RESEARCH LAB

Interactive quant research

Run Python, change assumptions, inspect results, and learn how research becomes into a profitable trading strategy

Python 3 · Runs in browser

research.ipynb

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

signal = model.predict(features)

p_value = test(edge).pvalue

# test, challenge, refine

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.ipynb

Alpha Research Premium

How to Create a Mean Reversion Strategy

Build the kind of mean reversion model used at quantitative firms using basic statistics and mathematics instead of machine learning.

Beginner · Python 3

.ipynb

Alpha Research Premium

How to Find a Statistical Anomaly

An example of researching a basic statistical anomaly using high-school statistics.

Beginner · Python 3

.ipynb

Alpha Research Premium

How to Handle Regime Shifts

Explore different models that can adapt to changes in the distribution of price returns (regime changes).

Intermediate · Python 3

.ipynb

Machine learning Premium

Researching Calendar Effects with XGBoost

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

Intermediate · Python 3

.ipynb

Alpha Research Free

What Is Overfitting?

Learn how an in-sample relationship can fail to generalise by comparing Bitcoin returns across training and testing periods.

Beginner · Python 3

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Launch a configured Python notebook directly in your browser.

Experiment

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

Interpret

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