Academic research often depends on datasets covering months or years of market history. Collecting that data observation by observation through paginated REST API calls adds unnecessary work before the research even begins.
FinFeedAPI Flat Files S3 API provides bulk access to historical OHLCV datasets through an S3-compatible interface, with GZIP-compressed CSV files organized by supported exchange, available OHLCV period, and date for research, statistical analysis, and repeatable historical studies.
Before testing a hypothesis, researchers often need to define and assemble the historical sample.
Which supported exchanges? Which OHLCV period? Which dates? Which files? How should they be loaded and processed?
For a small study, that may be manageable. Across years of observations, data preparation can become a project of its own.
Multi-year studies can require substantial historical data before analysis begins.
Collecting large numbers of historical observations through paginated endpoints creates additional request, pagination, and ingestion logic.
Researchers need to document exactly which historical data went into an experiment.
Downloading, organizing, validating, and loading the research sample can consume significant project time.
A result is easier to verify when the underlying dataset scope can be clearly described and reconstructed.
A reproducible market-data study should begin with a clearly defined dataset.
FinFeedAPI Flat Files organize historical OHLCV data around these dimensions.
That gives researchers a clearer starting point for documenting the market-data component of an experiment.
Instead of collecting historical observations through many paginated REST API calls, researchers can discover and retrieve date-partitioned files through S3-compatible operations such as LIST and GET. This is better suited to historical and batch-oriented workflows involving large datasets.
Historical OHLCV files are organized using supported exchange, available OHLCV period, and date.
Researchers can define the required scope and retrieve the corresponding historical partitions.
A file path can follow a structure such as s3://finfeedapi/E-IEXG/T-OHLCV+TP-1MIN/D-20260102.csv.gz
OHLCV datasets are delivered as CSV files, typically GZIP-compressed as .csv.gz, using the documented OHLCV schema. This format can be incorporated into common analytical environments, databases, notebooks, and data-processing tools.
Researchers can work with compatible object-storage tools and SDKs rather than building an observation-by-observation historical retrieval workflow. S3-compatible operations such as LIST, HEAD, and GET support dataset discovery, inspection, and retrieval.
File-based access gives researchers direct access to the OHLCV records used in their analysis. They can perform their own validation, filtering, transformations, and quality checks before running an experiment.
Retrieve historical OHLCV data covering months or years to investigate how market behavior changes across different periods.
Use datasets from supported exchanges to build studies comparing markets.
Researchers remain responsible for controlling for differences in market structure, trading calendars, instruments, and other factors that may affect comparability.
Use historical OHLCV data as input for realized-volatility calculations, regime studies, or other statistical models based on historical price behavior.
Define historical windows around selected events and retrieve the corresponding OHLCV files and periods for analysis.
The researcher determines the event methodology, control period, statistical model, and interpretation.
Historical datasets can be used as inputs for statistical or machine-learning experiments where repeated training and evaluation require substantial time-series data.
Researchers can document the supported exchange, OHLCV period, date range, file selection, and downstream processing used in an experiment. This makes the data scope easier to define, communicate, and reconstruct.
| Flat Files Capability | Research Application | Example |
|---|---|---|
| Historical OHLCV | Time-series research | Study historical price and volume behavior |
| Available OHLCV Periods | Different research horizons | Select an available aggregation period appropriate to the methodology |
| Supported Exchange Organization | Market-level research | Define which supported exchange datasets belong in the sample |
| Date Partitioning | Sample construction | Retrieve files corresponding to the research period |
| GZIP-Compressed CSV | Statistical analysis | Load and process .csv.gz datasets in research environments |
| S3-Compatible Access | Bulk retrieval | Use compatible object-storage tools and SDKs |
| Object Listing | Dataset discovery | Identify available prefixes and objects before downloading |
| Object Information | Dataset management | Inspect standard object information such as key, modification time, and size where available |
Flat Files provide historical market-data inputs. Researchers remain responsible for methodology, statistical validity, transformations, adjustments, bias controls, assumptions, and interpretation of results.
| Research Workflow | Before | After With Flat Files S3 API |
|---|---|---|
| Historical data retrieval | Large datasets may require many paginated REST API requests | Historical OHLCV can be retrieved as date-partitioned files through S3-compatible operations |
| Dataset definition | Data scope may be embedded inside collection scripts | Supported exchange, OHLCV period, and date can define the file-selection process |
| Long time ranges | Multi-year retrieval requires repeated pagination and request handling | Date-partitioned files support batch collection across historical periods |
| Data format | Different sources may require separate parsing logic | FinFeedAPI Flat Files use a documented GZIP-compressed CSV format |
| Research tooling | Custom REST ingestion may be required | S3-compatible access works with compatible tools and SDKs |
| Experiment replication | Recreating the original collection process can be difficult | Researchers can document the exact file scope used for the study |
| Dataset validation | Data must first be assembled from many responses | File-based datasets can be inspected and validated before analysis |
| Scaling a study | Expanding the period or market scope increases retrieval complexity | Additional historical file partitions can be incorporated into the dataset workflow |
FinFeedAPI provides an S3-compatible interface for discovering and retrieving Flat Files.
Researchers can use operations such as LIST, HEAD, and GET with compatible command-line tools, SDKs, scripts, and data-processing environments.
Historical OHLCV files are typically delivered as .csv.gz.
They can be decompressed and processed using environments that support CSV-based workflows, including Python, R, databases, notebooks, statistical software, and data-processing frameworks.
Researchers can list available prefixes and objects before retrieving data.
This helps separate dataset discovery and selection from the actual download and analysis process.
File-based access fits naturally into workflows where multiple historical partitions are discovered, downloaded, stored, processed, and analyzed together.
This is particularly useful when a research question requires substantially more history than a typical interactive REST query.
A historical market-data study can follow a clear sequence:
Flat Files do not automatically make a study reproducible. They make the historical data scope easier to define, document, retrieve, and reconstruct.
Use bulk historical OHLCV files instead of collecting large datasets through observation-by-observation paginated REST requests. Define the supported exchange, available OHLCV period, and historical range, discover the corresponding files, and retrieve them through an S3-compatible interface.
FinFeedAPI Flat Files S3 API gives academic researchers a practical historical data layer for long-horizon studies, market comparisons, statistical analysis, volatility research, event studies, model development, and reproducible dataset definition.
