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市場摘要

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投資想法

比特幣預期中的反彈目標市場經過2個月的磨底，總算有點反彈的樣子了。為什麼說反彈，因為從大週期看，調整好像尚未結束，所以這裏可能是一個abc走勢的b，所以我認為，這裏更傾向於反彈走勢。 不過反彈的力度也可能並不小，因為目前看，日線的均線ma200還在32000美元左右，依然有20%的空間，而從底部反彈，如果按照1.618和2.618計算，目標分別是2.5萬和3萬美元左右。 考慮到本輪反彈已經消耗了2個月的時間，留給多頭的時間不多了，這個反彈週期，大概率在9月中旬結束，之後進入新的調整週期。 建議：因為反彈走勢持續性一般，從2小時看到突破即調整的情況很多，所以不建議追高。保持底倉不動，剩下的資金高拋低吸。如果無法企及目標位，可以按照macd＋ma18的原則，觀察大級別分時是否出現頂部信號。
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2
相似的結構，能否帶來相似的獲利機會?混沌理論在本世紀初發現之後獲得大量的關注，包含碎形結構等等的股票實證研究也持續開展， 基本上相關的研究都指出了一件事：就是股票價格不可預期。 那麼這個理論與結構就沒有用了嗎? 個人認為其還是有相當的實用性，最簡單的，在市場打滾許久的投資人常常會有時候莫名的產生：這個點該賣了、這邊可以買了的想法， 然後最後這些投資人將這樣的走勢歸納為股性，意即每支股票有每支股票的走法，當一支股票到了哪裡就該反彈、到了哪裡會出現賣點， 雖然技術面或是基本面都不同，但股價走勢類似，這個股性，在我看來就是每支股票的碎形結構相同， 因此長期關注個股的老投資人自然而然的在潛意識中已經出現了哪邊可以買賣的想法。 而碎形結構會不會改變?當然會，否則如果走勢都一樣，股票也就喪失了他的迷人之處，但是碎形結構改變， 會需要相對長的時間或是突然出現一個基本面催化劑，因此短線上如果發現類似的碎形結構，我認為是值得投資人進場嘗試的。 比如這支神隆，我們看一下兩個框起來的量價結構，會發現有驚人的相似， 往上拉抬到一個高點，之後往下整理出一個低點，第一次沒有破前低而這次破了前低。 之後開始緩步往上推升經過一個整理之後再出現一個高點然後往下急殺。 第一次破底，而這次沒有破底，但結構與量能是雷同相似的， 那麼，我們是不是可以判斷在本波見底之後，會出現一波類似的往上推升行情? 目前也來到了類似的前波區間整理壓力，這邊會不會是一個好的相對切入點? 就有待市場驗證了。
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1
菱形菱形為反轉形態，屬於頭肩形態的變形，如果在相對低點發生屬於頭肩底，特性是先軋空再殺多。反之亦然。 以近期納指為例，先畫出 A B 兩條線，再分別以 A B 畫出平行線 C D。 漲幅測距為 F G 兩點的距離，加上突破點 E 。 台股指數近期也是菱形底，在本人以下貼文裡的補充有提到， 在政府基金宣佈不護盤的隔日，就已經有菱形的雛形，如果投機一點，價外買權多頭價差或是裸買 Call (視情況而定)，以上兩者的特性都是賺大賠小，並且在進場前就能控制住損失，都是可以使用的方式。 菱形也不一定是真的菱形，蒲型也能算是菱形的一種，技術分析雖然有技術兩字，其實還是有一些藝術的成分在裡頭。 以下是納指在網路泡沫始末 如果有興趣繼續深入瞭解，谷歌 Diamond Bottom 或是 Diamond Top 能找到一些資料。 不少人在指數操作喜歡看大戶或是法人的籌碼分佈，其實更應該研究的是，這些大戶為什麼會這樣佈局。
編輯精選
教育
中國解封預期大升，航空蓄勢待發中國封鎖至今已經兩年有多，在全球逐步邁向解封的時候，都在看這個全球第二大市場到底甚麼時候才願意與世界重新接軌。 關於中國防控是科學還是政治問題，就不再解釋，以免淪於口水抹黑。 這邊就用最簡單的方式來判斷中國是否準備好了。 1. 上個月中國宣布入境隔離天數降低，改為7+3 2. 昨天中國國產新冠口服藥批准上市 3. 這兩天兩國領袖要會談 上面這三個已知的消息，其實都給了中國解封預期大升的線索 另外還有個隱藏的消息就是10月中國20大會議，這個就先不討論。 回到線圖，可以發現國航拉升之後進入了盤整，這邊量能急縮是一個很標準的整理洗盤區間， 而如果以周線來看，也可以發現股價已經重新站上中長期關鍵均線並在均線附近進行整理。 股價會提早反應，如果9月中國逐步解封成真，那麼股價這時候開始啟動也是很正常的一件事。 國外航空股反應解封都會往上大漲，那麼中國的航空股沒理由不反應。 包含國泰航空、東方航空、南方航空的線圖都出現了上漲後整理的型態，這個是整個族群的運動， 因此個人認為相關類股將出現多頭的機會，提供各位參考
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9
美聯儲FOMC 7月利率決議、政策聲明及GDP數據前瞻 本月美聯儲FOMC將於北京時間28日凌晨02:00召開 同日晚08:30美國第二季度實際GDP年化季率初值數據公佈 料將引起市場大幅波動 <1>.7月加息75bp已成主流 6月份高達9.1%的CPI數據令市場一度預期本次FOMC加息100bp 但隨後美聯儲票委們迅速滅火降溫 目前市場預期9、11、12月份加息幅度逐漸收窄 年底或停止加息，明年初或開始降息 。 <2>.重點關注美聯儲主席鮑威爾會後講話 由於本次FOMC沒有點陣圖公佈及經濟預測 "美聯儲如何平衡高通脹風險與緊縮政策帶來的經濟衰退風險" 將成為後市走勢風向標。 <3>.美國第二季度實際GDP年化季率預期現分歧 目前市場給出的預期是0.9% 而向來以準確度較高著稱的亞特蘭大聯儲GDPNow模型預期為-1.59%(-1.6%) 兩者存在較大分歧 確定美國經濟衰退的依據通常是GPD與失業率 即GDP連續兩個季度負值、失業率持續6%以上才可以確定衰退 目前失業率較低暫不構成威脅 此次GDP數值公佈則陰晴不定 <4>.對於後市加息路徑及市場反應： 如果未來通脹數據逐步放緩 美聯儲則會放慢加息腳步直至降息 如果未來通脹仍然居高不下 那麼美聯儲將不得不在經濟衰退來臨之際，繼續大舉加息 這種不確定性因素則會持續促使市場的高波動性。
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4
APEUSDT 精美的諧波嵌套，短線持續偏空處理震盪完全就是諧波型態的主場 近期的各種幣的各個時間週期 完全有一大堆可以用做諧波交易經典案例的行情 如果用流量的邏輯來講的話 根本就像是這些 K 線圖 收了諧波的業配 需要給予它足夠的曝光的感覺 lol 諧波的有趣之處就是 是個 "完全沒得事後諸葛" 的交易方法 只要你稍微有點諧波基本概念 哪些成功哪些失敗基本上是一目瞭然的 而也就回到了非常公平的勝率和盈虧比的統計 以及每一次遇到諧波的時候的關鍵大哉問： “這一次你做不做” 例如昨天上午有看到看空蝙蝠、且選擇這次要做的交易者 說他有 6.93 的空單持倉一點都不奇怪 這就是照著規則交易下、市場大哥賞的禮物 短線還在走綠色諧波的 CD 段 到達 5.88 之前都還是會更加傾向留意做空交易機會 Let's see how it goes yo!
