01
Lookup
“Tesla: P/E, debt-to-equity, and free cash flow. Current vs. 3-year trend”
Single-entity snapshot with historical context. Sub-second.
Turn complex financial questions into definitive answers.
A purpose-built analytical engine for financial research. Not a chatbot. Not an API wrapper. Not a screener with a text box. Three free queries, no signup.
11,000+
US-listed stocks & ETFs
20+ yrs
of financials on US stocks
< 10s
typical response time
IHow it works
You describe the analysis. The engine computes it.
Step 01
Describe what you want
Multi-factor screens, temporal trends, cross-dataset event studies. Describe the analysis in plain English. The engine understands financial semantics.
Step 02
The engine does the rest
mrmarket.ai was purpose-built to resolve complex financial questions against structured data. It cross-references, aligns time dimensions, and computes. No guessing at any stage.
Step 03
Get structured results
Charts, tables, and saved screens. Every answer is reproducible, shareable, and refreshable when new data arrives.
IIThe problem
The analysis that moves a decision spans multiple datasets.
The questions that actually change your mind never fit in a single table. You want insider buying lined up against price action, or four straight quarters of rising cash flow screened against a moving average. Ask a chatbot and it answers fast, then quietly makes the numbers up. Open a terminal and the data is there, locked behind a query language you study for months and a bill past $25,000 a year. Build the pipeline yourself and you lose a week.
mrmarket.ai is the tool for exactly that work. You ask in plain English, it runs the computation against structured data, and you get an answer you can trust in seconds. Quick enough that you keep asking the next question, and the one after that.
IIIWhy it works
Computed, not predicted. Reproducible, not random.
Purpose-built engine
Not a language model with a database bolted on. mrmarket.ai was architected from the ground up to resolve complex financial queries against structured data.
Reproducible results
Same question, same logic, same answer. When new filings land, your saved screens update. The computation path never changes.
Designed to fail loud
If a question requires data outside our coverage, the engine surfaces an error rather than filling in the blanks. Built to tell you when it cannot answer.
IVCapabilities
What the engine handles.
Cross-dataset joins, consecutive pattern detection, forward return studies, point-in-time backtests, event correlation, and more. One sentence in, structured results out.
01 · cross-dataset
Cross-dataset joins
Combine fundamentals with prices, insider transactions with earnings, balance sheet ratios with sector benchmarks. Multiple datasets resolved in a single query.
02 · temporal
Temporal analysis
Consecutive growth detection, forward return studies, pre/post-event analysis. The engine handles time-aligned computation across different data frequencies.
03 · saved
Saved screens
Save any query as a persistent screen. Share it, refresh it, build a library of research workflows that stay current as new data arrives.
04 · automatic
Automatic visualization
The right chart for the data shape: rankings, time series, comparison tables. Computed from the result structure, not selected by the user.
VExamples
From simple questions to deep research.
02
Screen
“ROE above 15%, D/E below 1, revenue growth above 10%, FCF positive. Large cap only”
Multi-factor screen across three financial statements.
03
Cross-dataset
“Insider purchases over $500K where the stock dropped 20% in the following 3 months”
Insider filings joined with daily price data. Forward-return computation.
04
Temporal
“4 consecutive quarters of FCF growth, margin expansion, and revenue acceleration”
Sequential trend detection with multiple conditions. One sentence.
05
Event study
“For every stock where insiders bought >$1M in the 30 days before an earnings beat, what was the 63-trading-day return?”
Three datasets (insider filings, earnings, daily prices) resolved into a single result.
Free to try. Pro from $70/month, Max at $200/month.
See pricingVIBuilt For
A tool for those who do the work.
Profile 01
Quants & quantamental researchers
Cross-dataset analysis without the pipeline. Describe the study, get structured results. Spend your time on the analysis, not the infrastructure.
e.g. “Average 63-day return after insider purchases >$1M, grouped by sector, last 3 years”
Profile 02
RIAs & advisors
Run institutional-caliber screens for your clients, without the institutional data budget. Every number from a filing. Every chart shareable.
e.g. “Top 20 quality compounders: 5-year ROIC stability, margin expansion, FCF growth”
Profile 03
Swing traders & active investors
Cross-reference price action against fundamentals and insider activity before the next print.
e.g. “Stocks reporting next week where insiders bought in the last 90 days and the stock is below its 200-DMA”
VIIData
The cross-dataset analysis terminals were built for. Without the $25,000 price tag.
Daily prices, quarterly financials, earnings estimates, and insider filings sourced from publishers on the Nasdaq Data Link marketplace with point-in-time accuracy. Curated and maintained for the kind of cross-dataset queries that break generic tools.
VIII
Integrations
A dedicated analytical engine behind your AI.
mrmarket.ai exposes a full MCP interface. Connect it to Claude, ChatGPT, or Gemini and your AI gets a purpose-built analytical engine behind it. Multi-step research workflows, signal backtesting, comparative studies, pre-earnings watchlists. Your AI orchestrates. mrmarket.ai supplies the ground truth.
How it works
Claude
ChatGPT
Geminimrmarket.ai
query_data
mrmarket.ai
query_data
mrmarket.ai
query_data
Fundamentals
income, balance, cash flow
Prices
OHLC, returns, signals
Earnings
reported, estimates, calls
Filings
Form 4, 8-K, ownership
Your AI fans out parallel queries to mrmarket.ai. Each stack is one tool call resolving cross-entity joins. The AI synthesizes the answer.
Ask almost anything
“For every large-cap stock that gapped down 5%+ after earnings in the past 2 years, what is the 30/60/90-day recovery rate? Is it statistically significant vs. random 90-day windows?”
“Backtest this signal: buy stocks with insider purchases over $500K within 30 days before earnings, that beat EPS estimates. Hold 63 trading days. Show CAGR, max drawdown, and win rate vs. buying all post-beat stocks.”
“Build a pre-earnings watchlist: companies reporting in the next 2 weeks where insiders bought in the last 90 days, the stock is below its 200-DMA, and they beat estimates last quarter.”
Two-minute setup
Claude Code · one command
claude mcp add --transport http mrmarket https://mcp.mrmarket.ai/mcp
Claude Desktop · ChatGPT · Gemini — paste this URL
https://mcp.mrmarket.ai/mcp
500 free credits. Full engine access.
Every dataset. Every query type. No credit card.