Quantitative Finance - Research, Trading, Investing, & Algos

Quantitative Finance - Research, Trading, Investing, & Algos explores the quantitative approach to finance, covering research methods, trading strategies, investment analysis, algorithm development, and practical tools. It features insights from industry experts, educational material, and discussions on trends and applications in quantitative finance.

Quantitative Research Trading Strategies Investment Analysis Algorithm Development Financial Market Structure Machine Learning in Finance High-Frequency Trading Asset Allocation Educational Resources Industry Insights

The hottest Substack posts of Quantitative Finance - Research, Trading, Investing, & Algos

And their main takeaways
40 implied HN points β€’ 06 Feb 24
  1. Professor William F. Sharpe introduced the Sharpe Ratio in 1966.
  2. The Sharpe Ratio has evolved and been termed differently by other authors.
  3. The discussion on the Sharpe Ratio in the original paper has been broadened to cover more applications.
29 HN points β€’ 26 Jan 24
  1. Jane Street Capital created a card game called Figgie in 2013 to mimic commodities trading.
  2. Jane Street is known for using OCaml in tech and emphasizes functional programming.
  3. Figgie card game involves negotiating trades, aiming to make money, and can be played online against live and bot players.
20 implied HN points β€’ 02 Feb 24
  1. Sebastian Gutierrez is conducting a quick survey to understand the education and interests of his audience.
  2. The survey includes questions about education level, current work status, and fields like finance, math, economics, and computer science.
  3. The survey aims to help Sebastian tailor his newsletter content to better suit his readers' preferences.
20 implied HN points β€’ 29 Jan 24
  1. Backtesting is a useful tool for evaluating trading strategies based on historical data.
  2. Prop desks, banks, and hedge funds often have proprietary backtesting tools, but this one is unique for being open source and written in Python.
  3. The hftbacktest tool offers features like tick-by-tick simulation, order book reconstruction, and order fill simulation for high-frequency trading strategies.
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20 implied HN points β€’ 22 Jan 24
  1. Market microstructure focuses on how financial markets operate.
  2. The course covers various topics like liquidity, market transparency, and high-frequency trading.
  3. The lecturer, Egor Starkov, provides a comprehensive set of materials for the course.
2 HN points β€’ 08 Feb 24
  1. Ph.D. theses are a good framework for creating quant finance project ideas.
  2. Replicating part of a Ph.D. thesis can impress prospective employers by demonstrating historical context, purpose, execution, and a credible conclusion.
  3. Choosing a project related to quantitative finance that personally interests you increases the likelihood of seeing the project through to completion.
1 HN point β€’ 09 Feb 24
  1. Quantitative traders implement strategies developed by their team
  2. Traders collaborate with analysts and developers to create and test new trading ideas
  3. Executing, monitoring, and managing strategies is a team effort for quantitative traders
1 HN point β€’ 31 Jan 24
  1. High-frequency trading systems require co-location services, low-latency networks, trading software, algorithms, data, and development tools to work together effectively.
  2. The 1991 academic study on ultra-high frequency forex spot rate data emphasizes price revisions, statistical characteristics, impact of time aggregation, inter-relationships between currencies, and market efficiency.
  3. The study shows that high-frequency forex market dynamics exhibit unique statistical traits, fluctuations in value influenced by other currencies, and potential inefficiencies in market reactions to news.
1 HN point β€’ 24 Jan 24
  1. Asset allocation is important for individual investors to understand their portfolios and make informed decisions.
  2. The gain-pain index (GPI) is a tool developed to help investors analyze asset allocations based on risk preferences.
  3. The GPI tool helps investors understand the trade-offs involved in different asset allocations and determine optimal portfolio holdings.
0 implied HN points β€’ 12 Jan 24
  1. Upcoming content on Quantitative Finance at quantfinance.substack.com
  2. Subscribe for updates on Research, Trading, Investing, & Algos
  3. Connect with Sebastian Gutierrez for more information
0 implied HN points β€’ 03 Jun 25
  1. Learning about stochastic calculus, like Brownian motion and Itô’s Lemma, is important for understanding financial models. These concepts help us predict how prices will change over time.
  2. Mastering derivatives pricing, including the Black-Scholes model, is crucial for anyone dealing with options and risk management. It helps you figure out how much options should be worth.
  3. Exploring portfolio optimization techniques, like mean-variance, can help investors make better choices about how to allocate their money. It's about balancing risk and return effectively.