Exploring the future of financial data architecture: AI-native financial data foundation, large-scale financial data processing, and enterprise data quality & governance.
Tag: QuantFlow
I have been planning the UX components and research engine for QuantFlow. I thought both would become key pillars of the platform. The UX layer would simplify how engineers build data pipelines and how quants interact with data. The research engine would automate large parts of the quant research workflow, allowing users to focus on … Continue reading I Was Wrong: the Users of QuantFlow Won’t be Human
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
Data Ninjago (Financial Data Architecture) 