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美元走勢分析雖然我不做外匯，對於外匯市場的認知也有限。不過我還是比較關注美元的走勢，畢竟美元走勢對於全球資本市場的影響都是巨大的。
我對於美元的分析，主要是通過圖表比對，至於基本面，因為我的知識有限，大家可以關注其他專業人士的觀點。
美元過去2年一直在走上升週期，也就是從美國通脹加劇後，美聯儲加息帶來的美元升值週期。
但這個升值週期中，美元也不是單邊上升。對比上一次美元加息週期就可以看到這一點。2014年到2016年，漲幅31%。但主要升幅都是在2015年3月前完成的。一般在後加息時代，美元基本上都是震盪走勢。
所以我認為，現在美元的主升階段應該完成了，接下來應該進入震盪週期。就像2017年8月之後的走勢。
過去4個月，美元走的是單邊下跌行情，對比2008年以後的走勢，美元有4段下跌行情，跌幅在13%-17%之間，歷時大約一年。所以這4個月的調整，時間週期還不足以完成調整。但走單邊下跌概率也很小。所以我認為這裏可能會有進入反彈週期。也就是用abc的形態完成。
這一點在日線也可以找到依據。目前日線macd出現了背離，在連續下跌4個月之後，市場也需要一次反彈來修正指標。而在2017年8月那一次反彈，同樣出現了日線macd底背離。
雖然我們不能簡單地套用歷史，但至少可以作為參考。
預測一下btc的走勢雖然每一次幣圈的反彈都風風火火，不過該有的調整還是要有，單邊牛市的可能性不大。
從基本面看，美元尚未結束加息週期，全球經濟復蘇之路依然漫長，而幣圈也沒什麼值得興奮的大利好出現。
所以我之前判斷這裏應該是走大的B浪反彈。而這個反彈浪，有可能走abc的形態。經過這一波的拉升，目前已經擺脫了底部，所以我預測未來有可能走頭肩底的形態。
邏輯很簡單
1、我們看左側橢圓形位置，那裏曾經是多頭抵抗的區域，一定堆積了大量的套牢籌碼，所以衝擊這裏，需要一個時間，這樣的話，在右側對應一個平臺整理，非常合理。
2、本輪反彈，已經超過40%的漲幅，對於低位抄底的資金來說，有減倉欲望。
3、反彈如果走一波流，那就不是反彈了，而且直線拉升，也不符合交易所的利益，只有拉鋸戰，才能讓韭菜們樂此不疲地給交易所交稅。
當然，圖表預測僅供參考，具體操作，我們還是要遵守macd＋ma的策略。這個是我一貫的邏輯，老讀者應該很熟悉了，新讀者可以查看歷史文章。
今天是中國傳統節日春節前的除夕夜，在這裏，也祝願大家，在新的一年，鴻運當頭，萬事順利！
新年多頭回歸...美股即將站穩4200?! 訂單流觀點(SMC)我在十二月的視頻提到美股即將崩盤，一月初的TV文章也提到美股即將暴跌
然而我在1/14上一篇TV文章更新的時候表示我把我的空頭倉位部分止盈後通通平掉了
想必有些關注我的朋友們或許感到一頭霧水...難不成之前講的空頭觀點都是假的??
首先來說說基本面，通膨觸頂已經是事實，我也在視頻(判斷股市底部的關鍵)提到了即使如此我仍然看空
原因是高息的環境是不利股市的，企業融資成本會提升，欠錢的公司利息會還得很辛苦、有潛力的公司也很難融資擴增
同時就業市場強勁，失業率低的情況代表徵人不易，公司自然需要負擔更高的薪水聘請人才
這些因素將造成企業利潤下降，除了反映在財報與股價上，同時也可能面臨被迫裁員甚至倒閉的結果
2022年大家最關心的議題是通膨，我們這幾個月看到政府升息的政策奏效
2023年的新議題則是升息的後果...也就是經濟衰退的狀況
既然如此為何不繼續做空呢?
這麼說好了，市場就像一個注定要翻的船，上面滿滿的散戶有的看多、有的看空站在船的兩側
結果誰是正確的呢? 往往雙方都是錯的，因為市場可以先漲再跌、或是先跌再漲，觸發雙方的止損訂單
這感覺就有點像世足有的人壓法國贏、有的人壓阿根廷贏，結果90分鐘到了出現和局，通殺。
我們來看看技術面:
如果我上篇文章的預測成立，這個藍色區間是在吸籌空單，那麼一旦價格突破這個區間應該是區間上沿的假突破
此時我們可以看到價格起初於綠色區間(破壞塊Breaker)有非常好的反應，這也是我波段空單的進場點位
在3895部分止盈後，我們看到價格突破了影線上沿並於紅色箭頭(CPI公布日)站穩在影線的上方
這代表我原先主觀性的預測可能是錯誤的，聰明錢現在恐怕不是在吸籌空單，而是蓄勢待發要軋空!
標普在4200上方有極大量的買方流動性，也就是過多的散戶在做空，將一齊在這個位置認輸將他們的空單止損、買入平倉
同時會有追多的散戶在這個位置大量的買入股票期待市場今年會出現大漲!! 害怕錯過的FOMO情緒將在這個位置延燒
這就是典型的多頭陷阱(Bull Trap)而我不願意在這個時間點成為買方流動性的受害者
市場是動態的，太多人站在同一邊做空的時候，我只得下船靜觀其變...
我們可以看到周線級別幾乎所有人都在關注這條趨勢線
這個趨勢線突破之後，許多人將相信牛市已經來臨，「上升三角突破」、「頭肩底頸線突破」......
4200上方將是個極佳盈虧比的做空機會，屆時我會依據更多的訊息更新我的看法，在那之前我將專注在較靈活的短線機會上
希望您喜歡我的文章，有任何問題或想法請不吝留言指教!
想知道更多可以看我1/7分享的美股觀點
也歡迎訂閱我的頻道或加入免費的交流群以第一時間獲取免費的市場行情分析!
美國實戰日記 – 2023年1月19號交易有感
賺錢在交易市場上是一件困難的事情，因為需要一套交易系統來支持你的交易。而學習交易也是會產生成本的，例如學習費用。在臺灣學習交易系統特別昂貴。很多人購買了交易系統卻不知道它是否真的有效，這是因為很多老師並沒有公開他們的交易紀錄。確認一套交易方法是否真的能賺錢，以及確認這個方法是否適合自己，是你面臨的問題。斜槓收入也是現在很流行的，但需要檢查自己的交易系統是否能夠賺錢，才能實現斜槓收入。
如果你想要在交易市場上賺錢，你需要經過不斷的學習和實踐。首先，你需要學習一套交易系統，並確保它是有效的。你可以在了解交易系統之前，研究老師的交易紀錄，了解他們的成功率和盈利情況。之後，你需要實際應用這個交易系統，並不斷觀察和檢討你的交易結果。
當你確認你的交易系統是有效的後，你還需要不斷地學習新的知識和技巧，以改善你的交易結果。這包括學習新的技術分析工具、了解市場走勢和消息、閱讀專業文章和報導等。
另外，管理風險也是非常重要的。交易是有風險的，因此你需要確保你的交易結果不會被大幅度的負面影響。這可以通過設置止損點和限制交易量等手段來實現。
總之，在交易市場上賺錢需要不斷的學習和實踐，需要有一套有效的交易系統、不斷提升自己的知識和技能、以及適當的風險管理。
另外, 在交易市場上, 心理素質的管理也是非常重要的. 交易是有風險的, 因此需要良好的情緒管理, 避免被市場波動所影響. 要學習如何控制自己的情緒, 例如不要讓貪婪或恐慌影響交易決策. 亦需要有良好的獨立思考能力, 避免被市場與群體的思考影響. 這需要不斷的自我反省和自我調整.
