Stay current with the latest developments in Quantitative Finance, Python and R by exploring these in-depth articles.
Quantitative Finance & Trading Techniques
- Implied Volatility: Formulation, Computation, and Robust Numerical Methods – Tribhuvan Bisen, Quant Insider, presents a comprehensive framework for calculating implied volatility. The tutorial includes Python programming solutions, detailed convergence demonstrations, and clear visual examples, offering readers both technical depth and practical clarity.
- Calculate Synthetic NBBO from Prop Feeds – Databento demonstrates how to construct a synthetic NBBO by taking the best bid and offer seen across the different exchanges.
- Top Algo Trading Courses after MBA Finance – Chainika Thakar, QuantInsti, offers a possible path on how to transition from an MBA to Algo Trading.
- Trading Setbacks during Monthly Transitions – Brian Wilson, a Market Strategist at Market Structure EDGE, explores the unique difficulties traders encounter as the calendar shifts from one month to the next.
- Finding Edges – Euan Sinclair shares valuable perspectives on uncovering trading edges in this feature from the Robot Wealth Blog.
Programming & Data Analysis
- Exploring the Chain Rule with Step-by-Step Examples – Varun Divakar of QuantInsti offers clear, step-by-step examples that clarify each stage of the Chain Rule.
- The EClient and EWrapper API Classes – This tutorial introduces the EClient and EWrapper API classes, guiding you through the process of connecting to TWS and showcasing these essential functions.
- Visualizing Financial Markets with Matplotlib – PyQuant News demonstrates how to use Matplotlib and mplfinance to create candlestick charts
- Build a Stock Database Locally with Polars – Discover how to create a local stock database using Polars and DuckDB with PyQuant News.
Investment Strategies & Market Analysis
- Intelligent Concentration: A Synopsis of Warren Buffett and Diversification – Lloyd Everhart, a contributor to the Alpha Architect blog, analyzes trends in Warren Buffett’s portfolio concentration.
- Harnessing Sentiment Analysis in Financial Markets – PyQuant News explores how sentiment analysis is being applied in the financial sector, demonstrating practical, real-world uses that help investors better interpret market trends and make informed decisions.
- Unlocking REIT Returns: Real Estate Investment Factors – Larry Swedroe, writing for the Alpha Architect blog, explores a study that identifies and analyzes the primary factors driving returns in REITs.
- Machine Learning for Predictive Financial Analysis – PyQuant News explores cutting-edge techniques, opportunities, and obstacles inherent in analyzing financial data with advanced Machine Learning methods.
Disclosure: Interactive Brokers
The analysis in this material is provided for information only and is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad-based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation by IBKR to buy, sell or hold such investments. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.
The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Interactive Brokers, its affiliates, or its employees.
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