We Made Trading Signals Microsecond-Level Easy – 100 Factors in 40µs
DolphinDB announces a performance benchmark where it can compute 100 trading signal factors in 40 microseconds per row on a single-threaded CPU, aiming to reduce latency for real-time quant trading workflows.
Background
- DolphinDB is a high-performance time-series database designed for quantitative finance and big-data analytics, competing with tools like Kdb+ and ClickHouse.
- "Factors" in trading are mathematical formulas (e.g., moving averages, volatility ratios) computed from market data to generate buy/sell signals. The claim is that their system can compute 100 such factors in 40 microseconds (µs) — far faster than typical solutions.
- Speed at microsecond level matters in algorithmic trading, where firms compete on latency: winning trades often go to whoever can react fastest to market data.
- Prior context: most quant trading platforms struggle to compute many factors simultaneously without slowing down. DolphinDB's pitch is that it combines a time-series database with a built-in vectorized computing engine, avoiding the usual bottleneck of moving data between a database and a separate analytics tool.