Strategy Learner

In this project, I design a learning trading agent. I used a Reinforcement Learner-based approach, and create a Q-learning-based strategy using my Q-Learner. My learner works in the following way:

In the training phase, my learner will be provided with a stock symbol and a time period. It should use this data to learn a strategy. In the testing phase, my learner will be provided a symbol and a date range. All learning should be turned OFF during this phase. I will use exactly the same indicators as in the manual strategy project so I can compare my results. I optimize my indicators for time (vectorization), but there should be no changes to lookback windows or any other pertinent parameters.

If the date range is the same as used for the training, it is an in-sample test. Otherwise it is an out-of-sample test. My learner should return a trades dataframe like it did in the last project.

Overall, my tasks for this project include:

• Devise numerical/technical indicators to evaluate the state of a stock on each day.
• Build a strategy learner based on one of the learners described above that uses the indicators.
• Test/debug the strategy learner on specific symbol/time period problems.
• Write a report describing my learning strategy.

Built With

• python