以上呢就是最近的一個心得，下面來說明今天的操盤演練
接著大家請看第2張圖，這張圖顯示的是考特已經做好準備，確認好機會之後就把需要的部位已經打單子打好，這個商品考特使用了約625股數，在美國股市開盤之前就已經把單子掛號，因為考特是使用日線來做交易的，這樣子的交易方法非常適合考特，接下來就等待
考特目前所已經持有的部位，目前持有的天數已經來到第4天，那目前並沒有碰到任何的停損或者是停利，所以考特們的動作就是持續等待，並沒有其他需要注意的地方，那就祝大家新年快樂一切平安，數錢數到手抽筋(目前持有另一檔股)
#SPX #DJI 小牛要去那?先不看DJI 主要看SPX 因為關鍵在SPX
最終 期待已久的小牛終於來了
原則上SPX要能續攻 不然整體無法有效反轉
重點3條線 2個框框
3條線
1.橘色陰陽線 這條是大週期的延伸 會有很強黏性
力道也大 但是如果沒有相對量支持 被吸回來的機會
2.兩條 Dead or aLive 顧名思義 下去就拜拜 不然就空單拿好
3.Hearvy 過熱開反向
這邊是預估最好情況
2個框框
1.上方小框
這邊應該就是樂觀情況下的最高區域。但是也有可能再上去誘多
安全情況下 這邊就可以出場或拉高止盈
2.下方大框
這個區域是包含兩個長期趨勢內的關鍵位置
如果市場沒有那麼熱 基本上這邊就差不多了
影響因子
1.DXY 預估上 101.267 是反轉的開始
是否能到不一定 但目前可能隨時反轉
2.是否還會有壞消息 (公司倒閉/財報暴雷/金融業危機)
3.國際情勢繼續惡化
以上有兩個發生 那小牛就會提前結束
注意 只是小牛
聊聊DJI
這些是我私人季報內擷取出來的部分 供參考
明年度 預測不變下降白線雖然有轉緩(1轉2 開口收縮) 但整體形勢還是看跌為主 現階段還會是誘多 或者說小牛反彈 暫時頂部最高看白線 3 (這個情況有機會但是相對的是趴越高跌越快)
目前趨勢的發展最主要還是環繞在 通膨 形成因素
不言會就是
QE的資金氾濫 所以資金一天不到位(縮減) 反轉的一天就不會到來 當然FED/美國政府有些政策是有嘗試達到所謂的軟著陸 但是成效不
彰 另一點是現在主打的製造業回流反而加劇通膨 雖然製造業回流長期是有利於美國健全 但是這個時刻反而是毒藥 在這層影響下 之前以2008為模型基礎的假設就需要在做變動 V型反轉不再期待
1929 2000 2008
最常對比的三個年份
1929 TGD 經濟大蕭條
這個是最悲觀的人關注的時期 在我之前的模型內也是有把他列入參考 但是以現今來說 除非是爆發內戰或戰火延燒到本土(US) 不然代理人戰爭是不可能讓美股跌那麼深 另一個原因是 其他市場的崛起 會平衡掉不利因素。 簡單說明就是兩人三腳跑步。 只是當初1929只有兩個市場 (歐美) 沒有足夠協力的動能。 但是現在 光分區就可以 美 歐 東亞 中國 東南亞 中東 南美 非洲 要同時出事的可能性較低 (看東南亞沒有跟隊就知道不傻)
2000/2008 科技業(.com)的崛起跟金融業推波助瀾 Bubble Market 00的科技泡沫 資本轉向地產及金融服務市場 08後的泡沫 資本沒有縮手 反而再度推高了一切
基本上情況就上述兩句可以講完 結論就是貪婪(高估)及浮濫的資金 (利率1981-20%~1994-3%~2000~5%) 從歷史上看，網路經濟的繁榮可被視為類似於其它技術的繁榮——包括荷蘭鬱金香,1840年代的鐵路，1920年代的汽車和收音機，1950年代的電晶體，1960年代的分時共享計算機，以及 1980年代早期的家用電腦和生物技術 1990的.com
上述 我們可以得到第一點 市場除了正常波動外 一個時代的技術榮景同時也是一段經濟的循環
但2000~2020是個特殊的時期 沒有比較標竿性的產業可以當代表 但是仔細想一下會發現有些行業的改變
比如說 Blockbuster的消失 這是服務與資訊業的時代 實業與企業的交替 同時也是人力需求及薪資比的改變(這個課題水很深 有時間再做報告) 另外因為QE及利率降低 金融遊戲的改變契機因QE而延遲 且埋下更深的炸彈
基於此再對比歷史軌跡後 有個時期進入觀察名單中
60-80
長衰退 表哥心態 最好的防守
保暖思淫慾 吃飽太閒
這就是當初與現在的最佳寫照 老話說富不過三代 不是因為不會守 而是因為不會拼 要怎麼守 最好的防守是攻擊
體制不滿的人四處躁動 種種至今仍火熱的議題與藝術走向
同志平權 女性主義 種族平等 環保 反戰 性靈探索 實驗電影 搖滾樂 普普藝術 60年代的關鍵字
這也是美國一直以來在做的事 之前提過不在贅述 簡述這段歷史
50 黃金期 二戰後消費主義 保守主義 冷戰思維盛行的經濟繁榮時代 冷戰開始
60 狂歡反思期 反文化體制 富裕生活 追求精神平等
70 衰退期 停滯性通膨 商品供需失衡 匯率貶值 政策失誤
80 新自由主義 New Liberalism Reaganomics 蘇聯垮台 冷戰結束 貨幣資本 新時代的開啟 分開簡述後 可以看到什麼
50-70 是否可以90對照50年 只是黃金期是建立在美元霸權及蘇聯垮台/中東油權的勝利 60年的對比是否跟近年提倡環保/自由資本主義推行 只是 70年代的 衰退期被QE延後到現在 而比較尷尬的是 現在美國有意再製造一次新冷戰 為什麼? 答案很明顯
歷史總是極其相似 如果終點在 1983
那我們現在在哪裡
1968/1970/1974/1977
另外可以參考的方向是
現在的腳步會比以往快
所以往年復甦的腳步會縮短
其他有關CRYPTO的部分 就.......有空再寫吧。之前太多文章被封了 XD
另外 今年農曆新年期間多注意....... 什麼叫做驚喜
反彈轉回升的必要條件近期隨著美元轉弱台幣升值，台股隨即來了一波強勁的走勢，是否有可能延續下去？不妨直觀市場的走勢進行分析，免除外在雜音才是最好的方式。
一個多頭走勢必須是過前高，以黃金切割率判斷，要過前高 15152 (A)，必須先站上 15000。其次，再過15087，才有可能挑戰 15475 (B)。換言之，15000-15087 是一個密集壓力區間。
以年線(200 MA)的角度觀察，週五(1/13)開盤後，隨即挑戰年線。目前台股站上年線的股票不到四成，短期之內指數要挑戰年線的成功率不大，即使靠著權值股挑戰成功，籌碼面的不配合，也撐不了多久。
2022/12/6 有一個長黑向下跳空缺口(14955-14969)，不是不可能回補，但也沒必要拿著台幣去賭這種問題。
是不是買在最低點不重要，買在安全的位置才是最重要的。在此預祝各位新春假期愉快。
主要金融市場走勢一周綜述比特幣在沉寂數月後，終於爆發了，這當然和美聯儲加息步伐有著不可分開的原因。
隨著美元進入後加息週期，全球投資者對於金融資產的風險偏好明顯提升，而杠杆化率最高的加密市場，反應當然也應該是最敏感的。不過比特幣在之前數周，表現並不理想，一個長度2個月的橫盤，讓大家甚至懷疑市場能否走出像樣的反彈。
同期美股，亞洲股市，特別是黃金，都進入了大幅反彈週期，恒指更是以50%的反彈幅度，雄霸市場。但比特幣一直是不溫不火，直到本周，才開始有所起色。但比特幣大有後來居上的感覺，本周不僅是6連陽，而且漲幅達到24%，很多山寨幣的漲幅更大。看來加密貨幣市場高杠杆還是有著巨大的誘惑力。
我之前在文章中，分析了3日線底背離的走勢，也提示了幾個重要的壓力位，目前看剛好處於2.14-2.24的阻力區，理論上，這裏會有整理需求。當然，本輪反彈的目標，我還是看3日線的ma144，大約在3萬美元，而反彈週期，有可能持續到三季度。而後可能還有一次大級別的調整，因為之前我分析，這一波反彈，理論上是一個大的B浪反彈，之後經歷一次C浪後，才可能重新回到牛市。
當然，以上分析，都是預測，僅供參考，具體交易，我依然建議大家遵從macd＋ma的策略。
我們再看一下美元走勢，從114到現在，已經跌了10%，基本上和美國通脹見頂趨勢相同的路徑。不過美聯儲並沒有宣佈加息結束，即便是通脹見頂，也並不是說美元就進入熊市了。從歷史走勢看，在美元後加息時代，美元往往是高位震盪的走勢，況且全球經濟走勢還有很多不確定性，歐洲的地緣危機，中美抗衡，疫情反復，都可能造成市場的恐慌，而目前看最大的避險工具就是美元了，所以在美聯儲進入降息週期前，美元指數可能還會在100以上震盪，甚至不排除再次回到110以上的高位。
對於恒生指數，雖然從低位已經反彈了50%，但和之前的超跌走勢相比，依然還是有反彈空間，而從中國在後疫情週期，更需要香港發揮國際金融中心作用的角度看，港股的反彈機會也是存在的。從形態看，這裏有可能走出一個頭肩底的圖形，我認為，調整之後，繼續向上，挑戰ma144的概率很大。
而對於道指，市場分歧很大，有人分析，美國經濟可能軟著陸，也許不會進入衰退。對此，我覺得，經濟學家分析可能都有道理，但對於股市來說，並不是完全遵從經濟數據。我更相信市場有自身的規律。這裏的走勢，我也做了一個大膽的預測，就是在今年，道指可能會是震盪下行的走勢，從圖表看，如果繼續反彈，macd有個頂背離可能被處罰。美股經過反彈，基本上回到歷史高位 ，但上市公司的業績並沒有大幅上升，這對於支撐股價來說，是個不利因素。所以我傾向於看空美股，看多亞洲市場，特別是中國股市。
查看所有編輯精選的想法
社群趨勢
以太幣 ETH 交易想法 》ETH順勢做多機會，回踩後進場做多🔬研究幣種
ETHUSDTPERP ETHUSDT
📋幣種簡述
以太幣進行了一個短期壓力的突破，若是回踩到618的位置的話可以去佈置左側的多單
📈操作建議
這裡我們會建議以123法則的方式去做操作，個人會以2為主，3去做加倉，只是要注意的是比特幣相對來說型態比較看跌，所以在做這個多單的時候是一定要注意的。
若是有任何想要討論，分享的，歡迎進來DA的群組跟我們分享，更多的交易機會與解釋將會在群內！
美聯儲FOMC 2月利率決議前瞻
本周美聯儲將放緩加息幅度至25個BP至475BP已成共識，雖然近期通脹數據下行趨勢明顯，但此次聲明將繼續強調通脹高企。鮑威爾也將表示，放緩加息是為了更謹慎的調整貨幣政策，美聯儲打擊通脹的使命沒有改變。
美聯儲正在實現預期的利率峰值，但預計FOMC這次不會表明緊縮週期結束。措辭方面，政策聲明可能會將“持續加息”修改為“進一步加息”，暗示FOMC認為峰值將近，為了與未來加息的態度軟化形成對沖，FOMC可能會增加一個說法，“在一段時間內維持限制性利率是合適的”。
由於此次FOMC不發佈點陣圖，鮑威爾在記者會期間發表的言論較為重要，可能會強調未來數據對決議的重要性，因為3月決議前將公佈2次非農數據和2次CPI數據。
30/1/2023 黃金即日分析周一,無重要數據公佈
黃金上周收十字,周5窄幅震盪,日線亦收十字,強勢上升暫緩,市場進入整固階段。
長線升勢,短線震盪,今日策略高沽低買,先看區間1917.5-1934.5,未跌穿1917.5不看跌。
阻力1934.5,1949.3
支持1917.5
ETH突破三角收斂ETH已經突破三角收斂，但目前跌回收斂區間，關注收線的位置、1588.45和1566.60。
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若ETH的收盤價低於前低點1514.00，則會出現大型的頂部型態，將會反轉從今年初以來的上漲趨勢。
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突破趨勢線之後的走勢：
同一天之內，出現一根上漲k棒之後連續出現下跌k棒，代表上漲力道不足，可以關注日線的收線狀況。若日線的收盤價有回到趨勢線上方，則ETH來到前高1681.00的機會較大。
反之，關注ETH跌回三角收斂區間的走勢。
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1/30 Nasdaq 100 吊人反噬閃電延伸 看漲持續至12X40Nasdaq 100(那斯達克期貨) K線 吊人反噬，且出現N字形向上的閃電模式(即諧波理論的AB=CD型態)並作延伸，CD長度可以從1倍延伸到1.272倍或1.618倍，為看漲持續的訊號，測幅滿足點的理論值為
12495(=1.272倍) 或12740(=1.618倍)。
美元走勢分析雖然我不做外匯，對於外匯市場的認知也有限。不過我還是比較關注美元的走勢，畢竟美元走勢對於全球資本市場的影響都是巨大的。
我對於美元的分析，主要是通過圖表比對，至於基本面，因為我的知識有限，大家可以關注其他專業人士的觀點。
美元過去2年一直在走上升週期，也就是從美國通脹加劇後，美聯儲加息帶來的美元升值週期。
但這個升值週期中，美元也不是單邊上升。對比上一次美元加息週期就可以看到這一點。2014年到2016年，漲幅31%。但主要升幅都是在2015年3月前完成的。一般在後加息時代，美元基本上都是震盪走勢。
所以我認為，現在美元的主升階段應該完成了，接下來應該進入震盪週期。就像2017年8月之後的走勢。
過去4個月，美元走的是單邊下跌行情，對比2008年以後的走勢，美元有4段下跌行情，跌幅在13%-17%之間，歷時大約一年。所以這4個月的調整，時間週期還不足以完成調整。但走單邊下跌概率也很小。所以我認為這裏可能會有進入反彈週期。也就是用abc的形態完成。
這一點在日線也可以找到依據。目前日線macd出現了背離，在連續下跌4個月之後，市場也需要一次反彈來修正指標。而在2017年8月那一次反彈，同樣出現了日線macd底背離。
雖然我們不能簡單地套用歷史，但至少可以作為參考。
查看所有熱門想法
CVD - Cumulative Volume Delta (Chart)█ OVERVIEW
This indicator displays cumulative volume delta (CVD) as an on-chart oscillator. It uses intrabar analysis to obtain more precise volume delta information compared to methods that only use the chart's timeframe.
