Donn Fishbein started developing mechanized trading systems because of an interest in the scientific method and systems that could be tested. “The idea of letting the computer do the thinking appealed to me.”

The influence of the scientific method seems natural to someone with his background. He earned a medical degree from Jefferson Medical College in Philadelphia in 1981. He also earned a Ph. D. in neurophysiology at George Washington University in Washington, D.C., in 1986.

While working on his Ph. D. in Washington, he was affiliated with the National Institutes of Health from 1984 to 1986. One of his projects involved working with positron emission tomography (PET) scans to identify specific regions in the brain. He began using neural networks for automated image analysis. Because there were no commercial products available (the first NeuroShell didn’t appear until 1988), he wrote his own neural network for the project based on the backpropagation algorithm. The program ran on the mini-computers of the time and was moderately successful.

During this period he read about using neural networks to predict time series data, i.e., stock market data. He decided to use a neural network to predict the S&P 500 as an example for a talk he gave at NIH on the PET scan project. He noted that while the neural network for the image recognition portion of the talk generated a few questions, the S&P prediction section produced a lot of questions. “I decided that if intelligent people thought there was something to it, I should pursue work in that area,” said Fishbein.

He began experimenting with some general purpose neural net tools. Seven years ago he purchased the NeuroShell Trader. “I thought it was a lot of money at the time, but it was worth every penny in time savings. This tool was designed for traders,” according to Fishbein.

While working as a pathologist at Ohio, he designed some trading systems for Exchange Traded Funds (ETF) such as the QQQQ, SPY and IJR, the S&P 600 small cap fund. In 2000, that led to a talk at the Trenton Computer Festival, which was started by friends of his. The talk described how neural networks and genetic algorithms can be used in trading. He showed some sample systems, and people asked him how to get signals from the systems on a regular basis. He built a web site called www.NeuroQuant.com, and uploaded the signals on the weekends. He had an increasing number of requests to receive the signals on a daily basis, so he started a subscription service.

Currently, his original QQQQ system is showing a 36.1% annual return compared to 8.1% for buy and hold. A second QQQQ system shows a 25.5% return. His SPY system shows an 18.4% annual return compared to a buy and hold of 8.1%, and the IJR system has an annual return of 33.4% compared to 10.6% for buy and hold.

He said he’s had good success with the GRNN nets from the Trader’s Adaptive Net indicators add-on. He uses inputs from the Trader’s Price Momentum Indicator category as well as linear regression indicators.

He recently attended the NeuroShell Trader Advanced seminar in Florida. “I had a great time at the conference. The only con is that I didn’t ID granny. I just got the Adaptive Turboprop 2 add on; there’s a lot of promise there.”

His next projects include developing sector switching systems and intraday signals. “I work with things a while and test before going public. I haven’t had to take a system back yet because it didn’t work.” He is also in the process of launching a hedge fund based on neural network and genetic algorithm trading systems.