Dealing rangeThis script plots the current dealing ranges for the current time frame, otherwise if you want you can use it to pair it with the higher time frame ranges e.g. for time frames like 1min on the chart would be a 15 min range and the alignment is
1m -> 15m
3m -> 30m
5m -> 1h
15m -> 4h
1h -> 1D
4h -> 1W
1D -> 1M
1W -> 1Y
Example without tf alignment :
example with tf alignement :
Educational
250600 ORB Resistance Breakout v12 - Table ReorgHello traders, here is a Intraday LONG startegy for STOCKS based on the resistance breakout .
Time frame 5 Mins,
You may revise the MA values and max no of trades in a day (limit to 5 max).
Table with all data is provided, Sl is TSL based on 1 ATR value. Check the startegy with various inputs,
Good Luck ..
Position Size Calculatorcalculates what size position you should enter given desired risk and distance from SL in ticks on any symbol
abusuhil zonewatch
**Detailed Description of the “abusuhil zonewatch” Indicator and How to Use It**
---
## 1. General Overview
The **“abusuhil zonewatch”** indicator on TradingView is a comprehensive tool that combines four main features:
1. **User-Defined Horizontal Price Lines (Price Lines)** – allows you to draw up to seven custom support/resistance levels and receive alerts when price breaks them.
2. **ZoneWatch Multi-Timeframe Table** – displays the status of several technical indicators across seven timeframes (from 1 minute to 1 day) for the current instrument, marking each as “✓” (positive) or “✗” (negative), plus a summary percentage of positive signals per timeframe.
3. **Dominance Watch Table** – applies the same set of indicators to a chosen “dominance symbol” (e.g., BTC.D, ETH.D, or ETH/BTC) across the same seven timeframes, again showing “✓/✗” and a summary percentage.
4. **Smart Alerts** – two types of alerts:
* **ZoneWatch Smart Technical Alert**: triggers when a predefined percentage of indicators are positive simultaneously in selected timeframes.
* **Any Line Break Alert**: triggers the moment price closes above any active horizontal price line.
Together, these features give you both a quick visual of where price is relative to your manually drawn levels and a multi-timeframe technical read on both the instrument itself and its market dominance, with built-in alert logic.
---
## 2. Horizontal Price Lines (Price Lines)
### 2.1. Available Settings
* **Seven Price Lines** (Line 1 through Line 7), each with:
1. **Enable/Disable Checkbox** “Enable Line X”
2. **Price Input** “Line X Price” (user enters the exact price level)
3. **Color Picker** “Line X Color”
### 2.2. How It Works
* When you enable a line and specify a price and color, the script draws a horizontal line on the chart that extends 500 bars to the left and 500 bars to the right.
* If the price closes above that line (a bullish crossover), a small triangle shape (▲) in the same color appears directly beneath the candle that closed above it.
### 2.3. Practical Benefit
* You can mark key support or resistance levels without manually drawing them each time.
* The triangle alert clearly shows the exact candle that broke your level, letting you enter a trade or adjust your stop-loss precisely when it happens.
---
## 3. ZoneWatch Multi-Timeframe Table
### 3.1. Table Settings
* **Table Position**: Default is Top Right (“top\_right”), but you can choose Top Left, Bottom Left, or Bottom Right.
* **Font Size**: Adjustable from 6 to 24 points for readability.
* **ADX Threshold**: Default is 25 (range 10–50). This threshold is used when evaluating the ADX indicator for strength of trend.
### 3.2. Indicators Included (User Can Show/Hide Each)
1. **RSI (Relative Strength Index)**: RSI(14) > 50
2. **MACD (Moving Average Convergence Divergence)**: MACD Line > Signal Line (from MACD(12,26,9))
3. **Volume**: Current Volume > SMA(Volume, 20)
4. **SMA50**: Close Price > SMA(Close, 50)
5. **SMA200**: Close Price > SMA(Close, 200)
6. **Stochastic RSI**: %K > %D (from stoch(close, high, low, 14) and its 3-period SMA)
7. **VWAP (Volume-Weighted Average Price)**: Close Price > VWAP
8. **ADX (Average Directional Index)**: ADX(14) > ADX Threshold
You can enable or disable any of these eight via checkboxes in the inputs.
### 3.3. Timeframes Covered
The table applies all chosen indicators to seven different timeframes, each via `request.security` calls:
* **1m** (1 minute)
* **5m** (5 minutes)
* **15m** (15 minutes)
* **30m** (30 minutes)
* **1h** (1 hour)
* **4h** (4 hours)
* **1D** (1 day)
### 3.4. Table Layout and Logic
* **Top Row (Column Headers)**: Lists the seven timeframes (1m, 5m, 15m, 30m, 1h, 4h, 1D).
* **Next Rows (Indicator Rows)**: Each row corresponds to one of the enabled indicators (e.g., RSI, MACD, Volume, etc.). For each timeframe column, it shows:
* “✓” if the indicator condition is positive (e.g., RSI > 50 in that timeframe).
* “✗” if the condition is negative.
* Background coloring:
* Green (semi-transparent) if “✓”
* Red (semi-transparent) if “✗”
* **Bottom Row (Summary)**: Calculates how many of the enabled indicators are positive (“✓”) in each column (timeframe), then converts to a percentage:
* Percentage = (Number of “✓” symbols ÷ Total Number of Enabled Indicators) × 100
* If the percentage ≥ 75%, the cell background is light green.
* If 50% ≤ percentage < 75%, the cell background is light orange.
* If percentage < 50%, the cell background is light red.
### 3.5. Practical Benefit of the ZoneWatch Table
1. **Instant Multi-Timeframe Snapshot**
* You don’t need to switch between seven separate charts to see whether RSI, MACD, Volume, etc., are positive or negative in each timeframe.
* The table consolidates everything into one compact view, saving time.
2. **Identify the Strongest Timeframe Quickly**
* If you see, for example, that the 1h column has 6 out of 8 indicators positive (75%), you know the 1h is currently showing strong bullish momentum.
* You can use that as your primary trading timeframe or to confirm a trade you plan to enter in a shorter timeframe.
3. **Quantified Trend Strength**
* The Summary percentage row gives you an at-a-glance score of how “in agreement” the indicators are.
* If Summary ≥ 75% on a timeframe, you might treat that timeframe as particularly strongly trending, and plan trades accordingly.
4. **Customize to Your Strategy**
* Disable indicators you don’t use (e.g., if you never trade based on VWAP, simply uncheck it).
* Adjust the ADX threshold based on how strong you want that filter to be.
---
## 4. ZoneWatch Alerts
### 4.1. Smart Technical Alert
* **Purpose**: To notify you the moment a specified percentage of indicators are positive simultaneously in chosen timeframes.
* **Configuration**:
* **Enable Alerts** checkbox toggles the feature on/off.
* For each monitored timeframe (15m, 1h, 4h), you specify a “Required %” (e.g., 80% for 15m, 70% for 1h, 60% for 4h).
* When the Summary percentage for **all** selected timeframes is at or above the designated “Required %,” the indicator triggers the “ZoneWatch Smart Technical Alert.”
* **Benefit**:
* Ensures you only get a pop-up/email/push notification when multiple indicators align across multiple timeframes, reducing “noise” from single-timeframe signals.
* Helpful for traders who want to be alerted the moment a multi-timeframe consensus forms, rather than checking each timeframe manually.
### 4.2. Any Line Break Alert
* **Purpose**: To notify you immediately when price closes above any active horizontal Price Line.
* **How It Works**:
* For each enabled line (Line 1–Line 7), the script checks `ta.crossover(close, priceX)` (meaning price closing above priceX).
* If a crossover occurs on a given bar, the “Any Line Break Alert” triggers.
* **Benefit**:
* You don’t have to watch the chart constantly. As soon as price closes above your chosen level, you get an alert and can act (enter a long, adjust a stop, etc.).
---
## 5. Dominance Watch Table
### 5.1. What Is “Dominance”?
* **Dominance (e.g., BTC.D, ETH.D)** measures the proportion of total cryptocurrency market capitalization represented by Bitcoin or Ethereum.
* If **BTC.D** (Bitcoin Dominance) rises, more capital is flowing into Bitcoin relative to Altcoins.
* If **BTC.D** falls, more capital is moving into Altcoins.
* **ETHBTC** is simply the ETH/BTC trading pair—when ETHBTC is strong, Ethereum is outperforming Bitcoin.
### 5.2. Table Settings for Dominance
* **Dominance Symbol**: You choose from a dropdown:
* `CRYPTOCAP:BTC.D` (Bitcoin Dominance)
* `CRYPTOCAP:ETH.D` (Ethereum Dominance)
* `ethbtc` (ETH vs. BTC)
* `CRYPTOCAP:TOTAL` (Total Crypto Market Dominance)
* `CRYPTOCAP:TOTAL2` (Dominance of certain Altcoin categories)
* **Font Size**: Adjustable 8–24.
* **Table Position**: Default Bottom Right, but you can move it to any corner.
* **ADX Threshold**: Set between 10 and 50 to define “strong trend” for dominance.
### 5.3. Indicators Applied to Dominance
Exactly the same eight indicators used in ZoneWatch are now applied to whichever dominance symbol you select, over the same seven timeframes:
1. **RSI(14) > 50** for dominance price.
2. **MACD(12,26,9): MACD Line > Signal Line**.
3. **Volume > SMA(Volume, 20)**.
4. **Close Price > SMA(Close, 50)**.
5. **Close Price > SMA(Close, 200)**.
6. **Stochastic RSI: %K > %D** (from `stoch` plus its 3-period SMA).
7. **Close Price > VWAP**.
8. **ADX(14) > Dominance ADX Threshold**.
Each is evaluated via `request.security(symbolDominance, timeframe, …)`.
### 5.4. Table Layout and Logic
* **Top-Left Cell**: Displays the chosen dominance symbol (e.g., `CRYPTOCAP:BTC.D`), so you immediately know which dominance metric the table refers to.
* **Top Row (Headers)**: The seven timeframes (1m, 5m, 15m, 30m, 1h, 4h, 1D).
* **Indicator Rows**: One row per indicator (RSI, MACD, Volume, SMA50, …, ADX).
* Each cell shows “✓” if that indicator is positive in that timeframe for the chosen dominance symbol, or “✗” if negative.
* Background: Light green if “✓,” light red if “✗.”
* **Bottom Row (“Result %”)**:
* Counts how many of the eight indicators show “✓” in that timeframe, then calculates a percentage out of eight:
* Result % = (Number of “✓” symbols ÷ 8) × 100
* If Result % ≥ 50%, the cell background is light green. Otherwise, it is light red.
### 5.5. Practical Benefit of the Dominance Watch Table
1. **Gauge Where Money Is Flowing**
* A high bullish Result % in BTC.D suggests capital is rotating into Bitcoin.
* A low BTC.D Result % suggests capital is moving into Altcoins.
* A high Result % in ETHBTC indicates Ethereum is outperforming Bitcoin at the moment.
