a novel framework for stock portfolio trading that employs a 'Relaxation and Refinement' strategy to boost the Soft Actor-Critic (SAC) agent
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PLEASE NOTE: Find the basic version of the Hawkes scripts, which can be founded in the https://github.com/HongtengXu/PoPPy/
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TCN_GAT_zz1000.py is the training program for TCN and GAT, which introduced the scripts model.py for TCN model and gat.py for GAT model.
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SAC_zz1000.py is the traning program for SAC model, which introduced the scripts StockEnv_zz1000.py for trading environment and StcokAgent.py for agent model.
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To run R2-SAC, you should rewrite the test procedure for your trading strategy and get the hawkes scripts from the https://github.com/HongtengXu/PoPPy/.
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For some suggetions, we built AI4QTrading-patch-1 branch to help reproducing the strategy. And for commercial reason, some data need to be downloaded from the public data source.