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V.15:4 (162-169): Seasonality and Trading by Jeffrey Owen Katz, Ph.D., with Donna L. McCormick
V.15:4 (162-169): Seasonality and Trading by Jeffrey Owen Katz, Ph.D., with Donna L. McCormick
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Seasonality and Trading by Jeffrey Owen Katz, Ph.D., with Donna L. McCormick
This month, this trader and consultant looks at seasonality as the basis of a trading system.
My search for successful systems began with the brute-force application of neural network technology. I wanted to see if I could get a profitable trading system by using neural networks to generate trading signals based simply on recent price changes. As I anticipated, I found that this simplistic approach does not work very well. I then moved to simple rule-based systems, initially working with rules rooted in theory (for example, the thrust-retracement hypothesis). This provided better results: The systems worked but were not particularly tradable because the per-trade profit was on the small side. However, the results encouraged me enough to proceed with a more complex data-mining approach to rules. Instead of using rules based purely on theory, I let a genetic algorithm (GA) find the rules parameters (the lookbacks and thresholds) in a simple price-comparison model. I obtained better results, more tradable than the previous system, but still not ideal.
This month, this trader and consultant looks at seasonality as the basis of a trading system.
My search for successful systems began with the brute-force application of neural network technology. I wanted to see if I could get a profitable trading system by using neural networks to generate trading signals based simply on recent price changes. As I anticipated, I found that this simplistic approach does not work very well. I then moved to simple rule-based systems, initially working with rules rooted in theory (for example, the thrust-retracement hypothesis). This provided better results: The systems worked but were not particularly tradable because the per-trade profit was on the small side. However, the results encouraged me enough to proceed with a more complex data-mining approach to rules. Instead of using rules based purely on theory, I let a genetic algorithm (GA) find the rules parameters (the lookbacks and thresholds) in a simple price-comparison model. I obtained better results, more tradable than the previous system, but still not ideal.
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