"""""" #
"""
Copyright (c) 2020-2023, Dany Cajas
All rights reserved.
This work is licensed under BSD 3-Clause "New" or "Revised" License.
License available at https://github.com/dcajasn/Riskfolio-Lib/blob/master/LICENSE.txt
"""
import pandas as pd
import matplotlib.pyplot as plt
from xlsxwriter.utility import xl_range_abs, xl_range, xl_rowcol_to_cell, xl_col_to_name
import datetime
import riskfolio.src.PlotFunctions as plf
import riskfolio.src.RiskFunctions as rk
__all__ = [
"jupyter_report",
"excel_report",
]
__LICENSE__ = """Copyright (c) 2020-2023, Dany Cajas
All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
* Neither the name of Riskfolio-Lib nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."""
[文档]
def jupyter_report(
returns,
w,
rm="MV",
rf=0,
alpha=0.05,
a_sim=100,
beta=None,
b_sim=None,
kappa=0.30,
solver=None,
percentage=False,
erc_line=True,
color="tab:blue",
erc_linecolor="r",
others=0.05,
nrow=25,
cmap="tab20",
height=6,
width=14,
t_factor=252,
ini_days=1,
days_per_year=252,
bins=50,
):
r"""
Create a matplotlib report with useful information to analyze risk and
profitability of investment portfolios.
Parameters
----------
returns : DataFrame
Assets returns.
w : DataFrame of shape (n_assets, 1)
Portfolio weights.
rm : str, optional
Risk measure used to estimate risk contribution.
The default is 'MV'. Possible values are:
- 'MV': Standard Deviation.
- 'KT': Square Root Kurtosis.
- 'MAD': Mean Absolute Deviation.
- 'GMD': Gini Mean Difference.
- 'MSV': Semi Standard Deviation.
- 'SKT': Square Root Semi Kurtosis.
- 'FLPM': First Lower Partial Moment (Omega Ratio).
- 'SLPM': Second Lower Partial Moment (Sortino Ratio).
- 'CVaR': Conditional Value at Risk.
- 'TG': Tail Gini.
- 'EVaR': Entropic Value at Risk.
- 'RLVaR': Relativistc Value at Risk.
- 'WR': Worst Realization (Minimax).
- 'CVRG': CVaR range of returns.
- 'TGRG': Tail Gini range of returns.
- 'RG': Range of returns.
- 'MDD': Maximum Drawdown of uncompounded cumulative returns (Calmar Ratio).
- 'ADD': Average Drawdown of uncompounded cumulative returns.
- 'DaR': Drawdown at Risk of uncompounded cumulative returns.
- 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns.
- 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns.
- 'RLDaR': Relativistic Drawdown at Risk of uncompounded cumulative returns.
- 'UCI': Ulcer Index of uncompounded cumulative returns.
rf : float, optional
Risk free rate or minimum acceptable return. The default is 0.
alpha : float, optional
Significance level of VaR, CVaR, Tail Gini, EVaR, RLVaR, CDaR, EDaR and RLDaR. The default is 0.05.
The default is 0.05.
a_sim : float, optional
Number of CVaRs used to approximate Tail Gini of losses. The default is 100.
beta : float, optional
Significance level of CVaR and Tail Gini of gains. If None it duplicates alpha value.
The default is None.
b_sim : float, optional
Number of CVaRs used to approximate Tail Gini of gains. If None it duplicates a_sim value.
The default is None.
kappa : float, optional
Deformation parameter of RLVaR and RLDaR, must be between 0 and 1. The default is 0.30.
solver: str, optional
Solver available for CVXPY that supports power cone programming. Used to calculate RLVaR and RLDaR.
The default value is None.
percentage : bool, optional
If risk contribution per asset is expressed as percentage or as a value. The default is False.
erc_line : bool, optional
If equal risk contribution line is plotted.
The default is False.
color : str, optional
Color used to plot each asset risk contribution.
The default is 'tab:blue'.
erc_linecolor : str, optional
Color used to plot equal risk contribution line.
The default is 'r'.
others : float, optional
Percentage of others section. The default is 0.05.
nrow : int, optional
Number of rows of the legend. The default is 25.
cmap : cmap, optional
Color scale used to plot each asset weight.
The default is 'tab20'.
height : float, optional
Average height of charts in the image in inches. The default is 6.
width : float, optional
Width of the image in inches. The default is 14.
t_factor : float, optional
Factor used to annualize expected return and expected risks for
risk measures based on returns (not drawdowns). The default is 252.
