Algorithmic trading with Python involves using code to automate the buying and selling of financial instruments based on pre-defined strategies. Python’s simplicity and powerful libraries like pandas, NumPy, and scikit-learn make it ideal for analyzing market data, backtesting strategies, and deploying trading algorithms. Tools like ccxt and alpaca-trade-api enable real-time trading, while machine learning models enhance decision-making. Python’s async capabilities further streamline high-frequency trading. This approach reduces human error, reacts faster to market changes, and allows testing on historical data for robust strategies, making it a preferred choice for algorithmic traders.