The core concepts in this script come from our first CVD indicator , which displays CVD values as plot candles in a separate indicator pane. In this script, CVD values are scaled according to price ranges and represented on the main chart pane.
█ CONCEPTS
Bar polarity
Bar polarity refers to the position of the close price relative to the open price. In other words, bar polarity is the direction of price change.
Intrabars
Intrabars are chart bars at a lower timeframe than the chart's. Each 1H chart bar of a 24x7 market will, for example, usually contain 60 bars at the lower timeframe of 1min, provided there was market activity during each minute of the hour. Mining information from intrabars can be useful in that it offers traders visibility on the activity inside a chart bar.
Lower timeframes (LTFs)
A lower timeframe is a timeframe that is smaller than the chart's timeframe. This script utilizes a LTF to analyze intrabars, or price changes within a chart bar. The lower the LTF, the more intrabars are analyzed, but the less chart bars can display information due to the limited number of intrabars that can be analyzed.
Volume delta
Volume delta is a measure that separates volume into "up" and "down" parts, then takes the difference to estimate the net demand for the asset. This approach gives traders a more detailed insight when analyzing volume and market sentiment. There are several methods for determining whether an asset's volume belongs in the "up" or "down" category. Some indicators, such as On Balance Volume and the Klinger Oscillator , use the change in price between bars to assign volume values to the appropriate category. Others, such as Chaikin Money Flow , make assumptions based on open, high, low, and close prices. The most accurate method involves using tick data to determine whether each transaction occurred at the bid or ask price and assigning the volume value to the appropriate category accordingly. However, this method requires a large amount of data on historical bars, which can limit the historical depth of charts and the number of symbols for which tick data is available.
In the context where historical tick data is not yet available on TradingView, intrabar analysis is the most precise technique to calculate volume delta on historical bars on our charts. This indicator uses intrabar analysis to achieve a compromise between simplicity and accuracy in calculating volume delta on historical bars. Our Volume Profile indicators use it as well. Other volume delta indicators in our Community Scripts , such as the Realtime 5D Profile , use real-time chart updates to achieve more precise volume delta calculations. However, these indicators aren't suitable for analyzing historical bars since they only work for real-time analysis.
This is the logic we use to assign intrabar volume to the "up" or "down" category:
• If the intrabar's open and close values are different, their relative position is used.
• If the intrabar's open and close values are the same, the difference between the intrabar's close and the previous intrabar's close is used.
• As a last resort, when there is no movement during an intrabar and it closes at the same price as the previous intrabar, the last known polarity is used.
Once all intrabars comprising a chart bar are analyzed, we calculate the net difference between "up" and "down" intrabar volume to produce the volume delta for the chart bar.
█ FEATURES
CVD resets
The "cumulative" part of the indicator's name stems from the fact that calculations accumulate during a period of time. By periodically resetting the volume delta accumulation, we can analyze the progression of volume delta across manageable chunks, which is often more useful than looking at volume delta accumulated from the beginning of a chart's history.
You can configure the reset period using the "CVD Resets" input, which offers the following selections:
• None : Calculations do not reset.
• On a fixed higher timeframe : Calculations reset on the higher timeframe you select in the "Fixed higher timeframe" field.
• At a fixed time that you specify.
• At the beginning of the regular session .
• On trend changes : Calculations reset on the direction change of either the Aroon indicator, Parabolic SAR , or Supertrend .
• On a stepped higher timeframe : Calculations reset on a higher timeframe automatically stepped using the chart's timeframe and following these rules:
Chart TF HTF
< 1min 1H
< 3H 1D
<= 12H 1W
< 1W 1M
>= 1W 1Y
Specifying intrabar precision
Ten options are included in the script to control the number of intrabars used per chart bar for calculations. The greater the number of intrabars per chart bar, the fewer chart bars can be analyzed.
The first five options allow users to specify the approximate amount of chart bars to be covered:
• Least Precise (Most chart bars) : Covers all chart bars by dividing the current timeframe by four.
This ensures the highest level of intrabar precision while achieving complete coverage for the dataset.
• Less Precise (Some chart bars) & More Precise (Less chart bars) : These options calculate a stepped LTF in relation to the current chart's timeframe.
• Very precise (2min intrabars) : Uses the second highest quantity of intrabars possible with the 2min LTF.
• Most precise (1min intrabars) : Uses the maximum quantity of intrabars possible with the 1min LTF.
The stepped lower timeframe for "Less Precise" and "More Precise" options is calculated from the current chart's timeframe as follows:
Chart Timeframe Lower Timeframe
Less Precise More Precise
< 1hr 1min 1min
< 1D 15min 1min
< 1W 2hr 30min
> 1W 1D 60min
The last five options allow users to specify an approximate fixed number of intrabars to analyze per chart bar. The available choices are 12, 24, 50, 100, and 250. The script will calculate the LTF which most closely approximates the specified number of intrabars per chart bar. Keep in mind that due to factors such as the length of a ticker's sessions and rounding of the LTF, it is not always possible to produce the exact number specified. However, the script will do its best to get as close to the value as possible.
As there is a limit to the number of intrabars that can be analyzed by a script, a tradeoff occurs between the number of intrabars analyzed per chart bar and the chart bars for which calculations are possible.
Display
This script displays raw or cumulative volume delta values on the chart as either line or histogram oscillator zones scaled according to the price chart, allowing traders to visualize volume activity on each bar or cumulatively over time. The indicator's background shows where CVD resets occur, demarcating the beginning of new zones. The vertical axis of each oscillator zone is scaled relative to the one with the highest price range, and the oscillator values are scaled relative to the highest volume delta. A vertical offset is applied to each oscillator zone so that the highest oscillator value aligns with the lowest price. This method ensures an accurate, intuitive visual comparison of volume activity within zones, as the scale is consistent across the chart, and oscillator values sit below prices. The vertical scale of oscillator zones can be adjusted using the "Zone Height" input in the script settings.
This script displays labels at the highest and lowest oscillator values in each zone, which can be enabled using the "Hi/Lo Labels" input in the "Visuals" section of the script settings. Additionally, the oscillator's value on a chart bar is displayed as a tooltip when a user hovers over the bar, which can be enabled using the "Value Tooltips" input.
Divergences occur when the polarity of volume delta does not match that of the chart bar. The script displays divergences as bar colors and background colors that can be enabled using the "Color bars on divergences" and "Color background on divergences" inputs.
An information box in the lower-left corner of the indicator displays the HTF used for resets, the LTF used for intrabars, the average quantity of intrabars per chart bar, and the number of chart bars for which there is LTF data. This is enabled using the "Show information box" input in the "Visuals" section of the script settings.
FOR Pine Script™ CODERS
• This script utilizes `ltf()` and `ltfStats()` from the lower_tf library.
The `ltf()` function determines the appropriate lower timeframe from the selected calculation mode and chart timeframe, and returns it in a format that can be used with request.security_lower_tf() .
The `ltfStats()` function, on the other hand, is used to compute and display statistical information about the lower timeframe in an information box.
• The script utilizes display.data_window and display.status_line to restrict the display of certain plots.
These new built-ins allow coders to fine-tune where a script’s plot values are displayed.
• The newly added session.isfirstbar_regular built-in allows for resetting the CVD segments at the start of the regular session.
• The VisibleChart library developed by our resident PineCoders team leverages the chart.left_visible_bar_time and chart.right_visible_bar_time variables to optimize the performance of this script.
These variables identify the opening time of the leftmost and rightmost visible bars on the chart, allowing the script to recalculate and draw objects only within the range of visible bars as the user scrolls.
This functionality also enables the scaling of the oscillator zones.
These variables are just a couple of the many new built-ins available in the chart.* namespace.
For more information, check out this blog post or look them up by typing "chart." in the Pine Script™ Reference Manual .
• Our ta library has undergone significant updates recently, including the incorporation of the `aroon()` indicator used as a method for resetting CVD segments within this script.
Revisit the library to see more of the newly added content!
Look first. Then leap.
Smart Money Concepts [LuxAlgo]This all-in-one indicator displays real-time market structure (internal & swing BOS / CHoCH), order blocks, premium & discount zones, equal highs & lows, and much more...allowing traders to automatically mark up their charts with widely used price action methodologies. Following the release of our Fair Value Gap script, we received numerous requests from our community to release more features in the same category.
"Smart Money Concepts" (SMC) is a fairly new yet widely used term amongst price action traders looking to more accurately navigate liquidity & find more optimal points of interest in the market. Trying to determine where institutional market participants have orders placed (buy or sell side liquidity) can be a very reasonable approach to finding more practical entries & exits based on price action.
The indicator includes alerts for the presence of swing structures and many other relevant conditions.
Features
This indicator includes many features relevant to SMC, these are highlighted below:
Full internal & swing market structure labeling in real-time
Break of Structure (BOS)
Change of Character (CHoCH)
Order Blocks (bullish & bearish)
Equal Highs & Lows
Fair Value Gap Detection
Previous Highs & Lows
Premium & Discount Zones as a range
Options to style the indicator to more easily display these concepts
Settings
Mode: Allows the user to select Historical (default) or Present, which displays only recent data on the chart.
Style: Allows the user to select different styling for the entire indicator between Colored (default) and Monochrome.
Color Candles: Plots candles based on the internal & swing structures from within the indicator on the chart.
Internal Structure: Displays the internal structure labels & dashed lines to represent them. (BOS & CHoCH).
Confluence Filter: Filter non-significant internal structure breakouts.
Swing Structure: Displays the swing structure labels & solid lines on the chart (larger BOS & CHoCH labels).
Swing Points: Displays swing points labels on chart such as HH, HL, LH, LL.
