Daily and Historical Returns¶

Returns are implemented in finquant.returns.

The module provides functions to compute different kinds of returns of stocks.

finquant.returns.cumulative_returns(data, dividend=0)¶

Returns DataFrame with cumulative returns

\(\displaystyle R = \dfrac{\text{price}_{t_i} - \text{price}_{t_0} + \text{dividend}}{\text{price}_{t_0}}\)

Input:

data:	`pandas.DataFrame` with daily stock prices
dividend:	`float` (default= `0`), paid dividend

Output:

ret:	a `pandas.DataFrame` of cumulative Returns of given stock prices.

finquant.returns.daily_log_returns(data)¶

Returns DataFrame with daily log returns

\(R_{\log} = \log\left(1 + \dfrac{\text{price}_{t_i} - \text{price}_{t_{i-1}}}{\text{price}_{t_{i-1}}}\right)\)

Input:

data:	`pandas.DataFrame` with daily stock prices

Output:

ret:	a `pandas.DataFrame` of log(1 + daily percentage change of Returns)

finquant.returns.daily_returns(data)¶

Returns DataFrame with daily returns (percentage change)

\(\displaystyle R = \dfrac{\text{price}_{t_i} - \text{price}_{t_{i-1}}}{\text{price}_{t_{i-1}}}\)

Input:

data:	`pandas.DataFrame` with daily stock prices

Output:

ret:	a `pandas.DataFrame` of daily percentage change of Returns of given stock prices.

finquant.returns.historical_mean_return(data, freq=252)¶

Returns the mean return based on historical stock price data.

Input:

data:	`pandas.DataFrame` with daily stock prices
freq:	`int` (default= `252`), number of trading days, default value corresponds to trading days in a year

Output:

ret:	a `pandas.DataFrame` of historical mean Returns.