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Datasets Overview

FinBrain provides 12 alternative datasets focused on US-listed stocks and ETFs, normalized and ticker-mapped for direct integration into research pipelines, trading systems, and visual analysis. Each dataset is documented on its own page with sample responses, code examples, and use cases.

Coverage

Section titled “Coverage”
Metric Value
US stocks & ETFs 12,000+ (NYSE and NASDAQ)
Datasets 12
Historical depth 10 years on average (varies by dataset)
Price forecast coverage 28,000+ tickers across 20 global markets

The alternative datasets cover US-listed assets. Price Forecasts additionally span 20 global markets, including international equities, forex, crypto, and commodities.

Dataset Categories

Section titled “Dataset Categories”

We organize the dataset catalog into three categories based on the type of signal they provide.

Government & Regulatory

Section titled “Government & Regulatory”

Public disclosure data tied to government processes — congressional trading, lobbying activity, and federal contract awards. These datasets are difficult and time-consuming to aggregate independently and provide signals not found in price data alone.

Dataset Description Update Frequency
Congressional Trading US House and Senate member trading activity from STOCK Act filings As filed
Corporate Lobbying Federal LDA filings tracking corporate influence Quarterly
Government Contracts Federal contract awards mapped to ticker symbols Daily
Patent Filings USPTO granted patents mapped to ticker symbols Weekly

Social & Consumer Intelligence

Section titled “Social & Consumer Intelligence”

Signals from public social platforms, news media, professional networks, and app ecosystems. Useful for monitoring narrative shifts, retail attention, and consumer-facing company performance.

Dataset Description Update Frequency
News Sentiment AI-generated sentiment scores from financial news Daily
News Articles Recent financial news with source attribution Real-time
LinkedIn Metrics Employee counts and follower growth Weekly
App Store Ratings iOS and Android app performance Weekly
Reddit Mentions Ticker mentions across investing subreddits Every 4 hours

News Sentiment and News Articles are two views of the same underlying news dataset — aggregated daily scores and article-level records respectively.

Market & Trading Signals

Section titled “Market & Trading Signals”

Quantitative forecasts and market activity data — price predictions, analyst views, options positioning, and insider transactions.

Dataset Description Update Frequency
Price Forecasts Statistical price forecasts with confidence intervals Daily
Analyst Ratings Wall Street ratings, upgrades, downgrades, price targets Daily
Put/Call Ratios Options market sentiment and flow Daily
Insider Transactions SEC Form 4 filings tracking insider activity Daily

Common Patterns

Section titled “Common Patterns”

All FinBrain datasets share a consistent structure that makes them straightforward to integrate.

Standardized Response Envelope

Section titled “Standardized Response Envelope”

Every API response is wrapped in the same envelope:

{
  "success": true,
  "data": {
    "symbol": "AAPL",
    "name": "Apple Inc.",
    // dataset-specific fields
  },
  "meta": {
    "timestamp": "2026-04-18T12:00:00.000Z"
  }
}

This means the integration code for one dataset is structurally identical to the integration code for any other.

Ticker-Mapped at the Source

Section titled “Ticker-Mapped at the Source”

Every record is mapped to a stock ticker symbol at ingestion. You can query LinkedIn metrics, lobbying filings, government contracts, and Reddit mentions all by the same AAPL or MSFT symbol — no fuzzy matching, no name resolution, no manual joining.

Consistent Field Naming

Section titled “Consistent Field Naming”

Field names use camelCase across all datasets. Numeric values are returned as numbers (not strings). Dates use ISO 8601 format. This consistency matters when you’re joining datasets across endpoints in pipeline code.

Historical Depth

Section titled “Historical Depth”

Our longest-running datasets carry 10+ years of historical data, and corporate lobbying reaches back 15+ years. Newer series have shallower history and are being backfilled toward similar depth. Check individual dataset pages for exact coverage.

Ways to Access the Data

Section titled “Ways to Access the Data”

Every dataset is delivered through four interfaces. Choose the one that fits your workflow:

Interface Use Case
FinBrain Terminal Visual exploration, screening, ticker deep dives, no code
REST API Production pipelines, custom integrations, any language
Python SDK Quant research, backtesting, Jupyter workflows
MCP Integration LLM-powered research, AI assistants, semantic queries

The same data is available through every interface — pick whichever matches how your team works.

Next Steps

Section titled “Next Steps”