In quant research, the most painful work isn't strategy — it's data. Fragmented sources, inconsistent schemas, occasional bad values from individual sources, no one watching freshness — each can quietly invalidate your research conclusions.
Any single data provider has occasional missing values or wrong prices: - Trading halts handled inconsistently - Field semantics drifting across versions - API rate-limit gaps - Outright wrong prices on edge cases
Trusting one source is building on sand. But using multiple sources naively also fails — you immediately get the question: which source is right?
Compare the same metric across multiple independent sources. If the deviation exceeds threshold, auto-flag or fall back to the more-reliable source. This way occasional bad values don't silently pollute your factors.
Use physics-of-trading rules to catch dirty data: high ≥ low, volume ≥ 0, daily change within reasonable bounds, price ≥ 0. These catch huge classes of upstream bugs — e.g., a source returning market cap in the volume field gets caught immediately.
Every data category has a staleness threshold (real-time ticks: seconds; financial statements: per reporting cycle). Beyond threshold → alert. "Is the data fresh?" can't be a human-memory question — it needs central monitoring.
When a primary source has an outage, auto-switch to backup without breaking downstream consumers. Achieve true 7×24 with multi-source redundancy.
Stuffing data quality into each research project means N teams each solve the same problem N times, badly. Pulling it out into a stable platform service — that's what ReachRich does.