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千里之行 始于足下

Photo: Jimmy Chin ©

图表。聊天。交易。反复实践

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市场摘要

中国综合指数000001
SZSE综合指数399106
恒生HSI
标普500SPX
US 100 INDEXNDQ
日经225NI225
亿纬锂能300014
晨鸣纸业000488
兴齐眼药300573
立讯精密002475
超微半导体公司AMD
JD.com, IncJD
加密货币市值TOTAL
比特币BTCUSD
以太坊ETHUSD
SolanaSOLUSD
UniswapUNIUSD
Luna / 美元LUNAUSD
美元DXY
EUR/USDEURUSD
USD/JPYUSDJPY
GBP/USDGBPUSD
AUD/USDAUDUSD
USD/CADUSDCAD
黄金GC1!
白银SI1!
原油CL1!
天然气NG1!
棉花TT1!
比特币BTC1!
美国10年期国债US10Y
欧元 10YEU10Y
德国10年期国债DE10Y
日本10年期国债收益率JP10Y
英国10年期国债GB10Y
印度10年期国债IN10Y

观点

打不过龙头饶了谁? 估计A股很多的交易者很多人都赞成：做短线就是做龙头，而且是做全市场最强的龙头。这也包括我自己。因为这话也是游资大佬们流传下来的箴言。但是，从我个人实践来看量化交易做龙头要求极高，至少我还没有进入到做龙头盈利的窄门之内。也许未来某一天想明白了情况会有所改善吧。所以，不同阶段的认知就用不同阶段的工具也是必经之路。自从上个月把编号9526从龙头战法切换到杂毛战法+SB战法，回撤和收益率出现了改善。打龙头时候每天有几个涨停，但是一段时间下来发现资金曲线却是一路向东南的。不知道看到我文章的相同经历的朋友是否有同感呢？现在的主要改变就是不去rush涨停板，买到手的股票觉得好就多倒腾两天，目前来看是可以把曲线掰过来的。比如说持股5天的科信技术，前四天仓位和价格原因基本没赚到，但是今天一下就可以吃到肉了，不枉这几天的倒腾。 这让我想起来几年前学缠论的时候，缠中说禅提到炒股的三重境界：“看山是山，看水是水；看山不是山，看水不是水；看山还是山，看水还是水”。当时不明觉厉，尽管上网专门看了各种解释，觉得这个还是太神道儿了。随着时间消逝，自己似乎逐渐明白了三种境界的意思。如果你听懂了我的解释，就说明我确实明白了些。我并不会用什么深奥的佛学去说明含义。但是，我知道股市如战场，上战场要有武器，而量化交易者来说，好的策略就是好的武器。但是两点因素导致武器使用的效果不同：1. 使用者的认知（功力）；2. 使用的环境（平原用长枪，巷战用短刀）。可以确定的是人器合一方可在战场上保证性命，甚至建功立业。金庸老师笔下的神雕侠侣中有一个大神：独孤求败。他留下一个剑冢，埋了四把剑，小说中对这四把剑的那段描述，我认为，就是对于股市“看山水”三重境界的最好解释！ 第一把剑是无名利剑，青光闪闪，凌厉刚猛，可谓无坚不摧。这是独孤求败20岁前所用。这让我想起很多交易者通常在牛市末期开始自己交易生涯。初到股市，可以为牛犊不怕虎，每个人都像一把利剑，更确切的说在缺乏认知的情况下，每个新手都幻想自己是那把独孤求败无名利剑，想象着把自己在学业，工作上的能力和成就“复制”到股市当中来。因为新，所以青光闪闪，凌厉刚猛，喜欢动不动就梭哈，满脑子暴富思想，看不惯保守，没有耐心等待，“赢了会所嫩模儿，输了下海干活儿”。这个阶段的交易者是主力的最爱。 第二把剑是紫薇软剑。作为新韭菜在股市上碰的头破血流，锐气大减，经历一段时间的交易逐渐把棱角磨平，这时才叫真的走入股市。这个阶段的交易者因为发现“无名利剑”在熊市里面并不好用，“凌厉刚猛”的结果是经常碰破头。他发现，自己的狂躁、愤怒其实在股市里无人在乎，自己的能量原来毫无分量，自己的情绪在股海中连个涟漪都激不起来。很多老股民都开始告诉他一些生存的技巧就是要足够“圆滑”，吃了亏要“学着点”。为了进步，这个阶段的交易者开始拿起“紫薇软剑”，开始知道股市赚钱第一条：先活下来再说别的。他开始模仿成功大佬们的样子交易，学习他们的语录，学着试探，学着吃一堑长一智不断的调整，也学会了叫别人“大佬”。一段时间过去，他的柔软开始有了回报，亏损减少甚至扭亏为盈。但是紫薇的柔软也是有代价的，很多人在这个阶段过于谨慎，遇到大机会不敢像年少时那样果敢出击，可能会错过一波大行情。 第三把剑是玄铁重剑，这是独孤求败40岁以上所用的剑，也是杨过选中的剑。对应股市上，这个阶段的交易者又进了一步：所谓“重剑无锋，大巧不工”，经过一些年的交易，有些人会依旧耍着紫薇软剑，另一些人则在自省和追求中变得更加浑厚朴拙了。他们意识到过分的“软弱”，放弃初心，会错过大行情，大机会，往往只能成为“中人”，成就未必有多大。于是，这样的交易者宁愿简单一些，厚重一些，放弃一些交易的机会，学会大道至简。他开始简化自己的交易方式，把精力集中在更核心的少数事情上：资金流向，题材逻辑，情绪周期等。只不过玄铁重剑这个层次的交易者仍旧是有负担的，仍需要一个“器”（策略）来辅助自己，系统化，流程化的操作。 第四把剑是木剑。能到达这个阶段的交易者少之又少，因为他能卸下负担。木剑轻，不需要重剑那样端着，一个靠着灵敏的盘感和“肌肉记忆”进行交易盈利的很少再有系统性缺点。这就像炒股养家说的那个故事，没有哪个乒乓球高手是记着套路来打的，基本上都是靠着多年训练出来的感觉下意识的挥拍反击。木剑摆脱了重剑那种对“器”的依赖，没有了固定的策略，就没有了弱点。 到了这个阶段，一切的战法和策略都是桎梏。交易者到了这一步，不用刻意用任何策略，完全将自律和认知融入体内，就像来去自如的老司机，根据路况下意识挂挡，自由自在。回过来看独孤求败的这几把剑，每一次进步都会放弃一些东西，也拾起一些东西，活得由轻到重，又再次由重到轻。这里是点睛之笔：轻---重---轻 和 是山---不是山---又是山。这两者是不是很一致呢？这三重境界唯一变化的就是人，或者说人的认知。虽然都是轻，都是山，但和年少时候的认知产生了巨大的变化。这就是佛教里面说“渡人”的意思吧？ 当然我说起来简单，但是每一步都是异常艰辛，都需要时间和经历的沉淀。交易这条路， 事实是大多数人中途放弃了，没有进境了，早年梭哈，最后晚年还在梭哈。共勉！
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金汇得手：黄金震荡上涨 今日先多后空美元指数震荡收阴，今天暂时关注106.6-105.9区间，破位后参考周末分析。 黄金昨天1770企稳拉升，晚间最高1790附近，日线收阳。但看日线行情可以延续昨天走势继续上涨，也可以不破前高回落，日线阴阳转换。但是结合白银走势，黄金还是倾向上破继续涨一波。下方支撑1776附近触及可以多，但是如果走多不应该给到这个位置。下方还有一个是1783支撑，也可以说是日内的多空分水，不破可以先多，如果走强的话不排除1787先涨。上方目标先看前高1795破位还是看1803附近，也是618的位置，不破可以空一下。还是和以前分析一样，行情只有有效突破1814才可以继续上涨。 操作建议：1783附近多，见1776可以继续多，目标1795破位看1803附近，1803不破空一下。 原油昨天二次下探没有新低拉升，日线收阳。今天低多一波，日线支撑88.2附近触及可以多。不过行情上破后回落的低点在89.5附近，如果走强会在89.5上方运行，所以触及先多。上方目标先看昨天高点90.8附近，破位看91.8-92.7-93.7附近，趋势线阻力在93.7附近，不破做空。
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【上证指数】的波浪推演连载（3）接着上回的推演， 在小周期上， 上证指数走的依然是处于调整模式中， 正如上一篇连载的结构推演那样（见相关观点）， 大结构仍然维持首选计数不变， 小结构上， 正如上一篇的小周期连载推演所假设的那样， 目前走出了单锯齿的abc可能已经完成， abc起手的调整模式后续有多种演变， 最为多头的计数一为： 如图： 该锯齿abc为整个调整的全部， 目前正处于假设的c浪推动的一浪三中。 可能性最高的计数二为： 如图： 该锯齿为平台型的第一段，后续还有复杂的第二段和向下杀跌的第三段， 形成平台型， 该计数也是我个人较为倾向的计数， 从时间和幅度来说较为合理。 第三种计数为： 如图： 双锯齿型， 该计数也有一定可能， 当然由于单锯齿的调整模式变化比较多， 除了以上几种假设外， 还有联合型、三角形等假设， 等走出这类预期之外的走势， 我会持续追踪。 当然，指数的横盘给个股的启动创造了良好的条件， 各位也可以捕捉相应的机会。 ——————————————————————— 更多浪型演化的可能请关注视频频道， 本篇分析与预测仅用于行为金融学学术交流， 不构成买卖建议，不对任何交易行为负责。 金融市场具有高度风险， 请遵守您所在国家或地区的法律。 © 版权声明 文章版权归作者所有，未经允许请勿转载。
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XAUUSD （H4）XAUUSD 黄金日图到了供给区+多空转换位，价格暂时维持在开盘价附近，无明确方向 但是因为H4 目前是上升趋势，更愿意尊重趋势 交易方向以做多为主 交易计划： Plan A 价格如果上破起跌点1813，上方空间打开，回踩最新形成的需求区做多 Plan B 价格回踩下方上涨波段的0.382+需求区+多空转换位 入场：1760 止损：1750 入场方式：确认入场 TP1--前高1794 TP2--追踪推保护 Plan C 如果1850被跌破，回抽做空， 入场：1850 止损：1860 TP1--1730附近，3：1左右盈亏比走人 Plan D 回踩上涨波段的0.618+颈线破位+起涨点 入场：1824 止损：1710 TP1--2：1，1852减仓 TP2--前高：1794附近 TP3--追踪推保护
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腾讯：黑暗即将过去，上涨波段将要到来1、就基本面而言，腾讯仍将保有 中国互联网行业 龙头地位 很长一段时间。 从760跌到290已是明显超跌，至少可以期待一个修复行情，牛市将是大概率事件。 2、日线看， 走势是两个笔中枢的趋势下（400-500）+（300-400）。 现在处于日线第二个笔中枢的离开段， 从量价看，近三天是典型的恐慌抛售+放量滞跌，这意味着自动反弹或许即将到来， 反弹后，需要关注是否能够出现成功的二次测试。 3、日线次级别， 6月底，400元开始的下跌属于三笔式背驰。 因此，288附近见底的概率是不小的。 总的来说，腾讯已经到了比较不错的价值投资的位置，尽管右侧交易的准确时机还没见到。 HKEX:700
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更新上周对S5FI的观察美股一切按脚本进行，就是二季度GDP看错了，没想到是负值，但路径没变。技术性衰退又如何，表示美联储变鸽了，这便是现在的逻辑。而核心PCE也看对了，连续三连降后又反弹了。而且非核心PCE跳到6.8，仅次1982年6.9。老鲍不说了嘛，他最关注的依然是PCE，可是现在没人理会了。当看似逆逻辑上涨后，便出解析出各种因由。我的理解，其实就是嘴上说着担心宏观基本面。暗里却盯着是技术面操作。上周谈到的thrust就是光在spx勤劳地划趋势线所找不到的技术面。 这是几天前的图，thrust走了一大半。不知道是什么的同志看上一文。 这是今天的图。原来预计thrust起码两周完成，谁知道一周就干完了，甚至继续往上。虽然thrust到位了，但并不代表标普一定会转跌或回踩。只是说明最好吃的那块肉已经被吃完了，之后可能继续上涨，但那个slope就没有陡了。但也有可能开始转跌。07，08年也有两次thrust，可这两次后美股继续崩盘。其后十多年的thrust之后美股仍能持续上涨。所以这thrust并不能推断后续走势，只能说明机会成本最低的那一段已经结束。 这两周看到过的财报，大部份的财报多数是beat，但利润多数是下降。最大共同点是毛利率下降。上一文提过了毛利率问题，这里不再讲。看过很多财报，总的来讲看到的是消费者开始节约，企业开始节流。这都是开始经济下行。虽然说现在是技术性衰退，但6月中才加息0.75，有很多对经济影戏在Q2财报里还没完全反映。然而7月又来0.75，这fed连续猛踩刹车的效果估计应该到Q3或以后才完全显现。 目前看，市场认为经济差，fed必然转鸽。鸽fed就等于是牛市。通胀也会短期内下降。然后押注经济只是软着陆。涨到这样了，肯定不是bear market rally。就算是硬着陆，股市将来都会比现在涨得多。要是现在不买，肯定被通胀淹没。你买我买大家买。这想法不能说有错。美股历史总保持上涨，要是人人保持这心态，股市应该没有下跌的机会。在又开始下跌时，人又会产生很多不一样的想法，就很容易动摇当初的坚定。这就是人性。技术面其实反映的正是人性。 这次技术性衰退更令我相信这张图。之前都发过，不厌其烦再发一次。 这图显示过去50年7次衰退时，进入衰退期间，标普触底前的必须满足几个宏观条件。分别是PMI在50以下。失业率已经上升。两年和十年期利率倒挂后开始陡峭。最后是利率正常已经，或开始转向。尤其利率政策，即便鸽了，老鲍还是明确表示继续升息直到控制通胀为止。显然所有都没达标。 如果目前只是经济减速，而非衰退，股市或许就像Tom Lee说，V型反弹，年末通胀降到2%，标普4800以上新高。高油价，高通胀，以及加息周期下仍可以半年内出新高的确会刷新很多老司机认知。 然而什么事都有可能发生。所以绝不能把现金藏在床下。之前已经买入covered call etf和债券etf，今日卖掉部分大涨的个股，再加仓这些etf。其实债券在升息环境不应该买，可我认为，如果经济下行长债利率应该降低。同时fed加息减缓，债可能已经超跌。所以我认为不论之后美股不论涨跌，债还是能拿着的，只收益较少而已。收益率低一向是我的特点。 我觉得要是真的只是温和衰退，一些中小盘，在美国本土经营的零售股比big tech更值得押注。这版块的pe基本跌到5左右。big tech现在pe都在30以上。既然最好吃的部分都被吃完了，就挑些反弹时没人爱吃的。赌赌这些零售股，这版块也是超级落后。要是指数还要测试底部，big tech这些权重股应该是当仁不让。尤其谷歌亚麻，都是最近split，这相当吸引小散户。当散户都不觉得是bear market rally时，当然追爆过去只能远观的高大上。到跑的时候，散户们也是会乱跑。但这些pe爬地上的小盘股可能就在第一波时就已经触底了，可能再跌也跌不多。 老毛病，一堆废话才切入主题。返回正图。这个Breadth Thrust indicator。熟悉我的老同志都知道是什么。稍微介绍一下给新同志。简单就是纽交所advance除以advance加上decline之和的过去10天均值。advance是收盘时上涨个股数量，decline则是下跌数量。这数值超过0.6代表超买，低于0.4代表超卖。这里用上SMA算10天均值。可以曲线上破0.6数值后，标普开始回踩。这几年都没有出错。所有之后短期出现回踩几率应该挺高，但并不代表是中长期多翻空的信号。如果抄底收益已经不错，其实可以考虑先获利，之后再来过回马枪。 再进一步看。当Thrust由0.4以下跑到0.6以上，同时S5FI开始翻转，标普短期必定回踩。可是S5FI并没有跟随Thurst回踩，而出现背离，指数将继续上涨。就是说如果这次thrust向下，而S5FI不跟随下跌，出现背离。这次反弹极大可能是可信的反弹。下一次下跌或回调很可能是6月16日底部半年后的事。这样的话，标普可能早已经回到比目前水平更高的位置，那现在就不是什么bear market rally了。 如果真的出现背离，这的确和图三所示出现矛盾。可要是真正衰退出现在2023，那么两者间就没有矛盾，而是我当初所推断第三季度开始回测，第四季度见真底部的看法并不准确。若到2023才出现图三所示的情景，标普到时候未必低于6月16日的底。虽然目前宏观环境很糟。全球衰退的可能性很高，但我的角度，我仍是尊重技术面，因为技术面最后反映的是人的交易行为。要是出现背离，我还是会再降低一些现金，即便对宏观继续怀疑态度。 暂时就这样，目前情绪高涨，估计很多谨慎的人开始动摇，fear of missing out买入。 再看看吧。开始要忙了，下次更新估计一个月后，同志们保重哦。
