Semi-Quant Auto
Future market strategy automation
01 / Overview
A high-performance algorithmic trading system engineered for future markets. Semi-Quant Auto minimizes human error and emotion by automating complex quantitative strategies. Developed purely in Python, the pipeline manages massive streams of market data, executes split-second decisions, and visualizes live performance through dynamic charting dashboards.
03 / The Hardest Path
Ensuring near-zero latency data processing and avoiding exchange API rate limits during high market volatility.
04 / Challenges
Handling continuous, massive streams of tick data caused memory leaks in early versions. Additionally, rendering real-time charts blocked the main execution thread.
05 / Solutions
I rewrote the data ingestion pipeline using Python's asyncio for concurrent processing. I also decoupled the trading logic from the UI charting, running them on separate processes to ensure executions never lagged.