A collection of production-grade quantitative trading strategies, financial engineering models, and market microstructure simulators.

Some of my core projects:

1. Transaction Cost Analysis (TCA) & Execution Simulator (C++20)

An institutional-grade intraday simulator to model market microstructure and optimize algorithmic execution.

• Microstructure Model: Geometric Brownian Motion (GBM) price paths coupled with a U-shaped intraday volume profile and an Almgren-Chriss square-root market impact model (temporary vs. permanent impact decay).
• Algorithms Implemented: Naive TWAP, Volume-Tracking VWAP, and Implementation Shortfall (Almgren-Chriss closed-form optimal trajectory) trading off timing risk ($\lambda$) against slippage.
• Metrics: Implementation Shortfall (Perold 1988) vs. arrival price, VWAP slippage in bps, and peak participation-rate tracking.

2. Regime-Based Momentum Strategy — FX Majors (Python)

A systematic FX trading strategy gated by a 3-State Gaussian Hidden Markov Model (HMM) to decouple structural trends from volatility states.

• Mathematical Framework: Baum-Welch (EM) parameter estimation and Forward Algorithm filtering (zero look-ahead bias). Walk-forward expanding windows refitted every 63 trading days.
• Risk Management: Dynamic, regime-conditional stop-losses scaled by daily realized volatility. Regime transitions instantly trigger position-sizing adjustments (Trend High Vol vs. Trend Low Vol vs. Mean-Reverting Range).

3. Statistical Arbitrage & Pairs Trading Engine (Python)

An end-to-end framework applying cointegration and time-series econometrics to equity pairs.

• Methodology: Two-step Engle-Granger cointegration procedure, Augmented Dickey-Fuller (ADF) testing, and Ornstein-Uhlenbeck (OU) stochastic process parameter fitting for precise mean-reversion speed modeling.
• Statistical Rigor: Implements Bonferroni corrections to control family-wise error rates during multi-pair mining, backed by out-of-sample walk-forward testing.

4. Delta-Hedging Options Pricer & Simulator (Python & Streamlit)

A derivatives pricing suite featuring cross-method valuation and dynamic risk replication tracking.

• Pricing Engines: Analytical Black-Scholes-Merton, Binomial Trees for American options, and Monte Carlo paths for exotic structures.
• Dynamic Hedging: A continuous simulation layer that tracks real-time Greek sensitivities ($\Delta, \Gamma, \ Vega, \Theta$), calculating path-dependent P&L leakage during discrete rebalancing under volatile regimes.

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💻 Tech Stack & Tooling

• Languages: C++20 (STL, High-Performance Structs), Python (3.14+), VBA (Excel)
• Libraries: NumPy, Pandas, SciPy, Scikit-Learn, Statsmodels, Streamlit
• Data Pipelines: SQL (Data Queries/Aggregation), Bloomberg BQL Integration

(https://www.linkedin.com/in/jaime-ruiz-marín-09a05127b/)

Contacto/Contact info: 📫 jaimeruiz018@gmail.com