Fourier Analysis for Forex predictions

Fourier series is a powerful mathematical tool used in time series analysis. Time series analysis deals with the analysis of data points collected over time. Fourier series is a mathematical representation of a periodic function in terms of its component frequencies. This technique can be used to decompose a time series into its constituent frequencies and gain insights into the underlying patterns.

The first step is to obtain historical forex price data and preprocess it for analysis. This involves importing the data into Python, converting it into a suitable format, and performing any necessary data cleaning or filtering.

2. Polynomial Fitting:

Performing a polynomial fit in time series forecasting involves fitting a polynomial function to a set of time series data to capture the underlying trends or patterns in the data. The main reason for performing a polynomial fit is to model the data and identify trends, so that they can be extrapolated into the future to make forecasts.

3. Detrending:

Detrending in time series analysis is the process of removing the long-term trend or pattern from a set of data to reveal other patterns and fluctuations. It is important for improving statistical analysis and making the data stationary. Techniques such as linear regression, moving averages, and differencing can be used to detrend the data.

3.Fast Fourier Function:

4.Signal Reconstruction

5.Evaluation

The signal is distored after reconstruction but upon closer inspection the predicted and the actual values seem to have some predictive capabiliites

The general direcction seems to be the same at the very least fourier analysis can be used as a trend filter.Upon further research I came across an indicator by LuxAlgo which might work on a similar concept.

LuxAlgos model seems to be more robust and with different funtionality.

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