def get_ef_points(self):
"""
Get all the points for the Efficient Frontier running optimizer.
If verbose=True calculates elapsed time for each point and the total elapsed time.
"""
main_start_time = time.time()
df = pd.DataFrame()
# left part of the EF
for i, target_cagr in enumerate(self.target_cagr_range_left):
start_time = time.time()
row = self.minimize_risk(target_cagr)
df = df.append(row, ignore_index=True)
end_time = time.time()
if self.verbose:
print(f"left EF point #{i + 1}/{self.n_points} is done in {end_time - start_time:.2f} sec.")
# right part of the EF
range_right = self.target_cagr_range_right
if range_right is not None: # range_right can be a DataFrame. Must put an explicit "is not None"
n = len(range_right)
for i, target_cagr in enumerate(range_right):
start_time = time.time()
row = self.maximize_risk(target_cagr)
df = df.append(row, ignore_index=True)
end_time = time.time()
if self.verbose:
print(f"right EF point #{i + 1}/{n} is done in {end_time - start_time:.2f} sec.")
df = Frame.change_columns_order(df, ['Risk', 'CAGR'])
main_end_time = time.time()
if self.verbose:
print(f"Total time taken is {(main_end_time - main_start_time) / 60:.2f} min.")
self._ef_points = df
def ef_points(self) -> pd.DataFrame:
"""
DataFrame of weights and risk/return values for the Efficient Frontier.
The columns of the DataFrame:
- weights
- mean return
- CAGR
- risk (std)
All the values are annualized.
"""
target_rs = self.mean_return_range
df = pd.DataFrame(dtype="float")
for x in target_rs:
row = self.minimize_risk(x, monthly_return=True)
df = df.append(row, ignore_index=True)
df = Frame.change_columns_order(df, ["Risk", "Mean return", "CAGR"])
return df