Tag Archives: forecast

Addaptron Software Releases New Stock Market Software SMT1

Addaptron Software announced a software release, SMT1 (Stock Market Tools, release 1), a new advanced software system for End-Of-Day (EOD) traders. One of new advantages is all-in-one output forecast signal. This signal (number) is the result of processing data by Artificial Intelligence (AI) Forecast Module. The set of data consists of technical indicators, waves prediction, pattern filter, and cycles extrapolation. Based on Machine learning results, AI decides how to interpret all relevant data and express the conclusion in a single number.

SMT1 is intended for EOD traders with intermediate or advanced knowledge in the Stock Market and computer software. The software consists of four major functionalities: Forecast, Backtest, Simulation, and Tracking. SMT1 is provided with User’s Manual which helps to understand the general structure of the software, connections between functional modules, and how effectively utilize all features.

The software uses EOD historical prices data as input. SMT1 includes a free Downloader that allows downloading EOD historical quotes files of selected symbols (some of most traded leveraged ETFs) from Addaptron Software server for free. Optionally, users can use own input data files. User’s Manual explains how to use own input files.

The main concept of the software is to work (i.e., predict, simulate and optimize trading performance) with the group of well-traded leveraged ETFs to maximize overall return. Each ETF has inverse counterpart and represents different industries that allows finding a potential winner every day. Although the software is suited to a specific niche, users can try to use own group of symbols.

Stock Market traders use different types of sell signal to exit position. Since exit signal cannot be reliable enough, some traders use stop loss and profit target to exit position. Addaptron Software has done numerous computer simulations to learn if adding more exit conditions can improve trading return. The research discovered that a better trading return in the long run can be achieved by using as many as four conditions for exit. This multi-trigger exit concept has been implemented in SMT1 as a new 4-Way Exit Method. This is another SMT1 advantage.

The software also includes an extra feature to record buy-sell transactions, analyze a current position, recommend the action, and measure trading performance. Since AI is able to optimize many settings parameters, the number of user-defined parameters is minimized so that users can save time.

Downloading and installing SMT1 is a very easy process and explained step-by-step on download page . All retail traders are eligible for free fully-functional version during initial 30-day period.

The example of SMT1 user interface: tab-page Simulation (back testing)
The example of SMT1 user interface: tab-page Simulation (back testing)

Release of SMFT-1 Updated Version

Addaptron Software is pleased to announce the release of a new 2013.05 version of Stock Market Forecast Tools SMFT-1. It is an integrated system that includes three major programs: the most popular software program SMAP-3 for stock market cycles analysis and forecast, NNSTP – Neural Network Stock Trend Predictor, and FTA-2 – a modified version of InvAn-4 that is a comprehensive tool used by serious investors for years. The new version includes several improvements, such as, models optimization, ability to read more different input file formats, and optional feature to enable a free Downloader.

Release of SMFT-1 Updated Version

SMFT-1 consists of the tools that employ fundamental ratios rating model, technical analysis, chart pattern analysis, Elliott Wave theory, cycle analysis, candlesticks model, trend lines analysis, regression models, etc. The calculations are empowered by Neural Network. The implemented methods are statistically proven and widely used.

Most methods are provided with back-test calculation to estimate the accuracy of forecast within the recent performance period. The back-testing computations also may play an important role if more than one method is used. It allows estimating a weight of each method in a composed result; the weights that are proportional to the ability of the methods to predict the price.

Updated Version of TraDeSS-1 Has Been Released

Addaptron Software is pleased to announce the release of updated 2012.06.01 version of Trading Decision Support System TraDeSS-1. Now Simulation module allows optimizing algorithms and their parameters, as well as, performing comparative analysis for many symbols. At the end of batch mode simulation, it prints a summarized list of best buy-sell algorithms and parameters configuration for each symbol. The list is sorted from best to worst performers.

Updated Version of TraDeSS-1 Has Been ReleasedNow Relative Performance Forecast module allows printing output results in HTML-format that include additional information about prediction. It shows the forecast generated by each selected method, as well as, the weights of forecasts that were assigned in a composite forecast. If more than two methods are used, table includes RMSD (it is normalized root-mean-square deviation that is to estimate the degree of forecasts concurrence; the less amount means better consensus among different methods).

The new version is able to read input historical prices files of three different formats. The software recognizes these formats automatically. To enable reading any other format files, users should send the request.

Using Parabolic SAR with Neural Network for Predicting

Parabolic SAR (SAR stands for Stop-And-Reverse) is a trend-following indicator that has been used by many traders for decades. Its major application is in trading systems to define a trailing stop, i.e., to protect profit when a price trend changes. The term “parabolic” appeared to characterize the indicator parabola shape that is due to using an accelerating factor in the formula. SAR is especially effective in a trending market. To make it more effective in a sideways market, it is often used in conjunction with other indicators.

SAR indicator gives a strong signal when a price trend is about to reverse, therefore, this indicator can be used for prediction. To compare the predictive ability of SAR with other indicators, it has been implemented into the technical analysis module of Fundamental-Technical Analyzer FTA-2. SAR calculations have been used to collect statistics based on the forecast simulations for major indexes and ETFs during August-October 2011 period. As a result, SAR’s position was mostly in “top ten” indicators list.

Using Parabolic SAR with Neural Network for Predicting

The research and presented chart are made by Fundamental-Technical Analyzer FTA-2, one of the software modules that enables composing Neural Network forecasts of many indicators with weights accordingly to each indicator’s predictive ability.

Omitting logical rules for accelerating factor and reversal conditions, a recurring core formula for SAR is the following:

SAR (current point) = AF * [EP – SAR (previous point)] + SAR (previous point)

AF – Acceleration Factor (normally starts from 0.02 and increases by 0.02 if each next point reaches a new extreme, saturates until 0.2);
EP – Extreme Point (lowest low or highest high).

To summarize, Parabolic SAR can be enriched by combining it with Neural Network and successfully used for predicting stock market prices. Combing it with Neural Network allows extracting more statistically stable patterns and, therefore, providing a better accuracy in the forecast. As simulations showed, improved results can be achieved if SAR is transformed into more sensitive indicator by subtracting it from close price (it indicates the degree of SAR and price convergence).

S&P-500 Forecast for the Next Week – for October 18-22, 2010

The charts below show S&P-500 forecast for the period from October 18 to October 22, 2010. The forecast is a possible slight uptrend.

S&P-500 Forecast for the Next Week - for October 18-22, 2010

The forecasts have been calculated using Neural Network Stock Trend Predictor NNSTP-2 and Investment Analyzer InvAn-4 (pattern similarity).