6-9 session & levels6-9 Session & Levels - Customizable Range Analysis Indicator
Description:
This indicator provides comprehensive session-based range analysis designed for intraday traders. It calculates and displays key levels based on a customizable session period (default 6:00-9:00 AM ET).
Core Features:
Session Tracking
Monitors user-defined session times with timezone support
Displays session open, high, and low levels
Highlights session range with optional box visualization
Shows previous day RTH (Regular Trading Hours: 9:30 AM - 4:00 PM) levels
Range Levels
25%, 50%, and 75% range levels within the session
Range deviations at 0.5x, 1.0x, and 2.0x multiples
Fibonacci extension levels (customizable, default 1.33x and 1.66x)
Optional fill zones between Fibonacci levels
Time Zone Highlighting
Marks the 9:40-9:50 AM period as a potential reversal zone
Vertical lines with shading to identify key time windows
Statistical Analysis
Calculates mean and median extension levels based on historical sessions
Displays statistics table showing current range, average range, range difference, and z-score
Customizable sample size (1-100 sessions) for statistical calculations
Option to anchor extensions from either session open or high/low points
Input Settings Explained:
Session Settings
Levels Session Time: Define your session window in HHMM-HHMM format (default: 0600-0900)
Time Zone: Choose from UTC, America/New_York, America/Chicago, America/Los_Angeles, Europe/London, or Asia/Tokyo
Anchor Settings
Show Session Anchor: Toggle the session anchor line (marks session open price at 6:00 AM)
Anchor Style/Color/Width: Customize appearance (Solid/Dashed/Dotted, color, 1-4 width)
Show Anchor Label: Display price label for the anchor
Session Open Line: Similar options for the session open reference line
Range Box Settings
Show Range Box: Display a shaded rectangle highlighting the session high-to-low range
Range Box Color: Set the box background color and transparency
Range Levels (25%/50%/75%)
Show Range Levels: Toggle all three intermediate levels on/off
Individual Level Styling: Each level (25%, 50%, 75%) has its own color, style, and width settings
Show Range Level Labels: Display price labels for each level
Range Deviations
Show Range Deviations: Toggle deviation levels on/off
0.5x/1.0x/2.0x Settings: Each deviation multiplier can be customized with its own color, line style (Solid/Dashed/Dotted), and width
Show Range Deviation Labels: Display labels showing the deviation price levels
Previous Day RTH Levels
Show Previous RTH Levels: Display yesterday's regular trading hours high and low
RTH High/Low Styling: Separate color, style, and width settings for each level
Show Previous RTH Labels: Toggle price labels for RTH levels
Time Zones
Show 9:40-9:50 AM Zone: Highlight this specific time period with vertical lines and shading
Zone Color: Set the background fill color for the time zone
Zone Label Color/Text: Customize the label appearance and text
Fibonacci Extension Settings
Show Fibonacci Extensions: Toggle Fib levels on/off
Fib Extension Color/Style/Width: Customize line appearance
Show Fib Extension Labels: Display price labels
Fib Ext Level 1/2: Set custom multipliers (default 1.33 and 1.66, range 0-5 in 0.1 increments)
Show Fibonacci Fills: Display shaded zones between Fib levels
Fib Fill Color: Customize the fill color and transparency
Session High/Low Settings
Show Session High/Low Lines: Display the actual session extremes
Style/Color/Width: Customize line appearance
Show Labels: Toggle price labels for high/low levels
Extension Stats Settings
Show Statistical Levels on Chart: Display mean and median extension levels based on historical data
Extension Anchor Point: Choose whether to anchor from "Open" or "High/Low" of the session
Number of Sessions for Statistics: Set sample size (1-100, default 60) for calculating averages
Mean/Median High Extension: Separate styling for each statistical level (color, style, width)
Mean/Median Low Extension: Separate styling for downside statistical levels
Tables
Show Statistics Table: Display a summary table with current range, average range, difference, z-score, and sample size
Table Position: Choose from 9 positions (Bottom/Middle/Top + Center/Left/Right)
Table Text Size: Select from Auto, Tiny, Small, Normal, Large, or Huge
Display Settings
Projection Offset: Number of bars to extend lines forward (default 24)
Label Size: Choose from Tiny, Small, Normal, or Large
Price Decimal Precision: Set decimal places for price labels (0-6)
How It Works:
The indicator tracks the specified session period and calculates the session's open, high, low, and range. At the end of the session (9:00 AM by default), it projects all configured levels forward for the trading day. The statistical features analyze the last N sessions (you choose the number) to calculate typical extension behavior from either the session open or the session high/low points.
The z-score calculation helps identify whether the current session's range is normal, expanded, or contracted compared to recent history, allowing traders to adjust expectations for the rest of the day.
Use Case:
This indicator helps traders identify key support and resistance levels based on early session price action, understand current range context relative to historical averages, and spot potential reversal zones during specific time periods.
Note: This indicator is for informational purposes only and does not constitute investment advice. Always perform your own analysis before making trading decisions.
"马斯克+100万" için komut dosyalarını ara
Stock Relative Strength Rotation Graph🔄 Visualizing Market Rotation & Momentum (Stock RSRG)
This tool visualizes the sector rotation of your watchlist on a single graph. Instead of checking 40 different charts, you can see the entire market cycle in one view. It plots Relative Strength (Trend) vs. Momentum (Velocity) to identify which assets are leading the market and which are lagging.
📜 Credits & Disclaimer
Original Code: Adapted from the open-source " Relative Strength Scatter Plot " by LuxAlgo.
Trademark: This tool is inspired by Relative Rotation Graphs®. Relative Rotation Graphs® is a registered trademark of JOOS Holdings B.V. This script is neither endorsed, nor sponsored, nor affiliated with them.
📊 How It Works (The Math)
The script calculates two metrics for every symbol against a benchmark (Default: SPX):
X-Axis (RS-Ratio): Is the trend stronger than the benchmark? (>100 = Yes)
Y-Axis (RS-Momentum): Is the trend accelerating? (>100 = Yes)
🧩 The 4 Market Quadrants
🟩 Leading (Top-Right): Strong Trend + Accelerating. (Best for holding).
🟦 Improving (Top-Left): Weak Trend + Accelerating. (Best for entries).
⬜ Weakening (Bottom-Right): Strong Trend + Decelerating. (Watch for exits).
🟥 Lagging (Bottom-Left): Weak Trend + Decelerating. (Avoid).
✨ Significant Improvements
This open-source version adds unique features not found in standard rotation scripts:
📝 Quick-Input Engine: Paste up to 40 symbols as a single comma-separated list (e.g., NVDA, AMD, TSLA). No more individual input boxes.
🎯 Quadrant Filtering: You can now hide specific quadrants (like "Lagging") to clear the noise and focus only on actionable setups.
🐛 Trajectory Trails: Visualizes the historical path of the rotation so you can see the direction of momentum.
🛠️ How to Use
Paste Watchlist: Go to settings and paste your symbols (e.g., US Sectors: XLK, XLF, XLE...).
Find Entries: Look for tails moving from Improving ➔ Leading.
Find Exits: Be cautious when tails move from Leading ➔ Weakening.
Zoom: Use the "Scatter Plot Resolution" setting to zoom in or out if dots are bunched up.
PRO Trade Manager//@version=5
indicator("PRO Trade Manager", shorttitle="PRO Trade Manager", overlay=false)
// ============================================================================
// INPUTS
//This code and all related materials are the exclusive property of Trade Confident LLC. Any reproduction, distribution, modification, or unauthorized use of this code, in whole or in part, is strictly prohibited without the express written consent of Trade Confident LLC. Violations may result in civil and/or criminal penalties to the fullest extent of the law.
// © Trade Confident LLC. All rights reserved.
// ============================================================================
// Moving Average Settings
maLength = input.int(15, "Signal Strength", minval=1, tooltip="Length of the moving average to measure deviation from (lower = more sensitive)")
maType = "SMA" // Fixed to SMA, no longer user-selectable
// Deviation Settings
deviationLength = input.int(20, "Deviation Period", minval=1, tooltip="Lookback period for standard deviation calculation")
// Signal Frequency dropdown - controls both upper and lower thresholds
signalFrequency = input.string("More/Good Accuracy", "Signal Frequency", options= ,
tooltip="Normal/Highest Accuracy = ±2.0 StdDev | More/Good Accuracy = ±1.5 StdDev | Most/Moderate Accuracy = ±1.0 StdDev")
// Set thresholds based on selected frequency
upperThreshold = signalFrequency == "Most/Moderate Accuracy" ? 1.0 : signalFrequency == "More/Good Accuracy" ? 1.5 : 2.0
lowerThreshold = signalFrequency == "Most/Moderate Accuracy" ? -1.0 : signalFrequency == "More/Good Accuracy" ? -1.5 : -2.0
// Continuation Signal Settings
atrMultiplier = input.float(2.0, "TP/DCA Market Breakout Detection", minval=0, step=0.5, tooltip="Number of ATR moves required to trigger continuation signals (Set to 0 to disable)")
// Visual Settings
showMA = false // MA display removed from settings
showSignals = input.bool(true, "Show Alert Signals", tooltip="Show visual signals when price is overextended")
// ============================================================================
// CALCULATIONS
// ============================================================================
// Calculate Moving Average based on type
ma = switch maType
"SMA" => ta.sma(close, maLength)
"EMA" => ta.ema(close, maLength)
"WMA" => ta.wma(close, maLength)
"VWMA" => ta.vwma(close, maLength)
=> ta.sma(close, maLength)
// Calculate deviation from MA
deviation = close - ma
// Calculate standard deviation
stdDev = ta.stdev(close, deviationLength)
// Calculate number of standard deviations away from MA
deviationScore = stdDev != 0 ? deviation / stdDev : 0
// Smooth the deviation score slightly for cleaner signals
smoothedDeviation = ta.ema(deviationScore, 3)
// ============================================================================
// SIGNALS
// ============================================================================
// Overextended conditions
overextendedHigh = smoothedDeviation >= upperThreshold
overextendedLow = smoothedDeviation <= lowerThreshold
// Signal triggers (crossing into overextended territory)
bullishSignal = ta.crossunder(smoothedDeviation, lowerThreshold)
bearishSignal = ta.crossover(smoothedDeviation, upperThreshold)
// Track if we're in bright histogram zones
isBrightGreen = smoothedDeviation <= lowerThreshold
isBrightRed = smoothedDeviation >= upperThreshold
// Track if we were in bright zone on previous bar
wasBrightGreen = smoothedDeviation <= lowerThreshold
wasBrightRed = smoothedDeviation >= upperThreshold
// Detect oscillator turning up after bright green (buy signal)
// Trigger if we were in bright green and oscillator turns up, even if no longer bright green
oscillatorTurningUp = smoothedDeviation > smoothedDeviation
buySignal = barstate.isconfirmed and wasBrightGreen and oscillatorTurningUp and smoothedDeviation <= smoothedDeviation
// Detect oscillator turning down after bright red (sell signal)
// Trigger if we were in bright red and oscillator turns down, even if no longer bright red
oscillatorTurningDown = smoothedDeviation < smoothedDeviation
sellSignal = barstate.isconfirmed and wasBrightRed and oscillatorTurningDown and smoothedDeviation >= smoothedDeviation
// ============================================================================
