Historical Stock Market Data via S3 Flat Files

Access large-scale daily OHLCV stock market data through an S3-compatible Flat Files API. Data is delivered as gzip-compressed CSV files, organized by exchange and date for efficient retrieval and processing. This structure makes it easy to run long-term market analysis, build and validate trading strategies, power quantitative research, and feed data pipelines without relying on rate-limited REST APIs or custom ingestion systems.
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50
TB
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Historical market data
7
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Official SDKs
5+
+
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Core data types

What data will you get?

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Historical OHLCV Market Data
Access historical OHLCV (Open, High, Low, Close, Volume) datasets. Data is pre-aggregated and ready for analysis.
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Daily T+1 Data Snapshots
All OHLCV datasets are delivered on a T+1 daily basis. Each file represents a completed and validated trading period.
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Bulk Historical Coverage
Flat Files provide bulk access to historical OHLCV data. This is intended for large-scale research and backtesting workflows.
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CSV File Format
All datasets are delivered as CSV files. This allows easy loading into databases, data warehouses, and analytics tools.
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S3-Compatible Data Access
Files are distributed via an S3-compatible API. Standard object storage clients can be used for authentication and downloads.
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Time-Series Structured Data
OHLCV files are structured as time-series data, ordered by time. No additional aggregation is required before use.
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Consistent File Schema
Each dataset follows a stable and consistent schema across delivery dates. This reduces maintenance effort in downstream data pipelines.
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Partitioned by Date
Files are organized by delivery date, enabling selective downloads. This helps limit data transfer to only the required periods.
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Optimized for Offline Processing
Flat Files are designed for offline and batch processing, not real-time access. They fit machine learning, historical modeling, and analytical workloads.
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Data for Archive and Research
T+1 OHLCV Flat Files support long-term storage and reproducibility. This makes them suitable for audits, research replication, and compliance use cases.

Why use S3-based flat files instead of traditional market data APIs?

Traditional market data APIs are designed for real-time or small historical queries. They work well when you need a few symbols or a limited date range, but they quickly become slow, expensive, and restrictive at scale. S3-based flat files are built for bulk access. Instead of making millions of API calls, teams can download entire datasets in parallel using standard S3 tools, without rate limits or complex request logic. This approach is especially useful for quantitative research, machine learning, and large-scale backtesting.
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Flat Files S3 API allows you to...

Run large-scale historical backtests
Use bulk OHLCV datasets to backtest strategies over long time horizons. Flat files remove API rate limits and incremental querying constraints.
Train and validate machine learning models
Load T+1 historical OHLCV data into offline pipelines for feature engineering and model training. This supports reproducible experiments and consistent training datasets.
Perform long-range market research
Analyze multi-year price and volume trends using complete historical snapshots. Flat files are well suited for macro analysis and cross-period comparisons.
Build reproducible analytics and reports
Work with fixed, versioned daily datasets instead of continuously changing API responses. This makes research results and reports easier to reproduce and audit.
Integrate market data into data warehouses
Ingest CSV OHLCV files via S3-compatible storage into data lakes or warehouses. This enables batch processing with SQL, Spark, or other analytics frameworks.

Use cases

Use case: Academic Research

Any research in finance, economics, and data science depends on access to vast, high-quality historical market data. Researchers face the challenge of sourcing comprehensive, granular, and unbiased datasets that are large enough to test hypotheses, train complex models, and produce statistically significant results.

Learn how we solved that
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