Financial Analysis and Algorithmic Trading with Python

In today's fast-paced financial markets, data-driven decision-making and automated trading strategies are essential skills for finance professionals and retail investors alike. Python has become the industry-standard language for financial analysis, offering powerful tools to import, analyze, and model market data. This comprehensive text-based course guides you from Python fundamentals to financial engineering concepts. You will transition from manual calculation methods to automated workflows, learning how to evaluate portfolio risk, calculate key metrics like the Sharpe ratio, and build robust backtesting pipelines for algorithmic trading. What you'll learn: - Understand core financial concepts, including stock returns, volatility, risk, and the Capital Asset Pricing Model (CAPM). - Analyze time-series financial data using pandas, numpy, and modern data ingestion tools. - Optimize investment portfolios using the Sharpe Ratio and Markowitz Efficient Frontier mathematical frameworks. - Implement statistical models such as EWMA, ARIMA, and trend analysis to forecast market behaviors. - Build and backtest algorithmic trading strategies using clean, modern Python code and type hints. - Evaluate trading strategy performance, drawdown, and risk metrics using systematic backtesting principles. The course begins with foundational Python programming and key financial definitions before moving into data manipulation and visualization. You will then progress through portfolio theory, time-series forecasting, and systematic backtesting methodologies. This course is designed for beginners in programming and finance; no prior coding experience is required, as we start with the absolute basics of Python. Start reading today to unlock the power of Python for automated financial analysis and algorithmic trading.