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Introducing the New QuantFlow Website

· 3 min read
QuantFlow Team
QuantFlow Team
Quantitative Financial Intelligence Platform

We're excited to introduce the new QuantFlow website — a platform designed to communicate both the system we are building and the ideas behind it.

This is not just a product site. It is a place where system design, quantitative research, and practical implementation come together.

Why a New Website?​

QuantFlow sits at the intersection of data engineering, quantitative research, and machine learning.

To properly understand its value, it's not enough to describe features — we also need to explain:

  • how the system is designed
  • why certain architectural decisions were made
  • how it fits into real-world quantitative workflows

What We'll Share​

1. System Design​

We will provide detailed insights into how QuantFlow is built across all four components:

DataInfra — the engine-agnostic data foundation:

  • Multi-source ingestion with declarative feed provider YAML
  • Common Data Model (CDM) with Pydantic validation
  • QFSQL — an engine-agnostic SQL dialect for field mappings, compiling to BigQuery, Snowflake, DuckDB, and PostgreSQL
  • Auto-generated dbt pipelines and four-layer data quality enforcement

MarketState — market structure reconstruction:

  • 8 bar types (fixed + information-driven) via a single-pass Numba fused kernel
  • Order book snapshot reconstruction from tick data
  • Label Engine with triple barrier, fixed horizon return, trend scanning, and time-series labeling

FeatureDAG — the compiler for quantitative features:

  • Formula Language — a mathematical DSL with ~40 functions compiled to an IR DAG via Python's ast module
  • 125+ FeatureTypes and 14 MFP packs across 6 dimensions
  • 4-stage pipeline: AST compiler → IR DAG → lowering → execution
  • 50+ compile-time schema contracts catch errors before any data is touched

Execution Layer — dual-backend runtime:

  • Batch (Polars) for research — lazy evaluation, Arrow zero-copy, in-process deployment
  • Streaming (DolphinDB) for live trading — deploy-and-forget, sub-ms latency, consolidated engines
  • Mode polymorphism: tick / bar / tick_to_bar
  • Dagster orchestrates the batch pipeline with 5-stage asset lineage and per-stage retries

2. Product and Business Perspective​

Beyond the system itself, we will discuss:

  • how quantitative teams build and scale research pipelines
  • the challenges of data fragmentation and feature engineering
  • where QuantFlow fits within the broader quant ecosystem
  • design trade-offs between flexibility, performance, and usability

3. Theoretical Foundations​

We will also explore the underlying concepts that inform the system:

  • market microstructure and event-driven data
  • financial data modeling and time alignment
  • feature engineering for machine learning
  • causality and leakage prevention

Our Goal​

The goal of this platform is to bridge:

  • system design and real-world usage
  • practical engineering and theoretical understanding

We aim to make QuantFlow not only a tool, but also a reference point for how modern quantitative systems are built.

Explore​

We're building QuantFlow as both a system and a framework for thinking about quantitative finance.

— The QuantFlow Team