// ATR-BASED CONTINUATION SIGNALS
// ============================================================================
// Calculate ATR for distance measurement
atrLength = 14
atr = ta.atr(atrLength)
// Track price levels when ANY sell or buy signal occurs (original or continuation)
var float lastSellPrice = na
var float lastBuyPrice = na
// Initialize tracking on original signals
if sellSignal
lastSellPrice := close
if buySignal
lastBuyPrice := close
// Continuation Sell Signal: Price moved up by ATR multiplier from last red dot
// Disabled when atrMultiplier is set to 0
continuationSell = atrMultiplier > 0 and barstate.isconfirmed and not na(lastSellPrice) and close >= lastSellPrice + (atrMultiplier * atr)
// Continuation Buy Signal: Price moved down by ATR multiplier from last green dot
// Disabled when atrMultiplier is set to 0
continuationBuy = atrMultiplier > 0 and barstate.isconfirmed and not na(lastBuyPrice) and close <= lastBuyPrice - (atrMultiplier * atr)
// Update reference prices when continuation signals trigger (reset the 3 ATR counter)
if continuationSell
lastSellPrice := close
if continuationBuy
lastBuyPrice := close
// Combine original and continuation signals for plotting
allBuySignals = buySignal or continuationBuy
allSellSignals = sellSignal or continuationSell
// Track if a signal occurred to keep it visible on dashboard
// Signals trigger at barstate.isconfirmed (bar close)
var bool showBuyOnDashboard = false
var bool showSellOnDashboard = false
// Update dashboard flags immediately when signals occur
if allBuySignals
showBuyOnDashboard := true
showSellOnDashboard := false
else if allSellSignals
showSellOnDashboard := true
showBuyOnDashboard := false
else if barstate.isconfirmed
// Reset flags on bar close if no new signal
showBuyOnDashboard := false
showSellOnDashboard := false
// ============================================================================
// PLOTTING
// ============================================================================
// Professional color scheme
var color colorBullish = #00C853 // Professional green
var color colorBearish = #FF1744 // Professional red
var color colorNeutral = #2962FF // Professional blue
var color colorGrid = #363A45 // Dark gray for lines
var color colorBackground = #1E222D // Chart background
// Dynamic line color based on value
lineColor = smoothedDeviation > upperThreshold ? colorBearish :
smoothedDeviation < lowerThreshold ? colorBullish :
smoothedDeviation > 0 ? color.new(colorBearish, 50) :
color.new(colorBullish, 50)
// Plot the deviation oscillator with dynamic coloring
plot(smoothedDeviation, "Deviation Score", color=lineColor, linewidth=2)
// Plot zero line
hline(0, "Zero Line", color=color.new(colorGrid, 0), linestyle=hline.style_solid, linewidth=1)
// Subtle fill for overextended zones (without visible threshold lines)
upperLine = hline(upperThreshold, "Upper Threshold", color=color.new(color.gray, 100), linestyle=hline.style_dashed, linewidth=1)
lowerLine = hline(lowerThreshold, "Lower Threshold", color=color.new(color.gray, 100), linestyle=hline.style_dashed, linewidth=1)
fill(upperLine, hline(3), color=color.new(colorBearish, 95), title="Overextended High Zone")
fill(lowerLine, hline(-3), color=color.new(colorBullish, 95), title="Overextended Low Zone")
// Histogram style visualization (optional alternative)
histogramColor = smoothedDeviation >= upperThreshold ? color.new(colorBearish, 20) :
smoothedDeviation <= lowerThreshold ? color.new(colorBullish, 20) :
smoothedDeviation > 0 ? color.new(colorBearish, 80) :
color.new(colorBullish, 80)
plot(smoothedDeviation, "Histogram", color=histogramColor, style=plot.style_histogram, linewidth=3)
// ============================================================================
// BUY/SELL SIGNAL MARKERS
// ============================================================================
// Plot buy signals at -3.5 level (includes both initial and extended signals)
plot(allBuySignals ? -3.5 : na, title="Buy Signal", style=plot.style_circles,
color=color.new(colorBullish, 0), linewidth=4)
// Plot sell signals at 3.5 level (includes both initial and extended signals)
plot(allSellSignals ? 3.5 : na, title="Sell Signal", style=plot.style_circles,
color=color.new(colorBearish, 0), linewidth=4)
// ============================================================================
// ALERTS - SIMPLIFIED TO ONLY TWO ALERTS
// ============================================================================
// Alert 1: Long Entry/Short TP - fires on ANY green dot (original or continuation)
alertcondition(allBuySignals, "Long Entry/Short TP", "Long Entry/Short TP")
// Alert 2: Long TP/Short Entry - fires on ANY red dot (original or continuation)
alertcondition(allSellSignals, "Long TP/Short Entry", "Long TP/Short Entry")
// ============================================================================
// DATA DISPLAY
// ============================================================================
// Create a professional table for current readings
var color tableBgColor = #1a2332 // Dark blue background
var table infoTable = table.new(position.middle_right, 2, 2, border_width=1,
border_color=color.new(#2962FF, 30),
frame_width=1,
frame_color=color.new(#2962FF, 30))
if barstate.islast
// Determine status
statusText = overextendedHigh ? "OVEREXTENDED ↓" :
overextendedLow ? "OVEREXTENDED ↑" :
smoothedDeviation > 0 ? "Buyers In Control" : "Sellers In Control"
statusColor = overextendedHigh ? color.new(colorBearish, 0) :
overextendedLow ? color.new(colorBullish, 0) :
color.white
// Background color for status cell
statusBgColor = color.new(tableBgColor, 0)
// Status Row
table.cell(infoTable, 0, 0, "Status",
bgcolor=color.new(tableBgColor, 0),
text_color=color.white,
text_size=size.normal)
table.cell(infoTable, 1, 0, statusText,
bgcolor=statusBgColor,
text_color=statusColor,
text_size=size.normal)
// Signal Row - always show
table.cell(infoTable, 0, 1, "Signal",
bgcolor=color.new(tableBgColor, 0),
text_color=color.white,
text_size=size.normal)
// Show signal if flags are set (will stay visible during the bar)
if showBuyOnDashboard or showSellOnDashboard
// Green dot (buy signal) = "Long Entry/Short TP" with arrow up, white text on green background
// Red dot (sell signal) = "Long TP/Short Entry" with arrow down, white text on red background
signalText = showBuyOnDashboard ? "↑ Long Entry/Short TP" : "↓ Long TP/Short Entry"
signalColor = showBuyOnDashboard ? color.new(colorBullish, 0) : color.new(colorBearish, 0)
table.cell(infoTable, 1, 1, signalText,
bgcolor=signalColor,
text_color=color.white,
text_size=size.normal)
else
table.cell(infoTable, 1, 1, "Watching...",
bgcolor=color.new(tableBgColor, 0),
text_color=color.new(color.white, 60),
text_size=size.normal)
Key Support and ResistanceKEY SUPPORT AND RESISTANCE - USER GUIDE
========================================
OVERVIEW
This indicator automatically identifies and displays key support and resistance levels based on swing highs and swing lows. It uses pivot point detection to mark significant price levels where the market has previously shown reactions, helping traders identify potential entry/exit points and key decision zones.
KEY FEATURES
• Automatic Level Detection: Identifies swing highs (resistance) and swing lows (support) using pivot point analysis
• Dynamic Line Management: Displays only recent levels within a specified lookback period to keep charts clean
• Auto-Extending Lines: Projects support/resistance levels forward to anticipate future price interactions
• Color-Coded Levels: Red lines for resistance, green lines for support for easy visual identification
========================================
PARAMETERS
========================================
Left Bars (Default: 10)
• Minimum: 5 bars
• Number of bars to the left of the pivot point
• Higher values = more significant levels but fewer signals
• Lower values = more sensitive detection but may include minor swings
Right Bars (Default: 10)
• Minimum: 5 bars
• Number of bars to the right of the pivot point
• Must be confirmed by price action before the level is drawn
• Balances between confirmation delay and signal accuracy
Show Last N Bars (Default: 200)
• Minimum: 10 bars
• Only displays support/resistance levels detected within the most recent N bars
• Keeps your chart clean by removing outdated levels
• Adjust based on your trading timeframe and style
Line Extension Length (Default: 48)
• Minimum: 1 bar
• How many bars forward the support/resistance lines extend
• Helps visualize potential future price interactions
• Longer extensions useful for swing trading, shorter for day trading
========================================
HOW TO USE
========================================
FOR SWING TRADERS
1. Use default settings (10/10) or increase to 15/15 for more significant levels
2. Set "Show Last N Bars" to 300-500 to capture longer-term levels
3. Look for price reactions when approaching these levels
4. Combine with volume analysis for confirmation
FOR DAY TRADERS
1. Consider reducing Left/Right Bars to 7-8 for more frequent signals
2. Set "Show Last N Bars" to 100-150 to focus on recent action
3. Reduce "Line Extension Length" to 20-30 bars
4. Watch for intraday bounces or breakouts at these levels
TRADING STRATEGIES
Bounce Trading (Mean Reversion)
• Enter long when price approaches green support lines
• Enter short when price approaches red resistance lines
• Use stop loss just beyond the support/resistance level
• Best in ranging or consolidating markets
Breakout Trading (Trend Following)
• Wait for price to break through resistance (bullish) or support (bearish)
• Confirm with increased volume
• Previous resistance becomes new support (and vice versa)
• Best in trending markets
Multi-Timeframe Analysis
• Check higher timeframe levels for major support/resistance zones
• Use lower timeframe levels for precise entry/exit timing
• Confluence of multiple timeframe levels creates strong zones
========================================
IMPORTANT NOTES
========================================
Line Confirmation Delay
• Lines appear with a delay equal to "Right Bars" parameter
• This delay ensures the pivot point is confirmed
• Real-time level detection requires price action confirmation
Chart Clarity
• Maximum 500 lines can be displayed (TradingView limitation)
• Adjust "Show Last N Bars" if chart becomes too cluttered
• Old lines automatically delete when outside the lookback period
False Signals
• Not all support/resistance levels will hold
• Use additional confirmation (volume, candlestick patterns, other indicators)
• Markets can break through levels, especially during high-impact news
BEST PRACTICES
1. Combine with Other Analysis: Use alongside trend indicators, volume, and price action patterns
2. Context Matters: Consider overall market trend and structure
3. Risk Management: Always use stop losses; don't rely solely on S/R levels
4. Market Conditions: More effective in liquid, actively traded markets
5. Backtesting: Test settings on your specific instrument and timeframe before live trading
TROUBLESHOOTING
Too Many Lines?
• Increase "Left Bars" and "Right Bars" values
• Decrease "Show Last N Bars" value
Too Few Lines?
• Decrease "Left Bars" and "Right Bars" values
• Increase "Show Last N Bars" value
Lines Not Appearing?
• Ensure sufficient price data is loaded on your chart
• Check that "Right Bars" have passed since the last swing point
• Verify indicator is properly loaded (refresh if needed)
TECHNICAL DETAILS
• Uses ta.pivothigh() and ta.pivotlow() functions for level detection
• Implements array-based line management for efficient rendering
• Automatic cleanup of outdated lines to maintain performance
• Overlay indicator - displays directly on price chart
Disclaimer: This indicator is for educational and informational purposes only. It does not constitute financial advice. Always conduct your own research and risk assessment before making trading decisions.
