Explore the autoregressive based drift detection method (ADDM) for identifying concept drift and market regime changes in trading. Learn how ADDM enhances ML strategies and supports backtesting in finance....

The autoregressive (AR) model is a key tool for time series forecasting in trading. This guide covers its formula, calculation, and step-by-step model building, including a Python implementation....

Explore the ARTFIMA model for trading, its key parameters, and how to estimate it in R. Learn how to backtest a trading strategy using the ARTFIMA model to assess its effectiveness....

Learn about the TGAN algorithm, how it creates synthetic data, and its use in backtesting trading strategies. Explore the benefits, challenges, and applications of TGAN in time-series analysis....

Set up Forex trading using the Interactive Brokers API with Python. Explore a trading setup for intermediate-advanced users and an algorithmic trading platform for beginners to trade Forex with IB API effectively....

Learn about the risk-constrained Kelly Criterion to make your trading have less drawdown and better strategy performance!...

Learn about the TVP-VAR model that is being heavily used in macroeconomics. It can give you better results than a basic VAR for trading. Enjoy it!...

Looking for a quicker way to compute the Boruta-Shap algorithm? Don’t miss the opportunity to find it here! Learn how to code it in Python using a brand-independent GPU!...

The triple-barrier method in a couple of seconds? Once you handle huge amounts of data and want to use the triple barrier method, you’ll go All in with this GPU-based code!...

Spending too much time with CPU-based models? Learn how to run your machine learning algos with the GPU-based RAPIDS libraries from Nvidia! Run a trading strategy quicker, learn here!...