Game of Go, trail-and-error and trading?

Let’s continue our immersion into the captivating world of our algorithm. In this narrative, we will explore how it draws inspiration from the game of Go, crafting unique strategies, and how the trial-and-error method enhances its performance.

Our algorithm, inspired by the principles of the game of Go, actively explores countless combinations, anticipating moves ahead. Thus, it devises unique strategies hitherto unused in practice. Upon detecting recurring patterns, the algorithm leverages its prior knowledge and incorporates microoutputs for optimal solutions.

The second phase of our algorithm’s training involves the trial-and-error method, where it captures successful outcomes. Remarkably, strategy scenarios do not rely on existing trading strategies, excluding bias. We avoid incorporating human strategies, encouraging the AI to be guided by market trends, turbulence, and other parameters.

Opinions today vary about the future of artificial intelligence. Some fear its radical impact on the environment, while others believe AI is intricately woven into our lives, optimizing routines. Our product falls into the latter category: AI serves as an additional mechanism, unifying diverse strategies using analyzed data for maximum efficiency.