使用 AI 進行日間交易： 如何利用 ChatGPT 和 Grok 做出更智能的加密貨幣決策

加密貨幣日間交易的規則正在快速改變。這曾經需要數小時的手動分析但現在只需幾秒鐘，這得益於新一代 AI 工具。像 OpenAI 的 ChatGPT 和 Elon Musk 的 Grok (由 xAI 打造) 等人工智能助手被稱為加密交易中的“新秘籍”。

社交媒體上的交易員分享了使用這些大語言模型的故事，例如掃描市場情緒、生成交易腳本，甚至執行自動化策略，有時宣稱在日內獲得數千美元的利潤。雖然其中一些傳聞（例如使用 Grok 驅動的機器人將 0.1 SOL 變成 312 SOL 在三日內）聽起來幾乎難以置信，但它們強調了一個關鍵點：AI 為 24/7 的加密市場日間交易員提供了一個優勢。

但您如何正確利用 AI 平台進行日間交易，而限制又在何處？這本綜合指南將帶您了解在加密貨幣日間交易工作流程中實際使用 AI 工具的方法——從即時發現機會到結構化交易計劃和風險管理。

在這篇文章中，我們將探討 ChatGPT 和 Grok 的具體應用例子，使用 AI 進行交易的優缺點，以及一些“生活小竅門”，以便在不掉入常見陷阱的情況下獲取這些工具的最大收益。重要的是，我們將強調 AI 並不取代人類的判斷或策略——它只是輔助它。合理使用，AI 可以幫助打破加密貨幣市場的干擾，並為您的交易帶來紀律。不小心使用，它可導致誤導或放大錯誤。

最後，您將了解如何利用 AI 進行更快速的分析和更明智的決策，同時控制交易。目的是幫助您在信息高速流動的世界中更智能地交易。讓我們一起探索。

##什麼是加密貨幣日間交易？

加密貨幣日間交易是指在同一天（甚至幾分鐘內）進行進出交易，以從短期價格波動中獲利。與長期投資或“持有”不同，日間交易是一種快節奏的動量驅動風格。日間交易員可能在觀察 5 分鐘、15 分鐘或 1 小時的價格圖表，尋找顯示即將波動的模式。例如，他們可能會發現經典的突破模式——例如某個幣價在狹窄區間內盤整，然後開始飆升——並及時進場抓住快速上升。常見的技術指標如 RSI（相對強弱指數）或 MACD（移動平均線收斂與背離）通常用於確認這些設置。典型的日間交易由計劃的進場點、止損以控制風險（交易失敗時），以及某個水平的獲利目標組成。

實際上，加密貨幣日間交易員的工作流程可能如下所示： 掃描市場尋找有潛力的設置，進場（例如在關鍵阻力突破後不久買入），緊貼止損點設置在新支援位下方，並目標在下一阻力位或預定的獲利與風險比（如 2:1）賣出。所有這些過程都是在幾小時甚至幾分鐘內完成的——頭寸在一天結束前被關閉，因此稱為“日間交易”。這需要紀律、快速決策及嚴格風險管理。情緒必須得到控制；追逐上升或持有虧損的交易在這種風格中可能是災難性的。

為什麼加密貨幣日間交易特別具有挑戰性？ 首先，加密貨幣市場的波動相當大，並且它們在全球範圍內 24/7 運作。沒有“收盤鐘”——在週日凌晨 3 點時或週一下午 3 點時，幣價可輕易飆升或崩跌。交易量及流動性可能相差甚大；某些代幣的訂單簿稀薄，使其容易發生劇烈波動。此外，社交媒體情緒在加密貨幣價格中扮演著重要角色。單一有影響力的推文或 X 平臺上的突然趨勢可能令代幣飆升或急跌。在加密貨幣中，新聞和炒作是實時傳播的，散戶交易員同樣迅速對這些信號作出行動。這使得僅依賴技術圖表或傳統分析更困難——眼光必須時刻盯住來自社交渠道、新聞網站和社區論壇的信息流。

總之，加密貨幣的日間交易是一種高速的“忙碌”，考驗著您解析信息和果斷行動的能力。這正是 AI 工具進入的地方。AI 在迅速分析大量數據和識別模式上表現出色。在加密貨幣日間交易的背景下，這意味著一台 AI 可以比人類更快地掃描數百條推文、新聞文章和鏈上指標，可能在交易機會顯著出現在價格圖上之前提醒您。接下來的部分將深入闡述如何使用 AI 找到並執行這些快速交易，以及如何將 AI 集成到日間交易員的工具包中。

為什麼 AI 工具在加密交易中提供優勢？

加密貨幣市場以互聯網速度移動——交易員也必須如此。僅靠人眼和雙手通常難以跟上螢幕上高速披露的價格數據、推文、新聞通知及技術信號。這就是人工智能提供強大優勢的地方：速度和廣度的分析。AI 系統能夠在幾秒內解析信息並識别出模式，而這需要某人數小時才能完成（如果他們不會錯過的話）。

舉例來說，想像一下某種山寨幣在 X 上突然被更頻繁地提及，這表示有興趣或炒作激增。人工交易員可能在風潮已經形成後注意到這些聊天，或可能根本注意不到如果他們沒有關注該硬幣的社群。像 Grok 這樣的 AI 工具幾乎可以立即檢測到這種實時情緒峰值。Grok 設計用於在實時掃描 X 並量化情緒——它可以告訴您，例如“$XYZ 代幣的提及次數在過去一小時增加了 7 倍”，甚至會總結情緒是多頭還是空頭為主。提前捕捉到這些信息可讓您在代幣大幅漲價前進場，而不是在漲勢後追趕。尤其在加密貨幣中，零售驅動的上漲（尤其是 meme 言幣或新的炒作代幣）通常始於這類突如其來的社交媒體熱度。

另一個來自 AI 的優勢在於結構化和管理您的決策並非僅僅關於原始警示；正確解讀並明智行動卻是關鍵。像 ChatGPT 這樣的工具在這方面能夠充當思想交流平台甚至是交易指導。許多日間交易員常常在做出匆忙決策或未能充分計劃他們的交易（例如，不設置明確的止損或目標收益）時掙扎。ChatGPT 可以被引導將粗略的交易想法轉換為一個明確的計劃。如果 Grok （或您自己的分析）指出某代幣的情緒是看多的，且技術指標看起來有前景，你可以將這些事實輸入ChatGPT，並詢問：“在這個情況下，什麼會是短期交易的一個明智進場點和止損？” AI 將描述一個可能的計劃，例如：“價格強勁回升至 $0.50 以上後進場，將止損設置在 $0.45（即最近支援位下方），並考慮在接近下一阻力位的 $0.60 處獲利。”這類結構化的輸出幫助您直切噪音與情緒，專注於關鍵水平和風險管理。就像有一位助手總是提醒您應履行的交易規則。

重要的是，AI 可以從多個角度同時進行分析。人類可能擅長技術分析或緊跟一個新聞數據源，但 AI 可以同時綜合技術、基本面及情緒數據。例如，ChatGPT（通過正確的提示或插件）可以接受鏈上數據（如 Nansen 提供的鯨錢包動態），將其結合情緒數據（或許是來自 LunarCrush 或 Grok 的摘要），甚至加入一些技術背景（如果您提供指標讀數），然後給您為什麼幣正在移動的全貌。這樣的多面向分析能夠突出您可能會漏掉的事情如果您僅專注於某一個方面。交易員可能在圖表上看到價格突破，但 AI 可能增加說，“此外，社交媒體樂觀增加且交易量大幅提高，顯示此波動可能有後續。”或者它可能警告，“價格上升，但情緒其實混雜且有少數大持有者正在向交易所轉賬代幣（潛在為賣出），因此需要小心。”

所有這些優勢歸結為一個主要的好處：AI 可以幫助您更快、更明智地做出交易決策。它作為您分析的增倍器。某分析指出，結合人類判斷和 AI 工具可為交易員創造一個強大的混合工作流程。一些真實交易員已經使用 ChatGPT 進行技術解釋、策略回測，甚至編寫交易機器人，展示這些 AI 應用不僅僅是理論的——它們同樣能在交易實地運作。 且當與 TradingView 等平台或 CoinMarketCap 和 Glassnode 等數據源結合時，AI 變得更具效率，填補了原始數據與可行洞見之間的空白。

不過，我們要明確：速度並不等同於確定性。AI 不會提供您一個水晶球；它只是更快且全面地處理信息。加密貨幣市場仍然可能令您（及 AI）驚訝。您可能會收到一個趨勢的早期警報，但該趨勢有可能無法持續或預料之外地反轉。事實上，接下來的部分也涉及此問題的關鍵翻轉面——依賴 AI 的限制和陷阱。首先，讓我們深入探討如何逐步在您的日間交易策略中使用 Grok 和 ChatGPT 這樣的 AI 平台。

