Exploring the future of financial data architecture: AI-native financial data foundation, large-scale financial data processing, and enterprise data quality & governance.
Tag: Quantitative Research
Every stock behaviour pattern has parameters. A compression breakout depends on the rolling window. A VWAP reclaim depends on the volume threshold. A failed breakout depends on the forecast horizon. Change one number and a "profitable" pattern becomes noise. The typical workflow: tweak a parameter → rerun → query results → squint at a table … Continue reading QuantFlow: Brute-Force Grid Search for Stock Behaviour Patterns
Context: Some trading ideas often pop into my mind unexpectedly, but most of the time I am too lazy to investigate them further. The root cause of this "laziness" is not the effort required to explore the idea itself, but rather the amount of pre-work needed before I can actually start working on it. Collecting … Continue reading QuantFlow: Detecting Trading Opportunities Through Market Lead-Lag Profiling
For many years, a large portion of systematic strategies relied on relatively low-frequency signals. These approaches worked well when they were under-explored, but over time they have been widely researched, increasingly arbitraged, and structurally compressed in edge. As a result, a growing share of remaining opportunity has shifted toward market microstructure — order flow dynamics, … Continue reading How QuantFlow Handles Large-Scale Market Data
One of the reasons — actually, the core reason — I chose DolphinDB as the built-in streaming engine for QuantFlow's streaming execution layer is that it's really fast, even for the kind of complicated computation that requires chained steps. Thanks to that speed, and with QuantFlow's MarketState engine and FeatureDAG compiler on top, we can … Continue reading QuantFlow Fun – Build a Low-Latency Feature Monitor Dashboard
Data Ninjago (Financial Data Architecture) 
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