Project Record

FIX Market Data Pipeline

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Primary source: https://ionitsa.com/projects/fix-parser.md
Canonical page: https://ionitsa.com/projects/fix-parser/

Hedge fund execution pipeline that normalizes FIX messages into validated market events for analytics and controls.

Domains

Hedge FundExecution

Capability

Market Data Engineering

Methods

Data IngestionData QualityNormalization

Executive Summary

Execution systems emit FIX messages in different formats across venues and versions. Without a reliable ingestion layer, downstream reconciliation, compliance, and reporting pipelines receive inconsistent or corrupted data. I built a parser that normalizes FIX tag-value streams into structured market events, validates checksums, and resolves tag numbers to human-readable field names.

The pipeline handles multiple delimiters, surfaces validation failures for review, and feeds clean JSON or internal structs to trade labeling and reconciliation systems. Operations teams get a single, auditable entry point for execution data.

This demonstrates financial data ingestion, schema normalization, and data-quality controls for hedge fund infrastructure.

Problem

Raw FIX logs are tag-value streams with no schema enforcement. Venues use | or SOH (ASCII 1) as delimiters; checksums are optional and sometimes wrong. Downstream consumers (e.g. trade labeling, reconciliation) require structured fields and human-readable aliases (e.g. ClOrdID, Side) instead of raw tag numbers.

Input

Supports both | and SOH delimiters in messages such as:

8=FIX.4.4|35=D|49=BUY|56=SELL|...|10=072|

Validation

Checksum is computed modulo 256 over the message body before 10=. Invalid checksums are logged and optionally rejected so bad data does not propagate.

Implementation

Tokenize by delimiter, parse tag=value pairs, resolve tag numbers to standard FIX dictionary names.
Optional strict mode: reject messages with missing required tags or failed checksum.
Output: JSON or internal structs for order/execution events, with aliases for reporting and dashboards.

Trade-offs

Strict validation improves data quality but can drop valid messages from non-standard venues. We run in lenient mode by default and surface validation failures to a separate review queue.