An LCS for Stock Market Analysis Christopher Mark Gore [email protected] Computer Science...
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Transcript of An LCS for Stock Market Analysis Christopher Mark Gore [email protected] Computer Science...
An LCS for Stock Market AnalysisChristopher Mark Gore
[email protected]://www.cgore.com
Computer Science 401Evolutionary Computation
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.
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.
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.
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]
Resources for XCS
• Stewart W. Wilson: http://www.prediction-dynamics.com
• Classifier Fitness Based on Accuracy. (original paper)
• An Algorithmic Description of XCS. (a simple overview of the XCS algorithm)
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.
What data is available?
On a daily, weekly, or monthly basis:• Opening price.• High price.• Low price.• Closing price.• Trading volume.
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.
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.
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.
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.
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.
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.
Questions? (hopefully answers too)
?