Naharul98 / Digital-Signal-Processing-In-Trading

Using Discrete Fourier transform to transform and eliminate noise in asset price time series and identify repeating patterns to exploit and backtest.

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 not stationary, then detrend the data to remove seasonal factors to make it stationary
4. Eliminate noise in the pattern to better model it, this is where digital signal processing come in
5. Apply Discrete Fourier Transform to split the time series into different wave functions
6. Only take the most prevalent wave functions and rebuild the time series. This would theoretically remove noise within the time series.
7. Try to take advantage of this repeating pattern in the market
8. Incorporate risk management measures in the strategy