Applications-of-Convex-Optimization

About

This repository contains a Python package, ConvexTrader, for applications of convex application methods to portfolio optimization. Created by members of the Amherst College Quant Club as part of the 2024-2025 school year, this package is based upon a paper by Dhyey Mavani '25, which can be found here.

Installation & Use

Installation

To run the code locally, you can install with pip

pip install ConvexTrader

Using Optimization Techniques

from ConvexTrader import Portfolio
from ConvexTrader import Trade, TradeType
from ConvexTrader.single_period_optimization import single_period_optimization
from ConvexTrader.portfolio_exceptions import ValidationError, OptimizationError

# Example Portfolio
portfolio = Portfolio()
trades = [
    Trade("AAPL", 100, 150.0, datetime(2023, 1, 1), TradeType.BUY),
    Trade("GOOGL", 50, 2000.0, datetime(2023, 1, 2), TradeType.BUY),
    Trade("MSFT", 75, 300.0, datetime(2023, 1, 3), TradeType.BUY),
]
for trade in trades:
    portfolio.execute_trade(trade)

# Example Trade (single period)

r_t = np.array([0.05, 0.07, 0.02])
w_t = sample_portfolio.weights_vector
gamma = 1.0

def phi_trade(z):
    return cp.sum(cp.abs(z))

def phi_hold(w):
    return cp.sum(cp.square(w))

# this returns the optimal trade vector to be used
result = single_period_optimization(r_t, w_t, gamma, phi_trade, phi_hold)

Running Tests

To run the test suite, use

pytest

Contributing

We welcome contributions in the form of coding new applications of convex optimization towards trading, or anything to help improve the tool. Here's how you can help:

Setting Up a Virtual Environment

1. Clone the repository:

   git clone https://github.com/yourusername/Applications-of-Convex-Optimization.git
   cd Applications-of-Convex-Optimization

2. Create a virtual environment:

   python3 -m venv env

3. Activate the virtual environment:

   • On macOS and Linux:

     source env/bin/activate

   • On Windows:

     .\env\Scripts\activate

Installing Dependencies and Hooks

1. Install the required dependencies from the requirements.txt file:

   pip install -r requirements.txt

2. Set Up Pre-Commit Hooks:
   Install and configure pre-commit hooks to maintain code quality:

   pip install pre-commit
   pre-commit install

   To manually run the hooks and verify code compliance, use:

   pre-commit run

Adding Tests and Changes

1. Testing and Coverage Requirements:

   • Write tests for any new code or modifications.
   • Use pytest for running tests:

     pytest

   • Ensure the test coverage is at least 90%

2. Add and Commit Your Changes:

   • Follow the existing code style and structure.
   • Verify that all pre-commit hooks pass and the test coverage meets the minimum requirement.

   git add .
   pre-commit run
   git commit -m "Description of your changes"

3. Push Your Branch: Push your changes to your forked repository:

   git push --setup-upstream origin

4. Open a PR for us to review

Thank you for your interest in contributing to ConvexTrader! Your efforts help make this project better for everyone.

Contact

For any questions about the package, please reach out to trading at amherst.edu