# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
from datetime import datetime
import scipy.stats as stats
import pandas as pd
from qf_lib.analysis.rolling_analysis.rolling_analysis import RollingAnalysisFactory
from qf_lib.analysis.tearsheets.abstract_tearsheet import AbstractTearsheet
from qf_lib.common.enums.grid_proportion import GridProportion
from qf_lib.common.enums.plotting_mode import PlottingMode
from qf_lib.common.utils.error_handling import ErrorHandling
from qf_lib.containers.series.qf_series import QFSeries
from qf_lib.documents_utils.document_exporting.element.chart import ChartElement
from qf_lib.documents_utils.document_exporting.element.grid import GridElement
from qf_lib.documents_utils.document_exporting.element.new_page import NewPageElement
from qf_lib.documents_utils.document_exporting.element.paragraph import ParagraphElement
from qf_lib.documents_utils.document_exporting.element.table import Table
from qf_lib.plotting.charts.line_chart import LineChart
from qf_lib.plotting.charts.regression_chart import RegressionChart
from qf_lib.plotting.charts.returns_heatmap_chart import ReturnsHeatmapChart
from qf_lib.plotting.decorators.axes_formatter_decorator import AxesFormatterDecorator, PercentageFormatter
from qf_lib.plotting.decorators.axes_position_decorator import AxesPositionDecorator
from qf_lib.plotting.decorators.data_element_decorator import DataElementDecorator
from qf_lib.plotting.decorators.legend_decorator import LegendDecorator
from qf_lib.plotting.decorators.line_decorators import HorizontalLineDecorator
from qf_lib.plotting.decorators.title_decorator import TitleDecorator
from qf_lib.plotting.helpers.create_returns_bar_chart import create_returns_bar_chart
from qf_lib.plotting.helpers.create_returns_distribution import create_returns_distribution
from qf_lib.plotting.helpers.create_skewness_chart import create_skewness_chart
from qf_lib.settings import Settings
[docs]@ErrorHandling.class_error_logging()
class TearsheetWithBenchmark(AbstractTearsheet):
"""Creates a PDF report, which additionally contains a benchmark.
Can be used with or without the benchmark
Parameters
----------
settings: Settings
settings of the project
pdf_exporter: PDFExporter
tool that creates the pdf with the result
strategy_series: QFSeries
timeseries of the trading of the strategy
benchmark_series: QFSeries
timeseries of the benchmark
live_date: datetime
if set it is used to generate the cone chart
title: str
title of the document
"""
def __init__(self, settings: Settings, pdf_exporter, strategy_series: QFSeries, benchmark_series: QFSeries,
live_date: datetime = None, title: str = "Strategy Analysis"):
super().__init__(settings, pdf_exporter, strategy_series, live_date, title)
self.benchmark_series = benchmark_series
def build_document(self):
series_list = [self.strategy_series, self.benchmark_series]
# First Page
self._add_header()
self._add_perf_chart(series_list)
self._add_relative_performance_chart(self.strategy_series, self.benchmark_series)
self._add_statistics_table(series_list)
# Second Page
self.document.add_element(NewPageElement())
self._add_header()
self.document.add_element(ParagraphElement("\n"))
self._add_returns_statistics_charts()
self._add_ret_distribution_and_similarity()
self._add_rolling_table()
# Third Page
self.document.add_element(NewPageElement())
self._add_header()
self._add_cone_and_quantiles()
self._add_underwater_and_skewness()
self._add_rolling_return_chart(series_list)
self.document.add_element(ParagraphElement("\n"))
self._add_rolling_vol_chart(series_list)
self.document.add_element(ParagraphElement("\n"))
self._add_rolling_alpha_and_beta(series_list)
def _add_returns_statistics_charts(self):
grid = self._get_new_grid()
# Monthly returns heatmap - Strategy
heatmap_chart = ReturnsHeatmapChart(self.strategy_series, title="Monthly Returns - Strategy")
grid.add_chart(heatmap_chart)
# Annual returns bar chart - Strategy
annual_ret_chart_strategy = create_returns_bar_chart(self.strategy_series, title="Annual Returns - Strategy")
grid.add_chart(annual_ret_chart_strategy)
# Monthly returns heatmap - Benchmark
heatmap_chart = ReturnsHeatmapChart(self.benchmark_series, title="Monthly Returns - Benchmark")
grid.add_chart(heatmap_chart)
# Annual returns bar chart - Benchmark
annual_ret_chart_benchmark = create_returns_bar_chart(self.benchmark_series, title="Annual Returns - Benchmark")
grid.add_chart(annual_ret_chart_benchmark)
self.document.add_element(grid)
# put the same x axis range on both histograms
min_x = min([annual_ret_chart_strategy.get_decorator("data_element").data.min(),
