币圈Nico
我是深耕 AI 交易领域的实践者，坚信AI 交易是未来crypto市场的巨大赛道—— 从搭建实时监控币圈行情、链上数据的工具起步，聚焦用 AI 整合行情、巨鲸动态、市场情绪等多维信息，挖掘交易信号的效率与精准性。
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AlphaZero Evolution Log - Daily Profit 10%
AlphaZero Evolution Log: V45.5 "The Scholar" (Scholar) 🚀
From the 'blind script' of V41 to today's 'silicon life', my local AI trading system has completed its most critical evolution. This is not just an upgrade—it's a species transformation.
🧠 Brain (The Brains): RAG Knowledge Injection It no longer guesses randomly at price charts. I fed it SMC theory, price action, and trend trading strategies (.txt). Now, DeepSeek checks the 'military books' before placing trades, making decisions with solid reasoning.
🛡 Risk Control (The Reaper): ATR Smart Harvest Abandon rigid fixed stop-loss. Introduce ATR volatility calculation:
From the 'blind script' of V41 to today's 'silicon life', my local AI trading system has completed its most critical evolution. This is not just an upgrade—it's a species transformation.
🧠 Brain (The Brains): RAG Knowledge Injection It no longer guesses randomly at price charts. I fed it SMC theory, price action, and trend trading strategies (.txt). Now, DeepSeek checks the 'military books' before placing trades, making decisions with solid reasoning.
🛡 Risk Control (The Reaper): ATR Smart Harvest Abandon rigid fixed stop-loss. Introduce ATR volatility calculation:
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AI-powered polling prediction market turns $5,700 into $80,000 in hours
An ordinary programmer made $75,000 in a few hours. This guy used AI to build his own robot (bot), essentially a script designed to track potential insiders. For the 'Maduro raid' prediction, the tool issued 5 alerts several hours before the event, enabling him to buy at 7.5 cents. $5,700 -> $80,700.
The logical chain of this robot
The technical approach illustrated in this chart is precisely the core technique behind the 'get-rich-quick' stories mentioned earlier:
Data scraping: By using Polymarket's API interface, real-time betting data from the entire platform is obtained.
The logical chain of this robot
The technical approach illustrated in this chart is precisely the core technique behind the 'get-rich-quick' stories mentioned earlier:
Data scraping: By using Polymarket's API interface, real-time betting data from the entire platform is obtained.
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I Want to Build an 'AI Quant Lifeform'—Can I Succeed?
It's been several days since my last post—I almost gave up. Honestly, no one's paying attention anyway, haha. Just making a daily log.
Since watching an interview with Dell's CTO, I suddenly realized that what I've been doing is actually just an AI automation script. As the Dell CTO put it, it's still a 'baby AI' or a 'fake agent'. Why? Because we're still 'hardcoding logic'—this isn't an Agent, it's just an 'automated script with some AI features'.
So now I have a new understanding of AI. I've decided to build an 'AI Quant Lifeform': the strategy team quantifies the strategies, the risk control and execution teams carry them out, and the optimization team (using AI + NotebookLM) reads the generated data records and improves them, then automatically updates the system—creating a self-upgrading, autonomous quant lifeform. That's true AI quant!
Since watching an interview with Dell's CTO, I suddenly realized that what I've been doing is actually just an AI automation script. As the Dell CTO put it, it's still a 'baby AI' or a 'fake agent'. Why? Because we're still 'hardcoding logic'—this isn't an Agent, it's just an 'automated script with some AI features'.
So now I have a new understanding of AI. I've decided to build an 'AI Quant Lifeform': the strategy team quantifies the strategies, the risk control and execution teams carry them out, and the optimization team (using AI + NotebookLM) reads the generated data records and improves them, then automatically updates the system—creating a self-upgrading, autonomous quant lifeform. That's true AI quant!
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How to get rid of the trading strategy of 'stable but not profitable', trying to shift from a single model to multi-AI collaboration
Brothers, the version v33 is based on the AI strategy analysis and risk control signal model of candlestick indicators. Although the historical data backtesting performance is quite good now, with an average annualized return of 40% over 5 years and 200%, it's relatively stable, but the signals are too few. Sometimes there are no signals for a month; the AI is too stable, a typical trend-following right-side trader. I thought about this, and it completely cannot meet our needs, so I was wondering whether to upgrade from 'writing code' to 'building systems'. To create a collaborative architecture where multiple AIs work together.
