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棉花TT1!比特币BTC1!美国10年期国债US10Y欧元 10YEU10Y
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比亚迪电子00285.HK - 图形分析 - 看涨行情比亚迪电子 HKEX:285 于 近日 2023年01月23日突破阻力线 （大约26.25）价位， 并在突破前出现底部整理（看图中橙色的主力成本 - 18.80-28.50），配合 HKEX:HSI1! 恒生指数目前处于上涨趋势，故比亚迪电子目前可谓属于“看涨行情”。目前的回调都是属于黄金买入时机。整体而言，看涨比亚迪电子 HKEX:285 。反之，如果跌破支撑 25 HKD，可以进行止损，等待机会。
操作策略：
1. 依趋势投资而言，个人是认为突破点即为最佳买入的时机，即26-28 为合适的买入区。
2. 若要控制风险，建议以重要支撑点 即 25 设为止损价位。如果跌破25，可以执行止损。反之，则可以继续持有，直到合适的价位再卖出。（看图）
3. 目前市价位为 28.15 ，若以此价位假设为买入点，则距离止损价位 25， 其风险为大约 8%-9% , 属于风险可控的时机。
分析：
1. 比亚迪电子已经由最高价位大约63.30 跌至 近期阶段性最低大约 13.20 ， 跌幅高达 大约 79%。超越过半跌幅 此为“跌深”。
2. 比亚迪电子 於 2022年05月30日至2022年12月06日 -出现 巨大买入量 （尤其是2022年06月27日至2022年06月28日），可视为主力入场支撑(尤其是)，并形成了重要的“主力成本价位”，即 18.80 至 25.85 价位区。此可视为“止跌反弹”，并形成底部形态。
3. 比亚迪电子 突破主要压力线，即 26.25（ 2023年01月23日），然而突破并没带大量， 可视为“看涨”讯号“ 但需要小心观察因为也许会继续在整理。同时，这正是趋势投资中的”随势而为 ”的最佳时机，故 26-28 为合适的买入区黄金区。
4. 长期而言，“看涨”。然而如果涨后，却跌破重要支撑25，表示涨势不利，需要整理，则行情需要重新评估。
5. 投资必有风险，故可以将风险控制可承受范围，故 25 设为止损价位。如果趋势不对，那止损离场，保留实力，等待下次机会。
看法：
1. 一般而言，如果跌破支撑（看历史图），视为危险已至，若跌破支撑并抛售带量，表示主力离场，一般股价将面临下跌趋势（看图）。
2. 目前的突破涨，但没带大量，表示涨力可能会不足，故投资要谨慎，随时要准备止损的可能。然而目前恒生指数带涨，故随着大势而涨的可能性也相当高。
3. 以上之意，即为依趋势而投资，随势而为。
注意：
此为个人爱好分析，仅供交流参考，并非投资建议。投资必有风险，谨慎掂量。
澳元持续走强澳元兑美元 AUDUSD向上触及0.71大关，1月涨幅接近6%。
澳大利亚第四季度 CPI 通胀涨幅超过预期
澳大利亚统计局 (ABS) 的数据显示，截至 12 月 31 日的三个月内，CPI 通胀较上一季度增长 1.9%。 该读数高于预期的 1.6%，以及上一季度的 1.8%。按年率计算，CPI 通胀上涨 7.8%，高于预期的 7.5% 和上一季度的 7.3%。
近几个月来，澳洲联储放慢了加息步伐，导致通胀飙升。央行在 2022 年从历史低点累计加息 300 个基点 (bps) 后通胀仍然居高不下，该CPI数据现在给澳大利亚储备银行 (RBA) 施加了更大的压力，或继续加息 0.1%。
我的交易分析--周评小结间隔了两个星期没有写日志。
之前比较混乱，连连失守。又逢过年多事多病。完全静不下心来。
现在是什么状况呢？还是有点看不清楚。
随便聊聊，自己也好好捋一捋。
12月份开始，我就看多美指的反弹行情。我的判断认为至少要到106，情况好的话会上冲109。然后阶段下行。
事实上，反弹是存在的，徘徊挣扎于105，止步于105.6而已。
我有点固执了，死活认为106是硬标准。
市场的动荡，每天的更新和推动，哪有什么硬标准。都是资金表态的反应显现。
现在回头看来，上不了106，本身就是弱势的表达。
现在美指来到101.99，何去何从？
形态上，从日线的摇摇欲坠和幅度上来看，大概率是还有一波空头势能，完全能让美指再走到101之下。
环境上，各个机构和我所知市场的氛围，对美指也是抱有怀疑观望/看空态度。
然后还有原油市场的不安分，以及我所莫名担心的债务上限的基本面问题。
种种情况综合来判断，继续向下的概率是极高的。
欧元暂时先看到1.0960之上。
再跌一段，或许才开始有一波像样的反弹。
虎年黄金最后一弹！在2023年的展望中，我曾提及，黄金叠加圣诞新年和中国春节的上升行情会让黄金触碰到1880-1920区域。但是预料到黄金这一波的上涨，没有预料到黄金会涨的如此凶猛，如今黄金已经突破我原来所说的1880-1920的重点区域，创出了新高，接下来后续会如何演绎呢？
