Futures Quant System

Problem to Solve

Futures strategy work often breaks down in engineering details: market data from CTP needs to be cleaned and aligned, while strategy scripts, risk thresholds, and order execution are maintained separately. Backtests can be hard to reproduce, and failure modes such as network drops, malformed data, or insufficient margin tend to surface late. This skill frames the workflow as a set of modules: data acquisition, strategy development, risk management, transaction execution, and strategy backtesting.

How It Works and Where It Fits

It is built around the vn.py CTP interface and a Python workflow, and it processes inputs through parameters such as content, format, and options. The documented capabilities cover real-time market data such as price, volume, and open interest, strategy types such as trend following, mean reversion, and arbitrage, and monitoring of position and funding risk. For domestic Chinese futures, the material references CTP interfaces for the Shanghai, Dalian, and Zhengzhou exchanges.

Keep these boundaries in mind:
- Treat API keys as highly sensitive, with least privilege and rotation;
- Use request queuing and backoff for high-frequency calls to respect platform rate limits;
- Large files and long inputs may increase memory and inference cost, so chunking or segmentation is advisable;
- In complex market conditions, avoid fully automated trading without manual review and explicit stop-loss limits.