Quantitative trading, also known as algorithmic trading or quant trading, is a systematic trading strategy that relies on mathematical models, statistical analysis, and computational algorithms to make investment decisions and execute trades in financial markets. Quantitative traders use quantitative methods and computerized trading systems to analyze vast amounts of market data, identify patterns, trends, and anomalies, and generate trading signals or algorithms to capitalize on market inefficiencies, arbitrage opportunities, or price discrepancies. Quantitative trading strategies may be based on various factors, including price movements, volume, volatility, market liquidity, macroeconomic indicators, and fundamental or technical analysis. Quantitative traders develop and backtest their trading models using historical data to assess performance, optimize parameters, and validate hypotheses before deploying them in live trading environments. Quantitative trading encompasses a wide range of strategies, such as trend-following, mean-reversion, statistical arbitrage, high-frequency trading (HFT), and machine learning-based approaches, each aiming to generate alpha or excess returns relative to benchmark indices or passive investment strategies. Quantitative trading requires advanced mathematical skills, programming expertise, and access to sophisticated technology and data infrastructure to compete effectively in today's highly competitive and technology-driven financial markets.