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We have developed an efficient tool for intraday stock market forecasting based on Neural Networks and Wavelet Decomposition. This software has been tested on real data obtaining excellent results. SMF Tool gives Buy/Sell signals with a high degree of accuracy. SMF accepts, as input, a sequence of given length N. The system can determine if at least one of future prices - within an observation window of fixed length M - will be higher or lower than current price. SMF package has been tested with Italian Futures over a period of 3 years, more than 600 days of effective trading. Training data and testing data have been randomly selected from this data set, without any overlapping. Stock market data have been downloaded at http://www.ccg.it: these data are uniformly sampled each minute.

Why Wavelets
Wavelets can localize data in time-scale space. At high scales (shorter time intervals), the wavelet has a small time support and is thus, better able to focus on short lived, strong transients like discontinuities, ruptures and singularities. At low scales (longer time intervals), the wavelet's time support is large, making it suited for identifying long periodic features. Wavelets have a intuitive way of characterizing the physical properties of the data. At low scales, the wavelet characterizes the data's coarse structure; its long-run trend and pattern. By gradually increasing the scale, the wavelet begins to reveal more and more of the data's details, zooming in on its behavior at a point in time. Wavelet analysis is the analysis of change. A wavelet coefficient measures the amount of information that is gained by increasing the frequency at which the data is sampled, or what needs to be added to the data in order for it to look like it had been measured more frequently. For instance, if a stock price does not change during the course of a week, the wavelet coefficients from the daily scale are all zero during that week. Wavelet coefficient that are non-zero at high scales typically characterize the noise inherent in the data. Only those wavelets at very fine scales will try to follow the noise, whereas those wavelets at coarser scales are unable to pick up the high frequency nature of the noise.



 

 

 

 

 

 

Figure 1. Wall Street



Why Neural Networks
Since the early 90's when the first practically usable types emerged, artificial neural networks (ANNs) have rapidly grown in popularity. They are artificial intelligence adaptive software systems that have been inspired by how biological neural networks work. Their use comes in because they can learn to detect complex patterns in data. In mathematical terms, they are universal non-linear function approximators meaning that given the right data and configured correctly, they can capture and model any input-output relationships. This not only removes the need for human interpretation of charts or the series of rules for generating entry/exit signals but also provides a bridge to fundamental analysis as that type of data can be used as input. In addition, as ANNs are essentially non-linear statistical models, their accuracy and prediction capabilities can be both mathematically and empirically tested. In various studies neural networks used for generating trading signals have significantly outperformed buy-hold strategies as well as traditional linear technical analysis methods. While the advanced mathematical nature of such adaptive systems have kept neural networks for financial analysis mostly within academic research circles, in recent years more user friendly neural network software has made the technology more accessible to traders.

Index Terms: Matlab source code, price, neural networks, stock market prediction, neural network, wavelet, decomposition, wavelets, stock market forecasting, data, model, business, financial, analysis, target, marketing, optimization.


Demo code (protected P-files) available for performance evaluation. Matlab, Matlab Image Processing Toolbox, Matlab Neural Network Toolbox and Matlab Wavelet Toolbox are required.

Release
Date
Major features
1.0
2007.03.10


We recommend to check the secure connection to PayPal, in order to avoid any fraud.
This donation has to be considered an encouragement to improve the code itself.

Stock Market Forecaster - Release 1.0 - Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 360 EUROS (less than 504 U.S. Dollars).

Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of Stock Market Forecaster Based on Wavelets and Neural Networks.

Alternatively, you can bestow using our banking coordinates:
Name :
Luigi Rosa
Address :
Via Pozzo Strada 5 10139 Torino Italy
Bank name:
Poste Italiane
Bank address:
Viale Europa 190 00144 Roma Italy
IBAN (International Bank Account Number) :
IT-50-V-07601-03600-000058177916
BIC (Bank Identifier Code) :
BPPIITRRXXX


The authors are not commodity trading advisors. The information on this site is for trading education only. There are no trading recommendations for any one individual made on this site and this information is paper trades for trading education. All trades are extremely risky and only risk capital should be used when trading. The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 14 SP1. Matlab, Matlab Image Processing Toolbox, Matlab Neural Network Toolbox and Matlab Wavelet Toolbox are required. The code provided has to be considered "as is" and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.

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