Internal Order Blocks: Enables Internal Order Blocks & allows the user to select how many most recent Internal Order Blocks appear on the chart.
Swing Order Blocks: Enables Swing Order Blocks & allows the user to select how many most recent Swing Order Blocks appear on the chart.
Equal Highs & Lows: Displays EQH/EQL labels on chart for detecting equal highs & lows.
Bars Confirmation: Allows the user to select how many bars are needed to confirm an EQH/EQL symbol on chart.
Fair Value Gaps: Displays boxes to highlight imbalance areas on the chart.
Auto Threshold: Filter out non-significant fair value gaps.
Timeframe: Allows the user to select the timeframe for the Fair Value Gap detection.
Extend FVG: Allows the user to choose how many bars to extend the Fair Value Gap boxes on the chart.
Highs & Lows MTF: Allows the user to display previous highs & lows from daily, weekly, & monthly timeframes as significant levels.
Premium/Discount Zones: Allows the user to display Premium, Discount, and Equilibrium zones on the chart
Usage
Users can see automatic CHoCH and BOS labels to highlight breakouts of market structure, which allows to determine the market trend. In the chart below we can see the internal structure which displays more frequent labels within larger structures. We can also see equal highs & lows (EQH/EQL) labels plotted alongside the internal structure to frequently give indications of potential reversals.
In the chart below we can see the swing market structure labels. These are also labeled as BOS and CHoCH but with a solid line & larger text to show larger market structure breakouts & trend reversals. Users can be mindful of these larger structure labels while trading internal structures as displayed in the previous chart.
Order blocks highlight areas where institutional market participants open positions, one can use order blocks to determine confirmation entries or potential targets as we can expect there is a large amount of liquidity at these order blocks. In the chart below we can see 2 potential trade setups with confirmation entries. The path outlined in red would be a potential short entry targeting the blue order block below, and the path outlined in green would be a potential long entry, targeting the red order blocks above.
As we can see in the chart below, the bullish confirmation entry played out in this scenario with the green path outlined in hindsight. As price breaks though the order blocks above, the indicator will consider them mitigated causing them to disappear, and as per the logic of these order blocks they will always display 5 (by default) on the chart so we can now see more actionable levels.
The Smart Money Concepts indicator has many other features and here we can see how they can also help a user find potential levels for price action trading. In the screenshot below we can see a trade setup using the Previous Monthly High, Strong High, and a Swing Order Block as a stop loss. Accompanied by the Premium from the Discount/Premium zones feature being used as a potential entry. A potential take profit level for this trade setup that a user could easily identify would be the 50% mark labeled with the Fair Value Gap & the Equilibrium all displayed automatically by the indicator.
Conclusion
This indicator highlights all relevant components of Smart Money Concepts which can be a very useful interpretation of market structure, liquidity, & more simply put, price action. The term was coined & popularized primarily within the forex community & by ICT while making its way to become a part of many traders' analysis. These concepts, with or without this indicator do not guarantee a trader to be trading within the presence of institutional or "bank-level" liquidity, there is no supporting data regarding the validity of these teachings.
Harmonic Patterns Based Trend FollowerEarlier this week, published an idea on how harmonic patterns can be used for trend following. This script is an attempt to implement the same.
🎲 Process
🎯 Derive Zigzag and scan harmonic patterns for last 5 confirmed pivots
🎯 If a pattern is found, highest point of pattern will become the bullish zone and lower point of the pattern will become bearish zone.
🎯 Since it is trend following method, when price reaches bullish zone, then the trend is considered as bullish and when price reaches bearish zone, the trend is considered as bearish.
🎯 If price does not touch both regions, then trend remains unchanged.
🎯 Bullish and bearish zone will change as and when new patterns are formed.
🎲 Note
Patterns are not created on latest pivot as last pivot will be unconfirmed and moving. Due to this, patterns appear after certain delay - patterns will not be real time. But, this is expected and does not impact the overall process.
When new pattern formed
When price breaks over the zones
🎲 Output
🎯 Patterns formed are drawn in blue coloured lines. Due to pine limitation of max 500 lines, older patterns automatically get deleted when new ones come.
🎯 Bullish Zone and Bearish Zone are plotted in green and red colours and the zone will change whenever new pattern comes along.
🎯 Bar colors are changed according to calculated trend. Trend value can be 1 or -1 based on the current trend. You can also find the value in data window.
🎯 For simplicity purpose, input option for selection of specific patterns are not provided and also pattern names are not displayed on the chart.
Machine Learning: Lorentzian Classification█ OVERVIEW
A Lorentzian Distance Classifier (LDC) is a Machine Learning classification algorithm capable of categorizing historical data from a multi-dimensional feature space. This indicator demonstrates how Lorentzian Classification can also be used to predict the direction of future price movements when used as the distance metric for a novel implementation of an Approximate Nearest Neighbors (ANN) algorithm.
█ BACKGROUND
In physics, Lorentzian space is perhaps best known for its role in describing the curvature of space-time in Einstein's theory of General Relativity (2). Interestingly, however, this abstract concept from theoretical physics also has tangible real-world applications in trading.
Recently, it was hypothesized that Lorentzian space was also well-suited for analyzing time-series data (4), (5). This hypothesis has been supported by several empirical studies that demonstrate that Lorentzian distance is more robust to outliers and noise than the more commonly used Euclidean distance (1), (3), (6). Furthermore, Lorentzian distance was also shown to outperform dozens of other highly regarded distance metrics, including Manhattan distance, Bhattacharyya similarity, and Cosine similarity (1), (3). Outside of Dynamic Time Warping based approaches, which are unfortunately too computationally intensive for PineScript at this time, the Lorentzian Distance metric consistently scores the highest mean accuracy over a wide variety of time series data sets (1).
Euclidean distance is commonly used as the default distance metric for NN-based search algorithms, but it may not always be the best choice when dealing with financial market data. This is because financial market data can be significantly impacted by proximity to major world events such as FOMC Meetings and Black Swan events. This event-based distortion of market data can be framed as similar to the gravitational warping caused by a massive object on the space-time continuum. For financial markets, the analogous continuum that experiences warping can be referred to as "price-time".
Below is a side-by-side comparison of how neighborhoods of similar historical points appear in three-dimensional Euclidean Space and Lorentzian Space:
This figure demonstrates how Lorentzian space can better accommodate the warping of price-time since the Lorentzian distance function compresses the Euclidean neighborhood in such a way that the new neighborhood distribution in Lorentzian space tends to cluster around each of the major feature axes in addition to the origin itself. This means that, even though some nearest neighbors will be the same regardless of the distance metric used, Lorentzian space will also allow for the consideration of historical points that would otherwise never be considered with a Euclidean distance metric.
Intuitively, the advantage inherent in the Lorentzian distance metric makes sense. For example, it is logical that the price action that occurs in the hours after Chairman Powell finishes delivering a speech would resemble at least some of the previous times when he finished delivering a speech. This may be true regardless of other factors, such as whether or not the market was overbought or oversold at the time or if the macro conditions were more bullish or bearish overall. These historical reference points are extremely valuable for predictive models, yet the Euclidean distance metric would miss these neighbors entirely, often in favor of irrelevant data points from the day before the event. By using Lorentzian distance as a metric, the ML model is instead able to consider the warping of price-time caused by the event and, ultimately, transcend the temporal bias imposed on it by the time series.
For more information on the implementation details of the Approximate Nearest Neighbors (ANN) algorithm used in this indicator, please refer to the detailed comments in the source code.
█ HOW TO USE
Below is an explanatory breakdown of the different parts of this indicator as it appears in the interface:
Below is an explanation of the different settings for this indicator:
General Settings:
Source - This has a default value of "hlc3" and is used to control the input data source.
Neighbors Count - This has a default value of 8, a minimum value of 1, a maximum value of 100, and a step of 1. It is used to control the number of neighbors to consider.
Max Bars Back - This has a default value of 2000.
Feature Count - This has a default value of 5, a minimum value of 2, and a maximum value of 5. It controls the number of features to use for ML predictions.
Color Compression - This has a default value of 1, a minimum value of 1, and a maximum value of 10. It is used to control the compression factor for adjusting the intensity of the color scale.
Show Exits - This has a default value of false. It controls whether to show the exit threshold on the chart.
Use Dynamic Exits - This has a default value of false. It is used to control whether to attempt to let profits ride by dynamically adjusting the exit threshold based on kernel regression.
Feature Engineering Settings:
Note: The Feature Engineering section is for fine-tuning the features used for ML predictions. The default values are optimized for the 4H to 12H timeframes for most charts, but they should also work reasonably well for other timeframes. By default, the model can support features that accept two parameters (Parameter A and Parameter B, respectively). Even though there are only 4 features provided by default, the same feature with different settings counts as two separate features. If the feature only accepts one parameter, then the second parameter will default to EMA-based smoothing with a default value of 1. These features represent the most effective combination I have encountered in my testing, but additional features may be added as additional options in the future.
Feature 1 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
Feature 2 - This has a default value of "WT" and options are: "RSI", "WT", "CCI", "ADX".
Feature 3 - This has a default value of "CCI" and options are: "RSI", "WT", "CCI", "ADX".
Feature 4 - This has a default value of "ADX" and options are: "RSI", "WT", "CCI", "ADX".
Feature 5 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
Filters Settings:
Use Volatility Filter - This has a default value of true. It is used to control whether to use the volatility filter.
Use Regime Filter - This has a default value of true. It is used to control whether to use the trend detection filter.
Use ADX Filter - This has a default value of false. It is used to control whether to use the ADX filter.
Regime Threshold - This has a default value of -0.1, a minimum value of -10, a maximum value of 10, and a step of 0.1. It is used to control the Regime Detection filter for detecting Trending/Ranging markets.
ADX Threshold - This has a default value of 20, a minimum value of 0, a maximum value of 100, and a step of 1. It is used to control the threshold for detecting Trending/Ranging markets.
Kernel Regression Settings:
Trade with Kernel - This has a default value of true. It is used to control whether to trade with the kernel.
Show Kernel Estimate - This has a default value of true. It is used to control whether to show the kernel estimate.
Lookback Window - This has a default value of 8 and a minimum value of 3. It is used to control the number of bars used for the estimation. Recommended range: 3-50
Relative Weighting - This has a default value of 8 and a step size of 0.25. It is used to control the relative weighting of time frames. Recommended range: 0.25-25
Start Regression at Bar - This has a default value of 25. It is used to control the bar index on which to start regression. Recommended range: 0-25
Display Settings:
Show Bar Colors - This has a default value of true. It is used to control whether to show the bar colors.
Show Bar Prediction Values - This has a default value of true. It controls whether to show the ML model's evaluation of each bar as an integer.