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【教学贴】｜【中通客车】天地板带来的启发，一文揭示什么是投机市场的人性与反人性，以及如何从中获利！ 序 今天下午两点， 从4月27日开始连板上涨启动的大妖股中通客车， 开始以市价单的形式从涨停价27.97砸盘到当天的跌停价22.89， 再次上演这一天地板的奇观， 这不经让人想起了投机大师利弗莫尔的一句名言 “投机像山丘一样古老，人性更古不变。” 那么既然今天的主题是投机市场的人性与反人性， 我们就从【中通客车】今天的天地板走势中去窥探一二， 简单到底什么是人性，什么又是反人，以及作为市场参与者的我们如何从中获利的。 ---------------------------------------------------------------------------------------------------- 一：认清市场的本质 本质上来说，作为个人投资者大多数人参与的合约市场、期货市场、外汇市场， 基本上都是零和甚至于负和博弈的市场， 而股票市场由于其承载者上市公司这一价值载体， 在长期来说是一个正和市场（因为公司的价值随着公司的成长是会越来越增长的）， 但是对于短期投机者来说，扣除印花税等摩擦成本，也是个负和市场， （因为短周期来说，公司的实际成长价值来不及得到体现）， 这或许也是对于一个没有成熟投机或者投资体系的人来说，不要参与短线投机的原因， 正和游戏的难度显然比负和游戏的难度要低不少，赚钱的概率自然也就越大。 那么找出在这一负和市场中长期挣钱和长期输钱的玩家， 显然对我们有着重要的意义。 输钱玩家可以找到自身的问题，以及为什么会输钱， 并学习盈利玩家为什么会盈利的原因， 去趋利避害，提高自己的交易绩效。 --------------------------------------------------------------------------------------------------- 二：人性与盈亏的关系 现在市场上的两类人已经做了划分， 简单的我们可以称之为赢家和输家， 赢家又不认识所有的散户， 他们是怎么长期把钱从散户的口袋搬运到自己的口袋的呢？ 没错，就是利用人性。 人性就是赢家，（或者叫做庄家，或者叫做主力，或者称作smart money）用来操控并诱导输家的工具！ 因为人性每个人都有，包括这些赢家自己，人性是人类在千百万年进化以来，难以抹除的本性， 因为原始人类需要在自然环境中生存， 需要在面对野兽的攻击时足够的恐惧， 让人类能够分泌出肾上腺素去增强体能和力量快速的逃跑， 躲避野兽的攻击； 在找到食物和水源是能够足够的贪婪， 让人类能够比别的同伴抢到更加多的生存资源， 从而可以长久的生存下去。 这些贪婪与恐惧的本能随着人类千百万年来的进化， 并没有被剔除， 而是被不断的强化， 金融交易市场作为一个直接投入现代人最重要的生存资源进行博弈的“赌场”， 贪婪与恐惧的本性更是极具的放大。 这对于一个没有受过系统训练的投资者来说，要战胜贪婪与恐惧也是巨大的考验。 而利用贪婪与恐惧这一人性武器， 聪明的盈利玩家们可以知道 如何让你忍不住去追涨站在最高点去接过他们早已获利丰厚的筹码， 如何让你吓的在最低点去抛出握在手中的便宜筹码， 更何况很多的盈利玩家还有拥有许多个人投资者所不具有的内幕消息、媒体资源、资金优势等武器。 所以，对于一个没有受过系统交易训练和学习的个人投资者 长期要战胜这些聪明的盈利的玩家，是很难的。 ---------------------------------------------------------------------------------------------------- 三：如何利用人性去获利 巴菲特曾经说过：“别人贪婪时我恐惧，别人恐惧时我贪婪”； 而李嘉诚则说：“永远不要赚最后一个铜板”。 可见任何的投资家或者商人，都深谙人性之极的大道理， 那么什么是人性之极的大道理呢？怎么反过来利用人性去获利呢？ 经过我的多年的观察、总结、和验证，有以下几个点： 1、相对的高位的利多往往是主力在出货，相对低位的利空，往往是主力用来吸货。 这一现象在股票市场屡见不鲜了。 比如在上周7月13日随着美国六月末季度cpi数据的高于预期的公布，如下图所示： 整个市场再次的对通胀引发的美元加息预期开始担忧起来，并引发散户投资者恐慌性的抛售， 那么此时我们可以通过观察底部的量能，问自己一个问题，如此大的放量产生的分歧， 到底是谁在做多，谁在做空呢？ 这样重要的经济数据是面向谁的呢？ 整个市场长期又是谁在盈利，谁长期又是在亏损呢？ 那答案很明显了，是主力或者说机构在买入，而看着数据操作的散户在卖出， 再往左边看看这里是不是相对低位的地方？ 相信此时的你一定不会因为市场恐慌的大跌而随波逐流的卖出吧？ 如果你懂波浪，就会知道此时的5浪或者c浪是一个人性之极反转浪，表示情绪的极端释放， 关于波浪与人性也是个有意思的话题， 如果你能看到这里相信你对我们的分享是有兴趣的，那么请你积极点赞并关注我的账号， 并分享给更多的小伙伴，在关注粉丝达到300人后，我会回来填掉今天挖的这个坑。 那么再进一步深入， 你有没有想过为什么要在这个时间点上做和散户完全相反的动作呢？ 同样的这个坑也会在达到一定的粉丝数后解锁。 2、快速往关键高位逼近或超过往往是为了出货，犹豫的拉破关键高位往往是真实的突破，做空亦然。 这里的关键点在于如何去区分高位和低位，这也是让很多人困扰的地方， 既然这里为了出货和吸货，那么这里一定是主力资金发生过大量换手的地方 主力有很强的意愿（或许是主力资金的作战计划，或许是主力资金的自己的心里价位，又或许是这里有很多套牢的筹码需要主力资金去解套）总之这里就是主力的目标出货的价位，那么这里就是我们所说的高地位， 如图： 我们可以明显的看到该股的历史走势上，处于高位和低位的表现如何， 如果感兴趣，你还可以在trading view中打开下方的“E、D、S”等财务数据标签， 去看看这些个股及其重要的基本面数据和该股走势以及高地位的关系，相信你会有不错的发现。 这里由于篇幅和时间的限制， 很多更加有趣的内容无法分享出来， 但是点赞分享和互动是我分享的原动力， 如果你喜欢我的分享请积极的点赞分享和互动吧。 以上内容均为原创， 如需转载请注明出处。 ——————————————————————— 更多浪型演化的可能请关注视频频道， 本篇分析与预测仅用于行为金融学学术交流， 不构成买卖建议，不对任何交易行为负责。 金融市场具有高度风险， 请遵守您所在国家或地区的法律。 © 版权声明 文章版权归作者所有，未经允许请勿转载。
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都是右侧交易，传统型突破交易和预判式突破，其中有何玄机？ 都是右侧交易，传统型突破交易和预判式突破，其中有何玄机？ 我们先写定义 传统型突破：行情经历过盘整后，出现标志性的阳线或者阴线，在该时间级别收盘价（最好是整点收盘价）脱离原来的盘整区域，朝着盘整区前的方向运行，在突破时刻进行入场。 预判式突破：行情在盘整中期或者末期，通过综合判断，笃定该级别行情会朝着原来盘整前的方向运行，并在未突破盘整区间的制高点或最低点前，进场的交易。 最影响你账户盈利的因素，当然是首当其冲的盈亏比，预判式要比传统型突破的获利区间要大得多。 其次，预判式突破，比较难判断，因为是盘整的途中会出现不同形式，任何主力都会使出浑身解数让你以为，不是回调，而是反转，在行情回调到预定点位时，他会以某种形态让交易员害怕而放弃或者草草离场。 传统型突破，相对来说容易判断，我们只要盯着横盘前的最高点即可，但也有致命的缺陷，最惧怕假突破，一旦回落，就有可能与之前的行情形成双顶背离的风险，这就是我们做这类交易心理经常承受巨大压力的根本原因。 两者各有优劣，控制好风险，不存在一方压到一方的说法。 只预判式更让人在交易的过程中加强交易信心，因为你观察的因素要比传统性突破的因数要多得多，你的方法论，你的条件设定都将在常年累月的实践中不断优化而厚积薄发！ 案例： 类似我tv上的近期两个交易计划，就是两种风格的体现，对照着看或许会有启发。 《美加，美加多头收集筹码不充分，1小时计别计划空单！》 （点击下方图片跳转当时页面） 《美日，前首相被枪击，难道日元不来一点波动吗？美日做多》（点击下方图片跳转当时页面） 好了今天这里只是抛砖引玉，如果点赞够多，我们有时间再做技术上的判断分享。
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【上证A股】的小周期分析，以及调整浪型的推演。上证目前如上期关于a股的分析（见下方链接）所写的那样， 目前处于第一段推动完成后的调整模式中， 该调整模式目前有一段疑似的向下推动完成， 该推动用橙色推动浪标示出为某一级别的a浪或者是1浪， 目前运行在某一级别的b浪或者2浪中， 那么我们观察后续的离开段， 如果是一笔急跌打到了较为浅的位置， 如图： 后续较为容易演变为平台或者联合型或者三角型（较为特殊如果走出该浪型，会再开分析）的漫长调整， （由于篇幅关系上图只给出了一种平台的调整模式走法。 如果在完成该b或者2调整后， 再一次的往下深度调整， 如图： 达到或者击穿3090附近， 但是并未打过该段行情的启动点， 则较大可能以幅度换取空间， 结束整段调整重回升势。 当然，以上只是个人比较倾向于的两种浪型计数方案， 还有可能橙色线段标识出的只是一段大的下跌推动的一部分， 如果是该走势， 待走出后会更新相关分析。
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美联储FOMC 7月利率决议、政策声明及GDP数据前瞻本月美联储FOMC将于北京时间28日凌晨02:00召开 同日晚08:30美国第二季度实际GDP年化季率初值数据公布 料将引起市场大幅波动 <1>.7月加息75bp已成主流 6月份高达9.1%的CPI数据令市场一度预期本次FOMC加息100bp 但随后美联储票委们迅速灭火降温 目前市场预期9、11、12月份加息幅度逐渐收窄 年底或停止加息，明年初或开始降息 。 <2>.重点关注美联储主席鲍威尔会后讲话 由于本次FOMC没有点阵图公布及经济预测 "美联储如何平衡高通胀风险与紧缩政策带来的经济衰退风险" 将成为后市走势风向标。 <3>.美国第二季度实际GDP年化季率预期现分歧 目前市场给出的预期是0.9% 而向来以准确度较高著称的亚特兰大联储GDPNow模型预期为-1.59%(-1.6%) 两者存在较大分歧 确定美国经济衰退的依据通常是GPD与失业率 即GDP连续两个季度负值、失业率持续6%以上才可以确定衰退 目前失业率较低暂不构成威胁 此次GDP数值公布则阴晴不定 <4>.对于后市加息路径及市场反应： 如果未来通胀数据逐步放缓 美联储则会放慢加息脚步直至降息 如果未来通胀仍然居高不下 那么美联储将不得不在经济衰退来临之际，继续大举加息 这种不确定性因素则会持续促使市场的高波动性。
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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！！！
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教学
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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。
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都是右侧交易，传统型突破交易和预判式突破，其中有何玄机？ 都是右侧交易，传统型突破交易和预判式突破，其中有何玄机？ 我们先写定义 传统型突破：行情经历过盘整后，出现标志性的阳线或者阴线，在该时间级别收盘价（最好是整点收盘价）脱离原来的盘整区域，朝着盘整区前的方向运行，在突破时刻进行入场。 预判式突破：行情在盘整中期或者末期，通过综合判断，笃定该级别行情会朝着原来盘整前的方向运行，并在未突破盘整区间的制高点或最低点前，进场的交易。 最影响你账户盈利的因素，当然是首当其冲的盈亏比，预判式要比传统型突破的获利区间要大得多。 其次，预判式突破，比较难判断，因为是盘整的途中会出现不同形式，任何主力都会使出浑身解数让你以为，不是回调，而是反转，在行情回调到预定点位时，他会以某种形态让交易员害怕而放弃或者草草离场。 传统型突破，相对来说容易判断，我们只要盯着横盘前的最高点即可，但也有致命的缺陷，最惧怕假突破，一旦回落，就有可能与之前的行情形成双顶背离的风险，这就是我们做这类交易心理经常承受巨大压力的根本原因。 两者各有优劣，控制好风险，不存在一方压到一方的说法。 只预判式更让人在交易的过程中加强交易信心，因为你观察的因素要比传统性突破的因数要多得多，你的方法论，你的条件设定都将在常年累月的实践中不断优化而厚积薄发！ 案例： 类似我tv上的近期两个交易计划，就是两种风格的体现，对照着看或许会有启发。 《美加，美加多头收集筹码不充分，1小时计别计划空单！》 （点击下方图片跳转当时页面） 《美日，前首相被枪击，难道日元不来一点波动吗？美日做多》（点击下方图片跳转当时页面） 好了今天这里只是抛砖引玉，如果点赞够多，我们有时间再做技术上的判断分享。
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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！
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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。😂
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教学
85
