strategy-lab/to_explore/pyquantnews/14_RiskParity.ipynb

This code constructs and optimizes a financial portfolio using historical stock data. It uses the Riskfolio library to estimate asset returns and covariance. The code further optimizes the portfolio for risk parity and visualizes the resulting asset allocation and risk contributions. Additionally, it demonstrates the impact of adding a constraint on expected returns. This is useful for portfolio management and risk assessment in finance.

In [ ]:

import yfinance as yf
import riskfolio as rp
import warnings
warnings.filterwarnings("ignore")

Define a list of stock tickers to include in the portfolio

In [ ]:

assets = ["JCI", "TGT", "CMCSA", "CPB", "MO", "APA", "MMC", "JPM",
          "ZION", "PSA", "BAX", "BMY", "LUV", "PCAR", "TXT", "TMO",
          "DE", "MSFT", "HPQ", "SEE", "VZ", "CNP", "NI", "T", "BA"]

Sort the list of tickers alphabetically

In [ ]:

assets.sort()

Download historical stock data for the specified tickers from Yahoo Finance

In [ ]:

data = yf.download(assets, start="2016-01-01", end="2019-12-30")

Calculate daily returns based on adjusted closing prices

In [ ]:

returns = data['Adj Close'].pct_change().dropna()

Create a Portfolio object using the calculated returns

In [ ]:

port = rp.Portfolio(returns=returns)

Estimate expected returns and covariance matrix using historical data

In [ ]:

port.assets_stats(method_mu='hist', method_cov='hist', d=0.94)

Optimize the portfolio for risk parity using mean-variance optimization

In [ ]:

w_rp = port.rp_optimization(
    model="Classic",  # use historical
    rm="MV",  # use mean-variance optimization
    hist=True,  # use historical scenarios
    rf=0,  # set risk free rate to 0
    b=None  # don't use constraints
)

Plot the asset allocation of the optimized portfolio

In [ ]:

ax = rp.plot_pie(w=w_rp)

Plot the risk contribution of each asset in the optimized portfolio

In [ ]:

ax = rp.plot_risk_con(
    w_rp,
    cov=port.cov,
    returns=port.returns,
    rm="MV",
    rf=0,
)

Set a constraint for the minimum level of expected returns for the portfolio

In [ ]:

port.lowerret = 0.0008

Optimize the portfolio for risk parity with the added constraint on expected returns

In [ ]:

w_rp_c = port.rp_optimization(
    model="Classic",  # use historical
    rm="MV",  # use mean-variance optimization
    hist=True,  # use historical scenarios
    rf=0,  # set risk free rate to 0
    b=None  # don't use constraints
)

Plot the asset allocation of the constrained optimized portfolio

In [ ]:

ax = rp.plot_pie(w=w_rp_c)

Plot the risk contribution of each asset in the constrained optimized portfolio

In [ ]:

ax = rp.plot_risk_con(
    w_rp_c,
    cov=port.cov,
    returns=port.returns,
    rm="MV",
    rf=0,
)

PyQuant News is where finance practitioners level up with Python for quant finance, algorithmic trading, and market data analysis. Looking to get started? Check out the fastest growing, top-selling course to get started with Python for quant finance. For educational purposes. Not investment advise. Use at your own risk.