Use ATR Offset - This has a default value of false. It controls whether to use the ATR offset instead of the bar prediction offset.
Bar Prediction Offset - This has a default value of 0 and a minimum value of 0. It is used to control the offset of the bar predictions as a percentage from the bar high or close.
Backtesting Settings:
Show Backtest Results - This has a default value of true. It is used to control whether to display the win rate of the given configuration.
█ WORKS CITED
(1) R. Giusti and G. E. A. P. A. Batista, "An Empirical Comparison of Dissimilarity Measures for Time Series Classification," 2013 Brazilian Conference on Intelligent Systems, Oct. 2013, DOI: 10.1109/bracis.2013.22.
(2) Y. Kerimbekov, H. Ş. Bilge, and H. H. Uğurlu, "The use of Lorentzian distance metric in classification problems," Pattern Recognition Letters, vol. 84, 170–176, Dec. 2016, DOI: 10.1016/j.patrec.2016.09.006.
(3) A. Bagnall, A. Bostrom, J. Large, and J. Lines, "The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms." ResearchGate, Feb. 04, 2016.
(4) H. Ş. Bilge, Yerzhan Kerimbekov, and Hasan Hüseyin Uğurlu, "A new classification method by using Lorentzian distance metric," ResearchGate, Sep. 02, 2015.
(5) Y. Kerimbekov and H. Şakir Bilge, "Lorentzian Distance Classifier for Multiple Features," Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, 2017, DOI: 10.5220/0006197004930501.
(6) V. Surya Prasath et al., "Effects of Distance Measure Choice on KNN Classifier Performance - A Review." .
█ ACKNOWLEDGEMENTS
@veryfid - For many invaluable insights, discussions, and advice that helped to shape this project.
@capissimo - For open sourcing his interesting ideas regarding various KNN implementations in PineScript, several of which helped inspire my original undertaking of this project.
@RikkiTavi - For many invaluable physics-related conversations and for his helping me develop a mechanism for visualizing various distance algorithms in 3D using JavaScript
@jlaurel - For invaluable literature recommendations that helped me to understand the underlying subject matter of this project.
@annutara - For help in beta-testing this indicator and for sharing many helpful ideas and insights early on in its development.
@jasontaylor7 - For helping to beta-test this indicator and for many helpful conversations that helped to shape my backtesting workflow
@meddymarkusvanhala - For helping to beta-test this indicator
@dlbnext - For incredibly detailed backtesting testing of this indicator and for sharing numerous ideas on how the user experience could be improved.
Hurst Diamond Notation PivotsThis is a fairly simple indicator for diamond notation of past hi/lo pivot points, a common method in Hurst analysis. The diamonds mark the troughs/peaks of each cycle. They are offset by their lookback and thus will not 'paint' until after they happen so anticipate accordingly. Practically, traders can use the average length of past pivot periods to forecast future pivot periods in time🔮. For example, if the average/dominant number of bars in an 80-bar pivot point period/cycle is 76, then a trader might forecast that the next pivot could occur 76-ish bars after the last confirmed pivot. The numbers/labels on the y-axis display the cycle length used for pivot detection. This indicator doesn't repaint, but it has a lot of lag; Please use it for forecasting instead of entry signals. This indicator scans for new pivots in the form of a rainbow line and circle; once the hi/lo has happened and the lookback has passed then the pivot will be plotted. The rainbow color per wavelength theme seems to be authentic to Hurst (or modern Hurst software) and has been included as a default.
RedK K-MACD : a MACD with some more musclesMoving Averages are probably the most commonly used analysis tools, and MACD is possibly the first charting indicator a trader gets to learn about.
MACD Basic concept
----------------------------
Without repeating all the tons of documentation about what MACD does, let's quickly re-visit the MACD concept from a 10-mile altitude (note we're keen on simplifying here rather than being technically accurate - so please forgive the use of any "common lingos")
- MACD goal is to represent the distance between 2 Moving Averages (MAs) - one fast and one slow, relatively - as an unrestricted zero-based oscillator.
- The value of the main MACD line is the distance, or the displacement between the 2 MA's
- usually a signal line is used (which is another MA of that distance value) to enable better visualization of the change (and rate of change, since this is all depicted on a time axis) of that displacement - this represents price momentum (price movement in the recent period versus movements for a relatively longer period).
- the difference between the main MACD line and its signal is then represented as a histogram above and below the zero line. in this case, that histogram is really redundant, since it shows a value that is already represented visually by the main line and its signal line.
How K-MACD is different
---------------------------------
K-MACD takes that simple concept of the classic MACD and expands around it - the idea is to use the same simple approach to representing price momentum while bringing in more insight to price moves in the short, medium and long terms, ability to represent more than 2 MA's and to enable better identification of tradeable patterns (like Volatility Contraction and others) - while still keeping things simple and visually clean.
K-MACD is an indicator that allows us to view how price moves against 3 moving averages: a fast / slow pair, and a "market" Filter or Baseline (very long) that will be used as a flag for Bear/Bull market mode. Many traders and trading literature use the 200 day (40 week) SMA as that key filter
so in total, there are 4 MA lines in K-MACD (excluding the "orange" signal line):
* Price Proxy: Which is a very fast moving average that will represent the price itself - let's use a WMA(3) or something close to that here - there will be a signal line to enable better visualization of this similar to a classic MACD - that's the orange line
* Fast & Slow MA's : Use whatever represents the "medium term" momentum for your trading - Some traders use 20 and 50, others use 10 and 20 .. if on your price chart, you keep using a pair of MA's for this, use the same settings in K-MACD - these will be represented by the 3-color Momentum Bars that fluctuate above and below the baseline
* Filter/Baseline MA: Should be your long (Bullish/Bearish Mode) MA. so 100 or 200 or any other value you consider your market to be bearish below and bullish above. on K-MACD this is actually the blue zero line - everything else is "relative" to it
Review the sample chart which explains various elements and the "price chart" setup that K-MACD represents. With K-MACD you can clean up your chart from those various Moving Averages - or use a different set than the ones you already have K-MACD represent - or other indicators (like ATR channels..etc)
Other "muscles" in the K-MACD
---------------------------------------------
- Relative vs Classic Calculation Mode
A key issue with the classic MACD is that the displacement between the 2 moving averages is represented as "absolute or direct" values - as the price of the underlying increases with time, you can't really use these values to make useful comparison between the past and now (see below example) - also you can't use them to compare 2 different instruments.
- The "Relative" calculation option in K-MACD addresses that issue by relating all "distances" to the Baseline MA as percentage (above or below) - you can see this clear when you look at the above chart the far left versus the far right and compare K-MACD with the classic MACD - the Classic option is still available
- More MA "type" options for all MA lines: choose between SMA, EMA, WMA, and RSS_WMA (which i use a lot in my trading and is my default for the Price Proxy)
- More Alerts: a total or 9 alerts (in 3 groups) are available with K-MACD (Momentum above or below baseline, Price Proxy crossing signal line, and Price Proxy crossing baseline)
- New 52 week High / Low markers: These will show as Green/red circles on the zero line in K-MACD. this will only work for 1D timeframe and above, i'm just using a simple approach and would like to keep it that way.
- i know i added some more features not covered above :) -- if you have questions about any of the settings, feel free to ask below
Closing thoughts
-------------------------
K-MACD is a combination of couple of indicators i published in the past (xMACD and Mo_Bars) - so you can go back and read about them if needed - I then added improvements to accommodate ideas from swing trading literature and common practices that i plan to focus on in future. So K-MACD is really part of my own trading setup.
I assume here that most traders are familiar with what a MACD is - so kept this post short - if you thing we should expand more about the concepts covered here let me know in the comments - i can make some separate posts with examples and more details.
I hope many fellow traders find this work useful - and feel free let me know in comments below if you do.
Fair value bands (Dynamic risk levels, fair value metrics)— Overview
Fair value bands, like other band tools, depict dynamic points in price where price behaviour is normal or abnormal, i.e. trading at/around mean (price at fair value) or deviating from mean (price outside fair value). Unlike constantly readjusting standard deviation based bands, fair value bands are designed to be smooth and constant, based on typical historical deviations. The script calculates pivots that take place above/below fair value basis and forms median deviation bands based on this information. These points are then multiplied up to 3, representing more extreme deviations.
By default, the script uses OHLC4 and SMA 20 as basis for the bands. Users can form their preferred fair value basis using following options:
Price source
- Standard OHLC values
- HL2 (High + low / 2)
- OHLC4 (Open + high + low + close / 4)
- HLC3 (High + low + close / 3)
- HLCC4 (High + low + close + close / 4)
Smoothing
- SMA
- EMA
- HMA
- RMA
- WMA
- VWMA
- Median
Once fair value basis is established, some additional customization options can be employed:
Trend mode
Direction based
Cross based
Trend modes affect fair value basis color that indicates trend direction. Direction based trend considers only the direction of the defined fair value basis, i.e. pointing up is considered an uptrend, vice versa for downtrend. Cross based trends activate when selected source (same options as price source) crosses fair value basis. These sources can be set individually for uptrend/downtrend cross conditions. By default, the script uses cross based trend mode with low and high as sources.
Cross based (downtrend not triggered) vs. direction based (downtrend triggered):
Threshold band
Threshold band is calculated using typical deviations when price is trading at fair value basis. In other words, a little bit of "wiggle room" is added around the mean based on expected deviation. This feature is useful for cross based trends, as it allows filtering insignificant crosses that are more likely just noise. By default, threshold band is calculated based on 1x median deviation from mean. Users can increase/decrease threshold band width via input menu for more/less noise filtering, e.g. 2x threshold band width would require price to cross wiggle room that is 2x wider than typical, 0x erases threshold band altogether.
Deviation bands
Width of deviation bands by default is based on 1x median deviations and can be increased/decreased in a similar manner to threshold bands.
Each combination of customization options produces varying behaviour in the bands. To measure the behaviour and finding fairest representation of fair and unfair value, some data is gathered.
— Fair value metrics
Space between each band is considered a lot, named +3, +2, +1, -1, -2, -3. For each lot, time spent and volume relative to volume moving average (SMA 20) is recorded each time price is trading in a given lot:
Depending on the asset, timeframe and chosen fair value basis, shape of the distributions vary. However, practically always time is distributed in a normal bell curve shape, being highest at lots +1 to -1, gradually decreasing the further price is from the mean. This is hardly surprising, but it allows accurately determining dynamic areas of normal and abnormal price behaviour (i.e. low risk area between +1 and -1, high risk area between +-2 to +-3). Volume on the other hand is typically distributed the other way around, being lowest at lots +1 to -1 and highest at +-2 to +-3. When time and volume are distributed like so, we can conclude that 1) price being outside fair value is a rare event and 2) the more price is outside fair value, the more anomaly behaviour in volume we tend to find.
