quanteda.generate_return_series

Module Contents

Functions

generate_return_series(expected_annual_return, ...[, ...])

Generate a DataFrame with independent time series of returns.
quanteda.generate_return_series.generate_return_series(expected_annual_return, annual_volatility, n_rows=365, freq='D', num_series=1, dist='normal', random_state=524, start_date='2024-01-01')[source]

Generate a DataFrame with independent time series of returns.

Parameters:

  • expected_annual_return (float) – Expected annualized return as a decimal (e.g., 0.05 for 5%).

  • annual_volatility (float) – Annualized volatility as a decimal (e.g., 0.2 for 20%).

  • n_rows (int, default 365) – Number of days, hours, or minutes (rows) to generate.

  • freq ({'D', 'H', 'min'}, default 'D') – Frequency of returns (‘D’ for daily, ‘H’ for hourly, ‘min’ for minute).

  • num_series (int, default 1) – Number of independent time series (columns) to generate.

  • dist ({'normal', 'lognormal'}, default 'normal') – Type of return distribution. Only support Normal and Log-normal distribution.

  • random_state (int, default 524) – Seed for numpy random number generation.

  • start_date (str, default '2024-01-01') – Start date for the series in the format ‘YYYY-MM-DD’.

Returns:

A DataFrame with columns as independent return time series.

Return type:

pandas.DataFrame

Examples

>>> generate_return_series(0.05, 0.2, n_rows=3, freq='D', num_series=3, dist='normal', random_state=123, start_date='2024-01-01')
            series_1  series_2  series_3
2024-01-01 -0.012345  0.016767  0.022222
2024-01-02  0.043215  0.019105 -0.005555
2024-01-03 -0.001011  0.003333 -0.011111