Oops ReversalThis indicator identifies bullish Oops reversal days — a classic setup refined for stronger confirmation and early detection.
✅ Criteria for Signal (on current day):
Gap Down Open:
The stock opens below the previous day's low, suggesting initial weakness.
Shallow Intraday Dip:
Price doesn't fall more than 1% below the open, indicating the sell-off lacks follow-through.
Strong Reversal:
During the same day, the stock rallies and crosses the previous day's low by at least 1%, confirming strength and buyer control.
Indicators and strategies
Internal Pivot Pattern [LuxAlgo]The Internal Pivot Pattern indicator is a novel method allowing traders to detect pivots without excessive delay on the chart timeframe, by using the lower timeframe data from a candle.
It features custom colors for candles and zigzag lines to help identify trends. A dashboard showing the accuracy of the pattern is also included.
🔶 USAGE
We define a pivot as the occurrence where the middle candle over a specific interval (for example, the most recent 21 bars) is the highest (pivot high) or the lowest (pivot low). This method commonly allows for identifying swing highs/lows on a trader's chart; however, this pattern can only be identified after a specific number of bars has been formed, rendering this pattern useless for real-time detection of swing highs/lows.
This indicator uses a different approach, removing the need to wait for candles to form on the user chart; instead, we check the lower timeframe data of the current candle and evaluate for the presence of a pivot given the internal data, effectively providing pivot confirmation at the candle close.
An internal pivot low pattern is indicative of a potential uptrend, while an internal pivot high is indicative of a potential downtrend.
Candles are colored based on the last internal pivot detected, with blue candle colors indicating that the most recent internal pivot is a pivot low, indicating an uptrend, while an orange candle color indicates that the most recent internal pivot is a pivot high, indicating a downtrend.
🔹 Timeframes
The timeframe setting allows controlling the amount of lower timeframe data to consider for the internal pivot detection. This setting must be lower than the user's chart timeframe.
Using a timeframe significantly lower than the user chart timeframe will evaluate a larger amount of data for the pivot detection, making it less frequent, while using a timeframe closer to the chart timeframe can make the internal pivot detection more frequent, and more prone to false positives.
🔹 Accuracy Dashboard
The Accuracy Dashboard allows evaluating how accurate the detected patterns are as a percentage, with a pattern being judged accurate if subsequent patterns are detected higher or lower than a previous one.
For example, an internal pivot low is judged accurate if the following internal pivot is higher than it, indicating that higher highs have been made.
This dashboard can be useful to determine the timeframe setting to maximize the respective internal pivot accuracy.
🔶 SETTINGS
Timeframe: Timeframe for detecting internal swings
Accuracy Dashboard: Enable or disable the Accuracy Dashboard.
🔹 Style
Internal Pivot High: Color of the dot displayed upon the detection of an internal pivot high
Internal Pivot Low: Color of the dot displayed upon the detection of an internal pivot low
Zig-Zag: Color of the zig-zag segments connecting each internal pivot
Candles: Enable candle coloring, with control over the color of the candles highlighting the detected trend
RED E Support & ResistanceThe “RED-E Support & Resistance” indicator is designed to assist traders in visualizing key levels of support and resistance on a chart by employing ATR (Average True Range) to create dynamic horizontal zones. This indicator automatically plots robust support and resistance bands that can help identify potential areas where price may reverse, consolidate, or react. These levels are particularly beneficial for traders who employ concepts like Smart Money analysis, as they illustrate zones where institutional trading activity might occur.
How It Works:
• The indicator uses ATR-based calculations to determine the placement of the support and resistance zones. This approach accounts for market volatility, making the zones adaptive to changing conditions.
• The Zone Thickness parameter allows users to customize the width of the plotted zones, enhancing visibility and fitting them to their specific trading style.
• The support and resistance zones extend horizontally across the chart, providing clear reference points for potential price reactions.
Practical Application:
• Trend Analysis: Identify areas of significant price resistance and support to understand potential turning points or trends in the market.
• Risk Management: Use these zones to better inform stop-loss placements or set profit targets.
• Confirmation Tool: Combine the indicator with other technical analysis tools for confirmation of potential trade entries or exits.
Customization Options:
• Change the colors of the support and resistance zones for better integration with different chart themes.
• Adjust the ATR Length and Multiplier to fine-tune the sensitivity of the zones based on personal preferences and the characteristics of the asset being analyzed.
Disclaimer:
This indicator is for educational and informational purposes only. It is not intended to serve as investment advice or a recommendation to buy or sell any financial instrument. Always perform your own research and consider consulting with a financial professional before making trading decisions. Trading involves significant risk, and past performance does not guarantee future results.
3 hours ago
Release Notes
The “RED-E Support & Resistance” indicator is designed to assist traders in visualizing key levels of support and resistance on a chart by employing ATR (Average True Range) to create dynamic horizontal zones. This indicator automatically plots robust support and resistance bands that can help identify potential areas where price may reverse, consolidate, or react. These levels are particularly beneficial for traders who employ concepts like Smart Money analysis, as they illustrate zones where institutional trading activity might occur.
How It Works:
• The indicator uses ATR-based calculations to determine the placement of the support and resistance zones. This approach accounts for market volatility, making the zones adaptive to changing conditions.
• The Zone Thickness parameter allows users to customize the width of the plotted zones, enhancing visibility and fitting them to their specific trading style.
• The support and resistance zones extend horizontally across the chart, providing clear reference points for potential price reactions.
Practical Application:
• Trend Analysis: Identify areas of significant price resistance and support to understand potential turning points or trends in the market.
• Risk Management: Use these zones to better inform stop-loss placements or set profit targets.
• Confirmation Tool: Combine the indicator with other technical analysis tools for confirmation of potential trade entries or exits.
Customization Options:
• Change the colors of the support and resistance zones for better integration with different chart themes.
• Adjust the ATR Length and Multiplier to fine-tune the sensitivity of the zones based on personal preferences and the characteristics of the asset being analyzed.
Disclaimer:
This indicator is for educational and informational purposes only. It is not intended to serve as investment advice or a recommendation to buy or sell any financial instrument. Always perform your own research and consider consulting with a financial professional before making trading decisions. Trading involves significant risk, and past performance does not guarantee future results.
GTrader-ICT All In One-Comumnity VersionMeet the **GTrader-ICT All In One **, a comprehensive toolkit designed to integrate key Inner Circle Trader (ICT) concepts directly onto your chart. This powerful overlay indicator consolidates multiple essential tools, streamlining your technical analysis and helping you identify key temporal and price-based events.
📚 References & Inspiration
This indicator stands on the shoulders of giants. With the help of **tradeforopp** and **LuxAlgo**. The concepts and some implementation details were referenced from the following excellent, publicly available scripts:
ICT Killzones: The session drawing and pivot logic is adapted from tradeforopp
ICT Macros: The macro detection and plotting functionality is inspired by the work of Lux Algo , particularly their widely-used indicators covering ICT concepts.
🎯 Core Features
* **ICT Killzones:** Visualize critical trading sessions with customizable boxes. You can easily toggle and style the **Asia**, **London**, and **New York (AM, Lunch, PM)** sessions to focus on the liquidity and volatility that matter most to your strategy.
* Fully customizable session times and colors.
* Timezone support to align sessions with your local or preferred trading time (defaults to `America/New_York`).
* **ICT Macros:** Automatically identify and plot specific, short-duration time windows where institutional algorithms are known to be active (e.g., `09:50-10:10`, `14:50-15:10`, etc.).
* Plots the high/low range of the macro, providing clear levels of interest.
* Utilizes 1-minute data for precision, even when viewing on 3-minute or 5-minute charts.