Viewing metric calculations
Metric calculation highlights can be enabled from the input menu, resulting in a lot based coloring and visibility of each lot counter (time, cumulative relative volume and average relative volume) in data window:
— Alerts
Available alerts are the following:
Individual
- High crossing deviation band (bands +1 to +3 )
- Low crossing deviation band (bands -1 to -3 )
- Low at threshold band in an uptrend
- High at threshold band in a downtrend
- New uptrend
- New downtrend
Grouped
- New uptrend or downtrend
- Deviation band cross (+1 or -1)
- Deviation band cross (+2 or -2)
- Deviation band cross (+3 or -3)
— Practical guide
Example #1 : Risk on/risk off trend following
Ideal trend stays inside fair value and provides sufficient cool offs between the moves. When this is the case, fair value bands can be used for sensible entry/exit levels within the trend.
Example #2 : Mean reversions
When price shows exuberance into an extreme deviation, followed by a stall and signs of exhaustion (wicks), an opportunity for mean reversion emerges. The higher the deviation, the more volatility in the move, the more signalling of exhaustion, the better.
Example #3 : Tweaking bands for desired behaviour
The faster the length of fair value basis, the more momentum price needs to hit extreme deviation levels, as bands too are moving faster alongside price. Decreasing fair value basis length typically leads to more quick and aggressive deviations and less steady trends outside fair value.
Dynamic Linear Regression ChannelsPlots new linear regression channels from points where a previous channel is broken thus keeping the length of bars in the trend dynamic. Regression channels are useful in detecting trend changes, support and resistance levels and to trade mean reversions.
Note: Setting higher values of upper and lower deviation may result in error if the price never breaks the channel and the script references too many bars than supported.
Musashi_Fractal_Dimension === Musashi-Fractal-Dimension ===
This tool is part of my research on the fractal nature of the markets and understanding the relation between fractal dimension and chaos theory.
To take full advantage of this indicator, you need to incorporate some principles and concepts:
- Traditional Technical Analysis is linear and Euclidean, which makes very difficult its modeling.
- Linear techniques cannot quantify non-linear behavior
- Is it possible to measure accurately a wave or the surface of a mountain with a simple ruler?
- Fractals quantify what Euclidean Geometry can’t, they measure chaos, as they identify order in apparent randomness.
- Remember: Chaos is order disguised as randomness.
- Chaos is the study of unstable aperiodic behavior in deterministic non-linear dynamic systems
- Order and randomness can coexist, allowing predictability.
- There is a reason why Fractal Dimension was invented, we had no way of measuring fractal-based structures.
- Benoit Mandelbrot used to explain it by asking: How do we measure the coast of Great Britain?
- An easy way of getting the need of a dimension in between is looking at the Koch snowflake.
- Market prices tend to seek natural levels of ranges of balance. These levels can be described as attractors and are determinant.
Fractal Dimension Index ('FDI')
Determines the persistence or anti-persistence of a market.
- A persistent market follows a market trend. An anti-persistent market results in substantial volatility around the trend (with a low r2), and is more vulnerable to price reversals
- An easy way to see this is to think that fractal dimension measures what is in between mainstream dimensions. These are:
- One dimension: a line
- Two dimensions: a square
- Three dimensions: a cube.
--> This will hint you that at certain moment, if the market has a Fractal Dimension of 1.25 (which is low), the market is behaving more “line-like”, while if the market has a high Fractal Dimension, it could be interpreted as “square-like”.
- 'FDI' is trend agnostic, which means that doesn't consider trend. This makes it super useful as gives you clean information about the market without trying to include trend stuff.
Question: If we have a game where you must choose between two options.
1. a horizontal line
2. a vertical line.
Each iteration a Horizontal Line or a Square will appear as continuation of a figure. If it that iteration shows a square and you bet vertical you win, same as if it is horizontal and it is a line.
- Wouldn’t be useful to know that Fractal dimension is 1.8? This will hint square. In the markets you can use 'FD' to filter mean-reversal signals like Bollinger bands, stochastics, Regular RSI divergences, etc.
- Wouldn’t be useful to know that Fractal dimension is 1.2? This will hint Line. In the markets you can use 'FD' to confirm trend following strategies like Moving averages, MACD, Hidden RSI divergences.
Calculation method:
Fractal dimension is obtained from the ‘hurst exponent’.
'FDI' = 2 - 'Hurst Exponent'
Musashi version of the Classic 'OG' Fractal Dimension Index ('FDI')
- By default, you get 3 fast 'FDI's (11,12,13) + 1 Slow 'FDI' (21), their interaction gives useful information.
- Fast 'FDI' cross will give you gray or red dots while Slow 'FDI' cross with the slowest of the fast 'FDI's will give white and orange dots. This are great to early spot trend beginnings or trend ends.
- A baseline (purple) is also provided, this is calculated using a 21 period Bollinger bands with 1.618 'SD', once calculated, you just take midpoint, this is the 'TDI's (Traders Dynamic Index) way. The indicator will print purple dots when Slow 'FDI' and baseline crosses, I see them as Short-Term cycle changes.
- Negative slope 'FDI' means trending asset.
- Positive most of the times hints correction, but if it got overextended it might hint a rocket-shot.
TDI Ranges:
- 'FDI' between 1.0≤ 'FDI' ≤1.4 will confirm trend following continuation signals.
- 'FDI' between 1.6≥ 'FDI' ≥2.0 will confirm reversal signals.
- 'FDI' == 1.5 hints a random unpredictable market.
Fractal Attractors
- As you must know, fractals tend orbit certain spots, this are named Attractors, this happens with any fractal behavior. The market of course also shows them, in form of Support & Resistance, Supply Demand, etc. It’s obvious they are there, but now we understand that they’re not linear, as the market is fractal, so simple trendline might not be the best tool to model this.
