Building out a multi objective reinforcement learning model and creating custom indicators along the way is not something I would have been able to do or even think of doing before starting this course. Seems like stuff that Phd level individuals would be working on and so I am gladly soaking up all that I can with this project. I’m sure that the theories and more mathematical parts are covered for but the build itself I really need to know how to set these things up rather than building them from scratch. I am taking this course along with open source which has been a great pairing because I had no idea there were so many projects that were open source like Pandas for example. I guess I really didn’t know where it came from before taking these classes even though I had used it but that led me to try and find more libraries that I could use for this project. Learning new technologies has been the most rewarding so far and I think coming away from this class will help me in the future by going out and learning without much guidance. Other cool things on top of coding is learning about formulas in the financial world like the one here:
Learning about all the indicators has been interesting to know how to trade in the future and this is something that I will be looking into for much longer than the duration of this course.
Tools and Style
So my team is based in all different time zones so there can be commits happening at all times of the day. Since this project has taken a lot of research at the beginning it started off a little slow just to get our bearings but has ramped up pretty quickly and commits are starting to come in daily. For our project tracking we have been using Jira to make sure that our deadlines are being met and that we are all on the same page with what work needs to be done to hold each other accountable. I find it pretty easy to use since I use it at work and its so nice to have such a simpler board to keep us focused without customer bugs and tech debt that has built up over the years.
Success?
I think we will get a working product for sure. If it ends up tanking our paper trading accounts is another but we are definitely trying to optimize the strategies as best we can. We have been using the backtesting library which you can fine tune to give some insane results but of course hind sight is 20/20. I think we will get a better idea of profitability when we feed all the features into the model and train for the signal or buy or sell coming up in the next two weeks. This is the part that gets really exciting and seeing the sample models start to learn with the data we present. I started out by giving the lunar lander sample a go and helped the idea behind reinforcement learning get more solidified.
Overall I feel good about the project at this point and excited to see how we wrap it all together in the coming weeks! Check out my most recent post here!
