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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.

New Stock Market Forecast Tools SMFT-2 Released

Addaptron Software released a new Stock Market Forecast Tools SMFT-2. It is an integrated advanced system that is the next generation improved software based on older SMFT-1 version and a few recent development projects.


New Stock Market Forecast Tools SMFT-2

 


Most prediction modules provided with back-test calculation to estimate the accuracy of forecast within the recent performance periods. Additionally, the back-testing computations 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.

SMFT-2 currently includes five major modules:

  • TA Predictor – prediction for day or week period based on technical analysis, pattern recognition and Neural Networks (generates composite result). Back-analysis models optimization and batch calculation for comparative analysis included.
  • Waves – Elliott Wave model: back-test optimization, up to 10 waves forecast.
  • Cycles – prediction based on cycle analysis.
  • Week day – search for maximum performance using price behavior depending on week day. It allows discovering the best entry/exit days of week; batch calculation included.
  • Month day – search for maximum performance using price behavior depending on month day. It allows discovering the best entry/exit days of month; batch calculation included.

The implemented methods are statistically proven and widely used. All modules share the same EOD (end-of-day) input data. The software is provided with a free Downloader that allows downloading EOD historical quotes files from the Internet for free. A fully-functional software SMFT-2 during initial 30-day period is free. The software and associated documentation are delivered via download links over the Internet. For technical requirements, installation instruction, and download link, visit SMFT-2 download page.

Prediction Combined with Simple Algorithm Provides Stable Return

Any prediction can fail but if it is combined with well-tested buy-sell rules, the result is much better. Addaptron Software provides predictions of major ETFs prices (Open, High, Low, Close) for the next day. This article describes one of the simplest algorithms to use one-day candle prediction data. The algorithm consists of four simple rules:

  1. Place Buy Limit order with predicted Low price before the stock market is opened.
  2. As soon as the Buy Limit order is completed, place Sell Limit order with predicted High price.
  3. Cancel the order if it is not completed during the first half of the stock market day.
  4. If the stock market is about to close but the order is not completed, cancel the Sell Limit order and place Sell Market order.

The following charts and tables demonstrate one of the practical examples of described above approach (symbol BOIL).

Predicted and actual prices chart (dark green and red filled candles – predicted, light green and red line candles – actual):

The chart of predicted and actual prices


Predicted and actual prices table:

The table of predicted and actual prices


Intraday performance chart (actual prices) for the same period:

The intraday prices


The result of using the described algorithm:

The result of using the described algorithm


The example is based on the following assumptions: 300 shares are used for trading, transaction fee is $10 so that 2 transactions (buy and sell) within day cost $20. Another example (symbol SSO) can be find here

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.

Advanced Software System for Professional and Institutional Traders

Advanced Software System for Professional and Institutional Traders

After years of intensive development Addaptron Software has released Trading Decision Support System TraDeSS-1, advanced computer program for institutional traders and investors. It is a comprehensive and effective software to help finding the best trading opportunities, maximizing profitability using several predictive models with back-testing features, and optimizing algorithms by running simulations.

TraDeSS-1 is equipped with an advanced forecasting state-of-the-art system. The predicting can be done using nine forecast methods of different nature. Only one method, a combination of a few ones, or all together can be used. Each method is provided with back-test calculation to estimate the accuracy of forecast within the recent performance period. The back-testing computations play an important role if more than one method is selected. It allows assigning a weight to each method in a composed result; the weights are proportional to the ability of the methods to predict the price.

The comparative analysis of simulations shows that systems based on predicted entry-exit signals generate a better profit in around 70% cases than random-entry trading systems. As well as, it should be noted that a multi-model forecast provides a significant improvement over the best individual forecast. It can be explained by the existence of many different independent factors contributing to the error in each forecast which is normally distributed around an actual value.

