https://github.com/10mohi6/portfolio-backtest-python
portfolio-backtest is a python library for backtest portfolio asset allocation on Python 3.7 and above.
https://github.com/10mohi6/portfolio-backtest-python
asset-allocation backtest cvar etf hrp minimum mutual-fund portfolio python stock tangency variance
Last synced: about 2 months ago
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portfolio-backtest is a python library for backtest portfolio asset allocation on Python 3.7 and above.
- Host: GitHub
- URL: https://github.com/10mohi6/portfolio-backtest-python
- Owner: 10mohi6
- Created: 2021-04-13T13:03:05.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-04-21T02:39:24.000Z (about 4 years ago)
- Last Synced: 2025-04-14T04:14:15.241Z (about 2 months ago)
- Topics: asset-allocation, backtest, cvar, etf, hrp, minimum, mutual-fund, portfolio, python, stock, tangency, variance
- Language: Python
- Homepage:
- Size: 1.1 MB
- Stars: 28
- Watchers: 1
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# portfolio-backtest
[](https://pypi.org/project/portfolio-backtest/)
[](https://opensource.org/licenses/MIT)
[](https://codecov.io/gh/10mohi6/portfolio-backtest-python)
[](https://travis-ci.com/10mohi6/portfolio-backtest-python)
[](https://pypi.org/project/portfolio-backtest/)
[](https://pepy.tech/project/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
```python
from portfolio_backtest import Backtest
Backtest(tickers=["VTI", "AGG", "GLD"]).run()
```
### advanced run
```python
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))
```
```python
[{'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}}]
```
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### 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)
```python
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))
```
```python
{'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