# Copyright 2016-present CERN – European Organization for Nuclear Research
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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from typing import Sequence
import numpy as np
import pandas as pd
from qf_lib.common.utils.returns.max_drawdown import max_drawdown
from qf_lib.containers.dataframe.cast_dataframe import cast_dataframe
from qf_lib.containers.dataframe.qf_dataframe import QFDataFrame
from qf_lib.containers.dataframe.simple_returns_dataframe import SimpleReturnsDataFrame
[docs]class InitialRiskStatsFactory:
FAILED = "Failed"
SUCCEEDED = "Succeeded"
def __init__(self, max_accepted_dd: float, target_return: float):
assert max_accepted_dd > 0, "Draw-down should be a positive number"
self._max_accepted_dd = max_accepted_dd
self._target_return = target_return
[docs] def make_stats(self, initial_risks: Sequence[float], scenarios_list: Sequence[QFDataFrame]) -> QFDataFrame:
"""
Creates a pandas.DataFrame showing how many strategies failed (reached certain draw down level) and how many
of them succeeded (that is: reached the target return and not failed on the way).
Parameters
----------
initial_risks: Sequence[float]
list of initial_risk parameters where initial_risk is a float number
scenarios_list: Sequence[pandas.DataFrame]
list with scenarios (QFDataFrame) where each DataFrame corresponds to one initial_risk value
Each DataFrame has columns corresponding to different scenarios and its indexed by Trades' ordinal number.
Its values are returns of Trades.
Returns
-------
pandas.DataFrame
DataFrame indexed with initial_risk values and with columns FAILED (fraction of scenarios that failed)
and SUCCEEDED (fraction of scenarios that met the objective and didn't fail on the way)
"""
result = QFDataFrame(
index=pd.Index(initial_risks), columns=pd.Index([self.FAILED, self.SUCCEEDED]), dtype=np.float64)
for init_risk, scenarios in zip(initial_risks, scenarios_list):
# calculate drawdown for each scenario
scenarios_df = cast_dataframe(scenarios, SimpleReturnsDataFrame) # type: SimpleReturnsDataFrame
max_drawdowns = max_drawdown(scenarios_df)
total_returns = scenarios_df.total_cumulative_return()
failed = max_drawdowns >= self._max_accepted_dd
reached_target_return = total_returns >= self._target_return
succeeded = ~failed & reached_target_return
num_of_scenarios = scenarios_df.num_of_columns
failed_normalized = failed.sum() / num_of_scenarios
succeeded_normalized = succeeded.sum() / num_of_scenarios
result.loc[init_risk, [self.FAILED, self.SUCCEEDED]] = [failed_normalized, succeeded_normalized]
return result