Fortunately, statistical methods combined with computer power can be a good solution to make the candlestick patterns recognition works less time-consuming and more effective. These days, Neural Network (NN) can help to automate a candlestick patterns recognition task. NN should be properly trained in order to be able to predict the next candlestick parameters with the highest probability. One of the obvious problems of implementing a candlestick pattern NN predicting system is a formalization of inputs.

Numerous tests show many possibilities of improving NN candlestick patterns recognition systems. For example, output result can be composed from selected optimized calculations based on different historical periods. Also there are many different ways to formalize the shapes and relative positions of candlesticks. As well as, as back- and forward-testing show, it is reasonable to re-train NN for each particular type of shares and latest historical periods to make the forecast more accurate.

Optimal Solution. There is an automated tool FTA-2 (free use of fully-functional version for one month). It has module which enables using Neural Network to recognize typical candlestick patterns and predict future prices (open, high, low, close). This module predicts only one next candlestick but the prediction can be successfully used for different widths of candlestick, i.e., the number of trading days in one candlestick. The module has been enhanced to calculate result that is composed from different historical periods that allows making the forecast more accurate. Also it can perform comparative forecast analysis for many symbols.

Useful resources:

• Candlestick basics – major signals
• Neural Network basics – introduction
• The computer program which enables using Neural Network to recognize typical candles patterns and predict future prices – software FTA-2

The charts below shows how actual 5-day performance (yellow line) differ from predicted performances by these two methods. The top half is the comparison of Neural Network prediction, bottom half – Cycle Analysis. Green bars mean buy signals, red – sell*.

Three major conclusions for this particular historical period:

1. Cycle Analysis prediction gives signals too early, Neural Network prediction – too late.
2. In average, the prediction by Cycle Analysis showed slightly better accuracy than the one by Neural Network.
3. It seems logical to combine these two methods to improve the accuracy.

* ) The calculations have been performed by an integrated experimental system that combined two applications: Cycle Analysis predictor SMAP-3 and Neural Network predictor NNSTP-2

Charts represent S&P-500 forecast for February 22-26, 2010. The calculations have been performed using Neural Network Stock Trend Predictor NNSTP-2 and Stock Market Predictor SMAP-3 (cycle analysis). A summarized prediction could be a moderate uptrend (1-2%) with flat or downtrend ending. Back-testing fails can be explained by increasing news factor or a possible reversal driven by fundamental changes.