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2
兩年底部淬鍊 量價慣性改變 曾經的生技股王能否再現光芒?這檔我本人也追蹤了一段時間，之前上漲追進去，再被市場殺下來，我都有份。 拉長時間來看，這個整理跨度的時間與空間都很大，對於投資人來說想從這個超大型整理型態抓到進場點其實很難， 畢竟進場點還是要看較為短期的股價表態，而如果假設這個底部是一個整理型態，那麼追突破的價格可能與現在的價格差距相當大。 但就資金周轉時間的效率來看，或許等到上去表態追突破也不失為一個成功的方式，這邊選擇就交給投資人自行判斷了。 這個兩年的底部我個人觀察到的是近期上漲的量價慣性改變，過去可以發現整理區的大量幾乎都發生在相對高點， 但最近的大量卻是在相對低點，這是第一個我看到的不同。 再來看到MACD指標在這個整理區間出現了一個長期的背離走勢，而近期也是來到了整理區低點附近之後出現短期的背離， 這個短期背離之外又有長期背離，基本上我認為是一個大波段抄底的好時機。 另外在趨勢線判斷上也可以發現整理期間的兩次大型下降整理出現時間縮短、斜率增加的趕底走勢， 一般來說我們希望看到的是整理時間拉長然後斜率降低出現橫盤整理之後的突破，大部分股票的走勢也是這樣走。 那為何這檔股票的走勢會與其他不同? 我認為這是因為主力有時間的壓力。 一般的股票從底部起漲，多半都會伴隨著基本面的改善，像是三率上升或是市場出現相關的改善，這些的關鍵影響因素大部分都會是時間， 這就是費式時間轉折的奧秘。 但這檔股票與一般股票不一樣，時間對他來說與一般股票的影響不同，一般股票時間越長可能對於股價的幫助是越好，因為市場的持續改善或是三率的持續增加。 而她的股價影響是來自於藥證、來自於三期解盲的市場信心又或是藥物的授權，這邊時間如果拖越長反而越不利， 生技股的笑話就是解盲前不敢買、解盲後買(賣)不到。 因此她的底部就會出現這種趕底的走勢，所以生技股也是特別容易出現破底翻的族群。 所以這個整理區間的斜率、時間都出現加速趕底的情況，我認為她的大方向趨勢馬上就要改變。 回到標題，兩年的底部淬鍊、量價結構慣性出現改變，我認為屬於合一的光芒即將綻放。
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教育類想法

諧波教學（1）：加特利、蝙蝠形態的識別和應用在2019年分享了壹組諧波教學，講解的是諧波模式中的理想比例，但是完美的比例在市場中很難遇見，很多小夥伴私聊，多分享壹些諧波教學 不確定能不能持續連載，有時間會持續更新壹些幹貨教學 壹年多過去了，看到很多人放棄了諧波學其他技術，也看到很多人徹底被市場淘汰。投機像山嶽壹樣古老，重要的是笑到最後，而不是壹時的春風得意 樓主的諧波學自於斯科特.卡尼，並非原創，也沒有經過主觀性的改編，如有和您學的諧波不太壹樣，不要著急反駁，技術是壹成不變的，而交易系統卻很難雷同，適合妳的才是最好的 比如賽弗，很多人喜歡交易這個形態，但我不能把它納入我的交易系統，因為賽弗和鯊魚形態有嚴重的重疊，和以前壹樣，依然喜歡分享，有精力的話會繼續分享諧波相關的知識 2021年，祝大家順風 、順水、順財神！！！
編輯精選
教育
2
菱形菱形為反轉形態，屬於頭肩形態的變形，如果在相對低點發生屬於頭肩底，特性是先軋空再殺多。反之亦然。 以近期納指為例，先畫出 A B 兩條線，再分別以 A B 畫出平行線 C D。 漲幅測距為 F G 兩點的距離，加上突破點 E 。 台股指數近期也是菱形底，在本人以下貼文裡的補充有提到， 在政府基金宣佈不護盤的隔日，就已經有菱形的雛形，如果投機一點，價外買權多頭價差或是裸買 Call (視情況而定)，以上兩者的特性都是賺大賠小，並且在進場前就能控制住損失，都是可以使用的方式。 菱形也不一定是真的菱形，蒲型也能算是菱形的一種，技術分析雖然有技術兩字，其實還是有一些藝術的成分在裡頭。 以下是納指在網路泡沫始末 如果有興趣繼續深入瞭解，谷歌 Diamond Bottom 或是 Diamond Top 能找到一些資料。 不少人在指數操作喜歡看大戶或是法人的籌碼分佈，其實更應該研究的是，這些大戶為什麼會這樣佈局。
編輯精選
教育
穩健盈利的核心,什麼是交易系統？交易系統包涵哪些東西？關於我對交易系統的一些理解，歡迎交流指正~“交易系統”這個詞彙想必大家都不陌生，但市場上真正擁有一套“穩健盈利”交易系統的人數實在屈指可數，今天和大家聊一聊我對交易系統的一些理解及應用方法，有不足的地方希望高手能夠批評指正，歡迎交流~ 首先，在聊交易系統之前我們必須要搞清楚一個市場的真相，無論技術分析還是基本面價值投資方法，又或是一些玄學預測方法，都無法準確無誤的預測市場下一步的走勢。最可怕的應該就是賺了錢的交易員偶爾也會做分析師的事情，導致大眾堅信市場是可以預測的，在明白市場無法預測之前，我們談交易系統簡直就是無稽之談，浪費時間... 個人認為一套完整併且符合科學依據的交易系統主要包含以下三個核心方面： 一.交易策略 二.資金管理 三.個人心理 以上三大核心因素缺一不可。 一.交易策略： 在明白了市場無法預測之後，我們應該搞清楚我們所應用的所有的交易方法，例如各種類型的指標，形態學，傳統量價關係，價格行為學，波浪理論，江恩理論，纏論，諧波，威克夫，亞當理論，磁區理論，價格模型等等，任何一種交易方法最終都要形成一個買賣點，而這個買賣點都只能歸為“交易策略”或者稱為交易信號，我們統稱為交易策略，市場上有非常多的人把一套交易策略簡單的理解為一套交易系統，其實這是非常致命的誤區...開發出一套交易策略之後，我們還需要花大量時間統計出該策略的三大要素： 1.交易勝率 2.平均盈虧比（注意是平均） 3.交易頻率 以上三大要素如果不能建立在科學的基礎之上，那麼該策略就是一套注定走向失敗的策略。交易勝率是該交易策略的成功率，平均盈虧比是平均盈利和平均虧損的比值，很多朋友在交易之前就是計算出該筆交易的參考盈虧比，用這樣的方法在市場上長期交易，我認為這樣的方法是不合理的，交易頻率是交易次數。其中，交易勝率和平均盈虧比必須確保該策略在長期大量交易之後是盈利的，這裡引出另外一個概念“期望值”，期望值是通過大量統計之後用交易勝率和平均盈虧比通過數學公式計算出的一個數值，交易勝率和平均盈虧比越高，該策略的期望值也越高，交易勝率，平均盈虧比，交易頻率三者理論上越高越好，單一提升已經比較困難，想三者同步提升更是難上加難，這也是很多交易員能夠穩健盈利但收益率十分低的原因，我用的期望值的計算公式如下：【勝率*（平均盈利/平均損失）】-（失敗率*1）】得出的數值必須為“正數”，該數值越高越好，但只能做為一個理論參考值。 二.資金管理： 聊完了交易策略之後現在我們回到交易系統的第二大核心要素資金管理上面，資金管理我的理解其實就是“倉位規模定義方法”，資金管理必須能夠回答以下問題:我們能夠開多大倉位？我們能夠虧損多少？以上兩個問題必須是準確數值，不能是理論數值，這和每個交易者的資金情況不一樣，和心理承受能力也不一樣。資金管理的方法比較多，適合自己交易系統的方法才是最好的，但其核心作用是使我們能夠在市場上長期生存，徹底排除除賬戶歸0的風險也叫“破除破產風險”並且盈利時能夠擴大我們的收益。 三.個人心理： 個人心理是通過大量交易之後，確保我們的交易策略和資金管理都建立在科學的基礎之上，即“正期望值的交易策略，我們所採用的資金管理也能夠徹底破除破產風險",內心能夠使我們真正堅定執行該交易系統的一種心理支柱，沒有該心理支柱的支撐，即使是再好的交易系統拿到我們手中也很難做到堅定不移的執行，這也是購買別人的交易系統的不靠譜之處，更何況市場上出售的不一定是能夠穩健盈利的系統，個人心理支柱只能在開發交易系統和測試統計交易系統還有大量的交易過程之中慢慢形成，聽別人說，看書，复盤等等方法個人覺得作用微乎其微... 個人認為一套合理的交易系統主要包含以下學科：數學，概率論，統計學及個人心理學。 “市場無聖經，盈利必須建立在科學的基礎之上”... 如果您贊同我的觀點，請點贊，評論，擴散，不勝感激。 祝君交易愉快！
編輯精選
教育
7
關於道式理論123&2B法則道式理論 123法則 法則1- 突破壓力趨勢線 嘗試突破操作 風險較大 無法判定真偽突破 法則2- 底部逐漸托高 Higher Low 嘗試回撤站穩操作 風險較小 盈虧較好掌控 法則3- 突破前高價位(法則1最高價位) Higher High 結構走勢明確 終結下降走勢 _______________________ 2B法則 針對假突破進行操作 假設趨勢可能反轉 在上升趨勢中 價量逐漸衰竭 供給過於需求 需求逐漸耗盡 未能有效突破壓力區間 嘗試空頭操作 止損價位設於前高上方 風險較好控制 盈虧較好掌控
編輯精選
教育
8
關於熊市要記住的4件事大家好！👋 哇，這5個月真的夠了！資產全都打水漂了，到處都是巨大的吞噬K線。並且納斯達克指數在本周正式收於熊市區域。對於加密交易者來說，比特幣、以太坊和其他一些加密資產已被削減了一半或更多。儘管標準普爾500指數從高點僅下跌了13-14%，但幾乎可以肯定地說，在過去兩年我們所看到的幾乎是大規模牛市之後，我們現在正式進入熊市。 因為這可能是社群中許多人第一次經歷熊市，我們認為發佈一些關於熊市需要記住的關鍵事項會很有幫助，以便人們可以駕馭這個新的市場狀態。 讓我們開始吧! 1.) 波動性讓您的部位在盈虧方面感覺更大💥 熊市通常比牛市帶來更大的資產價格波動。在過去20天，我們看到指數的日均波動約為3%，遠高於2021年約0.9%的滾動20天平均值。在相同數量的資本下，這種平均範圍的回升代表著以美元計算，您的盈虧變動可能比“平常”大得多。2020年3月，標準普爾500指數的日均波動幅度超過5%！ 記住這一點很重要，因為盈虧會對交易者的心理產生巨大影響。許多專業的經紀人和對沖基金控制了這一個因素，減少了風險敞口，使每日投資組合波動率接近其目標。有一些基金被授權這樣做。雖然您可以按照自己的交易計劃自由地做自己喜歡的事情，但這是一個關鍵的期望！期望會比平常有更大的變動。 2.) 熊市平均持續約2年📉 2年的數字主要是指*股票*熊市的平均持續時間。到目前為止，在加密貨幣中，熊市的平均持續時間約為9個月。相比之下，在股票中，平均牛市持續時間超過6年。因此，雖然熊市往往比股市的牛市要短很多，但它們也往往更令人難忘。 最近，熊市越來越短 — 2020年的最後一次熊市只持續了幾個月。一些人將此歸因於美聯儲的介入越來越多，而另一些人則經常聲稱，我們現在在21世紀享有的更好的通訊建設讓資訊定價更快。雖然趨勢肯定是朝著越來越短的熊市發展，但它們的持續時間往往比人們預期的要長得多。請適度地調整您的期望！ 3.) 現金是一種部位💵 雖然美元目前通膨很高，約為7-10%（取決於您查看的數字），但每天一美元的購買力實際上並沒有太大變化。而一股SPY每天的購買力變化得很快，而且最近它的購買力下降得更快。在熊市最重要的是要記住，保持活力是*最重要的事情。只要不爆倉，還可以活著明天再戰鬥，逃離表現不佳的資產以換取現金是一種選擇。 最近一直在發生這種情況。您看看主要的資產類別，如果人們似乎正以現金賣出。債券、股票、黃金、加密貨幣 — 它們都以現金出售。在“避險”環境中，保守派參與者通常會從股票等風險資產轉向國債等“更安全”的資產。也就是說，隨著美聯儲升息和通膨居高不下，人們似乎放棄他們可以在債券中獲得的3%報酬率，轉而支持使用總體較靈活的現金。對沖的另一種選擇是賣空您認為表現不佳的資產，或買入您投資組合的看跌期權（如果有）。您可以在期權市場上直接看到還在低檔價格的標的。 4.) 抄底是很困難的🎣 雖然我們作為交易員的工作是尋找具有積極預期價值的機會，但從歷史上看，抄底一直具有很大的挑戰性。在2020年的崩盤中，許多著名的對沖基金在崩盤時對沖不足，在崩盤後則對沖過度。實際上，一些世界上最聰明的人在選擇可能的抄底部位方面做得很差。 除非你有一個非常長期的策略，允許隨著時間的推移一致地部署平均成本(DCA)，否則試圖在下降趨勢的市場中抄底可能是一個成功率非常低的策略。 好。這就是新手在熊市要記住的4件事。正如我們所提到的，在一個更加困難的市場中最重要的事情就是活著！🐻 祝您週末愉快！😄 -TradingView團隊
編輯精選
教育
6
2022/5/10 [消失的特輯系列] 教科書不會教你的事情：實戰中不同盤型的量價差異，殺多盤、軋空盤如何判斷和操作？2022/5/10 教科書不會教你的事情：實戰中不同盤型的量價差異，殺多盤、軋空盤如何判斷和操作？ BITSTAMP:BTCUSD COINBASE:ETHUSD ~開場嘮叨~ 大部分投資人其實不太懂市場主力鯨魚正在進行什麼策略、什麼目的、或盤勢進行到了什麼階段， "大都只看價格漲跌" 多數人都將注意力放在投資商品的價格變化上，每天看著價格上上下下、心情也跟著起伏不定， 每當價格下跌便覺得是主力在出貨割韭菜、每當價格上漲又覺得主力進場希望無窮， 看著價格變化，搞得自己低點恐慌、高點貪婪，不斷上演追高殺低的戲碼， 價格其實是你在投資時，最不需要去看的東西，價格只是盤勢進度位置的標記，並不那麼重要， 真正重要的是量價分析 + 市場籌碼分析(鏈上數據分析) + 投資心理學分析+ 市場情緒分析， 你在市場中真正要學的是透過量價分析、市場籌碼分析、投資心理學分析去看懂鯨魚主力在演什麼戲、市場盤勢進度進展到哪裡了， 而不是每天看著價格變化、擔心均線有沒有跌破漲破、或是一堆指標變化，搞得自己六神無主、暈頭轉向而沒有頭緒， 但人性往往都會驅使你只盯著價格的變化，除非你意識到並下定決心跳脫出來，不然你永遠只能躊躇不前。 另外，也常常看到新手投資人會犯的一個錯誤，他們總異想天開的想去預測市場的未來走向， 請記住，務必對市場保持敬畏的心、並尊重市場， 當你在市場一段時間後，你就會真正了解市場永遠是不可預測的，尤其是在這個94.39%BTC籌碼高度集中在2%鯨魚錢包地址的加密貨幣市場中， 千萬不要試著去預測市場。 而是要根據市場變化，準備好牛熊市的多空兩方交易策略， 如同以前到現在，我文章末段總會出現的一句話： I think that I can't predict the direction of market, but I am ready for a strategy of bullish or bearish . ~正文開始~ 市場中可以將盤型簡單分成三種，分別為正常盤、殺多盤、軋空盤，其中殺多盤和軋空盤為特殊盤型，也是大家俗稱的低檔鈍化和高檔鈍化。 投資人如果經驗不足、不熟悉盤型，無法從技術圖上直接判別出來的話，這邊教各位快速使用視覺化Stoch隨機指標去輔助自己判斷(參數值可修改調整為9,1,3或 9,3,3使用)。 一、正常盤： 1.隨機指標輔助判別： KD值約落在20-80之間，如圖中 綠色方框 的對應位置。 2.盤型目的： 為一般市場盤勢，正常尺度下的洗盤和調節出貨，可使用 量價關係搭配鯨魚主力籌碼狀況 來判別和擬訂交易策略。 3.量價關係： 有利多方的情況下，上漲時隨著價格創高，其成交量應該溫和遞增；下跌修正時，隨著價格創低，其成交量應該逐漸量縮。 反之，若上漲時隨著價格創高，而成交量沒有溫和遞增的話，則會開始累積多方背離度數，此時便有下跌修正來測試支撐的需求， 累積的多方背離度數越多，之後下跌修正的力道將會更猛烈。 另外，若下跌時隨著價格創低，而成交量持續量縮的話，則會開始累積空方背離度數，此時便有上漲測試壓力的需求， 累積的空方背離度數越多，之後上漲反彈測試壓力的力道就會更激烈。 二、殺多盤： 1.隨機指標輔助判別： KD值將會維持在20以下，並且持續3天以上形成低檔鈍化的現象，如圖中的 橘色方框 位置。 2.盤型目的： 殺多盤的主要目的就是要逼出市場信心較堅定的投資人籌碼，讓他們心態炸裂潰堤， 當籌碼分析判別鯨魚主力仍持續建倉時，且市場仍健康的情況下，殺多盤是為了下一輪新的上漲拉升做準備，藉此強力清洗市場。 加密貨幣市場價格跌個15~20%殺做期貨、槓桿的投資人、跌個30-50%殺現貨，把市場上信心不足的浮額和投機客都爆倉清洗乾淨～ 3.量價關係： 在殺多盤的情況下不用細看量價關係了，若有主力鯨魚持續建倉+籌碼換手的情況下通常是持續多度空方背離。 