portbt 0.1.8

Description:

portbt 0.1.8

portbt
portbt is a Python library designed to make backtesting a custom portfolio of assets easy and intuitive. With PortBT, you can test a range of rebalancing strategies and asset allocations using just a few lines of code.
Features

Simple and intuitive code
Flexible rebalancing options

Getting Started
To get started with PortBT, simply install the library using pip
pip install portbt

Import it into your Python project. From there, you just have to create a Portfolio object with the asset prices to define your portfolio.
import portbt as pbt

# sample from brazil, so decimal "," and sep ";"
prices = pd.read_csv('sample/prices.csv', sep=';', decimal=',')
prices.index = pd.to_datetime(prices.index) # index has to be datetime
prices[prices.columns] = prices[prices.columns].astype(float)
prices.name = 'prices'
prices.index.name = 'date'

print(prices)

Output
BBAS3 BOVA11 ITUB4 PETR4 SMAL11 VALE3
date
2008-11-28 5.848646 36.595901 6.496692 7.174176 23.760300 13.265061
2008-12-01 5.848646 34.744900 6.289404 6.580500 22.656500 12.286927
2008-12-02 5.828202 35.001598 6.447920 6.544737 22.606501 11.829182
2008-12-03 5.848646 35.293800 6.511325 6.905950 22.426001 11.983371
2008-12-04 5.971348 35.122101 6.567414 6.652028 22.636900 11.607532
... ... ... ... ... ... ...
2023-04-03 38.650002 98.300003 24.030001 24.490000 86.500000 80.309998
2023-04-04 39.290001 98.510002 24.510000 24.270000 87.000000 78.040001
2023-04-05 39.150002 97.629997 24.490000 24.350000 85.650002 76.889999
2023-04-06 39.020000 97.589996 24.410000 24.000000 85.879997 76.750000
2023-04-10 39.040001 98.660004 24.670000 24.510000 86.349998 78.230003


Creating the portfolio and backtesting
portfolio = pbt.Portfolio(prices)

backtest = portfolio.run_backtest(rebalance=False)

# backtest.prices -> prices for each asset, "raw data"
# backtest.values -> values for each asset (starting capital = 1)
# backtest.exposure -> exposure for each asset
# backtest.result -> backtest result, starting from 1
# backtest.all_dates -> all dates for the backtesting, if needed
# backtest.rebal_dates -> rebalace dates only

# example
print(backtest.exposure)

Output
BBAS3 BOVA11 ITUB4 PETR4 SMAL11 VALE3
date
2008-11-28 0.166667 0.166667 0.166667 0.166667 0.166667 0.166667
2008-12-01 0.174991 0.166140 0.169408 0.160510 0.166862 0.162088
2008-12-02 0.174798 0.167769 0.174094 0.160021 0.166893 0.156424
2008-12-03 0.173115 0.166956 0.173505 0.166643 0.163393 0.156388
2008-12-04 0.177667 0.167009 0.175911 0.161351 0.165789 0.152272
... ... ... ... ... ... ...
2023-04-03 0.253178 0.102909 0.141708 0.130782 0.139475 0.231949
2023-04-04 0.257288 0.103096 0.144492 0.129566 0.140236 0.225321
2023-04-05 0.258185 0.102898 0.145396 0.130913 0.139037 0.223571
2023-04-06 0.258177 0.103195 0.145399 0.129457 0.139871 0.223901
2023-04-10 0.255589 0.103228 0.145400 0.130815 0.139155 0.225814

Portfolio backtesting - main function
Portfolio.run_backtest() -> Backtest

Parameters:

- rebalance: bool (required)
If 'True', the backtest will rebalance itself.

- weights: str or dict, default 'ew'
If 'dict', than it is the weight for each asset (number between 0 and 1),
The sum can't be different than one.
Example:
asset_weights = {
'asset1': 0.3,
'asset2': 0.2,
'asset3': 0.5,
}
If str (has to be 'ew'), runs the backtest using equal weight for all
assets (1 / number of assets).

- rebal_freq: str, default '1M'
Rebalance frequency. Has the same valid inputs as pandas.DataFrame.resample()
function.

TODO

Performance metrics and visualizations
Reports
Yahoo Finance implementation

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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