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portfolio-backtest is a python library for backtest portfolio asset allocation on Python 3.7 and above.

Project description

portfolio-backtest

PyPI License: MIT codecov Build Status PyPI - Python Version Downloads

portfolio-backtest is a python library for backtest portfolio asset allocation on Python 3.7 and above.

Installation

$ pip install portfolio-backtest
$ pip install PyPortfolioOpt

Usage

basic run

from portfolio_backtest import Backtest

Backtest(tickers=["VTI", "AGG", "GLD"]).run()

tangency-portfolio.png minimum-variance-portfolio.png hierarchical-risk-parity-portfolio.png minimum-cvar-portfolio.png cumulative-return.png

advanced run

from portfolio_backtest import Backtest
import pprint

bt = Backtest(
    tickers={
        "VTI": 0.6,
        "AGG": 0.25,
        "GLD": 0.15,
    },
    target_return=0.1,
    target_cvar=0.025,
    data_dir="data",
    start="2011-04-10",
    end="2021-04-10",
)
pprint.pprint(bt.run(plot=True))
[{'Annual volatility': '10.9%',
  'Conditional Value at Risk': '',
  'Cumulative Return': '160.9%',
  'Expected annual return': '9.6%',
  'Sharpe Ratio': '0.70',
  'portfolio': 'Your Portfolio',
  'tickers': {'AGG': 0.25, 'GLD': 0.15, 'VTI': 0.6}},
 {'Annual volatility': '6.3%',
  'Conditional Value at Risk': '',
  'Cumulative Return': '102.3%',
  'Expected annual return': '7.0%',
  'Sharpe Ratio': '0.79',
  'portfolio': 'Tangency Portfolio',
  'tickers': {'AGG': 0.67099, 'GLD': 0.0, 'VTI': 0.32901}},
 {'Annual volatility': '4.3%',
  'Conditional Value at Risk': '',
  'Cumulative Return': '53.3%',
  'Expected annual return': '4.3%',
  'Sharpe Ratio': '0.53',
  'portfolio': 'Minimum Variance Portfolio',
  'tickers': {'AGG': 0.91939, 'GLD': 0.00525, 'VTI': 0.07536}},
 {'Annual volatility': '4.0%',
  'Conditional Value at Risk': '',
  'Cumulative Return': '48.7%',
  'Expected annual return': '4.1%',
  'Sharpe Ratio': '0.51',
  'portfolio': 'Hierarchical Risk Parity Portfolio',
  'tickers': {'AGG': 0.89041, 'GLD': 0.05695, 'VTI': 0.05263}},
 {'Annual volatility': '',
  'Conditional Value at Risk': '0.5%',
  'Cumulative Return': '52.1%',
  'Expected annual return': '4.2%',
  'Sharpe Ratio': '',
  'portfolio': 'Minimum CVaR Portfolio',
  'tickers': {'AGG': 0.93215, 'GLD': 0.0, 'VTI': 0.06785}},
 {'Annual volatility': '7.7%',
  'Conditional Value at Risk': '',
  'Cumulative Return': '166.5%',
  'Expected annual return': '10.0%',
  'Sharpe Ratio': '1.04',
  'portfolio': 'Semi Variance Portfolio (target return 10.0%)',
  'tickers': {'AGG': 0.39504, 'GLD': 0.0, 'VTI': 0.60496}},
 {'Annual volatility': '',
  'Conditional Value at Risk': '2.5%',
  'Cumulative Return': '251.3%',
  'Expected annual return': '13.3%',
  'Sharpe Ratio': '',
  'portfolio': 'Return Maximize CVaR Portfolio (target CVaR 2.5%)',
  'tickers': {'AGG': 0.08851, 'GLD': 0.0, 'VTI': 0.91149}}]

advanced-your-portfolio.png advanced-tangency-portfolio.png advanced-minimum-variance-portfolio.png advanced-hierarchical-risk-parity-portfolio.png advanced-minimum-cvar-portfolio.png advanced-return-maximize-cvar-portfolio-(target-cvar-2.5%).png advanced-semi-variance-portfolio-(target-return-10.0%).png advanced-cumulative-return.png

Provides a method (discrete_allocation) that can be converted into an actual allocation available for purchase by entering the latest price and desired portfolio size ($ 10,000 in this example)

from portfolio_backtest import Backtest

bt = Backtest(
    tickers={
        "VTI": 0.6,
        "AGG": 0.25,
        "GLD": 0.15,
    }
)
print(bt.discrete_allocation(total_portfolio_value=10000))
{'Discrete allocation': {'VTI': 28, 'AGG': 21, 'GLD': 9}, 'Funds remaining': '$109.45'}

Supported Portfolio

  • Your Portfolio
  • Hierarchical Risk Parity Portfolio
  • Tangency Portfolio
  • Minimum Variance Portfolio
  • Minimum CVaR Portfolio
  • Semi Variance Portfolio
  • Return Maximize CVaR Portfolio

Project details


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