生活小竅門 #1：利用 AI 情緒分析發現早期趨勢

在加密貨幣交易中使用 AI 的最強大用途之一即是在實時掃描社交情緒以發現早期趋势。 內容：趨勢。 在加密世界，社交媒體的熱度往往先於價格變化，尤其是對於山寨幣和meme代幣。如果你能在他人湧入之前，捕捉到正在增長的敘事或標籤，你就有可能設置一個交易。像Grok這樣的AI工具就是專門為這項任務定製的。

Grok是什麼？ Grok是一個由xAI（Elon Musk的AI倡議）開發的會話AI，與X和網絡搜索原生集成。可以將Grok視為一個獲得實時網絡訪問權限的AI聊天機器人，並具有專門的能力，可以閱讀X的數據流。它可以提取最新帖子，分析情緒，甚至在被提示時閱讀圖表或新聞文章。雖然ChatGPT的基礎版本只培訓到某個截止日期的數據，且默認情況下不瀏覽網絡，Grok是為了保持最新而建的——根據xAI，它擁有“任何AI模型中最實時的搜索能力。” 這使得Grok對於需要最新資訊的交易者特別有用。

使用Grok捕捉熱度激增： 假設你是一位日間交易者，正在尋找當天的下一個熱門幣。在過去，你可能要手動瀏覽加密貨幣推特或檢查趨勢詞，這在某些情況下是不可靠且緩慢的。使用Grok，你可以直接問：“現在加密貨幣推特上有什麼趨勢？” 或者更具體地，“在過去一小時內有任何山寨幣標籤的大量提及嗎？”。Grok會掃描X上的帖子並回報諸如：“我看到$ABC幣的提及量異常激增，主要是正面的情緒，人們對於傳聞中的交易所上市感到興奮”等。

作為具體例子，交易者曾利用Grok來監控Pi Network的Pi幣等代幣，當突然出現熱度時。一個示例提示可能是：“今天Pi幣在X上的情緒如何？”。Grok可能會以綜合摘要回覆說：“Pi幣的提及量大幅上升。多頭對於潛在的價格目標$1到$1.25感到樂觀，因為有強大的社區，或許還有一些合作消息；空頭則警告它可能跌至$0.40，因為有即將到來的令牌解鎖、集中化問題和KYC擔憂。” 這類答案對於交易者是非常有價值的：不僅告訴你Pi幣正在被熱捧（這可能促使你立即調出價格圖表），還給出了一個平衡觀點，告訴你為什麼人們看多或看空。換句話說，AI不僅告訴你“每個人都很興奮，買吧！”，而是展示來自社交媒體的多空論點，讓你自行判斷這種興奮是否基於真實的東西還是存在警告信號。

解讀情緒信號： 假設Grok報告某代幣的提及量大幅激增，並伴隨著壓倒性的正面情緒（例如，很多“月亮”和“火箭”的表情符號）。經驗表明，情緒激增通常先於短期價格提升，尤其是在小市值的幣種中。一名精明的日間交易者可以將此信息作為早期警報：有事情在醞釀，是時候調查$ABC幣了。然而，並非每一個熱度激增都是可信的——加密貨幣推特可能是協調性拉盤或者虛假信息的地雷區。AI也可能誤讀諷刺或協調機器人帖子為“正面情緒”。因此，應將情緒作為進一步分析的促使因素，而非單獨的交易信號。良好的做法是將其與快速技術檢查結合（價格是否真的在上漲？成交量是否在增長？）以及基本檢查（是否有真實消息？）。接下來我們將介紹這些內容。但作為第一步，AI情緒分析就像是你的雷達——它掃描廣泛區域，當有值得注意的事情出現在社交視野中時，它會大喊“嘿，過來看看這裡！”

實際案例： 在2025年6月初，Solana的DeFi活動悄然激增。其總鎖定價值（TVL）從約60億美元飆升至約90億美元，短期內現其生態系統的真正動量。那些關注數據或DeFi新聞的交易者開始注意到此趨勢，但插入情緒的AI可能會更早捕捉到Solana項目的社交媒體熱度。如果Grok當時在掃描，它可能會標記Solana的DeFi協議提及量增加，或者Solana的總體熱度。交易者看到該警報後，可以檢查Solana的價格圖表，並注意到一個看漲的設置，利用早期提示來計劃多頭交易。事實上，社會情緒和基本因素往往交叉——在Solana的案例中，TVL的一個基本指標上升和正面的聊天可能是齊頭並進的。教訓是AI可以幫助嗅探出價格變動的背景。不是盲目地飛行，你會知道某些東西正在上漲的原因（例如，“DeFi TVL上升50%，社區持樂觀態度”），這可以讓你更有信心地駕駛浪潮，或者反之，如果熱度聽起來很脆弱，則要保持謹慎。

最後，關於訪問和限制的一點說明：Grok提供了一個免費層（供X用戶使用），具有有限的查詢——大約每兩小時10條消息加上一些圖片分析。這可能足以每天進行幾次情緒掃描，但如果你是活躍的日間交易者，你可能會輕易超過這個限制。付費層（如X Premium或Premium+或專用的SuperGrok訂閱）允許更頻繁的查詢，甚至還有一個“思考模式”用於更深入的分析。有了付費計劃，你可能會在一天中不斷運行多個幣種的Grok掃描。請記住，無論你能運行多少查詢，Grok是一個洞察工具，而不是交易終端——它不會為你執行交易。你必須將其輸出拿去使用並在你的交易所或平台上下交易決策。此外，情緒分析並非萬無一失：在快節奏的拉升中，Grok可能會稍晚幾分鐘觸發趨勢話題，或者可能誤解上下文（例如，將諷刺解讀為負面情緒）。使用它作為早期警告和研究工具。當它喊道“代幣$XYZ正在趨勢！”時，你的下一步是驗證該趨勢，而不是盲目進行交易。這就是技術指標和其他分析發揮作用的地方——這將引我們得到下一個生活技巧。

生活技巧 #2：使用AI快速檢查技術指標和圖表

一旦像Grok一樣的AI向你發出潛在機會的警報（或者即便你自己找到了），日間交易的下一步就是技術分析——基本上是閱讀價格圖表以決定進場和出場點。技術交易者使用RSI、移動平均線、MACD、布林帶等指標來估量動量並識別支撐/阻力位。手動為多個幣種進行這些操作可能會很繁瑣，但AI可以像隨叫隨到的技術分析師一樣行動，即時獲取指標讀數甚至解釋其含義。

使用AI進行快速TA（技術分析）檢查： 假設比特幣正在發生變動，你想知道它是否超買或有繼續空間。你可以問Grok或ChatGPT（配有插件或更新數據）：“比特幣的RSI現在是多少，這意味著什麼？”。 在一個真實的例子中，一位用戶詢問了Grok在特定日期（2025年7月9日）的比特幣RSI。Grok從實時數據（可能來自CoinMarketCap或類似來源）中獲得並回答：“截至2025年7月9日，比特幣的14天RSI為54，顯示出中立的動量。” 這個簡短的答案使你免去了翻閱圖表設置和自行計算RSI的麻煩。更重要的是，它提供了背景——54即不是超買也不是超賣（一般來說RSI > 70或 < 30），因此是“中立動量”。

對於日間交易者來說，該信息在塑造你的交易方面是有用的。如果RSI在突然價格飆升中達到80（高度超買），AI對此的警告可能會警告你不要晚入一個行情——或許表明動能已經過度。相反，如果RSI較低且開始向上彎曲，情緒也在轉為正面，這可能會加強看漲的設置。AI可以檢索並彙總各種指標：移動均線值、MACD狀態（是否有看漲交叉？）、波動性測量等。某些AI，如果與圖表平台連接，可能甚至會生成圖表模式的快速文本描述（例如，“ETH正測試其兩周前未能突破的阻力位約$2,000”）。事實上，如果你提供正確的數據，ChatGPT可以很擅長闡明技術分析。比如，交易者曾利用ChatGPT來解讀一組指標讀數如：“BTC 1小時圖：RSI = 72，MACD剛剛出現看漲交叉，成交量正在上升。這意味著什麼？” ChatGPT可能會回應以類似於“RSI 72意味著BTC接近超買領域，但看漲的MACD交叉伴隨著成交量的增加，表示強而有力的上升动力。這可能暗示短期內會繼續上升，但 如果RSI變得更高，請注意潛在的回調。” 本質上，它提供了對技術狀況的第二意見。