📚 Optimization over the other original indicators
We add the custom input for macros session, users just need to input the from/to hour: minute format, and they will be converted into session objects in pinescript
The macro draws function is optimized, removing redundant draws, leading to better performance
Add "Distance from Macro Line to Chart" option
Add "Session Drawings Limit" for better performance
⚠️ Notes on TradingView Warnings
You may encounter some warnings from TradingView when using this script. These are generally expected due to the script's advanced, event-driven nature:
1. **Function Call Consistency:** The function 'box.new' should be called on each calculation for consistency, which may appear. This happens because drawing elements (like session boxes) are intentionally created only on the *first bar* of a new session, not on every single bar. This is a necessary design choice for performance and to prevent duplicate drawings.
2. **Potential for Repainting/Slow Load:** The **Macro** feature uses the `request.security_lower_tf()` function to get accurate 1-minute data. This can trigger warnings about performance or slow loading times. This is a known trade-off for achieving the precision required for the feature.
Silver BulletSilver Bullet is a trading tool built for finding cleaner, higher-probability setups. It focuses on key windows of market movement and adds helpful tools like daily range levels and candlestick patterns.
Whether you’re trading breakouts or reversals, Silver Bullet gives you a clearer view of the market and more confidence in your setups.
⸻
🔹 Trading Setup #1: Macro Time
The Macro Time setting offers two modes: Macro Bullet and Silver Bullet. Both help traders focus on specific times when the market tends to deliver clean moves.
• Macro Bullet is based on the high and low of a full macro session. It automatically detects the session’s range and bias, then offers optimal entries for either Long or Short setups. Once the session resolves, it provides Fibonacci-based levels for entry, target, and stop loss.
• Silver Bullet is based on ICT concepts and focuses on the hourly range for London, NY AM, and NY PM sessions. It’s designed for quick time blocks and highlights key levels as the session unfolds.
To use this setup, set Macro Time to “ICT Sessions” and select your preferred mode under Bullet Mode.
⸻
🔹 Trading Setup #2: Daily Range
Enable Daily Range to draw Fibonacci levels based on either the previous day’s candle or the current day’s developing range. These levels help you identify potential support, resistance, and midpoint zones throughout the day.
With the current day’s range, levels automatically update in real time as new highs or lows form — keeping your chart aligned with evolving price action.
⸻
🔹 Trading Setup #3: Candlestick Patterns
Turn on Candlestick Patterns to automatically highlight clean reversal signals such as Hammers, Hanging Men, Shooting Stars, and Tweezers. Each pattern is detected using specific criteria and trend filters to reduce noise and improve reliability. They work especially well as confirmation signals around key levels or session zones.
Silver Bullet brings structure, clarity, and precision to your intraday trading. By combining time-based bias, price action levels, and pattern recognition, it helps you trade with purpose — not guesswork. Use one setup or combine all three for a complete view of the market, tailored to your style and session of choice.
FEDFUNDS Rate Divergence Oscillator [BackQuant]FEDFUNDS Rate Divergence Oscillator
1. Concept and Rationale
The United States Federal Funds Rate is the anchor around which global dollar liquidity and risk-free yield expectations revolve. When the Fed hikes, borrowing costs rise, liquidity tightens and most risk assets encounter head-winds. When it cuts, liquidity expands, speculative appetite often recovers. Bitcoin, a 24-hour permissionless asset sometimes described as “digital gold with venture-capital-like convexity,” is particularly sensitive to macro-liquidity swings.
The FED Divergence Oscillator quantifies the behavioural gap between short-term monetary policy (proxied by the effective Fed Funds Rate) and Bitcoin’s own percentage price change. By converting each series into identical rate-of-change units, subtracting them, then optionally smoothing the result, the script produces a single bounded-yet-dynamic line that tells you, at a glance, whether Bitcoin is outperforming or underperforming the policy backdrop—and by how much.
2. Data Pipeline
• Fed Funds Rate – Pulled directly from the FRED database via the ticker “FRED:FEDFUNDS,” sampled at daily frequency to synchronise with crypto closes.
• Bitcoin Price – By default the script forces a daily timeframe so that both series share time alignment, although you can disable that and plot the oscillator on intraday charts if you prefer.
• User Source Flexibility – The BTC series is not hard-wired; you can select any exchange-specific symbol or even swap BTC for another crypto or risk asset whose interaction with the Fed rate you wish to study.
3. Math under the Hood
(1) Rate of Change (ROC) – Both the Fed rate and BTC close are converted to percent return over a user-chosen lookback (default 30 bars). This means a cut from 5.25 percent to 5.00 percent feeds in as –4.76 percent, while a climb from 25 000 to 30 000 USD in BTC over the same window converts to +20 percent.
(2) Divergence Construction – The script subtracts the Fed ROC from the BTC ROC. Positive values show BTC appreciating faster than policy is tightening (or falling slower than the rate is cutting); negative values show the opposite.
(3) Optional Smoothing – Macro series are noisy. Toggle “Apply Smoothing” to calm the line with your preferred moving-average flavour: SMA, EMA, DEMA, TEMA, RMA, WMA or Hull. The default EMA-25 removes day-to-day whips while keeping turning points alive.
(4) Dynamic Colour Mapping – Rather than using a single hue, the oscillator line employs a gradient where deep greens represent strong bullish divergence and dark reds flag sharp bearish divergence. This heat-map approach lets you gauge intensity without squinting at numbers.
(5) Threshold Grid – Five horizontal guides create a structured regime map:
• Lower Extreme (–50 pct) and Upper Extreme (+50 pct) identify panic capitulations and euphoria blow-offs.
• Oversold (–20 pct) and Overbought (+20 pct) act as early warning alarms.
• Zero Line demarcates neutral alignment.
4. Chart Furniture and User Interface
• Oscillator fill with a secondary DEMA-30 “shader” offers depth perception: fat ribbons often precede high-volatility macro shifts.
• Optional bar-colouring paints candles green when the oscillator is above zero and red below, handy for visual correlation.
• Background tints when the line breaches extreme zones, making macro inflection weeks pop out in the replay bar.
• Everything—line width, thresholds, colours—can be customised so the indicator blends into any template.
5. Interpretation Guide
Macro Liquidity Pulse
• When the oscillator spends weeks above +20 while the Fed is still raising rates, Bitcoin is signalling liquidity tolerance or an anticipatory pivot view. That condition often marks the embryonic phase of major bull cycles (e.g., March 2020 rebound).
• Sustained prints below –20 while the Fed is already dovish indicate risk aversion or idiosyncratic crypto stress—think exchange scandals or broad flight to safety.
Regime Transition Signals
• Bullish cross through zero after a long sub-zero stint shows Bitcoin regaining upward escape velocity versus policy.
• Bearish cross under zero during a hiking cycle tells you monetary tightening has finally started to bite.
Momentum Exhaustion and Mean-Reversion
• Touches of +50 (or –50) come rarely; they are statistically stretched events. Fade strategies either taking profits or hedging have historically enjoyed positive expectancy.
• Inside-bar candlestick patterns or lower-timeframe bearish engulfings simultaneously with an extreme overbought print make high-probability short scalp setups, especially near weekly resistance. The same logic mirrors for oversold.
Pair Trading / Relative Value
• Combine the oscillator with spreads like BTC versus Nasdaq 100. When both the FED Divergence oscillator and the BTC–NDQ relative-strength line roll south together, the cross-asset confirmation amplifies conviction in a mean-reversion short.
• Swap BTC for miners, altcoins or high-beta equities to test who is the divergence leader.
