Pyinsurance 1.1.5

Tools to backtest and create your own portfolio insurance strategy

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 data 0.4 matplotlib 3.3.4 ============ ========

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 :

.. 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()

.. figure:: pictures/output.png :alt: 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()

.. figure:: pictures/output2.png :alt: png