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. โ
4-Week Module
Live Online Sessions
Beginners
What Youโll Learn in Module 1
In Module 1, we focus on establishing the core skills that youโll leverage throughout your algorithmic trading journey. โ
โ Financial Data Manipulation with Python
Learn to master NumPy for financial calculations, analyze portfolio performance metrics, and implement essential quantitative formulas in Python.
โ 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.
โ Forecasting & Backtesting Fundamentals
Discover forecasting techniques, construct and validate backtesting frameworks, and evaluate strategy performance using statistical methods for robust results.
โ Portfolio Analysis & Optimization
Develop skills in analyzing portfolio returns, calculating risk metrics, and implementing optimization algorithms to build efficient asset allocations.
โ Practical Coding Exercises
Consolidate your learning with hands-on coding challenges and receive real-time feedback to reinforce key concepts.
โ Foundational Trading Scripts
Build your first end-to-end Python trading scriptโcovering 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 scriptsโnot 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 โ 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: youโll code strategies live, backtest them on historical data, and deploy prototypes to simulated trading environmentsโall within the module.
Detailed Curriculum
Week 1: Python Foundations for Financial Analysis
Practical NumPy Exercises
โข Vectorized calculations for moving averages, returns, and risk metrics
โข Implement essential financial formulas (e.g., CAGR, Sharpe ratio) in Python
Portfolio Performance Analysis
โข Use pandas to load and manipulate asset price data
โข Compute portfolio returns, volatility, and drawdowns with real datasets
Week 2: Forecasting & Backtesting Fundamentals
Introduction to Forecasting Techniques
โข Explore time-series models (ARIMA, exponential smoothing) and regression-based approaches
โข Validate forecast accuracy with walk-forward analysis
Backtesting Framework Design
โข Use Python (Alpaca Trade API, os, logging) to programmatically place and manage orders
โข Conduct live coding session: backtest a moving-average crossover strategy
Week 3: Generative AI Overview & Multi-Agent Systems
Generative AI in Finance
โข Understand core concepts of LLMs and AI agents for trading applications
โข Explore FinGPT architecture and use cases
Building a Multi-Agent Trading System
โข Orchestrate data-ingestion, signal-generation, and alerting agents
โข Hands-on exercise: deploy agents to fetch news sentiment and technical indicators
Week 4: FinGPT Application & Module 1 Capstone
Practical FinGPT Coding
โข Craft prompts for signal generation and parameter recommendations
โข Integrate FinGPT outputs into Python scripts for automated decision support โ
End-to-End FinGPT Solution
โข 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 โ
This four-week path ensures you gain hands-on experience with Python, quantitative methods, and GenAIโculminating 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. โ
Youโll benefit most if you:
Are an Aspiring Quantitative Analyst seeking hands-on NumPy and pandas experience with real financial data. โ
Are a Python Developer wanting to apply your coding skills to portfolio analysis, forecasting, and backtesting. โ
Are an Early-Career Data Scientist aiming to specialize in financial applications of data science and introductory GenAI techniques. โ
Are a Self-Directed Investor interested in systematic trading approaches, from risk metrics to simple AI agents. โ
If youโre ready to master the core Python and GenAI foundations that power professional-grade algo trading systems, Module 1 is your first step.