- I’ve noticed that when the Musashi version of the 'FDI' indicator start making a cluster of multicolor dots, this end up being an attractor, I tend to draw a rectangle as that area as price tend to come back (I still researching here).
Extra useful stuff
- Momentum / speed: Included by checking RSI Study in the indicator properties. This will add two RSI’s (9 and a 7 periods) plus a baseline calculated same way as explained for 'FDI'. This gives accurate short-term trends. It also includes RSI divergences (regular and hidden), deactivate with a simple check in the RSI section of the properties.
- BBWP (Bollinger Bands with Percentile): Efficient way of visualizing volatility as the percentile of Bollinger bands expansion. This line varies color from Iced blue when low volatility and magma red when high. By default, comes with the High vols deactivated for better view of 'FDI' and RSI while all studies are included. DDWP is trend agnostic, just like 'FDI', which make it very clean at providing information.
- Ultra Slow 'FDI': I noticed that while using BBWP and RSI, the indicator gets overcrowded, so there is the possibility of adding only one 'FDI' + its baseline.
Final Note: I’ve shown you few ways of using this indicator, please backtest before using in real trading. As you know trading is more about risk and trade management than the strategy used. This still a work in progress, I really hope you find value out of it. I use it combination with a tool named “Musashi_Katana” (also found in TradingView).
Best!
Musashi
Multi-timeframe Harmonic PatternsHello friends. In recent months I have been busy with my academic research and haven't had much time to publish new scripts. To fill the gap of these months, I decided to publish the indicator Multi-timeframe Harmonic Patterns . Harmonic technical chart patterns can predict the next price trend and provide traders with clues to the price direction, which is one of the indicators widely used by professional traders.
(1) Indicator description
This indicator is built on ZigZag Multi Time Frame with Fibonacci Retracement@LonesomeTheBlue . Thanks to LonesomeTheBlue for contributing the awesome indicator
The indicator supports 6 different timeframes , and 25 different harmonic patterns
This indicator supports indicating key indicator prices: entry price, stop loss price, and two take profit prices
(2) Key parameters
timeframe resolution: The timeframe of the harmonic pattern
pivot high/low source: Calculation method of high/low pivot points
timeframe pivot period: Minimum period of high/low pivot points
delay for confirmations: Wait for N candles to confirm the chart pattern
bullish/bearish colors: Bullish/bearish pattern colors
enable harmonic patterns: Enable current harmonic patterns
show harmonic patterns: Show harmonic patterns found
show trading prices of patterns: Show key prices of harmonic patterns
(3) Supported Patterns:
Gartlay
Cypher
Bat
Deepcrab
Crab
Butterfly
Shark
0-5
AB=CD
3-Drives
Anti-Gartlay
Anti-Cypher
Anti-Bat
Anti-Crab
Anti-Butterfly
Anti-Shark
Black-Swan
White-Swan
Descending-Triangle
Ascending-Triangle
Symmetrical-Triangle
Headers&Shoulders
Inverse-Headers&Shoulders
Double-Top
Double-Bottom
————————————————————————————————————————
各位朋友大家好。最近几个月我忙于自己的学术研究没有过多时间更新脚本。为弥补这几个月的空缺，我决定发布该 多时间周期的谐波指标 。谐波技术图表形态在一定程度上可以预测下一个价格走势，为交易者提供价格方向的线索，是广大专业交易人员广泛使用的指标之一。
(1) 指标说明
该指标建立于 ZigZag Multi Time Frame with Fibonacci Retracement@LonesomeTheBlue ，感谢LonesomeTheBlue贡献的出色指标
该指标支持 6种不同的时间周期 ，以及 25种不同的谐波形态
该指标支持指示关键的指标价格：入场价格、止损价格、以及两种止盈价格
(2) 关键参数
timeframe resolution: 谐波形态的时间周期
pivot high/low source: 高/低枢纽点的计算方式
timeframe pivot period: 高/低枢纽点的最小周期
delay for confirmations: 等待N个蜡烛以确认图表形态
bullish/bearish colors: 看涨/看跌的形态颜色
enable harmonic patterns: 使能当前的谐波形态
show harmonic patterns: 显示被发现的谐波形态
show trading prices of patterns: 显示谐波形态的关键价格
(3) 支持形态:
Gartlay
Cypher
Bat
Deepcrab
Crab
Butterfly
Shark
0-5
AB=CD
3-Drives
Anti-Gartlay
Anti-Cypher
Anti-Bat
Anti-Crab
Anti-Butterfly
Anti-Shark
Black-Swan
White-Swan
Descending-Triangle
Ascending-Triangle
Symmetrical-Triangle
Headers&Shoulders
Inverse-Headers&Shoulders
Double-Top
Double-Bottom
查看所有熱門腳本
如何建立頂級交易計劃大家好！ 👋
今天，我們來看一下，如何透過幾個簡單的步驟建立一個勢不可擋的交易計劃。
雖然許多成功的交易者在識別交易時，經常使用不同的“變數”，但所有良好的交易計劃的核心決策過程基本上相同。因此，我們要討論一些您在交易計劃中不可錯過的關鍵事項。讓我們開始吧👇
資產選擇🏦🏦
所有好的交易計劃都需要定義他們將如何選擇要交易的資產。對於期貨和外匯交易者來說，這是一個相對簡單的過程，因為可交易商品的範圍很小。然而，對於股票和加密交易者來說，可交易商品的範圍是巨大的。您要如何確定哪些商品提供最多的機會和最佳的風險/回報？擁有一套明確的標準來尋找您想要交易的機會，將您的策略預期價值最大化是絕對必要的。
例如：股票當沖交易者可能會在成交量/股數超過X時，搜尋隔夜跳空幅度超過4%的股票。或者，加密貨幣波段交易者可能會尋找具有超賣或超買條件的流動性加密貨幣，這些條件可能會帶來均值回歸機會。
不管是什麼資產，對於交易者來說，通常有兩個關鍵點可以確保您正在尋找的東西：
波動性✅
流動性✅
如果資產沒有足夠的流動性，隨著時間的推移，將很難擴大規模和退出更大的部位。
如果資產沒有足夠的波動性，那麼在小幅交易區間內很難產生絕對收益。但並非總是如此，因為一些期權策略希望從低波動性中獲利，但對於現貨交易者來說，這絕對是至關重要的。
執行邏輯🧠🧠
一旦您知道想要交易什麼資產，下一步就是定義什麼才是真正的交易機會。幾乎所有資產每天都在變動 — 您可以自己定義哪些“設定”來提供最佳風險/回報？
最好的交易計劃具有像決策樹的邏輯，因此交易者不必在該過程中考慮太多 — 所有艱難的決定，都是在當下的情況之前就做出的。
這些決策樹可能會變得無限複雜，但只要您建立並熟悉自己的執行邏輯，那麼您就可以遵循，並隨著時間的推移對其進行改進。
建立決策邏輯時需要考慮兩個重要因素：
方向✅
執行✅
雖然一些交易者樂於在任一方向交易，但許多交易者只樂於在一個方向交易，因為它可以更容易地簡化您在交易中尋找的東西。正因為如此，大多數基金和交易者首先會想出一個“觀點”。
例如：“我只會在資產高於20天移動平均線時尋找多頭交易。”
或者
“如果ISM PMI大於50，那麼我只會考慮購買股票。”
然後，一旦您知道您的交易方向（可以兩者兼具！），實際上，準確地弄清楚是什麼讓您進場和出場交易是很重要的。
例如：“如果我正在一個趨勢資產中尋找多頭進場，我只會在30天的高點買入，且將停損設定在30天的低點。”
擁有方向和執行力有助於明確了解什麼是交易機會，什麼是僅存在於您腦海中的藍圖。這也是風險控制，讓您擺脫糟糕情況的關鍵。
資金管理💵💵
如果您在單筆交易中損失了所有規模過大的資產，那麼尋找資產交易並根據高質量的計劃交易它們就無關緊要了。因此，最好的交易計劃是透過計劃最壞的情況來考慮風險和回撤。
風險控制的常用策略包含使用主題限制、行業限制等來調整交易規模（例如：在任何時候風險不該超過您資本的1-5%）。 風險是您在進場時做出的決定，而不是在出場時做出的決定。
進行交易時，確切地知道您的風險是什麼，以及如何適用於您的整體部位管理策略。有關更多詳細資訊，請參閱 這篇文章 .
所以，這些您都了解了！使用3個步驟，快速建立一個無懈可擊的交易計劃，以應對市場的衝擊。
那麼，您還在等什麼？快來試試吧😉
-TradingView團隊❤️
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！！！
創造巨額利潤的“Vegas隧道交易法”優缺點系統性教學~在閱讀該教學觀點之前我先先聲明，任何交易投資方法都有其優缺點，在熟練掌握一套交易方法和對市場有客觀的認識之前，直接採用任何交易方法和大資金進行交易無疑是賭博行為，大家在學習完這套交易方法後，感興趣的朋友要多進行複盤和練習才能徹底了解該方法的優缺點，才能熟練使用，我本人並不採用”Vegas隧道交易法“來進行實盤交易，但偶爾會用該方法參考進行行情分析，我所採用的是我自己開發的交易方法，原因並不是因為Vegas隧道交易法不具備實盤交易價值，恰好相反，這套交易方法具備十分有用的獲利價值，目前在各個投資交易市場依舊有人靠這套方法獲得巨額利潤，但交易之路只有適合自己的方法才是最好的，Vegas這套方法比較簡單，但並不代表不具備強大的盈利價值，認真學習每個人都可以學會並且熟練掌握。
Vegas(維加斯）隧道交易法
1.起源：在距今20多年前，有一位華爾街頂級對沖基金經理名為Vegas, 他靠自己開發的交易方法在市場中賺取了巨額利潤之後將這套交易方法在網上進行了公開，並以自己的名字命名。名為“Vegas隧道交易法”，從此這套交易法逐步開始走向大眾的視野。
2.定義：Vegas隧道交易法所採用的指標工具為EMA,其指標參數為:EAM144,EMA169,EMA12,EMA576,EMA676.其中EMA144和EMA169為“隧道區”Vegas本人認為是價格多空力量主要分水嶺，起到支撐位和壓力位作用，EMA12做為“過濾器”使用，EMA576和EMA676做為大趨勢方向的研判和輔助。