========================================
中文使用指南
========================================
概述
本指標自動識別並顯示基於波段高點和低點的關鍵支撐阻力位。使用樞軸點檢測標記市場先前反應的重要價格水平，幫助交易者識別潛在的進出場點和關鍵決策區域。
主要功能
• 自動水平檢測：使用樞軸點分析識別波段高點（阻力）和波段低點（支撐）
• 動態線條管理：僅顯示指定回看期內的近期水平，保持圖表清晰
• 自動延伸線條：將支撐阻力水平向前投影，預測未來價格互動
• 顏色編碼：紅線表示阻力，綠線表示支撐，便於視覺識別
========================================
參數說明
========================================
左側K棒數（預設：10）
• 最小值：5根K棒
• 樞軸點左側的K棒數量
• 數值越高 = 水平越重要但訊號越少
• 數值越低 = 檢測更敏感但可能包含次要波動
右側K棒數（預設：10）
• 最小值：5根K棒
• 樞軸點右側的K棒數量
• 必須經過價格行為確認後才繪製水平
• 在確認延遲和訊號準確性之間取得平衡
顯示最近N根K棒內的點（預設：200）
• 最小值：10根K棒
• 僅顯示最近N根K棒內檢測到的支撐阻力水平
• 透過移除過時水平保持圖表清晰
• 根據您的交易時間框架和風格調整
線條延伸長度（預設：48）
• 最小值：1根K棒
• 支撐阻力線向前延伸的K棒數
• 幫助視覺化潛在的未來價格互動
• 較長延伸適合波段交易，較短適合當沖交易
========================================
使用方法
========================================
波段交易者
1. 使用預設設定（10/10）或增加至15/15以獲得更重要的水平
2. 將「顯示最近N根K棒」設為300-500以捕捉長期水平
3. 觀察價格接近這些水平時的反應
4. 結合成交量分析進行確認
當沖交易者
1. 考慮將左右側K棒減少至7-8以獲得更頻繁的訊號
2. 將「顯示最近N根K棒」設為100-150以專注於近期行情
3. 將「線條延伸長度」減少至20-30根K棒
4. 觀察日內在這些水平的反彈或突破
交易策略
反彈交易（均值回歸）
• 當價格接近綠色支撐線時做多
• 當價格接近紅色阻力線時做空
• 在支撐阻力水平之外設置止損
• 在區間或盤整市場中效果最佳
突破交易（趨勢跟隨）
• 等待價格突破阻力（看漲）或支撐（看跌）
• 以增加的成交量確認
• 先前的阻力成為新的支撐（反之亦然）
• 在趨勢市場中效果最佳
多時間框架分析
• 檢查更高時間框架的主要支撐阻力區域
• 使用較低時間框架進行精確的進出場時機
• 多個時間框架水平的匯合創造強大區域
========================================
重要注意事項
========================================
線條確認延遲
• 線條出現時會有等於「右側K棒數」參數的延遲
• 此延遲確保樞軸點被確認
• 實時水平檢測需要價格行為確認
圖表清晰度
• 最多可顯示500條線（TradingView限制）
• 如果圖表變得太雜亂，請調整「顯示最近N根K棒」
• 超出回看期的舊線會自動刪除
假訊號
• 並非所有支撐阻力水平都會守住
• 使用額外確認（成交量、K棒型態、其他指標）
• 市場可能突破水平，特別是在重大新聞期間
最佳實踐
1. 結合其他分析：與趨勢指標、成交量和價格行為型態一起使用
2. 背景很重要：考慮整體市場趨勢和結構
3. 風險管理：始終使用止損；不要僅依賴支撐阻力水平
4. 市場條件：在流動性高、活躍交易的市場中更有效
5. 回測：在實盤交易前，在您的特定商品和時間框架上測試設定
故障排除
線條太多？
• 增加「左側K棒數」和「右側K棒數」數值
• 減少「顯示最近N根K棒」數值
線條太少？
• 減少「左側K棒數」和「右側K棒數」數值
• 增加「顯示最近N根K棒」數值
線條未出現？
• 確保圖表上載入了足夠的價格數據
• 檢查自上次波動點以來是否已過「右側K棒數」
• 驗證指標是否正確載入（如需要請刷新）
技術細節
• 使用 ta.pivothigh() 和 ta.pivotlow() 函數進行水平檢測
• 實施基於陣列的線條管理以實現高效渲染
• 自動清理過時線條以保持性能
• 疊加指標 - 直接顯示在價格圖表上
免責聲明：本指標僅供教育和資訊目的。不構成財務建議。在做出交易決策前，請務必進行自己的研究和風險評估。
RSI Profile [Kodexius]RSI Profile is an advanced technical indicator that turns the classic RSI into a distribution profile instead of a single oscillating line. Rather than only showing where the RSI is at the current bar, it displays where the RSI has spent most of its time or most of its volume over a user defined lookback period.
The script builds a histogram of RSI values between 0 and 100, splits that range into configurable bins, and then projects the result to the right side of the chart. This gives you a clear visual representation of the RSI structure, including the Point of Control (POC), the Value Area High (VAH), and the Value Area Low (VAL). The POC marks the RSI level with the highest activity, while VAH and VAL bracket the percentage based value area around it.
By combining standard RSI, a distribution profile, and value area logic, this tool lets you study RSI behavior statistically instead of only bar by bar. You can immediately see whether the current RSI reading is located inside the dominant zone, extended above it, or depressed below it, and whether the recent regime has been biased toward overbought, oversold, or neutral territory. This is particularly useful for swing traders, mean reversion systems, and anyone who wants to integrate RSI context into a more profile oriented workflow.
🔹 Features
1. RSI-Based Distribution Profile
-Builds a histogram of RSI values between 0 and 100.
-The RSI range is divided into a user-defined number of bins (e.g., 30 bins).
-Each bin represents a band of RSI values, such as 0–3.33, 3.33–6.66, ..., 96.66–100.
-For each bar in the lookback period, the script:
-Finds which bin the RSI value belongs to
Adds either:
-1.0 → if using time/frequency
-volume → if using volume-weighted RSI distribution
This creates a clear profile of where RSI has been concentrated over the chosen lookback window.
2. Time / Volume Weighting Mode
Under Profile Settings, you can choose:
-Weight by Volume = false
→ Profile is built using time spent at each RSI level (frequency).
-Weight by Volume = true
→ Profile is built using volume traded at each RSI level.
This flexibility allows you to decide whether you want:
-A pure momentum structure (time spent at each RSI)
-Or a participation-weighted structure (where higher-volume zones are emphasized)
3. Configurable Lookback & Resolution
-Profile Lookback: number of historical bars to analyze.
-Number of Bins: controls the resolution of the histogram:
Fewer bins → smoother, fewer gaps
More bins → more detail, but potentially more visual sparsity
-Profile Width (Bars): defines how wide the histogram extends into the future (visually), converted into time using average bar duration.
This provides a balance between performance, clarity, and visual density.
4. Value Area, POC, VAH, VAL
The script computes:
-POC (Point of Control)
→ The RSI bin with the highest total value (time or volume).
-Value Area (VA)
→ The range of RSI bins that contain a user-specified percentage of total activity (e.g., 70%).
-VAH & VAL
→ Upper and lower RSI boundaries of this Value Area.
These are then drawn as horizontal lines and labeled:
-POC line and label
-VAH line and label
-VAL line and label
This gives you a profile-style view similar to classical volume profile, but entirely on the RSI axis.
5. Color Coding & Visual Design
The histogram bars (boxes) are colored using a smart scheme:
-Below 30 RSI → Oversold zone, uses the Oversold Color (default: green).
-Above 70 RSI → Overbought zone, uses the Overbought Color (default: red).
-Between 30 and 70 RSI → Neutral zone, uses a gradient between:
A soft blue at lower mid levels
A soft orange at higher mid levels
Additional styling:
-POC bin is highlighted in bright yellow.
-Bins inside the Value Area → lower transparency (more solid).
-Bins outside the Value Area → higher transparency (faded).
This makes it easy to visually distinguish:
-Core RSI activity (VA)
-Extremes (oversold/overbought)
-The single dominant zone (POC)
🔹 Calculations
This section summarizes the core logic behind the script and highlights the main building blocks that power the profile.
1. Profile Structure and Bin Initialization
A custom Profile type groups together configuration, bins and drawing objects. During initialization, the script splits the 0 to 100 RSI range into evenly spaced bins, each represented by a Bin record:
method initBins(Profile p) =>
p.bins := array.new()
float step = 100.0 / p.binCount
for i = 0 to p.binCount - 1
float low = i * step
float high = (i + 1) * step
p.bins.push(Bin.new(low, high, 0.0, box(na)))
2. Filling the Profile Over the Lookback Window
On the last bar, the script clears previous drawings and walks backward through the selected lookback window. For each historical bar, it reads the RSI and volume series and feeds them into the profile:
if barstate.islast
myProfile.reset()
int start = math.max(0, bar_index - lookback)
int end = bar_index
for i = 0 to (end - start)
float r = rsi
float v = volume
if not na(r)
myProfile.add(r, v)
The add method converts each RSI value into a bin index and accumulates either a frequency count or the bar volume, depending on the chosen mode:
method add(Profile p, float rsiValue, float volumeValue) =>
int idx = int(rsiValue / (100.0 / p.binCount))
if idx >= p.binCount
idx := p.binCount - 1
if idx < 0
idx := 0
Bin targetBin = p.bins.get(idx)
float addedValue = p.useVolume ? volumeValue : 1.0
targetBin.value += addedValue
3. Finding POC and Building the Value Area
Inside the draw method, the script first scans all bins to determine the maximum value and the total sum. The bin with the highest value becomes the POC. The value area is then constructed by expanding from that center bin until the desired percentage of total activity is covered:
for in p.bins
totalVal += b.value
if b.value > maxVal
maxVal := b.value
pocIdx := i
float vaTarget = totalVal * (p.vaPercent / 100.0)
float currentVaVol = maxVal
int upIdx = pocIdx
int downIdx = pocIdx
while currentVaVol < vaTarget
float upVol = (upIdx < p.binCount - 1) ? p.bins.get(upIdx + 1).value : 0.0
float downVol = (downIdx > 0) ? p.bins.get(downIdx - 1).value : 0.0
if upVol == 0 and downVol == 0
break
if upVol >= downVol
upIdx += 1
currentVaVol += upVol
else
downIdx -= 1
currentVaVol += downVol
Hybrid -WinCAlgo/// 🇬🇧
Hybrid - WinCAlgo is a weighted composite oscillator designed to provide a more robust and reliable signal than the standard Relative Strength Index (RSI). It integrates four different momentum and volume metrics—RSI, Money Flow Index (MFI), Scaled CCI, and VWAP-RSI—into a single 0-100 oscillator.
This powerful tool aims to filter market noise and enhance the detection of trend reversals by confirming momentum with trading volume and volume-weighted average price action.
⚪ What is this Indicator?
The Hybrid Oscillator combines:
* RSI (40% Weight): Measures fundamental price momentum.
* VWAP-RSI (40% Weight): Measures the momentum of the Volume Weighted Average Price (VWAP), providing strong volume confirmation for trend strength.
* MFI (10% Weight): Measures money flow volume, confirming momentum with liquidity.
* Scaled CCI (10% Weight): Tracks market extremes and potential trend shifts, scaled to fit the 0-100 range.
⚪ Key Features
* Composite Strength: Blends four different market factors for a multi-dimensional view of momentum.
* Volume Integration: High weights on VWAP-RSI and MFI ensure that momentum signals are backed by trading volume.
* Advanced Divergence: The robust formula significantly enhances the detection of Bullish and Bearish Divergences, often providing an earlier signal than traditional oscillators.
* Customizable: Adjustable Lookback Length (N) and Individual Component Weights allow users to fine-tune the oscillator for specific assets or timeframes.
* Visual Clarity: Uses 40/60 bands for earlier Overbought/Oversold indications, with a gradient-styled background for intuitive visual interpretation.
⚪ Usage
Use Hybrid – WinCAlgo as your primary momentum confirmation tool:
* Divergence Signals: Trust the indicator when it fails to confirm new price highs/lows; this signals imminent trend exhaustion and reversal.
* Accumulation/Distribution: Look for the oscillator to rise/fall while the price is ranging at a bottom/top; this confirms hidden buying or selling (accumulation).
* Overbought/Oversold: Use the 60 band as the trigger for potential selling/shorting signals, and the 40 band for potential buying/longing signals.
* Noise Filter: Combine with a higher timeframe chart (e.g., 4H or Daily) to filter out gürültü (noise) and focus only on significant momentum shifts.
---
STRAT - MTF Dashboard + FTFC + Reversals v2.7# STRAT Indicator - Complete Description
## Overview
A comprehensive multi-timeframe STRAT trading system indicator that combines market structure analysis, flip levels, Full Timeframe Continuity (FTFC), and reversal pattern detection across 12 timeframes.