Since sometimes predictions can fail, to preserve a capital amount in a volatile market, the software enables simulating different risk management approaches. Depending on the character of particular trading shares and the current market conditions some ideas can work better than others. To optimize the strategy in a particular case, the software enables testing different algorithm configurations and finding automatically the best ones. It is especially important for exit points to minimize losses (and ultimately maximize an overall profit). All optimizations can be done automatically by scanning 64 possible logical combinations and adjusting numerical parameters.

TraDeSS-1 has a functionality that allows estimating a hypothetical maximum possible profit in case of 100% accurate forecast. Although an actual forecast cannot be so accurate, this feature combined with comparative analysis enables discovering the best trading opportunities among different types of financial instruments. Calculating maximum theoretical return allows finding optimal buy and sell signals. Also it helps estimating a reasonable amount of initial investment at given transaction fee. Users can choose to re-invest each time a new or the same amount and see the difference in results.

The software has forward testing and assets management features. It allows monitoring the simulated or actual completed transactions, reflecting total trading activity, and evaluating the success of trading in overall. It enables working with many separate data files that is convenient in case of managing multiple assets and keeping the archives of older activities.

TraDeSS-1 has also a few independent tools, such as, technical indicators predictor, cycle analysis forecast, Neural Network (NN) forecast, fundamental 3-month rating model, etc. The detailed description is presented in User’s Manual (accessible from menu Help after downloading and installing the software). To download the software, the registration is required: http://www.addaptron.com/m/registr.php

Candlestick Patterns Decoded by Neural Network

Candlestick patterns can signal a trend continuation or reversal. Many traders and investors know how to benefit from candlesticks analysis. As a rule, a simple chart analysis is used that requires analysts to remember and correctly interpret, at least, typical candlestick patterns. However, the number of possible different candlestick shapes and their combinations in a row can be huge. Besides, some patterns interpretations might be considered as contradictory or doubtful.

Fortunately, statistical methods combined with computer power can be a good solution to make the candlestick patterns recognition works less time-consuming and more effective. These days, Neural Network (NN) can help to automate a candlestick patterns recognition task. NN should be properly trained in order to be able to predict the next candlestick parameters with the highest probability. One of the obvious problems of implementing a candlestick pattern NN predicting system is a formalization of inputs.

Numerous tests show many possibilities of improving NN candlestick patterns recognition systems. For example, output result can be composed from selected optimized calculations based on different historical periods. Also there are many different ways to formalize the shapes and relative positions of candlesticks. As well as, as back- and forward-testing show, it is reasonable to re-train NN for each particular type of shares and latest historical periods to make the forecast more accurate.

Optimal Solution. There is an automated tool FTA-2 (free use of fully-functional version for one month). It has module which enables using Neural Network to recognize typical candlestick patterns and predict future prices (open, high, low, close). This module predicts only one next candlestick but the prediction can be successfully used for different widths of candlestick, i.e., the number of trading days in one candlestick. The module has been 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.

Candlestick Patterns Decoded by Neural Network

Useful resources:

  • Candlestick basics – major signals
  • Neural Network basics – introduction
  • The computer program which enables using Neural Network to recognize typical candles patterns and predict future prices – software FTA-2

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)

where:
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).

Elliott Wave Neural Network Forecast

Stock Market Forecast Tools SMFT-1Stock Market Forecast Tools SMFT-1 has been updated. One of the major changes is a new module in Technical Analyzer TA-1 sub-system. The new module analyzes waves and predicts the next extreme (high or low price) similar to Elliott Wave theory of recurrent stock market price structures. Fundamentally, the Elliott 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, a more general approach has been developed – the software takes into consideration all extracted waves. Due to employing Neural Network (NN) the module enables identifying both the price and date of extremes. The software has a flexibility in tuning and optimizing computing parameters by users. It allows choosing the number of counted waves in each case for NN training in a range within 9..24. Also one can set the amount of iterations for NN learning from 50 to 5,000.

Elliott Wave Neural Network Forecast

TA-1 Waves module interface