3.具體用法：當價格和EMA12同時向上突破隧道區（EMA144和EMA169)，並且價格處於EMA576和EMA676之上時，則後市行情看多 。當價格和EMA12向下跌破隧道區，並且價格處於EMA576和EMA676之下時，則後市行情看空，多空頭寸止損為隧道區的另一側。
注意：1.EMA12做為過濾器使用，其作用是區分真假突破，當價格突破隧道區但EMA12沒有突破時，視為假突破,不參與操作,只有當價格和EMA12同時突破隧道區時才視為真突破。
2.EMA576和EMA676做為大趨勢方向研判方法，其核心邏輯是順應趨勢方向，無論何時都要順著EMA576和EMA676方向操作，主動放棄方向不一致的交易機會。
3.Vegas交易法開發者本人在公開的時候有所保留，比如具體的止盈位置，倉位比例等等，都是學習者需要進行優化的地方，或者使用該方法進行行情參考。
優缺點：缺點，根據我個人的交易經驗，Vegas隧道交易法明顯的缺點在於震盪行情的時候，EMA144，EMA169隧道區和EMA576和EMA676輔助線容易形成均線黏合，反復交叉，導致無法正常識別到可靠的交易機會，但可以使用行情週期來進行優化，過濾掉一些勝率比較低的震盪週期，專注於捕捉趨勢行情。
優點，能夠有效的識別趨勢反轉階段，及時轉換牛熊思維，從而不會導致踏空大部分大單邊行情，另外，“Vegas隧道交易法”本質上屬於趨勢交易法，能夠有效捕捉趨勢行情，從而獲得比較可觀的盈虧比。
對交易新手的建議-- 先從理解「交易規則」開始吧！
2022 年至今，無論是股市、幣圈（以及曇花一現的 NFT 圈）、還是美金以外的匯市，投資者幾乎是哀鴻遍野
相對的，如果你追問「那前幾年大漲的時候應該還是賺不少吧」？
這時候又是幾家歡樂幾家愁、如人飲水冷暖自知。
確實，有不少人靠著這幾年的行情發家致富、財富自由；
但更有為數不少的人還總是持續在 「行情好的時候都沒賺到、行情不好的時候深深被套」 的虧損泥沼中循環。
今天我們就來聊一下參與市場時，行情的波動明明的確是有起有落
但為什麼多數市場參與者的投資績效，卻常常有相似的 起起落落落落落落落落落的體驗呢
也就是： 「虧損的根本原因是什麼？」
搞懂了這些原因，回來檢視一下此刻你的認知和狀態
再踏入這個戰場其實一點也不晚的！
為了聊這個話題，首先要區分的是投資和交易，兩種截然不同的路線
最簡單的區分方式之一，就是面對一個大跌的交易日
你去問投資者，大多數都是愁雲慘霧的；你去問交易者，至少有半數以上非常興奮
金融市場是一個， 「讓有能力的人把認知變現」 的戰場，
投資者的戰場是關於 「找到價值被低估的標的」 的能力；
交易者的戰場則是關於 「執行好足以盈利的交易規則」 的能力
兩者的追求、努力方向和技能樹都有不少落差，
而如果你赫然發現，你既沒有在找尋價值被低估的標的、也沒有在執行足以盈利的規則的話
沒錯，你很有可能就僅僅是在賭博、碰運氣而已，而十賭九輸這句話大家想必並不陌生！
當然，如果非常很嚴謹的定義的話，還是會發現其實兩者之間是有不少交集和模糊地帶的。
例如很多人會在買入的時候想說是短線交易、小賺一點就跑，結果跌下來再說服自己這是長期投資；
但最終希望每一個參與者，最起碼在一開始就要想清楚，自己參與這個市場，是來投資還是來交易的？
這對於搞懂虧損的根本原因，也有很大的幫助。
今天我們還是把重點放在交易者的戰場，如前面提到的，是關於 「執行好足以盈利的交易規則」 ，
而這句話又可以拆解成三段，分別是「執行好」 、「足以盈利」、以及「交易規則」
篇幅有限，今天我們先把重點放在「交易規則」，
如果大家反應不錯的話，後續再把另外兩集陸續補上！
Let's Go！！
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交易規則
所謂的交易規則，就是一套可以幫助你在交易過程中的每一個時間節點，
都清楚知道此刻應該做些什麼事的操作說明書，主要可以分為三個階段和四種規則：
入場前 - 風控規則
風控規則的最高指導原則就是，「任何時候都要確保自己能夠接受最差的狀況」；
這件事甚至是從入金前就開始了的；
也是為什麼總是會建議大家，只用輸光也不影響生活的閒錢來做交易；主要可以逐步拆解和設定以下幾個問題
1. 可以拿來交易的閒錢有多少？
2. 每一筆交易願意虧多少？ （一般建議是帳戶金額的 2% 以下）
3. 一個交易日的虧損上限是多少？ （例如是 6% ，則不能同時開超過三單）
4. 一週的虧損上限是多少？
5. 當日盈利到多少時，不允許再轉盈為虧？
6. 其他風控問題
風控規則是一個交易員最基礎的素養，設定的足夠嚴謹的話，哪怕是遇到最誇張的黑天鵝，
也會是你完全可以承受的風險，而只有在輸得起的狀況下，才更有機會在交易市場中成功，
如果你從來沒有想過關於風控規則的事，那建議可以至少先試著回答上面幾個問題！
入場前 - 入場規則
入場規則的關鍵在於，每次交易都可以找到明確、邏輯類似的入場點和停損點；
如果我們用拆解問題常用的 4W1H 來理解的話，大概需要做到下列這些事：
1. What
-- 要做什麼品種、出現什麼信號的時候要入場？
2.Why
-- 是否清楚這個信號背後的邏輯，為什麼出現時你要操作，有沒有反覆驗證過策略的有效性？
3. Where
-- 入場點在哪裡？停損點在哪裡？ 如果回答不了這兩個問題，就沒有辦法依照風控規則設定的單筆風險來計算開倉的倉位大小
4. When
--每天能夠操作的交易時段是幾點到幾點
5.How
--具體的入場方式是什麼？ 市價單？突破單？限價單？
強烈建議大家如果是已經有一點交易經驗、或是學過一些技術分析的話，
可以用上面這 4w1h 來檢視一下現在你的交易方法，是否具備提供完整入場規則的邏輯！
如果任何一個點有點疑慮的話，也就找到了接下來可以努力精進的目標了！
入場後 - 出場規則
正所謂「會買的是徒弟、會賣的是師傅」，絕大多數坊間的技術分析門派，都在嘗試著定義對於入場點的判定和優化，
著墨在出場規則的卻明顯較少、但這卻是真正影響交易成敗的關鍵！
因為「小賺就跑、虧損死扛」的壞習慣，所導致的悲慘盈虧比，其實才是多數交易者虧損的主因；
股票作手回憶錄中提到的交易關鍵是「不要失去你的部位」，也是在講同一件事。
交易有對有錯很正常，關鍵在於如何在對的時候賺更多、錯的時候虧更少。
出場規則的世界其實很單純，就只是在回答一個問題而已：「入場的 100% 持倉，要如何分配出場理由」？
為了做到這件事，大家可以試著靜下心來思考，從自己開始交易以來，「有哪些理由會讓你把手中的持倉出掉」？
如果是觸發停損點，那當然是 100% 全數離場，真的要思考的是那些沒有打掉停損點的交易的分配，
下面我也會列一些常見的出場理由，而大家的任務就是要思考，手中的 100% 子彈要如何分配？
打掉停損點 - 100% 停損出場
沒有打掉停損點 - 100% 的比例如何分配？
較好的出場理由 ： 可重複的、有客觀方法判定和事前設定的
1. 特定盈虧比 / 目標價
走到了預先設定的盈虧比目標或是特定的價格時，就操作減倉
我個人的交易習慣是 1:1 半倉，在出場規則的邏輯裡，其實就是定義了 1:1 盈虧比出場 50%
2. 反轉信號
行情運行過程中出現了和你做的方向相反的信號，例如 pin bar / inside bar / 吞沒等等，這裡的關鍵是酌情減倉是 OK 的，但也沒必要因為這點風吹草動就全跑
3. 左側支撐 / 阻力
跟反轉信號邏輯類似，差異是在入場前你就可以找到左側的參考目標位了，
有的人喜歡到目標全跑、我個人還是比較偏好酌情減倉留著尾倉參與後續行情
4. 推保護 （修改停損點位置）
我的尾倉永遠都是因為保護被打掉而出場的，這提供了我「如果遇到大行情時，手中還有倉位參與」的可能性；
較差的出場理由
1. 感覺
感覺要漲了、感覺要跌了、感覺好可怕、感覺抱不住等等
感覺總有個理由，理由來自反轉信號或左側支撐阻力還可以被前述邏輯包含，如果只是單純的第六感，
那下一個問題應該是「你的第六感準嗎？」，如果答案是否定的，那就別參考它了吧。
2. 他人建議
很多人喜歡對別人的交易品頭論足，更多人會因為別人的評論而影響自己的執行
交易是一件關於把自己認知變現的生意，其實任何人，哪怕是什麼頗負盛名的交易界大佬的任何觀點，
都不應該是影響你的交易執行的理由。
3. 其他理由
仔細想想還有什麼前面沒提到的理由，也會讓你把手中的倉位出掉呢？
歡迎大家在留言中補充，可以一起來討論，它屬於較好的還是較差的出場理由
出場後 - 優化規則
有規則並不等於你的規則就能盈利，所以一定會需要事前的測試、驗證，通常會在模擬盤和覆盤上練功、累積樣本，
確定可以在足夠樣本下仍然能盈利才考慮上實盤；
而正式開始實盤交易之後，也還是要定期檢視目前的每個規則的表現，是否有需要的調整和優化的；
這裡有一個很大的誤區是，有的人會用「規則是死的、人是活的」來作為違反規則或是例外的藉口，
但更應該做的事其實是，規則是活的，因為每次優化的時候都會調整；
但是平時交易的時候，都是以當前的規則為準，而絕不是每次都動不動說這次是例外。
優化規則的設定關鍵在於，「對於表現不好的交易數據對症下藥」，
而決定一個交易策略成敗的交易數據不外乎勝率和盈虧比，
表現不好的交易者基本上也只有三種可能性，
優化規則的目的就是在遇到以下這三種可能性的時候，
能夠適當的調整你的風控、入場、出場規則，以讓你的交易處在一個持續有在進步的狀態，大概可以關注以下幾件事：
1. 累積多少筆數檢視一次交易成績？ 通常是 30 筆
2. 遇到三種虧損狀態的解決方案，虧損基本上只有這三種可能性：
勝率太低-- 調整方向往提高入場規則的篩選標準，降低沒必要入場的交易對勝率的污染
盈虧比太低-- 調整方向往出場規則的比例分配為準，目標是讓「成功的交易有機會賺到更多」
勝率盈虧比都低 -- 可以考慮先回模擬盤調整狀態、並適當的調整核心入場策略
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講到這裡，可能已經篩選掉了為數不少的市場參與者了，
因為絕大多數的人，都是在 「沒有規則的情況下做交易」
如果交易是一個戰場的話，規則就像是你的武器和裝備；
而在沒有規則的情況下的交易，基本上就和在戰場上裸奔一樣危險
很多人會說自己做交易虧損是因為沒有交易紀律，
我常常都會反問對方說，那你的規則是什麼？ 而對方卻一臉茫然；
所謂的紀律是把一套規則執行到位的能力，後續也會聊到；
但如果你連規則都沒有，那根本就沒有紀律的用武之地
試著先從上述的四個規則的維度，嘗試總結或設計一下自己的交易規則吧！
如此一來、在有規則的基礎之上，就剩下兩個主線任務了，分別是「讓規則更好」以及「把規則執行好」，
那今天這集就到這裡告一段落，感謝大家的收看，我們下集待續！
盤整期的關注點位以及操作手段在一段盤整行情中，有幾個位置我會特別的關注
1、一段上漲後的第一個回踩
Equal low左方的那個紅圈，那個位置重要的原因是他是一個潛在的equal low形成的點位
2、一段行情第一個形成的higher high
這個位置重要的原因主要是和後續的假突破有關
3、Higher high過後的第一個lower high
這個位置重要的原因是因為這邊是一個潛在的equal high點位
關注完這三個點並確認equal high和equal low成形之後，我們就可以準備吃主力的豆腐了
首先，最重要的一點 「壓力支撐只是大資金的陷阱」
你可能會覺得，怎麼可能？這很合理阿，上面被套下面想買
沒有，請看我後面的解釋，你就會發現，壓力支撐都是拿來破的，一切都是騙人的
但在這之前，我們要先介紹一個重要的觀念叫liquidity pool
liquidity pool是一個價位區間，意思是，只要到了這個價位，機構就可以大量成交，就這樣想就好，非常簡單
1、equal low的成形
equal low的成形就是騙局，每次接近equal low然後沒有跌破，都是在告訴趨勢交易者說這邊有很強的支撐
所以下方就會堆積多方大量的止損賣單，或是突破空單，這個時候這邊就形成了一個sell side liquidity pool
只要機構把價格壓下這個區間就可以獲得前面所說的兩個來源所提供的大量的成交，這邊也就是昨天提過的sell to buy
2、equal high的成形
equal high的成形也是騙局，每次接近equal high然後沒有漲破，都是在告訴趨勢交易者說這裡有很強的阻力
所以上方就會堆積大量空方的止損買單，或是大量的突破多單在這邊，這個時候這邊就形成了一個buy side liquidity pool
只要機構把價格稍微拉上去這個區間，那它的賣單就可以大量成交
這邊小幅度的漲破稱為stop hunt(獵殺停損)
大幅度的漲破叫做buy to sell
3、技術型態所形成的W底的頸線和M頂的頸線都是一個很好的Liquidity pool，這也是2B法則背後的原理
而2B的延伸的3B其實也就是機構沒有在上次的B中獲得足夠的流通性，所以只好再B一次的緣故
這個只是我解讀市場的方式，不一定正確，沒有人能完美的預測市場，但你看市場的觀點會決定你的操作
Enjoy the trade
如何計算槓桿於風險範圍內如何計算槓桿於風險範圍內
相信部份交易者或是新進人員
沒有實質計算過最大風險
就索性的進場操作
並造成不可逆之虧損
倉位管理 &盈虧比
是交易最基礎之風險概念
較好的盈虧比 可使容錯率提高
相對較差的盈虧比 往往較要求高勝率
想要長期的資金水位上升
必需遵守風險管理的原則
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如何利用止損價位去計算槓桿及保證金
假設風險止損範圍於3%內
是可以接受之風險範圍
進場價位 離止損價位相差2.78%
那是否可以套用參數
10x槓桿 保證金運用10% 止損價位符合風險3%內
5x槓桿 保證金運用20% 止損價位符合風險3%內
且目標價位與止損價位符合風險報酬比
槓桿 保證金 風險3%
100x 1% 0.3%
50x 2% 0.6%
25x 4% 1.2%
20x 5% 1.5%
10x 10% 3.0%
5x 20% 6.0%
可以依照交易系統調整適合之參數
錯過交易機會？你應該知道的三件事交易的目的是賺錢
但這個過程容易產生心魔
這個心魔來自於現實跟想像不同
錯過交易機會就是其中的一種
過程大約如下
1、我們會觀察或是發現到一個交易機會
2、我們感到開心，開心的理由很簡單，因為它是一個機會
3、也許我們會追蹤它一下下，但之後就忘了
4、等到想起來的時候，才發現，它已經跑完
5、更為重要的是，這個機會是賺錢的
6、這個時候，想像跟現實是不同的
7、不開心就油然生出了
那面對這件事情
我們該怎麼做呢？有三件事是你現在就可以思考的
首先，每個人都是不一樣的
所以我的方法不一定適用在你身上
但是大方向是可以去類比
錯過交易機會是否常發生？
是否已經有一個方法能夠讓這種事情發生機率下降？
心理知道它是正常現象，而且能夠接受這個現象
先從這三個方法著手
再把細節補起來就可以減少這類事情的發生了
查看所有教育的想法