## Core Features
### 1. **Multi-Timeframe STRAT Dashboard**
- Displays STRAT combos (1, 2u, 2d, 3) across 12 timeframes: 1m, 5m, 15m, 30m, 1H, 4H, 12H, Daily, Weekly, Monthly, Quarterly, Yearly
- Color-coded directional bias (green/red/doji)
- Inside bars (●) and Outside bars (●) highlighted
- Current timeframe marked with ★
### 2. **HTF Flip Levels with Smart Grouping**
- Displays higher timeframe (HTF) flip levels (open prices) as labels on the right side
- Automatically groups multiple timeframes at the same price level (e.g., "★ 1H/4H/D")
- Current timeframe flip level always displayed with ★ marker
- Color-coded: Green (above price) / Red (below price)
### 3. **Full Timeframe Continuity (FTFC)**
- User-selectable 4 timeframes for FTFC analysis (default: D, W, M, Q)
- Green line: FTFC Up (highest open of 4 timeframes)
- Red line: FTFC Down (lowest open of 4 timeframes)
- Identifies when price is above/below all 4 timeframe opens
### 4. **Hammer & Shooting Star Detection**
- **Hammer Pattern**: Long lower wick (≥2x body), small upper wick, signals potential bottom reversal
- **Shooting Star Pattern**: Long upper wick (≥2x body), small lower wick, signals potential top reversal
- Scans last 100 bars (adjustable) and marks ALL historical patterns
- Chart markers: 🔨 (Hammer) below bars, 🔻 (Shooting Star) above bars
- Dashboard column shows reversal patterns for each timeframe
- Adjustable wick-to-body ratio sensitivity (1.5 to 5.0)
### 5. **Debug Tables**
- **FTFC Debug**: Shows close vs. 4 timeframe opens, confirms all-green/all-red conditions
- **Reversal Debug**: Real-time analysis of current bar - body size, wick measurements, ratios, and pattern qualification
## Settings
### Display Settings
- Dashboard position (9 options: top-left to bottom-right)
- Dashboard text size (tiny to huge)
- Label offset and text size
- Toggle individual features on/off
### FTFC Settings
- Select 4 custom timeframes for continuity analysis
- Default: Daily, Weekly, Monthly, Quarterly
### Reversal Settings
- **Wick to Body Ratio**: Sensitivity for pattern detection (default 2.0)
- **Lookback Bars**: How many historical bars to scan (default 100, max 500)
- Show/hide reversal markers on chart
- Show/hide reversal debug table
## Use Cases
1. **Momentum Trading**: Identify STRAT setups (2-2, 2-1-2 reversals, 3-bar plays) across multiple timeframes
2. **Swing Trading**: Use HTF flip levels as support/resistance and FTFC for trend confirmation
3. **Reversal Trading**: Catch hammer/shooting star patterns at key levels for counter-trend entries
4. **Multi-Timeframe Analysis**: Confirm alignment across timeframes before entering trades
## How to Use
### For STRAT Traders
- Look for 2-1-2 reversal setups in the dashboard
- Watch for inside bars (●) at HTF flip levels for breakout trades
- Use outside bars (●) to identify potential volatility expansion
### For Reversal Traders
- 🔨 Hammers after downtrends = potential long entries
- 🔻 Shooting stars after uptrends = potential short entries
- Combine with HTF flip levels for high-probability setups
### For Trend Followers
- FTFC green line above = bullish structure
- FTFC red line below = bearish structure
- Enter when price breaks and holds above/below FTFC levels
## Visual Elements
- **Green Labels**: HTF flip levels above current price (resistance)
- **Red Labels**: HTF flip levels below current price (support)
- **Lime Line**: FTFC Up (highest timeframe open)
- **Red Line**: FTFC Down (lowest timeframe open)
- **🔨 Icon**: Hammer pattern (potential reversal up)
- **🔻 Icon**: Shooting Star pattern (potential reversal down)
- **★ Symbol**: Current timeframe or multiple timeframes grouped
## Performance Notes
This indicator performs 12 multi-timeframe security calls and may take 15-30 seconds to calculate on initial load. This is normal for comprehensive MTF analysis.
## Version
v2.7 - Simplified reversal detection, current TF labeling, optimized performance
---
**Perfect for**: STRAT traders, multi-timeframe analysts, reversal pattern traders, swing traders looking for high-probability setups with confluence across timeframes.
SMC Statistical Liquidity Walls [PhenLabs]📊 SMC Statistical Liquidity Walls
Version: PineScript™ v6
📌 Description
The SMC Statistical Liquidity Walls indicator is designed to visualize market volatility and potential reversal zones using advanced statistical modeling. Unlike traditional Bollinger Bands that use simple lines, this script utilizes an “Inverted Sigmoid” opacity function to create a “fog of war” effect. This visualizes the density of liquidity: the further price moves from the equilibrium (mean), the “harder” the liquidity wall becomes.
This tool solves the problem of over-trading in low-probability areas. By automatically mapping “Premium” (Resistance) and “Discount” (Support) zones based on Standard Deviation (SD), traders can instantly see when price is overextended. The result is a clean, intuitive overlay that helps you identify high-probability mean reversion setups without cluttering your chart with manual drawings.
🚀 Points of Innovation
Inverted Sigmoid Logic: A custom mathematical function maps Standard Deviation to opacity, creating a realistic “wall” density effect rather than linear gradients.
Dynamic “Solidity”: The indicator is transparent at the center (Equilibrium) and becomes visually solid at the edges, mimicking physical resistance.
Separated Directional Bias: distinct Red (Premium) and Green (Discount) coding helps SMC traders instantly recognize expensive vs. cheap pricing.
Smart “Safe” Deviation: Includes fallback logic to handle calculation errors if deviation hits zero, ensuring the indicator never crashes during data gaps.
🔧 Core Components
Basis Calculation: Uses a Simple Moving Average (SMA) to determine the market’s equilibrium point.
Standard Deviation Zones: Calculates 1SD, 2SD, and 3SD levels to define the statistical extremes of price action.
Sigmoid Alpha Calculation: Converts the SD distance into a transparency value (0-100) to drive the visual gradient.
🔥 Key Features
Automated Premium/Discount Zones: Red zones indicate overbought (Premium) areas; Green zones indicate oversold (Discount) areas.
Customizable Density: Users can adjust the “Steepness” and “Midpoint” of the sigmoid curve to control how fast the walls become solid.
Integrated Alerts: Built-in alert conditions trigger when price hits the “Solid” wall (2SD or higher), perfect for automated trading or notifications.
Visual Clarity: The center of the chart remains clear (high transparency) to keep focus on price action where it matters most.
🎨 Visualization
Equilibrium Line: A gray line representing the mean price.
Gradient Fills: The space between bands fills with color that increases in opacity as it moves outward.
Premium Wall: Upper zones fade from transparent red to solid red.
Discount Wall: Lower zones fade from transparent green to solid green.
📖 Usage Guidelines
Range Period: Default 20. Controls the lookback period for the SMA and Standard Deviation calculation.
Source: Default Close. The price data used for calculations.
Center Transparency: Default 100 (Clear). Controls how transparent the middle of the chart is.
Edge Transparency: Default 45 (Solid). Controls the opacity of the outermost liquidity wall.
Wall Steepness: Default 2.5. Adjusts how aggressively the gradient transitions from clear to solid.
Wall Start Point: Default 1.5 SD. The deviation level where the gradient shift begins to accelerate.
✅ Best Use Cases
Mean Reversion Trading: Enter trades when price hits the solid 2SD or 3SD wall and shows rejection wicks.
Take Profit Targets: Use the Equilibrium (Gray Line) as a logical first target for reversal trades.
Trend Filtering: Do not initiate new long positions when price is deep inside the Red (Premium) wall.
⚠️ Limitations
Lagging Nature: As a statistical tool based on Moving Averages, the walls react to past price data and may lag during sudden volatility spikes.
Trending Markets: In strong parabolic trends, price can “ride” the bands for extended periods; mean reversion should be used with caution in these conditions.
💡 What Makes This Unique
Physics-Based Visualization: We treat liquidity as a physical barrier that gets denser the deeper you push, rather than just a static line on a chart.
🔬 How It Works
Step 1: The script calculates the mean (SMA) and the Standard Deviation (SD) of the source price.
Step 2: It defines three zones above and below the mean (1SD, 2SD, 3SD).
Step 3: The custom `get_inverted_sigmoid` function calculates an Alpha (transparency) value based on the SD distance.
Step 4: Plot fills are colored dynamically, creating a seamless gradient that hardens at the extremes to visualize the “Liquidity Wall.”
💡 Note
For best results, combine this indicator with Price Action confirmation (such as pin bars or engulfing candles) when price touches the solid walls.
MTF RSI Stacked + AI + Gradient MTF RSI Stacked + AI + Gradient
Quick-start guide & best-practice rules
What the indicator does
Multi-Time-Frame RSI in one pane
• 10 time-frames (1 m → 1 M) are stacked 100 points apart (0, 100, 200 … 900).
• Each RSI is plotted with a smooth red-yellow-green gradient:
– Red = RSI below 30 (oversold)
– Yellow = RSI near 50
– Green = RSI above 70 (overbought)
• Grey 30-70 bands are drawn for every TF so you can see extremities at a glance.
Built-in AI (KNN) signal
• On every close of the chosen AI-time-frame the script:
– Takes the last 14-period RSI + normalised ATR as “features”
– Compares them to the last N bars (default 1 000)
– Votes of the k = 5 closest neighbours → BUY / SELL / NEUTRAL
• Confidence % is shown in the badge (top-right).
• A thick vertical line (green/red) is printed once when the signal flips.
How to read it
• Gradient colour tells you instantly which TFs are overbought/obove sold.
• When all or most gradients are green → broad momentum up; look for shorts only on lower-TF pullbacks.
• When most are red → broad momentum down; favour longs only on lower-TF bounces.
• Use the AI signal as a confluence filter, not a stand-alone entry:
– If AI = BUY and 3+ higher-TF RSIs just crossed > 50 → consider long.
– If AI = SELL and 3+ higher-TF RSIs just crossed < 50 → consider short.
• Divergences: price makes a higher high but 1 h/4 h RSI (gradient) makes a lower high → possible reversal.
Settings you can tweak
AI timeframe – leave empty = same as chart, or pick a higher TF (e.g. “15” or “60”) to slow the signal down.
Training bars – 500-2 000 is the sweet spot; bigger = slower but more stable.
K neighbours – 3-7; lower = more signals, higher = smoother.
RSI length – 14 is standard; 9 gives earlier turns, 21 gives fewer false swings.
Practical trading workflow
Open the symbol on your execution TF (e.g. 5 m).
Set AI timeframe to 3-5× execution TF (e.g. 15 m or 30 m) so the signal survives market noise.
Wait for AI signal to align with gradient extremes on at least one higher TF.
Enter on the first gradient reversal inside the 30-70 band on the execution TF.
Place stop beyond the swing that caused the gradient flip; target next opposing 70/30 level on the same TF or trail with structure.
Colour cheat-sheet
Bright green → RSI ≥ 70 (overbought)
Bright red → RSI ≤ 30 (oversold)
Muted colours → RSI near 50 (neutral, momentum pause)
That’s it—one pane, ten time-frames, colour-coded extremes and an AI confluence layer.
Keep the chart clean, use price action for precise entries, and let the gradient tell you when the wind is at your back.
CCI Threshold HistogramSynopsis
The Custom CCI Indicator by Simon20cent enhances traditional CCI analysis with adjustable smoothing and a momentum-based histogram. The histogram highlights key thresholds, turning green above +100 and red below –100 to clearly identify strong bullish or bearish momentum. Both the CCI and smoothed CCI lines can be toggled for a cleaner view, making this tool effective for spotting momentum shifts, breakout conditions, and potential entry zones with improved clarity.
NQ-VIX Expected Move LevelsNQ -VIX Daily Price Bands
This indicator plots dynamic intraday price bands for NQ futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = Daily Open + (NQ Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily Open - (NQ Price × VIX ÷ √252 ÷ 100)
The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's open
Lower band (red) contracts from the current day's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current NQ price and VIX level
Daily Open
Expected move
NQ-VIX Expected Move LTF LevelsNQ -VIX LTF Price Bands
This indicator plots dynamic intraday price bands for NQ futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = (Input TF Open) + (NQ Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
Lower Band = Daily Open - (NQ Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
The calculation uses the square root of Input TF ÷ (23h in min) to convert annualized VIX volatility into an expected TF move, then scales it as a percentage adjustment from the current TF input's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current TF's open
Lower band (red) contracts from the current TF's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current input TF
Current NQ price and VIX level
Current input TF Open
Expected move
Viprasol Elite Advanced Pattern Scanner# 🚀 Viprasol Elite Advanced Pattern Scanner
## Overview
The **Viprasol Elite Advanced Pattern Scanner** is a sophisticated technical analysis tool designed to identify high-probability double bottom (DISCOUNT) and double top (PREMIUM) patterns with unprecedented accuracy. Unlike basic pattern detectors, this elite scanner employs an AI-powered quality scoring system to filter out false signals and highlight only the most reliable trading opportunities.