2. **Confirm or Reject Trades**
* If you plan to buy an Altcoin, you might wait until BTC.D Result % is below 50%—meaning money is leaving Bitcoin for other cryptocurrencies.
* If you plan to buy Bitcoin, you’d like to see BTC.D Result % above 50% (signals aligning toward more Bitcoin dominance).
* To switch between BTC and ETH, use ETHBTC: if its Result % is above 50%, Ethereum strength is stronger than Bitcoin strength right now.
3. **Synergy with ZoneWatch**
* If ZoneWatch says BTC/USD is very bullish on 1h (for example, 70% of indicators positive), and BTC.D is also bullish (80% positive), that confirms broader Bitcoin strength—offering extra confidence in a Bitcoin trade.
* If ZoneWatch is bullish on an Altcoin pair but BTC.D is also strong, that may signal caution—money might flow back into Bitcoin before pumping Alts.
---
## 6. How to Use the Indicator in Practice
### 6.1. Choose Your Timeframes and Indicators
* Decide which timeframes are most relevant to your style (scalping—1m to 15m; swing—1h to 4h; position—1D).
* In **ZoneWatch settings**, enable only the indicators you actually use. For example, if you don’t trade based on VWAP, simply uncheck “Show VWAP.”
* Adjust the **ADX Threshold** to your preference: a higher threshold (e.g., 30) means you only count ADX as positive when trend strength is very strong; a lower threshold (e.g., 20) is more sensitive.
### 6.2. Draw and Monitor Price Lines
* Under “Price Lines,” enable the lines (1–7) that correspond to support/resistance levels you’ve identified on the chart. Enter their exact prices and pick distinct colors.
* When the price closes above a line (a bullish break), you’ll see a small ▲ marker beneath the candle. That is your cue to check the ZoneWatch and Dominance tables for confirmation before entering a trade.
### 6.3. Read the ZoneWatch Table for Multi-Timeframe Confirmation
* Look at the **Summary %** row to see which timeframes have the highest percentage of positive indicators.
* For instance:
* If **1h** is 75% (6 out of 8 indicators positive), that indicates a fairly strong bullish bias on the 1-hour chart.
* If **4h** is only 40%, that suggests the 4-hour timeframe isn’t confirming bullish momentum—proceed with caution on longer trades.
* Use this to choose the “main” timeframe for your trade or to confirm a pattern you see on the price chart.
### 6.4. Check the Dominance Watch Table for Liquidity Flow
* Select the dominance symbol you need: e.g., `CRYPTOCAP:BTC.D` if you’re deciding whether to trade Bitcoin or Altcoins.
* If **BTC.D Result % ≥ 60%** on the 4h or 1D timeframe, that signals that Bitcoin is currently commanding more market share, which is bullish for Bitcoin and potentially bearish for Altcoins.
* If **BTC.D Result % < 50%** while your ZoneWatch is bullish on an Altcoin pair, that means money is indeed flowing into Alts—reinforcing an Altcoin trade.
* For ETH vs. BTC decisions, use **ETHBTC**: if ETHBTC Result % ≥ 50%, Ethereum is stronger than Bitcoin right now.
### 6.5. Set Up Smart Alerts
1. **ZoneWatch Smart Technical Alert**:
* Enable it, then choose which timeframes you want to monitor (e.g., 15m and 1h).
* Set “Required % 15m = 80%,” “Required % 1h = 70%.”
* Once both 15m and 1h summary percentages meet or exceed those thresholds, you receive an on-screen alert or e-mail/push (depending on your TradingView alert settings).
2. **Any Line Break Alert**:
* As soon as price closes above any enabled line, you get a separate alert. No need to watch the chart continuously; you’ll be notified the instant a level breaks.
### 6.6. Entry, Stop-Loss, and Take-Profit Strategy
* **Entry**:
1. Price breaks your chosen horizontal line (look for ▲ marker).
2. ZoneWatch summary % for your target timeframe is above your desired threshold (e.g., ≥ 70%).
3. Dominance Watch is aligned (e.g., BTC.D ≥ 60% if buying Bitcoin, or BTC.D < 50% if buying Altcoins).
4. Then place your buy/sell order.
* **Stop-Loss**:
* Place just below (for longs) or above (for shorts) the last broken Price Line or a relevant moving average (e.g., SMA50) that is negative in ZoneWatch.
* **Take-Profit**:
* Monitor ZoneWatch: if summary % drops below a certain level (e.g., from 70% to 50%), that may signal waning momentum—consider taking profits.
* Also watch Dominance: if BTC.D surges while you hold an Altcoin, it may be time to exit or rotate to Bitcoin.
---
## 7. Key Takeaways and Benefits
1. **Integrated Price + Dominance Analysis**
* By combining user‐drawn support/resistance (Price Lines) with a multi-timeframe technical read and a dominance/market-share read, you get a full picture of both price action and where market liquidity is flowing—essential for high-probability trade setups.
2. **Quantified, Objective Signals**
* Instead of interpreting charts subjectively, ZoneWatch turns each indicator into a “✓/✗” binary, and the Summary % gives you a clear numerical score of how bullish or bearish each timeframe is.
* Dominance Watch does the same for market-share indicators—no guessing, just numbers.
3. **Time Savings & Simplicity**
* Rather than opening seven chart tabs and reviewing eight indicators on each, you get everything in two compact tables.
* You can immediately see whether, for example, the 1h is bullish or bearish across multiple metrics, plus whether Bitcoin or Ethereum dominance is going in your favor.
4. **Highly Customizable**
* Enable or disable any of the eight indicators.
* Adjust the ADX thresholds for either the instrument or the dominance symbol.
* Move and resize tables to fit your chart layout.
* Choose exactly which timeframes and percentage thresholds to use for smart alerts.
5. **Actionable Alerts**
* **Smart Technical Alerts** let you know the moment multiple indicators align across multiple timeframes.
* **Line Break Alerts** notify you instantly when price breaks your important levels.
6. **Multi-Strategy Compatibility**
* Scalpers can focus on 1m–15m signals.
* Swing traders can rely on 1h–4h signals.
* Position traders and investors can watch the daily (1D) summary.
* All with the same single Pine v6 script.
---
### In Summary
The **“abusuhil zonewatch”** indicator is a one-stop solution for:
* **Drawing and monitoring key price levels** with instant alerts on breaks.
* **Gauging technical momentum** across seven timeframes for any TradingView symbol.
* **Tracking market dominance metrics** (BTC.D, ETH.D, ETH/BTC, etc.) in parallel, using the same technical indicators.
* **Receiving intelligent, multi-timeframe alerts** when your predefined criteria are met.
By consolidating all of these functions into one Pine v6 script, you save hours of manual charting and multi-tab analysis—delivering a clear, actionable read on both price action and market liquidity flows. Whether you are a day trader, swing trader, or long-term investor, this tool empowers you with objective, quantified signals on both the instrument and its broader market context.
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**وصف دقيق وتفصيلي لمؤشر “abusuhil zonewatch” وخدماته وكيفية الاستفادة منه**
---
## ١. لمحة عامة عن المؤشر
“abusuhil zonewatch” هو أداة تقنية متكاملة مخصّصة لمنصّة TradingView، تجمع بين:
1. **رسم خطوط سعرية أفقية يحدّدها المستخدم** (Price Lines).
2. **جدول متعدّد الأُطُر الزمنية (ZoneWatch Table)** لعرض حالة مجموعة من المؤشرات الفنية عبر سبعة أُطر زمنية (من الدقيقة إلى اليومي).
3. **جدول دوميننس موازٍ (Dominance Watch Table)** لتحليل هيمنة البيتكوين/الإيثيريوم (أو “ethbtc”) باستخدام نفس المؤشرات الفنية على نفس الأُطر الزمنية.
4. **تنبيهات ذكية** تُنبّه عند تحقق شروط فنية محدّدة:
* توافق الأغلب من المؤشرات (ZoneWatch Smart Technical Alert).
* كسر السعر لأيٍّ من الخطوط الأفقية (Any Line Break Alert).
بهذه الوظائف، يمكّن المؤشر المتداول أو الباحث من تتبُّع مستويات سعرية أساسية يختارها بنفسه، وفحص حالة الزخم والاتجاه الفني لأصل معين (سعره) مع ربط ذلك بتحليل هيمنة السوق (Dominance) في آنٍ واحد.
---
## ٢. رسم الخطوط الأفقية (Price Lines)
* **الإعدادات المتوفرة**
* سبع مستويات سعرية (Line 1 إلى Line 7).
* لكل مستوى:
1. تفعيل/تعطيل (Enable Line X).
2. رقم السعر المطلوب (Line X Price).
3. لون الخط (Line X Color).
* **آلية العمل**
* إذا فعَّل المستخدم مستوى سعر وخطّ لونَّه، يُرسم هذا الخط أفقيًا على الشارت ممتدًّا 500 شمعة إلى اليسار و500 شمعة إلى اليمين.
* في لحظة اختراق السعر (إغلاق شمعة صاعدًا) للخارج أعلى الخط، تظهر علامة (مثلث صغير) أسفل الشمعة بلون الخط نفسه، دلالة على كسر المقاومة (أو إذا كان المستوى أردناً، دلالة على اختراقه صعودًا).
* **الفائدة العملية**
* رسم مستويين أو ثلاثة أو أكثر لتمثيل مناطق دعم/مقاومة يهمّك مراقبتها دون الحاجة للرسم يدويًا كلّ مرة.
* الإشارة المرئية (مثلث) تسهل عليك تحديد اللحظة الدقيقة لكسر السعر للمستوى، مما يتيح سرعة في فتح صفقة أو تعديل أوامر وقف الخسارة.
---
## ٣. جدول ZoneWatch الفني (ZoneWatch Table)
### ٣.١. مكونات الجدول
* **الموقع الافتراضي:** في الزاوية اليمنى العليا (Top Right) من الشارت (قابل للتعديل بين أعلى يسار، أعلى يمين، أسفل يسار، أسفل يمين).
* **حجم الخط:** قابل للتعديل بين 6 و24 نقطة حسب تفضيل المستخدم.
* **عتبة ADX (ADX Threshold):** قيمة يمكن ضبطها (افتراضيًا 25) لاستخدامها في اختبار قوة الاتجاه.
### ٣.٢. المؤشرات الفنية المدرجة
يمكن للمستخدم إظهار أو إخفاء كلٍ مما يلي بحسب رغبته، عبر مربعات اختيار (Checkboxes):
1. **RSI (Relative Strength Index):** يُختبر إذا كان RSI (14) > 50.
2. **MACD (Moving Average Convergence Divergence):** يُختبر إذا كان خط MACD > خط الإشارة.
3. **حجم التداول (Volume):** يُختبر إذا كان الحجم الحالي > متوسط SMA20 للحجم.
4. **SMA50:** يُختبر إذا كان سعر الإغلاق > متوسط SMA50.
5. **SMA200:** يُختبر إذا كان سعر الإغلاق > متوسط SMA200.
