Addaptron Software Release 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): 

SMFT-2 is a New Stock Market Prediction Software

SMFT-2 (Stock Market Forecast Tools) is a new integrated system by Addaptron Software. It is the next generation of the software that intended to replace older SMFT-1 version. As a result of a few recent development projects, the new version is based on new advanced methods, provides more accurate prediction, and is easier to use.

Prediction modules is built with back-test calculation to estimate the accuracy of forecast within the recent performance periods. Additionally, the back-testing computations is important if more than one method is used. It allows estimating the 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 is free during initial 30-day period. 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.

About Trading Decision Support System TraDeSS-1

Trading Decision Support System TraDeSS-1 is for institutional traders and investors who deal with ETFs, commodities, and big-volume equities. 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 principal amount in a volatile market, the software enables simulating different risk management approaches. Depending on the character of particular trading assets 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 for equities, etc. The detailed description is presented in User's Manual (accessible from menu Help after downloading and installing the software). The software is available via the registration (no payment data collected and no obligation to buy).

A fully-functional software during initial 30-day period is free. To continue using the software after 30 days, the subscription is required (subscription link is available from the software interface). Technical support and updates are included. Annual lease and perpetual license are available. Paid on-site training (how to use the software) can be provided.

SP-500 Index Cycle Analysis Forecast for Spring and Summer of 2012

There is a simple predictive model that is built on an assumption that the stock market has a semi-cyclical nature. Many technical analysts use cycle analysis in their comprehensive research. The cycles may not be stable all the time but the probability of repeating patterns can be big enough to get a consistent trading profit.

Cycle analysis can be made using charts. However, the distinctive cycles in pattern can be masked by more powerful factors (fundamental data, bad/good news, global events, etc.) that over-drive the market time-from-time. Therefore, a special software can be very useful for extracting hidden cycles.

The chart below shows S&P-500 index forecast for April-May and summer months of 2012. The calculation has been performed using Stock Market Predictor SMAP-3. According to this forecast the stock market might continue its uptrend until the end of May, then have some correction in June and top in July.

Predicting the Next Move of the Market by Elliott Wave

The Elliot Wave model is based on a crowd psychology that changes between optimistic and pessimistic states creating patterns that can be fitted to natural sequences. These days, the Elliott Wave principle can be improved and used in stock market forecast. Combining this method with Neural Network (NN) helps to eliminate the subjectivity in counting waves.

Instead of assuming that waves obey only the sequence of Fibonacci, harmonic, or fractal ratios, a more general approach can be used to process all extracted waves. Besides, employing NN enables identifying both the price and date of extremes. Although the Elliott Wave does not generate always accurate and consistent forecasts, its result can be successfully used as an additional input for making a trading or investing decision in modern market conditions.

The chart and the portion of output result below can give a clue for a possible next move of S&P-500 for the next week:

Symbol: ^GSPC
Total waves number: 126
Cases number: 108
Target: 1,382.35$ at 2012-02-21 (change 3.10% from 2012-02-14)
     No        Date                $           %   up/down  period
    115  2012-01-02         1,284.62        2.88    100.00     3
    116  2012-01-04         1,265.26       -1.51      0.00     2
    117  2012-01-11         1,296.82        2.49    100.00     5
    118  2012-01-12         1,277.58       -1.48      0.00     1
    119  2012-01-20         1,322.28        3.50    100.00     5
    120  2012-01-24         1,306.06       -1.23      0.00     1
    121  2012-01-26         1,333.47        2.10    100.00     2
    122  2012-01-30         1,300.49       -2.47      0.00     2
    123  2012-02-09         1,354.32        4.14    100.00     8
    124  2012-02-10         1,337.35       -1.25      0.00     1
    125  2012-02-13         1,353.35        1.20    100.00     1
    126  2012-02-14         1,340.83       -0.93      0.00     1

Chart and calculation results by SMFT-1 (a version of FTA-2 sub-system Waves module interface)

Automated Recognition of Candlestick Patterns

The idea of using the chart with candlesticks (or candles) for predicting market prices is very old. Two centuries ago, Japanese rice trader found that the candlesticks pattern chart could be used as a tool to predict future prices in a free market with a natural demand-supply balance. The method was improved later by others and today it is successfully used by many traders and investors in the stock market.

A candlestick is presented using high, low, opening, and closing prices during a certain trading period, for example, trading day. A regular candlestick figure consists of Real Body, Upper Shadow, and Lower Shadow. The Real Body size is proportional to the difference between opening and closing prices. Real Body can be two types - white (green) for uptrend and black (red) for downtrend. Upper Shadow size is proportional to the difference between either high price and closing price in case of uptrend or high price and opening price in case of downtrend. Similarly, Lower Shadow size is proportional to the difference between either low price and opening price in case of uptrend or low price and closing price in case of downtrend.



