Coding

In this article, we present a comprehensive guide to the Python code used to replicate the backtest of the trend-following strategy described in our paper, “A Century of Profitable Industry Trends.” This strategy utilizes Kenneth French’s database to construct a long-only, industry-based trend-following portfolio. The dataset spans daily data from 48 industry portfolios between 1926 and 2024. Our analysis reveals that momentum-based portfolios significantly outperform a passive Buy & Hold approach over the past century. Notably, the strategy’s parameters were not optimized in-sample, indicating potential for enhanced performance with further tuning.

In this article, we discuss using Matlab for a trend-following backtest outlined in “A Century of Profitable Industry Trends”. We use Kenneth French’s data to compare a momentum-based strategy against a Buy & Hold approach. The results show that momentum portfolios can outperform, and we conclude with a Matlab script that lets readers test how different parameters might improve profitability.

Explore our guide on backtesting the S&P500 ETF (SPY) using seven years of free Alpaca data. Learn a proven intraday momentum strategy to potentially beat the market, distilled into clear, actionable insights. Perfect for traders seeking effective, data-driven techniques.

Dive into our latest exploration of Python-based backtesting with two years of free SPY ETF data from Polygon. This post expands on the momentum strategies from ‘Beat the Market’, providing detailed Python code and analysis to assess their profitability and effectiveness.

In the world of finance, MATLAB is a powerful tool used by quantitative researchers to run statistical inferences and backtest systematic trading strategies. In this blog post, we share the MATLAB code that has been used to study the profitability of the strategy presented in our paper “Beat the Market: An Effective Intraday Momentum Strategy […]