【教学贴】｜【中通客车】天地板带来的启发，一文揭示什么是投机市场的人性与反人性，以及如何从中获利！ 序 今天下午两点， 从4月27日开始连板上涨启动的大妖股中通客车， 开始以市价单的形式从涨停价27.97砸盘到当天的跌停价22.89， 再次上演这一天地板的奇观， 这不经让人想起了投机大师利弗莫尔的一句名言 “投机像山丘一样古老，人性更古不变。” 那么既然今天的主题是投机市场的人性与反人性， 我们就从【中通客车】今天的天地板走势中去窥探一二， 简单到底什么是人性，什么又是反人，以及作为市场参与者的我们如何从中获利的。 ---------------------------------------------------------------------------------------------------- 一：认清市场的本质 本质上来说，作为个人投资者大多数人参与的合约市场、期货市场、外汇市场， 基本上都是零和甚至于负和博弈的市场， 而股票市场由于其承载者上市公司这一价值载体， 在长期来说是一个正和市场（因为公司的价值随着公司的成长是会越来越增长的）， 但是对于短期投机者来说，扣除印花税等摩擦成本，也是个负和市场， （因为短周期来说，公司的实际成长价值来不及得到体现）， 这或许也是对于一个没有成熟投机或者投资体系的人来说，不要参与短线投机的原因， 正和游戏的难度显然比负和游戏的难度要低不少，赚钱的概率自然也就越大。 那么找出在这一负和市场中长期挣钱和长期输钱的玩家， 显然对我们有着重要的意义。 输钱玩家可以找到自身的问题，以及为什么会输钱， 并学习盈利玩家为什么会盈利的原因， 去趋利避害，提高自己的交易绩效。 --------------------------------------------------------------------------------------------------- 二：人性与盈亏的关系 现在市场上的两类人已经做了划分， 简单的我们可以称之为赢家和输家， 赢家又不认识所有的散户， 他们是怎么长期把钱从散户的口袋搬运到自己的口袋的呢？ 没错，就是利用人性。 人性就是赢家，（或者叫做庄家，或者叫做主力，或者称作smart money）用来操控并诱导输家的工具！ 因为人性每个人都有，包括这些赢家自己，人性是人类在千百万年进化以来，难以抹除的本性， 因为原始人类需要在自然环境中生存， 需要在面对野兽的攻击时足够的恐惧， 让人类能够分泌出肾上腺素去增强体能和力量快速的逃跑， 躲避野兽的攻击； 在找到食物和水源是能够足够的贪婪， 让人类能够比别的同伴抢到更加多的生存资源， 从而可以长久的生存下去。 这些贪婪与恐惧的本能随着人类千百万年来的进化， 并没有被剔除， 而是被不断的强化， 金融交易市场作为一个直接投入现代人最重要的生存资源进行博弈的“赌场”， 贪婪与恐惧的本性更是极具的放大。 这对于一个没有受过系统训练的投资者来说，要战胜贪婪与恐惧也是巨大的考验。 而利用贪婪与恐惧这一人性武器， 聪明的盈利玩家们可以知道 如何让你忍不住去追涨站在最高点去接过他们早已获利丰厚的筹码， 如何让你吓的在最低点去抛出握在手中的便宜筹码， 更何况很多的盈利玩家还有拥有许多个人投资者所不具有的内幕消息、媒体资源、资金优势等武器。 所以，对于一个没有受过系统交易训练和学习的个人投资者 长期要战胜这些聪明的盈利的玩家，是很难的。 ---------------------------------------------------------------------------------------------------- 三：如何利用人性去获利 巴菲特曾经说过：“别人贪婪时我恐惧，别人恐惧时我贪婪”； 而李嘉诚则说：“永远不要赚最后一个铜板”。 可见任何的投资家或者商人，都深谙人性之极的大道理， 那么什么是人性之极的大道理呢？怎么反过来利用人性去获利呢？ 经过我的多年的观察、总结、和验证，有以下几个点： 1、相对的高位的利多往往是主力在出货，相对低位的利空，往往是主力用来吸货。 这一现象在股票市场屡见不鲜了。 比如在上周7月13日随着美国六月末季度cpi数据的高于预期的公布，如下图所示： 整个市场再次的对通胀引发的美元加息预期开始担忧起来，并引发散户投资者恐慌性的抛售， 那么此时我们可以通过观察底部的量能，问自己一个问题，如此大的放量产生的分歧， 到底是谁在做多，谁在做空呢？ 这样重要的经济数据是面向谁的呢？ 整个市场长期又是谁在盈利，谁长期又是在亏损呢？ 那答案很明显了，是主力或者说机构在买入，而看着数据操作的散户在卖出， 再往左边看看这里是不是相对低位的地方？ 相信此时的你一定不会因为市场恐慌的大跌而随波逐流的卖出吧？ 如果你懂波浪，就会知道此时的5浪或者c浪是一个人性之极反转浪，表示情绪的极端释放， 关于波浪与人性也是个有意思的话题， 如果你能看到这里相信你对我们的分享是有兴趣的，那么请你积极点赞并关注我的账号， 并分享给更多的小伙伴，在关注粉丝达到300人后，我会回来填掉今天挖的这个坑。 那么再进一步深入， 你有没有想过为什么要在这个时间点上做和散户完全相反的动作呢？ 同样的这个坑也会在达到一定的粉丝数后解锁。 2、快速往关键高位逼近或超过往往是为了出货，犹豫的拉破关键高位往往是真实的突破，做空亦然。 这里的关键点在于如何去区分高位和低位，这也是让很多人困扰的地方， 既然这里为了出货和吸货，那么这里一定是主力资金发生过大量换手的地方 主力有很强的意愿（或许是主力资金的作战计划，或许是主力资金的自己的心里价位，又或许是这里有很多套牢的筹码需要主力资金去解套）总之这里就是主力的目标出货的价位，那么这里就是我们所说的高地位， 如图： 我们可以明显的看到该股的历史走势上，处于高位和低位的表现如何， 如果感兴趣，你还可以在trading view中打开下方的“E、D、S”等财务数据标签， 去看看这些个股及其重要的基本面数据和该股走势以及高地位的关系，相信你会有不错的发现。 这里由于篇幅和时间的限制， 很多更加有趣的内容无法分享出来， 但是点赞分享和互动是我分享的原动力， 如果你喜欢我的分享请积极的点赞分享和互动吧。 以上内容均为原创， 如需转载请注明出处。 ——————————————————————— 更多浪型演化的可能请关注视频频道， 本篇分析与预测仅用于行为金融学学术交流， 不构成买卖建议，不对任何交易行为负责。 金融市场具有高度风险， 请遵守您所在国家或地区的法律。 © 版权声明 文章版权归作者所有，未经允许请勿转载。
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交易表现心理学：第1篇越是来之不易的成功，越是荣耀万分。熟练的飞行员在穿越狂风暴雨中赢得荣誉。 - 爱比克泰德。 大家好！ 👋 本周，我们认为深入探讨一个不太常见的话题会很有趣：表现心理学 — 并讨论它与交易的关系。具体来说，我们将研究以下问题：究竟是什么推动了一位交易者的表现出色？ 从流程的角度来看，有抱负的交易者可以从其它表现学科（如体育运动）中获得很多东西，以便更好地了解达到他们目标的必要步骤。让我们开始吧！ 时间是精通的共同要素 ⏰ 精通是随着时间的推移而建立的。首先通过探索，然后是认知构建，之后是规划良好的练习。 为了精通而投入所需的大量时间和精力，个人通常会在情感上与该领域建立联系，从而建立长久关系。 几乎所有表现出色的交易者都表现出对交易本身固有的、内在的热爱。这意味着热爱分析图表、制定策略、观察市场，并试图在脑海中将这些碎片拼凑在一起。在这个框架中 —交易不是一份工作，它是一门 手艺 。如果你只是喜欢地位、生活方式或收入，那么你很可能无法达到这个职业的真正高度。表现最好的交易者花费数小时进行交易；不是因为他们想要，而是因为他们热爱。 寻找利基市场 ❤️ 伟人不是靠努力才变得伟大；他们努力工作是因为他们找到了一个伟大的利基市场：一个能够激发他们的才能、兴趣和想象力的领域。世界上最好的投手可能会成为一个糟糕的击球手。 如果你刚开始你的旅程（或迷失方向），需要考虑的事情是尝试找到你真正引起共鸣的利基市场。由于医院和银行有轮岗计划，让新人接触不同类型的经验，因此它们非常重视其它职业和金融机构的利基市场。 那么，为什么个体交易者不这样做呢？集中思考的一个好方法是为自己构建一个轮换计划。这是最受欢迎的资产类别和交易风格的列表。对每一个都谷歌一下，或者在TradingView上寻找观点，看看有什么最能引起你的共鸣。通过实际找到自己喜欢日复一日地做的事情，来为自己的长期精通做好准备。 流动资产类别： -股票 -货币 -加密货币 -期货 -固定收入 -波动性 样式（时间周期）： -日内 - 持有时间为几秒到几小时 -波段 - 持有时间为数天至数周 -仓位 - 持有时间为数周至数月 哪种持有风格适合你的气质？你喜欢学习什么主题？ 学习过程 ✅ 在交易和生活中，我们经常听到“熟能生巧”。更好的说法可能是“充分的练习成就完美”。练习时间是如何规划的，这决定了拥有五年经验的表现者和拥有一年经验但重复练习五次的表现者之间的区别。所以，你应该如何规划你的练习？ 在表现心理学中，有一个概念被称为“学习循环”。它有三个部分。 表现 -> 反馈 -> 学习（重复）。 这是至关重要的，因为反馈是改进的关键。交易是一项单独的运动，这意味着弄清楚如何整合允许反思的反馈过程是十分关键的。 盈利/亏损是反馈，但它们单独作为你的反馈机制可能会出现一些问题。即使是执行最漂亮交易的最优秀交易者，在给定的日子里也可能处于事实的反面。过程为王。从你的表现中获取与盈亏无关的反馈，以便你可以跟踪决策的输入。有些交易者做大量笔记，有些记录他们的屏幕，有些记录与盈亏无关的数据点（睡眠时间、水合作用、情绪等）。 （我们在图表中内置了一个笔记功能，你可以将其用于此目的。） 如果你将所有这些项目收集在一起，为打造精通创建一个长期蓝图，它看起来应该像这样： 1.) 找出你真正喜欢交易的地方 2.) 更深入地探索它 3.) 随着时间的推移坚持下去，让你内在的享受激励你度过起伏 4.) 以这样一种方式规划你在这段时间内的表现，这样你就可以为自己产生反馈 5.) 结合反馈不断改进你的流程。让学习循环成为你长期表现的引擎。 - 希望你享受阅读，祝你愉快！ - TradingView团队
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Pine脚本

Bjorgum Double Tap█   OVERVIEW Double Tap is a pattern recognition script aimed at detecting Double Tops and Double Bottoms. Double Tap can be applied to the broker emulator to observe historical results, run as a trading bot for live trade alerts in real time with entry signals, take profit, and stop orders, or to simply detect patterns. █   CONCEPTS How Is A Pattern Defined? Doubles are technical formations that are both reversal patterns and breakout patterns. These formations typically have a distinctive “M” or a “W” shape with price action breaking beyond the neckline formed by the center of the pattern. They can be recognized when a ​pivot fails to break when tested for a second time and the retracement that follows breaks beyond the key level opposite. This can trap entrants that were playing in the direction of the prior trend. Entries are made on the breakout with a target projected beyond the neckline equal to the height of the pattern. Pattern Recognition Patterns are recognized through the use of ​zig-zag; a method of filtering price action by connecting swing highs and lows in an alternating fashion to establish trend, ​support and resistance, or derive shapes from price action. The script looks for the highest or lowest point in a given number of bars and updates a list with the values as they form. If the levels are exceeded, the values are updated. If the direction changes and a new significant point is made, a new point is added to the list and the process starts again. Meanwhile, we scan the list of values looking for the distinctive shape to form as previously described. █   STRATEGY RESULTS Back Testing Historical back testing is the most common method to test a strategy due in part to the general ease of gathering quick results. The underlying theory is that any strategy that worked well in the past is likely to work well in the future, and conversely, any strategy that performed poorly in the past is likely to perform poorly in the future. It is easy to poke holes in this theory, however, as for one to accept it as gospel, one would have to assume that future results will match what has come to pass. The randomness of markets may see to it otherwise, so it is important to scrutinize results. Some commonly used methods are to compare to other markets or benchmarks, perform statistical analysis on the results over many iterations and on differing datasets, walk-forward testing, out-of-sample analysis, or a variety of other techniques. There are many ways to interpret the results, so it is important to do research and gain knowledge in the field prior to taking meaningful conclusions from them. 