依然是先说结论：黄金极大概率会冲击2000，不排除冲击2070前高的极小可能性，但是创出新高我认为几乎等同于零！
后续在中国春节结束到美联储二月议息会议后开始回落！ 全年黄金依然维持震荡偏空的观点， 不会走出牛市，除非美国经济出现崩溃
黄金为什么极大概率会冲击2000呢？我们先从简单的k线形态来看，黄金三个小阴k线被昨天的一根大阳k线直接吞没并且创出新高，没有任何的顶部的形态迹象，反而是多头的空中加油迹象！
第二点，对比这一轮涨势和距离我们比较近两次熟悉的涨势的行情，2020年黄金新高和2022年俄罗斯乌克兰冲突的二次冲高，我们发现在多头最疯狂的阶段还没有到来，也就是所谓的90度上冲的阶段在这轮还没有看到，目前的行情还是涨后有回踩，有休息，那种歇斯底里的上涨的行情还没有到来，但是那种直接上冲的行情往往都是一段上涨行情的结束！所以黄金还存在上冲的动能，同时从rsi的指标来看，天图级别还没有触及80的超极端值区域，周线级别也刚触及70，一切还在酝酿！等待最后一冲！
第三点 黄金自从底部的超级大双底1610-1730的2倍目标区域恰好在2000附近.
第四点黄金月线级别的供给区差不多就是以2000作为防守，投机资金一定会继续利用情绪继续上攻！
为什么我认为黄金不会走出牛市？
黄金从11月到现在走的逻辑是 美国衰退预期叠加通胀下降带动名义利率预期从而使得实际利率快速下降的行情，同时美元的走弱，空头仓位的回补季节性因素也一起助推了这一波黄金的上涨。如果市场预期的二月加息25bp的靴子落地，同时验证衰退的数据没有进一步的下滑。
如果黄金要继续走高，那么继续助推黄金上涨的动力和催化剂会是什么呢？
1.美国的需求崩塌了，美国的金融业出现了系统性的风险，美联储需要快速大幅度降息，把实际利率降到0，拉需求，稳定市场信心
2. 美联储误判通胀形势，提前降息，导致通胀反弹！70年代噩梦重演！
不要忘记了黄金最直接最简单的观察指标10年期美债的实际利率依然在1.2以上！同时今年上半年会见到全期限结构的实际利率转正的情景！美国的就业数据依然强劲，软着陆也不是不可能！美联储多次强调了吸取过去教训，不会轻易放松利率！
所以黄金在这一波情绪宣泄后，我个人认为不会走出牛市！
最后从情绪面，很多投行2023年的贵金属展望都是黄金将在今年走出牛市！券商投行的研报看看就好，且不说大名鼎鼎的中金过去几年的新年预测都是错的离谱，拿最近的美国九大投行在2021年末发布的对2022年的美股的预测来看，也是全军覆没，一个没对！同时最近我发现外网 内网 财经大v 小v都开始推黄金了，接下来就到散户和平时不关注黄金的投资者开始大张旗鼓的推荐黄金了！行情总是在狂热中死亡，在绝望中诞生！
这是虎年最后一篇黄金展望，在这里预祝各位投资者在兔年交易顺利，财源滚滚！
赚了指数不赚钱**一些思考**
揉搓之后，指数飘红，北上再大买94亿，不过这个和我没关系。昨日持仓带来的不利已经乱了节奏，再加上今天又错误的开仓。导致错过好的行情，赚了指数不赚钱。这个时候我才真正意识到市场理解力的差距。这些日子关注一些大佬实盘，很多个股见解相同我和大佬通持仓，但是我却无法累计。大佬却是推土机，在短线行情日子不好的情况，小盈大盈，就是不亏，确实厉害。我对照了下，目前差距主要是，对于个股理解差距，仓位管理的差距，以及耐心的差距和心态的差距。技术上可以交叠，但是加仓哪个，去掉哪个，我则总是本末倒置。
**短线情绪方面**
在北上资金助攻下，上证指数继续上扬，这也预示节后的红包行情。量能再次站上零轴，在来回拉锯之后，希望明日大盘可以确立上涨方向，在春节之前完美收官。短线情绪则维持低位，依旧很难做，稍有不慎就会吃面。事实上，短线情绪不如昨日，从统计数据上，涨停数量从35减少到29，涨停成功率从76%减少为71%， 跌停板数量则从4变为9。而且躺在跌停板上都是前期强势股：金发拉比、兔宝宝、西安饮食、隆基机械、泰永长征、天威视讯、宏达高科等。
短线情绪和量能vs上证指数来自TradingView
**持仓复盘**
昨天打了科瑞技术的收盘，也没有连板预期，所以竞价就清了，开盘卖在了最高点。这笔操作还是很不错的。通达股份因为低开，吓得我赶紧清仓了，但是后面疏忽，其来了个弱转强，最终竟然在尾盘竞价抢上涨停，我这笔非常垃圾的卖在全天低点上，这是要干嘛，这是什么信号？！
远望谷不及预期，卖在高点上，但是因为主要卖在低开，所以回撤并没有完全防守住，这也是目前很大一个问题。再次说明芯片渣男本性难改！
弘业期货做T，及时修正了认知，追打了个涨停，也把仓位提上来了。国盛金控虽然卖在水上，已经是全天低点了， 后面弱转强没资金在排板，等腾出来已经涨停了，不给买的机会了。这两个看出来的问题是：最近真正能连板的股都买的最少，这就是理解力的差距吧。另外，就是杂毛股买入的太多，试错单可以有，但是还是要加深理解力，强化连板股的识别，并降低杂毛的仓位。
**今日持仓第一名，科安达**
昨天晚上发酵的“交通强国”概念，但是这只是最萎靡的一只，被早盘竞价给骗了，拿着它看着其他“交通强国”呼呼的涨，这个心情真是万匹......
