Please find the project inside Zip file that contain the multiple folders
- State : in this folder you will find the state.py file
- Agent :inside this folder you will get Agent.py file
- Trading.ipynb file that contains functionality:
3.1 Data pre-processing 3.2 Agent is trained with 51 Episode. Input here are following parameters:
- Stock1_name: this is first stock name, which is Apple - aapl.us
- Stock2_name: this is second stock name, which is Amazon - amzn.us
- episode_count: This is number of episodes which agent till train on
- Start_balance: This is the initial starting cash, which is $ 10,000
- Training: This is number of records used for trading i.e. number of days on each episode of training will run
- Test: This is number of days on which test run will be executed
3.3 Evaluate and final program that predict the total portfolio value for one episode
- Models are saved in model directory
To execute the program, you would need to run the Trading.IPynb file with input as stated above and then look at the result
- There are other files: Testing- Google n Walmart.ipynb and Testing-IBM n GE.ipynb. These can be used to test the model generated in Trading.ipynb and stored in Models directory
References
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MACHINE LEARNING FOR TRADING: GORDON RITTER: https://cims.nyu.edu/~ritter/ritter2017machine.pdf
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Financial Trading as a Game: A Deep Reinforcement Learning Approach: Huang, Chien-Yi https://arxiv.org/pdf/1807.02787.pdf
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Convergence of Q-learning: a simple proof: Francisco S. Melo: http://users.isr.ist.utl.pt/~mtjspaan/readingGroup/ProofQlearning.pdf
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https://medium.com/@chinmaya.mishra1/deep-dive-in-to-reinforcement-learning-10fa30b418f9
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David Silver’s lectures about deep reinforcement learning