- Any index as an equivalent of the overall stock market, sector, industry, ETF, or well-traded shares can be used to find an optimal timing.
- SMAP forecasts performance for 1/4 of historical period. For example, if the period of historical quote data entered as input is equal 16 years, prediction is calculated for the next 4 years; for 4 months of history, prediction is one month.
- Using back testing helps to find an optimal time frame for better predictability.
- Annual return is calculated on a decompounded basis, i.e., for example, 20% displayed annual return means 44% total for two-year period.
- SMAP can be used also for intraday trading.

The stock market does not follow just a linear trend - it has some deviations from a linear function. Some cycles are well-known, such as, four-year presidential cycle, annual and quarterly fiscal reporting cycles, etc. In addition, some cycles are defined by intrinsic characteristic properties of the system. The stock market performance curve can be considered as a sum of the cyclical functions with different periods and amplitudes. It is not easy to analyze the repetition of typical patterns in stock market performance because cycles mask themselves - sometimes they overlap to form an abnormal extremum or offset to form a flat period. It is clear that a simple chart analysis has a certain limit in identifying cycles parameters and using them for predicting.

Addaptron Software has developed Stock Market Analyzer-Predictor SMAP, computer program, which is able not only to extract basic cycles of the stock market (indexes, sectors, or well-traded shares) but also to predict an optimal timing to buy or sell stocks. SMAP calculation mainly based on extracting basic cyclical functions with different periods, amplitudes, and phases from historical quote curve. To detect correctly major cycles, the historical price data are transformed from time domain to frequency domain (spectrum).

At the beginning, instead of calculating the prediction for the time period forward, SMAP does a simulation of forecast on relevant past data in order to estimate the accuracy of prediction with certain parameters (such simulation called "back testing" - the process of evaluating prediction on prior time periods). Then it calculates the prediction for the time period forward using internal optimized parameters. Back testing also allows finding an optimal time frame. The more difference between actual curve and forecasted, the worse prediction accuracy is.

By selecting data with different historical periods, user can identify the major cycles, which have a dominant effect in a particular time frame. To build an extrapolation (predicted curve), SMAP uses the following two-step approach: (1) applying spectral (time series) analysis to decompose the curve into basic functions, (2) composing these functions beyond the historical data. Additionally, SMAP enables finding optimal timing to buy/sell by analyzing months of year, days of month, and days of week (the calculation is based on statistical analysis). Also it can be used for intraday trading (no source data provided).

SMAP has a user-friendly easy-to-use interface. This software is intended for investors with a basic knowledge in stock investing.

- Using a pure cycle analysis without additional methods and adjustments
- Decoupling the fitting area (time interval where solution is searched) from historical data
- Predetermining the period values and number of cycles to be included in the solution
- Estimating each cycle statistical stability within selected historical period
- Estimating the deviation of a solution from actual data in both areas - fitting and including unused (one can see the solution data set compared to a selected historical time interval whether or not a whole historical time period was included in the analysis)
- Using linear or logarithmic scale to calculate the solution and plot the chart
- Using the price value in absolute units ($) or in percentages (relative to minimal value)
- Working with expandable chart scale

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