S&P 500 Stock Allocation Optimizer

A Streamlit app that allocates a portfolio across chosen S&P 500 stocks with linear programming and SLSQP, under sector constraints.

2 solvers
Linear programming and SLSQP compared in one view
503
S&P 500 tickers to choose from
8 of 9
Commits by Kurniadi

Role: Group project: app, data loading, charts and the linear programming model
Period: Nov 2024 to Dec 2024
Project type: Featured
Room in the 3D HQ: L3:stock-allocation-optimizer

Results

2 solvers
Linear programming and SLSQP compared in one view
PuLP and SciPy SLSQP with the same constraints (the optimization module in the repository). This uses historical returns, not a guarantee of future returns.
503
S&P 500 tickers to choose from
The ticker list in the repository; prices load live from Yahoo Finance.
8 of 9
Commits by Kurniadi
Repository commit history (one commit by a teammate adds the SLSQP solver).

Problem

An investor choosing among S&P 500 stocks needs a way to compare allocations using historical returns while keeping sector exposure in check, such as limits on tech and oil and gas and a minimum share per stock.

Contribution

I built the Streamlit app: stock selection, price download with yfinance for a chosen date range, cumulative return charts in Plotly and the results view.
I modelled the allocation as a linear program in PuLP that maximizes cumulative return over the selected historical period, under tech, non-tech, oil and gas and minimum proportion constraints.
A teammate added the same model with SciPy's SLSQP solver, so the app compares both allocations side by side.