Tag Archives: technical indicators

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)

Pre-Release Announcement

Addaptron Software has been making steady progress with the development of the next generation of high quality software tools for investors/traders. Although there is still a lot of work ahead, Addaptron Software is getting ready to deliver a new software product, SMT-1 (Stock Market Tools, version 1.0). Its first (beta) release is scheduled for October-November 2018.

One of the achievements is all-in-one output forecast signal. This signal is combined from technical indicators, waves, and cycles data by Artificial Intelligence (AI) module. Also based on prediction accuracy, AI decides what time-frame signals to include. There are three types of output: (1) positive numbers for up trending symbols, (2) negative numbers for down trending ones, and (3) zero numbers for uncertain prediction (when AI is unable to provide a reliable prediction result).

In short, SMT-1 has the following benefits:

  • Its comprehensive AI forecast output is combined into a single list of ranked symbols. Such a simple concept enables most investors/traders to use the software easily.
  • Since AI is able to optimize many settings parameters, the number of user-defined parameters is minimized so that users can save time.
  • The software recommends entry/exit prices that allows users just to place a limit buy or limit sell order for the next market day.
  • The software has a back-test simulation functionality that allows users to try different trading strategies.
  • Except a calculated sell signal, the software has an ability to maximize trading profit by optimizing additional sell-trigger parameters.
  • The software includes an extra feature to analyze a current position, recommend the action, track buy-sell transactions, and measure trading performance.
A screenshot of SMT-1 alpha version, main interface
A screenshot of SMT-1 alpha version, main interface

The SMT-1 release will represent a leap forward in usability, functionality, performance and value for Addaptron Software product users. Visit our website addaptron.com at the end of 2018 to download a beta version of SMT-1 and take advantage of this huge upgrade and promotional deal.

Best vs. Many Technical Indicators: When Error Is Useful

Some indicators can provide a better prediction than others so that it seems logically to use the best selected ones to build a composite forecast. On the other hand, even the best indicators can fail. The questions is how to get a consistent good accuracy in predicting – by using only a few best indicators or many good ones. The answer is not obvious and, therefore, a factual comparative analysis would be needed to shed some light on this issue. This short report is based on limited statistical researches; it is an attempt to reach a certain conclusion.

About Expert Method. Apparently, the more good forecasts are taken into consideration, the more precise can be an approximation to actual value. There is Expert Method. This method can be explained by following. As example, an experimentalist shows a pen and asks a group of about 40 people to write down their estimate of the length. Then he collects notes and calculates the average number – normally it is almost 100% accurate. Why it works? Because everyone makes errors in different directions so that averaging gives a precise result.

The Details of Experiment. To find an optimal number of top performing indicators, two tests have been done – using artificial data and real market data. Artificial data allow performing forward testing with more consistent statistics. Although back-testing has been done on out-of-sample sets, it did not have the same forward-testing success every time. Forward testing showed that in average few indicators might produce less accurate prediction than many.

Best vs. Many Technical Indicators: When Error Is Good
The researches and presented chart are made by Technical Analyzer TA-1 (the software is able to compose Neural Network forecasts of many indicators with weights accordingly to each indicator’s predictive ability).

Conclusion. The main conclusion is that relying on a couple of best indicators might yields less consistent success over a long run than using many best and good ones. However, too many is another extreme and not good. The second conclusion is that the list of best indicators is not static – it evolves depending on many factors, including market conditions and, probably, on the number of traders employing particular indicators to make their buy-sell decisions. Thirdly, if the best current top of indicators is known, here is a magic number – it is around 30. And finally, better results are possible if indicators are combined accordingly to their latest back-testing ranking.

Divergence Indicators Demonstrate Better Predictive Abilities

Technical analysts use many different indicators. Not all indicators are equally good. Some of them have better predictive abilities than others at given conditions. Statistical results of the research for stocks and indexes during the last several months have showed the way to improve some regular indicators. In short, if an indicator is trend-differentially coupled with price – it demonstrates better predictive abilities than a pure indicator.

Calculating Divergence IndicatorAmong studied indicators are Relative Strength Index (RSI) and Moving Average Convergence/Divergence (MACD) indicators. They have been transformed to a slope of line and differentially-coupled with a price line slope. Indicators and price transformation to line slopes has been performed using Least Squares Linear Regression within a sliding 10-day period (moving window). A comparative analysis of indicators’ predictive abilities showed that these two coupled ones are better than around 90% of all other (57) the most popular indicators.

The chart below shows an example of such forecast:

The research results presented here have been calculated using a feature of Technical Analyzer TA-1 (TA). Except analyzing chart with indicators and historical data, TA enables to perform a 10-day forecast using Artificial Neural Network. The calculations can be done on the basis of one selected technical indicator or all available. If all indicators used, TA decides how much weight should be assigned for each indicator’s forecast in a composite result by using back-testing for particular market conditions and a specific stock. Each weight is proportional to a predictive ability of a corresponding technical indicator.

S&P-500 Forecast for November 8-19, 2010

This week a stock market rally was driven mostly by news and may last more days ahead. Concerning technical analysis prediction, the chart below can give a clue of S&P-500 index forecast for the next two weeks of November, 2010. According to this forecast the uptrend may continue only 2-3 days and then it can reverse to downside.

S&P-500 Forecast for November 8-19, 2010

This forecast composed from many technical indicators with weights accordingly to the predictive ability of each indicator. It is a short-term (10 trading days) forecast using Neural Network. The used software tool is Investment Analyzer InvAn-4.