👉  In short, it would be naive to place trust in one good backtest and expect positive results to continue. For this reason, results have been omitted from this publication. Repainting Repainting is simply the difference in behaviour of a strategy in real time vs the results calculated on the historical dataset. The strategy, by default, will wait for confirmed signals and is thus designed to not repaint. Waiting for bar close for entires aligns results in the real time data feed to those calculated on historical bars, which contain far less data. By doing this we align the behaviour of the strategy on the 2 data types, which brings significance to the calculated results. To override this behaviour and introduce repainting one can select "Recalculate on every tick" from the properties tab. It is important to note that by doing this alerts may not align with results seen in the strategy tester when the chart is reloaded, and thus to do so is to forgo backtesting and restricts a strategy to forward testing only. 👉  It is possible to use this script as an indicator as opposed to a full strategy by disabling "Use Strategy" in the "Inputs" tab. Basic alerts for detection will be sent when patterns are detected as opposed to complex order syntax. For alerts mid-bar enable "Recalculate on every tick" , and for confirmed signals ensure it is disabled. █   EXIT ORDERS Limit and Stop Orders By default, the strategy will place a stop loss at the invalidation point of the pattern. This point is beyond the pattern high in the case of Double Tops, or beneath the pattern low in the case of Double Bottoms. The target or take profit point is an equal-legs measurement, or 100% of the pattern height in the direction of the pattern bias. Both the stop and the limit level can be adjusted from the user menu as a percentage of the pattern height. Trailing Stops Optional from the menu is the implementation of an ATR based trailing stop. The trailing stop is designed to begin when the target projection is reached. From there, the script looks back a user-defined number of bars for the highest or lowest point +/- the ATR value. For tighter stops the user can look back a lesser number of bars, or decrease the ATR multiple. When using either Alertatron or Trading Connector, each change in the trail value will trigger an alert to update the stop order on the exchange to reflect the new trail price. This reduces latency and slippage that can occur when relying on alerts only as real exchange orders fill faster and remain in place in the event of a disruption in communication between your strategy and the exchange, which ensures a higher level of safety. 👉  It is important to note that in the case the trailing stop is enabled, limit orders are excluded from the exit criteria. Rather, the point in time that the limit value is exceeded is the point that the trail begins. As such, this method will exit by stop loss only. █   ALERTS Five Built-in 3rd Party Destinations The following are five options for delivering alerts from Double Tap to live trade execution via third party ​API solutions or chat bots to share your trades on social media. These destinations can be selected from the input menu and alert syntax will automatically configure in alerts appropriately to manage trades. Custom JSON JSON, or JavaScript Object Notation, is a readable format for structuring data. It is used primarily to transmit data between a server and a web application. In regards to this script, this may be a custom intermediary web application designed to catch alerts and interface with an exchange ​API. The JSON message is a trade map for an application to read equipped with where its been, where its going, targets, stops, quantity; a full diagnostic of the current state and its previous state. A web application could be configured to follow the messages sent in this format and conduct trades in sync with alerts running on the TV server. Below is an example of a rendered JSON alert: { "passphrase": "1234", "time": "2022-05-01T17:50:05Z", "ticker": "ETHUSDTPERP", "plot": { "stop_price": 2600.15, "limit_price": 3100.45 }, "strategy": { "position_size": 0.1, "order_action": "buy", "market_position": "long", "market_position_size": 0, "prev_market_position": "flat", "prev_market_position_size": 0 } } Trading Connector Trading Connector is a third party fully autonomous Chrome extension designed to catch alert webhooks from TradingView and interface with MT4/MT5 to execute live trades from your machine. Alerts to Trading Connector are simple; just select the destination from the input drop down menu, set your ticker in the "TC Ticker" box in the "Alert Strings" section and enter your URL in the alert window when configuring your alert. Alertatron Alertatron is an automated algo platform for cryptocurrency trading that is designed to automate your trading strategies. Although the platform is currently restricted to crypto, it offers a versatile interface with high flexibility syntax for complex market orders and conditions. To direct alerts to Alertatron, select the platform from the 3rd party drop down, configure your ​API key in the ”Alertatron Key” box and add your URL in the alert message box when making alerts. 3 Commas 3 Commas is an easy and quick to use click-and-go third party crypto ​API solution. Alerts are simple without overly complex syntax. Messages are simply pasted into alerts and executed as alerts are triggered. There are 4 boxes at the bottom of the "Inputs" tab where the appropriate messages to be placed. These messages can be copied from 3 Commas after the bots are set up and pasted directly into the settings menu. Remember to select 3 Commas as a destination from the third party drop down and place the appropriate URL in the alert message window. Discord Some may wish to share their trades with their friends in a Discord chat via webhook chat bot. Messages are configured to notify of the pattern type with targets and stop values. A bot can be configured through the integration menu in a Discord chat to which you have appropriate access. Select Discord from the 3rd party drop down menu and place your chat bot URL in the alert message window when configuring alerts. 👉  For further information regarding alert setup, refer to the platform specific instructions given by the chosen third party provider. █   IMPORTANT NOTES Setting Alerts For alert messages to be properly delivered on order fills it is necessary to place the following placeholder in the alert message box when creating an alert. {{strategy.order.alert_message}} This placeholder will auto-populate the alert message with the appropriate syntax that is designated for the 3rd party selected in the user menu. Order Sizing and Commissions The values that are sent in alert messages are populated from live metrics calculated by the strategy. This means that the actual values in the "Properties" tab are used and must be set by the user. The initial capital, order size, commission, etc. are all used in the calculations, so it is important to set these prior to executing live trades. Be sure to set the commission to the values used by the exchange as well. 