Marco Corpa Criado

Quantitative Researcher & Machine Learning Engineer

BSc in Mathematics & Statistics (UCM) and MSc in AI applied to Financial Markets (MIAX - BME). Building deep learning, market microstructure, and generative time-series models.

View Quantitative ProjectsView Resume (PDF)

About Me

Marco Corpa Criado

I'm a Mathematics & Statistics graduate from Universidad Complutense de Madrid, currently completing a Master's in Quantitative Finance (MIAX). I'm ambitious, driven by a genuine hunger to keep learning, and I thrive when tackling complex, unfamiliar problems — whether that means building a regularization model from scratch or designing a portfolio optimization algorithm. I combine strong analytical rigour with a collaborative mindset, and I'm currently working toward CFA Level I (November 2026) as I pursue a long-term career as a Quantitative Portfolio Manager.

Languages

Spanish — NativeEnglish — Advanced

Education

B.Sc. in Mathematics & Statistics

Universidad Complutense de Madrid (UCM)

2021 – 2025

Rigorous training in probability, statistical inference, stochastic processes, and mathematical optimization.

MIAX — Master's in Quantitative Finance

In Progress

Instituto BME

2025 – Present

Advanced quantitative methods applied to derivatives pricing, portfolio management, algorithmic trading, and financial risk.

CFA Level I Candidate

In Progress

CFA Institute

Exam: November 2026

Technical & Quantitative Skills

Programming & Frameworks

PythonPyTorchTensorFlow / KerasSQLRMATLABC++ (Foundations)

Quantitative Finance & Trading

Market Microstructure & LOB DynamicsMonte Carlo SimulationVaR / CVaR (Kupiec Backtesting)Fixed Income Analytics (DV01, Duration, Convexity)Derivatives Pricing & GreeksPortfolio Optimization (Markowitz)

Machine Learning & XAI

TimeVAE (Generative Modeling)Autoencoders (Anomaly Detection)Explainable AI (SHAP, Integrated Gradients)Fair Machine LearningLightGBM & Scikit-learnTime Series Econometrics (GARCH, ARIMA)

Mathematics & Statistics

Probability TheoryStochastic ProcessesStatistical InferenceLinear Algebra & Matrix DecompositionNumerical Optimization

Developer Tools & Platforms

GitGitHubLaTeXLinux / BashVS CodeJupyter

Quantitative & ML Projects

Synthetic Financial Time-Series Generation via TimeVAE
Implemented a Time-series Variational Autoencoder (TimeVAE) in TensorFlow/Keras to synthesize realistic non-stationary asset return paths. Preserved volatility clustering, autocorrelation structures, and heavy-tailed market distributions, benchmarked via Kullback-Leibler (KL) divergence.
PythonTensorFlowTimeVAEGenerative AITime Series
View on GitHub
Market Microstructure & HFT Efficiency on BME Tick Data
Engineered a high-throughput pipeline to analyze granular tick-by-tick Limit Order Book (LOB) data from Bolsa de Madrid (BME). Tested the Efficient Market Hypothesis (EMH), bid-ask bounce effects, order-flow toxicity, and liquidity consumption during sudden book imbalances.
PythonPandasMarket MicrostructureLOB DynamicsHFT
View on GitHub
Unsupervised Financial Anomaly Detection via Autoencoders
Designed deep recurrent and convolutional Autoencoder architectures in PyTorch for unsupervised anomaly detection across cross-market asset feeds. Optimized dynamic reconstruction error thresholds to detect flash crashes, regime shifts, and structural breaks in real time.
PythonPyTorchAutoencodersDeep LearningAnomaly Detection
View on GitHub
Fair Machine Learning & Explainable AI (XAI) in Credit Risk
Designed, trained, and audited a deep neural network on 307K+ credit applications. Implemented domain-constrained Keras layers, demographic parity FAIR loss (90.5% bias reduction), Monte Carlo Dropout for epistemic uncertainty, and SHAP/Integrated Gradients for full regulatory interpretability.
PythonKerasXAI (SHAP)Fair MLCredit Scoring
View on GitHub
Fixed Income Analytics & Portfolio Risk Engine
Built parametric, Historical, and Monte Carlo VaR/CVaR engines with Kupiec coverage backtesting. Designed a bond portfolio analytics suite computing full term-structure cash flow discounting, Macaulay/Modified Duration, Convexity, and DV01 sensitivity measures.
PythonNumPySciPyFixed IncomeRisk ManagementMonte Carlo
View on GitHub

Experience

Quantitative Research & Systematic Strategy Development

Ongoing

MIAX (BME) / Independent Research

2025 — Present

Developing deep learning and market microstructure models on high-frequency order book data. Engineering systematic backtesting engines, generative time-series pipelines (TimeVAE), and risk-attribution frameworks.

Private Academic Instructor in Mathematics & Statistics

Ongoing

Independent Tutor

2021 — Present

Instructing undergraduate and pre-university students in Linear Algebra, Differential Calculus, Probability Theory, and Statistical Inference. Designing rigorous problem sets and numerical computational routines.

Want to know more?

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