Quantitative trading has evolved from a niche strategy into a cornerstone of modern financial markets. As China’s financial ecosystem grows more sophisticated, investors are increasingly shifting away from emotional, news-driven decision-making toward data-backed, algorithmic approaches. Unlike traditional investing—which often relies on gut feelings or insider tips—quantitative trading uses historical data, statistical models, and automated execution to identify and capitalize on market inefficiencies.
In the U.S., algorithmic and quantitative strategies account for 70–80% of equity trading volume. In contrast, China's quant market is still in its growth phase, with adoption below 10%—primarily concentrated in commodity futures. However, the launch of stock index futures has bridged the gap between China’s securities and derivatives markets, enabling new opportunities for arbitrage, hedging, and systematic trading.
This guide explores the most prominent quantitative trading platforms in China, evaluating their data offerings, research tools, backtesting capabilities, live trading support, and community engagement. Whether you're a retail investor, independent quant, or institutional player, understanding these platforms is essential for building robust, scalable strategies.
What Defines a Strong Quant Platform?
A top-tier quantitative trading platform should offer:
- High-quality, clean historical and real-time market data
- Flexible research environments (e.g., Jupyter Notebooks)
- Accurate backtesting across multiple timeframes (daily, minute, tick)
- Seamless transition from simulation to live trading
- API access for customization and integration
- Active user communities for learning and troubleshooting
Below is an overview of leading domestic platforms that meet at least some of these criteria.
Leading Quantitative Trading Platforms in China
JoinQuant (聚宽)
JoinQuant stands out as one of the most comprehensive platforms for both beginners and experienced quants. It provides full Level-1 stock data since 2005, financial statements, rights adjustments, fund pricing, futures, macroeconomic indicators, and sector/concept classifications.
The platform runs on an IPython Notebook interface, supporting both Python 2 and 3. Users can backtest strategies across stocks, ETFs, funds, and futures at daily, minute, and tick levels. Simulation trading mirrors real-market conditions closely.
For live execution, JoinQuant partners with First Capital Securities to enable automated trading in equities, exchange-traded funds, and futures. Its vibrant JoinQuant Community offers tutorials, shared strategies, and expert insights—making it ideal for collaborative learning.
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TradeBlazer (开拓者)
Built on a high-performance C++ engine, TradeBlazer excels in speed and precision—especially for futures traders. The platform uses its proprietary TradeBlazer Language (TBL), offering deep control over strategy logic and charting components.
It supports backtesting and simulation for futures at multiple frequencies and enables fully automated real-time trading. While focused mainly on derivatives, its efficiency makes it a favorite among professional traders.
Active educational resources like “Investment Academy” and “TB Online School” help users master technical analysis and automation techniques.
FMZ (发明者)
FMZ differentiates itself by supporting not only traditional financial instruments but also global cryptocurrency markets. With APIs for Bitcoin, blockchain assets, and digital currency futures, it appeals to cross-asset quants.
Users can code in JavaScript, Python, C++, or use visual Blockly programming—lowering entry barriers. FMZ supports backtesting and simulated trading across funds, commodities, and crypto derivatives.
Its real-time signal engine connects to international exchanges, enabling global execution. The platform hosts a dynamic community featuring shared strategies ("Crowdsourced"), live observation tools ("Watch"), and a knowledge base ("Library").
Ricequant (米筐)
Ricequant delivers extensive data coverage: equities since 2005 (with minute-level granularity), ETFs over two decades, futures from 1999 onward, and options data. It also integrates sentiment analytics through alternative data sources.
The research environment uses Jupyter Notebooks with support for Python and MATLAB. Backtesting spans stocks, ETFs, and various futures contracts at daily and minute levels.
While Ricequant supports simulation trading seamlessly, real-time execution is currently limited to futures via partner brokers. The Ricequant Community fosters discussion and idea exchange among developers and analysts.
MyQuant (掘金)
MyQuant offers deep historical data—including tick-level stock prices over the past decade—and detailed corporate actions (dividends, splits). It includes continuous contracts for index and commodity futures.
