V.15:4 (162-169): Seasonality and Trading by Jeffrey Owen Katz, Ph.D., with Donna L. McCormick
Product Description
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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