Let's update how to train an intelligent agent. I will break it down into several tweets to teach you step by step how to use deepseek. First, choose the right model; here I am using Yuanbao as an example (Tencent, remember to pay me for the advertisement). Next, I will explain the role of prompt programming. You can think of AI as a hundred thousand whys. If you don't specify the scope of its responses, it will answer you randomly. Prompt programming is used to regulate its response scope and thinking logic. Here I use a simple prompt programming example: You need to define the role the AI will play: for instance, let it act as a very impressive cryptocurrency trader. This way, it will adopt this role and help you answer questions with knowledge about cryptocurrencies. The second step is to ensure that it learns all about candlestick chart knowledge so that it can understand candlesticks. The third step is to specify what you want it to help you with: for example, based on the indicators I provide, help me predict the system's trend for the next few minutes or days. The fourth step is to make it speak in human language, so you can get a preliminary intelligent agent prototype. At this point, the agent can only be considered to have a rough framework; further fine-tuning is needed, and later I will also teach everyone how to train their own intelligent agents. I am still that Mo Chen; follow me for more knowledge in the AI field. Friends are welcome to discuss together; I will answer any technical questions you have.