Educational articles on quantitative research, machine learning, market data, and methodology.
Examining the common reasons why backtested strategies fail to perform in live markets, from overfitting to unrealistic assumptions.
How lookahead bias can silently invalidate your research results and practical steps to prevent it.
A comprehensive guide to walk-forward testing methodology and why it produces more reliable research results.
An overview of how machine learning is being applied to quantitative finance research, including challenges and opportunities.
Techniques and best practices for creating meaningful features from financial time-series data.
Understanding the critical importance of out-of-sample testing in quantitative research and how to do it properly.
Exploring the often-overlooked impact of data quality on quantitative research outcomes.
How to recognize, prevent, and manage overfitting when developing quantitative trading strategies.
A deep dive into maximum drawdown as a risk metric, its calculation, interpretation, and limitations.