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ForexBot Contains code and data for machine learning-based forex trading strategy Based on the initial analysis of the minute-by-minute and daily data for various currency pairs, a classifier based on the previous n price movements performs poorly, ie not significantly better than a random model. Fast Fourier Transform "FFT" is an abbreviated calculation that can be computed in a fraction of the time.

FFT sacrifices phase relationships and concentrates only on cycle length and amplitude. The benefit of FFT is its ability to extract the predominate cycle s from a series of data e.

FFTs are based on the principal that any finite, time-ordered set of data can be approximated by decomposing the data into a set of sine waves. Each sine wave has a specific cycle length, amplitude, and phase relationship to the other sine waves.

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A difficulty occurs when applying FFT analysis to security prices, because FFTs were designed to be applied to non-trending, periodic data. The fact that security prices are often trending is overcome by "detrending" the data using either a linear regression trendline or a moving average.

To adjust for the fact that security data is not truly periodic, since securities are not traded on weekends and some holidays, the prices are passed through a smoothing function called a "hamming window. As stated above, it is beyond the scope of this book to provide complete interpretation of FFT analysis.

The End Point Fast Fourier Transform System

This indicator shows the three predominate cycle lengths and the relative strength of each of these cycles. The Interpreted FFT indicator always displays the most significant cycle days in this example on the left and the least significant cycle 27 days in this example on the right. The length of each cycle is determined by the numeric value of the indicator as read from the y-axis scales on the sides of the chart.

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The system presented in this package is an improved version of the EPFFT system presented in the papers. The EPFFT takes the FFT at each bar, filters the noisy price series using a unique noise filter in the frequency domain, and creates a one day ahead projected price value. This process is continued until the last bar of data is reached.

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The projected one day ahead values that are created at each bar are plotted. The EPFFT system follows the projected one day ahead price curve to generate it's buy and sell signals. This process gives a more robust noise filtered signal than a single frequency dominant cycle procedure. The Input parameters to the strategy and indicator are changeable and optimizable so that the user can develop his own parameter set on his price series and time frame of interest.

This system can easily be used on stocks and mutual funds. Please note that hypothetical Out-Of-Sample past performance is no guarantee of future results. There are no locks of any kind on the EasyLanguage source code. Set Mark equal to zero if XTime not equal to zero.