Hands-On Machine Learning for Algorithmic Trading

ML and algorithmic trading strategies

Quantitative strategies have evolved and become more sophisticated in three waves:

1. In the 1980s and 1990s, signals often emerged from academic research and used a single or very few inputs derived from market and fundamental data. These signals are now largely commoditized and available as ETF, such as basic mean-reversion strategies.
2. In the 2000s, factor-based investing proliferated. Funds used algorithms to identify assets exposed to risk factors like value or momentum to seek arbitrage opportunities. Redemptions during the early days of the financial crisis triggered the quant quake of August 2007 that cascaded through the factor-based fund industry. These strategies are now also available as long-only ...