Quantitative investment strategies based on mathematical and statistical modeling have become increasingly popular in recent years. With the advancement of computing power and data analytics, quant strategies opened up new possibilities for investors to systematically exploit market inefficiencies and generate alpha. However, learning quantitative investment can be challenging for individual investors without a strong math background. This article aims to provide key resources and learning paths for people who want to get started with quantitative investment strategies.
Major focus areas in quantitative investment
Quantitative investment is a broad field encompassing many techniques and asset classes. Some major focus areas include: statistical arbitrage, algorithmic trading, machine learning techniques, risk modeling, high frequency trading strategies, derivatives pricing models, etc. It’s important for beginners to get exposure to these different areas first before diving deep into a particular topic.
Introductory books on quantitative finance and modeling
For investors without an advanced degree in finance or math, some introductory books can help build up the knowledge foundation in quantitative analysis and modeling. Books like ‘Quantitative Methods in Finance’ by Thomas and ‘Quantitative Finance for Dummies’ by Ritchken provide accessible overviews of key concepts like portfolio theory, option pricing, risk management using basic math and Excel modeling. Andreas Clenow’s ‘Following the Trend’ introduces momentum and trend following strategies with simple statistical techniques.
Online courses on core quant skills
Beyond books, there are excellent interactive courses that teach programming skills for quantitative finance. For example, Datacamp has a ‘Quantitative Finance with Python’ course covering pandas, numpy, matplotlib for data analysis. Udemy offers ‘Machine Learning for Quantitative Finance’ to apply ML in trading strategies. Mastering programming languages like Python and R is pivotal to implement quant strategies.
Github repositories for quantitative strategies
One excellent resource is to look at open source Github repositories where quants share actual code for quantitative trading strategies. Repos like ‘Quantitative-Investment-Strategies’,’quantitative-trading-strategies’,’quantpedia_strategies’ provide peeks into different modeling techniques with source code examples. Studying real production-level code allows quants to move from theory to practice.
For beginners looking to explore quantitative investment, focus first on building up core knowledge through books, online courses and github code repositories. Get exposure to different quant techniques before specializing. Programming skills in Python/R are crucial to put strategies into production. With disciplined learning, quants can develop promising data-driven strategies.