NNSTP Predict stock prices for short terms.
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NNSTP Description

From Addaptron Software : 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 1-60 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. 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. The software is intended for traders with a basic knowledge in stock analysis.Version 2.0 predicts future share prices or 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 1-60 trading days. The forecasting is based on automatic scan of different inputs periods to define accuracy of each one by back testing. What's new in this version: Version 2.0 predicts future share prices or 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 1-60 trading days. The forecasting is based on automatic scan of different inputs periods to define accuracy of each one by back testing.

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