fastquant VS backtesting.py

https://github.com/enzoampil/fastquant -sort of a wrapper for backtrader that makes it very easy to run backtests and design trade strategies.

https://kernc.github.io/backtesting.py/ Nice analytics/visualisation but you need to do tricks to buy half a bitcoin. No native support of strategies based on multiple assets.

Hey, love the learning attitude! Some things to consider include (how are you actually going to write your code). If you want to spend more time experimenting with a signal (which with your background, seems like you do), try using a framework out there like backtesting.py, backtrader, or blankly (the last one is my favorite because I'm able to actually deploy live in one line whereas the others don't). These frameworks have built-in backtesting, data connections, etc. that should help you speed your dev up.

It’s super easy to get up and running with code. With the rise of data science as a field, datasets are far and wide. Accessible from just about any venue. Take a look at Kaggle, QuiverQuant, Yahoo Finance, or even directly from the brokerages and exchanges. Developers can easily download data directly as a .csv or .json and quickly get up and running by utilizing frameworks like backtesting.py or vectorbt. “Great, it seems like I can get up and running and I’ll have an awesome money making trading algorithm in no time”.... unfortunately, wrong. Why is this wrong? Well, simulation is NOT the real world. The real world is not a CSV file—the real world is a stream of events. Cause and effect. The real world works in a fashion where new data comes in, you make a decision, and then you figure it out, not “I have all of this data, let me run this all through time and figure it out”. Indeed, the data sources that you get in real-time are almost completely different from the data sources you use in simulation. Rather than .csv you use WebSockets; rather than QuiverQuant you use APIs; rather than backtesting frameworks you use more robust, event driven packages. Without it, you’re stuck duplicating code, rewriting it into an event-based system, and ultimately using that to go into production, and who knows if your code is going to change along the way.

I highly recommend using the Backtesting (https://github.com/kernc/backtesting.py) library. With some small modifications to data, you can quickly backtest any trading strategy.

backtesting.py is a good option. It should be pretty easy to make strategy to suite your needs.