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I am scratching my head with an optimization problem for Avellaneda and Stoikov market-making algorithm (optimizing the risk aversion parameter), and I've come across https://github.com/im1235/ISAC

which is using SACs to optimize the gamma parameter.


since SAC is a model-free reinforcement learning, does this mean it is not prone to overfitting?

or in other words, can it be applied to live to trade?

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Overfitting depends on whether your agent can generalize to the real world of trading, not on whether it is model free or not. When you train your agent with historical market data, or simulated data, you need to make sure the interactions with the market is as realistic as possible, which is hard especially in the case of market making, where your actions affect the market a lot. Personally, I think it very hard to make it work unless you have a really good market simulator. However, if it does work out-of-sample, you could start to run it alive and finetune it with real market data. I would prefer to train it directly by running the agent live in the very beginning, though it would be costly.

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I agree with autoencoder; your environment must really be precisely defined. Markets react to trades and creating accurate simulator for your environment is a challenging problem. I think overfitting will depend entirely on the definition of your environment and state space and can happen if you use out of the box algorithms. However, if such an accurately defined environment and state space does exist, it is not entirely impossible to create a successful RL application.

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