LATEST CONTENT
Access all my articles, essays, research papers, book summaries... by chronological order.
LATEST CONTENT
Advanced Statistical Modeling and Forecasting of High-Frequency Financial Data through Multivariate Time Series Analysis and Stochastic Processes
Quantitative Finance • Quantitative Research • Probability & Statistics • Statistical Analysis • Time-Series Analysis • Stochastic Processes • Asset Forecasting • High-Frequency Trading
LSTM Deep Learning Algorithm for Financial Asset Forecastin
Quantitative Finance • Asset Forecasting • Machine Learning • Artificial Intelligence • Deep Learning • LSTM Algorithm
Quantitative Research & Development Environment For Systematic Asset Trading (QRDE-SAT)
Quantitative Finance • Quantitative Research • Quantitative Research Tools • Python
The Structured Map of Asset Forecasting
Finance • Quantitative Finance • Asset Forecasting
FEATURED
-
![]()
Optimization of Lunar Trajectories & Rendezvous Strategies in Moon's Vinicity
Research project in collaboration with the German Aerospace Center (DLR) & ISAE-SUPAERO University to find optimal trajectories between the Moon and NRH Orbits.
-
![]()
Data Structures & Algorithms – Github Repository Project
Structured collection of the ~100 most relevant Data Structures & Algorithms (DSA) coding problems and their solutions, including explanations of all DSA concepts.
-
![]()
Pairs'24 Olympics iOS App
Developed a Swift-based iOS app replicating the Paris 2024 Olympics official app, designed for legacy unsupported iPhone devices.
-
![]()
QRDE-SAT - Quant Research & Development Environment for Systematic Asset Trading
Personal Python library for quantitative research, development, backtesting and deployment of Financial Trading Strategies.
-
![]()
PyFinPO - Python Financial Portfolio Optimization Library
Developed PyFinPO, a personal library aiming to provide a structured, direct and extensible infrastructure to perform Financial Portfolio Optimization in Python.
-
![]()
Financial Market-Regime Identification via Gaussian Mixture & Gradient Boosting ML
Published a two-phase framework integrating unsupervised and supervised machine learning techniques to detect and predict latent regimes in financial markets.
SUBSCRIBE TO MY NEWSLETTER
Receive periodic updates on new articles, projects, and technical insights.
I'm always open to discussing quantitative research opportunities, collaborative projects, or interesting technical problems. Feel free to reach out using the form below.
Álvaro Sánchez
Quantitative Researcher
Email:
alvarosf07@gmail.com