Addaptron Software

Investment Analyzers InvAn-3 and InvAn-4 are not available anymore. Most of their parts have been redeveloped and included in Fundamental-Technical Analyzer FTA-2.

Fundamental-Technical Analyzer FTA-2

Fundamental-Technical Analyzer FTA-2 Interface Example

Fundamental-Technical Analyzer FTA-2 (FTA) is designed to help stock market investors and traders with analysis and forecast. It is a comprehensive system with user-friendly interfaces that is easy to use. This software is intended for stock market participants with a basic knowledge in trading and investing.

FTA consists of six major modules: FTA 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 NN.

The module Waves analyzes price waves and predicts the next extreme (high or low in price) similar to Elliott Wave theory of recurrent stock market price structures. Fundamentally, the Elliot Wave model is based on a crowd psychology that follows between optimistic and pessimistic trends creating patterns that can be fitted to natural sequences. However, instead of assuming that waves obey only the sequence of Fibonacci, harmonic, or fractal ratios, the software has been designed to use a more general approach by taking into consideration all extracted waves. Due to employing NN it enables identifying both the price and date of extremes.

The module Candles enables using NN to recognize typical candles patterns and predict future prices. This module predicts only one next candle. However, the candles pattern prediction can be successfully used for different widths of candle, i.e., the number of trading days in one candle. The module is enhanced to calculate result that is composed from different historical periods that allows making the forecast more accurate. Also it can perform comparative forecast analysis for many symbols.

The pattern recognition forecast 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 the degree of patterns' similarity to predicted one.

Comprehensive 3-month Fundamental-Technical Ratings Model. FTA includes a comprehensive 3-month fundamental-technical ratings model (RM) that is based on key financial ratios and technical parameters reflecting a company-stock state and dynamics. In creating this model, the main idea was to combine three-month fundamental, technical, and timing forecasts. RM works with end-of-day (EOD) stock price data from the US and worldwide stock exchanges. It can use USD and foreign currency for calculation. Also RM has a few portfolio management features.

The main RM output result is a rank of stocks. The highest ranked stocks are expected to be the most probable best performers within the next three-month period. The rank is a result of comprehensive data processing. Stocks are ranked on the basis of the composite rating which is a combination of fundamental, technical, and timing ratings. This combination is calculated using Harmonic Averaged Quality Functions (HAQF) method. HAQF allows modeling quality of company and its stock very realistically.

RM fundamental analysis is performed on the basis of several key ratios and parameters (factors) to reflect company and its stock actual state and dynamics. Also it includes stock performance expectations on the basis of analysts' opinion and external ratings. RM fundamental rating is calculated using HAQF method.

RM technical analysis is based on the set of major technical indicators. RM reliably processes the signals from tens of indicators and combines them into a single rating. This rating is calculated using Neural Networks (NN) algorithm that allows reaching the best accuracy in the forecast. RM timing rating is used to define when better to buy or sell particular shares. The timing rating is calculated either on the basis of trend average deviation or cycles analysis forecast.

RM has some extra features. It calculates the recommended percentage of cash reserve depending on the current condition of all stocks in the database (similar to the idea of Robert Lichello). RM allows forecasting and plotting price of stocks' portfolios, an individual stock, index, or group of stocks from the same sector on the basis of cycles analysis.

RM Major Benefits

FTA-2 is a part of integrated system SMFT-1. Some additional info is available on Q & A page. The detailed description is presented in User's Manual (accessible from menu Help after downloading and installing SMFT-1).

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