Metadata-Version: 2.1
Name: portfolioperformance
Version: 1.0.0
Summary: Tool to test the out-of-sample performance of portfolio optimization models
Home-page: https://github.com/sidnand/portfolioperformance
Author: Siddharth Nand
Author-email: snand233@gmail.com
License: GNU
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown

# 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-Optimization-Interface/)

## 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 = <benchmark model>

# List of models
models = [
    benchmark,
    # <add other list of models>
]

app = App(<data path>, GAMMAS, TIME_HORIZON, models, dateFormat=<pandas datetime format>,
            dateRange=["01011990", "01012010"], delim=<"," or "\s+">,
            riskFactorPositions=[positions for risk factor column],
            riskFreePosition=<risk free asset column>)

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 project: `setapp `
4. 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!

