Quantitative trading introduction principles strategies and practical tools

Overview of Quantitative Trading

Quantitative trading is a trading method that uses computer programs and statistical models to make investment decisions based on big data. It transforms human intuition and experience into clear and regularly repeatable mathematical rules, automatically executing buy and sell orders, minimizing emotional interference, and enhancing trading efficiency and accuracy.

Operating Process

Quantitative trading typically consists of five stages: strategy development, strategy backtesting, risk management, program implementation, and live execution. First, a mathematical model is constructed by analyzing market data, verifying the strategy’s effectiveness using historical data, then setting position ratios and maximum drawdown, and finally programming the strategy and connecting it to the trading interface, allowing for continuous execution of buy and sell orders in actual operations.

Common Types of Strategies

Common quantitative strategies include momentum strategies (buying assets with strong upward trends), mean reversion (reversing operations when prices deviate from the average), arbitrage strategies (profiting from low-risk price differences across markets), and using machine learning models to discover complex market patterns. These strategies are often implemented using Python or specialized quantitative platforms and are repeatedly backtested.

A platform tool suitable for beginners

Beginners can choose to use Gate Strategy Square for automated trading strategies, or they can write backtesting strategies themselves using open-source frameworks like QuantConnect and Backtrader. BigQuant supports Chinese operations and offers a drag-and-drop building feature, lowering the programming threshold and making it easier for users without programming backgrounds to get started with quantitative trading.