mlfinlab.data_generation.correlated_random_walks
Contains methods for generating correlated random walks.
Module Contents
Functions
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Generates a synthetic time series of correlation and distribution clusters. |
- generate_cluster_time_series(n_series, t_samples=100, k_corr_clusters=1, d_dist_clusters=1, rho_main=0.1, rho_corr=0.3, price_start=100.0, dists_clusters=('normal', 'normal', 'student-t', 'normal', 'student-t'))
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Generates a synthetic time series of correlation and distribution clusters.
It is reproduced with modifications from the following paper: Donnat, P., Marti, G. and Very, P., 2016. Toward a generic representation of random variables for machine learning. Pattern Recognition Letters, 70, pp.24-31.
This method creates n_series time series of length t_samples. Each time series is divided into k_corr_clusters correlation clusters. Each correlation cluster is subdivided into d_dist_clusters distribution clusters. A main distribution is sampled from a normal distribution with mean = 0 and stdev = 1, adjusted by a rho_main factor. The correlation clusters are sampled from a given distribution, are generated once, and adjusted by a rho_corr factor. The distribution clusters are sampled from other given distributions, and adjusted by (1 - rho_main - rho_corr). They are sampled for each time series. These three series are added together to form a time series of returns. The final time series is the cumulative sum of the returns, with a start price given by price_start.
- Parameters:
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n_series – (int) Number of time series to generate.
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t_samples – (int) Number of samples in each time series.
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k_corr_clusters – (int) Number of correlation clusters in each time series.
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d_dist_clusters – (int) Number of distribution clusters in each time series.
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rho_main – (float): Strength of main time series distribution.
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rho_corr – (float): Strength of correlation cluster distribution.
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price_start – (float) Starting price of the time series.
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dists_clusters – (list) List containing the names of the distributions to sample from. The following numpy distributions are available: “normal” = normal(0, 1), “normal_2” = normal(0, 2), “student-t” = standard_t(3)/sqrt(3), “laplace” = laplace(1/sqrt(2)). The first disitribution is used to sample for the correlation clusters (k_corr_clusters), the remaining ones are used to sample for the distribution clusters (d_dist_clusters).
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- Returns:
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(pd.DataFrame) Generated time series. Has size (t_samples, n_series).