**今日持仓第二名， 弘业期货**
通过做了个BSB的T，把昨日弘业期货的仓位加上来了。没想到是它PK掉了泰永长征、湘菜股份、金发拉比，有些意外。
**今日持仓第三名，英飞拓**
有老妖跌停，有老妖异动，赶紧就低吸了几口。
**今日持仓第四名，跃岭股份**
一个蹭成飞借壳的家伙，这次看样子是要傍中航电测这个大款了。
2023.1.17 黄金交易计划2023.1.17
黄金交易计划
PLANA:进场理由：高位上涨趋势，目前处于周线级别供给区内，谨慎追多，反弹做空为主，下方2个支撑区做多。
1、入场方式：确认（15分钟反转K线进场）
2、入场位：1901-1892，止损位：1890
3、TP1: 1910，TP2: 1920。
跌至多2位置大胆多，上升趋势支撑，止损需求区下方。
PLANB:进场理由：高位上涨趋势，利用4小时供给区做空，可参考周线观点。
1、入场方式：确认入场
2、入场位：1919-11924，止损位：1930
3、TP1: 1902，TP2: 1893
如果下跌至1902后，反弹1910至15空，止损1920.
周线供给区，周线双顶位置，分批建仓，止损1937。
查看所有编辑精选观点
美元走势分析虽然我不做外汇，对于外汇市场的认知也有限。不过我还是比较关注美元的走势，毕竟美元走势对于全球资本市场的影响都是巨大的。
我对于美元的分析，主要是通过图表比对，至于基本面，因为我的知识有限，大家可以关注其他专业人士的观点。
美元过去2年一直在走上升周期，也就是从美国通胀加剧后，美联储加息带来的美元升值周期。
但这个升值周期中，美元也不是单边上升。对比上一次美元加息周期就可以看到这一点。2015年到2018年。但主要升幅都是在2015年3月前完成的。一般在后加息时代，美元基本上都是震荡走势。
所以我认为，现在美元的主升阶段应该完成了，接下来应该进入震荡周期。就像2017年8月之后的走势。
过去4个月，美元走的是单边下跌行情，对比2008年以后的走势，美元有4段下跌行情，跌幅在13%-17%之间，历时大约一年。所以这4个月的调整，时间周期还不足以完成调整。但走单边下跌概率也很小。所以我认为这里可能会有进入反弹周期。也就是用abc的形态完成。
这一点在日线也可以找到依据。目前日线macd出现了背离，在连续下跌4个月之后，市场也需要一次反弹来修正指标。而在2017年8月那一次反弹，同样出现了日线macd底背离。
虽然我们不能简单的套用历史，但至少可以作为参考。
金汇得手：黄金高位震荡 周五主多辅空操作美元指数依旧震荡，今天周五关注101.9-101.2区间，强阻力102.5强支撑101.1-100.5，周五小心行情是否破位。
黄金震荡走弱，日线收阴，单看日线黄金有回落的需求。但是结合周线，今天不支持大跌，所以有可能还是高位震荡为主。日线阻力1939附近，不破可以空一下，不过早盘在1932，也是日内多空分水。如果行情站稳1933看1939甚至1945，1932受阻的话看1923-1915附近。最近走势也没什么好分析的，重点建议欧美盘参与。
操作建议：早盘关注1932附近阻力，先涨接近1939或1945空，先跌接近1923或1915多，意外见1901还是建议多。
昨天由于震荡下跌，所以多单利润都不大，但是整体还是盈利。原油不管是79.9多还是82.1空，完全在预期之内。
原油也是震荡，日线支撑80.5，阻力82.2附近。行情突破82.2，看83.2，首次触及可以空。建议接近80.5或79.9多，接近82.2或83.2空，具体实盘为准。
妙手💮2023-1-27 BTC12小时级别又出现信号，如果再破22500则看低至21700今天已经回到重庆。
在春节期间，BTC和标普一直保持强势，尤其是特斯拉财报利好之下，纳指也一再突破关键阻力位。
BTC12小时级别在经过一段时间横盘之后，又一次出现下跌信号，这可能是空军最好的机会。
为什么说是空军最好的机会呢，
①在山寨币热点币猛涨之后，该补涨也补涨了，在市场各个热点轮动之后，缺乏新的人气品种给主力资金炒作对象。
②在闪电突袭23800之后，btc并未如之前维持强势推土机行情，反而留下了长长的上影线，k线结构不理想。
