Description

Model History

The Augmented Dickey-Fuller Test (ADF Test) is a statistical test used to check for the presence of a unit root in an autoregressive model, thereby determining the stationarity of a time series. On financial markets, it is widely used for analyzing time series of asset prices.

The test was developed and described by British statisticians David Dickey and Wayne Fuller in 1979. The augmented version of the test (ADF) was proposed to improve accuracy and reliability compared to the original Dickey-Fuller test.

Purpose of the Model in Financial Markets

Testing Stationarity: The ADF Test is used to determine whether a time series is stationary or follows a trend (i.e., it contains a unit root). Stationarity is crucial in time series modeling and econometric analysis because many methods require the data to be stationary.
Analysis of Integration: It is often used in cointegration analysis, where multivariate time series are examined for the presence of long-term relationships.
Algorithmic Trading Decisions: Checking for stationarity aids in the development of strategies based on time series, including pairs trading and time series forecasting.

Advantages of the Augmented Version Over the Ordinary Version

Consideration of Autocorrelation: The ADF Test includes additional lags of the differenced dependent variable in the model to account for high autocorrelation in time series data, making the test more reliable and accurate.
Flexibility: The augmented version allows for the incorporation of both random walks and deterministic trends, including linear trends, in the model estimation.
Reduction of Autocorrelated Residuals: The ADF Test addresses the issue of autocorrelated errors, which can distort results in the original Dickey-Fuller test.

Dickey–Fuller test

Functionality

The indicator has the following parameters for configuration:

Price source: the data source for price used in the calculation;
Lookback: the length of the series for evaluation;