# 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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from itertools import cycle
from typing import Sequence, List, Tuple
import matplotlib as mpl
from qf_lib.common.utils.confidence_interval.analytical_cone_oos import AnalyticalConeOOS
from qf_lib.containers.series.qf_series import QFSeries
from qf_lib.plotting.charts.chart import Chart
from qf_lib.plotting.decorators.data_element_decorator import DataElementDecorator
[docs]class ConeChartOOS(Chart):
"""
While using a simple cone (e.g. LineChart with Cone decorator) the results of the evaluation may be very different
depending on the live_start_date. To be immune to this, ConeChart plots only the ends of simple cones which start
at 1 periods, 2 periods, ..., n periods before the end of the backtested series. The period length depends
on the frequency of the data provided for the chart. If it has daily frequency, then the length of one period
will be 1 day.
Parameters
----------
oos_series: QFSeries
data to be plotted using ConeChartOOS - only the Out of sample data
is_mean_return: float
mean daily log return of the strategy In Sample
is_sigma: float
std of daily log returns of the strategy In Sample
cone_opacity: float
opacity of the cone
cone_stds: Sequence[float]
list/tuple of different standard deviations for which cones should be drawn
title: Optional[str]
title of the plot, by default it is 'Performance vs. Expectation'
"""
def __init__(self, oos_series: QFSeries, is_mean_return: float, is_sigma: float, cone_opacity: float = 0.3,
cone_stds: Sequence[float] = (1.0, 2.0), title: str = 'Performance vs. Expectation'):
super().__init__()
self.assert_is_qfseries(oos_series)
self.oos_series = oos_series
self.is_mean_return = is_mean_return
self.is_sigma = is_sigma
self.cone_opacity = cone_opacity
self.cone_stds = cone_stds
self.title = title
[docs] def plot(self, figsize: Tuple[float, float] = None):
self._setup_axes_if_necessary(figsize)
cone = AnalyticalConeOOS()
cone_data_frame = cone.calculate_aggregated_cone_oos_only(
self.oos_series, self.is_mean_return, self.is_sigma, 0)
strategy_tms = cone_data_frame['Strategy']
mean_tms = cone_data_frame['Expectation']
ax = self.axes
ax.plot(strategy_tms)
ax.plot(mean_tms)
cone_colors = cycle(Chart.get_axes_colors()[2:4])
# fill areas for every standard deviation
for cone_std in self.cone_stds:
upper_df = cone.calculate_aggregated_cone_oos_only(
self.oos_series, self.is_mean_return, self.is_sigma, cone_std)
lower_df = cone.calculate_aggregated_cone_oos_only(
self.oos_series, self.is_mean_return, self.is_sigma, -cone_std)
upper_bound = upper_df['Expectation']
lower_bound = lower_df['Expectation']
ax.fill_between(
cone_data_frame.index, lower_bound, upper_bound, color=next(cone_colors), alpha=self.cone_opacity)
ax.set_xlabel('Days in the past')
ax.set_ylabel('Current valuation')
ax.set_title(self.title)
ax.set_xlim(0, self.oos_series.size)
self._insert_valuation_text_box(cone, strategy_tms)
self._apply_decorators()
def _insert_valuation_text_box(self, cone, strategy_tms):
# add text box with average expectation over 20 days and overall horizon
one_sigma_df = cone.calculate_aggregated_cone_oos_only(self.oos_series, self.is_mean_return, self.is_sigma, 1)
one_sigma_tms = one_sigma_df['Expectation']
valuation_tms = (strategy_tms - 1) / (one_sigma_tms - 1) # type: QFSeries
valuation_total = valuation_tms.mean()
total_days = valuation_tms.size - 1 # first element is 0 days
selected_short_frame = 20
if total_days > selected_short_frame:
valuation20d = valuation_tms.head(selected_short_frame + 1).mean()
textstr = 'Valuation 20D = {:.2f}\nValuation {}D = {:.2f}'.format(valuation20d, total_days, valuation_total)
else:
textstr = 'Valuation {}D = {:.2f}'.format(total_days, valuation_total)
props = dict(boxstyle='square', facecolor='white', alpha=0.5, edgecolor='grey')
font_size = mpl.rcParams['legend.fontsize']
self.axes.text(
0.05, 0.95, textstr, transform=self.axes.transAxes, bbox=props, verticalalignment='top', fontsize=font_size)
[docs] def apply_data_element_decorators(self, data_element_decorators: List["DataElementDecorator"]):
pass