Welcome back to the Python for Fundamental Analysis series! We’ve built a strong quantitative toolkit over the last posts: While numbers are essential, they don’t capture management strategy, competitive dynamics, outlook, or the nuances behind performance. For this, we turn to qualitative sources, and one of the richest is the earnings call transcript. These quarterly calls offer direct…
Welcome back to the Python for Fundamental Analysis series! We’ve built a solid foundation by calculating individual metrics and ratios for single companies. Check out the previous posts: Analyzing a company in isolation, however, only tells part of the story. Is a P/E ratio of 25 high or low? Is a Net Profit Margin of 15% good…
Welcome back to the Python for Finance series! Having explored valuation (Post 1), financial statements (Post 2), profitability (Post 3), and liquidity (Post 4), we now examine another crucial aspect: Leverage. Leverage involves using debt to finance operations and assets. While it can magnify returns, it also increases financial risk, making companies more vulnerable during downturns due to…
Welcome back to the Python for Finance series! In our previous posts, we’ve fetched specific metrics like P/E (Post 1), retrieved full financial statements (Post 2), and visualized profitability trends (Post 3). Now, we shift our focus from profitability to another critical aspect of fundamental analysis: Liquidity. Liquidity refers to a company’s ability to meet its short-term financial…
Welcome back to our Python for Finance series! In the previous post (Post 2), we successfully automated the retrieval of the three core financial statements – the Income Statement, Balance Sheet, and Cash Flow Statement – using Python and the Financial Modeling Prep (FMP) API. We saw how pandas helps structure this data effectively. But raw financial statements, while informative,…
Welcome back to our series on Python for Technical Indicators! After covering Moving Averages and the Relative Strength Index (RSI), we’ll now explore another popular momentum indicator: Moving Average Convergence Divergence (MACD). Moving Average Convergence Divergence (MACD) is a popular trend-following momentum indicator. It gets its name because it tracks the relationship between a faster and a…
In the dynamic world of financial markets, technical indicators are indispensable tools for traders and analysts seeking to understand market sentiment and predict potential price movements. Our ongoing series exploring Python for finance now delves into another cornerstone indicator: the Relative Strength Index (RSI). This powerful momentum oscillator helps gauge the speed and persistence of price…
Beta neutrality is a powerful risk management strategy that can help protect your investments from broad market movements. In this article, I’ll walk you through how to analyze and neutralize the beta of a portfolio using Python, complete with code examples you can adapt for your own investments. What is Beta Neutrality? Beta neutrality is…
In our series on Python for finance, we’ve covered portfolio construction , risk management , backtesting , and optimization . Now, we’ll explore how to use Python to implement technical indicators, starting with moving averages. Disclaimer: This article was drafted with the assistance of artificial intelligence and reviewed by the editor prior to publication to ensure…
Welcome to the fifth article in our Python Portfolio Management series! So far, we’ve covered the basics of portfolio management, building efficient frontiers, implementing risk management techniques, and calculating beta and dollar sensitivity. Today, we’ll explore how to build a backtesting framework to evaluate the performance of your investment strategies using Python. Backtesting is a…