6. **Stochastic RSI:** يُختبر إذا كان خط %K > خط %D (من حساب Stoch RSI (14)).
7. **VWAP:** يُختبر إذا كان سعر الإغلاق > قيمة VWAP.
8. **ADX (Average Directional Index):** يُختبر إذا كانت قراءة ADX (من DMI (14,14)) > العتبة المحدّدة.
### ٣.٣. الأُطُر الزمنية المشمولة
يتم تطبيق كل مؤشر على سبعة أُطر زمنية منفصلة باستخدام دالة `request.security`، وهي:
* 1m (دقيقة واحدة)
* 5m (خمس دقائق)
* 15m (خمسة عشر دقيقة)
* 30m (ثلاثون دقيقة)
* 1h (ساعة واحدة)
* 4h (أربع ساعات)
* 1D (يومي)
### ٣.٤. طريقة العرض في الجدول
* **الصف الأول**: يحتوي على عناوين الأعمدة، وهي الأُطُر الزمنية (1m، 5m، …، 1D).
* **الصفوف التالية**: كل صف يخص مؤشرًا واحدًا (على سبيل المثال: صف RSI، صف MACD، صف Volume، …).
* تظهر في كل خلية علامة “✓” إذا كانت نتيجة المؤشر إيجابية في هذا الإطار، أو “✗” إذا كانت سلبية.
* لون خلفية الخلية يكون أخضر شفّاف عند الإشارة الإيجابية، أو أحمر شفّاف عند الإشارة السلبية.
* **الصف الأخير (Summary)**:
* يجمع عدد المؤشرات المفعّلة التي أظهرت علامة “✓” في كل عمود (أي في كل إطار).
* يحسب النسبة المئوية:
$$
\text{نسبة} = \bigl(\frac{\text{عدد العلامات الإيجابية (✓)}}{\text{إجمالي المؤشرات المفعّلة}}\bigr) \times 100
$$
* يُلوّن خلفية الخلية:
* أخضر شفاف إذا كانت النسبة ≥ 75%.
* برتقالي شفاف إذا كانت النسبة بين 50% و75%.
* أحمر شفاف إذا كانت النسبة < 50%.
### ٣.٥. الفائدة العملية من جدول ZoneWatch
1. **رؤية فورية لحالة الزخم والاتجاه متعدد الأُطُر الزمنية**:
* بدلاً من فتح سبع نوافذ مختلفة لكل إطار زمني، تحصل على ملخص مركّز باتجاه كل إطار ومؤشرات الزخم.
2. **تمييز الإطارات التي تشهد توافقًا فنيًا قويًا**:
* إذا رأيت عمود 1h يحتوي على ثلاث مؤشرات صاعدة من أصل أربعة أو خمسة مفعّلة، فهذا يعكس زخمًا إيجابيًا متوسّط الأجل.
3. **التخطيط واتخاذ القرار السريع**:
* يمكنك ـ على سبيل المثال ـ تأجيل فتح صفقة جديدة حتى يتحقق ملخص ≥ 70% على إطار 4h، ما يمنحك دلالة قوية على صعود/هبوط مستمر.
4. **تقليل “ضجيج” البيانات**:
* بدلاً من متابعة أرقام مؤشرات منفصلة في كل إطار، تكتفي بقراءة الرموز “✓/✗” والنسب المئوية، ما يسرّع عملية التحليل.
---
## ٤. تنبيهات ZoneWatch (Alerts)
### ٤.١. ZoneWatch ‒ Smart Technical Alert
* **الهدف:** إرسال إشعار عندما تكون نسبة الملخص (Summary) في إطار زمني معيّن أو أكثر أكبر من عتبة تحدّدها.
* **ضبط الإعدادات**:
* يمكن اختيار مراقبة ثلاثة أُطر زمنية بشكلٍ منفرد (15m، 1h، 4h).
* لكل إطار، تحدّد “Required %” (على سبيل المثال: 80% في 15m، 70% في 1h، 60% في 4h).
* إذا بلغت النسبة المئوية الملخّصة في كل إطار نشط القيمة المطلوبة أو أكثر، يُرسل التنبيه.
* **كيفية الاستفادة**:
* يسمح لك برفع الحدّ الأدنى المطلوب من اتفاق المؤشرات لكي تتلقى إشعارًا.
* يقلّص تنبيهات “الضجيج” ويُركّز على اللحظات التي يتحقق فيها توافق فني كبير عبر أكثر من إطار.
* بمجرد وصول النسب إلى العتبات المحددة، يمكنك فتح صفقة أو تعديل مراكزك مع العلم بأن الزخم موجود ومتوافق على أكثر من إطار.
### ٤.٢. ZoneWatch ‒ Any Line Break Alert
* **الهدف:** إرسال إشعار فوري عندما يخترق السعر أيًّا من الخطوط الأفقية المفعّلة إلى الأعلى.
* **آلية العمل**:
* لكل خط مفعّل، يُربط اختراق السعر (Crossover) بإشارة تنبيه.
* عند إغلاق شمعة يصعود فيها السعر فوق مستوى الخط، يُرسل التنبيه.
* **كيفية الاستفادة**:
* يتيح لك مراقبة مستويات دعم/مقاومة مهمة دون النظر المستمر إلى الشارت.
* بمجرد كسر السعر للمستوى، تحصل على تنبيه فوري بوقوع كسر مقاومة أو اختراق دعم، فتكون مستعدًا لدخول الصفقة أو تعديل وقف الخسارة.
---
## ٥. جدول Dominance Watch (Dominance Watch Table)
### ٥.١. مقدّمة حول “الدوميننس”
* **الدوميننس (Dominance)**: هو مصطلح يشير إلى نسبة سيولة سوقٍ محدّد (مثل سيولة البيتكوين) ضمن السوق الكلي للعملات الرقمية.
* إذا ارتفع “BTC.D” (هيمنة البيتكوين)، فهذا يعني أنّ سيولة أكبر تتدفق إلى البيتكوين مقارنةً بالعملات الأخرى.
* إذا انخفض “BTC.D”، فهذا يعكس أن السيولة تتحرك أكثر إلى العملات البديلة (Altcoins).
* **“ethbtc”**: هو ثنائي يعبّر عن سعر الإيثيريوم مقابل البيتكوين؛ ازدياده يعني أن الإيثيريوم يكسب زخمًا نسبيًّا مقابل البيتكوين.
### ٥.٢. إعدادات جدول Dominance Watch
* **رمز الدوميننس (Dominance Symbol)**:
* قائمة منسدلة تضمّ:
* `CRYPTOCAP:BTC.D` (هيمنة البيتكوين)،
* `CRYPTOCAP:ETH.D` (هيمنة الإيثيريوم)،
* `ethbtc` (سعر ETH مقابل BTC)،
* `CRYPTOCAP:TOTAL` (هيمنة سوق العملات الرقمية الكليَّة)،
* `CRYPTOCAP:TOTAL2` (هيمنة سوق العملات الخاصة/محدّدة).
* **حجم الخط (Font Size)**: قابل للتعديل بين 8 و24 نقطة.
* **موقع الجدول (Table Position)**: مثلاً “bottom\_right” أو أي زاوية أخرى يختارها المستخدم.
* **عتبة ADX (Dominance ADX Threshold)**: بين 10 و50 لتحديد قوة اتجاه الهيمنة.
### ٥.٣. المؤشرات الفنية المُطبَّقة على الدوميننس
يُطبَّق نفس ثمانية المؤشرات المُستعملة في ZoneWatch، ولكن على “قيمة الدوميننس/سعر ethbtc” عبر سبعة أُطر زمنية:
1. **RSI (14) > 50**.
2. **MACD (12,26,9): خط MACD > خط الإشارة**.
3. **حجم التداول > SMA20 للحجم** (للسعر/الدوميننس نفسه).
4. **سعر الإغلاق > SMA50**.
5. **سعر الإغلاق > SMA200**.
6. **Stochastic RSI: %K > %D**.
7. **سعر الإغلاق > VWAP**.
8. **ADX (DMI (14,14)) > العتبة المحددة**.
### ٥.٤. طريقة العرض في جدول Dominance Watch
* **الصف الأول**: يظهر في الخلية اليسرى العليا اسم رمز الدوميننس المختار (مثلاً `CRYPTOCAP:BTC.D`).
* **عناوين الأعمدة**: تبيّن الإطارات الزمنية (1m، 5m، 15m، 30m، 1h، 4h، 1D).
* **الصفوف التالية**: كل صف عبارة عن مؤشِّر (“RSI”، “MACD”، “Volume”، …).
* في كل خلية، تظهر “✓” إذا كانت نتيجة المؤشر إيجابية في ذلك الإطار (مثلاً: RSI > 50)، أو “✗” إذا كانت سلبية.
* لون خلفية الخلية: أخضر شفّاف للإيجابية، أحمر شفّاف للسلبية.
* **الصف الأخير (Result %)**:
* يجمع عدد “✓” من إجمالي 8 مؤشرات مُفعّلة، ثم يحسب النسبة المئوية.
$$
\text{نسبة DOM} = \bigl(\frac{\text{عدد العلامات الإيجابية}}{8}\bigr) \times 100
$$
* إذا كانت النسبة ≥ 50%، تُلوّن الخلفية أخضر شفاف؛ إذا أقلّ من 50%، تُلوَّن بالأحمر الشفاف.
### ٥.٥. الفائدة العملية من جدول Dominance Watch
1. **فهم اتجاه السيولة العام**:
* إذا كانت لوحة “BTC.D” اليومي فيها 6 أو 7 مؤشرات صاعدة (أي علامة “✓”)، فهذا دليل على زخم هيمنة البيتكوين وميول السوق للانتقال من العملات البديلة → البيتكوين.
* إذا كانت “ethbtc” 70% إيجابية على الإطار 1h، فهذا يعني أن قيمة الإيثيريوم تكسب زخمًا نسبيًّا أمام البيتكوين.
2. **دعم القرار**:
* عند شراء عملة بديلة (Altcoin)، يكون بعيدًا عن البيتكوين عمومًا؛ لذلك إذا انخفضت هيمنة البيتكوين (DOM أقلّ من 50–60%) وزادت إشارات “ethbtc”، قد تكون فرصة أفضل لدخول Altcoins.
* عند التفكير في شراء بيتكوين، فمن المفيد التأكد أن “BTC.D” إيجابي بمعظم مؤشرات RSI/ADX/… إلخ، للدلالة على مزيد من سيولة تدخل البيتكوين.
3. **التوقيت المشترك** مع ZoneWatch:
* استخدم “ملخص النسبة” في Dominance Watch لدعم أو رفض إشارة ZoneWatch.
* مثال عملي:
* إذا حضرنا إشارة ZoneWatch إيجابية 75% في إطار 1h للـ BTC/USD، ولكن Dominance Watch (BTC.D) سلبي بنسبة أقلّ من 40%، فقد يعكس هذا أن السيولة تتجه للـ Altcoins وليس للبيتكوين، وقد ننتظر تعديلًا أو نتوخّى الحذر.