.. math::
\begin{align}
\text{Annualized Return} & = \text{Return} \, \times \, \text{t_factor} \\
\text{Annualized Risk} & = \text{Risk} \, \times \, \sqrt{\text{t_factor}}
\end{align}
ini_days : float, optional
If provided, it is the number of days of compounding for first return.
It is used to calculate Compound Annual Growth Rate (CAGR). This value
depend on assumptions used in t_factor, for example if data is monthly
you can use 21 (252 days per year) or 30 (360 days per year). The
default is 1 for daily returns.
days_per_year: float, optional
Days per year assumption. It is used to calculate Compound Annual
Growth Rate (CAGR). Default value is 252 trading days per year.
bins : float, optional
Number of bins of the histogram. The default is 50.
ax : matplotlib axis of size (6,1), optional
If provided, plot on this axis. The default is None.
Raises
------
ValueError
When the value cannot be calculated.
Returns
-------
ax : matplotlib axis
Returns the Axes object with the plot for further tweaking.
Example
-------
::
ax = rp.jupyter_report(returns,
w,
rm='MV',
rf=0,
alpha=0.05,
height=6,
width=14,
others=0.05,
nrow=25)
.. image:: images/Report_1.png
.. image:: images/Report_2.png
.. image:: images/Report_3.png
.. image:: images/Report_4.png
"""
cov = returns.cov()
fig, ax = plt.subplots(
nrows=6,
figsize=(width, height * 6),
gridspec_kw={"height_ratios": [2, 1, 1.5, 1, 1, 1]},
)
ax[0] = plf.plot_table(
returns,
w,
MAR=rf,
alpha=alpha,
a_sim=a_sim,
kappa=0.30,
solver=None,
t_factor=t_factor,
ini_days=ini_days,
days_per_year=days_per_year,
ax=ax[0],
)
ax[2] = plf.plot_pie(
w=w,
title="Portfolio Composition",
others=others,
nrow=nrow,
cmap=cmap,
ax=ax[2],
)
ax[3] = plf.plot_risk_con(
w=w,
cov=cov,
returns=returns,
rm=rm,
rf=rf,
alpha=alpha,
a_sim=a_sim,
beta=beta,
b_sim=b_sim,
kappa=kappa,
solver=solver,
t_factor=t_factor,
percentage=percentage,
erc_line=erc_line,
color=color,
erc_linecolor=erc_linecolor,
ax=ax[3],
)
ax[4] = plf.plot_hist(
returns=returns,
w=w,
alpha=alpha,
a_sim=a_sim,
kappa=kappa,
solver=solver,
bins=bins,
ax=ax[4],
)
ax[[1, 5]] = plf.plot_drawdown(
returns=returns, w=w, alpha=alpha, kappa=kappa, solver=solver, ax=ax[[1, 5]]
)
year = str(datetime.datetime.now().year)
title = "Riskfolio-Lib Report"
subtitle = "Copyright (c) 2020-" + year + ", Dany Cajas. All rights reserved."
fig.suptitle(title, fontsize="xx-large", y=1.011, fontweight="bold")
ax[0].set_title(subtitle, fontsize="large", ha="center", pad=10)
return ax
[文档]
def excel_report(
returns,
w,
rf=0,
alpha=0.05,
t_factor=252,
ini_days=1,
days_per_year=252,
name="report",
):
r"""
Create an Excel report (with formulas) with useful information to analyze
risk and profitability of investment portfolios.
Parameters
----------
returns : DataFrame
Assets returns.
w : DataFrame of size (n_assets, n_portfolios)
Portfolio weights.
rf : float, optional
Risk free rate or minimum acceptable return. The default is 0.
alpha : float, optional
Significance level of VaR, CVaR, EVaR, DaR and CDaR.
The default is 0.05.
t_factor : float, optional
Factor used to annualize expected return and expected risks for
risk measures based on returns (not drawdowns). The default is 252.
.. math::
\begin{align}
\text{Annualized Return} & = \text{Return} \, \times \, \text{t_factor} \\
\text{Annualized Risk} & = \text{Risk} \, \times \, \sqrt{\text{t_factor}}
\end{align}
ini_days : float, optional
If provided, it is the number of days of compounding for first return.
It is used to calculate Compound Annual Growth Rate (CAGR). This value
depend on assumptions used in t_factor, for example if data is monthly
you can use 21 (252 days per year) or 30 (360 days per year). The
default is 1 for daily returns.
days_per_year: float, optional
Days per year assumption. It is used to calculate Compound Annual
Growth Rate (CAGR). Default value is 252 trading days per year.
name : str, optional
Name or name with path where the Excel report will be saved. If no
path is provided the report will be saved in the same path of
current file.