Event-Driven Tactics
• FOMC days: plot the oscillator on an hourly chart (disable ‘Force Daily TF’). Watch for micro-structural spikes that resolve in the first hour after the statement; rapid flips across zero can front-run post-FOMC swings.
• CPI and NFP prints: extremes reached into the release often mean positioning is one-sided. A reversion toward neutral in the first 24 hours is common.
6. Alerts Suite
Pre-bundled conditions let you automate workflows:
• Bullish / Bearish zero crosses – queue spot or futures entries.
• Standard OB / OS – notify for first contact with actionable zones.
• Extreme OB / OS – prime time to review hedges, take profits or build contrarian swing positions.
7. Parameter Playground
• Shorten ROC Lookback to 14 for tactical traders; lengthen to 90 for macro investors.
• Raise extreme thresholds (for example ±80) when plotting on altcoins that exhibit higher volatility than BTC.
• Try HMA smoothing for responsive yet smooth curves on intraday charts.
• Colour-blind users can easily swap bull and bear palette selections for preferred contrasts.
8. Limitations and Best Practices
• The Fed Funds series is step-wise; it only changes on meeting days. Rapid BTC oscillations in between may dominate the calculation. Keep that perspective when interpreting very high-frequency signals.
• Divergence does not equal causation. Crypto-native catalysts (ETF approvals, hack headlines) can overwhelm macro links temporarily.
• Use in conjunction with classical confirmation tools—order-flow footprints, market-profile ledges, or simple price action to avoid “pure-indicator” traps.
9. Final Thoughts
The FEDFUNDS Rate Divergence Oscillator distills an entire macro narrative monetary policy versus risk sentiment into a single colourful heartbeat. It will not magically predict every pivot, yet it excels at framing market context, spotting stretches and timing regime changes. Treat it as a strategic compass rather than a tactical sniper scope, combine it with sound risk management and multi-factor confirmation, and you will possess a robust edge anchored in the world’s most influential interest-rate benchmark.
Trade consciously, stay adaptive, and let the policy-price tension guide your roadmap.
High Probability Buy/Sell with SL & TP High-accuracy Buy/Sell signals with dynamic SL & Target—perfect for scalpers and swing traders,Smart trading signals with built-in risk management. Never miss a move.Auto Buy/Sell entries with real-time SL & TP levels—trade with confidence.Turn signals into strategy. Precision entries, clear exits.Your all-in-one trading assistant: entry, stop loss, and take profit—automated.Built for serious traders: Clean signals, sharp exits, and solid risk-reward.
Smart Order Blocks [Pro Version]Here’s a **clear, detailed "How It Works" explanation** for this indicator:
---
## ✅ **Smart Order Blocks \ – How It Works**
### **Purpose**
This indicator detects **Order Blocks (OBs)** based on **pivot highs and lows**, and automatically marks **Bullish** and **Bearish OB zones** on the chart with optional extensions and alerts. It is designed to help traders identify **institutional price levels** where liquidity is often engineered for future price moves.
---
### **Customization Options**
✔ **Source** → Choose between Wicks or Bodies for OB calculation.
✔ **Pivot Settings** → Adjust sensitivity for detecting pivots.
✔ **Extend OBs** → Keep zones visible until tapped, or fix a specific width.
✔ **Show Labels** → Displays OB type and strength on chart.
✔ **Colors** → Configure Bullish, Bearish, and Invalid OB colors.
---
### **Practical Usage**
* **Entry Strategy**:
* Wait for price to **revisit a Bullish OB** in an uptrend → Long entry.
* Wait for price to **revisit a Bearish OB** in a downtrend → Short entry.
* Combine with:
* **Market Structure (HH/HL or LH/LL)**.
* **Confirmation signals** (e.g., candlestick pattern, break of structure).
* **Risk Management** → Stop loss outside OB zone.
---
### ✅ **Summary in One Sentence**
The indicator automatically identifies **institutional OB zones**, shows their strength, extends them until mitigated, and alerts you when price interacts with these key liquidity levels, helping you trade like Smart Money.
---
FunctionADFLibrary "FunctionADF"
Augmented Dickey-Fuller test (ADF), The ADF test is a statistical method used to assess whether a time series is stationary – meaning its statistical properties (like mean and variance) do not change over time. A time series with a unit root is considered non-stationary and often exhibits non-mean-reverting behavior, which is a key concept in technical analysis.
Reference:
-
- rtmath.net
- en.wikipedia.org
adftest(data, n_lag, conf)
: Augmented Dickey-Fuller test for stationarity.
Parameters:
data (array) : Data series.
n_lag (int) : Maximum lag.
conf (string) : Confidence Probability level used to test for critical value, (`90%`, `95%`, `99%`).
Returns: `adf` The test statistic. \
`crit` Critical value for the test statistic at the 10 % levels. \
`nobs` Number of observations used for the ADF regression and calculation of the critical values.
Volume Peak BoxTH Description
Volume Peak Box
อินดิเคเตอร์นี้ใช้ตรวจจับช่วงที่มี Volume สูงผิดปกติ โดยใช้ Bollinger Band กับข้อมูล Volume ที่ดึงจาก Timeframe ที่ล็อกไว้ (เช่น 1 ชั่วโมง) และจะแสดงผลในรูปแบบ กล่องครอบช่วงราคาสูง–ต่ำ ของช่วง Volume Peak นั้น
🔧 วิธีทำงาน:
คำนวณ Bollinger Band จาก Volume ของ Timeframe ที่กำหนด
ถ้า Volume สูงกว่า Upper Band → ถือว่าเป็น Volume Peak
วาดกล่องครอบ High–Low ของแท่งที่อยู่ในช่วง Volume Peak
กล่องจะแสดงบนทุก Timeframe แต่ใช้ข้อมูลจาก Timeframe ที่ล็อกไว้เท่านั้น
🧠 เหมาะสำหรับการดู:
โซน Breakout
การเคลื่อนไหวของสถาบัน
ความไม่สมดุลของอุปสงค์/อุปทาน
เหมาะมากหากใช้ร่วมกับการอ่านพฤติกรรมราคาใน Timeframe ย่อย เพื่อดูปฏิกิริยาราคาต่อแรง Volume จาก Timeframe ใหญ่
________________
ENG Description
Volume Peak Box
This indicator detects volume spikes based on Bollinger Bands applied to volume from a locked timeframe (e.g. 1H), and draws a box around the price range during those peak periods.
🔧 How it works:
Calculates Bollinger Bands on volume from the selected timeframe.
If volume exceeds the upper band, it is marked as a volume peak.
When a volume peak starts and ends, the indicator draws a box covering the high–low price range during that period.
These boxes remain visible on all timeframes, but always reflect data from the locked timeframe.
🧠 Great for identifying:
Breakout zones
Institutional activity
Supply/demand imbalances
Tip: Use with lower timeframe price action to see how the market reacts near volume peaks from higher timeframes.
Uptrend Strength Checklist DashboardThe Uptrend Strength Checklist Dashboard is a powerful visual tool designed to help traders quickly evaluate the strength and quality of an uptrend using a combination of 20 widely-used technical conditions. It displays a clean, color-coded dashboard directly on the chart, summarizing key trend indicators in real-time.
🧠 What It Does:
This script checks 20 bullish criteria across different categories—momentum, trend alignment, volume, and price action. Each condition is scored individually and shown in a dashboard with checkmarks ✅ (condition met) or ❌ (condition not met).
