# Introduction The cryptocurrency sector is a difficult place to navigate, where volatility, complexity, and rapid innovation are abundant. Many investors and businesses are reluctant to brave these waters and few maximize the opportunities that are available in the space. With the power of our strategic trading algorithm, it is our mission to make a secure, easy to use application that can be an access point for anyone looking to minimize their risk while maximizing their rewards during their crypto journey. StackerAI strives to be a cutting edge tool for broad market adoption during the vast prospective onboarding which is taking shape. ### AI Trading AI trading, also known as algorithmic trading, refers to the use of artificial intelligence and advanced mathematical algorithms to automate the process of buying and selling financial assets, in our case cryptocurrencies, based on predefined rules or strategies. In AI algorithmic trading, various machine learning techniques, including neural networks, deep learning, natural language processing, and statistical models, are employed to analyze large volumes of historical and real-time market data. These algorithms can identify patterns, trends, and anomalies in the market, enabling them to make informed trading decisions without human intervention. The primary goals of AI algorithmic trading are to increase trading efficiency, reduce human error, minimize emotional bias, and potentially enhance returns by executing trades at optimal prices and timings. Additionally, AI algorithms can adapt and evolve over time as they learn from past trading experiences and market dynamics. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://stackerai.gitbook.io/stacker-ai/stacker-ai/introduction.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.