annual_ret_chart_benchmark.get_decorator("data_element").data.min(),
0]) # start at least form 0
max_x = max([annual_ret_chart_strategy.get_decorator("data_element").data.max(),
annual_ret_chart_benchmark.get_decorator("data_element").data.max()])
max_x = max_x + (max_x - min_x) / 8 # leave some space on the right to put the bar value
annual_ret_chart_strategy.set_x_range(min_x, max_x)
annual_ret_chart_benchmark.set_x_range(min_x, max_x)
def _add_ret_distribution_and_similarity(self):
grid = GridElement(mode=PlottingMode.PDF, figsize=self.half_image_size, dpi=self.dpi)
title = "Distribution of Monthly Returns - Strategy"
chart_strategy = create_returns_distribution(self.strategy_series, title=title)
grid.add_chart(chart_strategy)
chart = RegressionChart(self.benchmark_series, self.strategy_series)
grid.add_chart(chart)
title = "Distribution of Monthly Returns - Benchmark"
chart_benchmark = create_returns_distribution(self.benchmark_series, title=title)
grid.add_chart(chart_benchmark)
chart = RegressionChart(self.strategy_series, self.benchmark_series)
grid.add_chart(chart)
# put the same x axis range on both histograms
min_x = min([chart_strategy.series.min(), chart_benchmark.series.min()])
max_x = max([chart_strategy.series.max(), chart_benchmark.series.max()])
chart_strategy.set_x_range(min_x, max_x)
chart_benchmark.set_x_range(min_x, max_x)
self.document.add_element(grid)
def _add_underwater_and_skewness(self):
grid = GridElement(mode=PlottingMode.PDF, figsize=self.half_image_size, dpi=self.dpi)
# Underwater plot
grid.add_chart(self._get_underwater_chart(self.strategy_series, benchmark_series=self.benchmark_series))
# Skewness chart
chart = create_skewness_chart(self.strategy_series, title="Skewness")
grid.add_chart(chart)
self.document.add_element(grid)
def _add_rolling_table(self):
dtos = RollingAnalysisFactory.calculate_analysis(self.strategy_series, self.benchmark_series)
column_names = [
Table.ColumnCell("Rolling Return Period", css_class="left-align"),
"Strategy Average",
"Strategy Worst",
Table.ColumnCell("Strategy Best", css_class="right-align"),
"Benchmark Average",
"Benchmark Worst",
Table.ColumnCell("Benchmark Best", css_class="right-align"),
Table.ColumnCell("% Strategy outperform Benchmark", css_class="right-align")]
result = Table(column_names, grid_proportion=GridProportion.Sixteen, css_class="table rolling-table")
for dto in dtos:
result.add_row([Table.Cell(dto.period, css_class="right-line"),
Table.Cell(dto.strategy_average, "{:.2%}"),
Table.Cell(dto.strategy_worst, "{:.2%}"),
Table.Cell(dto.strategy_best, "{:.2%}"),
Table.Cell(dto.benchmark_average, "{:.2%}"),
Table.Cell(dto.benchmark_worst, "{:.2%}"),
Table.Cell(dto.benchmark_best, "{:.2%}"),
Table.Cell(dto.percentage_difference, "{:.2%}")])
self.document.add_element(result)
def _add_rolling_alpha_and_beta(self, timeseries_list):
freq = timeseries_list[0].get_frequency()
timeseries_list = [tms.dropna().to_simple_returns() for tms in timeseries_list]
df = pd.concat(timeseries_list, axis=1).fillna(0)
rolling_window_len = int(freq.value / 2) # 6M rolling
step = round(freq.value / 6) # 2M shift
legend = LegendDecorator()
chart = LineChart(start_x=df.index[0], end_x=df.index[-1])
line_decorator = HorizontalLineDecorator(0, key="h_line", linewidth=1)
chart.add_decorator(line_decorator)
def alpha_function(df_in_window):
strategy_returns = df_in_window.iloc[:, 0]
benchmark_returns = df_in_window.iloc[:, 1]
beta, alpha, _, _, _ = stats.linregress(benchmark_returns, strategy_returns)
return beta, alpha
rolling = df.rolling_time_window(rolling_window_len, step, alpha_function)
rolling = pd.DataFrame([[b, a] for b, a in rolling.values], columns=["beta", "alpha"], index=rolling.index)
rolling_element = DataElementDecorator(rolling["beta"])
chart.add_decorator(rolling_element)
legend.add_entry(rolling_element, "beta")
rolling_element = DataElementDecorator(rolling["alpha"], use_secondary_axes=True)
chart.add_decorator(rolling_element)
legend.add_entry(rolling_element, "alpha")
chart.add_decorator(legend)
chart.add_decorator(AxesFormatterDecorator(use_secondary_axes=True, y_major=PercentageFormatter(".1f")))
# modify axes position to make secondary scale visible
axes_position = list(self.full_image_axis_position)
axes_position[2] = axes_position[2] - 0.07
position_decorator = AxesPositionDecorator(*axes_position)
chart.add_decorator(position_decorator)
title_str = "Rolling alpha and beta [{} {} samples]".format(rolling_window_len, freq)
title_decorator = TitleDecorator(title_str, key="title")
chart.add_decorator(title_decorator)
self.document.add_element(ChartElement(chart, figsize=self.full_image_size, dpi=self.dpi))