## 🎯 Key Features
### Advanced Pattern Detection
- **DISCOUNT Patterns** (Double Bottoms): Identifies bullish reversal zones where price may bounce
- **PREMIUM Patterns** (Double Tops): Detects bearish reversal zones where price may decline
- Multi-point validation system (5-point structure)
- Symmetry analysis with customizable tolerance
### 🤖 AI Quality Scoring System
Each pattern receives a quality score (0-100) based on:
- **Symmetry Analysis** (32% weight): How closely the two bottoms/tops match
- **Trend Context** (22% weight): Strength of the preceding trend using ADX
- **Volume Profile** (22% weight): Volume confirmation at key points
- **Pattern Depth** (16% weight): Significance of the pattern's price range
- **Structure Quality** (16% weight): Overall pattern formation quality
Quality Grades:
- ⭐ **ELITE** (88-100): Highest probability setups
- ✨ **VERY STRONG** (77-87): Strong trade opportunities
- ✓ **STRONG** (67-76): Valid patterns with good potential
- ○ **VALID** (65-66): Acceptable patterns meeting minimum criteria
### 🎯 Intelligent Target System
Three target modes per pattern direction:
- **Conservative**: 0.618 Fibonacci extension (safer, closer targets)
- **Balanced**: 1.0 extension (moderate risk/reward)
- **Aggressive**: 1.618 extension (higher risk/reward)
Targets automatically adjust based on pattern quality score.
### 🔧 Advanced Filtering Options
- **Volatility Filter (ATR)**: Excludes patterns during extreme volatility
- **Momentum Filter (ADX)**: Ensures sufficient trend strength
- **Liquidity Filter (Volume)**: Confirms adequate trading volume
### 📊 Pattern Lifecycle Management
- Real-time neckline tracking with extension multiplier
- Pattern invalidation after extended wait period
- Breakout/breakdown confirmation
- Reversal detection (pattern failure scenarios)
- Target achievement tracking
### 🌈 Premium Visual System
- Color-coded quality levels
- Cyber-themed color scheme (Neon Green/Hot Pink/Purple/Cyan)
- Transparent fills for pattern zones
- Dynamic labels with pattern information
- Elite dashboard showing live pattern stats
## 📈 How To Use
### Basic Setup
1. Add indicator to your chart
2. Enable desired patterns (DISCOUNT and/or PREMIUM)
3. Adjust quality threshold (default: 65) - higher = fewer but better signals
4. Set your preferred target mode
### Trading DISCOUNT Patterns (Bullish)
1. Wait for pattern detection (labeled points 1-4)
2. Check quality score on dashboard
3. Entry on breakout above neckline (point 5)
4. Stop loss below the lowest bottom
5. Target shown automatically based on your mode
6. ⚠️ Watch for pattern failure (break below bottoms = SHORT signal)
### Trading PREMIUM Patterns (Bearish)
1. Wait for pattern detection (labeled points 1-4)
2. Check quality score on dashboard
3. Entry on breakdown below neckline (point 5)
4. Stop loss above the highest top
5. Target shown automatically based on your mode
6. ⚠️ Watch for pattern failure (break above tops = LONG signal)
## ⚙️ Input Settings Guide
### 🔍 Detection Engine
- **Left/Right Pivots**: Higher = fewer but cleaner patterns (default: 6/4)
- **Min Pattern Width**: Minimum bars between bottoms/tops (default: 12)
- **Symmetry Tolerance**: Max % difference allowed between levels (default: 1.8%)
- **Extension Multiplier**: How long to wait for breakout (default: 2.2x pattern width)
### ⭐ Quality AI
- **Min Quality Score**: Only show patterns above this score (default: 65)
- **Weight Distribution**: Customize what matters most (symmetry/trend/volume/depth/structure)
### 🔧 Filters
- **Volatility Filter**: Avoid choppy markets (recommended: ON)
- **Momentum Filter**: Ensure trend strength (recommended: ON)
- **Liquidity Filter**: Volume confirmation (recommended: ON)
### 💎 Target System
- Choose target aggression for each pattern type and direction
- Higher quality patterns get adjusted targets automatically
## 🎨 Visual Customization
- Adjust colors for DISCOUNT/PREMIUM patterns
- Set quality-based color coding
- Customize label sizes
- Toggle dashboard visibility and position
- Show/hide historical patterns
## 🚨 Alert System
Set up TradingView alerts for:
- 🚀 **LONG Signals**: DISCOUNT breakout, PREMIUM failure
- 📉 **SHORT Signals**: PREMIUM breakdown, DISCOUNT failure
- ✅ **Target Achievement**: When price hits your target
## 💡 Pro Tips
1. **Higher Timeframes = Better Signals**: Patterns on 4H, Daily, Weekly are more reliable
2. **Quality Over Quantity**: Focus on ELITE and VERY STRONG grades
3. **Combine with Trend**: DISCOUNT in uptrend, PREMIUM in downtrend = best results
4. **Watch Pattern Failures**: Failed patterns often provide strong counter-trend signals
5. **Adjust for Your Style**: Intraday traders use Conservative, swing traders use Aggressive
## 🔒 Pattern Invalidation
Patterns become invalid if:
- No breakout/breakdown within extension period
- Support/resistance levels are broken prematurely
- Pattern shown in faded colors = no longer active
## ⚠️ Risk Disclaimer
This indicator is a tool for technical analysis and does not guarantee profitable trades. Always:
- Use proper risk management
- Combine with other analysis methods
- Never risk more than you can afford to lose
- Past performance does not indicate future results
ES-VIX Expected Move LTF LevelsES-VIX LTF Price Bands
This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = (Input TF Open) + (ES Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
Lower Band = Daily Open - (ES Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
The calculation uses the square root of Input TF ÷ (23h in min) to convert annualized VIX volatility into an expected TF move, then scales it as a percentage adjustment from the current TF input's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current TF's open
Lower band (red) contracts from the current TF's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current input TF
Current ES price and VIX level
Current input TF Open
Expected move
Hurst Exponent - Detrended Fluctuation AnalysisIn stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analyzing time series that appear to be long-memory processes and noise.
█ OVERVIEW
We have introduced the concept of Hurst Exponent in our previous open indicator Hurst Exponent (Simple). It is an indicator that measures market state from autocorrelation. However, we apply a more advanced and accurate way to calculate Hurst Exponent rather than simple approximation. Therefore, we recommend using this version of Hurst Exponent over our previous publication going forward. The method we used here is called detrended fluctuation analysis. (For folks that are not interested in the math behind the calculation, feel free to skip to "features" and "how to use" section. However, it is recommended that you read it all to gain a better understanding of the mathematical reasoning).
█ Detrend Fluctuation Analysis
Detrended Fluctuation Analysis was first introduced by by Peng, C.K. (Original Paper) in order to measure the long-range power-law correlations in DNA sequences . DFA measures the scaling-behavior of the second moment-fluctuations, the scaling exponent is a generalization of Hurst exponent.
The traditional way of measuring Hurst exponent is the rescaled range method. However DFA provides the following benefits over the traditional rescaled range method (RS) method:
• Can be applied to non-stationary time series. While asset returns are generally stationary, DFA can measure Hurst more accurately in the instances where they are non-stationary.
• According the the asymptotic distribution value of DFA and RS, the latter usually overestimates Hurst exponent (even after Anis- Llyod correction) resulting in the expected value of RS Hurst being close to 0.54, instead of the 0.5 that it should be. Therefore it's harder to determine the autocorrelation based on the expected value. The expected value is significantly closer to 0.5 making that threshold much more useful, using the DFA method on the Hurst Exponent (HE).
• Lastly, DFA requires lower sample size relative to the RS method. While the RS method generally requires thousands of observations to reduce the variance of HE, DFA only needs a sample size greater than a hundred to accomplish the above mentioned.
█ Calculation
DFA is a modified root-mean-squares (RMS) analysis of a random walk. In short, DFA computes the RMS error of linear fits over progressively larger bins (non-overlapped “boxes” of similar size) of an integrated time series.
Our signal time series is the log returns. First we subtract the mean from the log return to calculate the demeaned returns. Then, we calculate the cumulative sum of demeaned returns resulting in the cumulative sum being mean centered and we can use the DFA method on this. The subtraction of the mean eliminates the “global trend” of the signal. The advantage of applying scaling analysis to the signal profile instead of the signal, allows the original signal to be non-stationary when needed. (For example, this process converts an i.i.d. white noise process into a random walk.)
We slice the cumulative sum into windows of equal space and run linear regression on each window to measure the linear trend. After we conduct each linear regression. We detrend the series by deducting the linear regression line from the cumulative sum in each windows. The fluctuation is the difference between cumulative sum and regression.
We use different windows sizes on the same cumulative sum series. The window sizes scales are log spaced. Eg: powers of 2, 2,4,8,16... This is where the scale free measurements come in, how we measure the fractal nature and self similarity of the time series, as well as how the well smaller scale represent the larger scale.
As the window size decreases, we uses more regression lines to measure the trend. Therefore, the fitness of regression should be better with smaller fluctuation. It allows one to zoom into the “picture” to see the details. The linear regression is like rulers. If you use more rulers to measure the smaller scale details you will get a more precise measurement.
The exponent we are measuring here is to determine the relationship between the window size and fitness of regression (the rate of change). The more complex the time series are the more it will depend on decreasing window sizes (using more linear regression lines to measure). The less complex or the more trend in the time series, it will depend less. The fitness is calculated by the average of root mean square errors (RMS) of regression from each window.
Root mean Square error is calculated by square root of the sum of the difference between cumulative sum and regression. The following chart displays average RMS of different window sizes. As the chart shows, values for smaller window sizes shows more details due to higher complexity of measurements.
The last step is to measure the exponent. In order to measure the power law exponent. We measure the slope on the log-log plot chart. The x axis is the log of the size of windows, the y axis is the log of the average RMS. We run a linear regression through the plotted points. The slope of regression is the exponent. It's easy to see the relationship between RMS and window size on the chart. Larger RMS equals less fitness of the regression. We know the RMS will increase (fitness will decrease) as we increases window size (use less regressions to measure), we focus on the rate of RMS increasing (how fast) as window size increases.
If the slope is < 0.5, It means the rate of of increase in RMS is small when window size increases. Therefore the fit is much better when it's measured by a large number of linear regression lines. So the series is more complex. (Mean reversion, negative autocorrelation).
If the slope is > 0.5, It means the rate of increase in RMS is larger when window sizes increases. Therefore even when window size is large, the larger trend can be measured well by a small number of regression lines. Therefore the series has a trend with positive autocorrelation.
If the slope = 0.5, It means the series follows a random walk.
█ FEATURES
• Sample Size is the lookback period for calculation. Even though DFA requires a lower sample size than RS, a sample size larger > 50 is recommended for accurate measurement.
• When a larger sample size is used (for example = 1000 lookback length), the loading speed may be slower due to a longer calculation. Date Range is used to limit numbers of historical calculation bars. When loading speed is too slow, change the data range "all" into numbers of weeks/days/hours to reduce loading time. (Credit to allanster)
• “show filter” option applies a smoothing moving average to smooth the exponent.
• Log scale is my work around for dynamic log space scaling. Traditionally the smallest log space for bars is power of 2. It requires at least 10 points for an accurate regression, resulting in the minimum lookback to be 1024. I made some changes to round the fractional log space into integer bars requiring the said log space to be less than 2.
• For a more accurate calculation a larger "Base Scale" and "Max Scale" should be selected. However, when the sample size is small, a larger value would cause issues. Therefore, a general rule to be followed is: A larger "Base Scale" and "Max Scale" should be selected for a larger the sample size. It is recommended for the user to try and choose a larger scale if increasing the value doesn't cause issues.
The following chart shows the change in value using various scales. As shown, sometimes increasing the value makes the value itself messy and overshoot.
When using the lowest scale (4,2), the value seems stable. When we increase the scale to (8,2), the value is still alright. However, when we increase it to (8,4), it begins to look messy. And when we increase it to (16,4), it starts overshooting. Therefore, (8,2) seems to be optimal for our use.
█ How to Use
Similar to Hurst Exponent (Simple). 0.5 is a level for determine long term memory.
