RESEARCH LAB

Interactive quant research, without the setup

Run Python, change assumptions, inspect results, and learn how a trading hypothesis becomes evidence in a real research workflow.

Python 3 · Runs in browser · No setup

research.ipynb

returns = df['close'].pct_change()

signal = model.predict(features)

p_value = test(edge).pvalue

# test, challenge, refine

EQUITY CURVE+12.8%

Notebook library

Choose a research workflow

Every notebook is editable, executable, and designed around a real quantitative question.

.ipynb

Statistical research Free

Investigate a calendar effect, test whether it survives out of sample, and measure whether trading costs erase the edge.

Beginner · 20 min · Python 3

.ipynb

Machine learning Premium

Train an XGBoost model on calendar features, validate its predictions, and test whether the apparent edge remains after fees.

Intermediate · 30 min · Python 3

Launch a configured Python notebook directly in your browser.

Change the code, assumptions, features, and execution model.

Inspect the evidence and decide whether the result is robust enough to matter.