Bad data in automated trading directly erodes P&L through mispriced executions, flawed risk calculations, and corrupted alpha signals. This custom validation workflow automates the detection of stale feeds, cross-exchange arbitrage violations, and statistical outliers in real-time pricing and reference data. The operational upside comes from preventing erroneous trades, reducing manual data-science firefighting, and ensuring downstream portfolio models and execution algos operate on clean, trustworthy inputs. Implementation requires integrating with Bloomberg, Refinitiv, or direct exchange feeds, deploying validation models, and establishing alerting logic.




