Algorithmic Trading – Strategy Design and Execution Techniques Course Overview
Algorithmic trading sits at the intersection of quantitative finance, market structure, and technology, and it increasingly shapes how institutional investors execute decisions and manage risk. Understanding how algorithms work, and how to evaluate them honestly, is becoming a core skill for anyone working in capital markets.This course covers the full lifecycle of a rule-based trading strategy: from sourcing and structuring market data, calculating returns and technical indicators, and translating rules into executable signals, through to backtesting with realistic transaction costs, evaluating risk-adjusted performance using metrics such as Sharpe ratio and maximum drawdown, and understanding how institutional execution algorithms manage market impact. AI tools are introduced at key points in the workflow — including data validation and performance interpretation, reflecting how they are practically applied in professional trading environments.
Who should take this course?
This course is designed for finance professionals and students pursuing a career in capital markets, sales and trading, asset management, or quantitative research who want to go beyond theoretical knowledge and develop hands-on experience building and evaluating algorithmic trading strategies.
Algorithmic Trading - Strategy Design and Execution Techniques Learning Objectives
- Explain the structure and purpose of algorithmic trading strategies, distinguishing between alpha-generating strategies and execution algorithms and describing how each is used across institutional trading environments.
- Build a complete rule-based trading strategy in Excel, constructing the indicator calculations, signal logic, position sizing, and transaction cost model from raw historical price data.
- Evaluate the validity of a backtest by identifying common sources of bias — including look-ahead bias, data snooping, and unrealistic cost assumptions — and applying corrections to produce a more defensible result.
- Measure and interpret strategy performance using industry-standard risk-adjusted metrics, including annualized return, Sharpe ratio, maximum drawdown, and win rate, and assess results in the context of a passive benchmark.
- Analyze the role of execution algorithms in institutional trading, comparing VWAP, TWAP, participation of volume, and implementation shortfall approaches across different liquidity and urgency conditions.
