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.

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.

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.

### What We Will Learn

In this course, we will focus on understanding the process of designing a successful trading strategy, and learning to use R to build and backtest a trading strategy. We will be making use of some popular R packages such as quantmod and quantstrat.

It is important to note that R can be used for analyzing data, building a strategy and backtest it. It is great for trading analytics. However, once you are ready to execute the strategy, i.e, ready to place orders based on strategy, you are going to do that in a real-time order execution system.

Note: Backtesting is one of the most important steps in building a successful quantitative trading strategy. Quants use their computational finance and programming skills to build complex trading strategies. However, before these strategies are executed in the live market, they are tested using historical data. Basically, the traders will feed the historical data into these algorithmic trading programs which will tell them how well their strategies performed on this historical data. This refers to backtesting and can help traders in finding flaws in their trading strategies and improvise them.