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SMFT-1 for Windows

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Editors’ Review

Download.com staff
SMFT-1 offers predictive stock analysis for traders. It uses neural networks, pattern matching, and back-testing to compose weighted forecasts, helping to maximize profit and minimize risk.

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SMFT-1 0/1

  • Pros

    • Uses back-testing to validate indicator reliability
    • Neural Network weights forecasts by predictive ability
    • Scans historical data for predictive pattern matching
    • Matches patterns using OHLCV correlation and deviation
    • Analyzes correlated asset pairs with time shifts

  • Cons

    • Relies on an internal historical database
    • Lacks automated trade execution or broker integration
    • Does not incorporate fundamental analysis data
    • No mention of real-time data integration
    • Analysis is limited to provided models and indicators
Icon of program: SMFT-1

SMFT-1 for Windows

Download now

Used SMFT-1 for Windows?

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Full Specifications

GENERAL
Release
Latest update
Version
2012.07
OPERATING SYSTEMS
Platform
Windows
Operating System
  • Windows XP
  • Windows 10
  • Windows Vista
  • Windows 2000
  • Windows 7
Additional Requirements
Microsoft .NET Framework 2.0
POPULARITY
Total Downloads
151
Downloads Last Week
13
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Developer’s Description

By Addaptron Software
Maximize profit and minimize risk for stock market trade.
SMFT-1 is intended for stock market traders and consists of 3 modules TA-1, SMAP-3, and NNSTP-2. Technical Analyzer TA-1 is based on predictive models including technical analysis and pattern recognition. Except technical analysis chart capabilities, the technical analysis module is able to determine which indicator should be trusted more for particular market conditions and specific shares using back-testing simulation. It composes the forecast with weights accordingly to predictive ability of each technical indicator using Neural Network. The pattern similarity module searches for the best matches by scanning all historical data from the internal database. It matches on the basis of maximum correlation and minimum deviation within given historical period using open, high, low, and close prices and volume data. The composite result is built as a weighted average with weights proportionally pattern degree of similarity. Also it has a correlation analysis tool to perform analysis and evaluate the future trend using a mutually-correlated pair (or in opposite correlation) with time shift.
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