RESEARCH LAB

Interactive quant research

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

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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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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
Alpha Research Premium

Improve Trading with Cross Returns

Find a predictive edge in other assets' lagged returns when an asset's own past returns have none.

Intermediate · Python 3
.ipynb
Machine learning Premium

Improve Trading with Model Stacking

Can stacking models on top of each other improve model performance?

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 Premium

Trading the Unconditional Mean

Can the average daily return of a coin be traded, or is it only a benchmark to beat?

Beginner · Python 3
.py
Strategy Deployment Premium

Unconditional Mean Trading Bot

The live Hyperliquid bot behind the unconditional mean strategy: how a research idea becomes code that trades.

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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01

Open

Launch a configured Python notebook directly in your browser.

02

Experiment

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

03

Interpret

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