👉  It is important to understand that the calculations on the account size take place from the beginning of the price history of the strategy. This means that if historical results have inflated or depleted the account size from the beginning of trade history until now, the values sent in alerts will reflect the calculated size based on the inputs in the "Properties" tab. To start fresh, the user must set the date in the "Inputs" tab to the current date as to remove trades from the trade history. Failure to follow this instruction can result in an unexpected order size being sent in the alert. █   FOR PINECODERS  • With the recent introduction of matrices in Pine, the script utilizes a matrix to track ​pivot points with the bars they occurred on, while tracking if that ​pivot has been traded against to prevent duplicate detections after a trade is exited.  • Alert messages are populated with placeholders ; capability that previously was only possible in alertcondition() , but has recently been extended to `strategy.*()` functions for use in the `alert_message` argument. This allows delivery of live trade values to populate in strategy alert messages.  • New arguments have been added to strategy.exit() , which allow differentiated messages to be sent based on whether the exit occurred at the stop or the limit. The new arguments used in this script are `alert_profit` and `alert_loss` to send messages to Discord
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Pine Script™策略
37
Fourier Extrapolator of Price w/ Projection Forecast [Loxx]Due to popular demand, I'm pusblishing Fourier Extrapolator of Price w/ Projection Forecast.. As stated in it's twin indicator, this one is also multi-harmonic (or multi-tone) trigonometric model of a price series xi, i=1..n, is given by: xi = m + Sum( a*Cos(w*i) + b*Sin(w*i), h=1..H ) Where: xi - past price at i-th bar, total n past prices; m - bias; a and b - scaling coefficients of harmonics; w - frequency of a harmonic ; h - harmonic number; H - total number of fitted harmonics. Fitting this model means finding m, a, b, and w that make the modeled values to be close to real values. Finding the harmonic frequencies w is the most difficult part of fitting a trigonometric model. In the case of a Fourier series, these frequencies are set at 2*pi*h/n. But, the Fourier series extrapolation means simply repeating the n past prices into the future. This indicator uses the Quinn-Fernandes algorithm to find the harmonic frequencies. It fits harmonics of the trigonometric series one by one until the specified total number of harmonics H is reached. After fitting a new harmonic , the coded algorithm computes the residue between the updated model and the real values and fits a new harmonic to the residue. see here: A Fast Efficient Technique for the Estimation of Frequency , B. G. Quinn and J. M. Fernandes, Biometrika, Vol. 78, No. 3 (Sep., 1991), pp . 489-497 (9 pages) Published By: Oxford University Press The indicator has the following input parameters: src - input source npast - number of past bars, to which trigonometric series is fitted; Nfut - number of predicted future bars; nharm - total number of harmonics in model; frqtol - tolerance of frequency calculations. The indicator plots two curves: the green/red curve indicates modeled past values and the yellow/fuchsia curve indicates the modeled future values. The purpose of this indicator is to showcase the Fourier Extrapolator method to be used in future indicators.
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Pine Script™指标
30
VisibleChart█   OVERVIEW This library is a Pine programmer’s tool containing functions that return values calculated from the range of visible bars on the chart. This is now possible in Pine Script™ thanks to the recently-released chart.left_visible_bar_time and chart.right_visible_bar_time built-ins, which return the opening time of the leftmost and rightmost bars on the chart. These values update as traders scroll or zoom their charts, which gives way to a class of indicators that can dynamically recalculate and draw visuals on visible bars only, as users scroll or zoom their charts. We hope this library's functions help you make the most of the world of possibilities these new built-ins provide for Pine scripts. For an example of a script using this library, have a look at the Chart ​VWAP indicator. █   CONCEPTS Chart properties The new chart.left_visible_bar_time and chart.right_visible_bar_time variables return the opening time of the leftmost and rightmost bars on the chart. They are only two of many new built-ins in the `chart.*` namespace. See this blog post for more information, or look up them up by typing "chart." in the Pine Script™ Reference Manual . Dynamic recalculation of scripts on visible bars Any script using chart.left_visible_bar_time or chart.right_visible_bar_time acquires a unique property, which triggers its recalculation when traders scroll or zoom their charts in such a way that the range of visible bars on the chart changes. This library's functions use the two recent built-ins to derive various values from the range of visible bars. Designing your scripts for dynamic recalculation For the library's functions to work correctly, they must be called on every bar. For reliable results, assign their results to global variables and then use the variables locally where needed — not the raw function calls. Some functions like `barIsVisible()` or `open()` will return a value starting on the leftmost visible bar. Others such as `high()` or `low()` will also return a value starting on the leftmost visible bar, but their correct value can only be known on the rightmost visible bar, after all visible bars have been analyzed by the script. You can plot values as the script executes on visible bars, but efficient code will, when possible, create resource-intensive labels, lines or tables only once in the global ​scope using var , and then use the setter functions to modify their properties on the last bar only. The example code included in this library uses this method. Keep in mind that when your script uses chart.left_visible_bar_time or chart.right_visible_bar_time , your script will recalculate on all bars each time the user scrolls or zooms their chart. To provide script users with the best experience you should strive to keep calculations to a minimum and use efficient code so that traders are not always waiting for your script to recalculate every time they scroll or zoom their chart. Another aspect to consider is the fact that the rightmost visible bar will not always be the last bar in the dataset. When script users scroll back in time, a large portion of the time series the script calculates on may be situated after the rightmost visible bar. We can never assume the rightmost visible bar is also the last bar of the time series. Use `barIsVisible()` to restrict calculations to visible bars, but also consider that your script can continue to execute past them. █   FUNCTIONS The library contains the following functions: barIsVisible()   Condition to determine if a given bar is within the users visible time range.   