因此，這時的籌碼分析就非常重要了，因為此時各種均線和指標基本上都會失效， 所以只需持續留意"投降成交量是否出現"和"主力鯨魚是否持續建倉+籌碼換手"即可。 強力的殺多盤很難判斷底部，只能從 "投降成交量" 去看，專業術語叫做投降 (Capitulation)，看信心堅定的投資人 (可能為散戶，或是大戶鯨魚們的廝殺：小鯨魚、中鯨魚、小機構）們是否終於忍不住投降，乖乖殺低賠錢賣出籌碼～ 因此量價方面，你會看到價格下跌途中的成交量可能在關鍵支撐價格都是連續量縮背離跌破的， 一直到某個關鍵支撐價格跌破、並出現近期相對大量的成交量時(像是圖中的黃色圈圈處)， 才有可能是殺多盤要結束的尾聲，所以強殺多盤中，在訊號上看到暴跌帶大量的黑k棒非常重要。 4.殺多盤如何應對？例子？ 既然都說了殺多盤主要目的為強力清洗市場，那麼在籌碼分析上就要看到散戶恐慌殺低賣出 (極度恐慌20以下最優)、並且鯨魚主力持續逢低買入+成功換手的痕跡。 當確立鯨魚和主力動向沒問題之後，殺多盤比起正常盤來說，其實就是額外的建倉好機會(送分題)，心態穩健且有經驗的操盤手可規劃好逢低往下金字塔建倉的策略。 例子像是我在2020/3月開始，3811-10000美元區間所建倉的比特幣，當大家都在殺多盤中恐慌殺跌時，我隨同鯨魚主力一起逢低建倉。 近3年內的例子則有2020/3月附近殺多盤（ETH約200-400美元)、2021/5-7月的殺多盤，我之前文章也為大家分析過，那時市場一樣在極度恐慌之中....ETH均價1900美元。2021/12-2022/1月的殺多盤，ETH現貨約3000美元附近 + 3倍槓桿均價約對應ETH價格2150-2300美元。最後則是近期2022/5月的殺多盤，ETH均價有機會來到1500-2300美元附近殺多震盪。 市場極度恐慌又來到10 ，近3年內只出現4次 10以下的指數，分別為：2020/3、2021/5、2022/1、2022/5也就是現在，3年內只做4次主要的買入建倉會難嗎？！ upload.cc 三、軋空盤： 1.隨機指標輔助判別： KD值將會維持在80以上，並且持續3天以上形成高檔鈍化的現象，如圖中 紫色方框 位置。 2.盤型目的： 當投資人們被震盪盤或殺多盤洗到覺得放棄希望後，認為市場即將要轉熊市時，就會開始轉為做空，此時空單便會持續累積大增。 軋空盤目的很簡單，就是清洗空軍+另外軋空手的投資人，並且把累積的空單當作上漲的燃料，不斷往上軋、快速上漲， 軋空過程中強迫做空的人回補買入，因此上漲力道為空軍投降的回補買進力道，只要投資人持續做空，價格就會一直背離往上漲。 3.量價關係： 在軋空盤的過程中和殺多盤一樣，量價會持續背離，而且各項指標基本上都會鈍化失效，所以操作上籌碼變化將是分析的重點。 請密切關注 "鯨魚主力的籌碼變化"和"空軍投降成交量是否出現"，以及"空單是否持續補足" ，藉此判斷軋空燃料是否還足夠。 只要投資人持續做空補足燃料的話， 且空軍投降成交量沒有出現的話，商品價格便可以不斷上漲。 另外，在籌碼分析中應留意主力鯨魚是否在軋空過程中持續大量出貨，如果主力鯨魚持續在軋空盤中持續出貨、或空方回補的投降成交量出現的話， 也將是軋空盤快速上漲的尾聲了，要留意緊接而來的快速下跌修正。 例子像是我去年文章分析萬海看空在最高點的例子，當時萬海股票價格連漲、市場一片樂觀看好， 但主力利用軋空盤高檔持續出貨，我提醒大家將有多殺多的修正危險時還被散戶們噓呢，結果隔兩天後就從320元附近一路下殺到150元趴在地上玩沙子了... 四、我的個人建倉進度呢？ 鯨魚的近年成本可以分成兩大波去看，我和鯨魚是一起建倉的： 第一大波2020/3月開始 BTC3850-10000美元上下建倉、ETH200-800美元，大量出場調節均價BTC 50000美元附近(2個多月高檔震盪)。 第二大波建倉，目前主要分成4個部分，中間少量來回做價差降低成本不看之外，ETH 1900(50%)、2900美元 (20%)、3100美元 (20%)、2200美元 (額外佔30%=10%*3倍槓桿純低買高賣幾趟)，3次總建倉+槓桿低吸高拋後均價約落在2200美元。 另備有20%緊急預備金對付非理性的殺多盤。 目前僅剩ETH1500-1900美元區間還沒建倉到(預計使用10%預備金)，以中長期投資來說，近期殺多盤將有機會出現不錯的分批買入時機，想建倉ETH現貨的朋友們可以留意一下，現貨建倉後就可以拿去FTX交易所活存放貸5-8%，每小時複利。 因為實在太多人詢問了，所以這邊統一簡單說明一下， 請不要傻傻模仿我的價格操作，因為我們的認知、操盤經驗、資金規劃管理經驗、心理能耐、甚至是建倉點位都完全不一樣 ；除非你有能耐可以和我一樣透過認知敢買在2020/3月 BTC 3811美元和ETH 200-400美元附近、2021/5-7月 BTC 30000美元和ETH 1900美元附近、2021/12-2022/1月 ETH 3000美元 + 3倍槓桿2150-2300美元附近。 我也不怕各位笑，這波建倉布局的過程中，我從相對高點到現在已經歷帳面獲利回吐一千多萬了(當然投資成本都早已拿回且有額外獲利，現在是用賺的錢在操作而已)，我的心一樣無動於衷，安安穩穩的生活約會、過日子，除非你的心理波動承受度和我一樣，否則否則請不要亂模仿跟著我的操作； 而是你要學習我操作的方式、邏輯和教學文章的內容 。 重申一次，從以前到現在我教各位的都是釣魚的方式、而不是直接給你們魚！ 這裡可不像外面一堆雜七雜八的鬼怪報明牌社團，永遠記得天下沒有白吃的午餐，如果有那就是詐騙。 各位好好努力充實自己吧！ ---------------------------------- 嚴厲聲明: CryptoQuant和Tradingview是我合作發表的主站！近期微信和金色財經出現不肖分子"大量"抄襲我的文章在招收會員騙財。 本人分析一律公開透明，僅提供客觀市場分析和教學，若有任何私下群組或連絡的情況皆為仿冒的詐騙分子！請大家留意勿上當！ ---------------------------------- As an analyst, I think that I can't predict the direction of market, but I am ready for a strategy of bullish or bearish . CryptoQuant區塊鏈數據分析師 Yu-Shiuan Chen
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【尹框Cyning】纏論教學第5講二、分型 分型是纏論K線系統中一個極為關鍵的概念 源自K線組合的一個完全分類，是一個純理論的推導。 最早提出分型概念的是諾貝爾獎獲得者——美國的比爾•威廉姆斯博士 他在《混沌操作法》一書中 將分型 (Fractal,臺灣翻譯為碎形）稱為上分型和下分型 纏中說禪在《教你炒 股票108課》中將分型加以簡化，稱為頂分型和底分型。 如圖1-2-1所示 連續三根經由包含處理後的K線，按完全分類方法 僅可以組合成四種不同的結構，其中2和4就是分型。 分型由連續三根經由包含處理後的構成 分型有兩種表現形式：頂分型、底分型。 1.頂分型 定義：三根經包含處理後的連續K線 若中間那根K線的高點最髙，那麽這種組合形態就叫頂分型 註意：定義中的K線，必須經包含處理後的K線。 如圖1-2-2所示 圖中兩組K線中，中間K線的高點都是最高的，這種組合形態就叫頂分型 頂分型的最高點稱為頂 如圖1-2-3所示，中間K線的最髙點叫做頂。 頂分型是上漲過程將出現轉折的信號 —只上升中的股票，如果連頂分型都沒有出現，就不應當急於賣出。 如圖1-2-4所示 上漲過程中一直未出現頂分型，暫時持有 在上漲過程中，第一次出現的頂分型 如果形態上不十分強烈，那麽一般情況下可能是中繼頂分型 不會立即導致行情向下轉折 但如果是第二次或三次連續出現頂 分型，就必須特別小心 這個時候的頂分型，向下轉折的概率非常大。 如圖1-2-5所示，當出現第二個頂分型B時 行情向下轉折的概率加大，應當引起重視。 假期愉快~
編輯精選
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【關於交易你可能會意外的 Points 】EP05 停更了幾週再度回來 今天還上傳了第一集的 “小嬰兒也會做交易” 在文章下方可以連過去的 Twitter 上 xDD 填上之前本系列埋的坑 也當作是個 TV 10,000 Followers 達標的小小彩蛋～～ ＝＝＝＝＝＝＝＝＝＝正文開始分隔線＝＝＝＝＝＝＝＝＝＝＝＝＝ 1. 所謂的交易紀律，指的是 「把自己的交易規則執行到位的能力」 但絕大多數會說自己的交易紀律不好的人 其實是連交易規則都還沒有 有一點搞錯重點 2. 所有的分析方法，包括基本面、技術面、籌碼面、消息面、甚至是盤感 都可以用 “事實 - 觀點 - 交易計畫” 的邏輯框架去拆解 3. 基本面從事實到觀點很順、成千上萬的分析師都可以用這件事養活自己 能靠基本分析混飯吃的往往也都有比較嚴謹的邏輯體系 但從觀點到交易計畫常常很卡 所以基本面分析師常常被挑戰的就是 「沒辦法落地到在哪裡買入哪裡出場」 4. 消息面從事實到觀點嚴重分歧，往往會有一個直覺的觀點、和對立的反直覺觀點 兩派其實都很能夠論述的 例如消息面是某某某大戶入場某某某標的 多頭會說 「哇 大戶入場了 跟著抱大腿」 空頭會說 「當你看到的時候已經是給別人抬轎的了」 最有趣的是兩方往往互有輸贏 而觀點到交易計畫更粗暴 除了 這個消息看多 然後 market buy ; 這個消息看空 然後 market sell 以外 幾乎沒有什麼合理的解決方案 5. 籌碼面的定義很廣，從籌碼分布、大戶進出到訂單流、包括幣圈的鏈上分析 都是廣義的籌碼面 挑戰和 4 舉的例子有點類似 但相對起來有更多 從觀點到交易計畫的選項 6. 盤感流有兩種，第一種真的是純玄學的盤感，講不出為什麼的 那就是沒有客觀事實，只有觀點然後跳交易計畫 這類交易也是最難以被複製和學習的 第二種是其實還是有他產生盤感的依據了邏輯 如果他真的有心整理而也真能成就一家之言 其實很多技術分析方法和指標，都是在嘗試客觀量化某個人的盤感由來 7. 放在最後的技術分析，當然也有一大堆派別是走不通這完整的三段的 但相對起來，一些邏輯比較完整的交易系統 都是可以順利的從客觀事實拿觀點、再從觀點拿交易計畫、落地成非常具體的入場、停損、停利方法 當然，邏輯客觀而完整的最大意義並不是就等於能盈利 而是更容易 被複製和學習 也能夠在相同基礎之上討論各種優化的方案 其實也是我可以一路發這 1,500 多篇、累積這麼多朋友追蹤的根基 包括我的交易執行邏輯，其實還是建立在盤面上的訊息 一樣算在廣義的技術分析內 也藉這篇的整理來感謝大家一路以來的支持囉～
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Pine腳本

Bjorgum Double Tap█   OVERVIEW Double Tap is a pattern recognition script aimed at detecting Double Tops and Double Bottoms. Double Tap can be applied to the broker emulator to observe historical results, run as a trading bot for live trade alerts in real time with entry signals, take profit, and stop orders, or to simply detect patterns. █   CONCEPTS How Is A Pattern Defined? Doubles are technical formations that are both reversal patterns and breakout patterns. These formations typically have a distinctive “M” or a “W” shape with price action breaking beyond the neckline formed by the center of the pattern. They can be recognized when a ​pivot fails to break when tested for a second time and the retracement that follows breaks beyond the key level opposite. This can trap entrants that were playing in the direction of the prior trend. Entries are made on the breakout with a target projected beyond the neckline equal to the height of the pattern. Pattern Recognition Patterns are recognized through the use of ​zig-zag; a method of filtering price action by connecting swing highs and lows in an alternating fashion to establish trend, ​support and resistance, or derive shapes from price action. The script looks for the highest or lowest point in a given number of bars and updates a list with the values as they form. If the levels are exceeded, the values are updated. If the direction changes and a new significant point is made, a new point is added to the list and the process starts again. Meanwhile, we scan the list of values looking for the distinctive shape to form as previously described. █   STRATEGY RESULTS Back Testing Historical back testing is the most common method to test a strategy due in part to the general ease of gathering quick results. The underlying theory is that any strategy that worked well in the past is likely to work well in the future, and conversely, any strategy that performed poorly in the past is likely to perform poorly in the future. It is easy to poke holes in this theory, however, as for one to accept it as gospel, one would have to assume that future results will match what has come to pass. The randomness of markets may see to it otherwise, so it is important to scrutinize results. Some commonly used methods are to compare to other markets or benchmarks, perform statistical analysis on the results over many iterations and on differing datasets, walk-forward testing, out-of-sample analysis, or a variety of other techniques. There are many ways to interpret the results, so it is important to do research and gain knowledge in the field prior to taking meaningful conclusions from them. 