Wavelet Transform for Filtering Financial Data in real - time for eSignal
The computerization and digital signal processing development let improve classical indicators essentially due to application of modern methods of information processing to prices. Indicators began to smooth better and to delay less. However Holy Grail has failed. First, the prices are non stationary, i.e. the characteristics of filters are varied during the time. Second, as different from technical problems, the kind of a signal and noise distributions for the price are unknown, i.e. nobody know what to filter actually. Third, being filtered by means of Fourier and similar methods prices change the previous values to the addition of the new data: we receive ideal trends under a history data but we can only trade them from right hand to left hand.
Fourier transformation is based on representation of initial series by the infinite sum of sinusoids with a various phase, amplitude and frequency. Recently wavelet transformations was widely adopted in various areas of data processing in which initial series are represented as the sum of some locally defined functions named wavelets. They are constructed by shifting and vertical and horizontal scaling of certain the prototype function. Wavelet transformation, in essence, is fractal that allows the effective using it in the technical analysis. First, it allows to carry out the multiscale analysis of prices, objectively identify trends on various scales by duration and amplitude, separate traders to various groups: scalpers, day traders, swing traders, position traders and long-term investors. The multiscale analysis can be interpreted as the analysis on various time frames. Second, it allows determine noise as the insufficient for reception of the profit amplitude and frequency movement of the prices that effectively allows filter the price series simply subtracting the lowest scale wavelets from it. Third, the additional filtration of white noise without delay is possible. Fourth, long-term trends are defined objectively. Fifth, wavelets do not contain optimized parameters in construct to standard indicators. Sixth, the used wavelets type is adapted to deal with the time ordered data and does not distorted on the last price values. Seventh, the used wavelet transformation is very effective computationally that allows use it in real time for the large massives of tick data. Eighth, it is effective to use wavelets as input data for neural networks and other methods of forecasting and recognition.
Windows XP / 2000 / 2003 or later.
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