A machine-learning model only sees the features it is given. Before training begins, data science teams need historical market data they can retrieve at scale, inspect, transform, and process repeatedly across experiments.
FinFeedAPI Flat Files S3 API provides bulk access to historical OHLCV datasets through an S3-compatible interface, with GZIP-compressed CSV files using the documented Flat Files schema and organized by supported exchange, available OHLCV period, and date. This gives data science teams a practical historical data layer for feature engineering, model development, experimentation, and batch analytics.
Testing a new feature sounds simple.
Calculate returns. Build rolling volatility. Add volume dynamics. Compare different lookback windows.
But each experiment depends on the historical dataset underneath it.
If retrieving, organizing, and processing that data requires a complicated collection pipeline, every new experiment inherits that complexity.
Feature generation across long time ranges or multiple supported exchanges can involve large datasets.
Collecting historical observations through many REST requests introduces pagination and ingestion logic before feature engineering starts.
Changing the market, date range, OHLCV period, or preprocessing logic can change the resulting features.
Missing observations, market schedules, outliers, alignment, and other data characteristics need explicit treatment.
Adding more dates, markets, features, or model variants can significantly increase processing requirements.
Available files and observations depend on supported exchanges, trading calendars, holidays, and available OHLCV periods.
A feature is not defined only by its formula.
Consider something as simple as historical volatility. The resulting feature can depend on:
FinFeedAPI provides the historical OHLCV layer.
Keeping those two layers separate makes experiments easier to define, debug, and repeat.
Instead of collecting large historical datasets through many paginated REST API calls, data science teams can discover and retrieve date-partitioned files through S3-compatible operations such as LIST and GET. This fits naturally into offline and batch-oriented feature pipelines.
Flat Files are organized by supported exchange, available OHLCV period, and date.
A pipeline can define its required scope and retrieve the corresponding available historical partitions.
For example: s3://finfeedapi/E-IEXG/T-OHLCV+TP-1MIN/D-20260102.csv.gz
Historical OHLCV data is delivered as GZIP-compressed CSV files using the documented Flat Files schema. Files can be decompressed and processed using common data science environments and data-processing frameworks.
The data-access layer can retrieve the required historical partitions.
A separate transformation layer can then handle cleaning, alignment, filtering, calculations, feature generation, and dataset construction.
This prevents feature logic from becoming tightly coupled to observation-by-observation API collection.
File-based datasets allow data scientists to inspect the OHLCV observations feeding a feature pipeline. Teams can apply their own checks for missing observations, unusual values, trading-calendar effects, or other conditions before those records become model inputs.
Use historical OHLCV observations to calculate returns over selected intervals and lookback windows.
Generate rolling volatility measures from historical price series using methodology defined by the data science team.
Use historical volume observations to construct relative volume, rolling averages, volume changes, or other derived signals.
Create features based on relationships between open, high, low, and close values within historical periods.
Build datasets using supported exchange data and investigate how model behavior changes across different market samples.
Teams remain responsible for instrument mapping, comparability, trading calendars, and other market-specific differences.
Retrieve historical partitions, transform the observations into features, generate target variables, and construct datasets for model development.
Keep the data scope defined while changing feature formulas, parameters, lookback windows, or modeling approaches.
This helps separate changes in feature engineering from unintended changes in the underlying dataset selection.
| Flat Files Capability | Data Science Application | Example |
|---|---|---|
| Historical OHLCV | Feature inputs | Build price-, return-, range-, and volume-based features |
| Available OHLCV Periods | Experiment design | Select an available aggregation period appropriate to the model |
| Supported Exchange Organization | Dataset segmentation | Define which supported market datasets belong in an experiment |
| Date Partitioning | Dataset construction | Select available historical partitions for training and evaluation periods |
| GZIP-Compressed CSV | Batch processing | Load .csv.gz files using the documented Flat Files schema |
| S3-Compatible Access | Dataset retrieval | Use compatible tools and SDKs with LIST, HEAD, and GET |
| Object Listing | Dataset discovery | Identify available historical objects before processing |
| Object Information | Pipeline management | Inspect standard object information such as key, modification time, and size where available |
FinFeedAPI provides historical OHLCV inputs. Feature definitions, transformations, labels, model architecture, train/test methodology, bias controls, and evaluation remain the responsibility of the data science team.
| Feature Engineering Workflow | Before | After With Flat Files S3 API |
|---|---|---|
| Historical data retrieval | Large datasets may require many paginated REST requests | Historical OHLCV can be retrieved as date-partitioned files |
| Dataset scope | Market and date selection may be embedded inside collection scripts | Supported exchange, period, and date can explicitly define the retrieval scope |
| Feature pipeline input | Data collection and feature logic may be tightly coupled | Historical files can form a separate input layer for feature processing |
| Experiment expansion | Adding more history increases API collection complexity | Additional available historical partitions can be added to batch workflows |
| Data inspection | Issues may only appear after responses have been assembled | File-based observations can be inspected before feature generation |
| Tool integration | REST ingestion logic may be required for each workflow | S3-compatible access and CSV fit common data-processing environments |
| Repeated experiments | Dataset collection may be repeated alongside each experiment | A defined historical dataset scope can be reused across feature experiments |
| Feature debugging | It can be difficult to separate data retrieval issues from feature logic | Retrieval, transformation, and feature-generation stages can be handled separately |
FinFeedAPI provides an S3-compatible interface for discovering and retrieving Flat Files.
Data teams can use operations such as LIST, HEAD, and GET with compatible SDKs, command-line tools, scripts, and processing systems.
Historical OHLCV files are delivered as GZIP-compressed CSV using the documented Flat Files schema.
They can be decompressed and processed with Python, notebooks, databases, dataframes, distributed processing systems, or other compatible environments.
Pipelines can list available prefixes and objects before deciding which historical partitions to retrieve.
This allows dataset selection to become an explicit stage of the workflow.
Historical file partitions can be incorporated into batch-processing pipelines for larger feature-generation jobs.
The architecture, concurrency, compute environment, caching, and processing strategy remain under the data team's control.
A data science workflow can move through a defined sequence:
The Flat Files layer solves historical data access. It does not solve feature quality, leakage, survivorship bias, target construction, train/test design, or model validity.
Use FinFeedAPI Flat Files S3 API to discover and retrieve historical OHLCV partitions by supported exchange, available period, and date, then apply your own validation, transformation, and feature-engineering methodology downstream.
Define the data-access layer once, then spend more of each experiment on the features and models that come after it.
