sample_data.py

#
# Python Module to Generate a
# Sample Financial Data Set
#
# Python for Algorithmic Trading
# (c) Dr. Yves J. Hilpisch
# The Python Quants GmbH
#
import numpy as np
import pandas as pd

r = 0.05  # constant short rate
sigma = 0.5  # volatility factor


def generate_sample_data(rows, cols, freq='1min'):
    '''
    Function to generate sample financial data.

    Parameters
    ==========
    rows: int
        number of rows to generate
    cols: int
        number of columns to generate
    freq: str
        frequency string for DatetimeIndex

    Returns
    =======
    df: DataFrame
        DataFrame object with the sample data
    '''
    rows = int(rows)
    cols = int(cols)
    # generate a DatetimeIndex object given the frequency
    index = pd.date_range('2021-1-1', periods=rows, freq=freq)
    # determine time delta in year fractions
    dt = (index[1] - index[0]) / pd.Timedelta(value='365D')
    # generate column names
    columns = ['No%d' % i for i in range(cols)]
    # generate sample paths for geometric Brownian motion
    raw = np.exp(np.cumsum((r - 0.5 * sigma ** 2) * dt +
                 sigma * np.sqrt(dt) *
                 np.random.standard_normal((rows, cols)), axis=0))
    # normalize the data to start at 100
    raw = raw / raw[0] * 100
    # generate the DataFrame object
    df = pd.DataFrame(raw, index=index, columns=columns)
    return df


if __name__ == '__main__':
    rows = 5  # number of rows
    columns = 3  # number of columns
    freq = 'D'  # daily frequency
    print(generate_sample_data(rows, columns, freq))