Using Neural Networks and Genetic Algorithms for Technical Analysis

Ä    Note: It is not necessary to read this section in order to use TradingSolutions. However, understanding the concepts used in the program can help you to get the most out of the intermediate and advanced features.

The previous topics have provided brief introductions to technical analysis, neural networks, and genetic algorithms. Now these concepts will be tied together to explain how neural networks and genetic algorithms can be used for technical analysis. In particular, four general methods will be discussed.

First, genetic algorithms could be used without neural networks to optimize constant inputs to entry/exit systems or calculate market timing signals. This allows general approaches to be fine-tuned to particular stocks or data.

Second, and probably most common, a neural network could be used to create a model for predicting the future price of a financial instrument, given the current and previous prices and other technical and/or fundamental data. The predicted price could then be used within an entry/exit system to produce a signal indicating when to buy or sell. The simplest entry/exit system would be: buy if the predicted price is greater than the current price and sell otherwise. Genetic algorithms could be used to optimize the neural networks inputs and parameters in order to produce the best model possible.

A third method would be to train a neural network to produce an entry/exit (buy/sell) signal. The desired entry/exit signal for the training phase could be produced by looking into the future to determine the optimum time to buy and sell given certain constraints and commission assumptions. The neural network could then be trained to produce this optimal signal using only current and historical data as inputs. Since the output of this type of neural model is an entry/exit signal, it can be used directly without the need for further processing by an entry/exit system. Again, genetic algorithms could be used to optimize the inputs and parameters to the neural network in addition to the entry/exit system thresholds.

Finally, a neural network could be used to create a model for predicting the performance of a stock for a certain period into the future. For example, the inputs to the model could be the current price, the percent gain over the last week, the percent gain over the last month, etc. and the desired output could be the percent gain for the next week. If this type of model was created for a number of stocks, such as the stocks in the S&P 500, these stocks could be sorted by the projected percent gain for the next week. The top ten could be purchased and held for that week then re-chosen again each week according to the models’ projections. This is only one simple way in which this type of model could be used for trading. As with the previous two methods for using neural networks, the networks inputs and parameters could be optimized using genetic algorithms.

These are just some of the ways neural networks and genetic algorithms can be used for technical analysis. Many other possibilities exist. Because TradingSolutions allows you the flexibility to decide the inputs and outputs, you are essentially limited only by your imagination.

&  Return to the Overview for this chapter.