Norgate Data
Professional-grade market data for quantitative backtesting and algorithmic trading.
Institutional-grade financial data refined for machine learning and quantitative research.
AlgoSeek is a premier financial data provider specializing in the curation of high-fidelity market data optimized for quantitative analysis and machine learning. As of 2026, AlgoSeek has positioned itself as the foundational layer for financial LLMs and algorithmic trading systems by offering 'Machine Learning Ready' datasets that eliminate the heavy lifting of data cleaning and normalization. Their technical architecture focuses on providing tick-level and intraday bar data across Equities, Futures, Options, and Forex. A key differentiator in their 2026 market position is their proprietary 'Equity Master' symbology, which tracks corporate actions and symbol changes with 100% survivorship-bias-free accuracy. This allows institutional researchers to simulate historical environments with surgical precision. The data is delivered through modern cloud native pipelines, supporting Parquet and Avro formats for high-speed ingestion into distributed computing frameworks like Spark and Snowflake. AlgoSeek remains the gold standard for firms requiring millisecond-resolution data for alpha generation, risk management, and training deep learning models on market microstructure.
Comprehensive symbology database tracking every ticker change, merger, and name change since 2007.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Pre-cleaned, normalized data delivered in columnar Parquet format for direct ingestion into ML models.
Provides every single trade and quote with millisecond timestamps from SIP and Direct feeds.
Automatic calculation of split and dividend adjustments across decades of historical data.
Metadata including sector, industry, market cap, and listing exchange linked to every ticker.
Engineered bars (OHLCV) ranging from 1 second to daily aggregates with custom session boundaries.
Rigorous data maintenance that includes delisted and bankrupt companies in the historical set.
Slippage and latency estimation errors due to low-resolution data.
Registry Updated:2/7/2026
Raw financial data is too messy for transformer model training.
Inaccurate VaR (Value at Risk) calculations from poor quality historical prices.