The total score out of 20 is then used to interpret the trend strength into 4 levels:
🔥 Very Strong Uptrend (18–20 points)
👍 Strong Uptrend (14–17 points)
🤔 Possible Uptrend Forming (8–13 points)
📉 Weak or No Uptrend (0–7 points)
📋 Checklist Criteria Includes:
Price above short/medium/long EMAs (7, 20, 50, 200)
EMAs stacked in bullish order
MACD Line & Histogram
RSI > 50 and ROC > 0
ADX > 25 and +DI > -DI
OBV trend and Bullish Volume Dominance
Price above Ichimoku Cloud, Tenkan > Kijun
Parabolic SAR bullish signal
Williams Alligator confirmation
Price > Bollinger Band Midline
Price > Previous Week’s High
🌐 Multilingual Support:
Supports both English and Arabic (العربية) language options, with all labels, tooltips, and trend messages dynamically translated based on user selection.
🎨 Customization Options:
Choose table position and size on chart
Customize all trend and table colors
Adjust all indicator input lengths to suit your strategy
✅ Perfect For:
Trend-following traders
Swing and position traders
Technical analysts looking for a structured signal confirmation tool
🔔 Note: This indicator does not generate buy/sell signals on its own but provides a visual checklist to help confirm the strength of an uptrend. Use it in conjunction with your entry/exit strategy and risk management rules.
GCM Volatility-Adaptive Trend ChannelScript Description
Name: GCM Volatility-Adaptive Trend Channel (GCM VATC)
Overview
The GCM Volatility-Adaptive Trend Channel (VATC) is a comprehensive trading tool that merges the low-lag, smooth-trending capabilities of the Jurik Moving Average (JMA) with the classic volatility analysis of Bollinger Bands (BB).
By displaying both trend and volatility in a single, intuitive interface, this indicator aims to help traders see when a trend is stable versus when it's becoming volatile and might be poised for a change.
Core Components:
JMA Trend System: At its core are three dynamically colored JMA lines (Baseline, Fast, and Slow) that provide a clear view of trend direction. The lines change color based on their slope, offering immediate visual feedback on momentum. A colored ribbon between the Baseline and Fast JMA visualizes shorter-term momentum shifts.
Standard Bollinger Bands: Layered on top are standard Bollinger Bands. Calculated from the price, these bands serve as a classic measure of market volatility. They help identify periods where the market is expanding (high volatility) or contracting (low volatility).
How to Use It
By combining these two powerful concepts, this indicator provides a unified view of both trend and volatility. It can help traders to:
Identify the primary trend direction using the smooth JMA lines.
Gauge the strength and stability of that trend.
See when the market is becoming volatile (bands widening) or consolidating (bands contracting), which can often precede a significant price move or a change in trend.
A Note on Originality & House Rules Compliance
This indicator does not introduce a new mathematical formula. Instead, its strength lies in the thoughtful combination of two well-respected, publicly available concepts: the Jurik Moving Average and Bollinger Bands. The JMA implementation is a standard public version. The goal was to create a practical, all-in-one tool for trend and volatility analysis.
This script is published as fully open-source in compliance with TradingView's House Rules. It utilizes standard, publicly available algorithms and does not contain any protected or hidden code.
Settings
All lengths, sources, and colors for the JMA lines and Bollinger Bands are fully customizable in the settings menu, allowing you to tailor the indicator to your specific trading style and asset.
I hope with this indicator Traders even Beginner can can control their emotions which increase the probabilities of the winning rates and cutting the losing strength
Purposely I Didn't plant the High low or Buy Sell signals in the chart. Because everything is in the chart where volatility Signal with the Bollinger Band and Buy Sell Signal in the JMA Dynamic colors. and that's enough to decide when to take trade and when not to.
Thank You and Happy Trading
Drawdown Distribution Analysis (DDA) ACADEMIC FOUNDATION AND RESEARCH BACKGROUND
The Drawdown Distribution Analysis indicator implements quantitative risk management principles, drawing upon decades of academic research in portfolio theory, behavioral finance, and statistical risk modeling. This tool provides risk assessment capabilities for traders and portfolio managers seeking to understand their current position within historical drawdown patterns.
The theoretical foundation of this indicator rests on modern portfolio theory as established by Markowitz (1952), who introduced the fundamental concepts of risk-return optimization that continue to underpin contemporary portfolio management. Sharpe (1966) later expanded this framework by developing risk-adjusted performance measures, most notably the Sharpe ratio, which remains a cornerstone of performance evaluation in financial markets.
The specific focus on drawdown analysis builds upon the work of Chekhlov, Uryasev and Zabarankin (2005), who provided the mathematical framework for incorporating drawdown measures into portfolio optimization. Their research demonstrated that traditional mean-variance optimization often fails to capture the full risk profile of investment strategies, particularly regarding sequential losses. More recent work by Goldberg and Mahmoud (2017) has brought these theoretical concepts into practical application within institutional risk management frameworks.
Value at Risk methodology, as comprehensively outlined by Jorion (2007), provides the statistical foundation for the risk measurement components of this indicator. The coherent risk measures framework developed by Artzner et al. (1999) ensures that the risk metrics employed satisfy the mathematical properties required for sound risk management decisions. Additionally, the focus on downside risk follows the framework established by Sortino and Price (1994), while the drawdown-adjusted performance measures implement concepts introduced by Young (1991).
MATHEMATICAL METHODOLOGY
The core calculation methodology centers on a peak-tracking algorithm that continuously monitors the maximum price level achieved and calculates the percentage decline from this peak. The drawdown at any time t is defined as DD(t) = (P(t) - Peak(t)) / Peak(t) × 100, where P(t) represents the asset price at time t and Peak(t) represents the running maximum price observed up to time t.
Statistical distribution analysis forms the analytical backbone of the indicator. The system calculates key percentiles using the ta.percentile_nearest_rank() function to establish the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of the historical drawdown distribution. This approach provides a complete picture of how the current drawdown compares to historical patterns.
Statistical significance assessment employs standard deviation bands at one, two, and three standard deviations from the mean, following the conventional approach where the upper band equals μ + nσ and the lower band equals μ - nσ. The Z-score calculation, defined as Z = (DD - μ) / σ, enables the identification of statistically extreme events, with thresholds set at |Z| > 2.5 for extreme drawdowns and |Z| > 3.0 for severe drawdowns, corresponding to confidence levels exceeding 99.4% and 99.7% respectively.
ADVANCED RISK METRICS
The indicator incorporates several risk-adjusted performance measures that extend beyond basic drawdown analysis. The Sharpe ratio calculation follows the standard formula Sharpe = (R - Rf) / σ, where R represents the annualized return, Rf represents the risk-free rate, and σ represents the annualized volatility. The system supports dynamic sourcing of the risk-free rate from the US 10-year Treasury yield or allows for manual specification.
The Sortino ratio addresses the limitation of the Sharpe ratio by focusing exclusively on downside risk, calculated as Sortino = (R - Rf) / σd, where σd represents the downside deviation computed using only negative returns. This measure provides a more accurate assessment of risk-adjusted performance for strategies that exhibit asymmetric return distributions.
The Calmar ratio, defined as Annual Return divided by the absolute value of Maximum Drawdown, offers a direct measure of return per unit of drawdown risk. This metric proves particularly valuable for comparing strategies or assets with different risk profiles, as it directly relates performance to the maximum historical loss experienced.
Value at Risk calculations provide quantitative estimates of potential losses at specified confidence levels. The 95% VaR corresponds to the 5th percentile of the drawdown distribution, while the 99% VaR corresponds to the 1st percentile. Conditional VaR, also known as Expected Shortfall, estimates the average loss in the worst 5% of scenarios, providing insight into tail risk that standard VaR measures may not capture.
To enable fair comparison across assets with different volatility characteristics, the indicator calculates volatility-adjusted drawdowns using the formula Adjusted DD = Raw DD / (Volatility / 20%). This normalization allows for meaningful comparison between high-volatility assets like cryptocurrencies and lower-volatility instruments like government bonds.
