Stock Forecast Methods

Stock Market Trading and Investing

Enabling Candlestick Forecast by Neural Network

A new 2012.02 version of Fundamental-Technical Analyzer FTA-2 has been released. Now it has a new module which enables using Neural Network to process past candlestick patterns and predict the future candlestick, i.e., its Open, High, Low, and Close prices. A candlestick can consists of one or many trading days. The module calculates result composed from different historical periods that allows making the forecast more accurate. Also it can perform comparative forecast analysis for many symbols.

Enabling Candlesticks Forecast by Neural Network

More details can be found on FTA-2 software webpage, as well as, in User’s Manual that is available from the main menu after the installation of FTA-2. To try the software for free, go to download page and follow the instruction.

About Candlestick Forecast Model

The idea of using the chart with candlesticks (or candles) for predicting market prices is very old. Two centuries ago, Japanese rice trader found that the candlesticks pattern chart could be used as a tool to predict future prices in a free market with a natural demand-supply balance. The method was improved later by others and today it is successfully used by many traders and investors in the stock market.

A candlestick is presented using high, low, opening, and closing prices during a certain trading period, for example, trading day. A regular candlestick figure consists of Real Body, Upper Shadow, and Lower Shadow. The number of candlesticks that is normally used for predicting can range within 1..12. Evidently, the number of different combinations of several candlesticks in a row can be big. Some believe that there are only 12 major candlestick patterns, others consider this number is 70 or even more. Anyway, in case of chart analysis, it is necessary to remember at least major patterns and process many charts in order to make forecast successful.

Apparently, statistical methods combined with computer power can be a good solution to make the candlestick patterns recognition work less time-consuming and more effective. For example, Neural Network (NN) can help to automate a candlestick patterns recognition task. NN should be properly trained in order to be able to recognize and predict further movements. One of the obvious problems of implementing a candlestick pattern NN predicting system is a formalization of inputs, i.e., the way how to express each candlestick shape and relative position of all candlesticks in numerical values.

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February 6, 2012 - Posted by | Stock Market Forecast, Stock Market Software | , , , ,

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