③各项指标都在日线乃至3日线出现高位钝化，迟迟未能修复，当fomo行情一过，回调也顺理成章。
④二月加息基本面并未明显变化，调查显示98%概率仍维持25基点加息。
所以，如果再破22500则目标将是图中第一道白框约21700一带支撑。
当然从18600突破开始，我就在观点中提出是“牛回”。从更大角度来看，周线月线都在上涨的趋势之中，下跌是为了以后更好的上涨。
2023-1-26 ETH多单进场
日内交易系统 模板—— 六大步骤：
一、找关键位：参考周期 1-4小时内各找上下各两个水平关键位
①支撑位：1500 1400
②压力位：1600 1700
二、判趋势和阶段：震荡（高空低多）
① 1小时参考周期：
分析： 主推浪——结束，调整浪：C结束
结论： 多头为主，15分钟顺势交易
②5/15分钟操作周期：
分析： 主推浪——多结束 ，调整浪——C阶段结束
结论： 多头为主
① +②总判：顺势交易 多为主。5/15分钟等多信号
三、找信号： 5-15分钟内找顺势信号2个
①孕线上破
②第二次回踩到关键位1580 附近未成新低
四、制定进场计划:
① 入场价：1593.82 ② 止损价： 1588
③ 止损差价：6 ④ 止盈价：1640 1680
⑤盈亏比（必大于1.5）：8.8
⑥参考仓位=单笔止损/止损差价： 20/6= 3
⑦实际仓位：1手头寸
⑧持仓方式：头寸+滚仓
⑨持仓时长： 顺势——多——适中
⑩进场方式：限价挂单/市价
★★★★★注意1：本单是逆势//顺势??
★★★★★注意2：本单最大止损 100 !不得抗单
五、执行计划：进场后对策
① 分批止盈：1.5:１ ② 统一止盈：
③ 分批止损： ④ 统一止损：
⑤滚动加仓/平仓：
⑥备用计划：到止盈目标附近的后手
六、复盘：
①心理状态：
②仓位管理：
③ 技术分析：
比特币周线压力位比特币周线压力位，未来会在紫色线压力下受制
币圈🐶男女
你欺负我没有点良心吗
我忍耐你这么久，
我也不愿欺骗自己，
我终于决定把你归还给人海，
就当风没吹过，
你没来过，
我没爱过。
心中不愿放下，
却不回头。
初五迎财神，港股恒指创新高香港恒生指数大幅上涨，续刷去年4月以来新高。各项港股开盘后也同样数据上涨，哔哩哔哩涨7.95%，美团涨2.56%。蔚来涨12.94%，理想汽车涨12.16%，小鹏汽车涨10.47%。腾讯涨3.83%收复400港元
港股得益于美联储加息放缓，美联储将在2月上旬公布议息结果，市场已预期是次加息幅度稍见降温，仅加0.25%。而投资者亦估计利率最终会升到5%至5.5%之间就会见顶，且纳指及标普500指数有向上突破21年底以来长期下降通道的向好走势，
但港股从去年10月低位已经反弹了超50%，较大的涨幅或许存在回调的风险，我司HK50产品将于27号北京时间晚上8点交割，交割后可以进行新合约的交易。
TradingView上的easyMarkets易信账户允许您将我们行业领先的交易条件、受监管的交易、极小的固定点差与TradingView 强大的交易者社交网络、高级图表和分析相结合。享受限价单无滑点、绝对止盈止损、固定点差、负余额保护、无隐藏费用或佣金以及平台间完美的融合。
"本网站包含的任何意见、新闻、研究、分析、价格、其他信息或第三方网站的链接均按“原样”提供，仅供参考，并非建议或推荐、研究或交易价格历史记录，或任何金融产品交易的提供或征集，因此不应被视为此类活动。所提供的信息不涉及任何特定的投资目标、财务状况和任何可能收到该信息的特定人士的需求。请注意，过去的表现并不是未来表现和/或结果的可靠指标。基于第三方过往表现或前瞻性分析并不能保证未来结果。实际结果可能与前瞻性或过去表现存在重大差异。easyMarkets易信不对所提供信息的准确性或完整性承担任何责任，也不对基于任何第三方提供的建议、预测或任何信息、任何投资造成的任何损失承担责任。"
查看所有热门观点
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 [LUX]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.