The Risk Efficiency Score represents a composite measure ranging from 0 to 100 that combines the Sharpe ratio and current percentile rank to provide a single metric for quick asset assessment. Higher scores indicate superior risk-adjusted performance relative to historical patterns.
COLOR SCHEMES AND VISUALIZATION
The indicator implements eight distinct color themes designed to accommodate different analytical preferences and market contexts. The EdgeTools theme employs a corporate blue palette that matches the design system used throughout the edgetools.org platform, ensuring visual consistency across analytical tools.
The Gold theme specifically targets precious metals analysis with warm tones that complement gold chart analysis, while the Quant theme provides a grayscale scheme suitable for analytical environments that prioritize clarity over aesthetic appeal. The Behavioral theme incorporates psychology-based color coding, using green to represent greed-driven market conditions and red to indicate fear-driven environments.
Additional themes include Ocean, Fire, Matrix, and Arctic schemes, each designed for specific market conditions or user preferences. All themes function effectively with both dark and light mode trading platforms, ensuring accessibility across different user interface configurations.
PRACTICAL APPLICATIONS
Asset allocation and portfolio construction represent primary use cases for this analytical framework. When comparing multiple assets such as Bitcoin, gold, and the S&P 500, traders can examine Risk Efficiency Scores to identify instruments offering superior risk-adjusted performance. The 95% VaR provides worst-case scenario comparisons, while volatility-adjusted drawdowns enable fair comparison despite varying volatility profiles.
The practical decision framework suggests that assets with Risk Efficiency Scores above 70 may be suitable for aggressive portfolio allocations, scores between 40 and 70 indicate moderate allocation potential, and scores below 40 suggest defensive positioning or avoidance. These thresholds should be adjusted based on individual risk tolerance and market conditions.
Risk management and position sizing applications utilize the current percentile rank to guide allocation decisions. When the current drawdown ranks above the 75th percentile of historical data, indicating that current conditions are better than 75% of historical periods, position increases may be warranted. Conversely, when percentile rankings fall below the 25th percentile, indicating elevated risk conditions, position reductions become advisable.
Institutional portfolio monitoring applications include hedge fund risk dashboard implementations where multiple strategies can be monitored simultaneously. Sharpe ratio tracking identifies deteriorating risk-adjusted performance across strategies, VaR monitoring ensures portfolios remain within established risk limits, and drawdown duration tracking provides valuable information for investor reporting requirements.
Market timing applications combine the statistical analysis with trend identification techniques. Strong buy signals may emerge when risk levels register as "Low" in conjunction with established uptrends, while extreme risk levels combined with downtrends may indicate exit or hedging opportunities. Z-scores exceeding 3.0 often signal statistically oversold conditions that may precede trend reversals.
STATISTICAL SIGNIFICANCE AND VALIDATION
The indicator provides 95% confidence intervals around current drawdown levels using the standard formula CI = μ ± 1.96σ. This statistical framework enables users to assess whether current conditions fall within normal market variation or represent statistically significant departures from historical patterns.
Risk level classification employs a dynamic assessment system based on percentile ranking within the historical distribution. Low risk designation applies when current drawdowns perform better than 50% of historical data, moderate risk encompasses the 25th to 50th percentile range, high risk covers the 10th to 25th percentile range, and extreme risk applies to the worst 10% of historical drawdowns.
Sample size considerations play a crucial role in statistical reliability. For daily data, the system requires a minimum of 252 trading days (approximately one year) but performs better with 500 or more observations. Weekly data analysis benefits from at least 104 weeks (two years) of history, while monthly data requires a minimum of 60 months (five years) for reliable statistical inference.
IMPLEMENTATION BEST PRACTICES
Parameter optimization should consider the specific characteristics of different asset classes. Equity analysis typically benefits from 500-day lookback periods with 21-day smoothing, while cryptocurrency analysis may employ 365-day lookback periods with 14-day smoothing to account for higher volatility patterns. Fixed income analysis often requires longer lookback periods of 756 days with 34-day smoothing to capture the lower volatility environment.
Multi-timeframe analysis provides hierarchical risk assessment capabilities. Daily timeframe analysis supports tactical risk management decisions, weekly analysis informs strategic positioning choices, and monthly analysis guides long-term allocation decisions. This hierarchical approach ensures that risk assessment occurs at appropriate temporal scales for different investment objectives.
Integration with complementary indicators enhances the analytical framework. Trend indicators such as RSI and moving averages provide directional bias context, volume analysis helps confirm the severity of drawdown conditions, and volatility measures like VIX or ATR assist in market regime identification.
ALERT SYSTEM AND AUTOMATION
The automated alert system monitors five distinct categories of risk events. Risk level changes trigger notifications when drawdowns move between risk categories, enabling proactive risk management responses. Statistical significance alerts activate when Z-scores exceed established threshold levels of 2.5 or 3.0 standard deviations.
New maximum drawdown alerts notify users when historical maximum levels are exceeded, indicating entry into uncharted risk territory. Poor risk efficiency alerts trigger when the composite risk efficiency score falls below 30, suggesting deteriorating risk-adjusted performance. Sharpe ratio decline alerts activate when risk-adjusted performance turns negative, indicating that returns no longer compensate for the risk undertaken.
TRADING STRATEGIES
Conservative risk parity strategies can be implemented by monitoring Risk Efficiency Scores across a diversified asset portfolio. Monthly rebalancing maintains equal risk contribution from each asset, with allocation reductions triggered when risk levels reach "High" status and complete exits executed when "Extreme" risk levels emerge. This approach typically results in lower overall portfolio volatility, improved risk-adjusted returns, and reduced maximum drawdown periods.
Tactical asset rotation strategies compare Risk Efficiency Scores across different asset classes to guide allocation decisions. Assets with scores exceeding 60 receive overweight allocations, while assets scoring below 40 receive underweight positions. Percentile rankings provide timing guidance for allocation adjustments, creating a systematic approach to asset allocation that responds to changing risk-return profiles.
Market timing strategies with statistical edges can be constructed by entering positions when Z-scores fall below -2.5, indicating statistically oversold conditions, and scaling out when Z-scores exceed 2.5, suggesting overbought conditions. The 95% VaR serves as a stop-loss reference point, while trend confirmation indicators provide additional validation for position entry and exit decisions.
LIMITATIONS AND CONSIDERATIONS
Several statistical limitations affect the interpretation and application of these risk measures. Historical bias represents a fundamental challenge, as past drawdown patterns may not accurately predict future risk characteristics, particularly during structural market changes or regime shifts. Sample dependence means that results can be sensitive to the selected lookback period, with shorter periods providing more responsive but potentially less stable estimates.
Market regime changes can significantly alter the statistical parameters underlying the analysis. During periods of structural market evolution, historical distributions may provide poor guidance for future expectations. Additionally, many financial assets exhibit return distributions with fat tails that deviate from normal distribution assumptions, potentially leading to underestimation of extreme event probabilities.
Practical limitations include execution risk, where theoretical signals may not translate directly into actual trading results due to factors such as slippage, timing delays, and market impact. Liquidity constraints mean that risk metrics assume perfect liquidity, which may not hold during stressed market conditions when risk management becomes most critical.
Transaction costs are not incorporated into risk-adjusted return calculations, potentially overstating the attractiveness of strategies that require frequent trading. Behavioral factors represent another limitation, as human psychology may override statistical signals, particularly during periods of extreme market stress when disciplined risk management becomes most challenging.
