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shenyichen105/Deep-Reinforcement-Learning-in-Stock-Trading

Factor research★ 323 GitHub starsPython⏱ No commits in over six months

Deep actor-critic model for learning pair trading strategies

What it is

A project that uses a deep actor-critic reinforcement learning model to learn pair trading strategies. It includes the model implementation and training process so the approach can be reproduced. Researchers and developers interested in RL applied to statistical arbitrage can use it for study and experiments. Pair trading results are sensitive to the chosen pairs and costs, so treat the findings as a research starting point.

At a glance

Research onlyOur rating, based on popularity, maintenance and how ready it is for real use.

Best for	Professional quants
Used for	Strategy research, Backtesting
Markets	US equities
Stack	Python
Learning curve	Steep learning curve
Practical value	Low practical value
Cost	Free and open source
Hardware	No GPU needed
Maintenance	No commits in over six months