Plagiarizing DeepSeek's underlying logic! I rewrote the quantitative system using the theory of evolution

DeepSeek has proved a truth in the desperate situation of computing power: minimalist rules can "force" extremely complex top-level wisdom from huge computing power. In this video, I "stealed" from DeepSeek and used the Go language to build a cold GA (genetic algorithm) evolution engine in the cloud, allowing tens of millions of parameter genes to fight each other in the extreme market conditions of 10 years. The "Gu King" who survives in the end will tell us what the physical limits are in the trading market. Bonus easter egg: How to solve the "illusion" problem when writing AI code at low cost? Be sure to see the actual combat later. Timestamp: 0:00 Past mistakes 0:24 Learning from Deepseek 1:08 Making small things bigger 1:50 GA system description 5:40 Vibe Coding: System practice 9:20 Another discussion on "overfitting" Evolutionary System Document: https://drive.google.com/file/d/1OAs-d_a0A_tWoa9dQibZKwR198IdbeV9/view?usp=drive_link ⚠️ Disclaimer: This video is only used as a technical sharing and idea discussion on AI programming and quantitative system architecture, and does not constitute any investment or financial advice. Cryptocurrency and financial markets carry extremely high risks and no one should rely on the content of this video for actual trading. You are responsible for your own profits and losses, and please be responsible for your own wallet. 📢 **About [Coke AI Lab]:** This is an AI technology implementation channel. Only share proven development flows, tool chains and product thinking. The content comes from our company’s internal technical training. **Subscribe to the channel and never miss the next issue:** #aiprogramming#programming#cursor #ai #claude #sonnet #quantitative trading#vibecoding # Trading system#btc