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mbt_gym is a module which provides a suite of gym environments for training reinforcement learning (RL) agents to solve model-based high-frequency trading problems such as market-making and optimal execution. The module is set up in an extensible way to allow the combination of different aspects of different models. It supports highly efficient implementations of vectorized environments to allow faster training of RL agents.

License: BSD 3-Clause "New" or "Revised" License

Python 4.24% Jupyter Notebook 39.42% Dockerfile 0.01% Shell 0.02% HTML 56.32%

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jjjerome avatar leandro-sbetancourt avatar nirmaysriharsha avatar rahulsavani avatar

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robertmay615 walkacross syndoom nirmaysriharsha ipsec praveen686 hyukgu thulc snowskid kihoon-eom zhangjielun1994 niceboy120 narumiruna zhuoxianlin2001 rxzb ericjohn prateekm01 rpiryani gerard-ompad sureniology lfamarantine andrewsiah mariobravolarrea qiancapital wk101 ghlian seonghyeonjo94 leandro-sbetancourt bladesaber jjwwon790626 ginger-tec qhdtls123 jimmypark4 sorokinvld qrl-lob zenithez leondeligny behzadazadie

mbt_gym's Issues

Fantastic project

Thank you so much for sharing your work! It would be perfect to add a notebook for feeding it with external data instead of stochastic data.

number of rows (= actions) returned by get_action in SbAgent

The old method

    # return self.model.predict(state, deterministic=True)[0].reshape(self.num_trajectories, self.num_actions)

used self.num_trajectories as the number of different actions to return, but this was failing in some cases due to a discrepancy between self.num_tracjectories and the actual state given to model predict

Leandro and Rahul changed this to

    return self.model.predict(state, deterministic=True)[0].reshape(state.shape[0], self.num_actions)

Are we happy with this?

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