Recent articles cover Python features and various topics in quantitative finance.
Python tutorials:
- A Practical Breakdown of Vector-Based vs. Event-Based Backtesting – Tribhuvan Bisen, Quant Insider, examines two types of backtesting strategies: Vector-Based and Event-Based, providing practical and ready-to-use code.
- Algorithmic Trading Guide: High-Frequency, Liquidity-Taking Strategy – Databento offers a tutorial on constructing a rule-based algorithmic trading strategy.
- Unleashing the Power of Python Live Trading: A Guide to Seamless Automated Financial Transactions – Dr. Hui Liu, IBridgePy, discusses how to get started with Python live trading.
- Foundations and Future of Quantitative Finance – PyQuant News explores how quantitative finance techniques and open-source tools are transforming the financial markets.
Quantitative Finance stories:
- Reinforcement Learning in Trading – Ishan Shah from QuantInsti demonstrates how to apply Reinforcement Learning techniques in trading.
- AI Models in High-Frequency Trading – PyQuant News evaluates the performance of AI models in High-Frequency trading.
- Cut Through the Noise! These Two Factors Tend to Drive Portfolio Success – Jose Ordonez, Vice President of Financial Education at Alpha Architect, shares insights on factors that impact portfolio performance.
- Good News and Bad News in US Stocks – Tim Quast, the founder and CEO of Market Structure EDGE, provides market insights across eleven sectors, including Big Tech.
- What Is an Exchange Fund? A Way to Diversify Without Triggering a Tax Bill – Jose Ordonez, Alpha Architect, explores the characteristics and benefits of Exchange Funds.
- The Hidden Effort Problem: Work More and Get Better Results? – Elisabetta Basilico, Alpha Architect blog, reviews an academic paper that introduces a new approach for inferring executive work patterns, without the need for direct observation.
- A Tumultuous Week for Quants? – Tim Quast, Market Structure EDGE, explores some of the mathematical and regulatory foundations of the stock market.
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It’s exciting to see the addition of the new ForecastTrader lesson. With the increasing use of AI in financial markets, I’m curious to see how this tool can help refine predictions and improve decision-making for both beginner and advanced traders.
Really appreciated the focus on reinforcement learning in trading—it’s encouraging to see educational platforms spotlight advanced AI techniques that are actually reshaping strategies in the field.
We hope that you continue to enjoy IBKR Quant!
We hope you continue to enjoy IBKR Quant!