---
## ٦. كيفية الاستفادة المتكاملة
1. **اختيار الإطار الزمني الرئيسي**
* قرّر ما إذا كنت متداولًا سريعًا (scalper) يركز على الإطارات 1m–15m، أو سوينغ تريدر يفضل 1h–4h، أو مستثمر يتابع الخلية اليومية (1D).
* فعّل أو أوقف مؤشرات ZoneWatch التي لا تتناسب مع استراتيجيتك (مثلاً، إذا كنت تعتمد فقط RSI و MACD و ADX في التداول اليومي، فأوقف “Volume” و“SMA200” إن لم تعدّها ضرورية).
2. **مراقبة Price Lines**
* حدد مستويات دعم/مقاومة حرجة تراها من تحليلك السابق على الشارت (مثل قمة تاريخية سابقة أو مستوى فيبوناتشي مهم).
* عندما يكسر السعر مستوى مفعّل صعودًا، سترى مثلثًا أخضر صغير أسفل الشمعة. هذا توقيت جيد لمراجعة قيمة المؤشرات في ZoneWatch وDominance Watch.
3. **قراءة ملخص ZoneWatch**
* اطلع على الصف الأخير (Summary) وقارن نسب “✓” بين الأعمدة.
* إذا كان عمود 4h عنده 70% من المؤشرات صاعدة (أي 5 من 7 أو 6 من 8)، فهذا يعني أن الإطار المتوسط الأمد يميل بقوة للاتجاه الصاعد، ويُعطيك ثقة أعلى في احتمال استمرار الصعود.
* بالمقابل، إذا كان ملخص 1h سلبيًا (أقل من 50% مؤشرات صاعدة)، قد يعني ذلك حدوث تصحيح جزئي قبل مواصلة الصعود.
4. **التأكد من Dominance Watch**
* إذا كنت تريد شراء بيتكوين، فتأكد من أن “BTC.D” يعرض نسبة ≥ 50–60% مشجّعًا على زيادة هيمنة البيتكوين.
* إذا كنت تفكر في Altcoins، افحص “BTC.D”: إذا ظهرت نسبة أقل من 50%، فهذا دليل على توزيع السيولة باتجاه Altcoins.
* إذا أردت التبديل من بيتكوين إلى إيثيريوم، يمكنك متابعة “ethbtc”: عندما يكون ملخص “ethbtc” على 4h ≥ 70%، يصبح انتقال السيولة إلى ETH محتملًا.
5. **ضبط التنبيهات الذكية**
* للاستخدام الأمثل لخاصية “Smart Technical Alert” في ZoneWatch، حدّد العتبات بناءً على مخاطرتك المقبولة.
* مثلاً:
* “Required %15m” = 75%،
* “Required %1h” = 65%،
* “Required %4h” = 60%.
* عندما تتحقق هذه النسب في الوقت نفسه، ستحصل على تنبيه، فتقوم بتفحُّص Dominance Watch بسرعة؛ إن كان الـ“BTC.D” أو “ethbtc” يدعم قرارك، تدخل الصفقة بثقة أعلى.
6. **قرارات الدخول والخروج**
* **دخول الصفقة (Long/Short):**
1. تأكّد من أن ملخص ZoneWatch في الإطار الذي تستهدفه أعلى من العتبة (على الأقل 60–70%).
2. تأكّد من أن Dominance Watch (التابع للرمز المناسب: BTC.D أو ethbtc) يظهر دعمًا سليماً (≥ 50–60% من المؤشرات إيجابية).
3. إذا كان السعر قد كسر مستوى مقاومة في Price Lines، فهذا يمثل تأكيدًا إضافيًا لفتح صفقة شراء.
* **وقف الخسارة (Stop Loss):**
* يُمكنك وضعه أسفل أحدث دعم في Price Lines، أو أسفل متوسط SMA50 أو SMA200 الذي يظهر سلبيًا في ZoneWatch.
* **جني الأرباح (Take Profit):**
* راقب لحظة انعكاس المؤشرات: إذا قلّت نسبة ملخص ZoneWatch (مثلاً من 80% إلى أقلّ من 50%)، فهذا مؤشر على انخفاض الزخم، يمكنك حينها جني الأرباح.
* إذا ارتفع DOM (مثل BTC.D) بشكل كبير، فقد يشير إلى عودة سيولة قوية للبيتكوين وضربة على عملات بديلة، لذا يمكنك الاستعداد لجني الأرباح أو الانتقال إلى BTC.
---
## ٧. نقاط مهمّة وملخّص الفوائد
* **تكامل بين السعر ودوميننس السوق**: يجمع المؤشر بين تحليل الشارت التقليدي (Price Lines وZoneWatch) وبين فهم توزيع السيولة عبر Dominance Watch، مما يوفّر رؤية شاملة ومتكاملة لاتجاهات السوق.
* **إشارات كمية موضوعية**: الاعتماد على “✓/✗” ونسب الـSummary يقلّل من الانحياز العاطفي، ويلزم المتداول باتّباع قواعد واضحة قبل الإقدام على الشراء أو البيع.
* **توفير الوقت وتبسيط التحليل**: بدلاً من فتح سبع نوافذ لكل إطار وإجراء اختبارات يدوية على ثمانية مؤشرات، تجد كل ذلك في جدول واحد، ويمكن للجدولين (ZoneWatch وDominance Watch) العمل المتوازي لتقديم نظرة شاملة في آن واحد.
* **مرونة عالية في الإعداد**:
* تستطيع تخصيص المؤشرات التي تهمّك فقط (مثلاً، إذا كنت لا تهتمّ بـSMA200 أو VWAP، كلّف بتعطيلها من الإعدادات).
* تستطيع تعديل حجم الخط وموقع الجدول ليتناسب مع دقة شاشتك ورغبتك.
* تضبط عتبات الـADX وعتبات النسبة في التنبيهات لمواءمة استراتيجياتك الشخصية.
* **إشعارات فورية لتسريع رد الفعل**:
* إشعار “Smart Technical” عندما تتوافق معظم المؤشرات عبر الأُطر الزمنية الحرجة.
* إشعار “Any Line Break” يساعدك على عدم فقدان لحظة اختراق مستويات الدعم/المقاومة الحرجة التي حددتها بيدك.
---
### في الختام
**مؤشر “abusuhil zonewatch”** هو أداة متعددة الجوانب تجمع بين رسم مستويات سعرية يدوية وجداول فنية متعدّدة الأُطُر الزمنية لمعرفة اتجاه الزخم، بالإضافة إلى جداول “دوميننس” لمتابعة حركة سيولة السوق. يساعد هذا الدمج المتزامن متخذي القرارات على تقييم فرص الشراء أو البيع بدقة أعلى، وتقليل الأخطاء المترتبة على التحليل الانعكاسي الجزئي.
باستخدامه، يستطيع المتداول:
* تحديد مناطق السعر الحرجة ومراقبتها تلقائيًا.
* قياس حالة أو قوة المؤشرات الفنية عبر أكثر من إطار زمني بجلسة واحدة.
* ربط تحركات السعر بحالة سيولة السوق (الهيمنة) لاتخاذ قرارات أكثر وعيًا.
* تلقّي تنبيهات ذكية تلائم استراتيجيته الخاصة لفتح الصفقات أو تعديلها بسرعة.
**كل ما يحتاجه المتداول هو ضبط الإعدادات (Price Lines، قائمة المؤشرات المفضّلة، عتبات ADX/النسب، رمز الدوميننس) لمتابعة السوق بشكلٍ متكامل دون عناء فتح نوافذ متعدّدة أو إجراء الاختبارات يدويًا.**
📊 📊 Support Resistance Channels (15M,D) by.DCX
🔥 The only indicator you’ll ever need for scalping or swing trading. Period.
💡 Tired of messy charts full of meaningless lines?
This script automatically draws only the most meaningful support and resistance levels based on pivots –
optimized for both 15-minute scalping and daily timeframe swing trading.
💥 Why This Indicator Is a Game-Changer:
✅ Automatic Precision Detection – Finds real, battle-tested levels using pivot highs/lows
✅ Smart Noise Reduction – Merges nearby zones to reduce clutter
✅ Timeframe-Aware Logic
🔹 Daily Zones (Blue): Always visible
🔹 15-Minute Zones (Purple): Only shown when viewing timeframes below 4H
✅ Fully Customizable
🔹 Adjust pivot sensitivity, number of lines, merge distance – everything
🧠 Who Should Use This?
Scalpers who want sniper-level entries based on proven intraday zones
Swing traders who need clean, reliable daily levels
Anyone who hates cluttered charts and wants clarity
📈 Pro Tips:
Lower the “Max S/R Levels” to show only the most critical zones
👉 Try setting to 3–5 to highlight institutional-grade levels
Tune “Merge Distance (%)” to filter overlapping zones (recommended: 0.3–0.5%)
💬 TL;DR
🧲 “You’ll never need to draw S/R lines manually again. This script does it smarter.”
Low Liquidity Marker📘 Indicator Description – Low Liquidity Marker
The Low Liquidity Marker is a simple yet powerful tool designed to highlight candles where Volume × Low Price falls below a customizable threshold — signaling potential low liquidity zones on the chart.
🔍 How it works:
It calculates volume × low for each candle.
When this value drops below your defined threshold, a red triangle is plotted below that bar.
These bars may indicate poor institutional participation or market inefficiency.
⚠️ Why it matters:
Low liquidity makes it difficult to build or exit large positions efficiently.
Stocks or instruments flagged by this tool may be suitable for small capital investments but are generally unsuitable for high-volume or institutional-grade trading.
Use this indicator to filter out illiquid setups when screening for quality trades.
🛠 Customizable Input:
Volume × Low Threshold: Tune this parameter based on your instrument or trading timeframe.
💡 Ideal For:
Retail traders avoiding illiquid zones.
Investors wanting to identify where the market lacks sufficient depth.
Enhancing trade filters in systematic or discretionary setups.
Forex Session Levels + Dashboard (AEST)Forex Session Indicators for Breakout and Retest Strategy (AEST)
Position Size CalculatorIt is a position size calculation with 0.05% buffer to take swift entry on either sides with 0.5% risk on your overall capital
Portfolio Dashboard by DTRThe Portfolio Dashboard by DTR is a sophisticated yet user-friendly Pine Script indicator for TradingView, designed to empower traders with a comprehensive tool for managing and monitoring investment portfolios. Supporting up to 10 stocks, it delivers real-time performance metrics, risk analysis, and market insights in an intuitive, customizable dashboard—perfect for traders of all experience levels.
Key Features
Real-Time Portfolio Metrics: Tracks Return on Investment (ROI), Day's Profit and Loss (PNL), Risk of Profit (ROP), and Average Daily Range (ADR) with color-coded indicators for quick insights.
Individual Stock Insights: Displays detailed data for each stock, including ticker, trading setup, Last Traded Price (LTP) or Stop Loss (SL) status, position size, risk, portfolio risk, Risk-Reward (RR) or Gain%, daily change%, portfolio impact, and optional ADR.
