half_tael_DQN

A foreign exchange trading bot with quite some personalities, utilizing CNN + DQN with persona-driven adjustments.

📌 v4.5 | 2019-09-02 | Refactor to work with Anita_v2.0 + Proper documentation.

A proper README.md file will be written once the project is fully developed (or once reached a certain milestone). During the developing stage, records will be update on CHANGELOG.md.

1. Purpose

Named after an unified currency of Qin dynasty, half_tael_DQN is aimed to be a foreign-exchange trading bot capable of trading among multiple currencies. Further, with the concept of Anita, we expect such a bot to be able to generate trading strategies subject to different persona-driven adjustments — as an overly simplified example: to provide a more "aggressive" or a more "conservative" trading strategy.

2. Structure

half_tale_DQN is composite by three main blocks of code:

1. A virtual trading platform which capable of communicating through OANDA API to retrieve historical price data among different currencies, be able to register all virtual trading signals (buy & sell among any two-currency for a given value, etc), to generate a trade_log, and to track your account balance accordingly. A relatively thoughtful exception-handling system is implemented as well.
   • Constituted by oanda_interface.py and trade_interface.py.
   • To operate this virtual trading platform, check 🔰 User Manual for trade_interface.py. A standalone demo is also offered as train_interface_demo.py.
2. A DQN model with some kind of preprocess (currently CNN), capable of taking DataFrame with format like ./arena_data as input and generates trade signals accordingly.
   • Constantly updating, currently as ./DQN_v3.0. Where FX_env.py defines the custom environment of foreign-exchange trading, RL_brain.py builds the logic of model, and model_interface.py runs the model buy integrating the former two together.
   • Other than the above three "core" files, model_config.py and train_interface.py are provided as the configuration panel and the entry point of this program.
3. An Anita evaluator to influent the reward of a (or a set of) trade action(s) according to persona-driven adjustments.
   • Constituted by anita_interface.py along.
   • To operate this evaluator, check 🔰 User Manual for anita_interface.py.

3. Run

1. Register a fxTrade Practice account at OANDA. Then look up your account_number at Manage Funds --> My Fund --> Account Summary --> v20 Account Number; and obtain your OANDA's API token by visiting Manage API Access --> Generate/Revoke.

2. Edit oanda_config.py by assigning your account number and API token to my_account_id and my_access_token respectively (as str).

3. Edit model_config.py with the desired parameters you'd like.

4. You can either identify a version of DQN model you'd like to run with, go into such folder (e.g. ./DQN_v3.0), locate the train_interace.py file, then run against it within your IDE.

   Or in terminal, cd into the root folder of half_tael_DQN, identify the folder which contains your desired version of model, then:

   python3 model_version_folder/train_interace.py
   

   As for the example of ./DQN_v3.0, the command should be:

   python3 DQN_v3.0/train_interface.py  
   

4. Contribution

To get involved, you are expected to uphold CONTRIBUTING.md.

If you are from Graphen.ai and want to carry on this project, you are recommended to branch out from tag v4.5 like:

git checkout -b your_branch_name v4.5

Since later version of half_tael_DQN might detach the Anita evaluator as it is a property of Graphen.ai.

5. Limitations

Though published, this is my (@choH) first semi-serious project and also my first exposure to AI. Thus, a lot of works here were done in a premature way. The project is published more for the purpose of exhibition and ease of future development — please use it with caution.

Known limitations of this project include but not limited to:

1. Only be able to register trade signals within the virtual trading platform, but not able to submit real trade request to OANDA.

• Only a very simple CNN is implemented as preprocess method to handle multiple currencies (>2) as input — since there will be multiple price data on every timestamp — and the benchmark is not exactly appealing.
• Only a simple double DQN is implemented as model.
• Only a very basic method is implemented for Anita_Trait.
• Lack of visualization, as the cost_log is the only graphical output of this model.

Check out Issue #22 for potential improvements for this project.

6. Acknowledgements

• Henry ZHONG (@choH)

  • Responsible for project management.
  • Developed the Virtual Trading Platform.
  • Developed the DQN model after commit ef6e716 (the origin of DQN_v2.5).
  • Refactored DQN models to work with the Virtual Trading Platform.
  • Developed a demonstrative Anita framework.

• Steven XING (@lx72)

  • Developed DQN_v1.0 and DQN_v2.0.
  • Partially developed DQN_v2.5.
  • Exited the project after commit ef6e716.

7. License

CC BY-NC 2.0