I’m still searching for something that holds my interest for more than a few days. I’ve spent a lot of time on quantitative trading over the past few years, getting nowhere. or rather, going backwards (losing money). More recently I’ve spent time on Machine Learning, hoping it could improve my trading in some way. Not sure I will even need to do trading anymore. If I have enough to live on after buying an apartment I won’t actually need to make any income.
So, what to do? I’m coming around to revisiting reinforcement learning, which is a very interesting area of ML, and could be related to trading should I ever go back to that. It’s a complex area of ML, but I think I’ve got enough of a handle on the basics to take another shot at it. And once I’ve got it set up, finding the right data will be the biggest challenge. Garbage In, Garbage Out, they say, so identifying useful inputs will be the challenge. Or I could input everything and let the algorithm sort out what’s useful. That’s the beauty of reinforcement learning. The code does all the work.
So, I’m re-reading Practical Deep Reinforcement Learning with Python by Ivan Gridin. He gives examples in both PyTorch and TensorFlow, so I should be able to follow along fairly easily, at least as far as the ML library is concerned. However I have had problems with Reinforcement Learning in the past because of changes to other libraries commonly used. A favourite is openai.gym which gives me some headaches. Setting up a system that actually works with the code is a real headache in this area of ML. Let’s see how it goes this time.
So (my favourite word for starting paragraphs), I could develop an RL (reinforcement learning) model that has various trading strategies as actions, PnL as reward, and an environment that includes all the data that might be relevant. Perhaps the simplest starting point would be to concentrate on opening/closing positions on a single instrument. After that I could move on to managing a portfolio.