Risk management in algorithmic trading is critical to ensure long-term sustainability and capital protection. This course explores how algorithms can be coded to limit exposure, manage position sizes, apply stop losses, and control drawdowns. We begin by understanding the types of risks involved in algo trading—market, liquidity, execution, slippage, and technical risks. Then we dive into key techniques like portfolio diversification, volatility-based sizing, dynamic stop-loss adjustments, and circuit-breaker mechanisms. The course includes case studies showing how poor risk control led to major trading losses and how these could have been avoided with better design. You'll learn how to set maximum daily loss limits, calculate value at risk (VaR), and monitor system health in real-time. Coding examples in Python will show how to embed these practices into trading bots. For traders building or using automated systems, this course offers crucial skills to protect capital and ensure consistency across volatile market cycles.