Market Condition Analysis: Evaluates broader market trends using NSE:CNXSMALLCAP data, categorizing conditions as CHOPPY, BULL MARKET, BEAR MARKET, SHAKEOUT, or BEAR RALLY with visual color cues.
Customization Options:
Input total capital (scalable in Thousands, Lacs, or Crores) and maximum risk percentage.
Choose from B&W, Blue, Green, Red, Purple, or Transparent themes, with Dark Mode support.
Adjust dashboard and gauge positions (top/middle/bottom, left/center/right) and text sizes (tiny to huge).
Toggle display options like LTP, % change color, total row, ADR column, RR/Gain%, and empty rows.
Risk Management Tools: Calculates position sizes, individual and portfolio-level risks, and offers visual gauges for total allocation (% invested) and open risk (% of max risk). Supports setting Stop Loss to Break-Even (SL=BE).
Chart Enhancements: Optionally displays entry and stop loss lines on the chart with customizable styles (Dashed, Dotted, Normal) and dynamic labels for precise trade management.
How It Works
Setup: Users input portfolio details—ticker symbols, quantities, entry prices, stop losses, exits, and setups—for up to 10 stocks, along with capital and risk settings.
Data Processing: The indicator fetches daily high, low, close, and previous close data to compute metrics like ADR, percentage change, and Day's PNL for each stock.
Visualization: On the last bar, it generates a detailed table summarizing portfolio and stock-level data, alongside two gauges for allocation and risk, positioned per user preferences.
Chart Integration: When enabled, entry and SL lines with labels appear on the chart for the current ticker, updating dynamically based on price action.
How to Use
Add to Chart: Apply the indicator to your TradingView chart.
Configure Settings: In the settings panel, enter your total capital, stock details, and customize themes, positions, and display preferences.
Monitor Portfolio: Use the dashboard to assess portfolio health, risk exposure, and market conditions in real time.
Manage Trades: Leverage chart lines and labels to execute and adjust trades with precision.
Benefits
Centralized Oversight: Consolidates all essential portfolio data into one view.
Enhanced Risk Control: Provides real-time risk metrics and visual tools for proactive management.
Flexible Design: Adapts to various trading strategies and aesthetic preferences.
Intuitive Interface: Combines detailed analytics with clear, visually appealing presentation.
Important Notes
Accuracy: Ensure correct ticker symbols (e.g., NSE:RELIANCE) and price inputs for reliable results.
Timeframes: Optimized for daily or intraday charts; updates occur on the last bar.
Dependencies: Market condition and ADR calculations rely on NSE:CNXSMALLCAP data availability.
Elevate your trading with the Portfolio Dashboard by DTR—a powerful, all-in-one solution for portfolio management on TradingView. Take control of your investments today!
Asian, London, and NY SessionThe Asian, London, NY Session indicator is a custom-built tool designed specifically for traders focusing on SPX500 and NASDAQ100. This indicator highlights the most critical trading sessions—Asian, London, and New York—with a strong emphasis on their overlap and key market reaction times.
Tailored for the UTC+8 (Singapore Time) zone, the indicator marks three essential times on the chart:
06:00 UTC+8 – Opening of the Asian session, where early momentum starts to form.
15:00 UTC+8 – Beginning of the London session, known for increased volatility and volume.
21:30 UTC+8 – Start of the New York session, coinciding with the U.S. stock market open, often the most active period for SPX500 and NASDAQ100.
By visually segmenting these sessions and highlighting these time anchors, the indicator helps traders:
Spot high-probability entry zones based on historical session behavior.
Align strategies with institutional trading hours.
Monitor session overlaps, which often lead to major market moves and liquidity shifts.
This tool is ideal for day traders, scalpers, and swing traders who want to better understand how price action behaves across global market sessions and time their trades accordingly.
iGTR_DailyDaily TF chart setup for index. Use it wisely with MACD or Alpha on same TF for a bigger momentum.
Based on multi TF analysis of BB & MA.
GEEKSDOBYTE IFVG w/ Buy/Sell Signals1. Inputs & Configuration
Swing Lookback (swingLen)
Controls how many bars on each side are checked to mark a swing high or swing low (default = 5).
Booleans to Toggle Plotting
showSwings – Show small triangle markers at swing highs/lows
showFVG – Show Fair Value Gap zones
showSignals – Show “BUY”/“SELL” labels when price inverts an FVG
showDDLine – Show a yellow “DD” line at the close of the inversion bar
showCE – Show an orange dashed “CE” line at the midpoint of the gap area
2. Swing High / Low Detection
isSwingHigh = ta.pivothigh(high, swingLen, swingLen)
Marks a bar as a swing high if its high is higher than the highs of the previous swingLen bars and the next swingLen bars.
isSwingLow = ta.pivotlow(low, swingLen, swingLen)
Marks a bar as a swing low if its low is lower than the lows of the previous and next swingLen bars.
Plotting
If showSwings is true, small red downward triangles appear above swing highs, and green upward triangles below swing lows.
3. Fair Value Gap (3‐Bar) Identification
A Fair Value Gap (FVG) is defined here using a simple three‐bar logic (sometimes called an “inefficiency” in price):
Bullish FVG (bullFVG)
Checks if, two bars ago, the low of that bar (low ) is strictly greater than the current bar’s high (high).
In other words:
bullFVG = low > high
Bearish FVG (bearFVG)
Checks if, two bars ago, the high of that bar (high ) is strictly less than the current bar’s low (low).
In other words:
bearFVG = high < low
When either condition is true, it identifies a three‐bar “gap” or unfilled imbalance in the market.
4. Drawing FVG Zones
If showFVG is enabled, each time a bullish or bearish FVG is detected:
Bullish FVG Zone
Draws a semi‐transparent green box from the bar two bars ago (where the gap began) at low up to the current bar’s high.
Bearish FVG Zone
Draws a semi‐transparent red box from the bar two bars ago at high down to the current bar’s low.
These colored boxes visually highlight the “fair value imbalance” area on the chart.
5. Inversion (Fill) Detection & Entry Signals
An inversion is defined as the price “closing through” that previously drawn FVG:
Bullish Inversion (bullInversion)
Occurs when a bullish FVG was identified on bar-2 (bullFVG), and on the current bar the close is greater than that old bar-2 low:
bullInversion = bullFVG and close > low
Bearish Inversion (bearInversion)
Occurs when a bearish FVG was identified on bar-2 (bearFVG), and on the current bar the close is lower than that old bar-2 high:
bearInversion = bearFVG and close < high
When an inversion is true, the indicator optionally draws two lines and a label (depending on input toggles):
Draw “DD” Line (yellow, solid)
Plots a horizontal yellow line from the current bar’s close price extending five bars forward (bar_index + 5). This is often referred to as a “Demand/Daily Demand” line, marking where price inverted the gap.
Draw “CE” Line (orange, dashed)
Calculates the midpoint (ce) of the original FVG zone.
For a bullish inversion:
ce = (low + high) / 2
For a bearish inversion:
ce = (high + low) / 2
Plots a horizontal dashed orange line at that midpoint for five bars forward.
Plot Label (“BUY” / “SELL”)
If showSignals is true, a green “BUY” label is placed at the low of the current bar when a bullish inversion occurs.
Likewise, a red “SELL” label at the high of the current bar when a bearish inversion happens.
6. Putting It All Together
Swing Markers (Optional):
Visually confirm recent swing highs and swing lows with small triangles.
FVG Zones (Optional):
Highlight areas where price left a 3-bar gap (bullish in green, bearish in red).
Inversion Confirmation:
Wait for price to close beyond the old FVG boundary.
Once that happens, draw the yellow “DD” line at the close, the orange dashed “CE” line at the zone’s midpoint, and place a “BUY” or “SELL” label exactly on that bar.
User Controls:
All of the above elements can be individually toggled on/off (showSwings, showFVG, showSignals, showDDLine, showCE).
In Practice
A bullish FVG forms whenever a strong drop leaves a gap in liquidity (three bars ago low > current high).
When price later “fills” that gap by closing above the old low, the script signals a potential long entry (BUY), draws a demand line at the closing price, and marks the midpoint of that gap.
Conversely, a bearish FVG marks a potential short zone (three bars ago high < current low). When price closes below that gap’s high, it signals a SELL, with similar lines drawn.
By combining these elements, the indicator helps users visually identify inefficiencies (FVGs), confirm when price inverts/fills them, and place straightforward buy/sell labels alongside reference lines for trade management.
Combined ATR Bands + VWAP + Moving Averages🔥 Ultimate Trading Combo: ATR Bands + VWAP + Moving Averages
This comprehensive indicator combines three powerful technical analysis tools in one clean interface:
📊 Features:
• ATR Bands - Dynamic support/resistance levels with step-line styling
• VWAP - Volume Weighted Average Price with orange dotted visualization
• Moving Averages - 50, 100, 200 periods with customizable colors
⚙️ Customizable Settings:
Toggle each component on/off independently
Adjustable ATR periods and multipliers
Multiple VWAP anchor periods (Session, Week, Month, Quarter, Year)
Configurable MA sources and periods
Custom colors and transparency levels
🎯 Perfect For:
Day traders seeking dynamic support/resistance
Swing traders using multiple timeframe analysis
Anyone wanting clean, professional chart visualization
💡 Created with AI assistance (Claude Sonnet 4)
Open source - feel free to modify and improve!
🔥 المؤشر الشامل: نطاقات ATR + VWAP + المتوسطات المتحركة
هذا المؤشر الشامل يجمع ثلاث أدوات تحليل فني قوية في واجهة واحدة نظيفة:
📊 المميزات:
• نطاقات ATR - مستويات دعم ومقاومة ديناميكية بتصميم خطوط متدرجة
• VWAP - متوسط السعر المرجح بالحجم مع عرض نقطي برتقالي
• المتوسطات المتحركة - فترات 50، 100، 200 مع ألوان قابلة للتخصيص
⚙️ إعدادات قابلة للتخصيص:
تشغيل/إيقاف كل مكون بشكل مستقل
فترات ومضاعفات ATR قابلة للتعديل
فترات ربط VWAP متعددة (جلسة، أسبوع، شهر، ربع، سنة)
مصادر وفترات MA قابلة للتكوين
ألوان مخصصة ومستويات شفافية
🎯 مثالي لـ:
المتداولين اليوميين الباحثين عن دعم/مقاومة ديناميكية
متداولي التأرجح باستخدام تحليل الإطارات الزمنية المتعددة
أي شخص يريد عرض مخططات نظيف ومهني
💡 تم إنشاؤه بمساعدة الذكاء الاصطناعي (Claude Sonnet 4)
مفتوح المصدر - لا تتردد في التعديل والتحسين!
#ATR #VWAP #MovingAverages #TechnicalAnalysis #Support #Resistance #DayTrading #SwingTrading #OpenSource #AI #تحليل_فني #دعم_مقاومة #متوسطات_متحركة #تداول_يومي
Categorical Market Morphisms (CMM)Categorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality
A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets
Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics
Theoretical Foundation: The Mathematical Revolution
Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics.
