Create your own insured portfolio using several Tools.
First ,To install, just use pip :
.. code:: python
pip install pyinsurance
Required Dependencies are listed below , such :
============ ========
Dependency Version
============ ========
arch 5.0.1
numpy 1.20.1
scipy 1.6.2
statsmodels 0.12.2
numba 0.52.1
setuptools 60.5.0
pandas 1.2.4
pyvar 0.0.1
============ ========
There is no dependency verification , so please, make sure to have
installed every required one before using the package.
**Example**
===========
To begin, let’s extract some included default data :
.. code:: python
import pyinsurance
from pyinsurance.pymolder import tipp_model
from pyinsurance.data.IRX import load as d1
from pyinsurance.data.sp500 import load as d2
import matplotlib.pyplot as plt
risky_Asset = d2()
safe_Asset = d1()/52 #we divided by 52 as we use weekly rates
**Let’s initalise our first insured portfolio now!**
For instance,we set our lock-in rate , minimum capital risk allocation ,
threshold for capital injection , allocate funds ,strategy’s percentage
floor ,multipler,benchmark returns and rebalancement cycle being
respectively equal to :
.. code:: python
lock_in_rate = 0.05
mcr = 0.40
tfci = 0.80
fund = 100
floor = 0.80
multiplier = 10
Benchmark_return = risk_Asset
Rebalancement_frequency = 52 # once a week -> 52 weeks a year
Running the ``tipp_model`` class :
.. code:: python
res = tipp_model(risk_Asset,safe_Asset,lock_in_rate,mcr,tfci,fund,\
floor,multiplier,risk_Asset,Rebalancement_frequency)
**Our strategy-insured backtest is ready !**
.. code:: python
import matplotlib.pyplot as plt
from pyinsurance.Metric_Generator.returns_metrics import Cumulative_ret
fig = plt.figure(figsize=(15,5))
ax0 = fig.add_subplot(111)
plt.plot(risk_Asset.index,Cumulative_ret(risk_Asset)*100,label = 'Non-Insured Performance')
plt.plot(risk_Asset.index,res.Fund,label = 'Fund Performance')
plt.plot(risk_Asset.index,res.Reference_capital,label = 'Reference Capital',linestyle="--")
plt.plot(risk_Asset.index,res.floor,label = 'Floor',linestyle="-.")
plt.legend()
plt.show()
.. image:: https://raw.githubusercontent.com/EM51641/pyinsurance-/main/pictures/output.png
And our capital injections through the period are presented as:
.. code:: python
fig = plt.figure(figsize=(15,5))
ax1 = fig.add_subplot(111)
plt.plot(risk_Asset.index,res.capital_reinjection,label = 'Injected Capital')
plt.legend()
plt.show()
.. image:: https://raw.githubusercontent.com/EM51641/pyinsurance-/main/pictures/output2.png
If you want to backtest the VaR, you can use the `varpy`_ library:
.. _varpy: https://github.com/EM51641/VaRpy
.. code:: python
import pyvar
from varpy.Backtester.bktst import Backtest
from varpy.Backtester.time_Significance import Testing
VaR , CVaR = Backtest(data, 500, 2, 0.05, model = 'EVT')
.. code:: python
fig = plt.figure(figsize=(15,5))
plt.plot(data[500:])
plt.plot(VaR, label = 'VaR')
plt.plot(CVaR, label = 'CVaR')
plt.legend()
plt.show()
.. image:: https://raw.githubusercontent.com/EM51641/pyinsurance-/main/pictures/output3.png
