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SMFT-1 2013.05
Last update:
Mon, 24 June 2013, 2:00:14 am
Submission date:
Mon, 24 June 2013, 2:00:14 am
Vote for this product
SMFT-1 2013.05
License:
Shareware
Download size:
1.89 MB
Platform:
Windows
Win2000,Win7 x32,Win7 x64,WinServer,WinVista,WinVista x64,WinXP
SMFT-1 description

Software tools for stock market traders and investors to maximize profitability

Comprehensive system for stock market traders and investors, consists of the modules: FTA-2, SMAP-3, and NNSTP-2. Fundamental-Technical Analyzer FTA-2 has technical indicators chart capabilities and determines which indicator should be trusted more for particular market conditions and specific shares using back-testing simulation. It composes the forecast from indicators signals with weights accordingly to predictive ability of each indicator using Neural Network (NN). The pattern similarity module searches for the best matches by scanning all historical data and ranks them on the basis of maximum correlation and minimum deviation within given period using EOD prices and volume data.

Another module analyzes waves and predicts a future high or low price similar to Elliott Wave theory of recurrent stock market price structures; the software takes into consideration all extracted waves. Due to employing NN it enables identifying both the price and date of extremes. Also NN is used to apply candlesticks pattern to predict the next candlestick (one or many days in width). FTA-2 has a fundamental-technical ratings model to predict stocks within 3-month that is based on key ratios and technical parameters reflecting a company-stock state and dynamics. Also it has portfolio management features.

Stock Market Analyzer-Predictor SMAP-3 is designed to analyze the stock market (indexes, ETFs, or well-traded shares), reveal cycles, build forecast, and find optimal timing to buy and sell. Additional analysis includes such cycles as, month of year, day of month, and day of week. It can be used for intraday trading. NNSTP-2 predicts future indexes, ETFs, or share prices (or percentage) using Fuzzy Neural Network (FNN). It operates automatically when creating the FNN, training it, and mapping to classify a new input vector. Input data are daily weighted or closing prices of the stock and the volume traded.

Requirements:
MS .NET Framework 2.0

What's new in this release:
a new module added


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