Market forecasting: the predictive power of price patterns
A stock price does not assert itself on the market to which buyers and sellers have to submit. Price is arrived at by the equilibrium in trading between supply and demand. Price is a relative value. Price is a means of keeping score of market action; a score based on the ongoing conflict between buyers and sellers. If buyers are in the ascendancy then a price increase is scored and if sellers have the upper hand then a price decrease ensues. It is the changes in stock prices that document the results of investor conflict; if the direction of price change persists then a price trend is established. It is the early recognition of trends of price change that permits maximum profitability of stock market invest-ment. Price alone is a poor indicator of market involvement. It is the consideration of all the other technical information relative to the price, and price change that reveals the flux of market structure.The dynamics of investor conflict have prognostic value if they can be discerned. Price can have some value when related to the large picture of price history. Here various price levels can indicate previous levels of price support or resistance to further price change and, therefore, may prove to do so again. But again price does not determine this, the market forces determine this. That it happens at a particular price is the result of an equilibrium of market forces. Violation of these areas of support and resistance are often very bearish or bullish respectively. If these areas are violated then they often change their roles, support now becomes resistance and resistance, support to further stock price changes. So, for price to be a meaningful study to enhance profitability in stock market investment, we must study relative changes in stock price, i.e. price patterns and trends. Price patterns frequently associated with the continuation or reversal of trends are recognizable.
There are about as many chart (or price) patterns as there are stock market analysts, and there are many of them! To give you an idea of the different patterns available to you, here is a partial listing: trend lines, support/resistance, fan lines, channel lines, retracement, speed resistance, gaps, reversal patterns, head and shoulder patterns, double tops/bottoms, triple tops/bottoms, saucers or rounding patterns, cup and handle, V-formations, triangles, diamonds, flags and pennants, wedge formation and trading ranges. Candlestick charts have their own series of price patterns such as hammers, doji, stars, dragonfly doji, spinning tops, and we could go on and on for a while. The most widely used charting methods are bar charts and candlestick charts; some traders also use point and figure charts.
 |
Figure 1. Price pattern |
||||||||||||||
|
FAQ
Do these price patterns work well ? We are currently using these patterns for our trading strategies  . You can download a protected version of code and try it. Click here to download the demo code.Which limitations does the demo code have ? Only speed: in demo code artificial delays have been inserted. Why does code run so slow ? Artificial delays have been inserted to protected code (each pattern requires about 25 minutes). In this way you will be able to evaluate the code with some restrictions. The source code is extremely fast and highly optimized: in some fractions of second data are processed and sell/buy signals are ready to be used. How long can I use protected code ? You can use protected demo code with no limit. I have not a lot of time to evaluate the code. What can I do ? Please email us luigi.rosa@tiscali.it your data (column vectors with the high, low, closing, and opening prices of a security in csv or any other formatted file). You will receive in 6-12 hours a candlestick chart with all price patterns that have been detected. I want to obtain complete source code. What have I to do ? In order to obtain the complete source code (the faster code, with no restrictions and no artificial delays) please follow the instructions reported below. A donation is required. Which patterns have been implemented ? We have implemented existing price patterns improving them with some major modifications. De facto new, high profit price patterns have been introduced. How does the code work ? Each pattern is detected by a specific function. Each function has as inputs four vectors: Opening, Highest, Lowest, and Closing price that occurred during the Time Interval of the bars. We usually perform out tests with 5-Minute Bar Chart but, of course, other choices are possible. The function returns as output a vector whose elements are the indices of bars where the current pattern has occurred. Have I to read a user manual ? It is not necessary to read any user manual. In the demo code you will find a lot of examples and applications. All discovered patterns are visualized in a candlestick chart. What is your current research in price patterns analysis ? We are currently studying the possibility of merging these algorithms with approaches based on Artificial Neural Networks and Genetic Algorithms. Why Matlab ? Matlab offers the possibility to create fully-customizable code, with a simple and intuitive programming language. |
|||||||||||||||
 |
Demo code (protected P-files) available for performance evaluation. Matlab Financial Toolbox is required.
|
||||||||||||||
|
|||||||||||||||
|
We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
|||||||||||||||
|
Stock Prediction Based on Price Patterns – Release 1.0 – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 61 EUROS (less than 85,4 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 Prediction Based on Price Patterns. Alternatively, you can bestow using our banking coordinates:
|
|||||||||||||||
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 Financial Toolbox is 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.
Popularity: 1% [?]
Stock Market Forecasting based on Neural Networks and Wavelet Decomposition
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 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.
|
||||||||||||||
|
|||||||||||||||
|
We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
|||||||||||||||
|
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:
|
|||||||||||||||
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.
Popularity: 1% [?]
Risk and Asset Allocation
A toolbox for risk and asset allocation from Attilio Meucci that allows for advanced risk and portfolio management.
These routines support the book “Risk and Asset Allocation” Springer Finance, by A. Meucci, see http://www.symmys.com
The routines include many new features:
- - more uni-, multi- and matrix-variate distributions
- - more copulas
- - more graphical representations
- - more analyses in terms of the location-dispersion ellipsoid.
- - best replication / best factor selection
- - FFT-based projection of a distribution to the investment horizon
- - caveats about delta/gamma pricing
- - step-by-step evaluation of a generic estimator
- - non-parametric estimators
- - multivariate elliptical maximum-likelihood estimators
- - shrinkage estimators: Stein and Ledoit-Wolf, Bayesian classical equivalent
- - robust estimators: Hubert M, high-breakdown minimum volume ellipsoid
- - missing-data techniques: EM algorithm, uneven-series conditional estimation
- - stochastic dominance
- - extreme value theory for VaR
- - Cornish-Fisher approximation for VaR
- - kernel-based contribution to VaR and expected shortfall from different risk-factors
- - mean-variance analysis and pitfalls (different horizons, compounded vs. linear returns, etc…)
- - Bayesian estimation (multivariate analytical, Monte Carlo Markov Chains, priors for correlation matrices)
- - estimation risk evaluation: opportunity cost of estimation-based allocations
- - Black Litterman allocation
- - robust optimization (calls SeDuMi to perform cone programming)
- - robust Bayesian allocation
- - more…
In addition to these MATLAB routines, at www.symmys.com the reader can find other freely downloadable complementary materials:
- - the “Technical Appendices”, a booklet with the proofs of the results presented in the books and used in the routines
- - the “Slides”, a set of presentations that walk the reader through the whole book
- - the “Errata”, a few typos in the first two reprints of the book
- - the “Sample”, an excerpt of the book.
Any feedback on the above materials is highly appreciated: please refer to www.symmys.com to contact the author.
Popularity: 1% [?]
Translator
Follow Us
Video Posts