Category Theory in Markets
Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms:
Objects = Market states (price levels, volume conditions, volatility regimes)
Morphisms = State transitions (price movements, volume changes, volatility shifts)
Functors = Structure-preserving mappings between timeframes
Natural Transformations = Coherent changes across multiple market dimensions
The Morphism Detection Engine
The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions:
Morphism Strength = exp(-normalized_change × (3.0 / sensitivity))
Threshold = 0.3 - (sensitivity - 1.0) × 0.15
This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity.
Functorial Analysis Framework
Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws:
Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)|
Functorial Integrity = max(0, 1.0 - average_error)
When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system.
Homotopy Type Theory: Path Equivalence in Markets
The Revolutionary Path Analysis
Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths:
Path Distance = Σ(weight × |normalized_path1 - normalized_path2|)
Homotopy Score = (correlation + 1) / 2 × (1 - average_distance)
Equivalence Threshold = 1 / (threshold × √univalence_strength)
The Univalence Axiom in Trading
The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals.
Universal Properties: The Four Pillars of Market Structure
Category theory's universal properties reveal fundamental market patterns:
Initial Objects (Market Bottoms)
Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object
Market Translation = All selling pressure naturally flows toward the bottom
Detection Algorithm:
Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1)
Signal = strength > 0.4 AND morphism_exists
Terminal Objects (Market Tops)
Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others
Market Translation = All buying pressure naturally flows away from the top
Product Objects (Market Equilibrium)
Mathematical Definition = Universal property combining multiple objects into balanced state
Market Translation = Price, volume, and volatility achieve multi-dimensional balance
Coproduct Objects (Market Divergence)
Mathematical Definition = Universal property representing branching possibilities
Market Translation = Market bifurcation points where multiple scenarios become possible
Consciousness Detection: Emergent Market Intelligence
The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations:
Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension
Fractal Score = log(range_ratio) / log(memory_period)
Multi-Scale Awareness:
Micro = Short-term price-SMA correlations
Meso = Medium-term structural relationships
Macro = Long-term pattern coherence
Volume Sync = Price-volume consciousness
Volatility Awareness = ATR-change correlations
When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses.
Advanced Input System: Precision Configuration
Categorical Universe Parameters
Universe Level (Type_n) = Controls categorical complexity depth
Type 1 = Price only (pure price action)
Type 2 = Price + Volume (market participation)
Type 3 = + Volatility (risk dynamics)
Type 4 = + Momentum (directional force)
Type 5 = + RSI (momentum oscillation)
Sector Optimization:
Crypto = 4-5 (high complexity, volume crucial)
Stocks = 3-4 (moderate complexity, fundamental-driven)
Forex = 2-3 (low complexity, macro-driven)
Morphism Detection Threshold = Golden ratio optimized (φ = 0.618)
Lower values = More morphisms detected, higher sensitivity
Higher values = Only major transformations, noise reduction
Crypto = 0.382-0.618 (high volatility accommodation)
Stocks = 0.618-1.0 (balanced detection)
Forex = 1.0-1.618 (macro-focused)
Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal)
Controls = composition error tolerance
Trending markets = 0.1-0.2 (strict structure preservation)
Ranging markets = 0.2-0.5 (flexible adaptation)
Categorical Memory = Fibonacci sequence optimized
Scalping = 21-34 bars (short-term patterns)
Swing = 55-89 bars (intermediate cycles)
Position = 144-233 bars (long-term structure)
Homotopy Type Theory Parameters
Path Equivalence Threshold = Golden ratio φ = 1.618
Volatile markets = 2.0-2.618 (accommodate noise)
Normal conditions = 1.618 (balanced)
Stable markets = 0.786-1.382 (sensitive detection)
Deformation Complexity = Fibonacci-optimized path smoothing
3,5,8,13,21 = Each number provides different granularity
Higher values = smoother paths but slower computation
Univalence Axiom Strength = φ² = 2.618 (golden ratio squared)
Controls = how readily equivalent structures are identified
Higher values = find more equivalences
Visual System: Mathematical Elegance Meets Practical Clarity
The Morphism Energy Fields (Red/Green Boxes)
Purpose = Visualize categorical transformations in real-time
Algorithm:
Energy Range = ATR × flow_strength × 1.5
Transparency = max(10, base_transparency - 15)
Interpretation:
Green fields = Bullish morphism energy (buying transformations)
Red fields = Bearish morphism energy (selling transformations)
Size = Proportional to transformation strength
Intensity = Reflects morphism confidence
Consciousness Grid (Purple Pattern)
Purpose = Display market self-awareness emergence
Algorithm:
Grid_size = adaptive(lookback_period / 8)
Consciousness_range = ATR × consciousness_level × 1.2
Interpretation:
Density = Higher consciousness = denser grid
Extension = Cloud lookback controls historical depth
Intensity = Transparency reflects awareness level
Homotopy Paths (Blue Gradient Boxes)
Purpose = Show path equivalence opportunities
Algorithm:
Path_range = ATR × homotopy_score × 1.2
Gradient_layers = 3 (increasing transparency)
Interpretation:
Blue boxes = Equivalent path opportunities
Gradient effect = Confidence visualization
Multiple layers = Different probability levels
Functorial Lines (Green Horizontal)
Purpose = Multi-timeframe structure preservation levels
Innovation = Smart spacing prevents overcrowding
Min_separation = price × 0.001 (0.1% minimum)
Max_lines = 3 (clarity preservation)
Features:
Glow effect = Background + foreground lines
Adaptive labels = Only show meaningful separations
Color coding = Green (preserved), Orange (stressed), Red (broken)
Signal System: Bull/Bear Precision
🐂 Initial Objects = Bottom formations with strength percentages
🐻 Terminal Objects = Top formations with confidence levels
⚪ Product/Coproduct = Equilibrium circles with glow effects
Professional Dashboard System
Main Analytics Dashboard (Top-Right)
Market State = Real-time categorical classification
INITIAL OBJECT = Bottom formation active
TERMINAL OBJECT = Top formation active
PRODUCT STATE = Market equilibrium
COPRODUCT STATE = Divergence/bifurcation
ANALYZING = Processing market structure
Universe Type = Current complexity level and components
Morphisms:
ACTIVE (X%) = Transformations detected, percentage shows strength
DORMANT = No significant categorical changes
Functoriality:
PRESERVED (X%) = Structure maintained across timeframes
VIOLATED (X%) = Structure breakdown, instability warning
Homotopy:
DETECTED (X%) = Path equivalences found, arbitrage opportunities
NONE = No equivalent paths currently available
Consciousness:
ACTIVE (X%) = Market self-awareness emerging, major moves possible
EMERGING (X%) = Consciousness building
DORMANT = Mechanical trading only
Signal Monitor & Performance Metrics (Left Panel)
Active Signals Tracking:
INITIAL = Count and current strength of bottom signals
TERMINAL = Count and current strength of top signals
PRODUCT = Equilibrium state occurrences
COPRODUCT = Divergence event tracking
Advanced Performance Metrics:
CCI (Categorical Coherence Index):
CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5)
STRONG (>0.7) = High structural coherence
MODERATE (0.4-0.7) = Adequate coherence
WEAK (<0.4) = Structural instability
HPA (Homotopy Path Alignment):
HPA = max_homotopy_score × functorial_integrity
ALIGNED (>0.6) = Strong path equivalences
PARTIAL (0.3-0.6) = Some equivalences
WEAK (<0.3) = Limited path coherence
UPRR (Universal Property Recognition Rate):
UPRR = (active_objects / 4) × 100%
Percentage of universal properties currently active
TEPF (Transcendence Emergence Probability Factor):
TEPF = homotopy_score × consciousness_level × φ
Probability of consciousness emergence (golden ratio weighted)
MSI (Morphological Stability Index):
MSI = (universe_depth / 5) × functorial_integrity × consciousness_level
Overall system stability assessment
Overall Score = Composite rating (EXCELLENT/GOOD/POOR)
Theory Guide (Bottom-Right)
Educational reference panel explaining:
Objects & Morphisms = Core categorical concepts
Universal Properties = The four fundamental patterns
Dynamic Advice = Context-sensitive trading suggestions based on current market state
Trading Applications: From Theory to Practice
Trend Following with Categorical Structure
Monitor functorial integrity = only trade when structure preserved (>80%)
Wait for morphism energy fields = red/green boxes confirm direction
Use consciousness emergence = purple grids signal major move potential
Exit on functorial breakdown = structure loss indicates trend end
Mean Reversion via Universal Properties
Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes
Confirm with Product states = equilibrium circles show balance points
Watch Coproduct divergence = bifurcation warnings
Scale out at Functorial levels = green lines provide targets
Arbitrage through Homotopy Detection
Blue gradient boxes = indicate path equivalence opportunities
HPA metric >0.6 = confirms strong equivalences
Multiple timeframe convergence = strengthens signal
Consciousness active = amplifies arbitrage potential
Risk Management via Categorical Metrics
Position sizing = Based on MSI (Morphological Stability Index)
Stop placement = Tighter when functorial integrity low
Leverage adjustment = Reduce when consciousness dormant
Portfolio allocation = Increase when CCI strong
Sector-Specific Optimization Strategies
Cryptocurrency Markets
Universe Level = 4-5 (full complexity needed)
Morphism Sensitivity = 0.382-0.618 (accommodate volatility)
Categorical Memory = 55-89 (rapid cycles)
Field Transparency = 1-5 (high visibility needed)
Focus Metrics = TEPF, consciousness emergence
Stock Indices
Universe Level = 3-4 (moderate complexity)
Morphism Sensitivity = 0.618-1.0 (balanced)
Categorical Memory = 89-144 (institutional cycles)
Field Transparency = 5-10 (moderate visibility)
Focus Metrics = CCI, functorial integrity
Forex Markets
Universe Level = 2-3 (macro-driven)
Morphism Sensitivity = 1.0-1.618 (noise reduction)
Categorical Memory = 144-233 (long cycles)
Field Transparency = 10-15 (subtle signals)
Focus Metrics = HPA, universal properties
Commodities
Universe Level = 3-4 (supply/demand dynamics) [/b
Morphism Sensitivity = 0.618-1.0 (seasonal adaptation)
Categorical Memory = 89-144 (seasonal cycles)
Field Transparency = 5-10 (clear visualization)
Focus Metrics = MSI, morphism strength
Development Journey: Mathematical Innovation
The Challenge
Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets.
The Breakthrough
The key insight came from recognizing that market states form a category where:
Price levels, volume conditions, and volatility regimes are objects
Market movements between these states are morphisms
The composition of movements must satisfy categorical laws
This realization led to the morphism detection engine and functorial analysis framework .
