portfolio-backtest 0.3.0

Description:

portfoliobacktest 0.3.0

portfolio-backtest






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()






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}}]









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