Faber Sanchez Fabersanchez

---

🔬 Executive Summary

Systems Engineer and Quantitative Developer specializing in the intersection of Artificial Intelligence and Algorithmic Trading.

I design and deploy multi-threaded execution engines, high-frequency data pipelines, and institutional-grade decision models. My engineering focus is entirely data-driven: optimizing execution latency, rigorously validating statistical models, and building scalable backend infrastructure for financial markets.

📊 Quantitative System Metrics (Live & Backtest Data)

Aggregated performance and engineering metrics across proprietary systems.

Metric Domain	Indicator	Value / Status	Stack Used
Execution Latency	Avg. Order Routing Time	`< 850 µs`	`Python / ZeroMQ`
Data Ingestion	Tick Processing Speed	`1.2M ticks/sec`	`PostgreSQL / TimescaleDB`
Model Validation	Avg. Sharpe Ratio (OOS)	`2.14`	`NumPy / Pandas`
Risk Management	Max Drawdown Cap	`Hard Stop @ 8.1%`	`PyTorch / Scikit-Learn`
System Uptime	Sentinel V19 Core	`99.99%`	`Docker / Linux`

⚙️ Core Architecture & Tech Stack

🚀 Featured Engineering Projects

🛡️ Sentinel V19

Autonomous AI Trading Engine

Multi-threaded algorithmic trading platform engineered for institutional market analysis, autonomous execution, and dynamic risk control.

Hybrid Architecture: Python core orchestration bridged with MQL5.
Multi-Agent Engine: Concurrent agents for regime detection and risk validation.
XAI Integration: Full auditability of decision logic and margin adjustments.

Tech: Python • MQL5 • Asyncio • PostgreSQL

📊 QuantLab

Quantitative Research Framework

End-to-end quantitative research environment built to design, backtest, and statistically validate predictive models.

Vectorized Engine: High-performance backtesting with zero look-ahead bias.
Advanced Analytics: Automated Sharpe, Max Drawdown, VaR, and Monte Carlo.
Custom Indicators: Proprietary mathematical metric implementations.

Tech: Python • Pandas • SciPy • FastAPI

📐 System Blueprint: Sentinel V19 Architecture

graph TD
    classDef market fill:#0d1117,stroke:#30363d,stroke-width:1px,color:#c9d1d9
    classDef engine fill:#161b22,stroke:#58a6ff,stroke-width:1.5px,color:#f0f6fc
    classDef exec fill:#092e20,stroke:#2ea043,stroke-width:1.5px,color:#f0f6fc
    classDef db fill:#21262d,stroke:#8b949e,stroke-width:1px,color:#c9d1d9

    A[Live Market Feed] -->|Tick Data| B(Ingestion Pipeline)
    B --> C{Async Event Bus}
    
    C -->|Stream| D[Feature Engineering]
    C -->|Stream| E[AI Regime Agent]
    
    D --> F[Multi-Agent Signal Matrix]
    E --> F
    
    F -->|Raw Alpha| G[Risk Management & Position Sizing]
    G -->|Validated Order| H[MQL5 Low-Latency Bridge]
    
    H -->|Execute| I[Exchange API]
    
    G -->|Audit Trail| J[(PostgreSQL Cluster)]
    I -->|Fill Reports| J

    class A,I market;
    class B,C,D,E,F,G engine;
    class H exec;
    class J db;

Navigation Menu