Lean repository, update a configuration file with the specific algorithm(s) and its language (Python or C#) that you are looking to run, and then to build and run the solution using Visual Studio. <\/p>\n\n\n\n
My approach was to first learn how to use the local method to make sure that I understood what was going on behind the scenes before using the more abstract CLI method. Luckily, Lean provides a sample algorithm that is automatically configured by default. So once I figured out how to update the configuration file and build and run the solution, I was able to confirm that I backtested the algorithm successfully because I was presented with metrics that would have contained meaningful data if a real algorithm was used.<\/p>\n\n\n\n
The CLI method actually took more effort to get up and running because it required some more specific installations (e.g., required Anaconda), but once I learned the basic commands to create and backtest a project, I definitely found it simpler to use that the local method. I received some help from my teammate Travis to create a basic algorithm that mocked making purchases of the stock UDOW over a one-year period using price data provided by Yahoo Finance. After some trial and error I was pleased to see that the algorithm was working correctly and I could see many interesting metrics that were now meaningfully populated. I am looking forward to learning more about QuantConnect Lean and am hoping to create some advanced and effective trading algorithms over the next couple of months!<\/p>\n\n\n\n
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