Raises
------
ValueError
When the report cannot be built.
Example
-------
::
rp.excel_report(returns,
w,
rf=0,
alpha=0.05,
t_factor=252,
ini_days=1,
days_per_year=252,
name="report")
.. image:: images/Excel.png
"""
n1 = w.shape[0]
n2 = returns.shape[0]
portfolios = w.columns.tolist()
dates = returns.index.tolist()
year = str(datetime.datetime.now().year)
days = (returns.index[-1] - returns.index[0]).days + ini_days
# Create a Pandas Excel writer using XlsxWriter as the engine.
writer = pd.ExcelWriter(name + ".xlsx", engine="xlsxwriter")
# Convert the dataframe to an XlsxWriter Excel object.
w.to_excel(writer, sheet_name="Resume", startrow=36, startcol=0)
returns.to_excel(writer, sheet_name="Returns", index_label=["Date"])
# Get the xlsxwriter objects from the dataframe writer object.
workbook = writer.book
worksheet1 = writer.sheets["Resume"]
worksheet2 = writer.sheets["Returns"]
worksheet3 = workbook.add_worksheet("Portfolios")
worksheet4 = workbook.add_worksheet("Absdev")
worksheet5 = workbook.add_worksheet("CumRet")
worksheet6 = workbook.add_worksheet("Drawdown")
worksheet7 = workbook.add_worksheet("devBelowTarget")
worksheet8 = workbook.add_worksheet("devBelowMean")
worksheet1.hide_gridlines(2)
worksheet2.hide_gridlines(2)
worksheet3.hide_gridlines(2)
worksheet4.hide_gridlines(2)
worksheet5.hide_gridlines(2)
worksheet6.hide_gridlines(2)
worksheet7.hide_gridlines(2)
worksheet8.hide_gridlines(2)
# Cell Formats
cell_format1 = workbook.add_format({"bold": True, "border": True})
cell_format2 = workbook.add_format({"bold": True, "font_size": 28, "right": True})
cell_format3 = workbook.add_format({"num_format": "0.0000%"})
cell_format4 = workbook.add_format({"num_format": "0.0000%", "border": True})
cell_format5 = workbook.add_format({"num_format": "yyyy-mm-dd", "bold": True})
cell_format6 = workbook.add_format({"num_format": "0.0000", "border": True})
cell_format7 = workbook.add_format(
{"num_format": "yyyy-mm-dd", "bold": True, "border": True}
)
cell_format8 = workbook.add_format({"num_format": "0,000", "border": True})
cols = xl_col_to_name(1) + ":" + xl_col_to_name(n2)
worksheet1.set_column(cols, 11, cell_format3)
worksheet2.set_column(cols, 9, cell_format3)
worksheet2.write(0, 0, "Date", cell_format1)
worksheet3.write(0, 0, "Date", cell_format1)
worksheet4.write(0, 0, "Date", cell_format1)
worksheet5.write(0, 0, "Date", cell_format1)
worksheet6.write(0, 0, "Date", cell_format1)
worksheet7.write(0, 0, "Date", cell_format1)
worksheet8.write(0, 0, "Date", cell_format1)
worksheet1.set_column("A:A", 36)
worksheet2.set_column("A:A", 10, cell_format5)
worksheet3.set_column("A:A", 10, cell_format5)
worksheet4.set_column("A:A", 10, cell_format5)
worksheet5.set_column("A:A", 10, cell_format5)
worksheet6.set_column("A:A", 10, cell_format5)
worksheet7.set_column("A:A", 10, cell_format5)
worksheet8.set_column("A:A", 10, cell_format5)
for i in range(0, n2):
r = xl_rowcol_to_cell(i + 1, 0)
formula = "=Returns!" + r + ""
worksheet2.write(i + 1, 0, dates[i], cell_format7)
worksheet3.write_formula(i + 1, 0, formula, cell_format7)
worksheet4.write_formula(i + 1, 0, formula, cell_format7)
worksheet5.write_formula(i + 1, 0, formula, cell_format7)
