V.17:11 (487-493): Filters For Neural Net Trading Models by Steve Helme
Product Description
Filters For Neural Net Trading Models by Steve Helme
A filter such as a moving average removes noise from around the trend, leaving behind the important signal. The behavior required of filters that preprocess data inputs for a neural network trading model differs from that for discretionary trading systems. Usually, discretionary traders who use filters such as moving averages are not dependent on the filter’s precise characteristics. When looking for breakouts or trend changes, traders are concerned with the general performance of an indicator, not the relatively small deviations. On the other hand, mechanical systems, and neural network systems in particular, can be very sensitive to the precise characteristics of the filtered indicators used as system inputs.
A filter such as a moving average removes noise from around the trend, leaving behind the important signal. The behavior required of filters that preprocess data inputs for a neural network trading model differs from that for discretionary trading systems. Usually, discretionary traders who use filters such as moving averages are not dependent on the filter’s precise characteristics. When looking for breakouts or trend changes, traders are concerned with the general performance of an indicator, not the relatively small deviations. On the other hand, mechanical systems, and neural network systems in particular, can be very sensitive to the precise characteristics of the filtered indicators used as system inputs.
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