A dataset brings multiple data points together into one organized resource. It can be small, like a list of prices for a single stock, or extremely large, such as years of market data, millions of trades, or detailed company financials. Datasets can be structured in tables, time series, CSV files, JSON objects, or database records.
Datasets are designed to make data easy to read, filter, compare, and analyze. They may contain one type of information — such as closing prices — or many types, like timestamps, open prices, high/low values, volumes, and corporate events. Financial datasets often include thousands of rows and many fields because markets produce new information every second.
High-quality datasets must be accurate, complete, and consistent. Errors, missing values, or unreliable formatting can affect analysis and lead to incorrect conclusions. For this reason, datasets often go through validation, cleaning, and standardization before users work with them.
Datasets are used across finance, research, analytics, trading, automation, and reporting. They allow people and systems to study trends, test strategies, create models, and make informed decisions.
Datasets are the foundation of modern analysis. They allow investors, analysts, and developers to track performance, compare results over time, study patterns, and build tools that depend on reliable information.
Analysts check for accuracy, completeness, and consistency. They review whether values are correct, whether any fields are missing, and if the data follows a predictable structure. They also look for unusual spikes or gaps that may indicate errors. Clean, well-organized datasets make analysis faster and more reliable.
A structured dataset allows software — such as spreadsheets, databases, or APIs — to read and process the data easily. Without structure, values can become mixed or misaligned, leading to incorrect results. Formats like CSV, JSON, and SQL tables use defined rules to keep data organized.
Traders and analysts use datasets to study historical prices, test trading strategies, track company fundamentals, examine correlations, and build risk models. Reliable datasets make it possible to identify trends, forecast outcomes, and evaluate how markets behave under different conditions.
A dataset containing five years of daily OHLC prices for a stock includes thousands of rows. Each row has a date, open, high, low, close, and volume value. Analysts use this dataset to study long-term trends or build technical indicators.
FinFeedAPI provides structured datasets through its Stock API, Currency API, SEC API, and Flat Files API. Developers use these datasets to power trading platforms, research tools, machine learning models, and dashboards.