👉  In short, it would be naive to place trust in one good backtest and expect positive results to continue. For this reason, results have been omitted from this publication. Repainting Repainting is simply the difference in behaviour of a strategy in real time vs the results calculated on the historical dataset. The strategy, by default, will wait for confirmed signals and is thus designed to not repaint. Waiting for bar close for entires aligns results in the real time data feed to those calculated on historical bars, which contain far less data. By doing this we align the behaviour of the strategy on the 2 data types, which brings significance to the calculated results. To override this behaviour and introduce repainting one can select "Recalculate on every tick" from the properties tab. It is important to note that by doing this alerts may not align with results seen in the strategy tester when the chart is reloaded, and thus to do so is to forgo backtesting and restricts a strategy to forward testing only. 👉  It is possible to use this script as an indicator as opposed to a full strategy by disabling "Use Strategy" in the "Inputs" tab. Basic alerts for detection will be sent when patterns are detected as opposed to complex order syntax. For alerts mid-bar enable "Recalculate on every tick" , and for confirmed signals ensure it is disabled. █   EXIT ORDERS Limit and Stop Orders By default, the strategy will place a stop loss at the invalidation point of the pattern. This point is beyond the pattern high in the case of Double Tops, or beneath the pattern low in the case of Double Bottoms. The target or take profit point is an equal-legs measurement, or 100% of the pattern height in the direction of the pattern bias. Both the stop and the limit level can be adjusted from the user menu as a percentage of the pattern height. Trailing Stops Optional from the menu is the implementation of an ATR based trailing stop. The trailing stop is designed to begin when the target projection is reached. From there, the script looks back a user-defined number of bars for the highest or lowest point +/- the ATR value. For tighter stops the user can look back a lesser number of bars, or decrease the ATR multiple. When using either Alertatron or Trading Connector, each change in the trail value will trigger an alert to update the stop order on the exchange to reflect the new trail price. This reduces latency and slippage that can occur when relying on alerts only as real exchange orders fill faster and remain in place in the event of a disruption in communication between your strategy and the exchange, which ensures a higher level of safety. 👉  It is important to note that in the case the trailing stop is enabled, limit orders are excluded from the exit criteria. Rather, the point in time that the limit value is exceeded is the point that the trail begins. As such, this method will exit by stop loss only. █   ALERTS Five Built-in 3rd Party Destinations The following are five options for delivering alerts from Double Tap to live trade execution via third party ​API solutions or chat bots to share your trades on social media. These destinations can be selected from the input menu and alert syntax will automatically configure in alerts appropriately to manage trades. Custom JSON JSON, or JavaScript Object Notation, is a readable format for structuring data. It is used primarily to transmit data between a server and a web application. In regards to this script, this may be a custom intermediary web application designed to catch alerts and interface with an exchange ​API. The JSON message is a trade map for an application to read equipped with where its been, where its going, targets, stops, quantity; a full diagnostic of the current state and its previous state. A web application could be configured to follow the messages sent in this format and conduct trades in sync with alerts running on the TV server. Below is an example of a rendered JSON alert: { "passphrase": "1234", "time": "2022-05-01T17:50:05Z", "ticker": "ETHUSDTPERP", "plot": { "stop_price": 2600.15, "limit_price": 3100.45 }, "strategy": { "position_size": 0.1, "order_action": "buy", "market_position": "long", "market_position_size": 0, "prev_market_position": "flat", "prev_market_position_size": 0 } } Trading Connector Trading Connector is a third party fully autonomous Chrome extension designed to catch alert webhooks from TradingView and interface with MT4/MT5 to execute live trades from your machine. Alerts to Trading Connector are simple; just select the destination from the input drop down menu, set your ticker in the "TC Ticker" box in the "Alert Strings" section and enter your URL in the alert window when configuring your alert. Alertatron Alertatron is an automated algo platform for cryptocurrency trading that is designed to automate your trading strategies. Although the platform is currently restricted to crypto, it offers a versatile interface with high flexibility syntax for complex market orders and conditions. To direct alerts to Alertatron, select the platform from the 3rd party drop down, configure your ​API key in the ”Alertatron Key” box and add your URL in the alert message box when making alerts. 3 Commas 3 Commas is an easy and quick to use click-and-go third party crypto ​API solution. Alerts are simple without overly complex syntax. Messages are simply pasted into alerts and executed as alerts are triggered. There are 4 boxes at the bottom of the "Inputs" tab where the appropriate messages to be placed. These messages can be copied from 3 Commas after the bots are set up and pasted directly into the settings menu. Remember to select 3 Commas as a destination from the third party drop down and place the appropriate URL in the alert message window. Discord Some may wish to share their trades with their friends in a Discord chat via webhook chat bot. Messages are configured to notify of the pattern type with targets and stop values. A bot can be configured through the integration menu in a Discord chat to which you have appropriate access. Select Discord from the 3rd party drop down menu and place your chat bot URL in the alert message window when configuring alerts. 