worksheet6.write_formula(i + 1, 0, formula, cell_format7)
worksheet7.write_formula(i + 1, 0, formula, cell_format7)
worksheet8.write_formula(i + 1, 0, formula, cell_format7)
labels_1 = [
"",
"",
"",
"",
"Profitability and Other Inputs",
"Total Days in DataBase",
"Mean Return (1)",
"Compound Annual Growth Rate (CAGR)",
"Minimum Acceptable Return (MAR) (1)",
"Alpha",
"",
"Risk Measures based on Returns",
"Standard Deviation (2)",
"Mean Absolute Deviation (MAD) (2)",
"Semi Standard Deviation (2)",
"First Lower Partial Moment (FLPM) (2)",
"Second Lower Partial Moment (SLPM) (2)",
"Value at Risk (VaR) (2)",
"Conditional Value at Risk (CVaR) (2)",
"Entropic Value at Risk (EVaR) (2)",
"Worst Realization (2)",
"Skewness",
"Kurtosis",
"",
"Risk Measures based on Drawdowns (3)",
"Ulcer Index (UCI)",
"Average Drawdown (ADD)",
"Drawdown at Risk (DaR)",
"Conditional Drawdown at Risk (CDaR)",
"Entropic Drawdown at Risk (CDaR)",
"Max Drawdown (MDD)",
]
for i in range(0, len(labels_1)):
if labels_1[i] != "":
worksheet1.write(i, 0, labels_1[i], cell_format1)
for i in range(0, len(portfolios)):
a = "Portfolio " + str(i + 1)
worksheet1.write(3, 1 + i, a, cell_format1)
worksheet1.write(36, 1 + i, a, cell_format1)
worksheet3.write(0, 1 + i, a, cell_format1)
worksheet4.write(0, 1 + i, a, cell_format1)
worksheet5.write(0, 1 + i, a, cell_format1)
worksheet6.write(0, 1 + i, a, cell_format1)
worksheet7.write(0, 1 + i, a, cell_format1)
worksheet8.write(0, 1 + i, a, cell_format1)
for j in range(0, len(portfolios)):
r_0 = xl_rowcol_to_cell(8, 1 + j) # MAR cell
r_1 = xl_range_abs(37, 1 + j, 36 + n1, 1 + j)
r_2 = xl_range_abs(1, 1 + j, n2, 1 + j)
for i in range(0, n2):
r_3 = xl_range(i + 1, 1, i + 1, n1)
r_4 = xl_rowcol_to_cell(i + 1, 1 + j)
r_5 = xl_range_abs(1, 1 + j, i + 1, 1 + j)
formula1 = "{=MMULT(" + "Returns!" + r_3 + ",Resume!" + r_1 + ")}"
formula2 = "=ABS(Portfolios!" + r_4 + "-AVERAGE(Portfolios!" + r_2 + "))"
formula3 = "=SUM(Portfolios!" + r_5 + ")"
formula4 = "=MAX(CumRet!" + r_5 + ")-CumRet!" + r_4
formula5 = (
"=MAX(Resume!"
+ r_0
+ "/ "
+ str(t_factor)
+ "-Portfolios!"
+ r_4
+ ", 0)"
)
formula6 = "=MAX(AVERAGE(Portfolios!" + r_2 + ")-Portfolios!" + r_4 + ", 0)"
worksheet3.write_formula(i + 1, 1 + j, formula1, cell_format3)
worksheet4.write_formula(i + 1, 1 + j, formula2, cell_format3)
worksheet5.write_formula(i + 1, 1 + j, formula3, cell_format3)
worksheet6.write_formula(i + 1, 1 + j, formula4, cell_format3)
worksheet7.write_formula(i + 1, 1 + j, formula5, cell_format3)
worksheet8.write_formula(i + 1, 1 + j, formula6, cell_format3)
r_6 = xl_rowcol_to_cell(9, 1 + j) # Alpha cell
r_7 = xl_rowcol_to_cell(17, 1 + j) # Value at Risk cell
AVG = "=AVERAGE(Portfolios!" + r_2 + ") * " + str(t_factor) + ""
CUM = (
"{=PRODUCT(1 + Portfolios!"
+ r_2
+ ")^("
+ str(days_per_year)
+ "/"
+ str(days)
+ ")-1}"
)
STDEV = "=STDEV(Portfolios!" + r_2 + ") * SQRT(" + str(t_factor) + ")"
MAD = "=AVERAGE(Absdev!" + r_2 + ") * SQRT(" + str(t_factor) + ")"
ALPHA = "=" + str(alpha)
VaR = (
"=-SMALL(Portfolios!"
+ r_2
+ ",ROUNDUP(COUNT(Portfolios!"
+ r_2
+ ")*"
+ r_6
+ ",0)) * SQRT("
+ str(t_factor)
+ ")"
)
CVaR = (
"=-((SUMIF(Portfolios!"
