Quantitative Trading Vs. Algorithmic Trading

While talking about quants and trading desks, you will often come across terms such as quantitative trading and algorithmic trading. So, what is quantitative trading and how does it differ from algorithmic trading. Let’s take a look.

Quantitative trading involves the development of trading strategies with the help of advanced mathematical models. It involves conducting research, analyzing historical data, and using complex mathematical and statistical models to find trading opportunities in order to make a profit. Traders who develop these quant-based trading strategies and execute these strategies are called quant traders. Quantitative trading is used mostly used by financial institutions and hedge funds, though individuals are also known to engage in such strategy building. Once the trading strategy is built, the trades can be executed manually or automatically using those strategies. The key idea is to pick investments or build a trading strategy solely based on mathematical analysis.

Algorithmic trading is a subset of quantitative trading that makes use of a pre-programmed algorithm. The algorithm, using the quantitative models, decides on various important aspects of the trade such as the price, timing, and quantity, and executes the trade automatically without human intervention. The algorithmic trading process involves making use of powerful computers to run these complex mathematical models and execute the trade orders. This involves automating the full process including order generation, submission, and the order execution. Algorithmic trading is often used by large institutional investors such as pension funds, and mutual funds, to break large orders into several smaller pieces. Since the information is received electronically, algo trading is also used by players such as hedge funds to automatically make decisions to order before other human traders even receive the information, thereby providing them with a huge advantage. 

Algorithmic trading can be used with any quantitative trading strategy to make the complete decision of entering the trade and executing it without human intervention. This could be a market-making strategy, spread, arbitrage, or even pure speculation.

Apart from algorithmic trading, quantitative trading includes high-frequency trading and statistical arbitrage.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.