Trading

Let's say you're tracking the price of a stock over three days. On Day 1, it goes up by $5. On Day 2, it goes down by $3. And on Day 3, it goes up by $2. This can be represented as a vector, where each component represents the price change for that day: ([+5, -3, +2]).

Machine Learning

In machine learning, especially in techniques like Natural Language Processing (NLP), words are often represented as vectors. This allows computers to "understand" and compare words based on their meanings. For example, the word "king" might be represented by a vector, and the word "queen" by another. By comparing these vectors, the machine can determine that these words are related in some way.

Algo Trading

In algorithmic trading, vectors can be used to represent a portfolio's distribution across various assets. For instance, if you invest 50% in Stock A, 30% in Stock B, and 20% in Stock C, your portfolio can be represented by the vector ([0.5, 0.3, 0.2]). This vector representation can be useful when optimizing portfolios or calculating potential risks and returns.