為什麼這是一個“生活技巧”？ 因為它大大減少了分析圖表所需的時間和認知負擔。與其手動檢查多個指標並回憶每個指標的含義，您可以將這一過程轉移給AI，並獲得井井有條的答案。這就像擁有一個新手分析師在您的團隊中，進行數據運算並為您提供亮點。如果您一天交易多個不同的幣種，這尤其有幫助；您無法在每一刻都成為每個圖表的專家，但AI可以根據需要為您提供快速統計。它還有助於確認自己的分析。或許您認為自己看到了看漲信號——如果您提供數據後ChatGPT也帶來了類似的看漲解釋，則增加了信心。如果指出了您遺漏的東西（“成交量在這一運動中實際上很低，這可能是一個警告信號”），則可以幫助您避免不良交易。

示例場景： 您收到Grok的警報，表明代幣XYZ目前引起了很多熱議。價格開始波動。您迅速詢問：“目前XYZ的主要技術指標是什麼？” 如果AI回應：“在內容:

15-minute chart, RSI is at 65 (slightly below overbought), there was a bullish MACD crossover an hour ago, and the price broke above its 50-period moving average,” you’ve got a snapshot of momentum. That sounds moderately bullish (momentum upward, but not extremely overbought yet). You might decide it’s worth entering a quick long trade, planning to ride the momentum for a short burst. On the other hand, if the AI said “RSI is 85 (very overbought) and the price is far above its moving averages after a parabolic jump”, you might either avoid the trade or be very cautious/tight with your stop, because such conditions can precede a sharp pullback.

來源及可靠性說明： AI like Grok can fetch indicator values from reliable data providers, but sometimes there might be slight delays or discrepancies. It’s always wise to double-check critical details on your own charting platform if possible. The AI might also simplify things a bit in explanation. For very precise trading, you’d still want to see the chart visually. But the AI gets you most of the way there faster. If you’re away from your main computer, an AI response on your phone could even help you decide if it’s worth rushing to your trading app or not.

超越指標 – 模式識別： More advanced uses of AI include identifying chart patterns or trends. Some traders use image recognition AI on chart screenshots to detect patterns (like “head and shoulders” or “triangles”). Grok actually allows image input on the paid tier, meaning you could potentially show it a chart and ask for analysis. Or you might describe the price action in words and have ChatGPT identify the pattern (e.g., “ETH has made higher lows for the past week while hitting a ceiling at $1,900 – what pattern is this?” and it might say ascending triangle). This goes into deeper TA, but it’s worth noting that AI can assist in those qualitative judgments too.

總結來說，AI通過提供指標讀數及快速解釋加速了技術分析。它幫助確認日交易決策中至關重要的動能或謹慎信號。然而，記住AI的技術判斷是基於您或它能獲得的數據—如果這些數據延誤或市場迅速轉變，AI不會神奇地預見這些。它不預測下個燭台；而是分析當前情況。因此，這些快速AI驅動的見解是對價格走勢觀察的補充，而不是替代。它們特別有助於保持客觀—例如，如果您情緒上傾向於做多，但AI指出“RSI超買和熊市背離”則可能讓您三思。接下來，我們將探討AI如何幫助處理即使是人類也覺得棘手的事情：過濾真實機會並避免陷阱如詐騙或操控。

生活秘訣 #3: 使用AI進行盡職調查——避免詐騙和FOMO陷阱

加密貨幣充滿了噪音和虛假信號。每天都有數十個新代幣推出，其中很多是模因幣或純屬詐騙品，社交媒體上更是不斷有無數傳聞流傳。對於每日交易者而言，追逐錯誤的“機會”可能是災難性的—您可能會跳入一個立即崩盤的泵，或購買一個最終被證明有根本缺陷（如智能合約後門或即將解鎖將釋放大量供應）的代幣。這是AI能夠作為您的研究助手的地方，並快速進行盡職調查，以幫助您避免地雷。

驗證再購買： Let’s say our AI sentiment scanner (Grok) alerts us that a new token $ABC is trending, with people shouting it could “go to the moon.” Before blindly apeing in, you take a step back and ask AI to check the token’s legitimacy and fundamentals. Grok can cross-reference social sentiment with web data to flag potential red flags. For example, you might prompt, “Is $ABC token likely a scam or legit? What are people saying about it beyond price hype?”. A well-designed AI prompt could lead Grok or ChatGPT (with web access) to gather information like: the token’s contract audit status, whether its developers are known or anonymous, any history of exploits, how distribution looks (are insiders holding a huge chunk?), etc.

在早前的Grok使用案例中，有人詢問Bittensor (TAO)，一個相對不太知名的代幣，以評估它是否是騙局。Grok返回了混合的情緒報告：多頭宣稱TAO有長期潛力和雄心勃勃的AI市場目標（有些甚至推測未來價格會大漲），但空頭指出非常有效的擔憂—項目集權化、內部人掌控代幣、過往黑客事件和治理不透明。這樣的答案就是一個很大的警告信號：如果您考慮短期交易TAO因為它在漲，知道有嚴重的基本面紅旗（且有聲音在指出問題）應該讓您保持謹慎。也許您會選擇完全不交易TAO，或者若交易，僅保持小倉位，嚴控止損，純粹作為快速動能交易處理，對項目的長期價值不抱信任。

模因幣瘋狂： During memecoin season, countless tokens (like the Pepes, Shibas, and variants thereof) can skyrocket and crash within hours. AI can help you sift through them by quickly summarizing what each coin is about and whether the hype is organic or possibly manipulated. For example, if $DOGE2.0 is trending, you could ask, “What is $DOGE2.0 and are there any red flags about it?”. The AI might scour community forums, token tracker sites, and news. An answer might be: “$DOGE2.0 is a new meme token with no real project behind it aside from the name, it’s up 300% today on hype. However, some users note that the top 5 wallets hold 50% of the supply (potential rug risk) and the liquidity is low. No audit information available.” Armed with that, you know it’s a pure speculative play – if you trade it, you’re basically gambling and should treat it as such. AI is doing in seconds what might take you hours of reading Etherscan data, Telegram groups, and so forth.

另一個例子：Grok和$GROK token。 有趣的是，有一個以AI本身命名的模因幣（$GROK）。根據報導，Grok（AI）可以評估$GROK token的情緒和信息，並指出它與詐騙的擔憂有關。AI沒有偏見—如果它見到有些事情被指有詐騙嫌疑或審計報告顯示“重大漏洞”，AI會告訴你。這些是您在交易代幣之前絕對想知道的事情。因此，一個生活秘訣是總是快速進行AI驅動的嗅探測試：“Grok，檢查[TokenName]是否有任何詐騙警示或重大問題。” 這不能保證安全，但它是一個快速的篩選器。

快速基本面分析： Beyond scam checking, AI can summarize legitimate fundamentals too. Say a token is pumping because it announced some partnership or a new product launch. If you’re late to the news, you can ask ChatGPT to “summarize the latest news about [Token] and its significance.” If it tells you, for example, “The token surged after announcing integration with Shopify for crypto payments, which could significantly increase its adoption”, that context helps you gauge if the pump has real legs or is just a short-lived reaction.

AI還能從網絡上收集關鍵數據點：如代幣的市值、流通供應量或解鎖計劃，若這些因素相關。或許您會詢問：“$ABC的市值和供應是多少，是否有任何重大代幣解鎖或事件即將發生？” 獲得這些數據能防止意外—如若您了解到有即將進行的大量代幣解鎖（常常會導致價格下跌），您可能就會避免在不合時宜的時候做多。

擊破誤導信息： 加密貨幣的一個危險之處是，有時不良行為者故意散播虛假信息來欺騙交易者，甚至AI。拉高出貨團隊可能會產生大量看似真實的“熱議”，其實並非如此。作為交易者，您必須保持懷疑。AI有助於收集信息，但不具備人類直覺來辨別欺騙。實際上，AI可能被協調的假活動所誤導—它可能看到很多正面帖子並得出強烈情緒，但卻沒意識到這些帖子來自於機器人或有償推銷活動。因此，您必須將AI的發現與您的判斷相結合。如果有些事情聽起來好得令人難以置信（“每個人在推特上都說這個代幣能夠在明天10倍且無風險！”），多半是假的。使用AI獲取論據和數據，然後加上一粒鹽及批判性思維。必要時檢查來源—如AI說“空頭警告集中化”，或許您可以快速驗證代幣的持有人分佈（大多數代幣追蹤器顯示前幾大持有者的百分比）。

記住CCN的警告：“不良行為者可以向系統提供不正確的信息，誘騙AI做出差的交易選擇”。一個精心策劃的拉抬計劃可能會創造出人工買入信號（例如虛假成交量或誘騙訂單），這可能會欺騙算法交易者。AI可能難以區分真實激增和虛假激增，如果數據看起來相似。因此，一個生存妙招就是始終添加一層確認。這引導到下一點：通過成交量和鏈上數據進行確認。

生活秘訣 #4: 通過成交量和鏈上數據確認信號（人-AI組合）

到了這個階段，我們已經有AI提供的情緒線索、技術讀數，甚至是基本面檢查。接下來的“秘訣”更是一種原則：不要盲目依賴任何單一來源—尤其不獨立依賴AI。總是從直接市場數據中尋求確認，如成交量、訂單簿和鏈上活動。將其視為AI洞察與市場現實之間的必要握手。AI可能說“每個人都看好TokenX”，但這是否轉化成實際購買？亦或者AI報告“TokenY看起來技術上很強”，但或許有Content: 內容: whale quietly selling into the pump. This is where you, the trader, must use the tools at your disposal (many of which AI can help interpret) to confirm that a potential trade is valid and not a head-fake.

