For six years, chievan's work as a quant researcher at a brokerage firm followed a familiar rhythm: data in, strategy out. Market data arrived from multiple APIs, passed through layers of cleaning and transformation, landed in databases, and ultimately fed into backtesting models. Routine, yes — but far from frictionless.
To help you avoid the hard-learned lessons, we've compiled the most common pain points developers run into, covering database and table management, SQL usage, scripting syntax, cluster operations, and other practical features.
Two things drain the most time in quant development: slow backtests, and frameworks that fall apart the moment you try to extend them. Wait five minutes for data to load, tweak a parameter, wait five more minutes. Then try adding a second asset class and discover that your matching engine, position tracking, and cash management are all tangled together.
High-frequency market data contains a level of market microstructure detail that daily OHLC data simply can't match — order book dynamics, trade impact, informed order flow, intraday liquidity patterns. The problem is that building strategies directly on tick data is expensive: you need serious infrastructure, the signal-to-noise ratio is brutal, turnover costs eat returns, and scaling up is genuinely hard.
Digital twins have long been understood as high-fidelity replicas of the physical world. Whether modeling entire cities through GIS systems or capturing the intricate details of buildings and factories via BIM, the traditional approach has focused on creating precise digital mappings of reality to enable simulation, analysis, and operational planning. But this paradigm is shifting.
Factor replication from research reports has traditionally been a core method for analysts seeking to expand their analytical frameworks and identify new alpha-generating opportunities.
The session delivered an in-depth technical breakdown of how DolphinDB's scripting language achieves C++-level performance—a technical masterclass for engineers and industry practitioners.
A decade ago, "let it run overnight" was an acceptable answer. Today, that timeline is obsolete.
In this article, we walk through DolphinDB's built-in plot() function using an FX option volatility surface as a running example, showing you how to build professional 2D and 3D charts with mouse-driven rotation, zoom, and hover tooltips.
The age when everything gets quantified is no longer coming. It's here.