👉  For further information regarding alert setup, refer to the platform specific instructions given by the chosen third party provider. █   IMPORTANT NOTES Setting Alerts For alert messages to be properly delivered on order fills it is necessary to place the following placeholder in the alert message box when creating an alert. {{strategy.order.alert_message}} This placeholder will auto-populate the alert message with the appropriate syntax that is designated for the 3rd party selected in the user menu. Order Sizing and Commissions The values that are sent in alert messages are populated from live metrics calculated by the strategy. This means that the actual values in the "Properties" tab are used and must be set by the user. The initial capital, order size, commission, etc. are all used in the calculations, so it is important to set these prior to executing live trades. Be sure to set the commission to the values used by the exchange as well. 👉  It is important to understand that the calculations on the account size take place from the beginning of the price history of the strategy. This means that if historical results have inflated or depleted the account size from the beginning of trade history until now, the values sent in alerts will reflect the calculated size based on the inputs in the "Properties" tab. To start fresh, the user must set the date in the "Inputs" tab to the current date as to remove trades from the trade history. Failure to follow this instruction can result in an unexpected order size being sent in the alert. █   FOR PINECODERS  • With the recent introduction of matrices in Pine, the script utilizes a matrix to track ​pivot points with the bars they occurred on, while tracking if that ​pivot has been traded against to prevent duplicate detections after a trade is exited.  • Alert messages are populated with placeholders ; capability that previously was only possible in alertcondition() , but has recently been extended to `strategy.*()` functions for use in the `alert_message` argument. This allows delivery of live trade values to populate in strategy alert messages.  • New arguments have been added to strategy.exit() , which allow differentiated messages to be sent based on whether the exit occurred at the stop or the limit. The new arguments used in this script are `alert_profit` and `alert_loss` to send messages to Discord
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Pine Script™策略
34
Fourier Extrapolator of Price w/ Projection Forecast [Loxx]Due to popular demand, I'm pusblishing Fourier Extrapolator of Price w/ Projection Forecast.. As stated in it's twin indicator, this one is also multi-harmonic (or multi-tone) trigonometric model of a price series xi, i=1..n, is given by: xi = m + Sum( a*Cos(w*i) + b*Sin(w*i), h=1..H ) Where: xi - past price at i-th bar, total n past prices; m - bias; a and b - scaling coefficients of harmonics; w - frequency of a harmonic ; h - harmonic number; H - total number of fitted harmonics. Fitting this model means finding m, a, b, and w that make the modeled values to be close to real values. Finding the harmonic frequencies w is the most difficult part of fitting a trigonometric model. In the case of a Fourier series, these frequencies are set at 2*pi*h/n. But, the Fourier series extrapolation means simply repeating the n past prices into the future. This indicator uses the Quinn-Fernandes algorithm to find the harmonic frequencies. It fits harmonics of the trigonometric series one by one until the specified total number of harmonics H is reached. After fitting a new harmonic , the coded algorithm computes the residue between the updated model and the real values and fits a new harmonic to the residue. see here: A Fast Efficient Technique for the Estimation of Frequency , B. G. Quinn and J. M. Fernandes, Biometrika, Vol. 78, No. 3 (Sep., 1991), pp . 489-497 (9 pages) Published By: Oxford University Press The indicator has the following input parameters: src - input source npast - number of past bars, to which trigonometric series is fitted; Nfut - number of predicted future bars; nharm - total number of harmonics in model; frqtol - tolerance of frequency calculations. The indicator plots two curves: the green/red curve indicates modeled past values and the yellow/fuchsia curve indicates the modeled future values. The purpose of this indicator is to showcase the Fourier Extrapolator method to be used in future indicators.
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Pine Script™指標
27
VisibleChart█   OVERVIEW This library is a Pine programmer’s tool containing functions that return values calculated from the range of visible bars on the chart. This is now possible in Pine Script™ thanks to the recently-released chart.left_visible_bar_time and chart.right_visible_bar_time built-ins, which return the opening time of the leftmost and rightmost bars on the chart. These values update as traders scroll or zoom their charts, which gives way to a class of indicators that can dynamically recalculate and draw visuals on visible bars only, as users scroll or zoom their charts. We hope this library's functions help you make the most of the world of possibilities these new built-ins provide for Pine scripts. For an example of a script using this library, have a look at the Chart ​VWAP indicator. █   CONCEPTS Chart properties The new chart.left_visible_bar_time and chart.right_visible_bar_time variables return the opening time of the leftmost and rightmost bars on the chart. They are only two of many new built-ins in the `chart.*` namespace. See this blog post for more information, or look up them up by typing "chart." in the Pine Script™ Reference Manual . Dynamic recalculation of scripts on visible bars Any script using chart.left_visible_bar_time or chart.right_visible_bar_time acquires a unique property, which triggers its recalculation when traders scroll or zoom their charts in such a way that the range of visible bars on the chart changes. This library's functions use the two recent built-ins to derive various values from the range of visible bars. Designing your scripts for dynamic recalculation For the library's functions to work correctly, they must be called on every bar. For reliable results, assign their results to global variables and then use the variables locally where needed — not the raw function calls. Some functions like `barIsVisible()` or `open()` will return a value starting on the leftmost visible bar. Others such as `high()` or `low()` will also return a value starting on the leftmost visible bar, but their correct value can only be known on the rightmost visible bar, after all visible bars have been analyzed by the script. You can plot values as the script executes on visible bars, but efficient code will, when possible, create resource-intensive labels, lines or tables only once in the global ​scope using var , and then use the setter functions to modify their properties on the last