Volume is king for confirmation: Volume is the amount of trading activity – a surge in price accompanied by a surge in volume typically indicates a more trustworthy move (lots of participants agreeing on the price direction), whereas a price move on thin volume can easily reverse. AI tools can retrieve volume data too, but you might observe it directly on your exchange or chart. A good practice is to ask, “Did this price breakout come with significantly higher trading volume than usual?” If not, be wary – it could be a false breakout. If yes, that’s a green light that the move had conviction. Some advanced AI prompts or tools (like certain TradingView indicators and AI scripts) can filter signals by volume for you. For instance, one trader used ChatGPT to code a strategy that only triggers buys when RSI conditions are met and volume is above a certain threshold. The AI not only wrote the code but even recommended adding volume filters to reduce false signals, showing that it “understood” the importance of volume confirmation.

Whale flow and on-chain checks: In crypto, large holders (“whales”) can heavily influence intraday price. If a whale decides to dump, no amount of bullish sentiment can hold the price up. Conversely, if whales are accumulating, dips may be short-lived. AI can help analyze on-chain data: for example, by feeding it data from sources like Nansen or Whale Alert. You might say, “ChatGPT, here are some recent large transactions for TokenZ. What do you infer?” If the data shows many large transfers from unknown wallets to exchanges, the AI might conclude: “Multiple whales appear to be depositing TokenZ to exchanges, possibly to sell – this could indicate selling pressure ahead.” That’s a big red flag if you were about to go long. On the other hand, large transfers from exchanges into personal wallets could imply accumulation or at least that big players aren’t looking to sell immediately.

Grok or ChatGPT with browsing can also summarize community insights on whale behavior. There might be discussions like “someone noticed the top wallet just reduced their holdings by 20% yesterday.” If you prompt the AI about whale activity, it might surface that info. Some sentiment tools (like Santiment or LunarCrush) also provide on-chain metrics such as active addresses or token holder changes – feeding those into an AI for interpretation is a smart hack. For example, “Active addresses on this network doubled in the past week while price rose 30%. Is that a good sign?” The AI would likely say yes – more active addresses can mean genuine network usage backing the rally, not just speculation.

Confirmation rules and multi-factor prompts: One effective way to use AI is to include confirmation criteria in your prompts. Instead of asking a generic “should I trade this?”, you can ask something like: “TokenX just broke out above $10 resistance. Volume is 2x the average. Social sentiment is positive and a few big buys were reported. Given these factors, does this seem like a confirmed breakout worth trading, and what could be a prudent stop-loss?”. A prompt like this forces ChatGPT to weigh multiple factors (price action + volume + sentiment + whales) and give a reasoned answer. It might respond, “It appears to be a well-supported breakout since volume is significantly above average and sentiment is bullish. The presence of big buys adds credence. A prudent stop-loss could be just below $10 (the old resistance, now support) to protect against a false breakout.” This kind of combined analysis is where AI shines – it synthesizes the confirmations you listed into a cohesive recommendation. Of course, it’s basing it on the info you provided; if any of those points were incorrect or outdated, the analysis would be off. But as long as you feed in accurate observations, the AI can help double-check your thesis.

Avoiding emotional or manipulated trades: One key benefit of requiring confirmation is that it filters out trades born of FOMO (fear of missing out) or manipulation. Emotional trades often happen when a trader acts on one strong signal in isolation – e.g., “everyone on Twitter is screaming buy, I don’t want to miss this” or “the price is pumping, I’ll just jump in.” If you impose a rule that “I only act if multiple factors align” (and even better, have AI remind you of that), you’ll likely skip those dubious setups. AI can literally be programmed to be your voice of reason. If you told ChatGPT your trading rules (e.g., “never buy a breakout without high volume; never trade just on hype without technical confirmation”) and then run your scenario by it, it will echo your rules back and apply them. For example: “This trade lacks a volume confirmation and thus might be driven by hype alone; according to your rules, it’s safer to wait.” That is exactly this lifehack. AI helps enforce those rules by quickly checking if they are met.

In practice: Imagine a situation – Dogecoin starts spiking because Elon Musk tweeted a meme (a classic scenario). Social sentiment goes through the roof (Grok says “Dogecoin mentions up 5x, mostly ecstatic”), price jumps 20% in minutes. An emotional trader might hit the buy button immediately hoping for another 100% day. But a disciplined approach would be: Check volume – yes, it’s high. Check if any whales are selling – perhaps on-chain data shows a known large holder moved coins to an exchange just now (uh oh). Prompt ChatGPT: “Dogecoin pumped 20% after Elon’s tweet, volume is high, but I see a huge transaction of 100M DOGE into Binance. Sentiment is euphoric. What’s a cautious approach?” ChatGPT might respond, “While momentum is strong due to hype, the large deposit suggests a whale might sell into this rally. A cautious approach is to wait for a pullback or confirmation that the rally can sustain. If entering, one could use a very tight stop-loss due to the risky nature of hype-driven spikes.” This analysis could save you from being the last buyer at the top of a hype spike. Instead, maybe you wait and indeed see the whale dump, price drops back – if you still believe in the move, you could enter on that dip rather than the peak.

In essence, confirmation is about aligning multiple independent indicators: price action, volume, sentiment, fundamental context, whale behavior. When they all point the same way, the trade probability is better. AI makes checking each of those faster and easier, but you as the trader orchestrate the process and make the final call. By using AI to enforce a checklist, you reduce impulsive decisions.

We have now identified opportunities, validated them technically and fundamentally, and confirmed them with real data. Suppose everything looks good – you’re ready to pull the trigger on a trade. The next step is executing and managing that trade properly, which is where structuring a plan comes in. That’s our next lifehack: using AI to structure the trade and even to reflect on it afterwards.

Lifehack #5: Structuring Trade Plans with ChatGPT – Entries, Exits and Risk Management

One of the best uses of ChatGPT for a trader is to help structure your trade plan before you hit that buy or sell button. Many day traders get into trouble not because they lack good trade ideas, but because they fail to plan the trade fully – they might not set a stop-loss, or they haven’t thought about where to take profits, or they’re uncertain how to size the position. ChatGPT can function like a knowledgeable coach or an algorithmic trading rule-set, guiding you to define these elements clearly before you enter. Think of it as writing a mini trading plan for each trade with AI’s help, so you approach it with discipline.

From signal to strategy: Let’s continue with an example for continuity. You’ve done your analysis on Token ABC: sentiment bullish (via Grok), technicals supportive (maybe above key level with good volume), fundamentals okay (no red flags). You decide you want to go long (buy) for a day trade. Instead of just buying immediately, you can ask ChatGPT to help outline the trade. For instance: “ChatGPT, I want to long Token ABC around $5. It’s breaking out on good news. Help me structure this trade: suggest a reasonable entry point (or confirmation), a stop-loss level to manage risk, and a take-profit target, given current market context.”

ChatGPT will take on this request and likely give a detailed answer such as: “Consider entering the trade if ABC confirms above $5 (to ensure the breakout is real). A sensible stop-loss might be placed just below $4.50, which was the recent support level, to cap downside if the breakout fails. For take-profit, you could aim for the next resistance around $6 (which is a previous high) or use a 2:1 reward-to-risk ratio. That means if you risk $0.50 per token (from $5 entry down to $4.50 stop), aim for about $1.00 gain – so target around $6.00. Additionally, you might plan to take partial profits if it reaches $5.50 and trail your stop-loss upward to protect profits.”

