Compute Growth Index#

The compute_growth_index function takes a pandas DataFrame of historical prices, an initial value for the investment, and optional parameters for initial and ending costs. It calculates the percentage change of the price data, adjusts it for the initial and ending costs, and computes the cumulative product. The resulting DataFrame contains the growth index for each column in the input DataFrame.

Function#

def compute_growth_index(dataframe, initial_value=100, initial_cost=0, ending_cost=0):
    """
    Compute the growth index of a given DataFrame of prices, accounting for initial and
    ending costs.

    Parameters
    ----------
    dataframe : pandas.DataFrame
        DataFrame containing price data. Index should be of datetime type.
    initial_value : float, optional
        Initial value of the investment. Default is 100.
    initial_cost : float, optional
        Initial cost as a percentage of the investment. Default is 0.
    ending_cost : float, optional
        Ending cost as a percentage of the investment. Default is 0.

    Returns
    -------
    pandas.DataFrame
        A DataFrame containing the growth index for each column in the input DataFrame.
    """
    if not isinstance(dataframe, pd.DataFrame):
        raise TypeError("Input must be a pandas DataFrame")

    initial_cost = initial_cost / 100
    ending_cost = ending_cost / 100

    GR = ((1 + dataframe.pct_change()).cumprod()) * (initial_value * (1 - initial_cost))
    GR.iloc[0] = initial_value * (1 - initial_cost)
    GR.iloc[-1] = GR.iloc[-1] * (1 * (1 - ending_cost))
    return GR

Parameters#

  • dataframe: A pandas DataFrame containing price data. The index should be of datetime type.

  • initial_value: A float, optional. The initial value of the investment. Default is 100.

  • initial_cost: A float, optional. The initial cost as a percentage of the investment. Default is 0.

  • ending_cost: A float, optional. The ending cost as a percentage of the investment. Default is 0.

Returns#

  • A pandas DataFrame containing the growth index for each column in the input DataFrame.

Example#

import ds4finance as dsf
import yfinance as yf
import pandas as pd

def download_data(ticker, start, end):
    data = yf.download(ticker, start=start, end=end, progress=False)
    adj_close = data['Adj Close']
    return adj_close

ticker = 'SPY'
start_date = '2015-12-31'
end_date = '2022-12-31'

# Load your price data into a pandas DataFrame
spy = pd.DataFrame(download_data(ticker, start_date, end_date))
spy.columns = ['spy']

dsf.compute_growth_index(spy)
spy
Date
2015-12-31 100.000000
2016-01-04 98.602058
2016-01-05 98.768794
2016-01-06 97.522931
2016-01-07 95.183209
... ...
2022-12-23 213.023382
2022-12-27 212.183320
2022-12-28 209.546324
2022-12-29 213.318236
2022-12-30 212.756325

1763 rows × 1 columns

dsf.compute_growth_index(spy, 200)
spy
Date
2015-12-31 200.000000
2016-01-04 197.204116
2016-01-05 197.537588
2016-01-06 195.045863
2016-01-07 190.366418
... ...
2022-12-23 426.046765
2022-12-27 424.366641
2022-12-28 419.092649
2022-12-29 426.636472
2022-12-30 425.512651

1763 rows × 1 columns