• In the efficient market hypothesis, market follows a random walk and Hurst exponent should be 0.5. When Hurst Exponent is significantly different from 0.5, the market is inefficient.
• When Hurst Exponent is > 0.5. Positive Autocorrelation. Market is Trending. Positive returns tend to be followed by positive returns and vice versa.
• Hurst Exponent is < 0.5. Negative Autocorrelation. Market is Mean reverting. Positive returns trends to follow by negative return and vice versa.
However, we can't really tell if the Hurst exponent value is generated by random chance by only looking at the 0.5 level. Even if we measure a pure random walk, the Hurst Exponent will never be exactly 0.5, it will be close like 0.506 but not equal to 0.5. That's why we need a level to tell us if Hurst Exponent is significant.
So we also computed the 95% confidence interval according to Monte Carlo simulation. The confidence level adjusts itself by sample size. When Hurst Exponent is above the top or below the bottom confidence level, the value of Hurst exponent has statistical significance. The efficient market hypothesis is rejected and market has significant inefficiency.
The state of market is painted in different color as the following chart shows. The users can also tell the state from the table displayed on the right.
An important point is that Hurst Value only represents the market state according to the past value measurement. Which means it only tells you the market state now and in the past. If Hurst Exponent on sample size 100 shows significant trend, it means according to the past 100 bars, the market is trending significantly. It doesn't mean the market will continue to trend. It's not forecasting market state in the future.
However, this is also another way to use it. The market is not always random and it is not always inefficient, the state switches around from time to time. But there's one pattern, when the market stays inefficient for too long, the market participants see this and will try to take advantage of it. Therefore, the inefficiency will be traded away. That's why Hurst exponent won't stay in significant trend or mean reversion too long. When it's significant the market participants see that as well and the market adjusts itself back to normal.
The Hurst Exponent can be used as a mean reverting oscillator itself. In a liquid market, the value tends to return back inside the confidence interval after significant moves(In smaller markets, it could stay inefficient for a long time). So when Hurst Exponent shows significant values, the market has just entered significant trend or mean reversion state. However, when it stays outside of confidence interval for too long, it would suggest the market might be closer to the end of trend or mean reversion instead.
Larger sample size makes the Hurst Exponent Statistics more reliable. Therefore, if the user want to know if long term memory exist in general on the selected ticker, they can use a large sample size and maximize the log scale. Eg: 1024 sample size, scale (16,4).
Following Chart is Bitcoin on Daily timeframe with 1024 lookback. It suggests the market for bitcoin tends to have long term memory in general. It generally has significant trend and is more inefficient at it's early stage.
Expected Move BandsExpected move is the amount that an asset is predicted to increase or decrease from its current price, based on the current levels of volatility.
In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution.
Note: Normal distribution is just an assumption, it's not the real distribution of return
Settings:
"Estimation Period Selection" is for selecting the period we want to construct the prediction interval.
For "Current Bar", the interval is calculated based on the data of the previous bar close. Therefore changes in the current price will have little effect on the range. What current bar means is that the estimated range is for when this bar close. E.g., If the Timeframe on 4 hours and 1 hour has passed, the interval is for how much time this bar has left, in this case, 3 hours.
For "Future Bars", the interval is calculated based on the current close. Therefore the range will be very much affected by the change in the current price. If the current price moves up, the range will also move up, vice versa. Future Bars is estimating the range for the period at least one bar ahead.
There are also other source selections based on high low.
Time setting is used when "Future Bars" is chosen for the period. The value in time means how many bars ahead of the current bar the range is estimating. When time = 1, it means the interval is constructing for 1 bar head. E.g., If the timeframe is on 4 hours, then it's estimating the next 4 hours range no matter how much time has passed in the current bar.
Note: It's probably better to use "probability cone" for visual presentation when time > 1
Volatility Models :
Sample SD: traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias
Parkinson: Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close
Garman Klass: Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient
Yangzhang Garman Klass Extension: Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps.
about 8 x efficient
Rogers: Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient
EWMA: Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster.
YangZhang: Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance.
Median absolute deviation: It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator.
Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on a larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters.
Standard deviations:
Standard Deviation One shows the estimated range where the closing price will be about 68% of the time.
Standard Deviation two shows the estimated range where the closing price will be about 95% of the time.
Standard Deviation three shows the estimated range where the closing price will be about 99.7% of the time.
Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability.
Manually Entered Standard Deviation shows the range of any entered standard deviation. The probability of that range will be presented on the panel.
People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended. Assuming zero mean is recommended when time is not greater than 1.
When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation.
Note: The significance of autocorrelation can be checked on an ACF indicator.
ACF
The multimeframe option enables people to use higher period expected move on the lower time frame. People should only use time frame higher than the current time frame for the input. An error warning will appear when input Tf is lower. The input format is multiplier * time unit. E.g. : 1D
Unit: M for months, W for Weeks, D for Days, integers with no unit for minutes (E.g. 240 = 240 minutes). S for Seconds.
Smoothing option is using a filter to smooth out the range. The filter used here is John Ehler's supersmoother. It's an advance smoothing technique that gets rid of aliasing noise. It affects is similar to a simple moving average with half the lookback length but smoother and has less lag.
Note: The range here after smooth no long represent the probability
Panel positions can be adjusted in the settings.
X position adjusts the horizontal position of the panel. Higher X moves panel to the right and lower X moves panel to the left.
Y position adjusts the vertical position of the panel. Higher Y moves panel up and lower Y moves panel down.
Step line display changes the style of the bands from line to step line. Step line is recommended because it gets rid of the directional bias of slope of expected move when displaying the bands.
Warnings:
People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails.
Different volatility models provide different properties if people are interested in the accuracy and the fit of expected move, they can try expected move occurrence indicator. (The result also demonstrate the previous point about the drawback of using normal distribution assumption).
Expected move Occurrence Test
The prediction interval is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between. E.g., If 1 SD range is 100 - 200, the price can go to 80 or 230 intrabar, but if the bar close within 100 - 200 in the end. It's still considered a 68% one standard deviation move.
ES-VIX Expected Move - Open basedES-VIX Daily Price Bands
This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = Daily Open + (ES Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily Open - (ES Price × VIX ÷ √252 ÷ 100)
The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's open
Lower band (red) contracts from the current day's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current ES price and VIX level
Daily Open
Expected move
AlphaRank MA Lens – Multi-Timeframe Moving Average MapAlphaRank MA Lens – Multi-Timeframe Moving Average Map
AlphaRank MA Lens is a clean, open-source moving-average overlay that turns price action into an easy-to-read trend map. It focuses on structure and context only — no signals, no backtest, no hype — just a clear view of where price sits relative to key moving averages.
The script plots the 10 / 20 / 50 / 100 / 150 / 200 / 730 moving averages with full color control and a single “MA Type” switch, so you can flip the whole stack between SMA and EMA in one click. Instead of loading multiple separate MA indicators, this puts the full trend stack in one tool.
An optional background highlight lets you choose a reference MA (for example the 200 MA) and softly shade the chart:
Green when price is above that MA
Red when price is below it
This makes trend regime changes easy to see at a glance.
How traders typically use it (education only):
10/20/50 MAs → short-term trend and momentum.
100/150/200/730 MAs → bigger structural trend and “where price lives” in the long-term range.
Many traders consider conditions healthier when price and the short MAs are stacked above the longer MAs, and weaker when price trades below them.
Follow my work: AlphaRank
This script is for educational and analytical purposes only and does not provide trading advice or performance promises. Always combine it with your own judgment, testing, and risk management.
Abu Basel IQOption 2m Signals//@version=5
indicator("Abu Basel IQOption 2m Signals", overlay = true, timeframe = "", timeframe_gaps = true)
//========================
// الإعدادات
//========================
emaFastLen = input.int(9, "EMA سريع (9)")
emaSlowLen = input.int(21, "EMA بطيء (21)")
rsiLen = input.int(14, "RSI Length", minval = 2)
rsiBuyLevel = input.float(50.0, "RSI حد الشراء (أعلى من)", minval = 0, maxval = 100)
rsiSellLevel= input.float(50.0, "RSI حد البيع (أقل من)", minval = 0, maxval = 100)
bbLen = input.int(20, "Bollinger Length")
bbMult = input.float(2.0, "Bollinger Deviation")
showSignals = input.bool(true, "إظهار الأسهم (CALL / PUT)")
showBg = input.bool(true, "تلوين الخلفية عند الإشارات")
//========================
// المؤشرات الأساسية
//========================
emaFast = ta.ema(close, emaFastLen)
emaSlow = ta.ema(close, emaSlowLen)
basis = ta.sma(close, bbLen)
dev = bbMult * ta.stdev(close, bbLen)
bbUpper = basis + dev
bbLower = basis - dev
rsi = ta.rsi(close, rsiLen)
// رسم المتوسطات والبولينجر
plot(emaFast, title = "EMA 9", linewidth = 2)
plot(emaSlow, title = "EMA 21", linewidth = 2)
plot(basis, title = "BB Basis", linewidth = 1)
plot(bbUpper, title = "BB Upper", linewidth = 1, style = plot.style_line)
plot(bbLower, title = "BB Lower", linewidth = 1, style = plot.style_line)
//========================
// دوال أشكال الشموع الانعكاسية
//========================
bodySize = math.abs(close - open)
fullRange = high - low
upperWick = high - math.max(open, close)
lowerWick = math.min(open, close) - low
isSmallBody = bodySize <= fullRange * 0.3
// Hammer صاعدة (ذيل سفلي طويل)
bullHammer() =>
lowerWick > bodySize * 2 and upperWick <= bodySize and close > open
// Shooting Star هابطة (ذيل علوي طويل)
bearShootingStar() =>
upperWick > bodySize * 2 and lowerWick <= bodySize and close < open
// Bullish Engulfing
bullEngulfing() =>
close > open and close < open and close > open and open < close
// Bearish Engulfing
bearEngulfing() =>
close < open and close > open and close < open and open > close
// تجميع أنماط صعود/هبوط
bullPattern = bullHammer() or bullEngulfing()
bearPattern = bearShootingStar() or bearEngulfing()
//========================
// شروط الدخول
//========================
// تقاطع المتوسطات
bullCross = ta.crossover(emaFast, emaSlow) // صعود
bearCross = ta.crossunder(emaFast, emaSlow) // هبوط
// شروط شراء CALL:
// 1) تقاطع EMA9 فوق EMA21
// 2) السعر فوق خط وسط البولنجر
// 3) RSI أعلى من 50
// 4) شمعة انعكاسية صاعدة (Hammer أو Engulfing)
callCond = bullCross and close > basis and rsi > rsiBuyLevel and bullPattern
// شروط بيع PUT:
// 1) تقاطع EMA9 تحت EMA21
// 2) السعر تحت خط وسط البولنجر
// 3) RSI أقل من 50
// 4) شمعة انعكاسية هابطة (Shooting Star أو Bearish Engulfing)
putCond = bearCross and close < basis and rsi < rsiSellLevel and bearPattern
//========================
// رسم الإشارات على الشارت
//========================
plotshape(showSignals and callCond, title="CALL 2m",
style=shape.labelup, location=location.belowbar,
text="CALL\n2m", size=size.tiny)
plotshape(showSignals and putCond, title="PUT 2m",
style=shape.labeldown, location=location.abovebar,
text="PUT\n2m", size=size.tiny)
// تلوين الخلفية عند الإشارات
bgcolor(showBg and callCond ? color.new(color.green, 85) :
showBg and putCond ? color.new(color.red, 85) : na)
//========================
// شروط التنبيه (Alerts)
//========================
alertcondition(callCond, title="CALL 2m Signal",
message="Abu Basel Signal: CALL 2m on {{ticker}} at {{close}}")
alertcondition(putCond, title="PUT 2m Signal",
message="Abu Basel Signal: PUT 2m on {{ticker}} at {{close}}")
ADX Forecast Colorful [DiFlip]ADX Forecast Colorful
Introducing one of the most advanced ADX indicators available — a fully customizable analytical tool that integrates forward-looking forecasting capabilities. ADX Forecast Colorful is a scientific evolution of the classic ADX, designed to anticipate future trend strength using linear regression. Instead of merely reacting to historical data, this indicator projects the future behavior of the ADX, giving traders a strategic edge in trend analysis.