Returns: (bool) True if the the calling bar is between the `chart.left_visible_bar_time` and the `chart.right_visible_bar_time`. high()   Determines the value of the highest `high` in visible bars.   Returns: (float) The maximum high value of visible chart bars. highBarIndex()   Determines the `bar_index` of the highest `high` in visible bars.   Returns: (int) The `bar_index` of the `high()`. highBarTime()   Determines the bar time of the highest `high` in visible bars.   Returns: (int) The `time` of the `high()`. low()   Determines the value of the lowest `low` in visible bars.   Returns: (float) The minimum low value of visible chart bars. lowBarIndex()   Determines the `bar_index` of the lowest `low` in visible bars.   Returns: (int) The `bar_index` of the `low()`. lowBarTime()   Determines the bar time of the lowest `low` in visible bars.   Returns: (int) The `time` of the `low()`. open()   Determines the value of the opening price in the visible chart time range.   Returns: (float) The `open` of the leftmost visible chart bar. close()   Determines the value of the closing price in the visible chart time range.   Returns: (float) The `close` of the rightmost visible chart bar. leftBarIndex()   Determines the `bar_index` of the leftmost visible chart bar.   Returns: (int) A `bar_index`. rightBarIndex()   Determines the `bar_index` of the rightmost visible chart bar.   Returns: (int) A `bar_index` bars()   Determines the number of visible chart bars.   Returns: (int) The number of bars. volume()   Determines the sum of volume of all visible chart bars.   Returns: (float) The cumulative sum of volume. ohlcv()   Determines the open, high, low, close, and volume sum of the visible bar time range.   Returns: ( ) A tuple of the OHLCV values for the visible chart bars. Example: open is chart left, high is the highest visible high, etc. chartYPct(​pct)   Determines a price level as a percentage of the visible bar price range, which depends on the chart's top/bottom margins in "Settings/Appearance".   Parameters:      pct : (series float) Percentage of the visible price range (50 is 50%). Negative values are allowed.   Returns: (float) A price level equal to the `pct` of the price range between the high and low of visible chart bars. Example: 50 is halfway between the visible high and low. chartXTimePct(​pct)   Determines a time as a percentage of the visible bar time range.   Parameters:      pct : (series float) Percentage of the visible time range (50 is 50%). Negative values are allowed.   Returns: (float) A time in UNIX format equal to the `pct` of the time range from the `chart.left_visible_bar_time` to the `chart.right_visible_bar_time`. Example: 50 is halfway from the leftmost visible bar to the rightmost. chartXIndexPct(​pct)   Determines a `bar_index` as a percentage of the visible bar time range.   Parameters:      pct : (series float) Percentage of the visible time range (50 is 50%). Negative values are allowed.   Returns: (float) A time in UNIX format equal to the `pct` of the time range from the `chart.left_visible_bar_time` to the `chart.right_visible_bar_time`. Example: 50 is halfway from the leftmost visible bar to the rightmost. whenVisible(​src, whenCond, length)   Creates an array containing the `length` last `src` values where `whenCond` is true for visible chart bars.   Parameters:      src : (series int/float) The source of the values to be included.      whenCond : (series bool) The condition determining which values are included. Optional. The default is `true`.      length : (simple int) The number of last values to return. Optional. The default is all values.   Returns: (float ) The array ID of the accumulated `src` values. avg(​src)   Gathers values of the source over visible chart bars and averages them.   Parameters:      src : (series int/float) The source of the values to be averaged. Optional. Default is `close`.   Returns: (float) A cumulative average of values for the visible time range. median(​src)   Calculates the median of a source over visible chart bars.   Parameters:      src : (series int/float) The source of the values. Optional. Default is `close`.   Returns: (float) The median of the `src` for the visible time range. vVwap(​src)   Calculates a volume-weighted average for visible chart bars.   Parameters:      src : (series int/float) Source used for the ​VWAP calculation. Optional. Default is `hlc3`.   Returns: (float) The ​VWAP for the visible time range.
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Pine Script™脚本库
25
Time█   OVERVIEW This library is a Pine Script™ programmer’s tool containing a variety of time related functions to calculate or measure time, or format time into string variables. █   CONCEPTS `formattedTime()`, `formattedDate()` and `formattedDay()` Pine Script™, like many other programming languages, uses timestamps in UNIX format, expressed as the number of milliseconds elapsed since 00:00:00 UTC, 1 January 1970. These three functions convert a UNIX timestamp to a formatted string for human consumption. These are examples of ways you can call the functions, and the ensuing results: CODE RESULT formattedTime(timenow) >>> "00:40:35" formattedTime(timenow, "short") >>> "12:40 AM" formattedTime(timenow, "full") >>> "12:40:35 AM ​UTC​" formattedTime(1000 * 60 * 60 * 3.5, "HH:mm") >>> "03:30" formattedDate(timenow, "short") >>> "4/30/22" formattedDate(timenow, "medium") >>> ​"Apr​ 30, 2022" formattedDate(timenow, "full") >>> "Saturday, April 30, 2022" formattedDay(timenow, "E") >>> "Sat" formattedDay(timenow, "dd.MM.yy") >>> "30.04.22" formattedDay(timenow, "yyyy.MM.dd G 'at' hh:mm:ss z") >>> "2022.04.30 AD at 12:40:35 UTC" These functions use str.format() and some of the special formatting codes it allows for. Pine Script™ documentation does not