Module 1: Foundations of GenAI & Python for Algorithmic Trading with Pablo Moreno
Master the Python and AI fundamentals to launch your algorithmic trading journey
Build a rock-solid foundation in quantitative finance: master NumPy-driven data manipulation, forecasting & backtesting frameworks, portfolio optimization, and Generative AI with FinGPT.
4-Week Module
Live Online Sessions
Beginners
What You’ll Learn in Module 1
In Module 1, we focus on establishing the core skills that you’ll leverage throughout your algorithmic trading journey.
✅ Financial Data Manipulation with Python
Learn to master NumPy for financial calculations, analyze portfolio performance metrics, and implement essential quantitative formulas in Python.
✅ Generative AI Fundamentals for Finance
Understand how GenAI and AI agents can enhance trading workflows: build multi-agent systems and apply FinGPT to solve real-world financial problems.
✅ Forecasting & Backtesting Fundamentals
Discover forecasting techniques, construct and validate backtesting frameworks, and evaluate strategy performance using statistical methods for robust results.
✅ Portfolio Analysis & Optimization
Develop skills in analyzing portfolio returns, calculating risk metrics, and implementing optimization algorithms to build efficient asset allocations.
✅ Practical Coding Exercises
Consolidate your learning with hands-on coding challenges and receive real-time feedback to reinforce key concepts.
✅ Foundational Trading Scripts
Build your first end-to-end Python trading script—covering data ingestion, signal generation, and basic strategy execution.
Meet Your Instructor
“Bridging AI with live markets: code, test, deploy, repeat.“
Pablo has built automated trading systems for hedge funds and taught thousands of professionals how to integrate GenAI into their quant workflows. His sessions blend theory with hands-on coding in Python and R, ensuring you graduate with deployable scripts—not just slides.
Key Expertise
Algorithmic Trading Strategy Design
Quantitative Finance & Portfolio Theory
Machine Learning Engineering (FinGPT, CustomGPT)
Low-Latency Systems & Market Microstructure
MLOps for Trading Applications
Current Roles
Instructor – Quantitative Finance & Algorithmic Trading, SkillUp Exchange
RPA & AI-Agents Guild Lead, Customertimes
Publications & Projects
Machine Learning in Power BI with R and Python
Generative AI and Python for Algorithmic Trading and Quantitative Finance at SkillUp Exchange
Teaching Philosophy:
Pablo believes in learning by doing: you’ll code strategies live, backtest them on historical data, and deploy prototypes to simulated trading environments—all within the module.
Detailed Curriculum
Week 1: Python Foundations for Financial Analysis
Practical NumPy Exercises
• Vectorized calculations for moving averages, returns, and risk metrics
• Implement essential financial formulas (e.g., CAGR, Sharpe ratio) in Python
Portfolio Performance Analysis
• Use pandas to load and manipulate asset price data
• Compute portfolio returns, volatility, and drawdowns with real datasets
Week 2: Forecasting & Backtesting Fundamentals
Introduction to Forecasting Techniques
• Explore time-series models (ARIMA, exponential smoothing) and regression-based approaches
• Validate forecast accuracy with walk-forward analysis
Backtesting Framework Design
• Use Python (Alpaca Trade API, os, logging) to programmatically place and manage orders
• Conduct live coding session: backtest a moving-average crossover strategy
Week 3: Generative AI Overview & Multi-Agent Systems
Generative AI in Finance
• Understand core concepts of LLMs and AI agents for trading applications
• Explore FinGPT architecture and use cases
Building a Multi-Agent Trading System
• Orchestrate data-ingestion, signal-generation, and alerting agents
• Hands-on exercise: deploy agents to fetch news sentiment and technical indicators
Week 4: FinGPT Application & Module 1 Capstone
Practical FinGPT Coding
• Craft prompts for signal generation and parameter recommendations
• Integrate FinGPT outputs into Python scripts for automated decision support
End-to-End FinGPT Solution
• Develop a complete workflow: fetch data, generate AI-driven signals, backtest, and report results
Module 1 Final Project
Deliver a Python notebook that:
Ingests multi-asset price data
Uses FinGPT to generate trading signals
Backtests performance and visualizes key metrics
Exports a basic execution-ready strategy script
This four-week path ensures you gain hands-on experience with Python, quantitative methods, and GenAI—culminating in a project you can showcase and build upon in subsequent modules.
Learning Format
Module 1: Foundations of GenAI & Python for Algorithmic Trading is ideal for anyone looking to establish a robust quantitative and AI-powered trading toolkit.
You’ll benefit most if you:
Are an Aspiring Quantitative Analyst seeking hands-on NumPy and pandas experience with real financial data.
Are a Python Developer wanting to apply your coding skills to portfolio analysis, forecasting, and backtesting.
Are an Early-Career Data Scientist aiming to specialize in financial applications of data science and introductory GenAI techniques.
Are a Self-Directed Investor interested in systematic trading approaches, from risk metrics to simple AI agents.
If you’re ready to master the core Python and GenAI foundations that power professional-grade algo trading systems, Module 1 is your first step.
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