As technology professionals, we are already aware that our world is increasingly data-driven. This is especially true in the realm of financial markets, where algorithmic trading has become the norm, leveraging complex algorithms to execute trades at speeds and frequencies that far outstrip human capabilities. In this world where milliseconds can mean the difference between profit and loss, algorithmic trading provides an edge by making trading more systematic and less influenced by human emotional biases.
But what if we could take this a step further? What if our trading algorithms could learn from their mistakes, adapt to new market conditions, and continually improve their performance over time? This is where reinforcement learning, a cutting-edge field in artificial intelligence, comes into play.