Wow – that’s a pretty thorough plan, right? ChatGPT basically just gave you a structured playbook: Entry trigger, stop placement, and profit targets. It might even explain the rationale (e.g., previous support/resistance, risk/reward). This is hugely beneficial, especially if you are someone who tends to skip these steps in the heat of the moment. The AI isn’t emotionally invested in the trade; it will coldly tell you where logic dictates cutting losses or taking gains.

In the Cointelegraph example, they illustrated this with TAO (Bittensor), which had mixed signals. They suggested prompts like: “Based on current bullish sentiment around TAO, what short-term price action would confirm momentum for a day trade?”. The answer would have guidedContent: the trader on what technical confirmation to wait for before buying (for example, “if TAO breaks above $X with volume, that confirms momentum”).

交易員在購買前等待的技術確認（例如，「如果TAO突破$X並伴隨成交量，這就確認了動能」）。

Another prompt: “Given bearish sentiment and risk factors for TAO, what are safe conditions for a short setup today?”.

另一個提示：「鑒於TAO的看空情緒和風險因素，今天做空設置的安全條件是什麼？」

ChatGPT would outline something like, “If TAO fails to break resistance at $Y and starts dropping on high volume, you could short with a stop at $Y+some margin, targeting a drop to the next support $Z. Ensure there’s no sudden positive news as that could invalidate the short.”

ChatGPT 可能會列出類似的計劃：「如果TAO未能突破$Y的阻力並且開始在高成交量下下跌，你可以在$Y+一些保證金設置止損，目標是跌至下一個支撐位$Z。確保沒有突然的好消息，因為這可能會使空頭計劃失效。」

These are very concrete plans.

這些是非常具體的計劃。

The benefit of an AI-written plan is that it externalizes your strategy – you can literally copy-paste or write it on a notepad and follow it.

AI撰寫計劃的好處 是它能外化你的策略 – 你可以直接複製粘貼或寫在記事本上並遵循它。

It’s much easier to stick to a plan that’s clearly defined. It also forces you to consider risk/reward.

更容易堅持明確定義的計劃。這也迫使你考慮風險/回報。

ChatGPT often reminds you about risk management because that’s ingrained in the trading knowledge it was trained on.

ChatGPT 經常提醒你關於風險管理，因為這是它訓練時嵌入的交易知識。

It might nudge you, “This setup offers roughly a 2:1 reward-to-risk. Ensure that fits your trading criteria.” or “If the trade goes in your favor, consider moving your stop to breakeven to protect capital.”

它可能會提醒你，「這個設置提供了大約2:1的獎勵/風險。確保這符合你的交易標準。」或者「如果交易對你有利，考慮將止損移至保本以保護資本。」

These little suggestions are the kind of thing professional traders do but novices might forget.

這些小建議是專業交易員會做的，但新手可能會忘記。

Position sizing and other parameters: You can take it a step further and ask the AI about position size.

頭寸大小和其他參數： 你可以更進一步，詢問AI關於頭寸大小。

For instance: “If my portfolio is $10,000 and I’m willing to risk 1% on this trade, how many tokens can I buy and where should my stop be exactly?”

例如：「如果我的投資組合是$10,000，我願意在這次交易中冒1%的風險，我可以購買多少代幣，我的止損應該設在哪裡？」

ChatGPT can do the math: 1% of $10k is $100 risk. If stop is $0.50 below entry, that’s $0.50 risk per token. So you can buy 200 tokens (because 200*$0.50 = $100 risk).

ChatGPT 可以計算：$10,000的1%是$100風險。如果止損設在進入點以下$0.50，那麼每個代幣的風險是$0.50。因此你可以購買200個代幣（因為200*$0.50 = $100風險）。

The AI will explain that calculation if prompted, effectively preventing you from accidentally oversizing your trade.

AI會在要求時解釋計算，從而有效防止你意外放大交易規模。

This is so valuable – many traders lose big because they bet too large. AI will consistently apply the formula if you ask it.

這非常有價值——很多交易員因賭注過大而損失慘重。如果你要求，AI會一致地應用這個公式。

Emotion management through planning: Having a plan reduces emotional trading.

透過計劃進行情緒管理： 擁有計劃能減少情緒化交易。

For example, if you have your stop and target set (maybe even entered into your trading platform), you’re less likely to panic-sell on a small dip or get greedy and not take profit.

例如，如果你設置了止損和目標（甚至可能輸入了交易平台），你就不太可能在小幅下跌時恐慌賣出，或變得貪婪而不盈利了結。

ChatGPT can even help pre-plan what to do if the trade starts going well or goes against you.

ChatGPT甚至可以幫助預先計劃如果交易開始順利或不利於你時該怎麼辦。

You might include in your prompt, “Also, how should I manage the trade if it starts winning or losing?” and it might answer, “If it moves in your favor by a decent margin (say, half the distance to the target), you could secure some profits or at least move your stop to your entry price (breakeven).

你可能在提示中包括，「如果交易開始盈利或虧損，我應該如何管理？」然後它可能會回答：「如果它以一個不錯的幅度（比如說，目標的一半距離）向有利於你的方向移動，你可以獲取一些利潤，或至少將你的止損移至進入價（保本）。

If it goes against you immediately and hits the stop-loss, accept the loss and do not hold hoping for a rebound – your stop is there to protect you.”

如果它立即對你不利並觸及止損，接受損失，不要指望反彈——你的止損是為了保護你。”

Having that reinforced can steel you to actually follow through.

加強這一點可以使你堅定地實際執行。

Post-trade reflection: This is part of trade planning in a holistic sense – planning to review your trade after the fact.

交易後反思： 這是整體意義上的交易計劃的一部分 – 計劃在事後檢討你的交易。

Many traders skip journaling because it's tedious. But it’s crucial for improvement.

許多交易員跳過寫日誌因為它很繁瑣。但這對於改進非常重要。

Here’s where ChatGPT steps in again (we’ll call it lifehack #6 officially, but it ties closely to planning):

這是ChatGPT再次介入的地方（我們將其正式稱為生活妙招#6，但它與計劃密切相關）：

After the trade, you can feed ChatGPT the details of what happened and ask for an analysis.

交易後，你可以向ChatGPT提供發生的詳細情況並要求分析。

For example, “I bought ABC at $5, stop $4.50, target $6. It hit $5.80 then reversed and hit my stop at $5 (I had moved stop up).

例如，「我在$5買入ABC，止損$4.50，目標$6。它達到了$5.80然後反轉，並擊中了我的止損$5（我已調高了止損）。」

Can you analyze what I might learn from this? Did I manage it well?”.

你能分析一下我可以從中學到什麼嗎？我管理得好嗎？

ChatGPT might respond with something like, “It seems you moved your stop-loss up to $5 (above your entry) which locked in some profit – that’s good practice.

ChatGPT 可能會這樣回應：「看起來你已經將止損位上調至$5（在你的進入價格之上），這鎖定了一些利潤——這是一個好的做法。

The trade didn’t reach the full $6 target, indicating maybe the resistance at $5.80 was stronger than anticipated (perhaps there was a previous high or a lot of sell orders there).

交易沒有達到完全的$6目標，這表明可能$5.80的阻力比預期的更大（也許那裡有之前的高點或大量賣出訂單）。

One lesson could be to watch interim resistance levels; taking partial profit around $5.80 could have been considered.

一個教訓可能是觀察中期阻力位；可以考慮在$5.80左右獲取部分利潤。

However, your risk management was sound, since you did not turn a winning trade into a loser.

然而，你的風險管理是合理的，因為你沒有將盈利交易變成虧損。

Overall, the trade was well managed, even though it didn’t fully hit the target.”

總體而言，交易管理良好，即使它沒有完全達到目標。」

By doing this kind of debrief with AI, you get a neutral perspective highlighting what went right or wrong.

通過與AI進行此類反饋，你能獲得一種中立的視角，指出正確或錯誤之處。

Over time, patterns might emerge (and ChatGPT can notice patterns if you feed it your last 10 trades, for instance).

隨著時間的推移，可能會出現一些模式（例如，ChatGPT 可以在提供最後10筆交易時注意到這些模式）。

It might say, “I notice in several trades you set a target that wasn’t reached and price reversed near a closer resistance.

它可能會說：「我注意到在幾次交易中，你設置的目標未達成，價格在更接近的阻力附近反轉。

Maybe incorporate more conservative profit targets or scale out of trades.”

或許應考慮設置更保守的利潤目標，或逐步減少交易。」

This reflective process can seriously improve your strategy.

這一反思過程可以大大改善你的策略。

It’s like having a trading mentor review your journal, even if you trade solo at home.

