Investment-Portfolio-Builder

Overview

Investment Portfolio Builder is a Python library that provides portfolio optimization methods, including classical mean-variance optimization techniques, Black-Litterman allocation, and more recent developments like shrinkage and Hierarchical Risk Parity. It is both extensive and easily extensible, making it useful for casual investors and professionals looking for a prototyping tool. Whether you are a fundamentals-oriented investor or an algorithmic trader, Investment Portfolio Builder can help you combine your alpha sources in a risk-efficient way.

A quick example

Here is an example on real life stock data, demonstrating how easy it is to find the long-only portfolio that maximises the Sharpe ratio (a measure of risk-adjusted returns).

import pandas as pd
from pypfopt import EfficientFrontier
from pypfopt import risk_models
from pypfopt import expected_returns

# Read in price data
df = pd.read_csv("tests/resources/stock_prices.csv", parse_dates=True, index_col="date")

# Calculate expected returns and sample covariance
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)

# Optimize for maximal Sharpe ratio
ef = EfficientFrontier(mu, S)
raw_weights = ef.max_sharpe()
cleaned_weights = ef.clean_weights()
ef.save_weights_to_file("weights.csv")  # saves to file
print(cleaned_weights)
ef.portfolio_performance(verbose=True)

This outputs the following weights:

{'GOOG': 0.03835,
 'AAPL': 0.0689,
 'FB': 0.20603,
 'BABA': 0.07315,
 'AMZN': 0.04033,
 'GE': 0.0,
 'AMD': 0.0,
 'WMT': 0.0,
 'BAC': 0.0,
 'GM': 0.0,
 'T': 0.0,
 'UAA': 0.0,
 'SHLD': 0.0,
 'XOM': 0.0,
 'RRC': 0.0,
 'BBY': 0.01324,
 'MA': 0.35349,
 'PFE': 0.1957,
 'JPM': 0.0,
 'SBUX': 0.01082}

Expected annual return: 30.5%
Annual volatility: 22.2%
Sharpe Ratio: 1.28

Expected returns

• Mean historical returns:
  • the simplest and most common approach, which states that the expected return of each asset is equal to the mean of its historical returns.
  • easily interpretable and very intuitive
• Exponentially weighted mean historical returns:
  • similar to mean historical returns, except it gives exponentially more weight to recent prices
  • it is likely the case that an asset's most recent returns hold more weight than returns from 10 years ago when it comes to estimating future returns.
• Capital Asset Pricing Model (CAPM):
  • a simple model to predict returns based on the beta to the market
  • this is used all over finance!