Module 2: Implementation with GenAI & Python for Algorithmic Trading with Pablo Moreno
Apply generative AI to build end-to-end trading systems and automate strategy workflows
Transform your Python coding skills into a systematic trading edge. Learn to design quantitative models, integrate LLM-driven signal generation, engineer real-time data pipelines, and deploy risk-managed strategies in productionโall powered by GenAI.
4-Week Module
Live Online Sessions
Advanced Level
What You’ll Learn in Module 2
Module 2 focuses on applying GenAI and Python to build, backtest, and deploy end-to-end algorithmic trading systems โ
โ Algorithmic Strategy Implementation
Hands-on guidance to configure, backtest, and optimize quantitative trading strategies using Python โ
โ Real-Time Data Pipeline Engineering
Build robust ingestion and preprocessing workflows (APIs, web scraping, AI agents) to feed your models โ
โ GenAI & LLM Automation
Leverage CustomGPT and fine-tuned LLMs to automate signal generation, synthetic data creation, and strategy logic โ
โ Dynamic Risk Management
Implement scenario analysis, stress-testing, and AI-driven hedging frameworks to protect your portfolio โ
โ CustomGPT for Trading Workflows
Tailor AI agents to your unique workflowโautomate order placement rules, trade alerts, and performance monitoring โ
โ Capstone Project & Deployment
Develop, deploy, and present a fully automated GenAI-powered trading system as your module capstone โ
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: Harnessing GenAI for Trading Signals
GenAI Signal Generation
โข Use LLMs (Hugging Face Transformers, CustomGPT) to craft and refine trading signals
โข Prompt-engineering techniques for financial contextsโstructure, parameters, and bias mitigation
Sentiment Analysis
โข Apply GenAI to parse and score financial news and social-media feeds
โข Integrate sentiment scores into your trading logic
Trend Prediction Strategies
โข Develop GenAI-powered models to forecast market movements
โข Automate parameter optimization for improved predictive accuracy
Week 2: Automating Your Trading Workflow
Infrastructure Setup
โข Architect a scalable trading infrastructureโlocal vs. cloud deployment
โข Secure key management and environment configuration
Order Execution & Management
โข Use Python (Alpaca Trade API, os, logging) to programmatically place and manage orders
โข Implement dynamic stop-loss, take-profit, and position-sizing routines
Alerts, Logging & Monitoring
โข Set up real-time notifications for trade events and system health
โข Develop comprehensive logging and performance dashboards
Week 3: Real-Time Data Ingestion & Preparation
API Integration
โข Retrieve market and alternative data (stock prices, news feeds) via REST and WebSocket APIs
โข Handle JSON, CSV, and streaming data formats
Data Cleaning & Preprocessing
โข Implement robust routines for missing data, outlier detection, and normalization
โข Use Pandas and Dataprep to structure large datasets for model input
Rate Limiting & Error Handling
โข Design resilient ingestion pipelines to respect API limits and recover from failures
โข Automate retries, backoffs, and alerting for data pipeline issues
Week 4: Deployment, Automation & Capstone Presentation
Production Deployment & Monitoring
โข โข Containerize your full trading system using Docker or deploy to cloud functions (AWS Lambda, GCP Cloud Run) โ
โข Set up CI/CD pipelines (GitHub Actions or GitLab CI) for automatic code updates and version control โ
โข Implement real-time health checks and performance dashboards to track live P&L, latency, and error rates
Workflow Automation & Orchestration
โข Schedule your end-to-end jobs (data ingestion โ backtest โ signal generation โ execution) with Airflow, Prefect, or cron โ
โข Integrate alerting (Slack, email) for trade events, system failures, and threshold breaches
โข Build resilience: retry logic, exponential backoff, and failover strategies to ensure uninterrupted operation
Module 3 Final Project
Capstone โ Build Your GenAI-Powered Trading System
End-to-End Integration
Deployment & Live Testing
Performance Evaluation
Ethics & Governance
This comprehensive package will serve as a launchpad for your professional AI creative journey.
Learning Format
Module 2: Using GenAI for Algorithmic Trading is perfect for developers and traders ready to elevate their Python-based strategies with generative AI. โ
Youโll benefit most if you:
Have basic trading scripts in Python and want to integrate AI-driven signal generation. โ
Plan to harness sentiment analysis from news and social media to inform your models. โ
Need to engineer prompts specifically for financial applications and LLM tasks. โ
Want to automate parameter tuning and optimize strategies using GenAI techniques. โ
If youโre ready to move beyond traditional backtests and leverage GenAI for smarter, more adaptive trading strategies, Module 2 is your next step.