+ r_2
+ ',"<="&(-'
+ r_7
+ "/SQRT("
+ str(t_factor)
+ ")),Portfolios!"
+ r_2
+ ")"
)
CVaR += (
"-ROUNDUP(COUNT(Portfolios!"
+ r_2
+ ")*"
+ r_6
+ ",0)*(-"
+ r_7
+ "/SQRT("
+ str(t_factor)
+ ")))/(COUNT(Portfolios!"
+ r_2
+ ")*"
+ r_6
+ ")-"
+ r_7
+ "/SQRT("
+ str(t_factor)
+ ")) * SQRT("
+ str(t_factor)
+ ")"
)
EVaR = (
"="
+ str(rk.EVaR_Hist(returns @ w.iloc[:, j], alpha=alpha)[0])
+ " * SQRT("
+ str(t_factor)
+ ")"
)
WR = "=-MIN(Portfolios!" + r_2 + ") * SQRT(" + str(t_factor) + ")"
MDD = "=MAX(Drawdown!" + r_2 + ")"
ADD = "=AVERAGE(Drawdown!" + r_2 + ")"
DaR = (
"=+LARGE(Drawdown!"
+ r_2
+ ",ROUNDUP(COUNT(Drawdown!"
+ r_2
+ ")*"
+ r_6
+ ",0))"
)
CDaR = (
"=((SUMIF(Drawdown!" + r_2 + ',">="&' + DaR[2:] + ",Drawdown!" + r_2 + ")"
)
CDaR += (
"-ROUNDUP(COUNT(Drawdown!"
+ r_2
+ ")*"
+ r_6
+ ",0)*"
+ DaR[2:]
+ ")/(COUNT(Drawdown!"
+ r_2
+ ")*"
+ r_6
+ ")+"
+ DaR[2:]
+ ")"
)
EDaR = "=" + str(rk.EDaR_Abs(returns @ w.iloc[:, j], alpha=alpha)[0])
UCI = "=SQRT(SUMSQ(Drawdown!" + r_2 + ")/COUNT(Drawdown!" + r_2 + "))"
MAR = "=" + str(rf)
FLPM = "=AVERAGE(devBelowTarget!" + r_2 + ") * SQRT(" + str(t_factor) + ")"
SLPM = (
"=SQRT(SUMSQ(devBelowTarget!"
+ r_2
+ ")/(COUNT(devBelowTarget!"
+ r_2
+ ") - 1))"
+ " * SQRT("
+ str(t_factor)
+ ")"
)
SDEV = (
"=SQRT(SUMSQ(devBelowMean!"
+ r_2
+ ")/(COUNT(devBelowMean!"
+ r_2
+ ") - 1))"
+ " * SQRT("
+ str(t_factor)
+ ")"
)
SKEW = "=SKEW(Portfolios!" + r_2 + ")"
KURT = "=KURT(Portfolios!" + r_2 + ")"
labels_2 = [
"",
"",
"",
"",
"",
str(days),
AVG,
CUM,
MAR,
ALPHA,
"",
"",
STDEV,
MAD,
SDEV,
FLPM,
SLPM,
VaR,
CVaR,
EVaR,
WR,
SKEW,
KURT,
"",
"",
UCI,
ADD,
DaR,
CDaR,
EDaR,
MDD,
]
for i in range(0, len(labels_2)):
if labels_1[i] in ["Skewness", "Kurtosis"]:
worksheet1.write_formula(i, 1 + j, labels_2[i], cell_format6)
elif labels_1[i] in ["Total Days in DataBase"]:
worksheet1.write_formula(i, 1 + j, labels_2[i], cell_format8)
elif labels_2[i] != "":
worksheet1.write_formula(i, 1 + j, labels_2[i], cell_format4)
merge_format = workbook.add_format({"align": "Left", "valign": "vjustify"})
merge_format.set_text_wrap()
worksheet1.set_row(1, 215)
worksheet1.merge_range("A2:K2", __LICENSE__.replace("2021", year), merge_format)
worksheet1.write(2, 0, "https://github.com/dcajasn/Riskfolio-Lib")
worksheet1.write(31, 0, "(1) Annualized, multiplied by " + str(t_factor))
worksheet1.write(32, 0, "(2) Annualized, multiplied by √" + str(t_factor))
worksheet1.write(33, 0, "(3) Based on uncompounded cumulated returns")
worksheet1.write(0, 0, "Riskfolio-Lib Report", cell_format2)
workbook.close()