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 pricesdividend: float
(default=0
), paid dividendOutput: 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 pricesOutput: 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 pricesOutput: 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 pricesfreq: int
(default=252
), number of trading days, default value corresponds to trading days in a yearOutput: ret: a pandas.DataFrame
of historical mean Returns.