Designing a Canonical Normalization Pipeline for Financial Data

Financial data arrives from exchanges, brokers, and vendors in dozens of formats. Field names collide, units differ, and identifiers are inconsistent. Normalizing this manually is error-prone and does not scale. I wanted a systematic, docs-first way to define a canonical format and keep it stable over time.

The Problem

Normalization projects usually run into:

  • Different field names for the same concept
  • Inconsistent data types and units
  • Missing or incomplete data
  • Varying update frequencies
  • Conflicting identifier schemes

I needed a pipeline that could normalize multiple sources into one canonical format while preserving data lineage and validation guarantees.

The Solution

Canonical Normalization Pipeline (CNP) is a docs-first algorithm specification for normalizing financial data into a stable canonical format.

Key Components

  • Algorithm Specification - Core normalization rules
  • Canonicalization Rules - Field mapping and transformations
  • Assumptions - Explicit constraints and tradeoffs
  • Field Specs - Detailed field definitions
  • Reference Flow - Invariant-only reference implementation

Design Principles

  • Specs before code
  • Documented assumptions
  • Change management for algorithm updates
  • Case studies for validation
  • Research notes for design decisions

Technical Highlights

Planned Architecture

  • Input normalization from multiple sources
  • Field mapping and transformation
  • Data validation and quality checks
  • Canonical output with lineage tracking
  • Error handling for edge cases

Documentation Structure

  • ALGORITHM.md - Core algorithm specification
  • CANONICALIZATION.md - Normalization rules
  • ASSUMPTIONS.md - Constraints and invariants
  • FIELD_SPECS.md - Canonical field definitions
  • REFERENCE_FLOW.md - Invariant-only reference flow
  • docs/ - PRD, decisions, research, case studies

Impact

  • Consistency across data sources
  • Reliability through validation and invariants
  • Scalability with repeatable transformations
  • Maintainability via documented change management

What Makes It Different

Most normalization efforts start coding immediately. CNP starts with specification, assumptions, and decision records. That reduces implementation risk and ensures the system handles real-world edge cases before any code is written.

Current Status

Specification Phase:

  • Algorithm documentation in progress
  • Canonicalization rules drafted
  • Field specifications defined
  • Case studies collected

Next Steps:

  • Validate specs with real data
  • Implement reference flow
  • Test across multiple sources
  • Iterate based on case studies

What I Learned

Documentation-first work keeps teams aligned and prevents costly rewrites. In normalization projects, a stable spec and change management process are the foundation that makes the technical implementation sustainable.

Links

  • Repository: Private (specification phase)
  • Documentation: Internal docs directory

This project is building the foundation for reliable financial data normalization across inconsistent sources.