cockles98/itau-quant-challenge-2025
Quantitative strategy for the Ibovespa that combines Topological Data Analysis (with Persistent Homology & Mapper), classical factors and meta-models, regime-sensitive HRP. Achieved top 4%.
This project helps quantitative traders and portfolio managers construct resilient investment portfolios. By analyzing market data, it identifies different market conditions and allocates assets to generate better risk-adjusted returns and protect against downturns. You input historical stock prices and market factors, and it outputs an optimized long-only portfolio strategy for Ibovespa assets with key performance indicators.
Use this if you need a quantitative strategy that dynamically adjusts asset allocation based on market regimes to improve returns and reduce risk, especially during turbulent periods.
Not ideal if you are looking for a short-term trading strategy, a portfolio that includes short-selling, or if you primarily invest outside of the Ibovespa.
Stars
21
Forks
3
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Feb 28, 2026
Commits (30d)
0
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