bar only. The example code included in this library uses this method. Keep in mind that when your script uses chart.left_visible_bar_time or chart.right_visible_bar_time , your script will recalculate on all bars each time the user scrolls or zooms their chart. To provide script users with the best experience you should strive to keep calculations to a minimum and use efficient code so that traders are not always waiting for your script to recalculate every time they scroll or zoom their chart. Another aspect to consider is the fact that the rightmost visible bar will not always be the last bar in the dataset. When script users scroll back in time, a large portion of the time series the script calculates on may be situated after the rightmost visible bar. We can never assume the rightmost visible bar is also the last bar of the time series. Use `barIsVisible()` to restrict calculations to visible bars, but also consider that your script can continue to execute past them. █   FUNCTIONS The library contains the following functions: barIsVisible()   Condition to determine if a given bar is within the users visible time range.   Returns: (bool) True if the the calling bar is between the `chart.left_visible_bar_time` and the `chart.right_visible_bar_time`. high()   Determines the value of the highest `high` in visible bars.   Returns: (float) The maximum high value of visible chart bars. highBarIndex()   Determines the `bar_index` of the highest `high` in visible bars.   Returns: (int) The `bar_index` of the `high()`. highBarTime()   Determines the bar time of the highest `high` in visible bars.   Returns: (int) The `time` of the `high()`. low()   Determines the value of the lowest `low` in visible bars.   Returns: (float) The minimum low value of visible chart bars. lowBarIndex()   Determines the `bar_index` of the lowest `low` in visible bars.   Returns: (int) The `bar_index` of the `low()`. lowBarTime()   Determines the bar time of the lowest `low` in visible bars.   Returns: (int) The `time` of the `low()`. open()   Determines the value of the opening price in the visible chart time range.   Returns: (float) The `open` of the leftmost visible chart bar. close()   Determines the value of the closing price in the visible chart time range.   Returns: (float) The `close` of the rightmost visible chart bar. leftBarIndex()   Determines the `bar_index` of the leftmost visible chart bar.   Returns: (int) A `bar_index`. rightBarIndex()   Determines the `bar_index` of the rightmost visible chart bar.   Returns: (int) A `bar_index` bars()   Determines the number of visible chart bars.   Returns: (int) The number of bars. volume()   Determines the sum of volume of all visible chart bars.   Returns: (float) The cumulative sum of volume. ohlcv()   Determines the open, high, low, close, and volume sum of the visible bar time range.   Returns: ( ) A tuple of the OHLCV values for the visible chart bars. Example: open is chart left, high is the highest visible high, etc. chartYPct(​pct)   Determines a price level as a percentage of the visible bar price range, which depends on the chart's top/bottom margins in "Settings/Appearance".   Parameters:      pct : (series float) Percentage of the visible price range (50 is 50%). Negative values are allowed.   Returns: (float) A price level equal to the `pct` of the price range between the high and low of visible chart bars. Example: 50 is halfway between the visible high and low. chartXTimePct(​pct)   Determines a time as a percentage of the visible bar time range.   Parameters:      pct : (series float) Percentage of the visible time range (50 is 50%). Negative values are allowed.   Returns: (float) A time in UNIX format equal to the `pct` of the time range from the `chart.left_visible_bar_time` to the `chart.right_visible_bar_time`. Example: 50 is halfway from the leftmost visible bar to the rightmost. chartXIndexPct(​pct)   Determines a `bar_index` as a percentage of the visible bar time range.   Parameters:      pct : (series float) Percentage of the visible time range (50 is 50%). Negative values are allowed.   Returns: (float) A time in UNIX format equal to the `pct` of the time range from the `chart.left_visible_bar_time` to the `chart.right_visible_bar_time`. Example: 50 is halfway from the leftmost visible bar to the rightmost. whenVisible(​src, whenCond, length)   Creates an array containing the `length` last `src` values where `whenCond` is true for visible chart bars.   Parameters:      src : (series int/float) The source of the values to be included.      whenCond : (series bool) The condition determining which values are included. Optional. The default is `true`.      length : (simple int) The number of last values to return. Optional. The default is all values.   Returns: (float ) The array ID of the accumulated `src` values. avg(​src)   Gathers values of the source over visible chart bars and averages them.   Parameters:      src : (series int/float) The source of the values to be averaged. Optional. Default is `close`.   Returns: (float) A cumulative average of values for the visible time range. median(​src)   Calculates the median of a source over visible chart bars.   Parameters:      src : (series int/float) The source of the values. Optional. Default is `close`.   Returns: (float) The median of the `src` for the visible time range. vVwap(​src)   Calculates a volume-weighted average for visible chart bars.   Parameters:      src : (series int/float) Source used for the ​VWAP calculation. Optional. Default is `hlc3`.   Returns: (float) The ​VWAP for the visible time range.
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Pine Script™腳本庫
24
Time█   OVERVIEW This library is a Pine Script™ programmer’s tool containing a variety of time related functions to calculate or measure time, or format time into string variables. █   CONCEPTS `formattedTime()`, `formattedDate()` and `formattedDay()` Pine Script™, like many other programming languages, uses timestamps in UNIX format, expressed as the number of milliseconds elapsed since 00:00:00 UTC, 1 January 1970. These three functions convert a UNIX timestamp to a formatted string for human consumption. These are examples of ways you can call the functions, and the ensuing results: CODE RESULT formattedTime(timenow) >>> "00:40:35" formattedTime(timenow, "short") >>> "12:40 AM" formattedTime(timenow, "full") >>> "12:40:35 AM ​UTC​" formattedTime(1000 * 60 * 60 * 3.5, "HH:mm") >>> "03:30" formattedDate(timenow, "short") >>> "4/30/22" formattedDate(timenow, "medium") >>> ​"Apr​ 30, 2022" formattedDate(timenow, "full") >>> "Saturday, April 30, 2022" formattedDay(timenow, "E") >>> "Sat" formattedDay(timenow, "dd.MM.yy") >>> "30.04.22" formattedDay(timenow, "yyyy.MM.dd G 'at' hh:mm:ss z") >>> "2022.04.30 AD at 12:40:35 UTC" These