⯁ Real-Time ADX Forecasting
For the first time, a public ADX indicator incorporates linear regression (least squares method) to forecast the future behavior of ADX. This breakthrough approach enables traders to anticipate trend strength changes based on historical momentum. By applying linear regression to the ADX, the indicator plots a projected trendline n periods ahead — helping users make more accurate and timely trading decisions.
⯁ Highly Customizable
The indicator adapts seamlessly to any trading style. It offers a total of 26 long entry conditions and 26 short entry conditions, making it one of the most configurable ADX tools on TradingView. Each condition is fully adjustable, enabling the creation of statistical, quantitative, and automated strategies. You maintain full control over the signals to align perfectly with your system.
⯁ Innovative and Science-Based
This is the first public ADX indicator to apply least-squares predictive modeling to ADX dynamics. Technically, it embeds machine learning logic into a traditional trend-strength indicator. Using linear regression as a predictive engine adds powerful statistical rigor to the ADX, turning it into an intelligent, forward-looking signal generator.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental method in statistics and machine learning used to model the relationship between a dependent variable y and one or more independent variables x. The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted value (e.g., future ADX)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept
β₁ = slope (rate of change)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the ADX projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error, the regression coefficients are calculated as:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ = summation
x̄ and ȳ = means of x and y
i ranges from 1 to n (number of data points)
These formulas provide the best linear unbiased estimator under Gauss-Markov conditions — assuming constant variance and linearity.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational algorithm in supervised learning. Its power in producing quantitative predictions makes it essential in AI systems, predictive analytics, time-series forecasting, and automated trading. Applying it to the ADX essentially places an intelligent forecasting engine inside a classic trend tool.
⯁ Visual Interpretation
Imagine an ADX time series like this:
Time →
ADX →
The regression line smooths these values and projects them n periods forward, creating a predictive trajectory. This forecasted ADX line can intersect with the actual ADX, offering smarter buy and sell signals.
⯁ Summary of Scientific Concepts
Linear Regression: Models variable relationships with a straight line.
Least Squares: Minimizes prediction errors for best fit.
Time-Series Forecasting: Predicts future values using historical data.
Supervised Learning: Trains models to predict outcomes from inputs.
Statistical Smoothing: Reduces noise and highlights underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Based on rigorous statistical theory.
Unprecedented: First public ADX using least-squares forecast modeling.
Smart: Uses machine learning logic.
Forward-Looking: Generates predictive, not just reactive, signals.
Customizable: Flexible for any strategy or timeframe.
⯁ Conclusion
By merging ADX and linear regression, this indicator enables traders to predict market momentum rather than merely follow it. ADX Forecast Colorful is not just another indicator — it’s a scientific leap forward in technical analysis. With 26 fully configurable entry conditions and smart forecasting, this open-source tool is built for creating cutting-edge quantitative strategies.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the ADX?
The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down.
The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength.
⯁ How to use the ADX?
The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones:
Strong Trend: When the ADX is above 25, indicating a strong trend.
Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend.
Neutral Zone: Between 20 and 25, where the trend strength is unclear.
⯁ Entry Conditions
Each condition below is fully configurable and can be combined to build precise trading logic.
📈 BUY
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
📉 SELL
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
🤖 Automation
All BUY and SELL conditions are compatible with TradingView alerts, making them ideal for fully or semi-automated systems.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Superior-Range Bound Renko - Strategy - 11-29-25 - SignalLynxSuperior-Range Bound Renko Strategy with Advanced Risk Management Template
Signal Lynx | Free Scripts supporting Automation for the Night-Shift Nation 🌙
1. Overview
Welcome to Superior-Range Bound Renko (RBR) — a volatility-aware, structure-respecting swing-trading system built on top of a full Risk Management (RM) Template from Signal Lynx.
Instead of relying on static lookbacks (like “14-period RSI”) or plain MA crosses, Superior RBR:
Adapts its range definition to market volatility in real time
Emulates Renko Bricks on a standard, time-based chart (no Renko chart type required)
Uses a stack of Laguerre Filters to detect genuine impulse vs. noise
Adds an Adaptive SuperTrend powered by a small k-means-style clustering routine on volatility
Under the hood, this script also includes the full Signal Lynx Risk Management Engine:
A state machine that separates “Signal” from “Execution”
Layered exit tools: Stop Loss, Trailing Stop, Staged Take Profit, Advanced Adaptive Trailing Stop (AATS), and an RSI-style stop (RSIS)
Designed for non-repainting behavior on closed candles by basing execution-critical logic on previous-bar data
We are publishing this as an open-source template so traders and developers can leverage a professional-grade RM engine while integrating their own signal logic if they wish.
2. Quick Action Guide (TL;DR)
Best Timeframe:
4 Hours (H4) and above. This is a high-conviction swing-trading system, not a scalper.
Best Assets:
Volatile instruments that still respect market structure:
Bitcoin, Ethereum, Gold (XAUUSD), high-volatility Forex pairs (e.g., GBPJPY), indices with clean ranges.
Strategy Type:
Volatility-Adaptive Trend Following + Impulse Detection.
It hunts for genuine expansion out of ranges, not tiny mean-reversion nibbles.
Key Feature:
Renko Emulation on time-based candles.
We mathematically model Renko Bricks and overlay them on your standard chart to define:
“Equilibrium” zones (inside the brick structure)
“Breakout / impulse” zones (when price AND the impulse line depart from the bricks)
Repainting:
Designed to be non-repainting on closed candles.
All RM execution logic uses confirmed historical data (no future bars, no security() lookahead). Intrabar flicker during formation is allowed, but once a bar closes the engine’s decisions are stable.
Core Toggles & Filters:
Enable Longs and Shorts independently
Optional Weekend filter (block trades on Saturday/Sunday)
Per-module toggles: Stop Loss, Trailing Stop, Staged Take Profits, AATS, RSIS
3. Detailed Report: How It Works
A. The Strategy Logic: Superior RBR
Superior RBR builds its entry signal from multiple mathematical layers working together.
1) Adaptive Lookback (Volatility Normalization)
Instead of a fixed 100-bar or 200-bar range, the script:
Computes ATR-based volatility over a user-defined period.
Normalizes that volatility relative to its recent min/max.
Maps the normalized value into a dynamic lookback window between a minimum and maximum (e.g., 4 to 100 bars).
High Volatility:
The lookback shrinks, so the system reacts faster to explosive moves.
Low Volatility:
The lookback expands, so the system sees a “bigger picture” and filters out chop.
All the core “Range High/Low” and “Range Close High/Low” boundaries are built on top of this adaptive window.
2) Range Construction & Quick Ranges
The engine constructs several nested ranges:
Outer Range:
rangeHighFinal – dynamic highest high
rangeLowFinal – dynamic lowest low
Inner Close Range:
rangeCloseHighFinal – highest close
rangeCloseLowFinal – lowest close
Quick Ranges:
“Half-length” variants of those, used to detect more responsive changes in structure and volatility.
These ranges define:
The macro box price is trading inside
Shorter-term “pressure zones” where price is coiling before expansion
3) Renko Emulation (The Bricks)
Rather than using the Renko chart type (which discards time), this script emulates Renko behavior on your normal candles:
A “brick size” is defined either:
As a standard percentage move, or
As a volatility-driven (ATR) brick, optionally inhibited by a minimum standard size
The engine tracks a base value and derives:
brickUpper – top of the emulated brick
brickLower – bottom of the emulated brick
When price moves sufficiently beyond those levels, the brick “shifts”, and the directional memory (renkoDir) updates:
renkoDir = +2 when bricks are advancing upward
renkoDir = -2 when bricks are stepping downward
You can think of this as a synthetic Renko tape overlaid on time-based candles:
Inside the brick: equilibrium / consolidation
Breaking away from the brick: momentum / expansion
4) Impulse Tracking with Laguerre Filters
The script uses multiple Laguerre Filters to smooth price and brick-derived data without traditional lag.
Key filters include:
LagF_1 / LagF_W: Based on brick upper/lower baselines
LagF_Q: Based on HLCC4 (high + low + 2×close)/4
LagF_Y / LagF_P: Complex averages combining brick structures and range averages
LagF_V (Primary Impulse Line):
A smooth, high-level impulse line derived from a blend of the above plus the outer ranges
Conceptually:
When the impulse line pushes away from the brick structure and continues in one direction, an impulse move is underway.
When its direction flips and begins to roll over, the impulse is fading, hinting at mean reversion back into the range.
5) Fib-Based Structure & Swaps
The system also layers in Fib levels derived from the adaptive ranges:
Standard levels (12%, 23.6%, 38.2%, 50%, 61%, 76.8%, 88%) from the main range
A secondary “swap” set derived from close-range dynamics (fib12Swap, fib23Swap, etc.)
These Fibs are used to:
Bucket price into structural zones (below 12, between 23–38, etc.)
Detect breakouts when price and Laguerre move beyond key Fib thresholds
Drive zSwap logic (where a secondary Fib set becomes the active structure once certain conditions are met)
6) Adaptive SuperTrend with K-Means-Style Volatility Clustering
Under the hood, the script uses a small k-means-style clustering routine on ATR:
ATR is measured over a fixed period
The range of ATR values is split into Low, Medium, High volatility centroids
Current ATR is assigned to the nearest centroid (cluster)
From that, a SuperTrend variant (STK) is computed with dynamic sensitivity:
In quiet markets, SuperTrend can afford to be tighter
In wild markets, it widens appropriately to avoid constant whipsaw
This SuperTrend-based oscillator (LagF_K and its signals) is then combined with the brick and Laguerre stack to confirm valid trend regimes.
7) Final Baseline Signals (+2 / -2)
The “brain” of Superior RBR lives in the Baseline & Signal Generation block:
Two composite signals are built: B1 and B2:
They combine:
Fib breakouts
Renko direction (renkoDir)
Expansion direction (expansionQuickDir)
Multiple Laguerre alignments (LagF_Q, LagF_W, LagF_Y, LagF_Z, LagF_P, LagF_V)
They also factor in whether Fib structures are expanding or contracting.
A user toggle selects the “Baseline” signal:
finalSig = B2 (default) or B1 (alternate baseline)
finalSig is then filtered through the RM state machine and only when everything aligns, we emit:
+2 = Long / Buy signal
-2 = Short / Sell signal
0 = No new trade
Those +2 / -2 values are what feed the Risk Management Engine.
B. The Risk Management (RM) Engine
This script features the Signal Lynx Risk Management Engine, a proprietary state machine built to separate Signal from Execution.
Instead of firing orders directly on indicator conditions, we:
Convert the raw signal into a clean integer (Fin = +2 / -2 / 0)
Feed it into a Trade State Machine that understands:
Are we flat?
Are we in a long or short?
Are we in a closing sequence?
Should we permit re-entry now or wait?
Logic Injection / Template Concept:
The RM engine expects a simple integer:
+2 → Buy
-2 → Sell
Everything else (0) is “no new trade”
This makes the script a template:
You can remove the Superior RBR block
Drop in your own logic (RSI, MACD, price action, etc.)
As long as you output +2 or -2 into the same signal channel, the RM engine can drive all exits and state transitions.
Aggressive vs Conservative Modes:
The input AgressiveRM (Aggressive RM) governs how we interpret signals:
Conservative Mode (Aggressive RM = false):
Uses a more filtered internal signal (AF) to open trades
Effectively waits for a clean trend flip / confirmation before new entries
Minimizes whipsaw at the cost of fewer trades
Aggressive Mode (Aggressive RM = true):
Reacts directly to the fresh alert (AO) pulses
Allows faster re-entries in the same direction after RM-based exits
Still respects your pyramiding setting; this script ships with pyramiding = 0 by default, so it will not stack multiple positions unless you change that parameter in the strategy() call.
The state machine enforces discipline on top of your signal logic, reducing double-fires and signal spam.
C. Advanced Exit Protocols (Layered Defense)
The exit side is where this template really shines. Instead of a single “take profit or stop loss,” it uses multiple, cooperating layers.
1) Hard Stop Loss
A classic percentage-based Stop Loss (SL) relative to the entry price.
Acts as a final “catastrophic protection” layer for unexpected moves.