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
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.
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
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.
Pearl Dynamics | DR | IDR | AutoTraderThis script is based on the teaching of the @TheMas7er DR | IDR concepts. The purpose of this script is to provide a MTF visualization of all the available data to make informed decision on the trades. The script also will contain entry models which can be used alongside the DR|IDR levels to enter trade and in future also integrate with broker to execute the trades automatically.
This script has the following features :-
1) Plots the ADR | ODR | RDR Session ranges
2) The ADR | ODR ranges can be expanded into the RDR Session
3) The previous RDR session can be expanded until the next RDR Session
4) Auto PLOT regular FIB Retracement levels or Plot the .5 STD levels
5) Muli timeframe FVG for 5m/15m/1h timeframes
6) Multi timeframe VIBs|GIBs|GAPs on Current | 5m | 15m| 1h | Daily timeframes
7) Higher timeFrame BSL | SLL based on 15m | 1h | 4h | Daily timeframes with adjustable look-back periods.
STRATEGY MODELS
1A) LONG/SHORT Entries based on the 5m Bullish / Bearish FVGs, The entry model has 4 candle pattern types for entry and they will be highlighted when the trade is taken for visualization
With the above model has generated 550% profit based on the 1 year backtest results on long and short setups in the RDR session.
// Future Work.
The following models will be release in the course of the next week.
// MODEL2
// ADR Middle Rejections
// MODEL3
// ADR Low Rejections
// MODEL4
// ADR High Rejections
// MODEL5
// ODR Middle Rejections
// MODEL6
// ODR Low Rejections
// MODEL7
// ODR High Rejections
// MODEL8
// VIB REJECTION PATTERNS MULTI CANDLE PATTERNS.
// MODEL9
// LONG/SHOT Entries During the ADR | ODR | RDR formation before confirmation bias which are advanced entries.
// Enable ADR AND ODR Sessions for Entry and Exit Models.
ALERTS
// Presently MODEL1A strategy alerts can be used, in the subsequent release there will be alerts added for all key price action movements.
AUTOMATION
// Integration will be added to send signals to Brokers like Tradovate and other brokers to Fully Automate the trade entries.
Credits :-
- This would not have been possible with the teaching of @TheMas7er and @ICT_Concepts, without there knowledge this would not have been possible.
- PineCoders for providing the amazing library of PineCoders/VisibleChart/4
Note :- Although the Indicator will work in any timeframe from data projection standpoint, the Entry / Exit Models have been tested only for the 5m timeframe.
Lower timeframes and Other timeframe optimizations will follow as we go along.
查看所有热门脚本
将现实抽象的感想1.假设当前我们所处的环境是虚拟的。所有的一切都是由比我们高一个面位的人发明设计的。
2.从编程角度上来说，要设计出当前世界这么复杂的一个东西，必然有许许多多的面向对象。对应的这些面向对象就有更多的属性。