functions use str.format() and some of the special formatting codes it allows for. Pine Script™ documentation does not yet contain complete specifications on these codes, but in the meantime you can find some information in the The Java™ Tutorials and in Java documentation of its MessageFormat class . Note that str.format() implements only a subset of the MessageFormat features in Java. `secondsSince()` The introduction of varip variables in Pine Script™ has made it possible to track the time for which a condition is true when a script is executing on a realtime bar. One obvious use case that comes to mind is to enable trades to exit only when the exit condition has been true for a period of time, whether that period is shorter that the chart's timeframe, or spans across multiple realtime bars. For more information on this function and varip please see our Using `varip` variables publication. `timeFrom( )` When plotting lines , boxes , and labels one often needs to calculate an offset for past or future end points relative to the time a condition or point occurs in history. Using xloc.bar_index is often the easiest solution, but some situations require the use of xloc.bar_time . We introduce `timeFrom()` to assist in calculating time-based offsets. The function calculates a timestamp using a negative (into the past) or positive (into the future) offset from the current bar's starting or closing time, or from the current time of day. The offset can be expressed in units of chart timeframe, or in seconds, minutes, hours, days, months or years. This function was ported from our Time Offset Calculation Framework . `formattedNoOfPeriods()` and `secondsToTfString()` Our final two offerings aim to confront two remaining issues: How much time is represented in a given timestamp? How can I produce a "simple string" timeframe usable with request.security() from a timeframe expressed in seconds? `formattedNoOfPeriods()` converts a time value in ​ms to a quantity of time units. This is useful for calculating a difference in time between 2 points and converting to a desired number of units of time. If no unit is supplied, the function automatically chooses a unit based on a predetermined time step. `secondsToTfString()` converts an input time in seconds to a target timeframe string in timeframe.period string format. This is useful for implementing stepped timeframes relative to the chart time, or calculating multiples of a given chart timeframe. Results from this function are in simple form, which means they are useable as `timeframe` arguments in functions like request.security() . █   NOTES Although the example code is commented in detail, the size of the library justifies some further explanation as many concepts are demonstrated. Key points are as follows:  • ​Pivot points are used to draw lines from. `timeFrom( )` calculates the length of the lines in the specified unit of time.   By default the script uses 20 units of the charts timeframe. Example: a 1hr chart has arrows 20 hours in length.  • At the point of the arrows `formattedNoOfPeriods()` calculates the line length in the specified unit of time from the input menu.   If “Use Input Time” is disabled, a unit of time is automatically assigned.  • At each ​pivot point a label with a formatted date or time is placed with one of the three formatting helper functions to display the time or date the ​pivot occurred.  • A label on the last bar showcases `secondsSince()` . The label goes through three stages of detection for a timed alert.   If the difference between the high and the open in ticks exceeds the input value, a timer starts and will turn the label red once the input time is exceeded to simulate a time-delayed alert.  • In the bottom right of the screen `secondsToTfString()` posts the chart timeframe in a table. This can be multiplied from the input menu. █   FUNCTIONS formattedTime(timeInMs, format)   Converts a UNIX timestamp (in milliseconds) to a formatted time string.   Parameters:      timeInMs : (series float) Timestamp to be formatted.      format : (series string) Format for the time. Optional. The default value is "HH:mm:ss".   Returns: (string) A string containing the formatted time. formattedDate(timeInMs, format)   Converts a UNIX timestamp (in milliseconds) to a formatted date string.   Parameters:      timeInMs : (series float) Timestamp to be formatted.      format : (series string) Format for the date. Optional. The default value is "yyyy-MM-dd".   Returns: (string) A string containing the formatted date. formattedDay(timeInMs, format)   Converts a UNIX timestamp (in milliseconds) to the name of the day of the week.   Parameters:      timeInMs : (series float) Timestamp to be formatted.      format : (series string) Format for the day of the week. Optional. The default value is "EEEE" (complete day name).   Returns: (string) A string containing the day of the week. secondsSince(cond, resetCond)   The duration in milliseconds that a condition has been true.   Parameters:      cond : (series bool) Condition to time.      resetCond : (series bool) When `true`, the duration resets.   Returns: The duration in seconds for which `cond` is continuously true. timeFrom(from, qty, units)   Calculates a +/- time offset in variable units from the current bar's time or from the current time.   Parameters:      from : (series string) Starting time from where the offset is calculated: "bar" to start from the bar's starting time, "close" to start from the bar's closing time, "now" to start from the current time.      qty : (series int) The +/- qty of units of offset required. A "series float" can be used but it will be cast to a "series int".      units : (series string) String containing one of the seven allowed time units: "chart" (chart's timeframe), "seconds", "minutes", "hours", "days", "months", "years".   Returns: (int) The resultant time offset `from` the `qty` of time in the specified `units`. formattedNoOfPeriods(​ms, unit)   Converts a time value in ​ms to a quantity of time units.   Parameters:      ms : (series int) Value of time to be formatted.      unit : (series string) The target unit of time measurement. Options are "seconds", "minutes", "hours", "days", "weeks", "months". If not used one will be automatically assigned.   Returns: (string) A formatted string from the number of `ms` in the specified `unit` of time measurement secondsToTfString(tfInSeconds, mult)   Convert an input time in seconds to target string TF in `timeframe.period` string format.   Parameters:      tfInSeconds : (simple int) a timeframe in seconds to convert to a string.      mult : (simple float) Multiple of `tfInSeconds` to be calculated. Optional. 1 (no multiplier) is default.   Returns: (string) The `tfInSeconds` in `timeframe.period` format usable with `request.security()`.