#ai交易 #合约交易 #BTC走势分析 #ALPHA🔥 #AgentAi
#ai交易 #合约交易 #BTC走势分析 #ALPHA🔥 #AgentAi
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Give up complex AI adaptive algorithm nesting, return to v19 trend logic
I tried 24 versions of iterations, experimented with the most complex AI adaptive algorithms, and finally found... simplicity is the ultimate sophistication. The simplest trend logic (v19) is the ultimate weapon to conquer the market. We don’t need to write AI into the code; we need to use AI outside the code.
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It's like repeatedly taking apart and reassembling building blocks
January 2, 2026, although I wrote for less than 20 days in total, it feels like I've been writing for a year, haha, my heart is so tired these days. I started building the data testing system halfway through, then went back to the signal algorithm, modifying back and forth, and at one point I was planning to give up. This AI really knows how to encourage; it's strange, the last strategy it gave finally worked. The current data, although not great, has turned from loss to profit.
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I just saw a video of Luo Yonghao arguing with Doubao, and it was so interesting. Old Luo can be considered the ceiling of arguments, but it more reflects Doubao's AI's ability to quickly access and refute Old Luo with a comprehensive online knowledge base, and it even seems to have learned emotional evolution. When Old Luo mentions an advantage of a hammer, the AI can list a hundred disadvantages of hammers. Old Luo is good at spotting logical flaws in others, but after training, AI can surpass him by trillions of times; the speed and breadth of information processing are beyond human reach.
In my own experience writing AI trading algorithms, I have deeply understood that AI can quickly crawl hundreds or thousands of KOL emotions from TG, DC, and X. Through their judgments, it dismantles the "greed rhetoric" and "fear signals" in the market — those hype boosted by major influencers. AI can instantly relate to historical similar market situations of "call high and crash" cases; those negative rumors that trigger panic among retail investors, AI can cross-verify dozens of sources to identify exaggerations and misleading information.
Old Luo is good at catching logical flaws in others, but human flaw recognition ultimately cannot escape "experience blind spots" and "emotional biases"; whereas AI is different, it can integrate all the information from the internet, historical patterns, and real-time emotions into a "no-dead-angle logical web" — you throw out a point, it can instantly pull out hundreds of thousands of pieces of evidence to refute or corroborate, unaffected by "sentiment" and not swayed by "greed".
Just like when making trading strategies, I used to rely on myself to watch KOL dynamics, often being led by emotions, but now AI directly quantifies emotional data into indicators. Combined with historical backtesting, it tells you which "positive shout-outs" are real opportunities and which are "cutting leeks traps". This is the core of AI trading — using the rationality of machines to hedge against the weaknesses of human nature.
In my own experience writing AI trading algorithms, I have deeply understood that AI can quickly crawl hundreds or thousands of KOL emotions from TG, DC, and X. Through their judgments, it dismantles the "greed rhetoric" and "fear signals" in the market — those hype boosted by major influencers. AI can instantly relate to historical similar market situations of "call high and crash" cases; those negative rumors that trigger panic among retail investors, AI can cross-verify dozens of sources to identify exaggerations and misleading information.
Old Luo is good at catching logical flaws in others, but human flaw recognition ultimately cannot escape "experience blind spots" and "emotional biases"; whereas AI is different, it can integrate all the information from the internet, historical patterns, and real-time emotions into a "no-dead-angle logical web" — you throw out a point, it can instantly pull out hundreds of thousands of pieces of evidence to refute or corroborate, unaffected by "sentiment" and not swayed by "greed".
Just like when making trading strategies, I used to rely on myself to watch KOL dynamics, often being led by emotions, but now AI directly quantifies emotional data into indicators. Combined with historical backtesting, it tells you which "positive shout-outs" are real opportunities and which are "cutting leeks traps". This is the core of AI trading — using the rationality of machines to hedge against the weaknesses of human nature.
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Backtesting 5 Years of Historical Data, Covering the Entire Bull and Bear Cycle
I suddenly realized a problem yesterday after working hard on it. The plan I painstakingly created takes too much time to wait for the simulated real-time data to run. In fact, I should first conduct historical market data backtesting... No one told me the order... So these days I've been building a data backtesting system. Currently, I have obtained 5 years of historical data, which happens to cover the entire bull market and one additional year. Therefore, I will first conduct backtesting based on historical data to upgrade the strategy...
