The kind of information hedge funds pay $50k for.

One of the most comprehensive stock research databases for studying the behavior of equities from different perspectives, all with the single intention of giving the investor an edge.

45,000+

Setups
studied

100+

Metrics
considered

10+

Years of
history

Historical Event Sample
CELH
Stock explosion
Earnings beat with raised full-year guidance
2023-11-02
+24.8%
IONQ
Big runner
Commercial contract update and volume expansion
2023-10-18
+31.6%
UPST
Earnings reaction
Revenue surprise and improved credit commentary
2023-09-14
+43.9%
RXRX
High-volume setup
Research partnership expansion after elevated volume
2023-08-24
-24.0%
SOUN
Momentum study
Product adoption update after prior theme strength
2023-07-12
+17.6%

Stock Explosions Database

  • Studies 40%+ single-day moves
  • Maps catalysts, volume behavior, and post-move structure
  • Built for recognizing extreme volatility conditions

Big Runners Database

  • Studies stocks that gained 100%+ in under 90 days
  • Tracks setup formation, acceleration, and continuation behavior
  • Built for understanding multi-week momentum cycles

Earnings Reactions Database

  • Studies post-earnings price reactions
  • Compares gaps, reversals, drifts, and follow-through
  • Built for reading how markets respond to new information

IPO Lifecycle Database

  • Studies first-year IPO behavior
  • Tracks basing, breakouts, failures, and early trend formation
  • Built for understanding new-issue market structure

How the database bundle was built

A research build that turns scattered market history into structured evidence.

1. Build the thesis

Each database begins with a clear research question: what kind of market behavior matters, what should be measured, and what information is actually needed to study it properly.

2. Collect the information

Relevant historical information is gathered from different sources, then tracked through confirmation from multiple sources before it is allowed into the research base.

3. Structure the metrics

Raw information is organized around the fields that matter: dates, catalysts, price behavior, volume context, setup type, and what happened after the initial move.

4. Clean the data

Inconsistencies, duplicate records, missing fields, and misleading edge cases are removed or resolved before the dataset becomes usable for serious study.

5. Build the study framework

The database is connected to a repeatable framework for comparing market behavior across different regimes, catalysts, setups, and outcomes.

6. Finalize the reports

The final research reports translate the database into interpretation, with one purpose: helping investors and traders understand where an edge may exist.

This was not a weekend project

Collecting the data is hard. Interpreting it is much harder.

Three months of dedicated work

A concentrated research build, not a casual spreadsheet exercise.

12GB of source material

Large enough to require disciplined reduction before it became useful.

Beyond desktop computing

The processing and checking required heavier compute than a normal workstation can handle.

Research-grade frameworks

Academic market studies and professional investment methodologies shaped the final interpretation.