Introduction: The First Roadblock in Quant Trading — Platform Choice
When most people decide to start quant trading on China's A-share market, their first search for "A-share quant trading platform" returns a flood of names: QMT, miniQMT, PTrade, MyQuant, xtquant, Hundsun…
These platforms appear to do the same thing, but their positioning is fundamentally different. Choose wrong, and you'll either waste time migrating later or, worse, put your strategy security at risk.
This article uses 2026 platform data to break down the three most popular options and answer: for each stage and need, which tool is the right fit?
The Three Platforms at a Glance
Before diving into details, remember these three lines:
- QMT/miniQMT = Execution powerhouse. Sub-millisecond latency, local data, highest strategy security
- PTrade (Hundsun) = Cloud-hosted lightweight option. Fast onboarding, no environment management needed
- MyQuant (掘金量化) = Research-first platform. Best data quality for factor research and strategy incubation
These three aren't competing — they cover different phases of the quant development pipeline.
QMT/miniQMT: The Go-To for Technical Quant Traders
Key Strengths
Sub-millisecond execution, fully local
Single-trade latency under 1ms, full in-memory architecture, tick data delivered at microsecond speed. Critically: market data is cached locally, strategy code never leaves your machine, multi-layer encryption throughout.
For valuable strategies, local execution is the most important moat.
Widest instrument coverage
Equities, futures, options, margin trading, HK Stock Connect, convertible bonds, ETFs — all in one platform.
miniQMT mode: developer paradise
Via the xtquant Python package, you can call QMT interfaces directly from Jupyter Notebook or VSCode, bypassing the GUI entirely. For Python-native developers, the experience is far superior to any domestic competitor.
# miniQMT example: subscribe to real-time tick data
from xtquant import xtdata
def on_data(data):
print(data)
xtdata.subscribe_quote('000001.SZ', period='tick', callback=on_data)
xtdata.run()
Caveats
- Windows 64-bit only — no Linux or Mac support
- Requires account opening at a partner broker (e.g., Guojin Securities) before access
- GUI is dated, but miniQMT mode rarely needs the GUI
PTrade (Hundsun): The Cloud-Hosted Lightweight Option
Key Strengths
Zero infrastructure management
Strategies run in the cloud, 24/7, without you maintaining a server. For users whose strategies are relatively simple and who want to minimize operational overhead, this is the biggest win.
Ready-made strategy templates
Stop-limit chasing, grid trading, VWAP order splitting — over a dozen components built in. Users without deep coding skills can go live by tweaking template parameters.
Caveats
- Strategy code runs in the cloud → strategy leak risk for high-value proprietary strategies
- Less Python customization depth than QMT
- If you have Python skills, a self-hosted VPS + miniQMT setup is strictly more flexible
MyQuant (掘金量化): Best Data Source for the Research Phase
Key Strengths
10+ years of historical tick data
This is MyQuant's core differentiator. Nearly a decade of daily/minute/tick history across equities, futures, and fundamental/industry data — a full quality tier above most free data sources.
For factor research, high-quality backtesting data is the foundation of everything.
Multi-language support
Python, C++, and Matlab all supported — researcher-friendly by design.
Caveats
- Live trading capability weaker than QMT (main advantage is the research phase)
- Individual retail investor live trading channels are limited
- Requires online connectivity; less local than QMT
Head-to-Head Comparison
| Dimension | QMT/miniQMT | PTrade | MyQuant |
|---|---|---|---|
| Focus | Live execution | Cloud-hosted live | Research + live |
| Latency | <1ms (lowest) | Millisecond | Not emphasized |
| Data security | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ |
| Historical data | Moderate | Moderate | 10yr+, best quality |
| Instrument coverage | Widest | Equities + futures | Equities + futures |
| Onboarding difficulty | Medium | Low | Medium-High |
| OS support | Windows only | Cloud | Cross-platform |
| Strategy security | Highest (local) | Lower (cloud) | High |
The Recommended Combination Path
The three platforms aren't mutually exclusive — the optimal approach is to combine them by phase:
[Research] MyQuant / qlib / backtrader
→ High-quality data, robust backtesting environment
↓
[Validation] MyQuant sim / QMT paper trading
→ Live-adjacent validation
↓
[Live Trading] miniQMT (technical) / PTrade (lightweight)
→ Deploy proven strategies
Four-step path from zero to live quant trading:
- Register on MyQuant, run your first backtest (free, best data quality)
- Open a Guojin Securities account, apply for miniQMT access
- Connect via
xtquantPython package, start with a daily-frequency strategy in paper trading - Once stable, optimize for higher frequency and algorithmic order splitting
Conclusion: First, Figure Out Which Phase You're In
The core question in platform selection isn't "which is best" — it's "what phase am I in right now?"
- Just starting strategy research → MyQuant: get your data and backtest infrastructure right first
- Want to go live fast with a simple strategy → PTrade: zero-ops, quick start
- Have Python skills, care about security and low latency → miniQMT is the long-term optimal choice
China's A-share quant regulations remain tight in 2026 (DMA restrictions, T+0 controls). Strategies must comply. But that doesn't stop you from building your toolchain today — so when a compliant window opens, you're ready to move.
Further reading: Quant factor research in practice (IC/IR/Barra multi-factor) / Python data sourcing for quant (tushare/akshare) / A-share risk control system design
Sources: CNBLOGS Quant Practice Series / MyQuant Official Documentation / 2026 Quant Community Synthesis
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