In quantitative trading, factor discovery is the foundation of alpha generation. Whether for high-frequency crypto strategies or medium-term systematic portfolios, the ability to efficiently compute, iterate, and deploy factors directly determines research velocity and production readiness.

Quantitative trading boils down to one core objective: identifying and exploiting market inefficiencies. Like a gecko frozen in anticipation of its prey, the quant trader must act with precision the instant an opportunity emerges—then resume the wait.

In quantitative trading, high-frequency calculation is a common requirement for research and investment strategies. However, the exponential growth in market data volumes presents significant challenges for traditional relational databases. To address these challenges, many practitioners have turned to distributed file systems using formats such as Pickle, Feather, Npz, HDF5, and Parquet for data storage, and Python for quantitative financial computations.

DolphinDB, an advanced analytical platform powered by a distributed time-series database, has emerged as a preferred solution for many brokerages and private equity firms. This article aims to provide a comparative analysis of the Python + HDF5 factor calculation approach against DolphinDB's integrated factor calculation solution.

This article compares the performance and features of DolphinDB Backtest with those of popular backtesting products on the market. Through performance comparisons, the article further demonstrates DolphinDB's competitive advantages in performance and features.

Today, we are excited to feature Cardinal Operations , the team behind the copt plugin, which brings high-performance mathematical optimization directly into the DolphinDB environment.

As a practitioner who has worked extensively with both DolphinDB and DuckDB, Boye has a clear-eyed view of what each engine does best.

In the DolphinDB ecosystem, independent developers are emerging as a vital force — transforming niche operational challenges into production-grade solutions that drive real efficiency gains. These aren't theoretical tools. They come directly from production environments, built by practitioners who've experienced the pain points firsthand.