TECHNICAL IMPLEMENTATION
Performance optimization ensures reliable operation across different market conditions and timeframes. All technical analysis functions are extracted from conditional statements to maintain Pine Script compliance and ensure consistent execution. Memory efficiency is achieved through optimized variable scoping and array usage, while computational speed benefits from vectorized calculations where possible.
Data quality requirements include clean price data without gaps or errors that could distort distribution analysis. Sufficient historical data is essential, with a minimum of 100 bars required and 500 or more preferred for reliable statistical inference. Time alignment across related assets ensures meaningful comparison when conducting multi-asset analysis.
The configuration parameters are organized into logical groups to enhance usability. Core settings include the Distribution Analysis Period (100-2000 bars), Drawdown Smoothing Period (1-50 bars), and Price Source selection. Advanced metrics settings control risk-free rate sourcing, either from live market data or fixed rate specification, along with toggles for various risk-adjusted metric calculations.
Display options provide flexibility in visual presentation, including color theme selection from eight available schemes, automatic dark mode optimization, and control over table display, position lines, percentile bands, and standard deviation overlays. These options ensure that the indicator can be adapted to different analytical workflows and visual preferences.
CONCLUSION
The Drawdown Distribution Analysis indicator provides risk management tools for traders seeking to understand their current position within historical risk patterns. By combining established statistical methodology with practical usability features, the tool enables evidence-based risk assessment and portfolio optimization decisions.
The implementation draws upon established academic research while providing practical features that address real-world trading requirements. Dynamic risk-free rate integration ensures accurate risk-adjusted performance calculations, while multiple color schemes accommodate different analytical preferences and use cases.
Academic compliance is maintained through transparent methodology and acknowledgment of limitations. The tool implements peer-reviewed statistical techniques while clearly communicating the constraints and assumptions underlying the analysis. This approach ensures that users can make informed decisions about the appropriate application of the risk assessment framework within their broader trading and investment processes.
BIBLIOGRAPHY
Artzner, P., Delbaen, F., Eber, J.M. and Heath, D. (1999) 'Coherent Measures of Risk', Mathematical Finance, 9(3), pp. 203-228.
Chekhlov, A., Uryasev, S. and Zabarankin, M. (2005) 'Drawdown Measure in Portfolio Optimization', International Journal of Theoretical and Applied Finance, 8(1), pp. 13-58.
Goldberg, L.R. and Mahmoud, O. (2017) 'Drawdown: From Practice to Theory and Back Again', Journal of Risk Management in Financial Institutions, 10(2), pp. 140-152.
Jorion, P. (2007) Value at Risk: The New Benchmark for Managing Financial Risk. 3rd edn. New York: McGraw-Hill.
Markowitz, H. (1952) 'Portfolio Selection', Journal of Finance, 7(1), pp. 77-91.
Sharpe, W.F. (1966) 'Mutual Fund Performance', Journal of Business, 39(1), pp. 119-138.
Sortino, F.A. and Price, L.N. (1994) 'Performance Measurement in a Downside Risk Framework', Journal of Investing, 3(3), pp. 59-64.
Young, T.W. (1991) 'Calmar Ratio: A Smoother Tool', Futures, 20(1), pp. 40-42.
20 EMA / 55 EMA Cross with 50 SMMA FilterBulish Bearish indicator for the 55 ema flipping abover or below 20 ema and abover or below 50 smma
Range Filter Strategy [Real Backtest]Range Filter Strategy - Real Backtesting
# Overview
Advanced Range Filter strategy designed for realistic backtesting with precise execution timing and comprehensive risk management. Built specifically for cryptocurrency markets with customizable parameters for different assets and timeframes.
Core Algorithm
Range Filter Technology:
- Smooth Average Range calculation using dual EMA filtering
- Dynamic range-based price filtering to identify trend direction
- Anti-noise filtering system to reduce false signals
- Directional momentum tracking with upward/downward counters
Key Features
Real-Time Execution (No Delay)
- Process orders on tick: Immediate execution without waiting for bar close
- Bar magnifier integration for intrabar precision
- Calculate on every tick for maximum responsiveness
- Standard OHLC bypass for enhanced accuracy
Realistic Price Simulation
- HL2 entry pricing (High+Low)/2 for realistic fills
- Configurable spread buffer simulation
- Random slippage generation (0 to max slippage)
- Market liquidity validation before entry
Advanced Signal Filtering
- Volume-based filtering with customizable ratio
- Optional signal confirmation system (1-3 bars)
- Anti-repetition logic to prevent duplicate signals
- Daily trade limit controls
Risk Management
- Fixed Risk:Reward ratios with precise point calculation
- Automatic stop loss and take profit execution
- Position size management
- Maximum daily trades limitation
Alert System
- Real-time alerts synchronized with strategy execution
- Multiple alert types: Setup, Entry, Exit, Status
- Customizable message formatting with price/time inclusion
- TradingView alert panel integration
Default Parameters
Optimized for BTC 5-minute charts:
- Sampling Period: 100
- Range Multiplier: 3.0
- Risk: 50 points
- Reward: 100 points (1:2 R:R)
- Spread Buffer: 2.0 points
- Max Slippage: 1.0 points
Signal Logic
Long Entry Conditions:
- Price above Range Filter line
- Upward momentum confirmed
- Volume requirements met (if enabled)
- Confirmation period completed (if enabled)
- Daily trade limit not exceeded
Short Entry Conditions:
- Price below Range Filter line
- Downward momentum confirmed
- Volume requirements met (if enabled)
- Confirmation period completed (if enabled)
- Daily trade limit not exceeded
Visual Elements
- Range Filter line with directional coloring
- Upper and lower target bands
- Entry signal markers
- Risk/Reward ratio boxes
- Real-time settings dashboard
Customization Options
Market Adaptation:
- Adjust Sampling Period for different timeframes
- Modify Range Multiplier for various volatility levels
- Configure spread/slippage for different brokers
- Set appropriate R:R ratios for trading style
Filtering Controls:
- Enable/disable volume filtering
- Adjust confirmation requirements
- Set daily trade limits
- Customize alert preferences
Performance Features
- Realistic backtesting results aligned with live trading
- Elimination of look-ahead bias
- Proper order execution simulation
- Comprehensive trade statistics
Alert Configuration
Alert Types Available:
- Entry signals with complete trade information
- Setup alerts for early preparation
- Exit notifications for position management
- Filter direction changes for market context
Message Format:
Symbol - Action | Price: XX.XX | Stop: XX.XX | Target: XX.XX | Time: HH:MM
Usage Recommendations
Optimal Settings:
- Bitcoin/Major Crypto: Default parameters
- Forex: Reduce sampling period to 50-70, multiplier to 2.0-2.5
- Stocks: Reduce sampling period to 30-50, multiplier to 1.0-1.8
- Gold: Sampling period 60-80, multiplier 1.5-2.0
TradingView Configuration:
- Recalculate: "On every tick"
- Orders: "Use bar magnifier"
- Data: Real-time feed recommended
Risk Disclaimer
This strategy is designed for educational and analytical purposes. Past performance does not guarantee future results. Always test thoroughly on paper trading before live implementation. Consider market conditions, broker execution, and personal risk tolerance when using any automated trading system.
Best Settings Found for Gold 15-Minute Timeframe
After extensive testing and optimization, these are the most effective settings I've discovered for trading Gold (XAUUSD) on the 15-minute timeframe:
Core Filter Settings:
Sampling Period: 100
Range Multiplier: 3.0
Professional Execution Engine:
Realistic Entry: Enabled (HL2)
Spread Buffer: 2 points
Dynamic Slippage: Enabled with max 1 point
Volume Filter: Enabled at 1.7x ratio
Signal Confirmation: Enabled with 1 bar confirmation
Risk Management:
Stop Loss: 50 points
Take Profit: 100 points (2:1 Risk-Reward)
Max Trades Per Day: 5
These settings provide an excellent balance between signal accuracy and realistic market execution. The volume filter at 1.7x ensures we only trade during periods of sufficient market activity, while the 1-bar confirmation helps filter out false signals. The spread buffer and slippage settings account for real trading costs, making backtest results more realistic and achievable in live trading.