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Pine Script™腳本庫
25
Relative Bandwidth FilterThis is a very simple script which can be used as measure to define your trading zones based on volatility. Concept This script tries to identify the area of low and high volatility based on comparison between Bandwidth of higher length and ATR of lower length. Relative Bandwidth = Bandwidth / ATR Bandwidth can be based on either Bollinger Band, Keltner Channel or Donchian Channel. Length of the bandwidth need to be ideally higher. ATR is calculated using built in ATR method and ATR length need to be ideally lower than that used for calculating Bandwidth. Once we got Relative Bandwidth, the next step is to apply Bollinger Band on this to measure how relatively high/low this value is. Overall - If relative bandwidth is higher, then volatility is comparatively low. If relative bandwidth is lower, then volatility is comparatively high. Usage This can be used with your own strategy to filter out your non-trading zones based on volatility. Script plots a variable called "Signal" - which is not shown on chart pane. But, it is available in the data window. This can be used in another script as external input and apply logic. Signal values can be 1 : Allow only Long -1 : Allow only short 0 : Do not allow any trades 2 : Allow both Long and Short
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Pine Script™指標
18
Breakout Probability (Expo)█ Overview Breakout Probability is a valuable indicator that calculates the probability of a new high or low and displays it as a level with its percentage. The probability of a new high and low is backtested, and the results are shown in a table— a simple way to understand the next candle's likelihood of a new high or low. In addition, the indicator displays an additional four levels above and under the candle with the probability of hitting these levels. The indicator helps traders to understand the likelihood of the next candle's direction, which can be used to set your trading bias. █ Calculations The algorithm calculates all the green and red candles separately depending on whether the previous candle was red or green and assigns scores if one or more lines were reached. The algorithm then calculates how many candles reached those levels in history and displays it as a percentage value on each line. █ Example In this example, the previous candlestick was green; we can see that a new high has been hit 72.82% of the time and the low only 28.29%. In this case, a new high was made. █ Settings Percentage Step The space between the levels can be adjusted with a percentage step. 1% means that each level is located 1% above/under the previous one. Disable 0.00% values If a level got a 0% likelihood of being hit, the level is not displayed as default. Enable the option if you want to see all levels regardless of their values. Number of Lines Set the number of levels you want to display. Show Statistic Panel Enable this option if you want to display the backtest statistics for that a new high or low is made. (Only if the first levels have been reached or not) █ Any Alert function call An alert is sent on candle open, and you can select what should be included in the alert. You can enable the following options: Ticker ID Bias Probability percentage The first level high and low price █ How to use This indicator is a perfect tool for anyone that wants to understand the probability of a breakout and the likelihood that set levels are hit. The indicator can be used for setting a stop loss based on where the price is most likely not to reach. The indicator can help traders to set their bias based on probability. For example, look at the daily or a higher timeframe to get your trading bias, then go to a lower timeframe and look for setups in that direction. ----------------- Disclaimer The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs. My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
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Pine Script™指標
59
Point of Control V2 The genesis of this project was to create a POC library that would be available to deliver volume profile information via pine to other scripts of indicators and strategies. This is a republish of an invite only script to open access This is the indicator version of the library function. A few points of significance: - Allows the choice of reset of the study period, day/week or bars. This is simple enough to expand to other conditions - Bar count resets starting from the beginning of the data set (bar index =0) vs bars back from the end of the data set - A 'period' in this context is the time between resets - the start of the POC (eg. start of Day or Week) until it resets (for example at the beginning of a next day or week) - Automates the determination of the increment level rather than the user specifying ticks or price brackets - Does not allow for setting the # of rows and then calculating the implied price increment levels - When a period is complete it is often useful to look back at the POCs of historical periods, or extend them forward. - This script will find the historical POCs around the current price and display them rather than extend all the historical POC lines to the right - This script also looks across all the period POCs and identifies the master POC or what I call the Grand POC, and also the next 3 runner up POCs This indicator is also available as a library. BINANCE:BTCUSDT NSE:NIFTY OANDA:XAUUSD NASDAQ:AAPL TVC:USOIL
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Pine Script™指標
8
Andean OscillatorThe following script is an original creation originally posted on the blog section of the broker Alpaca. The proposed indicator aims to measure the degree of variations of individual up-trends and down-trends in the price, thus allowing to highlight the direction and amplitude of a current trend. Settings Length : Determines the significance of the trends degree of variations measured by the indicator. Signal Length : Moving average period of the signal line. Usage The Andean Oscillator can return multiple information to the user, with its core interpretation revolving around the bull and bear components. A rising bull component (in green) indicates the presence of bullish price variations while a rising bear component (in red) indicates the presence of bearish price variations. When the bull component is over the bear component market is up-trending, and the user can expect new higher highs. When the bear component is over the bull component market is down-trending, and the user can expect new lower lows. The signal line (in orange) allows a more developed interpretation of the indicator and can be used in several ways. It is possible to use it to filter out potential false signals given by the crosses between the bullish and bearish components. As such the user might want to enter a position once the bullish or bearish component crosses over the signal line instead. Details Measuring the degree of variations of trends in the price by their direction (up-trend/down-trend) can be done in several way. The approach taken by the proposed indicator makes use of exponential envelopes and the naive computation of standard deviation. First, exponential envelopes are obtained from both the regular prices and squared prices, thus giving two upper extremities, and two lower extremities. The bullish component is obtained by first subtracting the upper extremity of the squared prices with the squared upper extremity of regular prices, the square root is then applied to this result. The bearish component is obtained in the same way, but makes use of the lower extremities of the exponential envelopes.
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Pine Script™指標
27
Auto Trendline Indicator (based on fractals)A tool that automatically draws out trend lines by connecting the most recent fractals. Description: The process of manual drawing out trend lines is highly subjective. Many times, we don’t trade what we see, but what we “want to see”. As a result, we draw lines pointing to the direction that we wishfully want price to move towards. While there are no right/wrong ways to draw trend lines, there are, however, systematic/unsystematic ways to draw trend lines. This tool will systematically draw out trend lines based on fractals. Additional feature: This tool will also plot out symbols (default symbol “X”) to signify points of crossings. This can be useful for traders considering to use trend lines as part of their trading strategies. Here is an interesting observation on the price actions of NASDAQ futures on a 5 second chart during regular trading hours on July 14, 2022. It’s a phenomenon. People like to see straight lines connecting HL/LH, etc., so it's possible for the market as a whole to psychologically react to these lines. However, it is important to note that is is impossible to predict the direction of price. In the case above, price could have tanked below auto-drawn trend line. Fractal based trend lines should only be taken as references and regarded as price levels. No studies have ever proven that the slope of trend lines can indicate price's future direction. More about fractals: To understand more about fractals: www.investopedia.com www.tradingview.com Contrary to what it sounds like, fractal in "technical analysis" does not refer to the recursive self-repeating patterns that appear in nature, such as the mesmerizing patterns found in snowflakes. The Fractal Markets Hypothesis claims that market prices exhibit fractal properties over time. Assuming this assertion to be true, then fractals can be used a tool to represent the chaotic movements of price is a simplified manner. The purpose of this exercise is to take a tool that is readily available (ie. in this case, TradingView’s built-in fractals tool), and to create a newer tool based on it. Parameters: Fractal period (denoted as ‘n’ in code): It is the number of bars bounding a high/low point that must be lower/higher than it, respectively, in order for fractal to be considered valid. Period ‘n’ can be adjusted in this tool. Traditionally, chartists pick the value of 5. The longer it is, the less noise seen on the chart, and the pivot point may also be exhibited in higher timeframes. The drawback is that it will increase the period of lag, and it will take more bars to confirm the printed fractal. Others: Intuitive parameters such as whether to draw historical trend lines, what color to use, which way to extend the lines, and whether or not to show points of crossings.
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Pine Script™指標
25

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