quanteda.generate_financial_metrics

Module Contents

Functions

generate_financial_metrics(random_returns_df[, ...])

Calculate financial metrics based on a DataFrame of random returns on time series.
quanteda.generate_financial_metrics.generate_financial_metrics(random_returns_df, annual_risk_free=0.0)[source]

Calculate financial metrics based on a DataFrame of random returns on time series.

Parameters:

  • random_returns_df (pandas.DataFrame) – DataFrame containing returns of stocks on time series in columns. Each column is an independent series of returns. The supported frequencies of the index is ‘D’ for daily, ‘H’ for hourly and ‘min’ for minutely.

  • annual_risk_free (float, default 0.00.) – Annualized risk-free rate as a decimal (e.g., 0.02 for 2%).

Returns:

A DataFrame containing financial metrics including count (number of returns), total return, annualized return, annualized volatility, and Sharpe ratio for each stock return series

Return type:

pandas.DataFrame

Examples

>>> random_returns_df = pd.DataFrame([
{'index': '2024-01-01 00:00:00', 'stock_1': -0.002286, 'stock_2': 0.002545, 'stock_3': 0.002997},
{'index': '2024-01-01 01:00:00', 'stock_1': 0.003045, 'stock_2': 0.002913, 'stock_3': -0.000855},
{'index': '2024-01-01 02:00:00', 'stock_1': -0.001142, 'stock_2': 0.000635, 'stock_3': -0.002592},
{'index': '2024-01-01 03:00:00', 'stock_1': 0.000971, 'stock_2': -0.000168, 'stock_3': 0.000074},
{'index': '2024-01-01 04:00:00', 'stock_1': -0.000392, 'stock_2': 0.002252, 'stock_3': -0.000342}
])

>>> generate_financial_metrics(random_returns_df, annual_risk_free=0.02)
            count   total_return    annual_return   annual_volatility       sharpe_ratio
stock_1     5           0.000196        0.343184        0.192332            1.680339
stock_2     5           0.008177        14.325234       0.124650            114.763058
stock_3     5           -0.000718       -1.258381       0.189806            -6.735214