EM51641 / pyinsurance-

Create your own insured portfolio using several Tools.

First ,To install, just use pip :

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

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 :

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 :

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 :

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 !

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()

And our capital injections through the period are presented as:

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()

If you want to backtest the VaR, you can use the varpy library:

import varpy
from varpy.Backtester.bktst import Backtest
from varpy.Backtester.time_Significance import Testing
VaR , CVaR = Backtest(data, 500, 2, 0.05, model = 'EVT')

fig = plt.figure(figsize=(15,5))
plt.plot(data[500:])
plt.plot(VaR, label = 'VaR')
plt.plot(CVaR, label = 'CVaR')
plt.legend()
plt.show()