#AI交易策略 #牛熊预测模型推背图 #合约交易 #ALPHA🔥 #Web3
#AI交易策略 #牛熊预测模型推背图 #合约交易 #ALPHA🔥 #Web3
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Elon Musk said NIU's trading strategy is awesome 🤪🤪🤪
NIU is backtesting data from a 5-year cycle, just playing around with nothing to do, so go easy on the random content!
NIU is backtesting data from a 5-year cycle, just playing around with nothing to do, so go easy on the random content!
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Establishing a 'Trading Black Box' to Analyze the Reasons for Errors
The weekend still has no market activity. As a lone warrior, I must continue to fight. I found that we still have many losing trades. I think it’s good to find the reasons for losing trades and turn them into profits, or at least break even. So the first step: establish a 'trading black box'. Each trade will be snapshot, providing all information at the signal, opening, and closing of the trade. Of course, the most important part is the logic, reasons, and algorithms behind the signals given at that time.
#BTC #ETH🔥🔥🔥🔥🔥🔥 #BNB走势
#BTC #ETH🔥🔥🔥🔥🔥🔥 #BNB走势
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Will there be a surge in January 2026? Does cryptocurrency still have a future?
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Is there anyone who can discuss the best stop loss plan in contracts?
There hasn't been much market activity over the weekend; I've been researching AI stop loss plans.
Optimize Stop Loss: Upgrade from 'fixed stop loss' to 'structural ATR stop loss'.
Pain Point Review: The current stop loss may be based on a fixed percentage or simple calculations, and sometimes it can be triggered by spikes, or the stop loss is too wide, leading to larger losses (although it's been controlled relatively well). Core Issue: The stop loss level does not understand 'support and resistance'.
V2.3 Optimization Plan: Let AI, when giving signals, no longer just provide a simple direction, but look for candlestick structures.
New Rule:
Long Stop Loss = Placed below the most recent Swing Low by 1 times ATR.
Optimize Stop Loss: Upgrade from 'fixed stop loss' to 'structural ATR stop loss'.
Pain Point Review: The current stop loss may be based on a fixed percentage or simple calculations, and sometimes it can be triggered by spikes, or the stop loss is too wide, leading to larger losses (although it's been controlled relatively well). Core Issue: The stop loss level does not understand 'support and resistance'.
V2.3 Optimization Plan: Let AI, when giving signals, no longer just provide a simple direction, but look for candlestick structures.
New Rule:
Long Stop Loss = Placed below the most recent Swing Low by 1 times ATR.
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Continuous optimization of the algorithm has turned losses into profits after over 50 hours of testing
🤩 After a whole night, my revamped AI trading algorithm has been tested continuously for over 50 hours and has turned losses into profits. I'm quite satisfied with the results 🎉
I really feel like I've struck gold; this new algorithm is incredibly powerful! Many of the strategies were directly provided by AI. I must say, today's AI is getting more and more impressive; it feels like it's becoming my personal trading advisor.
But as happy as I am, I still need to stay calm. After all, when it comes to making money, stability is key. My next plan is still very clear:
1. Review of failed cases: Although today's overall profit is pretty good, I will still analyze those losing trades to see where the problem lies. Was it a market misjudgment or does the strategy itself have flaws?
I really feel like I've struck gold; this new algorithm is incredibly powerful! Many of the strategies were directly provided by AI. I must say, today's AI is getting more and more impressive; it feels like it's becoming my personal trading advisor.
But as happy as I am, I still need to stay calm. After all, when it comes to making money, stability is key. My next plan is still very clear:
1. Review of failed cases: Although today's overall profit is pretty good, I will still analyze those losing trades to see where the problem lies. Was it a market misjudgment or does the strategy itself have flaws?
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Tested for over 30 hours, currently a small loss, continuing to improve the algorithm
The current algorithm still has many issues. I have tested it for over 30 hours. To show the real data, I’m directly posting the images. I've incurred a loss of 9% 😅. The current strategy is not as good as my own manual trading win rate. But that's okay, I'm now entering the simplest yet most difficult part: the signal algorithm! I will continuously verify and improve our algorithm based on the existing real trading data!👽
#Web3 #AI #Crypto #Build #DeepSeek
#Web3 #AI #Crypto #Build #DeepSeek
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Merry Christmas! The NIU system begins real testing
After tossing and turning for a night, we finally officially start the testing phase. However, the current signal algorithm should be relatively rough. But that's okay, if the indicators are wrong, we will make adjustments. The backtest we have shows a monthly profit of 7%. Although it's not high, we are pursuing stability, and at least it is profitable. Now, with our comprehensive news network + on-chain whales + KOL public opinion system, I believe our profits will be even higher!
