Hi QuantConnect Community,
I am contemplating about a trading project with the following workflow:
1. The research part includes fitting a random forest model on historical data and save the model
2. The live trading/backtesting part would involve invoking the saved model and feed in real-time data to get a prediction response
I am wondering if I could do both 1 and 2 on the QuantConnect Python Platform? Any pointers would be much appreciated. Thanks a lot.
Rahul Chowdhury
Hey Daniel,
Unfortunately at this time, there's no way to save your weights in research. We've opened an issue to implement ObjectStore in the research enviornment. For now, you can train your model in the research environment and then copy over the weights manually into the backtesting environment. If you want to save your weights somewhere, you can upload the weights to dropbox and then download it in the algorithm.
You can also checkout this blog post walking you through an application of random forest machine learning.
Best
Rahul
Daniel Zuo
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