3.可能当前的地球，或者说我们当前所生活的环境，可能就继承或者实现了一个父类。而这个父类有着一个自然法则的属性。
4.随着人类科技的发展，我们慢慢对这个自然法则属性有了一些简单的窥探，到近代有学者进行了归纳总结。
5.我们因此可以通过书本和定义好了的数学知识来窥探这个自然法则，将它其中的一部分抽象出来，给予具体定义，常见的就是大家常说的斐波那契数列。
6.以上是我从某个角度对自然法则的定义
如何制定顶级交易计划大家好！ 👋
今天，我们来看一下，如何通过几个简单的步骤制定一个势不可挡的交易计划。
虽然许多成功的交易者在识别交易时经常使用不同的“变量”，但所有好的交易计划的核心决策过程基本相同。因此，我们要讨论一些您自己的交易计划中不应错过的关键事项。让我们开始吧👇
资产选择🏦🏦
所有好的交易计划都需要定义他们将如何选择要交易的资产。对于期货和外汇交易者来说，这是一个相对直截了当的过程，因为可交易商品的范围很小。然而，对股票和加密交易者来说，可交易商品的范围是巨大的。您要如何确定哪些商品提供最多的机会和最佳的风险/回报？拥有一套明确的标准来寻找您想要交易的机会，对最大化您的策略的预期价值是绝对必要的。
例如，股票日内交易者可能会在交易量/股数超过X，搜索隔夜跳空幅度超过4%的股票。或者，加密货币波段交易者可能会寻找具有超卖或超买条件的流动性加密货币，这可能会带来均值回归机会。
不管是什么资产，对交易者来说，通常有两个关键点可以确保您正在寻找的东西：
波动性✅
流动性✅
如果资产没有足够的流动性，随着时间的推移，将很难扩大规模并退出更大的仓位。
如果资产没有足够的波动性，那么在小幅交易区间内很难产生绝对收益。情况并非总是如此，因为一些期权策略希望从低波动性中获利，但对于现货交易者来说，这绝对是至关重要的。
执行逻辑🧠🧠
一旦您知道想要交易什么资产，下一步就是定义什么才算是真正的交易机会。几乎所有资产每天都在变动 — 您可以为自己定义哪些“设置”来提供最佳风险/回报？
最好的交易计划具有读起来像决策树的逻辑，因此交易者不必在该过程中考虑太多 — 所有艰难的决定都是在当下的情况之前做出的。
这些决策树可能会变得无限复杂，但只要您建立并熟悉自己的执行逻辑，那么您就可以遵循它，并随着时间的推移改进它。
建立决策逻辑时需要考虑两个重要因素：
方向✅
执行✅
虽然一些交易者乐于在任一方向交易，但许多交易者只乐于在一个方向交易，因为它可以更容易地简化您在交易中寻找的东西。正因为如此，大多数基金和交易者会首先想出一个“观点”。
例如：“我只会在资产高于20天移动平均线时寻找多头交易。”
或者
“如果ISM PMI大于50，那么我只会考虑购买股票。”
然后，一旦您知道交易方向是什么（可以两者兼有！），实际上，准确地弄清楚是什么让您进入和退出交易变得很有必要。
例如：“如果我正在一个趋势资产中寻找多头入场，我只会在30天的高点买入，而将止损设置在30天的低点。”
拥有方向和执行力有助于明确什么是交易机会，什么是仅存在于您脑海中的形态。这也是控制风险和让您摆脱糟糕情况的关键。
现金管理💵💵
如果您在单笔交易中损失了所有规模过大的资产，那么寻找资产交易并根据高质量的计划交易它们就无关紧要了。正因为如此，最好的交易计划会通过计划最坏的情况来考虑风险和回撤。
控制风险的常用策略围绕着使用主题限制、行业限制等来调整交易规模（例如，在任何时候风险不超过您资本的1-5%） 风险是您在入场时做出的决定，而不是在出场时做出的决定。
进行交易时，确切地知道您在冒什么风险，以及它如何适合您的总体仓位管理策略。更多详细信息，请参阅 这篇文章 。
所以，您都了解了！用3个快速步骤，建立无懈可击的交易计划，准备好应对市场的冲击。
那么，您还等什么呢？开始工作吧😉
-TradingView团队 ❤️
【交易认知】投机思维之 “今天的黄金，你摸顶爆仓了吗？”
两种『做空交易者』的思维
做空者A-耐心等待阻力结构位，不断尝试摸顶，不断止损，直到成功。A要证明谁才是市场中最聪明的人，以此为乐。
做空者B-耐心在高位等待支撑结构失效。
哪一种最明智？
显然是B
做空者A不断止损来证明市场是对。
做空者B不在乎价格涨到哪里，他等待支撑失效。相比于A，是几乎0成本，无风险的选项。
做空者A凭感觉摸顶，因为A只是在用无意识交易——当A看到一个通道，马上会想到价格在碰到通道、或者阻力结构的时候，就是个交易机会。因为往常的经验就是这么告诉他的——当价格碰到通道，就会出现反转，随后暴跌。上次没抓到，这次一定要抓到。于是A条件反射的入场了。就像这个成语：“坐吃山_”——你那懒惰的，不假思索大脑，马上冒出了一个声音：“空”。这个不假思索的“空”，就如同摸顶交易者的交易行为，被潜意识驱动，并没有经过什么思考。
因为思考是痛苦的，大脑讨厌思考。
随后通道失效。A明白自己入场早了，暗骂自己应该在更大的通道入场，于是接下来不该发生的事情，每一件都发生了——做空者A在连续摸顶止损之后杀红眼睛，频繁交易，加大仓位企图摊平亏损，浮亏加仓。有时候A可以把单子扛回来，但今天A不走运，A爆仓了。辛苦了大半年的账户，在即将过年的时候，一无所有。A去了天台——抽了一支烟，看了会儿星星，他看了看星星，想到了已经离开人间的爸爸。爸爸曾经告诉他：“吃水不忘挖井人”。
做空者B显然可以分辨出哪里是支撑。他只盯着高位的支撑。当支撑失效，意味着空头动能强过多头，他明白会有大量的多头逃跑，导致价格崩盘，而空头将乘胜追击，B就是其中的一个。B赚得不多，但他明白关注的重点是什么，而无所谓价格能长多高。B的钱赚得稳稳当当，它通过复利、一致性和持续性，积少成多。
注：
我当然知道什么供需交易，但我同时也很清楚，供需思维，培养了一大批摸顶猜底的“专家”——如果你不知道这个房间谁是傻瓜，那么很显然，就是你自己。
很多人在问trdaingview电脑版安装问题，我分享下安装过程1、下载。
win10 版本 tvd-packages.tradingview.com
mac os版本 tvd-packages.tradingview.com
2、找到该安装包。
3、选择安装包->右键选择 打开方式->在microsoft store中查找应用->确定。
4、系统弹出应用商店搜索结果“应用安装程序”->进入并获取安装即可（最好提前登录microsoft账号，没登陆其实也可以下载安装）
5、选择安装包->右键选择 打开方式->应用安装程序->确定。
6、第一次安装会提示失败，根据窗口提示打开设置，并设置为“旁加载应用”模式。
7、再次双击安装包安装即可。
注意：win10需要专业版以上版本。
题外话：
BTC回调23000以下再看涨。😂
价格移动原理及盈利空间判断等价格移动原理及盈利空间判断等
1.类别：供需与结构位 原油-4h-做空
2.主题：
(1)价格移动的原理是什么?
