QMTv1.0.3

Retail investors in China use QMT to develop and backtest trading strategies for stocks, futures, and options. They leverage the built-in Python framework to implement algorithms like moving average crossovers, momentum strategies, or multi-stock rotation, enabling systematic trading without extensive coding expertise.

Financial institutions such as hedge funds or asset management firms utilize QMT for rigorous backtesting of quantitative models across Chinese markets. They analyze performance metrics like Sharpe ratio and maximum drawdown to validate strategies before deployment, supported by comprehensive data coverage including Level 2 and financial data.

Universities and training centers employ QMT to teach quantitative finance and algorithmic trading. Students learn to code strategies in Python, conduct backtests, and understand market mechanics using real Chinese market data, bridging theoretical knowledge with practical application in a controlled environment.

Proprietary trading firms use QMT for real-time execution of automated strategies on Chinese exchanges. Traders develop high-frequency or intraday strategies, monitor positions and orders via the platform, and optimize performance through integration with external tools like Excel via VBA for enhanced analytics.

Financial analysts and risk managers leverage QMT to simulate portfolio scenarios and assess exposure across stocks, bonds, and derivatives. They use historical data and backtesting features to evaluate strategy robustness under different market conditions, aiding in compliance and decision-making processes.

QMT generates revenue through licensing fees charged to brokerage firms and individual users for access to the platform. Subscriptions may include tiered pricing based on features like data access, backtesting capabilities, or support for advanced instruments such as options and futures.

The platform monetizes its extensive market data coverage by offering premium data feeds, such as Level 2 or historical tick data, to users. Additionally, it provides APIs like xtquant SDK for miniQMT, enabling integration with external systems and generating fees from developers and enterprises.

QMT partners with Chinese brokerages (e.g., Guojin, Huaxin) to offer the platform as a value-added service to their clients. Revenue is shared through referral agreements or bundled packages, enhancing customer retention and attracting quantitative traders to the brokerage's ecosystem.