Sentiment Quantization

Sentiment Quantization is the process of converting qualitative market opinions, social media discussions, and news sentiment into numerical data points. In the context of cryptocurrency and derivatives, this involves using natural language processing to assign scores to text, which are then used as inputs for algorithmic trading models.

By quantifying human emotion, traders can identify potential market turning points or confirm existing trends. This data often serves as a leading indicator, providing insight into retail investor fear or greed before it manifests in price action.

It allows quantitative funds to incorporate non-traditional data into their systematic strategies. The ultimate goal is to translate subjective noise into actionable signals that can be backtested and integrated into risk management systems.