This is the companion code for the paper Deep Hedging of Derivatives Using Reinforcement Learning by Jay Cao, Jacky Chen, John Hull, and Zissis Poulos. The paper is available here at SSRN.
The code requires gym (0.12.1), tensorflow (1.13.1), and keras (2.3.1).
Run python ddpg_per.py to start training. Run python ddpg_test.py to test a trained model.
To setup a trading scenario for training and testing, modify the trading environment instantiation parameter values in the code accordingly (env = TradingEnv(...) and env_test = TradingEnv(...) ).
Trained weights for all trading scenarios in the paper are provided in the weights folder.
Each set of weights are obtained after 2 or 3 rounds of trainings. Later round of trainings start with the best weights obtained from the previous round together with manually fine-tuned hyper-parameter values (learning rate, target network soft update rate, etc. See comments in the code for details.)
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The code structure is adapted from @xiaochus's github project Deep-Reinforcement-Learning-Practice.
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The implementation of prioritized experience replay buffer is taken from OpenAI Baselines.