Implementation Challenges
Computational Complexity = Category theory calculations are intensive
Real-time Performance = Markets don't wait for mathematical perfection
Visual Clarity = How to display abstract mathematics clearly
Signal Quality = Balancing mathematical purity with practical utility
User Accessibility = Making PhD-level math tradeable
The Solution
After months of optimization, we achieved:
Efficient algorithms = using pre-calculated values and smart caching
Real-time performance = through optimized Pine Script implementation
Elegant visualization = that makes complex theory instantly comprehensible
High-quality signals = with built-in noise reduction and cooldown systems
Professional interface = that guides users through complexity
Advanced Features: Beyond Traditional Analysis
Adaptive Transparency System
Two independent transparency controls:
Field Transparency = Controls morphism fields, consciousness grids, homotopy paths
Signal & Line Transparency = Controls signals and functorial lines independently
This allows perfect visual balance for any market condition or user preference.
Smart Functorial Line Management
Prevents visual clutter through:
Minimum separation logic = Only shows meaningfully separated levels
Maximum line limit = Caps at 3 lines for clarity
Dynamic spacing = Adapts to market volatility
Intelligent labeling = Clear identification without overcrowding
Consciousness Field Innovation
Adaptive grid sizing = Adjusts to lookback period
Gradient transparency = Fades with historical distance
Volume amplification = Responds to market participation
Fractal dimension integration = Shows complexity evolution
Signal Cooldown System
Prevents overtrading through:
20-bar default cooldown = Configurable 5-100 bars
Signal-specific tracking = Independent cooldowns for each signal type
Counter displays = Shows historical signal frequency
Performance metrics = Track signal quality over time
Performance Metrics: Quantifying Excellence
Signal Quality Assessment
Initial Object Accuracy = >78% in trending markets
Terminal Object Precision = >74% in overbought/oversold conditions
Product State Recognition = >82% in ranging markets
Consciousness Prediction = >71% for major moves
Computational Efficiency
Real-time processing = <50ms calculation time
Memory optimization = Efficient array management
Visual performance = Smooth rendering at all timeframes
Scalability = Handles multiple universes simultaneously
User Experience Metrics
Setup time = <5 minutes to productive use
Learning curve = Accessible to intermediate+ traders
Visual clarity = No information overload
Configuration flexibility = 25+ customizable parameters
Risk Disclosure and Best Practices
Important Disclaimers
The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure.
Recommended Usage
Never trade signals in isolation = always use confluence with other analysis
Respect risk management = categorical analysis doesn't eliminate risk
Understand the mathematics = study the theoretical foundation
Start with paper trading = master the concepts before risking capital
Adapt to market regimes = different markets need different parameters
Position Sizing Guidelines
High consciousness periods = Reduce position size (higher volatility)
Strong functorial integrity = Standard position sizing
Morphism dormancy = Consider reduced trading activity
Universal property convergence = Opportunities for larger positions
Educational Resources: Master the Mathematics
Recommended Reading
"Category Theory for the Sciences" = by David Spivak
"Homotopy Type Theory" = by The Univalent Foundations Program
"Fractal Market Analysis" = by Edgar Peters
"The Misbehavior of Markets" = by Benoit Mandelbrot
Key Concepts to Master
Functors and Natural Transformations
Universal Properties and Limits
Homotopy Equivalence and Path Spaces
Type Theory and Univalence
Fractal Geometry in Markets
The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis.
This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide.
Why CMM Dominates
Mathematical Foundation = Built on proven mathematical frameworks
Original Innovation = First application of category theory to markets
Professional Quality = Institution-grade metrics and analysis
Visual Excellence = Clear, elegant, actionable interface
Educational Value = Teaches advanced mathematical concepts
Practical Results = High-quality signals with risk management
Continuous Evolution = Regular updates and enhancements
The DAFE Trading Systems Difference
At DAFE Trading Systems, we don't just create indicators - we advance the science of market analysis. Our team combines:
PhD-level mathematical expertise
Real-world trading experience
Cutting-edge programming skills
Artistic visual design
Educational commitment
The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics.
"In mathematics you don't understand things. You just get used to them." - John von Neumann
"The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems
Trade with Mathematical Precision. Trade with Categorical Market Morphisms.
Created with passion for mathematical excellence, and empowering traders through mathematical innovation.
— Dskyz, Trade with insight. Trade with anticipation.
Price Label Right of Candle by bigbluecheesesimple code that places the last price to the immediate right of the candle/bar
useful if you have labels for other studies making the RHS bid/offer obscured or difficult to monitor
Quarterly Earnings with NPMThis indicator is designed in a way so that it can indicate the quarterly earnings and also it can show us the change in sales and net profit margin as shown by Mark Minervini in his classes.
NIFTY Option Buy Strategy MASTER v1This script is a complete option buying strategy framework for NIFTY, designed for both intraday and positional swing trades.
🔹 Built using multi-timeframe analysis (EMAs, MACD, RSI)
🔹 Combines key macro filters: India VIX, PCR, FII/DII net cash flows
🔹 Supports both Call (CE) and Put (PE) entries
🔹 Includes manual input dashboard for real-time market context
🔹 Trade logic includes:
Bollinger Band breakouts
Volume confirmation
VWAP filtering
EMA crossover + MACD alignment
Resistance/support proximity from option chain (manual)
📈 Smart Trade Management:
Multi-target system (e.g., exit 50% at RR=1, 50% at RR=2)
Trailing stop-loss after target 1 hits
Automatic exit on SL/TP or reverse signals
Visual markers for all entries, exits, and stops
📊 Built-in Dashboard:
Displays India VIX, PCR, FII/DII flows, and S/R levels
Strike price selection (ATM + offset logic)
🧪 Ideal for backtesting, alerts, and real-time execution.
Can be used with alerts + webhook for automated trading or signal generation.
⚠️ Note: This script is for educational purposes only. Always test on paper trading before going live.
Candle Body TableCandle Body Table is a lightweight, easy-to-use indicator that displays a live summary of candle “body strength” across multiple timeframes, along with how much time is left on each candle. Simply choose up to five timeframes (1, 5, 15, 30, and 60 minutes by default), adjust the table’s corner and font size, and you’ll always have a quick, at-a-glance view of:
OC (Body %): The percentage of the candle that’s composed of its body (|open – close| divided by high–low).
Strength: A label (Weak, Balanced, or Strong) based on the body percentage.
Time Left: How many minutes and seconds remain before the current candle closes.
The table updates in real time (using lookahead), coloring each row background green if that timeframe’s current candle is bullish, or red if it’s bearish. That way, you can instantly see which timeframes have strong momentum, which are balanced or weak, and exactly when each candle will finish.
Use Cases
Multi-Timeframe Momentum Check:
If you want to confirm that both your 1m and 5m candles have “Strong” bodies before entering a trade, Candle Body Table shows you that instantly. No more switching back and forth between charts—just glance at the table.
Time-Sensitive Entries/Exits:
Suppose you trade breakouts only at the close of a 5-minute candle. The “Time Left” column counts down so you know exactly when that candle is about to close—down to the second—letting you prepare your order.
Quick Visual Scan:
When markets are choppy, you may want to see which timeframes are weak or balanced rather than diving into each timeframe separately. If the 15m row says “Weak” (small body %), you might avoid taking a trend-following position at that moment.
Session Overlaps & Volatility Windows:
During London/N.Y. overlap or U.S. cash close, traders often check for stronger bodies on higher timeframes (e.g., 30m or 60m). The table immediately highlights if that timeframe’s candle body heats up, indicating increased volatility.
Swing-to-Scalp Transition:
If you typically scalp on 1m but only when the 15m candle is “Strong,” this table gives a green/red cue and a strength label. That makes it easier to wait patiently until multiple timeframes align.
FAQ
Q1. What does “OC” mean, and why is it shown as a percentage?
A1. “OC” stands for Open/Close difference. So it reflects how much of the candle’s total range (high–low) is taken up by its body(open-close). A high OC% means the candle body is large relative to its wick. In other words a strong Bullish/Bearish candle.
Q2. How is “Strength” determined?
A2. The script uses three buckets:
Weak if OC% ≤ 30%
Balanced if 30% < OC% ≤ 55%
Strong if OC% > 55%
This gives you a quick label instead of having to interpret raw percentages every time.
Q3. Why do some rows have a green background and others red?
A3. If close > open (bullish candle), that entire row’s background is shaded green(70%). If close < open (bearish candle), it’s shaded red(70%). If open = close (doji), there’s no background shade. This lets you instantly spot bullish vs. bearish candles across your chosen timeframes.
Q4. Will this repaint?
A4. No. Because each OHLC value is requested with lookahead_on, you see the live developing OHLC. However, once a candle closes, those values are final. The “Time Left” column dynamically changes throughout the bar but does not redraw past values.
Intraday Market State Table IndicatorThis indicator simply show RSI /ADX /HV /Market state for nifty and Banknifty on 5 min chart , with colour changing options , All the tool tips added for users .
DCA Investment Tracker Pro [tradeviZion]DCA Investment Tracker Pro: Educational DCA Analysis Tool
An educational indicator that helps analyze Dollar-Cost Averaging strategies by comparing actual performance with historical data calculations.
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💡 Why I Created This Indicator
As someone who practices Dollar-Cost Averaging, I was frustrated with constantly switching between spreadsheets, calculators, and charts just to understand how my investments were really performing. I wanted to see everything in one place - my actual performance, what I should expect based on historical data, and most importantly, visualize where my strategy could take me over the long term .
What really motivated me was watching friends and family underestimate the incredible power of consistent investing. When Napoleon Bonaparte first learned about compound interest, he reportedly exclaimed "I wonder it has not swallowed the world" - and he was right! Yet most people can't visualize how their $500 monthly contributions today could become substantial wealth decades later.
Traditional DCA tracking tools exist, but they share similar limitations:
Require manual data entry and complex spreadsheets
Use fixed assumptions that don't reflect real market behavior
Can't show future projections overlaid on actual price charts
Lose the visual context of what's happening in the market
Make compound growth feel abstract rather than tangible
I wanted to create something different - a tool that automatically analyzes real market history, detects volatility periods, and shows you both current performance AND educational projections based on historical patterns right on your TradingView charts. As Warren Buffett said: "Someone's sitting in the shade today because someone planted a tree a long time ago." This tool helps you visualize your financial tree growing over time.
This isn't just another calculator - it's a visualization tool that makes the magic of compound growth impossible to ignore.
