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JavierFernandezGuerra/README.md

Javier Fernández Guerra

ML & Quantitative Finance Specialist · Madrid, Spain
Quant Research & Data Products Intern @ SIX Group · BME

I work at the intersection of machine learning and capital markets — alpha generation, factor research, deep learning for time series, and the rigorous out-of-sample validation that turns a backtest into a strategy.

Background

  • Quant Research & Data Products Intern @ SIX Group · BME
  • MSc — AI Applied to Financial Markets @ Instituto BME — SIX Group · current GPA 9.23 / 10
  • BSc — Business Administration · Data Analytics @ Universidad Autónoma de Madrid · First Prize, Best Quantitative Thesis — L. R. Klein Institute for Economic Forecasting (2025)

Selected projects

Repo What it is
eurostoxx-ml-stock-selection Bachelor's thesis (1st Prize). Cross-sectional ML stock selection on the Euro Stoxx 50: 20+ factors, rolling-window OOS backtest, genetic-algorithm hyperparameter search. +11.3% annualised alpha vs benchmark, Sharpe 0.59 across 40+ quarterly periods.
miax-neural-networks Deep learning for S&P 500 return forecasting — MLP / RNN / CNN / mixed architectures, multiple input windows. Model-driven portfolio that ~doubles equal-weight benchmark returns on 2025 out-of-sample.
miax-finance Seven quantitative finance studies: fixed income, equities, derivatives (delta-hedged straddle on SPY), risk modelling (HMM, copulas), advanced backtesting, ML preprocessing à la López de Prado, fund of funds construction.
miax-bayesian-networks DAG-based causal discovery on macro and equity-style returns. Reproduces the factor mirage result (López de Prado & Zoonekynd, 2025) on real data.
miax-stock-toolkit Modular Python toolkit for equity data extraction, Monte Carlo path simulation and portfolio analytics. Typer CLI, typed dataclasses, clean architecture.
gbm-monte-carlo Geometric Brownian Motion + Monte Carlo for price-path simulation. Calibrated on historical data, with VaR, CVaR and maximum drawdown estimation.

Stack

Python · pandas · numpy · scikit-learn · XGBoost · TensorFlow / Keras · PyTorch · statsmodels · CVXPY · pgmpy · yfinance · matplotlib

Quant: factor models · alpha signals · time-series cross-validation · portfolio optimisation · risk modelling

Contact

LinkedIn · ferguerjavier@gmail.com

Always happy to talk about ML in financial markets — feel free to reach out.

Popular repositories Loading

  1. eurostoxx-ml-stock-selection eurostoxx-ml-stock-selection Public

    [ESP] Bachelor's Thesis (UAM) - First Prize 'Best Quantitative Thesis' (L.R. Klein Institute, 2025): Tree-based ML (Decision Tree, Random Forest, XGBoost + genetic algorithm) for Euro Stoxx 50 stoc…

    Jupyter Notebook

  2. gbm-monte-carlo gbm-monte-carlo Public

    [ENG, ESP] Monte Carlo simulation of S&P 500 prices using Geometric Brownian Motion, with risk metrics (VaR, CVaR, maximum drawdown) and visualisations. English and Spanish notebooks.

    Jupyter Notebook

  3. miax-stock-toolkit miax-stock-toolkit Public

    [ESP] Modular Python toolkit for equity data extraction (Yahoo, Alpha Vantage, custom providers), Monte Carlo simulation, portfolio analytics and Markdown reporting. Typer CLI, typed dataclasses an…

    Jupyter Notebook

  4. miax-finance miax-finance Public

    [ESP] Master’s Coursework (mIAx) - Quantitative Finance Studies: fixed income, equity microstructure, derivatives, risk management, backtesting, ML preprocessing (López de Prado), fund of funds.

    Jupyter Notebook

  5. miax-bayesian-networks miax-bayesian-networks Public

    [ESP] Master’s Coursework (mIAx): Bayesian networks for financial causal discovery: DAG inference on macro asset returns and reproduction of the factor mirage (López de Prado & Zoonekynd, 2025).

    Jupyter Notebook

  6. miax-neural-networks miax-neural-networks Public

    [ESP] Master’s Coursework (mIAx): Deep learning for S&P 500 return forecasting: 64-model grid (MLP/RNN/CNN/mixed) + López de Prado investigation track and a model-driven portfolio.

    Jupyter Notebook