Introduction to Quantitative Trading
This is a sample chapter from the ebook Quantitative Trading Strategies in R.
Quantitative trading involves developing and executing trading strategies based on quantitative research. The quants traders start with a hypothesis and then conduct extensive data crunching and mathematical computations to identify profitable trading opportunities in the market. The most common inputs to these mathematical models are the price and the volume data, though other data inputs are also used. Traders who develop these quant-based trading strategies and execute these strategies are called quant traders.

Trading Infrastructure
While the infrastructure to support quantitative and algorithmic trading is quite robust, the key to finding success is in identifying the right opportunities and building a solid trading strategy. Quants traders make use of programming tools such as R, Python, and Matlab to build and backtest their trading strategies before deploying them for real trade execution.
Who Uses Quantitative Trading?
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
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 trades 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.
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