The number of candlesticks that is normally used for predicting can range within 1..12. Evidently, the number of different combinations of several candlesticks in a row can be big. Some believe that there are only 12 major candlestick patterns, others consider this number is 70 or even more. Anyway, in case of chart analysis, it is necessary to remember at least major patterns and process many charts in order to make forecast successful.

Apparently, statistical methods combined with computer power can be a good solution to make the candlestick patterns recognition work less time-consuming and more effective. For example, Neural Network (NN) can help to automate a candlestick patterns recognition task. NN should be properly trained in order to be able to recognize and predict further movements. One of the obvious problems of implementing a candlestick pattern NN predicting system is a formalization of inputs, i.e., the way how to express each candlestick shape and relative position of all candlesticks in numerical values.

Preparing Data for Neural Network. The idea is simple - look at several candlesticks, recognize pattern, and predict the next candlestick. But how to convert a candlestick shape in numerical values? For simplicity, let's consider one major characteristic of each candlestick. In case of using six candlesticks to predict the performance within the next seventh one (actually the value of Real Body can be considered as an equivalent of performance), the data row for training neural network would be presented by the following:



In reality we need to use more input parameters including shadows, relative position of each candlestick, etc. so that it can be, for example, 60 inputs for each row:



The candlesticks pattern can be formalized in different ways. Which one is the best? It may depend on the type of NN that is used and statistical characteristic of input data. Evidently, only practical testing (out-of-sample test) can indicate which formalization can give the best result for most cases. Logically, all numbers that describe candlestick shape should be expressed in relative units. For example, Real Body size can be converted using the following formula:

RB = 100% * (C - O) / O

where RB - Real Body relative size, C - closing price, O - opening price.

Two more numbers can express the Upper an Lower Shadows relatively to Real Body. The following figure shows distances that used for calculation relative Upper and Lower Shadows:


US = 100% * c / a
LS = 100% * c / b

where US, LS - relative Upper and Lower Shadows correspondingly. US and LS can have values within 0..100%; minimum value equals 0 if Real Body size equals 0, and maximum value equals 100 if Shadow size equals 0.

Therefore, we can use these three numbers for formalizing one candlestick. The number of candlesticks that can be used for historical period can be up to 12. So that the number of inputs for NN can be equal 3 * 12 = 36.

Two More Inputs for Candlestick Pattern Recognition. As it was discussed above, we can use three major numbers to describe the pattern of one candlestick - relative size of Real Body, relative size of Upper Shadow and Lower Shadow. Also we can use 12 candlesticks with these three parameters for each as inputs. However, it would be insufficient to use only these parameters since each candlestick can have different position and their relative position traditionally is used for the analysis and prediction.

The simplest position parameter would be a percentage deviation from an average of all candlesticks position. It could be just closing prices of each period of candlestick. The formula for calculation:


CP_i = 100% * (C_i / C_aver - 1)

where CP_i - relative position of i-candlestick; C_i - closing price of i-candlestick; C_aver - average of all closing prices (all 12 candlesticks).

Except above introduced parameter, it could be reasonable to add one more parameter to distinguish negative and positive candlesticks (black and white) since it makes a significant difference to investors' psychology. So that each black candlestick would have 0 value, white - 1. To summarize, 5 parameters multiplied by 12 (the total number of candlesticks) give 60 inputs.

Numerous tests show many possibilities of improving NN candles patterns recognition abilities. For example, output result can be composed from selected optimized calculations based on different historical periods. As well as, there are many other different ways to formalize the shapes and relative positions of candlesticks.

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 candles patterns and predict future prices. This module predicts only one next candle but the candles pattern prediction can be successfully used for different widths of candle, i.e., the number of trading days in one candle. The module is 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.

Useful resources:

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

The Stock Market Remains Weak Despite Positive Reports

In general, the stock market demonstrates weakness despite better-than-expected US economic reports and news. The US unemployment rate dropped to 8.6% in November from 9% in October. It is a lowest level in more than 2.5 years, since March 2009. October US retail sales were 7.2% higher than the same month a year ago. Furthermore, the retail sales have been raising for 5 straight months.

Improvement in consumer spending was one of the reasons why the economy grew at the best of the year annual rate of 2.5% in the 3^rd quarter. US auto sales increased in 14% in November. Also during the 4-day Thanksgiving weekend consumer spending reached a record 16% in total sales from a year ago. Although some of these numbers might be reevaluated and revised in the future, in overall, they indicate improvements.