1. An LCS for Stock Market Analysis Christopher Mark Gore chris-gore@earthlink.net http://www.cgore.com Computer Science 401 Evolutionary Computation

2. What happened to conversation intervention? An LCS requires lots of training data. Conversation intervention would require hundreds, if not thousands, of conversations for both the training phase and for the evaluation phase. I currently only have one conversation.

3. Why stock market analysis? There is more data available than I can even use: daily data for thousands of stocks over the last 100 years. It is a very unpredictable problem, and therefore it is interesting. If this actually produces results, than it is a self-funding project.

4. Why not ZCS? ZCS doesn’t easily produce long decision chains ZCS runs a panmictic GA, which is slow. ZCS doesn’t apply selective pressure towards a complete mapping. ZCS often fails to evolve accurate generalizations.

5. Why XCS? XCS is basically a “fixed” ZCS. XCS is the subject of a lot of research. XCS is one of the easiest LCS’s to implement. XCS has performed well before for this. [Schulenburg and Ross: Strength and Money: An LCS Approach to Increasing Returns]

6. Resources for XCS Stewart W. Wilson: http://www.prediction-dynamics.com http://www.prediction-dynamics.com Classifier Fitness Based on Accuracy. (original paper) An Algorithmic Description of XCS. (a simple overview of the XCS algorithm)

7. How do you get lots of stock data? Pay too much money to any major investment company. OR Take it from Yahoo! Finance’s back- end. http://finance.yahoo.com Write a parser for their slightly modified CSV files, and a data retrieval program.

8. What data is available? On a daily, weekly, or monthly basis: Opening price. High price. Low price. Closing price. Trading volume.

9. What questions can we use in the learning classifier? Is today higher/lower than yesterday? Is today a week-long high/low? Is this an n-day high/low? Is this a high/low trading volume? Is the monthly trend up or down? … and many, many more.

10. Shulenburg and Ross Strength and Money: An LCS Approach to Increasing Returns. Three different trader types: 1.Price information only. 2.Primarily volume information, limited price information. 3.Price, volume, limited history, competitor’s information.

11. Simple trading methods These were used for comparison. Bank: ignore the stock, keep the money in the bank the whole time. Buy and Hold: ignore the bank account, keep the money in the stock for the long run. Trend Following: buy if yesterday is higher than the day before, sell if yesterday is lower than the day before.

12. Schulenburg and Ross versus Me S&R were interested in mostly what data was most useful for prediction. Therefore, they made simplistic agents purposefully, each with different restricted data. I am most interested in how good of performance I can produce. Therefore, I will give them as much data as possible.

13. Possible future improvements What about switching between several stocks, instead of just a single stock and bank? Perhaps there are strong inter-relations among the stocks. What other data would be useful? Perhaps non-market indicators would be useful. What predicates really matter? Perhaps an s-classifier would help.

14. My current status Completed: Yahoo! Finance data retrieval program. Yahoo! Finance CSV file parser. Most stock data support functions. In progress: XCS algorithm: ~25% completed. Not yet started: Current condition predicates. Trial runs: needs XCS algorithm.

15. Questions? (hopefully answers too) ?