yet contain complete specifications on these codes, but in the meantime you can find some information in the The Java™ Tutorials and in Java documentation of its MessageFormat class . Note that str.format() implements only a subset of the MessageFormat features in Java. `secondsSince()` The introduction of varip variables in Pine Script™ has made it possible to track the time for which a condition is true when a script is executing on a realtime bar. One obvious use case that comes to mind is to enable trades to exit only when the exit condition has been true for a period of time, whether that period is shorter that the chart's timeframe, or spans across multiple realtime bars. For more information on this function and varip please see our Using `varip` variables publication. `timeFrom( )` When plotting lines , boxes , and labels one often needs to calculate an offset for past or future end points relative to the time a condition or point occurs in history. Using xloc.bar_index is often the easiest solution, but some situations require the use of xloc.bar_time . We introduce `timeFrom()` to assist in calculating time-based offsets. The function calculates a timestamp using a negative (into the past) or positive (into the future) offset from the current bar's starting or closing time, or from the current time of day. The offset can be expressed in units of chart timeframe, or in seconds, minutes, hours, days, months or years. This function was ported from our Time Offset Calculation Framework . `formattedNoOfPeriods()` and `secondsToTfString()` Our final two offerings aim to confront two remaining issues: How much time is represented in a given timestamp? How can I produce a "simple string" timeframe usable with request.security() from a timeframe expressed in seconds? `formattedNoOfPeriods()` converts a time value in ​ms to a quantity of time units. This is useful for calculating a difference in time between 2 points and converting to a desired number of units of time. If no unit is supplied, the function automatically chooses a unit based on a predetermined time step. `secondsToTfString()` converts an input time in seconds to a target timeframe string in timeframe.period string format. This is useful for implementing stepped timeframes relative to the chart time, or calculating multiples of a given chart timeframe. Results from this function are in simple form, which means they are useable as `timeframe` arguments in functions like request.security() . █   NOTES Although the example code is commented in detail, the size of the library justifies some further explanation as many concepts are demonstrated. Key points are as follows:  • ​Pivot points are used to draw lines from. `timeFrom( )` calculates the length of the lines in the specified unit of time.   By default the script uses 20 units of the charts timeframe. Example: a 1hr chart has arrows 20 hours in length.  • At the point of the arrows `formattedNoOfPeriods()` calculates the line length in the specified unit of time from the input menu.   If “Use Input Time” is disabled, a unit of time is automatically assigned.  • At each ​pivot point a label with a formatted date or time is placed with one of the three formatting helper functions to display the time or date the ​pivot occurred.  • A label on the last bar showcases `secondsSince()` . The label goes through three stages of detection for a timed alert.   If the difference between the high and the open in ticks exceeds the input value, a timer starts and will turn the label red once the input time is exceeded to simulate a time-delayed alert.  • In the bottom right of the screen `secondsToTfString()` posts the chart timeframe in a table. This can be multiplied from the input menu. █   FUNCTIONS formattedTime(timeInMs, format)   Converts a UNIX timestamp (in milliseconds) to a formatted time string.   Parameters:      timeInMs : (series float) Timestamp to be formatted.      format : (series string) Format for the time. Optional. The default value is "HH:mm:ss".   Returns: (string) A string containing the formatted time. formattedDate(timeInMs, format)   Converts a UNIX timestamp (in milliseconds) to a formatted date string.   Parameters:      timeInMs : (series float) Timestamp to be formatted.      format : (series string) Format for the date. Optional. The default value is "yyyy-MM-dd".   Returns: (string) A string containing the formatted date. formattedDay(timeInMs, format)   Converts a UNIX timestamp (in milliseconds) to the name of the day of the week.   Parameters:      timeInMs : (series float) Timestamp to be formatted.      format : (series string) Format for the day of the week. Optional. The default value is "EEEE" (complete day name).   Returns: (string) A string containing the day of the week. secondsSince(cond, resetCond)   The duration in milliseconds that a condition has been true.   Parameters:      cond : (series bool) Condition to time.      resetCond : (series bool) When `true`, the duration resets.   Returns: The duration in seconds for which `cond` is continuously true. timeFrom(from, qty, units)   Calculates a +/- time offset in variable units from the current bar's time or from the current time.   Parameters:      from : (series string) Starting time from where the offset is calculated: "bar" to start from the bar's starting time, "close" to start from the bar's closing time, "now" to start from the current time.      qty : (series int) The +/- qty of units of offset required. A "series float" can be used but it will be cast to a "series int".      units : (series string) String containing one of the seven allowed time units: "chart" (chart's timeframe), "seconds", "minutes", "hours", "days", "months", "years".   Returns: (int) The resultant time offset `from` the `qty` of time in the specified `units`. formattedNoOfPeriods(​ms, unit)   Converts a time value in ​ms to a quantity of time units.   Parameters:      ms : (series int) Value of time to be formatted.      unit : (series string) The target unit of time measurement. Options are "seconds", "minutes", "hours", "days", "weeks", "months". If not used one will be automatically assigned.   Returns: (string) A formatted string from the number of `ms` in the specified `unit` of time measurement secondsToTfString(tfInSeconds, mult)   Convert an input time in seconds to target string TF in `timeframe.period` string format.   Parameters:      tfInSeconds : (simple int) a timeframe in seconds to convert to a string.      mult : (simple float) Multiple of `tfInSeconds` to be calculated. Optional. 1 (no multiplier) is default.   Returns: (string) The `tfInSeconds` in `timeframe.period` format usable with `request.security()`.
