Professional-grade market data for quantitative backtesting and algorithmic trading.
Norgate Data is a premier provider of historical and end-of-day market data specifically engineered for rigorous quantitative analysis. Unlike standard retail data feeds, Norgate's architecture focuses on the removal of survivorship bias by providing comprehensive access to delisted securities and historical index constituents. By 2026, Norgate has solidified its position in the 'Quant-Stack' by offering a seamless bridge between high-quality normalized data and Python-based machine learning environments. The tool utilizes the Norgate Data Updater (NDU), a localized database management system that synchronizes cloud-hosted data to a local machine, allowing for ultra-fast, low-latency access during backtesting without the overhead of REST API calls. Its technical superiority lies in its granular treatment of corporate actions—such as splits, dividends, and spin-offs—offering users the choice between total return or price-only adjustments. This makes it an essential utility for institutional-level backtesting on platforms like AmiBroker, Wealth-Lab, and specialized Python libraries where data integrity is the primary determinant of model alpha.
Maintains a database of every stock that ever traded on major exchanges, allowing users to test strategies on stocks that eventually went bankrupt or were merged.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Tracks exact dates of index entry and exit for major indices like S&P 500, Russell 2000, and Nasdaq 100.
Allows real-time toggling between Adjusted (Back-adjusted for splits/dividends) and Unadjusted data.
Synchronizes cloud data to a local high-performance binary storage format.
Comprehensive tracking of ticker changes, CUSIP changes, and defunct companies.
Native Python library that converts local binary data directly into Pandas DataFrames or NumPy arrays.
Links price data with time-stamped fundamental data like P/E ratios, Market Cap, and Dividend Yield.
Ensuring results are not skewed by only testing on stocks that are successful today.
Registry Updated:2/7/2026
Accessing large volumes of clean, structured financial data for training models.
Identifying price anomalies when a stock is added or removed from an index.