The latest updates on all JetBrains products and topics
In this credit risk analysis tutorial, we'll focus on the complex interplay between federal funds rates, 10-year Treasury yields, and corporate bond yields – key indicators that shape investment strategies, economic forecasts, and policy decisions.
In this data research, we dive deep into the numbers, comparing the frenzied valuations of the dot-com era to today's AI-fueled market. By examining key financial metrics of tech giants then and now, we aim to uncover patterns, disparities, and potential warning signs that could shed light on the sustainability of current tech valuations.
In this article, we'll dive deep into the numbers behind some of the biggest layoffs in recent tech history. Our mission? To uncover whether these workforce reductions truly allowed these behemoths to become more profitable.
Previously, neXDos prototyped, backtested, and future-proofed their trading algorithms by hosting JupyterLab and JupyterHub. But this approach was problematic due to complexity in many areas, including managing the server environment, carrying out frequent updates, and handling the associated plugins within JupyterLab.
In this post we’ll discuss how AI-driven data science can accelerate investment strategy implementation, enhance client relationships, and make investment management firms more agile.
In this article, we'll explore a case study where we perform a Monte Carlo analysis for Value at Risk (VaR) on a stock portfolio, leveraging the power of Python, Datalore, and AI Assistant.
The financial ecosystem relies heavily on Excel, but as data grows, it's showing its limitations. It's time for a change. Enter Python, a game-changer in finance. In this article, I'll guide you through financial data analysis and visualization using Python. We'll explore how this powerful tool can uncover valuable insights, empowering smarter decisions.
In this article, I'll walk through the process of backtesting a daily Dow Jones mean reversion strategy using Python in Datalore notebooks. To make it accessible even for those with limited coding experience, I'll leverage Datalore's AI Assistant capabilities.
Explore the essential Python tools and libraries for portfolio optimization, get a walk through the process of calculating fundamental portfolio metrics such as lognormal returns and Sharpe ratios, and learn how to implement an established portfolio optimization strategy – mean-variance optimization.