猎取流动性=获取止损单
(2)通过k线理解多空力量的转换
(3)打开下行空间
(4)判断盈利空间
3.案例分析
(1)供需不平衡发生区域
(2)价格移动的原理是什么?
猎取流动性=获取止损单
上假突破获取空单止损单
下真突破获取多单止损单
下破左侧机构建仓买入/做多 诱多的底部区域
(3)通过k线理解多空力量的转换
1)“下破左侧机构建仓买入/做多 诱多的底部区域”即成为多空分水岭。图表k线告诉我们趋势延续，方向空，成为多空力量转换的临界区域。
2)这两根k线收盘在 左侧多头区域下方，所有在上方做多的多头 ，宣告失败。
完成大周期结构性多空力量的转换。
(4)打开下行空间
(5)入场位：
(6)判断盈利空间
价格下行的目标，也较早期价格上行的方式有关
通过逐根k线多空力量分析，可得到TP1与TP2。
TP1 ：前低 盈亏比 10：1
TP2 ：左侧前期机构建仓位 盈亏比：15：1
谐波教学（1）：加特利、蝙蝠形态的识别和应用在2019年分享了一组谐波教学（在这篇教学下方的“相关观点”可以找到），讲解的是谐波模式中的理想比例，但是完美的比例在市场中很难遇见，很多小伙伴私聊，多分享一些谐波教学
不确定能不能持续连载，有时间会持续更新一些干货教学
一年多过去了，看到很多人放弃了谐波学其他技术，也看到很多人彻底被市场淘汰。投机像山岳一样古老，重要的是笑到最后，而不是一时的春风得意
楼主的谐波学自于斯科特.卡尼，并非原创，也没有经过主观性的改编，如有和您学的谐波不太一样，不要着急反驳，技术是一成不变的，而交易系统却很难雷同，适合你的才是最好的
比如赛弗，很多人喜欢交易这个形态，但我不能把它纳入我的交易系统，因为赛弗和鲨鱼形态有严重的重叠，和以前一样，依然喜欢分享，有精力的话会继续分享谐波相关的知识
2021年，祝大家顺风 、顺水、顺财神！！！
具有代表性的投机标的特征复盘币圈投机有一条普适性原则------买新不买旧。行情转暖的时候（即指熊市中的阶段性大反弹，也指牛市初期）高人气新项目都是值得重点关注投机标的
如20年的牛市初期，投机者该买的是SOL dot这种新出的高人气项目，而不是买上一轮的过气王者EOS。
目前处于熊市中的阶段性大反弹，行情也开始转暖。背后站着资本，人气高的项目最容易脱颖而出。如APT
背后的原理是，资本方往往掌控着舆论，他们会为自己主推的项目造势，也具备拉盘的实力。优秀的新项目往往具有独特的创新，意味着更多叙事性。有资本，有故事，已经具备的上涨最重要三个条件中的两个。
最后一个条件是筹码控制。
新项目代币上线的前半年初期流通量往往不大（具体要看代币模型），筹码集中拉盘难度小。早期投资人的代币往往没有解锁或者解锁了少量部分。
具备相同特质且未发币的项目SUI和几个L2项目值得重点关注。
高人气新项目完全解锁市值往往处于高估状态，这是一些老韭菜不敢入场的原因之一（我） 这种估价是对比一级市场估值，或者和同类项目横向对比而言。
但此时对短期投机者而言，更应该注重流通市值。以及重点监控代币解锁潮的时间点。在非大牛市的时候一定不能持有代币到解锁潮来的时候。代币解锁潮对币价的影响参考ICP FIL GLMR等 。
短期投机者应规避解锁潮
APT在熊市以高市值上币，随后币价一路下跌，这是可以提前判断的大概率事件。但谁也不知道币价跌到什么时候才是头。根据有限数据观察，可以参考一级市场融资时的代币价格。以这个价格做锚点。价格低于锚点买入就等于你用比一级市场更低的价格拿到了筹码。APT没有出现这种情况，这也是我对它关注不够的原因之一。它一直没有跌倒我的心理价格。
现在从技术面分析。
K1的暴跌是FTT爆雷带来的连锁反应。且K1成交量较大，筹码大量还手。大概率是散户止损，大户买进。随后A区域出现了像是底部整理的结构，但后面跌破了这个区间。底部不成立。
K2跌破了K1的低点创新低却没有什么成交量，说明没有多少持币人投降了，但是也意味着没什么买盘力量。K3是个止跌信号，随后5个交易日连续收短小阳线，成交量也不大，再次说明市场真的没什么抛压了。只需要一点点买盘就能推动价格上涨。
