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This document is the final report for the machine learning for trading course. I have implemented two manual strategies, a random tree learner-based strategy and one based on Q-learning.
Experiment 1
I have implemented two manual strategies. The first strategy buys on a bullish MACD cross with a MACD smaller than zero and sells on a bearish MACD cross with a MACD greater than one.
The second strategy uses MACD diff (the difference between the MACD and the MACD signal), RSI, and price SMA with a period of eight. I have plotted the metrics over their one, three, and five days return to find reasonable thresholds for the strategy.
Based on the scatter plots, I have created a list of buy and sell signals. Each signal uses the current number of shares owned and one of the three indicators. The following figure shows the result for both manual strategies compared to the benchmark. Both approaches do well in the in-sample period but worse afterward, which I expected because I cherry-picked the thresholds based on the in-sample period's scatter plots.
Next, I have implemented a random tree-based strategy learner. The learner uses a leaf size of five and no bagging. A smaller leaf size would result in overfitting to the in-sample data. But as the following screenshot shows, five works well, and the RT learner does well for the out of sample data.
I have also implemented a strategy learner based on Q-learning. The Q-learner uses fifteen training runs on the in-sample data. It mostly does well for the out of sample data, but it looks like the RT-based strategy learner is better.
I am using a bin-size of five for the three indicators mentioned before. That results in 375 (3x5x5x5) states with only about 500 in-sample data points. Probably the Q-learner is overfitting to the in-sample data. Indeed, with bin sizes of four, the Q learner performs better for the out-of-sample data.
Experiment 2
Experiment 2 aims to show that the strategy learner trades differently when there is a commission, and the impact is not zero. The RT-based trader does not consider the commission value, but the Q-learning based trader does.
However, it seems like a commission smaller than $10 does not affect the number of trades significantly. Only when the commission is around $50 or with a slippage of 1% we see considerably fewer transactions.
| commission | n_orders |
|---|---|
| 9.95 | 79 |
| 20 | 83 |
| 50 | 63 |
| 100 | 37 |
Closing Remarks
Machine Learning for Trading is a great course. It gives an excellent introduction to finance, trading, and machine learning without getting lost in technical or mathematical details. I have enjoyed building decision tree learners and a Q learner from first principles. At the same time, the course accurately teaches powerful libraries such as NumPy and Pandas.