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 smart-beta funds that tilt portfolios according to a given set of risk factors.
3. The third era is driven by investments in ML capabilities and alternative data to generate profitable signals for repeatable trading strategies. Factor decay is a major challenge: the excess returns from new anomalies have been shown to drop by a quarter from discovery to publication, and by over 50% after publication due to competition and crowding.

There are several categories of trading strategies that use algorithms to execute trading rules:

• Short-term trades that aim to profit from small price movements, for example, due to arbitrage
• Behavioral strategies that aim to capitalize on anticipating the behavior of other market participants
• Programs that aim to optimize trade execution, and
• A large group of trading based on predicted pricing

The HFT funds discussed above most prominently rely on short holding periods to benefit from minor price movements based on bid-ask arbitrage or statistical arbitrage. Behavioral algorithms usually operate in lower liquidity environments and aim to anticipate moves by a larger player likely to significantly impact the price. The expectation of the price impact is based on sniffing algorithms that generate insights into other market participants' strategies, or market patterns such as forced trades by ETFs.

Trade-execution programs aim to limit the market impact of trades and range from the simple slicing of trades to match time-weighted average pricing (TWAP) or volume-weighted average pricing (VWAP). Simple algorithms leverage historical patterns, whereas more sophisticated algorithms take into account transaction costs, implementation shortfall or predicted price movements. These algorithms can operate at the security or portfolio level, for example, to implement multileg derivative or cross-asset trades.