這就像有一位交易導師審查你的日誌，儘管你是在家獨自交易。

Limits of AI in planning: While ChatGPT is great at formulating plans, remember it is not clairvoyant.

AI在計劃中的限制： 雖然ChatGPT 擅長制定計劃，但請記住它不是千里眼。

It doesn’t know which trades will succeed. It might occasionally give a plan that looks good on paper but market conditions invalidate it (maybe overnight news changes everything).

它不知道哪些交易會成功。它可能偶爾會提供一個看起來不錯的計劃，但市場條件使其無效（可能是隔夜新聞改變了一切）。

So, you still need to be adaptable. Also, sometimes AI might not have the latest price context if it’s not connected live – you have to provide the data or at least approximate it.

所以，你仍然需要具備適應性。此外，有時AI可能沒有最新的價格背景，如果它未即時連接——你需要提供數據或至少進行估算。

The quality of the plan is only as good as the scenario described. If you mistakenly tell ChatGPT that a support is $4.50 when actually it was $4.30, the plan’s stop suggestion might be off.

計劃的質量僅取決於所描述的情境。如果你錯誤地告訴ChatGPT一個支撐位是$4.50，而實際是$4.30，那麼計劃的止損建議可能就會偏差。

So double-check critical levels yourself.

所以請自行仔細檢查關鍵水平。

Nevertheless, using AI to structure trades enforces discipline. It makes you articulate your strategy, which in itself can reveal if a trade is questionable.

然而，使用AI來構建交易強調了紀律性。它能使你清晰表達你的策略，而這本身可以揭示交易是否有疑問。

(If you can’t explain it clearly to ChatGPT, maybe you shouldn’t be doing it.).

（如果你無法清楚地向ChatGPT解釋，那麼也許你不應該這麼做）。

Many traders have started to incorporate ChatGPT in their workflow for exactly these reasons – it’s like a second pair of eyes and a logical partner that can catch your blind spots.

許多交易員出於這些原因已開始將ChatGPT納入他們的工作流程——這就像是一雙第二雙眼睛和一個邏輯合作夥伴，能夠抓住你的盲點。

It augments your process but doesn’t replace your decision. You hit the Buy/Sell, not the AI.

它增強了你的流程，但不取代你的決策。你下單買入/賣出，而不是AI。

Now, let’s address the bigger picture: after going through all these “lifehacks” and techniques, what are the overall pros and cons of using AI in day trading?

現在，讓我們看一下更大的畫面：經歷了所有這些「生活妙招」和技術後，在日間交易中使用AI的整體優缺點是什麼？

We’ve hinted at many already, but consolidating them will give a balanced perspective.

我們已經暗示了很多，但整合它們可以提供一個平衡的視角。

And beyond that, we’ll peer a bit into how AI is reshaping trading and what the future might hold – all while keeping in mind that the final responsibility lies with you, the trader.

除此之外，我們會稍微探討一下AI如何重塑交易和未來可能會怎樣——在記住最終責任在你，交易員。

Pros and Cons of Using AI for Crypto Day Trading

AI 用於加密貨幣日間交易的優缺點

Like any tool or technology, AI in trading comes with its advantages and disadvantages.

如同任何工具或技術，AI 在交易中同樣擁有其優勢和劣勢。

Understanding these will help you leverage the pros while mitigating the cons.

了解這些會幫助你利用優勢同時減少劣勢。

Let’s break them down:

讓我們來分析一下：

Pros (Advantages of AI in Day Trading):

優點（AI 在日間交易中的優勢）：

• Speed and Efficiency: AI can analyze vast amounts of data (prices, indicators, news, social feeds) in a fraction of the time it takes a human. This means quicker decision-making.

• 速度和效率： AI可以在比人類縮短的時間內分析大量數據（價格、指標、新聞、社交媒體）。這意味著更快速的決策。

What used to require hours of market scanning can now be done in seconds.

需要花費數小時的市場掃描現在可以在幾秒鐘內完成。

In a game where milliseconds can matter (especially for automated trading), this is a huge edge.

在毫秒相爭的遊戲中（特別是自動化交易），這是巨大的優勢。

Even for a retail day trader, catching a signal a few minutes early can be the difference between buying at a low price or a significantly higher one after everyone else catches on.

即使對零售日間交易員而言，提前幾分鐘抓住信號可能意味著以低價買入或在其他人跟上後以顯著更高的價格買入的差異。

• 24/7 Vigilance: Crypto markets never sleep, and frankly humans need to.

• 24/7 警戒： 加密貨幣市場不會休眠，而人類則需要。

AI bots and scanners can monitor markets 24/7 without fatigue.

AI 機器人和掃描器可以 24/7 監控市場而不會疲勞。

They can send you alerts at 3 AM if something important happens.

如果發生了重要事件，它們可以在凌晨3點發送警報給你。

You could, for example, set up a system where if Bitcoin’s price moves more than 5% outside business hours or if a particular token’s sentiment spikes overnight, you get a notification (perhaps via a ChatGPT integrated bot on Telegram or a Zapier workflow).

例如，你可以設置一個系統，如果比特幣價格在非上班時間波動超過5％或某個代幣情緒在一夜之間飆升，你會收到通知（可能通過集成了ChatGPT 的Telegram 機器人或Zapier流程）。

This ensures you don’t miss opportunities or disasters simply because you were away or resting.

這確保你不會僅僅因為離開或休息而錯過機會或災難。

• Multitasking and Breadth: AIs don’t get overwhelmed by multitasking.

• 多任務處理和廣度： AI 不會因多任務處理而不堪重負。

They can track dozens of coins, multiple indicators, and news sources all at once.

它們可以一次追蹤數十種幣、指標和新聞來源。

As a human, you might effectively follow a handful of markets closely; AI can extend your reach so you have a broader radar.

作為人類，你可能有效地密切關注一些市場；AI可以擴展範圍，使你擁有更廣泛的雷達。

For a trader wanting to find the single hottest mover of the day, this broad scanning ability is like having an army of interns feeding you intel from every corner of the crypto world.

對於想要找到當日最熱門的交易者來說，這種廣泛的掃描能力就像擁有一支實習生隊伍，從加密世界的每個角落向你提供情報。

• Objectivity and Emotional Neutrality: AI tools don’t experience greed, fear, or FOMO.

• 客觀性和情緒中立： AI 工具不會經歷貪婪、恐懼或FOMO（害怕錯過）。

They’ll give the same analysis whether the market is euphoric or in panic.

無論市場是狂歡還是恐慌，它們都會給出相同的分析。

This can act as a stabilizing force on your decision-making.

這可以作為一種穩定力量來影響你的決策。

For instance, if you’re feeling the rush of a potential big win and want to double down, an AI might bluntly point out that your risk would violate your own rules.

例如，如果你感覺有可能大贏的興奮並想加碼，一個AI可能會直接指出你的風險會違反你自己的規則。

Or in a slump, it won’t get despondent – it will still dutifully look for the next setup without bias.

或者在經濟低迷時，它不會感到沮喪——它仍會盡職地不帶偏見地尋找下一個設置。

It’s often said that successful trading is 80% psychology. AI can help keep your psychology in check by providing a rational counterpoint to emotional impulses.

人們常說成功的交易80%是心理學。AI可以通過提供理性的反駁來幫助控制你的心理情緒。

• Skill Augmentation and Learning: AI can augment your trading skills, not replace them.

• 技能增強和學習： AI 可以增強而不是取代你的交易技能。

It’s like having a tutor or co-pilot.

這就像擁有一位導師或副駕駛。

If you’re not great at reading balance sheets or whitepapers, AI can summarize them for you.

如果你不擅長閱讀資產負債表或白皮書，AI可以為你總結。

If you struggle with coding a strategy, AI can help write or backtest one (conceptually).

如果你在編寫策略時遇到困難，AI可以幫助編寫或回測一個（概念上）。

Over time, interacting with AI can actually make you a better trader by exposing you to systematic analysis and diverse perspectives.

隨著時間的推移，與AI互動實際上可以使你成為一個更好的交易者，讓你接觸系統分析和多樣化的觀點。

For example, you might absorb some of the risk management reminders that ChatGPT frequently mentions, internalizing those best practices.

例如，你可能會吸收一些ChatGPT 經常提到的風險管理提醒，將其內化為最佳實踐。

• Customization and Versatility: ChatGPT and similar models are extremely versatile.

• 客制化和多用途性： ChatGPT 和類似模型非常多用途。

You can tailor them to your needs with the right prompts.

你可以用正確的提示根據你的需求進行調整。

Whether you trade scalping five-minute charts or swing trade over several days, you can ask the AI to adjust its suggestions accordingly.

