def test_historical_mean_return():
orig = [13.178779135809942, 3.8135072274034982]
l1 = range(1, 101)
l2 = [10 * 0.2 + i * 0.25 for i in range(21, 121)]
d = {"1": l1, "2": l2}
df = pd.DataFrame(d)
ret = historical_mean_return(df, freq=252)
assert abs(ret["1"] - orig[0]) <= 1e-15
assert abs(ret["2"] - orig[1]) <= 1e-15
def comp_expected_return(self, freq=252):
"""Computes the Expected Return of the stock.
:Input:
:freq: ``int`` (default: ``252``), number of trading days, default
value corresponds to trading days in a year
:Output:
:expected_return: Expected Return of stock.
"""
return historical_mean_return(self.data, freq=freq)
def comp_mean_returns(self, freq=252):
"""Computes the mean returns based on historical stock price data.
See ``finquant.returns.historical_mean_return``.
:Input:
: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.
"""
return historical_mean_return(self.data, freq=freq)
def comp_expected_return(self, freq=252):
"""Computes the Expected Return of the portfolio.
:Input:
:freq: ``int`` (default: ``252``), number of trading days, default
value corresponds to trading days in a year.
:Output:
:expected_return: ``float`` the Expected Return of the portfolio.
"""
if not isinstance(freq, int):
raise ValueError("freq is expected to be an integer.")
pf_return_means = historical_mean_return(self.data, freq=freq)
weights = self.comp_weights()
expected_return = weighted_mean(pf_return_means.values, weights)
self.expected_return = expected_return
return expected_return