Trading Report Generator from CSVMany people use the Trading Panel. Unfortunately, it doesn't have a Performance Report. However, TradingView has strategies, and they have a Performance Report :-D
What if we combine the first and second? It's easy!
This script is a special strategy that parses transactions in csv format from Paper Trading (and it will also work for other brokers) and “plays” them. As a result, we get a Performance Report for a specific instrument based on our real trades in Paper or another broker.
How to use it :
First, we need to get a CSV file with transactions. To do this, go to the Trading Panel and connect the desired broker. Select the History tab, then the Filled sub-tab, and configure the columns there, leaving only: Side, Qty, Fill Price, Closing Time. After that, open the Export data dialog, select History, and click Export. Open the downloaded CSV file in a regular text editor (Notepad or similar). It will contain a text like this:
Symbol,Side,Qty,Fill Price,Closing Time
FX:EURUSD,Buy,1000,1.0938700000000001,2023-04-05 14:29:23
COINBASE:ETHUSD,Sell,1,1332.05,2023-01-11 17:41:33
CME_MINI:ESH2023,Sell,1,3961.75,2023-01-11 17:30:40
CME_MINI:ESH2023,Buy,1,3956.75,2023-01-11 17:08:53
Next select all the text (Ctrl+A) and copy it to the clipboard.
Now apply the "Trading Report Generator from CSV" strategy to the chart with the desired symbol and TF, open the settings/input dialog, paste the contents of the clipboard into the single text input field of the strategy, and click Ok.
That's it.
In the Strategy Tester, we see a detailed Performance Report based on our real transactions.
P.S. The CSV file may contain transactions for different instruments, for example, you may have transactions for CRYPTO:BTCUSD and NASDAQ:AAPL. To view the report is based on CRYPTO:BTCUSD trades, simply change the symbol on the chart to CRYPTO:BTCUSD. To view the report is based on NASDAQ:AAPL trades, simply change the symbol on the chart to NASDAQ:AAPL. No changes to the strategy are required.
How it works :
At the beginning of the calculation, we parse the csv once, create trade objects (Trade) and sort them in chronological order. Next, on each bar, we check whether we have trades for the time period of the next bar. If there are, we place a limit order for each trade, with limit price == Fill Price of the trade. Here, we assume that if the trade is real, its execution price will be within the bar range, and the Pine strategy engine will execute this order at the specified limit price.
Combined Time and Price IndicatorCammjayyy THis is a time frame indicator that changes the colors based on day highs and lows. really good for swings and hedging
Dynamic S/R System - Pivot + ChannelDynamic S/R System - Pivot + Channel
A comprehensive Support & Resistance indicator combining dual methodologies for institutional-grade price level analysis
📊 CORE FEATURES
Dual Detection System
• Pivot-Based Levels - Historical turning points with intelligent touch counting
• Dynamic Channel S/R - Trend-aware linear regression boundaries
• Smart Level Management - Auto-merges similar levels, removes weak/outdated ones
Volume Integration
• Multi-timeframe volume analysis using EMA oscillator and spike detection
• Volume confirmation for all breakout signals to filter false moves
• Real-time volume status (Normal/High/Spike) in live information panel
Intelligent Touch Counting
• Automatic level validation through touch frequency analysis
• Strength classification with visual differentiation (colors/thickness)
• Level labels showing exact touch count (S3, R5, etc.)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🎨 VISUAL ELEMENTS
Line System
Solid Lines: Pivot-based S/R levels
Dashed Lines: Dynamic channel boundaries
Color Coding:
• 🔵 Blue/🔴 Red: Standard support/resistance
• 🟠 Orange: Strong levels (multiple touches)
• 🟣 Purple: Channel S/R levels
Signal Labels
• "B" - Pivot S/R breakout with volume confirmation
• "CB" - Channel boundary breakout
• "Bull/Bear Wick" - False breakout detection (wick rejections)
Information Panel
Real-time analysis displays:
• Total resistance/support levels detected
• Closest S/R levels to current price
• Volume status and position relative to levels
• Current market position assessment
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
✅ KEY ADVANTAGES
Multi-Method Validation
Combines historical pivot analysis with dynamic trend channels for comprehensive market view
False Breakout Protection
• Volume confirmation requirements
• Wick analysis to identify failed attempts
• Multiple validation criteria before signal generation
Adaptive Level Management
• Automatically updates as new pivots form
• Removes outdated/weak levels
• Maintains clean, relevant level display
Institutional-Grade Analysis
• Touch counting reveals institutional respect levels
• Volume integration shows smart money activity
• Strength classification identifies high-probability zones
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⏰ OPTIMAL USE CASES
Best Timeframes
• Daily - Primary recommendation for swing trading
• 4-Hour - Intraday analysis and entries
• Weekly - Long-term position planning
Ideal Markets
• Crypto pairs (especially ETH/BTC, BTC/USD)
• Forex majors with good volume data
• Large-cap stocks with institutional participation
Trading Applications
• Entry/exit planning around key S/R levels
• Breakout confirmation with volume validation
• Risk management using nearest S/R for stops
• Trend analysis through channel dynamics
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⚙️ CONFIGURATION GUIDELINES
Conservative Setup (Higher Confidence)
Min Pivot Strength: 3-4
Volume Threshold: 25-30%
Max Levels: 6-8
Aggressive Setup (More Signals)
Min Pivot Strength: 2
Volume Threshold: 15-20%
Max Levels: 10-12
🔔 ALERT SYSTEM
Breakout Alerts
• Resistance/Support breaks with volume confirmation
• Channel boundary violations
• Approaching strong S/R levels
Advanced Notifications
• Strong level approaches (within 0.5% of price)
• False breakout detection
• Volume spike confirmations
📈 TRADING STRATEGY GUIDE
Entry Strategy
1. Wait for price to approach identified S/R level
2. Confirm with volume analysis (spike/high volume preferred)
3. Watch for wick formations indicating rejection
4. Enter on confirmed breakout with volume or bounce with rejection
Risk Management
• Use nearest S/R level for stop placement
• Scale position size based on level strength (touch count)
• Monitor volume confirmation for exit signals
Market Context
• Combine with higher timeframe trend analysis
• Consider overall market sentiment and volatility
• Use channel direction for bias confirmation
Transform complex S/R analysis into actionable trading intelligence with institutional-level insights for professional trading decisions.
Oops Reversal-Updatedoops reversal - manas arora updated to cover only if it closes above previous day high
ZigZag Based RSIDescription
ZigZag Trend RSI (ZZ-RSI) is an advanced momentum indicator that combines ZigZag-based trend detection with a trend-adjusted RSI to deliver smarter overbought and oversold signals. Unlike traditional RSI that reacts purely to price movement, this indicator adapts its sensitivity based on the prevailing trend structure identified via the ZigZag pattern.
By dynamically adjusting RSI thresholds according to market direction, ZZ-RSI helps filter out false signals and aligns RSI readings with broader trend context—crucial for trend-following strategies, counter-trend entries, and volatility-based timing.
