best ML algorithm for quant trading

The best machine learning algorithm for quantitative trading depends on factors like the trading strategy, available data, and objectives. Commonly used algorithms include linear regression for price prediction, support vector machines for classification, random forest for pattern recognition, recurrent neural networks for time series forecasting, and reinforcement learning for adaptive trading strategies. It’s important to consider factors like data quality and risk management, and experimentation is key to determining the most suitable algorithm for a specific trading scenario.

Long short-term memory: At the moment, several experts consider LSTM as the most promising stock prediction algorithm. It’s basically a type of RNN, but unlike standard RNNs, can process both single data points and more complex data sequences