Neural networks can discover patterns in data that humans might not notice and successfully predict the future trend. Addaptron Software has developed NNSTP-2, neural network computer tool, to help stock traders in predicting stock prices for short terms. NNSTP-2 predicts future share prices or their percentage changes (can be chosen in settings menu) using Fuzzy Neural Network (FNN). It operates automatically when creating the FNN, training it, and mapping to classify a new input vector.
Recommended forecasting horizon is within the range of one to around 60 trading days. The software predicts closing price or weighted one (can be chosen in settings menu). Input data are weighted closing price of the stock and the volume traded (EOD csv-files). The historical period should be multi-fold more than forecast period. The maximum number of predicted days is defined by the formula: (predicted days) = [(historical days) - 40] / 20. The input data transformed to characteristic matrices before training FNN. The forecasting is based on automatic scan of different inputs periods to define accuracy of each one by back testing. Then the final forecast is built on weighted averaging of all forecasts. Each weight is proportional to the accuracy of a certain input period forecast.
NNSTP-2 has a user-friendly easy-to-use interface. Historical stock quote data (EOD) are downloaded automatically from the Internet free-of-charge (US and worldwide stock exchanges). The software is intended for traders with a basic knowledge in stock analysis.
Please be aware that any prediction tool that based on statistical methods may not work well in case of the stocks that are not traded with high volume (like penny stocks) or in time of big news events.
Some additional info is available on Q&A page, as well as, from menu Help after downloading and installing NNSTP-2 demo version.
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The algorithm of using NNSTP-2 is simple - select input data, run forecast, and view result. The input data are historical quotes of the stock selected by user for some period (automatic downloading). Downloaded CSV-files from the Internet are stored in INPUT subdirectory and can be used for further processing. The output results are presented in a chart form and data table (in text-box and text file).
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