Quantitative trading is a trading strategy that uses mathematical and statistical models to analyze financial data and make investment decisions. It involves using algorithms and computer programs to identify patterns and trends in market data and execute trades based on those patterns. Quantitative traders, or quants for short, use large data sets to develop and test their trading algorithms. They often rely on high-speed computers and other advanced technologies to execute trades quickly and efficiently.

Quantitative trading techniques are utilized extensively by certain hedge funds, high-frequency trading (HFT) firms, algorithmic trading platforms, and statistical arbitrage desks. These techniques may involve rapid-fire order execution and typically have short-term investment horizons. The objective of trading is to calculate the optimal probability of executing a profitable trade.

A quantitative trading system consists of four major components:

• Strategy Identification: Finding a strategy, exploiting an edge, and deciding on trading frequency.
• Strategy Backtesting: Obtaining data, analyzing strategy performance, and removing biases.
• Execution System: Linking to a brokerage, automating the trading, and minimizing transaction costs.
• Risk Management: Monitoring portfolio risk, VAR, and tracking error.

All quantitative trading processes begin with an initial period of research. This research process encompasses finding a strategy, obtaining any data necessary to test the strategy, and trying to optimize the strategy for higher returns and/or lower risk.

Quantitative trading is an extremely sophisticated area of quant finance that requires extensive programming expertise, at the very least in a language such as MATLAB, R, or Python. However, as the trading frequency of the strategy increases, the technological aspects become much more relevant. Thus, being familiar with C/C++ will be of paramount importance.

In summary, quantitative trading is a trading strategy that uses mathematical and statistical models to analyze financial data and make investment decisions. It involves using algorithms and computer programs to identify patterns and trends in market data and execute trades based on those patterns. Quantitative traders use large data sets to develop and test their trading algorithms and often rely on high-speed computers and other advanced technologies to execute trades quickly and efficiently.