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🎯 What This Indicator Does
This educational indicator provides DCA analysis tools. Users can input investment scenarios to study:
Theoretical Performance: Educational calculations based on historical return data
Comparative Analysis: Study differences between actual and theoretical scenarios
Historical Projections: Theoretical projections for educational analysis (not predictions)
Performance Metrics: CAGR, ROI, and other analytical metrics for study
Historical Analysis: Calculates historical return data for reference purposes
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🚀 Key Features
Volatility-Adjusted Historical Return Calculation
Analyzes 3-20 years of actual price data for any symbol
Automatically detects high-volatility stocks (meme stocks, growth stocks)
Uses median returns for volatile stocks, standard CAGR for stable stocks
Provides conservative estimates when extreme outlier years are detected
Smart fallback to manual percentages when data insufficient
Customizable Performance Dashboard
Educational DCA performance analysis with compound growth calculations
Customizable table sizing (Tiny to Huge text options)
9 positioning options (Top/Middle/Bottom + Left/Center/Right)
Theme-adaptive colors (automatically adjusts to dark/light mode)
Multiple display layout options
Future Projection System
Visual future growth projections
Timeframe-aware calculations (Daily/Weekly/Monthly charts)
1-30 year projection options
Shows projected portfolio value and total investment amounts
Investment Insights
Performance vs benchmark comparison
ROI from initial investment tracking
Monthly average return analysis
Investment milestone alerts (25%, 50%, 100% gains)
Contribution tracking and next milestone indicators
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📊 Step-by-Step Setup Guide
1. Investment Settings 💰
Initial Investment: Enter your starting lump sum (e.g., $60,000)
Monthly Contribution: Set your regular DCA amount (e.g., $500/month)
Return Calculation: Choose "Auto (Stock History)" for real data or "Manual" for fixed %
Historical Period: Select 3-20 years for auto calculations (default: 10 years)
Start Year: When you began investing (e.g., 2020)
Current Portfolio Value: Your actual portfolio worth today (e.g., $150,000)
2. Display Settings 📊
Table Sizes: Choose from Tiny, Small, Normal, Large, or Huge
Table Positions: 9 options - Top/Middle/Bottom + Left/Center/Right
Visibility Toggles: Show/hide Main Table and Stats Table independently
3. Future Projection 🔮
Enable Projections: Toggle on to see future growth visualization
Projection Years: Set 1-30 years ahead for analysis
Live Example - NASDAQ:META Analysis:
Settings shown: $60K initial + $500/month + Auto calculation + 10-year history + 2020 start + $150K current value
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🔬 Pine Script Code Examples
Core DCA Calculations:
// Calculate total invested over time
months_elapsed = (year - start_year) * 12 + month - 1
total_invested = initial_investment + (monthly_contribution * months_elapsed)
// Compound growth formula for initial investment
theoretical_initial_growth = initial_investment * math.pow(1 + annual_return, years_elapsed)
// Future Value of Annuity for monthly contributions
monthly_rate = annual_return / 12
fv_contributions = monthly_contribution * ((math.pow(1 + monthly_rate, months_elapsed) - 1) / monthly_rate)
// Total expected value
theoretical_total = theoretical_initial_growth + fv_contributions
Volatility Detection Logic:
// Detect extreme years for volatility adjustment
extreme_years = 0
for i = 1 to historical_years
yearly_return = ((price_current / price_i_years_ago) - 1) * 100
if yearly_return > 100 or yearly_return < -50
extreme_years += 1
// Use median approach for high volatility stocks
high_volatility = (extreme_years / historical_years) > 0.2
calculated_return = high_volatility ? median_of_returns : standard_cagr
Performance Metrics:
// Calculate key performance indicators
absolute_gain = actual_value - total_invested
total_return_pct = (absolute_gain / total_invested) * 100
roi_initial = ((actual_value - initial_investment) / initial_investment) * 100
cagr = (math.pow(actual_value / initial_investment, 1 / years_elapsed) - 1) * 100
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📊 Real-World Examples
See the indicator in action across different investment types:
Stable Index Investments:
AMEX:SPY (SPDR S&P 500) - Shows steady compound growth with standard CAGR calculations
Classic DCA success story: $60K initial + $500/month starting 2020. The indicator shows SPY's historical 10%+ returns, demonstrating how consistent broad market investing builds wealth over time. Notice the smooth theoretical growth line vs actual performance tracking.
MIL:VUAA (Vanguard S&P 500 UCITS) - Shows both data limitation and solution approaches
Data limitation example: VUAA shows "Manual (Auto Failed)" and "No Data" when default 10-year historical setting exceeds available data. The indicator gracefully falls back to manual percentage input while maintaining all DCA calculations and projections.
MIL:VUAA (Vanguard S&P 500 UCITS) - European ETF with successful 5-year auto calculation
Solution demonstration: By adjusting historical period to 5 years (matching available data), VUAA auto calculation works perfectly. Shows how users can optimize settings for newer assets. European market exposure with EUR denomination, demonstrating DCA effectiveness across different markets and currencies.
NYSE:BRK.B (Berkshire Hathaway) - Quality value investment with Warren Buffett's proven track record
Value investing approach: Berkshire Hathaway's legendary performance through DCA lens. The indicator demonstrates how quality companies compound wealth over decades. Lower volatility than tech stocks = standard CAGR calculations used.
High-Volatility Growth Stocks:
NASDAQ:NVDA (NVIDIA Corporation) - Demonstrates volatility-adjusted calculations for extreme price swings
High-volatility example: NVIDIA's explosive AI boom creates extreme years that trigger volatility detection. The indicator automatically switches to "Median (High Vol): 50%" calculations for conservative projections, protecting against unrealistic future estimates based on outlier performance periods.
NASDAQ:TSLA (Tesla) - Shows how 10-year analysis can stabilize volatile tech stocks
Stable long-term growth: Despite Tesla's reputation for volatility, the 10-year historical analysis (34.8% CAGR) shows consistent enough performance that volatility detection doesn't trigger. Demonstrates how longer timeframes can smooth out extreme periods for more reliable projections.
NASDAQ:META (Meta Platforms) - Shows stable tech stock analysis using standard CAGR calculations
Tech stock with stable growth: Despite being a tech stock and experiencing the 2022 crash, META's 10-year history shows consistent enough performance (23.98% CAGR) that volatility detection doesn't trigger. The indicator uses standard CAGR calculations, demonstrating how not all tech stocks require conservative median adjustments.
Notice how the indicator automatically detects high-volatility periods and switches to median-based calculations for more conservative projections, while stable investments use standard CAGR methods.
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📈 Performance Metrics Explained
Current Portfolio Value: Your actual investment worth today
Expected Value: What you should have based on historical returns (Auto) or your target return (Manual)
Total Invested: Your actual money invested (initial + all monthly contributions)
Total Gains/Loss: Absolute dollar difference between current value and total invested
Total Return %: Percentage gain/loss on your total invested amount
ROI from Initial Investment: How your starting lump sum has performed
CAGR: Compound Annual Growth Rate of your initial investment (Note: This shows initial investment performance, not full DCA strategy)
vs Benchmark: How you're performing compared to the expected returns
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⚠️ Important Notes & Limitations
Data Requirements: Auto mode requires sufficient historical data (minimum 3 years recommended)
CAGR Limitation: CAGR calculation is based on initial investment growth only, not the complete DCA strategy
Projection Accuracy: Future projections are theoretical and based on historical returns - actual results may vary
Timeframe Support: Works ONLY on Daily (1D), Weekly (1W), and Monthly (1M) charts - no other timeframes supported
Update Frequency: Update "Current Portfolio Value" regularly for accurate tracking
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📚 Educational Use & Disclaimer
This analysis tool can be applied to various stock and ETF charts for educational study of DCA mathematical concepts and historical performance patterns.
Study Examples: Can be used with symbols like AMEX:SPY , NASDAQ:QQQ , AMEX:VTI , NASDAQ:AAPL , NASDAQ:MSFT , NASDAQ:GOOGL , NASDAQ:AMZN , NASDAQ:TSLA , NASDAQ:NVDA for learning purposes.
EDUCATIONAL DISCLAIMER: This indicator is a study tool for analyzing Dollar-Cost Averaging strategies. It does not provide investment advice, trading signals, or guarantees. All calculations are theoretical examples for educational purposes only. Past performance does not predict future results. Users should conduct their own research and consult qualified financial professionals before making any investment decisions.
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© 2025 TradeVizion. All rights reserved.
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment
## 1. Introduction
Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985).
The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014).
## 2. Theoretical Foundation
### 2.1 Momentum Theory and Market Anomalies
The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015).
### 2.2 Drawdown Analysis and Risk Management
Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators.
### 2.3 Adaptive Smoothing and Market Regimes
The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets.
## 3. Methodology
### 3.1 Core Algorithm Design
The RAMO algorithm consists of several interconnected components:
#### 3.1.1 Risk-Adjusted Momentum Calculation
The fundamental innovation of RAMO lies in its risk adjustment mechanism:
Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor)
Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05)
This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952).
#### 3.1.2 Multi-Algorithm Momentum Framework
RAMO supports three distinct momentum calculation methods:
1. Rate of Change: Traditional percentage-based momentum (Pring, 2002)
2. Price Momentum: Absolute price differences
3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997)
This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013).
### 3.2 Leading Indicator Components
#### 3.2.1 Momentum Acceleration Analysis
The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes:
Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n)
This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969).
#### 3.2.2 Linear Regression Prediction
RAMO incorporates linear regression-based prediction to project momentum values forward:
Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset)
This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes.
#### 3.2.3 Volume-Based Exhaustion Detection
The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns:
Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20)
This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989).
### 3.3 Statistical Normalization and Robustness
RAMO employs Z-score normalization with outlier protection to ensure statistical robustness:
Z_Score = (Value - Mean) / Standard_Deviation
Normalized_Value = max(-3.5, min(3.5, Z_Score))
This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions.
### 3.4 Adaptive Threshold Calculation
Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992):
Upper_Threshold = Mean + (Multiplier × Standard_Deviation)
Lower_Threshold = Mean - (Multiplier × Standard_Deviation)
This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992).
## 4. Implementation Details
### 4.1 Adaptive Smoothing Algorithm
The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility:
Volatility_Percentile = Percentrank(Volatility, 100)
Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100)
This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995).
### 4.2 Risk Environment Classification
RAMO classifies market conditions into three risk environments:
- Low Risk: Current_DD < 30% × Max_DD
- Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD
- High Risk: Current_DD ≥ 70% × Max_DD
This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014).
## 5. Signal Generation and Interpretation
### 5.1 Entry Signal Logic
RAMO generates enhanced entry signals through multiple confirmation layers:
1. Primary Signal: Crossover between indicator and signal line
2. Risk Filter: Confirmation of favorable risk environment for long positions
3. Leading Component: Early warning signals via acceleration analysis
4. Exhaustion Filter: Volume-based reversal detection
This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992).
### 5.2 Divergence Analysis
RAMO incorporates both traditional and leading divergence detection:
- Traditional Divergence: Price and indicator divergence over 3-5 periods
- Slope Divergence: Momentum slope versus price direction
- Acceleration Divergence: Changes in momentum acceleration
This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999).
## 6. Empirical Advantages and Applications
### 6.1 Risk-Adjusted Performance
The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980).
### 6.2 Regime Awareness
RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments.
### 6.3 Institutional Applications
The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013).
## 7. Limitations and Future Research
### 7.1 Parameter Sensitivity
Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions.
### 7.2 Market Microstructure Considerations
RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance.
### 7.3 Transaction Cost Integration
Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004).
## References
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Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press.
Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin.
Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985.
Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764.
Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press.
Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58.
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247.
Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84.
Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall.
Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing.
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Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291.
Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill.
Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082.
Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91.
Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance.
Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library.
Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill.
Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press.
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790.
Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314.
Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239.
Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
