Difference between revisions of "Q Trader Hints"
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We expect the following outcomes in evaluating your system: | We expect the following outcomes in evaluating your system: | ||
| − | * For ML4T-220, the trained policy should provide a cumulative return greater than 100% in sample | + | * For ML4T-220, the trained policy should provide a cumulative return greater than 100% in sample |
| − | * For ML4T-220, the trained policy should provide a cumulative return greater than 100% out of sample | + | * For ML4T-220, the trained policy should provide a cumulative return greater than 100% out of sample |
| − | * For AAPL, the trained policy should significantly outperform the benchmark in sample | + | * For AAPL, the trained policy should significantly outperform the benchmark in sample |
| − | * For SINE_FAST_NOISE, the trained policy should provide a cumulative return greater than 200% in sample | + | * For SINE_FAST_NOISE, the trained policy should provide a cumulative return greater than 200% in sample |
| − | * For UNH, the trained policy should significantly outperform the benchmark in sample | + | * For UNH, the trained policy should significantly outperform the benchmark in sample |
| − | * Additional test in which we train your learner with one data set, then test it out of sample with another data set. The out of sample performance should be worse than in sample | + | * Additional test in which we train your learner with one data set, then test it out of sample with another data set. The out of sample performance should be worse than in sample. |
Training and testing for each situation should run in less than 30 seconds. We reserve the right to use different time periods if necessary to reduce auto grading time. | Training and testing for each situation should run in less than 30 seconds. We reserve the right to use different time periods if necessary to reduce auto grading time. | ||
Revision as of 12:02, 18 July 2017
Overview
In this project you will apply the Q-Learner you developed earlier to the trading problem. It is not required, but we recommend that you reuse the indicators that you developed in the previous project for this one. Note that there is no regression or classification learning in this project (so no use of RTLearner or LinRegLearner). The indicators define most of the "state" for your learner, the additional component of state is whether or not you are currently holding a position long or short. The actions are BUY, NOTHING, SELL.
Overall, your tasks for this project include:
- Build a strategy learner based on your Q-Learner and previously developed indicators.
- Test/debug the strategy learner on specific symbol/time period problems
Scoring for the project will be based on trading strategy test cases.
Implement Strategy Learner
For this part of the project you should develop a learner that can learn a trading policy using your Q-Learner. You should be able to use your Q-Learner from the earlier project directly, with no changes. You will need to write code in StrategyLearner.py to "wrap" your Q-Learner appropriately to frame the trading problem for it. Utilize the template provided in StrategyLearner.py Overall the structure of your strategy learner should be arranged as below. Note that this is a suggestion, not a requirement:
For the policy learning part:
- Select several technical features, and compute their values for the training data
- Discretize the values of the features
- Instantiate a Q-learner
- For each day in the training data:
- Compute the current state (including holding)
- Compute the reward for the last action
- Query the learner with the current state and reward to get an action
- Implement the action the learner returned (BUY, SELL, NOTHING), and update portfolio value
- Repeat the above loop multiple times until cumulative return stops improving.
A rule to keep in mind: As in past projects, you can only be long or short 200 shares, so if your learner returns two BUYs in a row, don't double down, same thing with SELLs.
For the policy testing part:
- For each day in the testing data:
- Compute the current state
- Query the learner with the current state to get an action
- Implement the action the learner returned (BUY, SELL, NOTHING), and update portfolio value
- Return the resulting trades in a data frame (details below).
We expect the following outcomes in evaluating your system:
- For ML4T-220, the trained policy should provide a cumulative return greater than 100% in sample
- For ML4T-220, the trained policy should provide a cumulative return greater than 100% out of sample
- For AAPL, the trained policy should significantly outperform the benchmark in sample
- For SINE_FAST_NOISE, the trained policy should provide a cumulative return greater than 200% in sample
- For UNH, the trained policy should significantly outperform the benchmark in sample
- Additional test in which we train your learner with one data set, then test it out of sample with another data set. The out of sample performance should be worse than in sample.
Training and testing for each situation should run in less than 30 seconds. We reserve the right to use different time periods if necessary to reduce auto grading time.
