# 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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from collections import Sequence
from typing import Tuple
import numpy as np
from qf_lib.common.utils.returns.list_of_max_drawdowns import list_of_max_drawdowns
from qf_lib.containers.series.qf_series import QFSeries
from qf_lib.plotting.charts.chart import Chart
from qf_lib.plotting.charts.line_chart import LineChart
from qf_lib.plotting.decorators.axes_label_decorator import AxesLabelDecorator
from qf_lib.plotting.decorators.scatter_decorator import ScatterDecorator
from qf_lib.plotting.decorators.title_decorator import TitleDecorator
[docs]def create_dd_probability_chart(prices_tms: QFSeries, bear_market_definition: float = 0.2) -> Tuple[Chart, Chart]:
"""Creates drawdowns probability chart.
Parameters
----------
prices_tms: QFSeries
timeseries of prices
bear_market_definition: float
definition of bear market threshold
Returns
-------
Tuple[Chart, Chart]
Returns two charts - one showing the probability of drawdowns going beyond a certain level and one showing the
marginal increase of probability of drawdowns going beyond the given level.
"""
def count_dd_above_threshold(drawdown_series: Sequence, threshold: float):
return sum(1 for dd in drawdown_series if dd > threshold)
drawdowns, duration_of_drawdowns = list_of_max_drawdowns(prices_tms)
examined_dds = np.arange(0.01, bear_market_definition, 0.005)
percentage_ending_in_bear_market = []
nr_of_bear_markets = count_dd_above_threshold(drawdowns, bear_market_definition)
for examined_dd in examined_dds:
number_of_dds_above = count_dd_above_threshold(drawdowns, examined_dd)
percentage = nr_of_bear_markets / number_of_dds_above * 100
percentage_ending_in_bear_market.append(percentage)
chart = LineChart()
chart.add_decorator(ScatterDecorator(examined_dds * 100, percentage_ending_in_bear_market, edgecolors='black'))
chart.add_decorator(TitleDecorator(
"Percentage of drawdowns going beyond {:2.0f}%".format(bear_market_definition * 100)))
axis_dec = AxesLabelDecorator(
"examined drawdown [%]",
"chance that drawdown will go beyond {:2.0f}% in [%]".format(bear_market_definition * 100))
chart.add_decorator(axis_dec)
x_axis_values = examined_dds * 100
prob_of_dd_chart = LineChart()
prob_of_dd_chart.add_decorator(ScatterDecorator(
x_axis_values, percentage_ending_in_bear_market, edgecolors='black'))
prob_of_dd_chart.add_decorator(TitleDecorator(
"Percentage of drawdowns going beyond {:2.0f}%".format(bear_market_definition * 100)))
axis_dec = AxesLabelDecorator(
"examined drawdown [%]",
"chance that drawdown will go beyond {:2.0f}% in [%]".format(bear_market_definition * 100))
prob_of_dd_chart.add_decorator(axis_dec)
marginal_increase_in_prob_chart = LineChart()
diff = np.diff([0] + percentage_ending_in_bear_market)
marginal_increase_in_prob_chart.add_decorator(ScatterDecorator(x_axis_values, diff, edgecolors='black'))
marginal_increase_in_prob_chart.add_decorator(TitleDecorator(
"Marginal increase of probability of drawdowns going beyond {:2.0f}%".format(bear_market_definition * 100)))
axis_dec = AxesLabelDecorator(
"examined drawdown [%]",
"Marginal increase of chance that drawdown will go beyond {:2.0f}% in [%]".format(bear_market_definition * 100))
marginal_increase_in_prob_chart.add_decorator(axis_dec)
return prob_of_dd_chart, marginal_increase_in_prob_chart