此时价格顶到达压力位B的位置。 K4说明这个压力位已经没什么压力。K5一次性就突破A区域，A区域的卖压全部被吸收。意图很明显——它就是要涨
但此时我不敢追，也没有对它有特别关注，毕竟它涨的虽好，我手里的其他标的涨的也不差。我的交易重心也不在它那里。
我曾经在它3块的时候考虑过很久要不要买它，但最终还是觉得贵了，没买。现在5块了，我似乎更不应该买了。
在此前我也几度抄底山寨损失惨重，也让我对山寨有点敬而远之。总之有很多不太明显不太重要的理由阻止我去追涨它。
我们常说的1 2 3 法则是什么？ 啥又是2 B 结构？
1 2 3法则
1，趋势线被突破
2，上升趋势不再创新高，下降趋势不在创新低
3，上升趋势中价格向下突破前期回落低点或下降趋势中价格向上突破前期反弹高点
123法则是道氏理论对趋势发生转变的定义，注意第2点，有时候价格可能会出现短暂的假突破
（新高或者新低），但是很快回到前高一下（前低以上）。所以可以和2B法则相结合。
2B法则
1、在上升趋势中，如果价格已经穿越先前的高点但又没能持续上涨，随后又跌破先前高点，则趋势
可能发生反转；
2、在下降趋势中，如果价格已经穿越先前的地点但又没能持续下跌，随后又涨会先前低点，则趋势
可能发生反转；
需要注意的是1 2 3 法则并不是1 2 3顺序，我们在实际应用中一定要学会灵活多变的去应用。
1 2 3 代表是法则，但不是顺序。
如果仅发生两种情况，则代表可能的趋势变动。如果 1 2 3 种情况同时发生，则是道氏理论对趋势变动的确认；
2B基于1 2 3原理，才有实际用处。那就是趋势可能发生转变。而且你会更融会贯通，当然，2b是产生在
1 2 3 法则的2上面的，即是处理对新高突破与否的一种方法。新高突破了，但未能延续，那便可能是2b。
新高未能突破，那自然就是2的产生了。新高强势突破，那才是真的突破，那就是新的趋势诞生了。
Hamonic 谐波形态完整教学应TRADINGVIEW 粉丝要求，要求做一个完整的数据整理给大家，我经过对市场实战论证和图形的理解给大家整理出了谐波形态中的8个形态
分别是：
加特利
蝙蝠
完美蝙蝠
蝴蝶
螃蟹
深海螃蟹
赛福
鲨鱼
5-0
以及ABCD我就不做赘述了
因为谐波个人感觉者8个形态已经让你在市场上混的风生水起
关注我我们一起作为谐波的狂者追逐者
一起学习一起交流
一起在交易的路上共同前行！
查看所有教育观点
KDJ、VOLUME、MA三个指标金典组合，实测收益达到743%BINANCE:ETHUSDT这个指标的组合主要包含 KDJ、VOLUME、MA三个指标，应用在币安的ETHUSDT的现货交易对上。
回测的范围是2018年1月到现在，收益达到743%。
1、VOL是价格涨跌趋势的先行指标，成交量是一个相当重要的技术指标。在本策略中，使用当前的交易量是否大于过去周期为30的均量，作为做空做多的条件。
2、KDJ指标，震荡指标，当J线小于30时，表示已经超卖，多头的力量变强做多，当J线大于70时，处于已经超卖，空头的力量变强，做空。
3、双均线，快线大于慢线做多，快线小于慢线，做空。
这个指标的组合是非常金典的，大家可以试用研究。
来谈谈TOTAL，比特币，以太坊和狗币今天就谈这4个币日线的看法。
前几天砸盘后，我认为庄稼应该会拉一波，空单别急着进去，如果有再次回到支撑可以考虑接多。
狗币这个月可能会有大行情，我猜的，交易主意风险，就这样。
祝大家好运。
2023-1-18 ETH 5分钟 回测 《超级二进制》主推浪和ABC调整的 应用 应用的指标如下：
1、自动斐波那契调整：Auto Fib Retracement
2、自动Z字行：ZigZag++ 来自 DevLucem
3、VRVP：可见范围内的成交量分布图
其他配合： 趋势线 、ABC浪型
符合盈亏比1.5:1 的机会 4次以上。有的盈亏比达到4-5倍！
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