Module 3: Advanced Algorithmic Trading Strategies and Deployment with Pablo Moreno
Apply your skills to professional trading workflows, monetization, and performance optimization.
Transform your Python and GenAI trading expertise into career assets. Learn how to design, backtest, and deploy advanced algorithmic strategies, manage risk, build low-latency pipelines, and establish yourself as a quantitative trading specialist.
4-Week Module
Live Online Sessions
Advanced Level
What You’ll Learn in Module 3
Module 3 focuses on turning your coding and AI knowledge into real-world trading solutions. Youโll design sophisticated strategies, implement robust risk controls, and build the infrastructure needed to trade at scale.
โ Statistical Arbitrage & Pairs Trading
Identify and exploit fleeting mispricings between correlated assets.
โ Machine-Learning Signal Generation
Use scikit-learn and FinGPT models to classify market regimes and generate predictive signals.
โ Portfolio Optimization Techniques
Leverage PyPortfolioOpt and cvxopt to allocate capital for optimal riskโreturn trade-offs.
โ Dynamic Risk Management
Implement stop-loss/take-profit rules, smart position sizing, and advanced order types (iceberg, fill-or-kill).
โ High-Frequency Trading Foundations
Understand market microstructure, order-book dynamics, and latency arbitrage principles.
โ Low-Latency Data Pipelines
Build asynchronous data ingestors, in-memory caches, and order routers for real-time 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: Advanced Strategy Foundations
Statistical Arbitrage Techniques
โข Cointegration and mean-reversion tests in statsmodels
โข Signal generation via rolling z-scores and spread analysis
Pairs Trading Workflow
โข Pair selection using correlation and distance metrics
โข Backtesting framework with backtrader or custom scripts
Portfolio Construction Basics
โข Meanโvariance optimization with PyPortfolioOpt
โข Efficient frontier visualization and riskโreturn trade-offs
Week 2: Risk Management & Order Execution
Dynamic Stop-Loss & Take-Profit
โข Volatility-based trailing stops (ATR, standard deviation)
โข Profit-target frameworks and scaling out rules
Position-Sizing Methods
โข Fixed-fractional vs. Kelly-criterion sizing
โข Volยญatility parity and risk-parity allocation
Advanced Order Types & API Integration
โข Limit, stop-limit, iceberg, and fill-or-kill orders
โข Placing and managing orders via alpaca-trade-api or IBPy
Week 3: HFT & Market Microstructure
Market Microstructure Essentials
โข Anatomy of the order book and matching engine
โข Liquidity, tick size, and slippage considerations
Tick-Level Data Handling
โข Real-time data ingestion with asyncio
โข Un-memory caching using Redis or Memcached
Concurrency & Event-Driven Design
โข multiprocessing vs. threading vs. async workflows
โข Callback-driven architectures for low latency
Week 4: Low-Latency Pipelines & Deployment
Asynchronous Data Pipeline Construction
โข Normalizing and timestamping feeds for multiple venues
โข Fault-tolerant message queues (Kafka/RabbitMQ)
Performance Tuning
โข JIT compilation of hotspots with Numba
โข Profiling and bottleneck identification
Direct Exchange Connectivity
โข Socket programming for FIX or proprietary gateways
โข Failover strategies and resiliency patterns
Module 3 Final Project
For your capstone, you will deliver a comprehensive professional package including:
A backtested algorithmic strategy combining ML-driven signals and portfolio optimization
A risk-controlled execution script with dynamic order logic
A prototype low-latency data pipeline ingesting, caching, and normalizing live market data
A deployment plan and slide deck detailing performance metrics, latency benchmarks, and scaling considerations
This final project will serve as a launchpad for your career as a quantitative trading specialistโready to showcase real-world, deployable trading solutions.
Industry Applications
Module 3 prepares you to deploy your advanced algorithmic-trading and GenAI skills across diverse financial and fintech sectors:
This module is designed for advanced Python developers and quant traders who want to professionalize their algorithmic-trading workflows and deploy real-world strategies.
Youโll benefit most from Module 3 if you:
Already have basic trading scripts and want to scale up to statistical arbitrage, pairs trading, and ML-driven signal generation.
Need a structured risk-management framework with dynamic stop-loss/take-profit rules and smart position sizing for live markets.
Are building or maintaining low-latency systems, and want hands-on guidance with asyncio, in-memory caches, and direct exchange connectivity.
Seek to demonstrate a fully deployable trading pipelineโfrom data ingestion through backtest to order executionโin a capstone project you can showcase.
If youโre ready to turn your quant ideas into robust, real-world trading applications, Module 3 is the perfect next step.