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LiquidDinosaurs69 t1_j195usj wrote

Reply to comment by Careful-Temporary388 in [D] Tensorflow vs Pytorch for LSTM stock bot by Careful-Temporary388

RL is what you need to use when you want to learn to automatically act in an environment (buy and sell bitcoin in my case). Deep learning based RL requires a neural network for estimating the value of an action and for actor-critic methods there's also a policy network. So you can construct the neural networks that RL needs with an LSTM if you want.

I'm not sure if you want to create something that automatically figures out a strategy to buy and sell or if you just want to predict a stock price. If you just want to predict price then you don't need RL and just an LSTM will be sufficient.

I'm using stable baselines implementation of PPO (an RL algorithm). I'm using wavenet style stacked dilated convolutions as a feature extractor. It's not working though lol. I want to use an LSTM but stable baselines3 currently doesn't support it and I'm going to have to find a way to implement it myself.

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Careful-Temporary388 OP t1_j1e28sh wrote

Thanks for explaining. I wish you luck my man! Instead of having the bot act, I was just going to have it predict price and then hardcode actions depending on the prediction. Really though, I feel like the net result is the same. There's so many people who have tried this and I'm yet to really hear of a success story, so I doubt there's much money in it unless you're creative with your data sources and your approach. Like finding a correlation somewhere that you can manipulate - for example - selling grain stocks when the weather is predicted to be a certain way for long periods of time.

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LiquidDinosaurs69 t1_j1eexl1 wrote

I think some people have been able to conclusively show a profit with this actually. I know I’ve read at least one journal paper where they showed a profit. During my back testing I was actually able to get up to 2% profit per year which isn’t really worth it. Yeah I’ve heard using data from other sources like Twitter sentiments can work. Maybe weather would be useful too if you could find a way to use that data.

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