Algorithmic strategies, statistical arbitrage, grid trading, and systematic trend-following systems.
Quantitative and algorithmic trading replaces discretionary judgment with systematic, rules-based execution. By codifying entry and exit logic into algorithms, traders eliminate emotional interference and gain the ability to backtest across decades of data before risking capital. This category covers everything from simple automated crossover strategies to advanced statistical models, grid bots, and trend-following systems used by institutions and retail quants alike.
Algorithmic trading uses computer programs to execute trades based on predefined rules — from simple moving average crossovers to complex machine learning models. The key advantage is removing emotion, achieving consistent execution speed, and backtesting strategies across decades of data before risking real capital.
Statistical arbitrage (stat arb) exploits temporary mispricings between correlated assets using quantitative models. Pairs trading — the simplest form — goes long the underperformer and short the outperformer when their price ratio deviates beyond historical norms, profiting as the spread reverts to the mean.
Grid trading places buy and sell limit orders at regular price intervals above and below the current market price, creating a 'grid' that profits from range-bound oscillations. Each buy order has a corresponding sell order at a higher grid level, generating profit from each completed round trip. It works best in sideways markets and can be automated easily.
Trend-following systems identify and ride sustained directional moves using rules-based approaches — moving average crossovers, breakout channels (Donchian, Keltner), and momentum filters. The strategy accepts many small losses for occasional outsized winners, relying on fat-tailed distribution of returns in financial markets.