無論你是做五分鐘圖表的剝頭皮交易，還是幾天的波段交易，你可以要求AI 相應調整其建議。

It can shift between technical, fundamental, and sentiment-based analysis as you require.

它可以根據你的需求在技術、基本面和情緒分析之間切換。

Moreover, advanced users can integrate AI into their custom workflows – from plugging into spreadsheets to using APIs to automate data feeding.

此外，高級用戶可以將AI 集成到他們的自定義工作流程中——從插入電子表格到使用API自動化數據輸入。

The AI becomes part of a personalized trading toolkit.

AI成為個人化交易工具包的一部分。

• Automation Potential: With a bit of coding or no-code 工具，您實際上可以連接 AI 自動執行或管理交易。這涉及到交易機器人的領域，但值得注意。例如，您可以有一個腳本使用 AI 的輸出來觸發實際訂單（需謹慎處理）。據報導，一些平台如 Pionex 正在實驗結合 ChatGPT 界面和自動化算法的可能性。許多業餘交易者已經建立了自己的 ChatGPT 驅動的交易機器人，能夠掃描情緒並一併進行交易。如果謹慎操作，這意味著您可以在不一直盯著螢幕的情況下擴大交易或運行策略。

• 持續改善透過日誌： 使用 AI 進行日誌或交易回顧（如之前提到的）是一項提升勝率的巨大優勢。這為從錯誤中學習帶來系統化的方法。隨著時間的推移，這可以提高您的利潤率，因為您（在 AI 協助下）可以識別和消除不良習慣或無效策略。

總之，優點圍繞著速度、廣度、客觀性和增強技能。AI 就像一位不知疲倦的分析師，全天候為您工作，幫助您培養良好的交易習慣。

缺點（AI 在交易中的局限性和風險）：

• 缺乏人類直覺或上下文理解： 儘管 AI 功能強大，但缺乏數據模式之外的真正上下文理解。它無法以直覺方式評估市場情緒，也無法理解超出其訓練數據或輸入之外的人類行為。正如所提到的，在情感分析中，它可能會錯過諷刺或反諷之處。AI 也不真正理解可能影響加密社區的地緣政治細微差別或文化因素。例如，如果某代幣因為某個小圈子內的笑話而暴漲，AI 可能無法理解，它只會看到“提及量增加”並給出通用的看法。最重要的是，AI 無法本質上區別真假信號。如果有人通過偽造訂單或大規模發帖來操縱拉高價格，AI 將會照單全收。而有經驗的人類交易者有時能夠嗅出不對勁之處（例如，“這種價格走勢看起來像經典的拉高出貨，過於垂直，並且在奇怪的時間間隔有奇怪的波動量”）。AI 可能只是看到動量並助威。這種缺乏直覺意味著，如果您盲目依賴 AI，可能會被虛假行動所蒙騙。

• 數據限制和質量問題： ChatGPT 的基礎模型沒有即時數據。即便有些模型（如 Grok）依賴於可能存在輕微延遲或錯誤的數據源。如果 AI 報告一個指標或價格，可能使用的數據是幾分鐘前的，快速市場中可能已經過時。已經有案例表明，AI 提供的統計因為信息獲取方式而過時或有輕微偏差。此外，如果輸入數據錯誤或有偏差，輸出也會如此（垃圾進，垃圾出）。這就是為什麼我們強調在可靠平台上雙重檢查關鍵信息。此外，AI 免費版本可能無法訪問某些信息（例如，沒有插件的 ChatGPT 無法自行獲取當前價格）。AIs 通常缺乏每秒的實時精確性——如果您做高頻交易，它們不能替代直接市場訊息流。它們工作的層級是總結數分鐘或數小時的活動，而非微妙。

• 過度依賴風險： 如果您開始用 AI 做所有事情，那麼失去自身優勢或變得自滿的危險就存在。交易涉及創造力和適應性。如果每個人都使用相似的 AI 模型，很多人可能會得到相同的信號，導致交易擁擠。想像數百名交易員都從 ChatGPT 獲得突破「看漲」信號——他們可能一起湧入，諷刺的是，這創造了一個人滿為患的位置，當最早少數人退出時就可能崩潰。在股票市場中，分析師甚至推測，AI 驅動的策略可能導致無意的擁擠交易，行為難以預測。您不想將整個決策過程交給 AI，否則如果 AI 出錯，您會變得脆弱。這就像自動駕駛飛行——運行良好，直到某些事不按劇本來，而如果您實際上沒有真正「飛行」飛機，您可能反應不佳。

• 誤解和錯誤： AI 有時會出現錯誤。它可能出現幻覺——即它可能捏造一個聽起來合理但是不基於事實的答案。例如，如果您詢問一些冷門問題，比如“證券交易委員會是否批准過可能影響這種代幣的任何 ETF？”，如果它不知道，可能會猜測或混合事實。或者它可能混淆兩個名字相似的代幣。提示的模糊性也會導致奇怪的答案。例如，如果您問：“我現在應該買入這個代幣嗎？”，有時可能會偏向謹慎，另一次可能聽起來樂觀，取決於細微的措辭差異。這種不一致性和潛在錯誤意味著您不能將 AI 的輸出視為聖經。始終用獨立來源或邏輯證實關鍵結論。

• 無責任或風險承擔： 如果交易失敗，AI 不會承擔責任——您才會。值得重申：AI 不會承擔金錢風險，因此它不會感受到失敗的恐懼或痛苦。它可能高興地建議一項最終虧損10%的交易，而它毫不懊悔（即使您之後告訴它，它也只是禮貌地說“很遺憾發生了這種事”，但這不會返還您的錢！）。換句話說，AI 工具不關心您的資本——只有您自己才會關心。這將責任放在您身上，以實施風險管理。AI 可能建議止損，但不會為您執行，除非您對其編程。而如果您選擇忽視 AI 的風險建議，AI 也無法阻止您。因此，需要有紀律性來正確運用信息。

• 有限適應性和經驗學習： 除非您特定地將您的經驗反饋給 AI（即使這樣），它也不會像人類交易者從多年模式識別和直覺構建中那樣“學習”。長時間身處市場，您可能注意到某些無形資產（市場“感覺”或常見的陷阱模式）——AI 只能知道其數據中有什么內容。隨著每次交易的進行它並不真正變得更好，與理想情況下您會的方式不同。有一些方法可以整合學習（例如調整模型以適應您自己的交易數據，但對普通用戶而言這太高級）。基本上，通用 AI 不會因為您經常使用而自動改進。除非您明確地這樣整合它，不然它不會追踪您的資本曲線或適應您的風格。

• 技術和訪問問題： 有時 AI 服務可能會中斷或速度慢（尤其是您依賴在線服務時）。想像在快速市場中您向 ChatGPT 詢問關鍵問題但回應延遲或服務過載——可能會令人沮喪或讓您錯失良機。或者您的互聯網斷線，而您的交易應用在一處位置，AI 在另一處……這些都是實際問題。此外，由於收費牆或超出其允許範圍，它無法檢索某些數據。您可能會要求“檢查該代幣的白皮書 PDF，告訴我是否有紅旗”——除非您有插件或其他方式輸入，否則無法實現。因此，它並不是全能的。

• 成本和限制： 最佳 AI 使用通常需要訂閱或高級訂購。Grok 的免費使用是有限制的；ChatGPT 的免費版本知識截止且無網絡訪問（基於其基礎訓練）。要獲取實時數據，可能需要付費的 ChatGPT Plus 或其他服務，這需要花費。這些成本可能會加起來。如果您使用某種專門的 AI 交易平台，那些通常有費用或利潤分享。儘管這些開支可能值得，但小賬戶的新手交易者需謹慎不要在工具上過度支出相較於其資本。

• 安全和隱私： 如果不小心，您可能將敏感信息提供給 AI。例如，將您的交易所 API 鍵提供給 AI 服務是一個大忌（除非是自託管的解決方案且您信任）。曾經發生過 API 鍵通過第三方服務外泄導致黑客攻擊的事件。此外，您擁有的任何策略或優勢，如果與知名 AI 分享，理論上它可能成為其他人可以訪問的其知識一部分（取決於該 AI 的管理或訓練方式）。因此，使用這些工具時存在無意中洩露一些精髓風險——儘管 OpenAI 表示如果您選擇退出，他們不會將您對話的數據用於訓練等等。仍然需要謹慎。

總之，缺點強調 AI 並非萬能或自主：它可能被誤導，也可能誤導您，並且它免於責任。也有外部因素，例如服務限制和費用。了解這些，您可以策劃享受 AI 的好處同時防範陷阱。