Core Components
ZigZag Pattern Recognition:
Identifies significant swing highs and lows based on price deviation (%) and pivot sensitivity (length). The most recent pivot determines the prevailing trend direction:
🟢 Bullish: last swing is a higher high
🔴 Bearish: last swing is a lower low
⚪ Neutral: no recent significant movement
Trend-Weighted RSI:
Modifies traditional RSI input by emphasizing price changes in the direction of the trend:
In bull trends, upside moves are magnified.
In bear trends, downside moves are emphasized.
Dynamic RSI Zones:
Overbought and Oversold thresholds adapt to the trend:
In uptrends: higher OB and slightly raised OS → tolerate stronger rallies
In downtrends: lower OS and slightly reduced OB → accommodate stronger sell-offs
In neutral: default OB/OS values apply
How to Use
✅ Entries (Reversal or Mean Reversion Traders):
Look for oversold signals (green triangle) in downtrends or neutrals to catch potential reversals.
Look for overbought signals (red triangle) in uptrends or neutrals to fade momentum.
Confirm with price action or volume for higher conviction.
📈 Trend Continuation (Momentum or Trend-Followers):
Use the trend direction label (Bullish / Bearish / Neutral) to align your trades with the broader move.
Combine with moving averages or price structure for entry timing.
Avoid counter-trend signals unless confirmed by divergence or exhaustion.
🧠 Signal Interpretation Table (top right of chart):
Trend: Indicates the current market direction.
RSI: Real-time trend-adjusted RSI value.
Signal: OB/OS/Neutral classification.
Customization Options
ZigZag Length / Deviation %:
Adjust pivot sensitivity and filter out minor noise.
RSI Length:
Controls how fast RSI responds to trend-adjusted price.
Color Settings:
Personalize visual cues for trend direction and OB/OS backgrounds.
Alerts Included
📢 Overbought/oversold conditions
🔄 Trend reversals (bullish or bearish shift)
These alerts are ideal for automated strategies, mobile notifications, or algorithmic workflows.
Ideal For
Traders seeking smarter RSI signals filtered by market structure
Trend-followers and swing traders looking for reliable reversals
Those frustrated with false OB/OS signals in volatile or trending markets
Best Practices
Use in confluence with price structure, trendlines, or S/R levels.
For intraday: consider lowering ZigZag Length and RSI Length.
For higher timeframes: use higher deviation % and smoother RSI to reduce noise.
STOCK SCHOOL | SWING TRACKER Swing Tracker is a powerful tool that automatically identifies Higher Highs (HH), Higher Lows (HL), Lower Highs (LH), and Lower Lows (LL) directly on the chart, helping traders clearly understand market structure and trend direction. Designed for price action traders, it works seamlessly across all timeframes and instruments, offering clean visual labels for swing points to spot trend continuations or potential reversals. Whether you're following the trend or looking for structure shifts, Swing Tracker keeps you aligned with price action for smarter, more confident trading decisions.
Circuit Breaker Table (NSE Style)🛡️ NSE Circuit Breaker Table – With Volatility-Based Band Support
This script displays a real-time circuit breaker table for any stock, showing the Upper and Lower circuit limits in a clean 2x2 grid. It’s especially useful for Indian traders monitoring NSE-listed stocks.
✅ Key Features:
📊 Upper & Lower Limits based on the previous day’s close
⚡ Optional ATR-based dynamic volatility band calculation
🎨 Customizable font sizes (Small / Medium / Large)
✅ Table neatly positioned on the top-right corner of your chart
🟢 Upper circuit shown in green, 🔴 lower circuit in red
Works on all NSE stocks and adapts automatically to charted symbols
⚙️ Customization Options:
Use static percentage bands (e.g., 10%)
Or enable ATR mode to reflect dynamic circuit potential based on recent volatility
This tool helps you stay aware of where a stock might get halted — useful for momentum traders, circuit breakout traders, and anyone monitoring volatility limits during intraday sessions.
Casper SMC 5min ORB - Roboquant AI🚀 Key Features:
Opening Range (09:30–09:35 EST) breakout detection
Configurable entry type: Instant or Retracement
Adjustable Risk:Reward multiplier and contract sizing
Optional Trailing Stop Loss using ATR
Second-Chance trades if the first breakout fails
Visual markers for entries, SL/TP, trade status, and breakout validation
Day filter: Trade only on selected weekdays
Session management with configurable close time
Breakout validation using:
Wick percentage filters
Distance filters based on OR range size
🧠 AI-Style Logic Enhancements:
Smart filters for avoiding overextended or noisy breakouts
Full support for lookahead-safe logic via barstate.isconfirmed
Clean box-style trade visualization (entry, SL, TP zones)
Custom alerts for long and short entries
⚙️ Recommended Settings:
Use on 5-minute chart
Best for US indices/ Futures
📌 Note:
This script is for educational purposes only.
Performance preview on Tradingview is not accurate
Yoou need to adjust the settings and run a 1 year report
Always backtest thoroughly and consult your financial advisor before live trading.
Gabriel's Laguerre RSI📈 Gabriel's Laguerre RSI
An Adaptive Momentum Oscillator with Laguerre Filtering and Efficiency Ratio Smoothing
🔍 Overview
"Gabriel's Laguerre RSI" is an advanced momentum oscillator that combines the strengths of the Laguerre Recursive Smoothing, the Efficiency Ratio (ER) for dynamic responsiveness, and a Laguerre-based signal filter built on an Ultimate Smoother foundation. This hybrid construction provides a smoother, more adaptive RSI-like oscillator that responds effectively to both trend strength and cyclical price behavior.
🧠 Core Concepts
Laguerre RSI (LaRSI):
Uses a recursive Laguerre smoothing algorithm that applies layered feedback levels (L0 to L3) to separate upward and downward price movements. The result is a normalized oscillator that ranges between 0–100 and is more resistant to noise than traditional RSI.
Efficiency Ratio (ER):
Measures the signal-to-noise ratio in price movement over a given length. A higher ER implies trend dominance, while a lower ER indicates consolidation. This dynamic ER is used to adaptively control the Laguerre smoothing intensity, making the indicator self-adjusting to different market regimes.
Laguerre Filter (Signal Line):
A sophisticated smoothing function layered on top of the Laguerre RSI output. It uses the Ultimate Smoother (a zero-lag Butterworth-style smoother) and a multi-level Laguerre polynomial to create a responsive yet stable signal line that acts as a confirmation filter or crossover signal.
⚙️ Inputs
LRSI Source:
Select the input data for the oscillator (default is close).
LRSI Length:
Defines the window size for the Efficiency Ratio. The ER adapts the smoothing depth of the Laguerre RSI.
Laguerre Filter Length:
Sets the responsiveness of the Laguerre Filter signal line. Lower values = faster reaction; higher values = smoother trend confirmation.
📏 Levels and Visuals
Horizontal Lines:
80: Overbought zone
50: Median reference
20: Oversold zone
Plots:
Blue Line: Adaptive Laguerre RSI
Olive Line: Laguerre Filter (Signal Line)
📌 How to Use
Trend Confirmation:
Use crossovers between the Laguerre RSI and the Laguerre Filter for trend signals.
Bullish Signal: LRSI crosses above the filter
Bearish Signal: LRSI crosses below the filter
Overbought/Oversold Detection:
LRSI values above 80 or below 20 suggest potential reversal or mean-reversion zones, especially when divergence or volume confluence exists.
Volatility Adaptation:
Because the Efficiency Ratio dynamically controls the Laguerre smoothing, this indicator automatically adjusts to quiet and volatile markets, minimizing whipsaws.
🧪 Why It’s Powerful
Adaptive to market noise and trend quality (via ER)
Smooth yet responsive signal generation (via multi-layer Laguerre and Ultimate Smoother)
Reduced lag and enhanced reversal detection compared to standard RSI
