Market data infrastructure determines what traders can test, trust, and execute. It includes historical datasets, live feeds, storage, cleaning rules, entitlement management, and alerting when the data itself breaks.
Treat historical and live data as one system
Backtests often fail when historical data differs from live data. Symbol formats, timestamps, adjustments, session calendars, and vendor corrections should be consistent across research and production. The closer the datasets match, the easier it is to diagnose performance differences.
Validate data before strategies consume it
Price spikes, stale quotes, missing bars, duplicate ticks, and impossible volumes should be detected before they reach signal generation. Validation rules are not optional in automated trading because a single bad print can trigger a real order.
Design storage for replay and audit
Strong infrastructure lets teams replay market conditions, reconstruct orders, and investigate incidents. Time-series databases, object storage, and event logs can all play a role. What matters is that data is recoverable and tied to the decisions it influenced.
Strategic takeaway
Reliable market data infrastructure gives algorithmic traders a stable foundation. Without it, even sophisticated models are built on sand.
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