Algorithmic Trading Strategies and Portfolio Management Guide

Transitioning from manual investing to systematic, data-driven trading requires a solid understanding of quantitative models and execution algorithms. This text-only course guides you through the core concepts of automated trading systems and modern portfolio theory without overwhelming jargon. You will develop the skills to conceptualize, write, and backtest algorithmic trading strategies while managing risk across a diversified portfolio of assets. By studying written explanations and practical code examples, you will understand how to transition from basic market analysis to automated, rule-based execution. What you'll learn: Understand the fundamental terminology of algorithmic trading, market microstructure, and order books; Design and backtest quantitative trading strategies using modern Python libraries and historical data; Apply modern portfolio theory and optimization techniques to balance risk and return; Configure risk management protocols, position sizing rules, and stop-loss mechanisms; Analyze performance metrics like the Sharpe ratio, drawdown, and win rate to evaluate strategy viability; Explore modern execution algorithms and API integration patterns for automated order routing. The course starts with foundational definitions of market mechanics and algorithmic structures before moving into strategy design, backtesting methodologies, and portfolio optimization. You will progress from reading theoretical concepts to analyzing complete, structured code examples. Designed for beginners interested in quantitative finance, finance students, or retail traders looking to systematize their approach, this course requires no prior programming or advanced mathematics experience. Start building your systematic trading foundation today through our comprehensive written guide.