平衡觀點： 正如 Cointelegraph 的一篇文章恰當地指出，「AI 只有在數據和使用者的情況下才能做到最好」。將其作為優勢，而非依賴。這是一個強大的盟友，但您是有皮膚在遊戲中。最佳成果可能來源於協同：人類的創造力和直覺受 AI 的效率和一致性所指引。在下一部分中，我們將通過反思 AI 正在如何真正改變交易格局以及這對交易者未來意味著什麼來總結旅程——本質上，在這個勇敢的新世界中如何保持先進性。

AI 在加密交易中的未來——適應和演進

像 ChatGPT 和 Grok 這樣的 AI 工具在加密交易領域的崛起並不是一時的現象；這是市場運作方式更廣泛技術轉變的一部分。我們正在見證交易手冊的重寫。這對您作為一名交易者意味著什麼，如何隨著這些變化適應和演進？

首先，考慮一下我們在短短幾年內走到了哪裡。不久之前，「AI 在交易中的應用……」Trading” past was mostly dominated by quant hedge funds and proprietary algorithms requiring significant investment. However, today, any retail trader with an internet connection can access powerful AI models that offer previously unimagined capabilities at an individual level. Access to information is becoming more equitable. By early 2025, even mainstream financial brokers have started incorporating AI chatbots into their platforms. For example, Tiger Brokers launched "TigerGPT" with an AI model (DeepSeek) to enhance the analysis and trading experience for their users. Many other firms are using AI for risk management and strategy development. In the cryptocurrency space, exchanges and trading apps might do the same – visualize your exchange interface having a built-in “AI advisor” to consult before trading any coin. Indeed, some have already been exploring this direction; Binance, Crypto.com, and others have tested AI-driven functions in customer experience or analysis.

So, the future may see AI as an integral part of all trading platforms. For traders, this implies two main points:

1. AI Access Could Become Ubiquitous and Possibly a Commoditized Standard. Simply using AI might not provide an advantage when everyone has access to it. The edge will shift to how effectively you utilize it. Two traders using the same AI could achieve different results – the trader with better prompts, better judgment, and better integration into their strategy will win. It's akin to how everyone gained access to advanced charting software over the years; it didn't make everyone profitable, it just raised the baseline of analysis quality. So, continue developing your skills in interacting with AI, customizing it to your style, and avoid the trap of merely copycatting others.

2. Markets May Become Faster and More Efficient in Some Ways, Yet Also Susceptible to AI-driven Herd Behavior. If many algorithms and traders react to AI-identified signals, some patterns may self-reinforce rapidly (leading to sudden spikes or drops), and other patterns may be arbitraged away quicker (everyone notices the same arbitrage, so it disappears). We mentioned “crowded trades” – this is a real possibility. For example, as sentiment analysis AI signals become more common, by the time a trend is flagged, a swarm of bots might rush in, causing a sharper but perhaps fleeting move. Volatility might increase in micro-timeframes even as longer-term inefficiencies diminish. As a trader, you might need to act faster on those AI-identified scalps, or, conversely, find contrarian moves when the AI herd overshoots. Opportunities might also exist in trading against predictable AI behavior – an advanced concept where if you know many systems will purchase based on a particular signal, you position yourself slightly earlier and then sell into them. This is complex but plausible.

AI could potentially initiate feedback loops. Imagine an AI that reads news and trades, and a journalist using AI to write news based on market movements – this could create circular effects. Sounds sci-fi, but minor versions of this can occur (one AI-generated tweet triggers AI trading bots, causing a price change, which triggers another AI’s sentiment alert…and so forth). This indicates that sometimes moves might occur due to AI-on-AI activity rather than any human rationale. Identifying when something seems fundamentally inexplicable (maybe it's just algorithms chasing one another) will be an essential new skill.

On a positive note, AI could further democratize trading knowledge. More educational resources will become available through AI tutors; more individuals from non-traditional backgrounds can participate with AI's guidance. This could increase market participation and liquidity. We might witness the emergence of new AI tools designed explicitly for cryptocurrency trading – perhaps ones integrating on-chain data deeply with price action in an AI model. There may also be AI-driven social trading, where an AI analyzes top traders’ behaviors and suggests strategies to others.

Yet, there's also concern about regulation. If AI trading bots cause issues (like flash crashes or manipulative schemes), regulators might step in with new rules. We already know the SEC monitors trading algorithms in traditional markets. In crypto, the space is currently more open, but any significant incident could invite new regulations. For instance, if an AI-guided pump-and-dump scheme affects many people, expect demands for oversight on AI financial advice. Already, there's caution that some AI-driven strategies may edge into manipulation or at least blur accountability (who's responsible if an AI causes a market incident?). As a trader, remain aware of the evolving legal landscape, particularly if implementing fully automated strategies. The last thing anyone wants is to inadvertently break a rule because “the bot did it.”

We can't mention the future without considering AI itself will become more powerful. The current ChatGPT and Grok are impressive, but imagine a year or two from now – models might become even more accurate in predictions (to an extent) by incorporating real-time learning and more specialized training on financial data. We might see multimodal models observing candlestick charts like a human eye would, not just numbers. There's already research into AI that can “see” patterns visually. Or AI that listens to earnings calls and picks up sentiment from voice tones (for stocks, at least). In crypto, an AI might monitor not just text but also developer activity (GitHub commits), network congestion, etc., all simultaneously. As traders, embracing these advancements early can keep you ahead. Those clinging to purely manual old-school methods might find themselves outpaced in terms of speed and breadth.

Nevertheless, despite these advanced technologies, the core principles of trading will remain: managing risk, understanding market structure, and controlling one's emotions. AI doesn't change supply and demand; it only alters how we perceive and respond to it. Even in a market thoroughly imbued with AI, someone will lose and someone will win in each trade – that zero-sum (minus fees) nature persists. Good trading will still require patience, discipline, and adaptability. One could possess the best AI tools and still fail without proper risk management or if greed prevails. Conversely, even a basic approach can succeed if one adheres to sound strategies and adapts to new tools cautiously.

Adaptability may very well be the overarching skill here. Be prepared to adjust your strategies as the environment evolves with AI. Strategies may have shorter lifespans. For example, maybe a social sentiment strategy worked excellently in 2023. By 2025, too many people (and bots) employ it, and it’s no longer as effective. So, you tweak it, apply more filters, or explore varying time horizons. Human-driven contrarian strategies (doing what the AIs aren't) might gain popularity at some juncture, then the pendulum swings again.

In conclusion, the future of cryptocurrency day trading with AI is both exciting and dynamic. Those embracing the technology thoughtfully and remaining agile will likely find it an invaluable edge, akin to traders who first adopted electronic trading or algorithmic strategies found an advantage for a while. However, those who become complacent or overly reliant on AI may encounter difficulties when conditions change or when AI leads them astray.

The best approach: remain curious, keep learning, and treat AI as an extension of your own analysis rather than a replacement. Continue building your own market intuition and knowledge – that human element combined with AI's power is a formidable combination. As previously mentioned: use AI as an edge, not a crutch. The cryptocurrency markets will continue to evolve rapidly, and with AI in the mix, they might evolve faster than ever. Yet, the opportunity is enormous for those who ride the wave. Many traders are already quietly using ChatGPT, Grok, and other AI tools effectively, sometimes in unexpected ways. Now you have a comprehensive overview of how they do it and how you can do the same.

Final Thoughts: Day trading has always been about information and execution. AI is changing how we receive information and even how strategies are executed. It can serve as your co-pilot, analyst, and risk manager all in one – yet you remain the pilot, the final decision-maker. With the tips and examples in this guide, you should be well-prepared to begin integrating AI into your trading workflow. Start gradually: perhaps use ChatGPT to double-check a trade idea or Grok to scan morning sentiment. Feel the experience, see the results, and iteratively refine your process. The learning curve is part of the journey, but it's rewarding.

We live in an era where an individual, empowered by AI, can process market insights like a team of analysts – an almost unfair advantage if used wisely. But keep in mind, no tool guarantees profits. Every trade still involves risk and uncertainty. The market can and will behave in ways no model can foresee from time to time. When in doubt, fall back on fundamental risk management and conduct your own research alongside the AI’s suggestions. If an AI proposes something that doesn’t make sense to you, trust your own judgment and verify.

As you venture into AI-assisted trading, maintain a journal of what works and what doesn't (yes, even document your AI's performance!). You're effectively training yourself while using the AI. Over time, you’ll develop an instinct for when to rely on the AI and when to question it.

Ultimately, every trade still comes down to you – your decision, your funds, your responsibility. Yet, you're no longer alone in the cockpit; you have powerful helpers at your disposal. Use them wisely, stay astute, and best of luck in the markets!