Prediction Combined with Simple Algorithm Provides Stable Return

November 28th, 2014 No comments

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

April 26th, 2013 Comments off

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

June 2nd, 2012 Comments off

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

April 7th, 2012 Comments off

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

February 2nd, 2012 6 comments

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

November 22nd, 2011 1 comment

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

October 4th, 2011 Comments off

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

New Forecast-simulation Software to Maximize Trading Profit

September 13th, 2011 Comments off

New Stock Market SoftwareAddaptron Software released the first beta version of a new decision support software system for stock market traders – Trading Forecast Weekly TFW-1. It provides weekly EOD forecasts for 20 selected ETFs by cloud computing. TFW-1 allows choosing best trading opportunities by comparative analysis, assessing different strategies using back-test simulation, finding the best configuration of algorithms among 64 possible combinations and optimizing triggering parameters, as well as, monitoring results by stand-alone computing.

The statistical analysis of simulations shows that systems based on predicted entry-exit signals generate a better profit than random-entry trading systems. TFW-1 enables forecasts to increase a trading profit. The calculations are done on server-side by a robust artificial intelligence system. Since sometimes predictions can fail, to preserve capital in a volatile market, the software enables simulating different risk management approaches. TFW-1 simulation allows testing generated predictions combined with buy-sell signals.

Depending on the character of particular trading shares and current market condition, some ideas can work better than others. To optimize strategy for each particular case, the software enables testing a given idea and finding automatically the best set of algorithms. It is especially important for exit points to minimize losses and ultimately to maximize an overall profit. Some algorithms have customizable parameters that can also be optimized. All optimizations can be done either automatically by scanning 64 possible logical combinations and adjusting numerical parameters or manually.

TFW-1 allows monitoring the simulated or actual completed transactions, reflecting total trading activity, and evaluating the success of trading in overall by stand-alone computing. It enables working with many separate data files that is convenient in case of managing multiple assets and keeping the archives of older activities. You are welcome to download and use for free (during the first 30-day period) a fully-functional version of TFW-1.

Best vs. Many Technical Indicators: When Error Is Useful

August 25th, 2011 Comments off

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.

Software Tools Redeveloped and Integrated

August 3rd, 2011 Comments off

Stock Market Forecast Tools SMFT-1To address the challenges of the modern stock market, Addaptron Software has made improvements to its line of software products. Now a few decision support tools have been included in a single system – Stock Market Forecast Tools SMFT-1. This integrated system allows traders to use several computer programs for affordable monthly subscription fee. New users are granted to use the fully-functional system for free during the first 30 days.

Most changes have been made to former Investment Analyzers InvAn-3 and InvAn-4. Now the system that inherited their redeveloped parts is called Technical Analyzer TA-1. Among major differences, its technical indicators forecast has been improved with flexibility to adjust weights in composed result. Also a new module has been added to predict trends using correlation analysis with forward shifting.