Digital-Signal-Processing-In-Trading Transform asset price time series into stationary form apply Discrete Fourier Transform to eliminate noise and identify/model repeating patterns to exploit and backtest. Inspiration from Research Journals The project was inspired by the following academic journals: Journal 1 Journal 2 Journal 3 Project Demo Jupyter Notebook Demo Screenshots About the Project Discrete Fourier Transform This digital signal processing technique is used to de-noise the data. Discrete Fourier Transform Splits time series into a set of waves. These waves can be summed up to construct the original time series again! However, objective is to remove noise from original time series. Therefore, the concept is that, we only select a few set of waves with most amplitude -> and only use these to reconstruct the time series. the resulting time series will only have the most prominent characteristics of the original. Trading Strategy Overview 1. Transform original time series to find a repeating pattern/anomaly. 2. Check for stationarity using Augmented Dickey Fuller test 3. If n

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