C++ Neural Networks and Fuzzy Logic: Preface

Dual Confirmation Trading System

Jeremy Konstenius, discusses a trading system for the S&P 400 index with a holographic neural network,

which is unlike the feedforward backpropagation neural network. The holographic network uses complex

numbers for data input and output from neurons, which are mathematically more complex than feedforward

network neurons. The author uses two trained networks to forecast the next day’s direction based on data for

the past 10 days. Each network uses input data that is detrended, by subtracting a moving average from the

data. Network 1 uses detrended closing values. Network 2 uses detrended High values. If both networks agree,

or confirm each other, then a trade is made. There is no trade otherwise.

Network 1 showed an accuracy of 61.9% for the five−month test period (the training period spanned two

years prior to the test period), while Network 2 also showed an accuracy of 61.9%. Using the two networks

together, Konstenius achieved an accuracy of 65.82%.