Portfolio Performance

Tool to test the out-of-sample performance of portfolio optimization models. Based on the paper: Optimal Versus Naive Diversification:

I have also developed a playground website for this package so you can easily run these models on your own data. Link to website

Usage

Installation

pip install portfolioperformance

Models

# 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

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 changes!

Folder Structure

.
|-- __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.
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