How to do quantitative analysis well? Taking BTC as an example, ordinary people look at candlestick charts, smart people look at news, while technology needs to observe multiple dimensions. In addition to candlestick charts and news, it is also necessary to monitor institutional wallet flows, the movements of whales, and effectively use models to analyze trends, etc. The road to quantitative analysis is long and arduous.
The principle of automatic hair removal is implemented through packet capturing or Selenium, and can be completed using AI. A tutorial is being prepared; those in need can check my homepage.
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.
After thinking for a long time, I decided to post a tweet I’ve seen many predictions made using AI, but the methods are actually incorrect Why is that? Because data has a time sensitivity For example, the previous second and the next second are different So how can we solve this? In such cases, if there is no large computing power We can use prompt programming to solve it If everyone is interested, I will release an episode on how to train a model specifically for the cryptocurrency space using natural language
Created a framework for strategy, backtesting is okay, will run a dynamic backtest, sharing technology regularly, feel free to follow if you like #ai #量化 $SOL $BTC
As a somewhat amateur cryptocurrency analyst, I think the upcoming trend is either an increase or a decrease; sideways movement is also possible. As I mentioned before, I am not a very professional cryptocurrency analyst.
Chatting with an expert who dropped from A8 to A7, I haven't learned much else, but I have learned to keep a good mindset. If I dropped from A8 to A7, I would probably faint on the spot; an expert is still an expert.
#加密市场回调 Today while writing code, I suddenly felt that if market sentiment + indicators could predict an upward or downward trend 3 hours in advance, could any knowledgeable boss explain it? $BTC $ETH $SOL (The image is unrelated to the content)
When Bitcoin breaks through $90,000, will DeFi usher in a new spring? Bitcoin has once again set a historical record, breaking through the $89,000 barrier. This news is like a shot of adrenaline, instantly activating the vitality of the entire cryptocurrency market. However, amidst this excitement, the market seems to have lost its direction. The once glorious narrative of DeFi (Decentralized Finance) now appears somewhat lonely, as many people have money in hand but do not know where to invest it. DeFi: The Forgotten Golden Track Looking back to the last 'DeFi Summer', the DeFi market was like a carnival, with countless projects emerging like mushrooms after rain, and investors flocking to this new financial blue ocean. However, as the market gradually calms down, the development of DeFi seems to have entered a bottleneck period. In fact, the importance of DeFi has not diminished; rather, it has become more prominent with the development of blockchain technology. On-chain Finance: Beyond the Boundaries of Ethereum Although most initial DeFi projects were built on Ethereum, with the rise of public chains like Solana and BSC, the application scenarios of DeFi are no longer limited to a single platform. This marks that DeFi is becoming a true synonym for on-chain finance, rather than just an exclusive label of a specific public chain. This change not only broadens the application scope of DeFi but also provides fertile ground for more innovative projects. BounceBit: The Pioneer of CeDeFi In this context, BounceBit stands out with its unique CeDeFi (combination of centralized and decentralized finance) concept. BounceBit creatively combines the chain security of BTC with the compatibility of EVM (Ethereum Virtual Machine), aiming to realize asset value circulation between CeFi (Centralized Finance) and DeFi through liquidity custodial tokens. This innovation not only breaks down the barriers between traditional finance and decentralized finance but also provides users with more diversified sources of income. CeDeFi: The Future Path of Financial Innovation If the development of DeFi has encountered a bottleneck, it may be because our imagination for on-chain finance is not bold enough. The financial system in the real world remains centralized, and CeDeFi is precisely the bridge connecting these two worlds. By introducing more traditional financial assets into the DeFi field, CeDeFi is expected to inject new vitality into DeFi and open up the next growth cycle. $BTC