编辑精选
Pine Script™脚本库
25
Relative Bandwidth FilterThis is a very simple script which can be used as measure to define your trading zones based on volatility. Concept This script tries to identify the area of low and high volatility based on comparison between Bandwidth of higher length and ATR of lower length. Relative Bandwidth = Bandwidth / ATR Bandwidth can be based on either Bollinger Band, Keltner Channel or Donchian Channel. Length of the bandwidth need to be ideally higher. ATR is calculated using built in ATR method and ATR length need to be ideally lower than that used for calculating Bandwidth. Once we got Relative Bandwidth, the next step is to apply Bollinger Band on this to measure how relatively high/low this value is. Overall - If relative bandwidth is higher, then volatility is comparatively low. If relative bandwidth is lower, then volatility is comparatively high. Usage This can be used with your own strategy to filter out your non-trading zones based on volatility. Script plots a variable called "Signal" - which is not shown on chart pane. But, it is available in the data window. This can be used in another script as external input and apply logic. Signal values can be 1 : Allow only Long -1 : Allow only short 0 : Do not allow any trades 2 : Allow both Long and Short
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Pine Script™指标
18
Breakout Probability (Expo)█ Overview Breakout Probability is a valuable indicator that calculates the probability of a new high or low and displays it as a level with its percentage. The probability of a new high and low is backtested, and the results are shown in a table— a simple way to understand the next candle's likelihood of a new high or low. In addition, the indicator displays an additional four levels above and under the candle with the probability of hitting these levels. The indicator helps traders to understand the likelihood of the next candle's direction, which can be used to set your trading bias. █ Calculations The algorithm calculates all the green and red candles separately depending on whether the previous candle was red or green and assigns scores if one or more lines were reached. The algorithm then calculates how many candles reached those levels in history and displays it as a percentage value on each line. █ Example In this example, the previous candlestick was green; we can see that a new high has been hit 72.82% of the time and the low only 28.29%. In this case, a new high was made. █ Settings Percentage Step The space between the levels can be adjusted with a percentage step. 1% means that each level is located 1% above/under the previous one. Disable 0.00% values If a level got a 0% likelihood of being hit, the level is not displayed as default. Enable the option if you want to see all levels regardless of their values. Number of Lines Set the number of levels you want to display. Show Statistic Panel Enable this option if you want to display the backtest statistics for that a new high or low is made. (Only if the first levels have been reached or not) █ Any Alert function call An alert is sent on candle open, and you can select what should be included in the alert. You can enable the following options: Ticker ID Bias Probability percentage The first level high and low price █ How to use This indicator is a perfect tool for anyone that wants to understand the probability of a breakout and the likelihood that set levels are hit. The indicator can be used for setting a stop loss based on where the price is most likely not to reach. The indicator can help traders to set their bias based on probability. For example, look at the daily or a higher timeframe to get your trading bias, then go to a lower timeframe and look for setups in that direction. ----------------- Disclaimer The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs. My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
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Pine Script™指标
61
Point of Control V2 The genesis of this project was to create a POC library that would be available to deliver volume profile information via pine to other scripts of indicators and strategies. This is a republish of an invite only script to open access This is the indicator version of the library function. A few points of significance: - Allows the choice of reset of the study period, day/week or bars. This is simple enough to expand to other conditions - Bar count resets starting from the beginning of the data set (bar index =0) vs bars back from the end of the data set - A 'period' in this context is the time between resets - the start of the POC (eg. start of Day or Week) until it resets (for example at the beginning of a next day or week) - Automates the determination of the increment level rather than the user specifying ticks or price brackets - Does not allow for setting the # of rows and then calculating the implied price increment levels - When a period is complete it is often useful to look back at the POCs of historical periods, or extend them forward. - This script will find the historical POCs around the current price and display them rather than extend all the historical POC lines to the right - This script also looks across all the period POCs and identifies the master POC or what I call the Grand POC, and also the next 3 runner up POCs This indicator is also available as a library. BINANCE:BTCUSDT NSE:NIFTY OANDA:XAUUSD NASDAQ:AAPL TVC:USOIL
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Pine Script™指标
8
Andean OscillatorThe following script is an original creation originally posted on the blog section of the broker Alpaca. The proposed indicator aims to measure the degree of variations of individual up-trends and down-trends in the price, thus allowing to highlight the direction and amplitude of a current trend. Settings Length : Determines the significance of the trends degree of variations measured by the indicator. Signal Length : Moving average period of the signal line. Usage The Andean Oscillator can return multiple information to the user, with its core interpretation revolving around the bull and bear components. A rising bull component (in green) indicates the presence of bullish price variations while a rising bear component (in red) indicates the presence of bearish price variations. When the bull component is over the bear component market is up-trending, and the user can expect new higher highs. When the bear component is over the bull component market is down-trending, and the user can expect new lower lows. The signal line (in orange) allows a more developed interpretation of the indicator and can be used in several ways. It is possible to use it to filter out potential false signals given by the crosses between the bullish and bearish components. As such the user might want to enter a position once the bullish or bearish component crosses over the signal line instead. Details Measuring the degree of variations of trends in the price by their direction (up-trend/down-trend) can be done in several way. The approach taken by the proposed indicator makes use of exponential envelopes and the naive computation of standard deviation. First, exponential envelopes are obtained from both the regular prices and squared prices, thus giving two upper extremities, and two lower extremities. The bullish component is obtained by first subtracting the upper extremity of the squared prices with the squared upper extremity of regular prices, the square root is then applied to this result. The bearish component is obtained in the same way, but makes use of the lower extremities of the exponential envelopes.
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Pine Script™指标
27
Auto Trendline Indicator (based on fractals)A tool that automatically draws out trend lines by connecting the most recent fractals. Description: The process of manual drawing out trend lines is highly subjective. Many times, we don’t trade what we see, but what we “want to see”. As a result, we draw lines pointing to the direction that we wishfully want price to move towards. While there are no right/wrong ways to draw trend lines, there are, however, systematic/unsystematic ways to draw trend lines. This tool will systematically draw out trend lines based on fractals. Additional feature: This tool will also plot out symbols (default symbol “X”) to signify points of crossings. This can be useful for traders considering to use trend lines as part of their trading strategies. Here is an interesting observation on the price actions of NASDAQ futures on a 5 second chart during regular trading hours on July 14, 2022. It’s a phenomenon. People like to see straight lines connecting HL/LH, etc., so it's possible for the market as a whole to psychologically react to these lines. However, it is important to note that is is impossible to predict the direction of price. In the case above, price could have tanked below auto-drawn trend line. Fractal based trend lines should only be taken as references and regarded as price levels. No studies have ever proven that the slope of trend lines can indicate price's future direction. More about fractals: To understand more about fractals: www.investopedia.com www.tradingview.com Contrary to what it sounds like, fractal in "technical analysis" does not refer to the recursive self-repeating patterns that appear in nature, such as the mesmerizing patterns found in snowflakes. The Fractal Markets Hypothesis claims that market prices exhibit fractal properties over time. Assuming this assertion to be true, then fractals can be used a tool to represent the chaotic movements of price is a simplified manner. The purpose of this exercise is to take a tool that is readily available (ie. in this case, TradingView’s built-in fractals tool), and to create a newer tool based on it. Parameters: Fractal period (denoted as ‘n’ in code): It is the number of bars bounding a high/low point that must be lower/higher than it, respectively, in order for fractal to be considered valid. Period ‘n’ can be adjusted in this tool. Traditionally, chartists pick the value of 5. The longer it is, the less noise seen on the chart, and the pivot point may also be exhibited in higher timeframes. The drawback is that it will increase the period of lag, and it will take more bars to confirm the printed fractal. Others: Intuitive parameters such as whether to draw historical trend lines, what color to use, which way to extend the lines, and whether or not to show points of crossings.
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Pine Script™指标
26

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