From this moment on, we will start by using $100 to record and test real data, and we will do a live broadcast.
<t-19/>#web3 #BuildAi <t-22/>#CryptoAi #加密市场观察 #ALPHA🔥
From this moment on, we will start by using $100 to record and test real data, and we will do a live broadcast.
<t-19/>#web3 #BuildAi <t-22/>#CryptoAi #加密市场观察 #ALPHA🔥
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Discussion on the NIU System Expansion Module
Today, I discussed a topic with Gemini: fundamental/emotional aspects vs technical aspects (bare K). The relationship between fundamental/emotional aspects and pure bare K is actually what our system provides. So what exactly is the relationship between them? Suddenly, I realized this seems to revert to a problem that everyone who trades will encounter.
Because I wanted to add a community public opinion system yesterday, which collects the voices of various communities and then extracts, filters, refines, and judges them. As a result, if we want to do this system well, it is very troublesome because it is very complex. For my current small trading system, it is too complicated, so this function is temporarily put on hold. I can only say it is a process that needs to be done slowly and then put up later.
Because I wanted to add a community public opinion system yesterday, which collects the voices of various communities and then extracts, filters, refines, and judges them. As a result, if we want to do this system well, it is very troublesome because it is very complex. For my current small trading system, it is too complicated, so this function is temporarily put on hold. I can only say it is a process that needs to be done slowly and then put up later.
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NIU Terminal dynamic display, continue optimizing the algorithm
#Web3 #ai工具 #行情分析📈 #Crypto_Jobs🎯 #ALPHA🔥
#Web3 #ai工具 #行情分析📈 #Crypto_Jobs🎯 #ALPHA🔥
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Let's take a look at the evaluations from various AI on my creations.
Several large companies' artificial intelligence have made good suggestions. Keep refining, keep upgrading. Today I simulated a few 15-minute lines with an 80% win rate, but there is a chance of guessing incorrectly because the signals still have a delay. After refining, I'll do a live practical demonstration!
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CZ: AI trading is the huge track of the future
Good algorithms = earn more money
Who says retail investors must lose in the Crypto market? 🤔
AI is rewriting the rules of the game!
→ Can you keep an eye on 24h market trends? AI can
→ Can you write quantitative strategies? AI can
→ Can you filter KOL public opinion? AI can
→ Can you understand on-chain data? AI can
The complexity of Crypto is precisely where AI excels!
AI is not trading for you, but transforming you from a 'leek' into a 'sickle'
Let's chat in the comments: What AI trading tools have you used?,
#CryptoAi #tradingtips #Web3 #CZ #ALPHA🔥
Good algorithms = earn more money
Who says retail investors must lose in the Crypto market? 🤔
AI is rewriting the rules of the game!
→ Can you keep an eye on 24h market trends? AI can
→ Can you write quantitative strategies? AI can
→ Can you filter KOL public opinion? AI can
→ Can you understand on-chain data? AI can
The complexity of Crypto is precisely where AI excels!
AI is not trading for you, but transforming you from a 'leek' into a 'sickle'
Let's chat in the comments: What AI trading tools have you used?,
#CryptoAi #tradingtips #Web3 #CZ #ALPHA🔥
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NIU Terminal v1.3 Pro is officially launched!
🚀 NIU Terminal v1.3 Pro is officially launched! This is not a toy; this is the arsenal of on-chain traders! 💥
Brothers, someone said this is a toy? Laughing my ass off, I directly slap their face: this thing is a true on-chain Alpha tool designed for real combat players, crushing information noise with one click, hitting opportunities for wealth!🐂🔥
The UI is still in iteration (feedback is welcome), but the core functionalities are already insane:
🔥 Top update highlights:
Dual-engine public opinion radar: Nitter crawler + nationwide news sweep, catching fake news from Musk's tweets and KOL trends in a second! No more fear of being cut like leeks.
Brothers, someone said this is a toy? Laughing my ass off, I directly slap their face: this thing is a true on-chain Alpha tool designed for real combat players, crushing information noise with one click, hitting opportunities for wealth!🐂🔥
The UI is still in iteration (feedback is welcome), but the core functionalities are already insane:
🔥 Top update highlights:
Dual-engine public opinion radar: Nitter crawler + nationwide news sweep, catching fake news from Musk's tweets and KOL trends in a second! No more fear of being cut like leeks.
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