https://github.com/sidnand/portfolioperformance
Tool to test the out-of-sample performance of portfolio optimization models
https://github.com/sidnand/portfolioperformance
finance financial-portfolio-management modern-portfolio-theory pypi-package python
Last synced: 7 months ago
JSON representation
Tool to test the out-of-sample performance of portfolio optimization models
- Host: GitHub
- URL: https://github.com/sidnand/portfolioperformance
- Owner: sidnand
- License: gpl-3.0
- Created: 2022-08-26T06:37:33.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-03-31T06:32:08.000Z (over 1 year ago)
- Last Synced: 2025-02-18T02:21:27.479Z (8 months ago)
- Topics: finance, financial-portfolio-management, modern-portfolio-theory, pypi-package, python
- Language: Python
- Homepage: https://pypi.org/project/portfolioperformance/
- Size: 832 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Portfolio Performance
Tool to test the out-of-sample performance of portfolio optimization models. Based on the paper: [Optimal Versus Naive Diversification](https://www.dropbox.com/s/ty11g7fi3cqbtfq/DGU-RFS-Final.pdf?dl=0):
I have also developed a playground website for this package so you can easily run these models on your own data. [Link to website](https://sidnand.github.io/Portfolio-Lab/)
## Usage
### Installation
`pip install portfolioperformance`
### Models
```python
# Each model takes a name parameter
ew = EqualWeight("Equal Weight")
minVar = MinVar("Minimum Variance")
JagannathanMa = JagannathanMa("Jagannathan Ma")
minVarShortSellCon = MinVarShortSellCon("Minimum Variance with Short Sell Constrains")
kanZhouEw = KanZhouEw("Kan Zhou EW")
meanVar = MeanVar("Mean Variance (Markowitz)")
meanVarShortSellCon = MeanVarShortSellCon("Mean Variance with Short Sell Constrains")
kanZhou = KanZhou("Kan Zhou Three Fund")
bayesStein = BayesStein("Bayes Stein")
bayesSteinShortSellCon = BayesSteinShortSellCon("Bayes Stein with Short Sell Constrains")
macKinlayPastor = MacKinlayPastor("MacKinlay and Pastor")
```
### Example
```python
import numpy as np
from portfolioperformance import *
# Risk aversion levels
GAMMAS = [1, 2, 3, 4, 5, 10]
# Time horizons
TIME_HORIZON = [60, 120]
benchmark =
# List of models
models = [
benchmark,
#
]
app = App(, GAMMAS, TIME_HORIZON, models, dateFormat=,
dateRange=["01011990", "01012010"], delim=<"," or "\\s+">,
riskFactorPositions=[positions for risk factor column],
riskFreePosition=)
sr = app.getSharpeRatios()
sig = app.getStatisticalSignificanceWRTBenchmark(benchmark)
```
## API
### App Class
#### Parameters
- `path` (`str`): Path to a `csv` file
- `gammas` (`list[int]`): List of gammas values
- `timeHorizon` (`list[int]`): List of time horizons
- `models` (`list[Model]`): List of Model classes
- `dateFormat` (`str`): Pandas date time format, default = "%Y-%m-%d"
- `dateRange` (`[str, str]`): Start and end date, default = []
- `delim` (`str`): Delimitation type, either "," or "\s+" (whitespace), default = ","
- `logScale` (`bool`): Whether or not data is in log-scale, default = False
- `riskFactorPositions` (`list`): Risk factor asset column positions (where position 1 is the date column), default = []
- `riskFreePosition` (`int`): Risk free asset column position, default = 1
#### Methods
##### `getSharpeRatios() -> dict[str, float]`
Get the sharpe ratios
- Returns: - `dict[str, float]`: A dictionary with model name and sharpe ratio as a key-valued pair
##### `getStatisticalSignificanceWRTBenchmark(benchmark) -> dict[str, float]`
Get the statistical significance with respect to a benchmark model
* Parameters:
* `benchmark` (`Model`): Benchmark model
- Returns:
- `dict[str, float]`: A dictionary with model name and sharpe ratio as a key-valued pair
## Development
### Setup
1. Clone project: `git clone https://github.com/sidnand/portfolioperformance`
2. Install packages: `pip install -r requirements.txt`
3. Make changes!
### Folder Structure
```bash
.
|-- __init__.py # import /src/app.py and all models
|-- /src
|-- app.py # code for running all the optimiation models
|-- model.py # parent class to all the models
|-- modelNoGamma.py # class for models that don't take extra parameters
|-- modelGamma.py # class for models that take an extra gamma parameter; gamma is a list of constants for the investors risk-aversion level
|-- ./models # all the models
|-- ./utils
|-- filter.py # includes a function that is used to filter the parameters passed to a function
|-- quadprog.py # quadratic programming
|-- sharedOptions.py # models options that are in common with >2 models
|-- statistics.py # statistics functions
```
### Create Pull-Request
Please create a pull-request to include your changes onto this repo. These changes will be reflected on the playground website linked above.
1. Run ``pip install -e .`` to install the package locally.
2. Make changes.
3. Update version number in ``setup.py``. Please use the Semantic Versioning 2.0.0 system. [Click to learn more](https://semver.org/).
4. Run ``python setup.py sdist bdist_wheel`` to create a python wheel.
5. Create a pull-request!
## Release History
- 1.1.0
- `quadprog == 0.1.12` update
- Update code for latest `pandas` version
- 1.0.0
- Initial release of the package
- `Python <= 3.9.9`
- `quadprog <= 0.1.11`