Use Case

Turn insider filings into research insights.

Query 5+ years of structured PDMR transaction data across OMX Stockholm. Filter by insider role, company, sector, and transaction type -- through a visual screener, REST API, or AI agent.

The challenge

The data exists. Getting it research-ready is the hard part.

Insider transaction data is publicly available, but turning it into a structured, queryable research dataset takes significant effort -- or the right tool.

No structured historical dataset

Getting 5+ years of clean, queryable insider transaction data for Swedish equities requires normalizing fragmented regulatory sources into a usable format.

No cross-referencing by role or sector

You cannot easily ask "show me all CFO purchases in Swedish banks over the last 3 years" without building and maintaining your own database.

Static exports, no live queries

Most data sources give you a one-time export, not a filterable, sortable, API-queryable dataset you can iterate on as your research evolves.

AI cannot access it

You cannot point Claude or ChatGPT at insider data and ask analytical questions without first structuring and feeding it manually.

The solution

One dataset. Three ways to research.

Insiqta gives you structured, queryable insider transaction data through the interface that fits your research workflow -- visual exploration, programmatic access, or conversational AI.

Screener for exploration

Filter and sort 5+ years of transactions by company, insider role, sector, volume, and price. Spot patterns visually before writing a single line of code.

Visual filtering across every data dimension
Sort and rank to surface outliers instantly
Save views for recurring research lenses

API for systematic research

Pull structured data into Python, R, or any analytics stack. Full pagination, filtering, and sorting. Build factor models, run regressions, backtest strategies.

REST endpoints with full query parameters
JSON responses ready for dataframes
WebSocket feed for real-time updates

MCP for AI-powered analysis

Ask your AI agent "Which sectors had the highest insider buying in Q1 2025?" and get structured answers. No data wrangling required.

Plain English queries, structured results
Iterate on research questions conversationally
Works with Claude, OpenAI, Cursor, and any MCP client

Features

Everything you need for serious insider data research.

From historical depth to AI-native queries, every feature is designed to reduce time-to-insight.

5+ years of historical data

Deep enough for meaningful backtests, trend analysis, and academic research across multiple market cycles.

Rich filtering dimensions

Slice by insider role, company, instrument type, transaction nature, date range, price, and volume. Combine filters freely.

Consistent data model

Every record follows the same MAR schema. No normalization work required across different time periods or data sources.

API-first access

REST endpoints with full query parameters. Pull exactly the data you need into your notebook, pipeline, or dashboard.

AI-native queries

Use the MCP server to run exploratory analysis in plain English. Iterate on research questions without writing SQL or API calls.

Saved views for recurring analysis

Save filtered configurations as reusable research lenses. Come back to the same view weekly or monthly as new data arrives.

Who it's for

For anyone who treats insider data as a research input.

Whether you are testing a hypothesis, building a factor model, or writing a research report, Insiqta gives you the data layer you need.

Equity research analysts

Test investment theses against historical insider behavior. Ask: "Do insider purchases at small caps predict 6-month returns?"

Quantitative researchers

Build and backtest insider-activity factors. Combine with price data, fundamentals, or alternative data sources.

Portfolio managers

Add insider conviction as a signal layer when evaluating existing or potential positions across Swedish equities.

Academic researchers

Study insider trading patterns, information asymmetry, and regulatory effectiveness on the Swedish market.

Workflow

From hypothesis to insight.

Start with a question, explore the data, refine your filters, and export results -- all without building your own data pipeline.

1

Frame your question

Define the research hypothesis or pattern you want to investigate. What insider behavior are you looking for?

2

Query the data

Use the screener for visual exploration, the API for systematic pulls, or ask your AI agent directly.

3

Analyze and iterate

Slice by time period, insider role, company size, and sector. Refine your filters as patterns emerge.

4

Export and integrate

Feed results into your models, reports, or presentations via the API. Automate recurring research workflows.

Coverage

Full OMX Stockholm. Every filing. Every field.

Every MAR-notifiable insider transaction reported to Finansinspektionen, with full regulatory detail on every record -- ready for research from day one.

5+Years of historical data
100%OMX Stockholm coverage
Real-timeTransaction latency
Full MARRegulatory data fields

Available data fields on every transaction

Issuer name & LEI code
PDMR name & position
Closely associated person flag
Instrument type & ISIN
Transaction date & time
Volume & price
Currency & trading venue
Nature of transaction
Amendment & correction flags
Share option programme linkage
Notification date
Transaction status

FAQ

Frequently asked questions

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