The Clean Data Guide: How to Source Professional-Grade Historical Data in 2026
The gap between “retail” and “institutional” data has narrowed, but the risk of Data Pollution (survivorship bias, unadjusted splits, and “dirty” ticks) is at an all-time high due to the sheer volume of AI-generated noise. For a portfolio, sourcing clean data is the difference between a robust backtest and a “hallucinated” strategy. Professional-grade historical data […]
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