Supporting Python, C#, C++, and MATLAB, MyQuant caters to multi-language developers. Its hybrid backtesting system allows combined equity-futures strategy evaluation.
Simulation works across stocks and major futures products. Live trading requires manual approval; once enabled, users receive trade signals but execute manually through supported terminals.
BigQuant
BigQuant emphasizes AI-powered strategy development. Beyond standard market data, it integrates alternative datasets like news sentiment and social media trends—ideal for machine learning applications.
The platform supports Python-based modeling and offers pre-built AI modules. Backtesting covers equities and futures with high temporal resolution.
Although direct auto-trading isn’t offered, BigQuant pushes second-level signals via API to external trading systems. This makes it suitable for semi-automated workflows where human oversight remains critical.
Pobo Quant (真格)
As part of Pobo Finance, this platform specializes in derivatives: financial futures, commodity options, stock options. Data depth is strong in these areas.
Strategy development uses Python with full API access. Backtesting covers daily to tick-level resolution for options and futures.
Simulation relies on third-party tools, while live trading focuses on futures execution. Community activity is moderate compared to others.
Uqer (优矿)
Uqer offers one of the richest datasets in China: financial reports post-2007, cross-market ETFs, bond/commodity indices, media content feeds, e-commerce signals, and quant factor libraries.
Its notebook-style research environment supports Python 2 only—a limitation for modern developers. Backtesting spans equities, funds, futures, and indices.
Despite strong analytical capabilities, Uqer does not currently support live trading—a significant constraint for practitioners seeking end-to-end automation.
GFQuant (广发证券)
Backed by GF Securities, this institutional-grade platform provides comprehensive equity data since 2005—including margin financing details—and covers funds, futures, macro indicators.
Python-based research with API integration allows custom modeling. Backtesting and simulation go down to tick level.
Live trading involves signal delivery followed by manual order placement—limiting full automation potential.
GTJAQuant (国泰君安)
Offered by Guotai Junan Securities, GTJAQuant delivers broad data coverage: financials since 2005, fund portfolios, index series (600+), CFFEX products, technical factors from Tushare.
Supports Python strategy design and multi-asset backtesting. Simulation runs at tick frequency.
No direct live trading interface exists yet. Community engagement is present but less active than open platforms like JoinQuant.
MindGo (同花顺)
Powered by Tonghuashun (10jqka), MindGo combines deep data—including QuantdI, iFinD—with a Jupyter-based research environment.
Backtesting includes T+0/T+D instruments like forex and futures. Simulation supports all granularities.
Currently lacks live execution functionality. Community presence is limited.
Other notable platforms include CSC Quant (中信建投), WindQuant (万矿), and Guoren (果仁)—the latter being a no-code solution ideal for beginners exploring rule-based investing without coding.
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Core Keywords
- Quantitative trading platforms
- Algorithmic trading China
- Backtesting tools
- Automated trading systems
- Python for finance
- Stock market data API
- Futures trading automation
- AI in investing
Frequently Asked Questions
Q: Which platform is best for beginners?
A: JoinQuant and Guoren are excellent starting points. JoinQuant offers coding flexibility with strong educational resources; Guoren provides a no-code interface ideal for non-programmers.
Q: Can I trade live on all these platforms?
A: No. Only platforms like JoinQuant, TradeBlazer, FMZ, Ricequant, and Pobo Quant support direct or semi-automated live trading. Others like Uqer and GTJAQuant currently lack full execution capabilities.
Q: Do any platforms support cryptocurrency trading?
A: Yes—FMZ is the most developed in this space, offering global crypto exchange connectivity and backtesting for digital asset strategies.
Q: Are there free plans available?
A: Most platforms offer free tiers with basic data and limited backtesting. Premium features—such as tick data or live API access—require paid subscriptions.
Q: How important is community support?
A: Extremely. Active communities accelerate learning through shared code snippets, debugging help, and strategy discussions. JoinQuant and FMZ lead in this area.
Q: Is AI widely used in Chinese quant platforms?
A: Increasingly so. BigQuant specializes in AI-driven models using alternative data. Others integrate machine learning libraries within Python environments.