Total Market Dataset
Aggregate fundamentals and the six analytics across the entire US-listed universe, the market as a single line item.
What data it carries
The whole US-listed universe rolled up into one set of top-down series.
- Market overview · aggregate market cap, revenue, EBITDA, net income and company counts
- The six analytics · growth, profitability, valuation, financial strength, efficiency and management effectiveness at the market level
- Dispersion & trend · aggregate margins, multiples and growth over time, TTM / FY / Q
Best real-life usage
- Top-down / macro read: is the market expensive (aggregate P/E, EV/EBITDA) and are margins expanding or compressing?
- Market-cycle context: track aggregate revenue and earnings growth and valuation across cycles.
- Portfolio benchmarking: compare a portfolio's blended valuation and growth to the whole market.
Combine with other datasets
Each analysis below pairs this dataset with another to answer a question neither can alone.
Decompose the market into sectors and industries, see which are driving aggregate growth and which are dragging valuation.
Place a single company against the market aggregate for relative valuation and growth.
Compare how concentrated institutional ownership is for one company against typical ownership concentration across the market.
Relate market-level valuation and regime to factor performance for timing and tilt decisions.
Over MCP with AI agents
Connect an AI agent to the CSIMarket MCP server (mcp.csimarket.com) and it can call these endpoints as tools, no glue code. Ask in plain language; the agent picks the endpoints, fetches the data, and composes the answer.
- “How does the overall market's valuation and margin trend look this quarter versus a year ago?”, the agent pulls the total-market overview + growth + valuation and summarizes.
- Automate market commentary for a newsletter or dashboard: the agent fetches the latest aggregates and writes the paragraph.
Available for AI training
AI / LLM trainingThe Total Market Dataset is licensable for AI and machine-learning work: training or fine-tuning a model on it, generating embeddings, indexing it as a retrieval corpus for RAG, or building a derivative model whose weights carry learned representations of it.
It is a separate licence from an API or MCP subscription. A subscription covers querying the data and working with the answer. It does not cover training a model on the data or persisting it into a durable AI knowledge base. Four licence types do: model training (LLM and ML), derivative model, RAG and vector index, and enterprise unlimited.
Delivered for pipeline use: CSV in long or wide form, Parquet for ML pipelines, JSON and NDJSON, and API streaming for automated workloads.