2) Standard Trailing Stop
A percentage-based Trailing Stop (TS) that:
Activates only after price has moved a certain percentage in your favor (tsActivation)
Then trails price by a configurable percentage (ts)
This is a straightforward, battle-tested trailing mechanism.
3) Staged Take Profits (Three Levels)
The script supports three staged Take Profit levels (TP1, TP2, TP3):
Each stage has:
Activation percentage (how far price must move in your favor)
Trailing amount for that stage
Position percentage to close
Example setup:
TP1:
Activate at +10%
Trailing 5%
Close 10% of the position
TP2:
Activate at +20%
Trailing 10%
Close another 10%
TP3:
Activate at +30%
Trailing 5%
Close the remaining 80% (“runner”)
You can tailor these quantities for partial scaling out vs. letting a core position ride.
4) Advanced Adaptive Trailing Stop (AATS)
AATS is a sophisticated volatility- and structure-aware stop:
Uses Hirashima Sugita style levels (HSRS) to model “floors” and “ceilings” of price:
Dungeon → Lower floors → Mid → Upper floors → Penthouse
These levels classify where current price sits within a long-term distribution.
Combines HSRS with Bollinger-style envelopes and EMAs to determine:
Is price extended far into the upper structure?
Is it compressed near the lower ranges?
From this, it computes an adaptive factor that controls how tight or loose the trailing level (aATS / bATS) should be:
High Volatility / Penthouse areas:
Stop loosens to avoid getting wicked out by inevitable spikes.
Low Volatility / compressed structure:
Stop tightens to lock in and protect profit.
AATS is designed to be the “smart last line” that responds to context instead of a single fixed percentage.
5) RSI-Style Stop (RSIS)
On top of AATS, the script includes a RSI-like regime filter:
A McGinley Dynamic mean of price plus ATR bands creates a dynamic channel.
Crosses above the top band and below the lower band change a directional state.
When enabled (UseRSIS):
RSIS can confirm or veto AATS closes:
For longs: A shift to bearish RSIS can force exits sooner.
For shorts: A shift to bullish RSIS can do the same.
This extra layer helps avoid over-reactive stops in strong trends while still respecting a regime change when it happens.
D. Repainting Protection
Many strategies look incredible in the Strategy Tester but fail in live trading because they rely on intrabar values or future-knowledge functions.
This template is built with closed-candle realism in mind:
The Risk Management logic explicitly uses previous bar data (open , high , low , close ) for the key decisions on:
Trailing stop updates
TP triggers
SL hits
RM state transitions
No security() lookahead or future-bar access is used.
This means:
Backtest behavior is designed to match what you can actually get with TradingView alerts and live automation.
Signals may “flicker” intrabar while the candle is forming (as with any strategy), but on closed candles, the RM decisions are stable and non-repainting.
4. For Developers & Modders
We strongly encourage you to mod this script.
To plug your own strategy into the RM engine:
Look for the section titled:
// BASELINE & SIGNAL GENERATION
You will see composite logic building B1 and B2, and then selecting:
baseSig = B2
altSig = B1
finalSig = sigSwap ? baseSig : altSig
You can replace the content used to generate baseSig / altSig with your own logic, for example:
RSI crosses
MACD histogram flips
Candle pattern detectors
External condition flags
Requirements are simple:
Your final logic must output:
2 → Buy signal
-2 → Sell signal
0 → No new trade
That output flows into the RM engine via finalSig → AlertOpen → state machine → Fin.
Once you wire your signals into finalSig, the entire Risk Management system (Stops, TPs, AATS, RSIS, re-entry logic, weekend filters, long/short toggles) becomes available for your custom strategy without re-inventing the wheel.
This makes Superior RBR not just a strategy, but a reference architecture for serious Pine dev work.
5. About Signal Lynx
Automation for the Night-Shift Nation 🌙
Signal Lynx focuses on helping traders and developers bridge the gap between indicator logic and real-world automation. The same RM engine you see here powers multiple internal systems and templates, including other public scripts like the Super-AO Strategy with Advanced Risk Management.
We provide this code open source under the Mozilla Public License 2.0 (MPL-2.0) to:
Demonstrate how Adaptive Logic and structured Risk Management can outperform static, one-layer indicators
Give Pine Script users a battle-tested RM backbone they can reuse, remix, and extend
If you are looking to automate your TradingView strategies, route signals to exchanges, or simply want safer, smarter strategy structures, please keep Signal Lynx in your search.
License: Mozilla Public License 2.0 (Open Source).
If you make beneficial modifications, please consider releasing them back to the community so everyone can benefit.
Single AHR DCA (HM) — AHR Pane (customized quantile)Customized note
The log-regression window LR length controls how long a long-term fair value path is estimated from historical data.
The AHR window AHR window length controls over which historical regime you measure whether the coin is “cheap / expensive”.
When you choose a log-regression window of length L (years) and an AHR window of length A (years), you can intuitively read the indicator as:
“Within the last A years of this regime, relative to the long-term trend estimated over the same A years, the current price is cheap / neutral / expensive.”
Guidelines:
In general, set the AHR window equal to or slightly longer than the LR window:
If the AHR window is much longer than LR, you mix different baselines (different LR regimes) into one distribution.
If the AHR window is much shorter than LR, quantiles mostly reflect a very local slice of history.
For BTC / ETH and other BTC-like assets, you can use relatively long horizons (e.g. LR ≈ 3–5 years, AHR window ≈ 3–8 years).
For major altcoins (BNB / SOL / XRP and similar high-beta assets), it is recommended to use equal or slightly shorter horizons, e.g. LR ≈ 2–3 years, AHR window ≈ 2–3 years.
1. Price series & windows
Working timeframe: daily (1D).
Let the daily close of the current symbol on day t be P_t .
Main length parameters:
HM window: L_HM = maLen (default 200 days)
Log-regression window: L_LR = lrLen (default 1095 days ≈ 3 years)
AHR window (regime window): W = windowLen (default 1095 days ≈ 3 years)
2. Harmonic moving average (HM)
On a window of length L_HM, define the harmonic mean:
HM_t = ^(-1)
Here eps = 1e-10 is used to avoid division by zero.
Intuition: HM is more sensitive to low prices – an extremely low price inside the window will drag HM down significantly.
3. Log-regression baseline (LR)
On a window of length L_LR, perform a linear regression on log price:
Over the last L_LR bars, build the series
x_k = log( max(P_k, eps) ), for k = t-L_LR+1 ... t, and fit
x_k ≈ a + b * k.
The fitted value at the current index t is
log_P_hat_t = a + b * t.
Exponentiate to get the log-regression baseline:
LR_t = exp( log_P_hat_t ).
Interpretation: LR_t is the long-term trend / fair value path of the current regime over the past L_LR days.
4. HM-based AHR (valuation ratio)
At each time t, build an HM-based AHR (valuation multiple):
AHR_t = ( P_t / HM_t ) * ( P_t / LR_t )
Interpretation:
P_t / HM_t : deviation of price from the mid-term HM (e.g. 200-day harmonic mean).
P_t / LR_t : deviation of price from the long-term log-regression trend.
Multiplying them means:
if price is above both HM and LR, “expensiveness” is amplified;
if price is below both, “cheapness” is amplified.
Typical reading:
AHR_t < 1 : price is below both mid-term mean and long-term trend → statistically cheaper.
AHR_t > 1 : price is above both mid-term mean and long-term trend → statistically more expensive.
5. Empirical quantile thresholds (Opp / Risk)
On each new day, whenever AHR_t is valid, add it into a rolling array:
A_t_window = { AHR_{t-W+1}, ..., AHR_t } (at most W = windowLen elements)
On this empirical distribution, define two quantiles:
Opportunity quantile: q_opp (default 15%)
Risk quantile: q_risk (default 65%)
Using standard percentile computation (order statistics + linear interpolation), we get:
Opp threshold:
theta_opp = Percentile( A_t_window, q_opp )
Risk threshold:
theta_risk = Percentile( A_t_window, q_risk )
We also compute the percentile rank of the current AHR inside the same history:
q_now = PercentileRank( A_t_window, AHR_t ) ∈
This yields three valuation zones:
Opportunity zone: AHR_t <= theta_opp
(corresponds to roughly the cheapest ~q_opp% of historical states in the last W days.)
Neutral zone: theta_opp < AHR_t < theta_risk
Risk zone: AHR_t >= theta_risk
(corresponds to roughly the most expensive ~(100 - q_risk)% of historical states in the last W days.)
All quantiles are purely empirical and symbol-specific: they are computed only from the current asset’s own history, without reusing BTC thresholds or assuming cross-asset similarity.
6. DCA simulation (lightweight, rolling window)
Given:
a daily budget B (input: budgetPerDay), and
a DCA simulation window H (input: dcaWindowLen, default 900 days ≈ 2.5 years),
The script applies the following rule on each new day t:
If thresholds are unavailable or AHR_t > theta_risk
→ classify as Risk zone → buy = 0
If AHR_t <= theta_opp
→ classify as Opportunity zone → buy = 2B (double size)
Otherwise (Neutral zone)
→ buy = B (normal DCA)
Daily invested cash:
C_t ∈ {0, B, 2B}
Daily bought quantity:
DeltaQ_t = C_t / P_t
The script keeps rolling sums over the last H days:
Cumulative position:
Q_H = sum_{k=t-H+1..t} DeltaQ_k
Cumulative invested cash:
C_H = sum_{k=t-H+1..t} C_k
Current portfolio value:
PortVal_t = Q_H * P_t
Cumulative P&L:
PnL_t = PortVal_t - C_H
Active days:
number of days in the last H with C_k > 0.
These results are only used to visualize how this AHR-quantile-driven DCA rule would have behaved over the recent regime, and do not constitute financial advice.
new_youtube_strategy//@version=5
strategy("Dow + Homma 1m Scalper (15m filter)", overlay=true, margin_long=100, margin_short=100, initial_capital=10000)
//===== INPUTS =====
maLen = input.int(50, "Trend SMA Length", minval=5)
htf_tf = input.timeframe("15", "Higher TF")
priceTolPct = input.float(0.05, "SR tolerance %", step=0.01)
wickFactor = input.float(2.0, "Hammer/ShootingStar wick factor", step=0.1)
dojiThresh = input.float(0.1, "Doji body % of range", step=0.01)
risk_RR = input.float(2.0, "Reward:Risk", step=0.1)
capitalRiskPct = input.float(1.0, "Risk % of equity per trade", step=0.1)
//===== 1m TREND (SMA) =====
sma1 = ta.sma(close, maLen)
sma1Up = sma1 > sma1
sma1Down = sma1 < sma1
uptrend1 = close > sma1 and sma1Up
downtrend1 = close < sma1 and sma1Down
//===== 15m TREND VIA request.security =====
sma15 = request.security(syminfo.tickerid, htf_tf, ta.sma(close, maLen), lookahead=barmerge.lookahead_off)
sma15Up = sma15 > sma15
sma15Down = sma15 < sma15
uptrend15 = close > sma15 and sma15Up
downtrend15 = close < sma15 and sma15Down
//===== SWING HIGHS/LOWS (LOCAL EXTREMA) =====
var int left = 3
var int right = 3
swHigh = ta.pivothigh(high, left, right)
swLow = ta.pivotlow(low, left, right)
//===== SR FLIP LEVELS =====
var float srSupport = na
var float srResistance = na
// when a swing high is broken -> new support
if not na(swHigh)
if close > swHigh
srSupport := swHigh
// when a swing low is broken -> new resistance
if not na(swLow)
if close < swLow
srResistance := swLow
//===== CANDLE METRICS =====
body = math.abs(close - open)
cRange = high - low
upperW = high - math.max(open, close)
lowerW = math.min(open, close) - low
isBull() => close > open
isBear() => close < open
bullHammer() =>
cRange > 0 and
isBull() and
lowerW >= wickFactor * body and
upperW <= body
bearShootingStar() =>
cRange > 0 and
isBear() and
upperW >= wickFactor * body and
lowerW <= body
isDoji() =>
cRange > 0 and body <= dojiThresh * cRange
bullEngulfing() =>
isBear() and isBull() and
open <= close and close >= open
bearEngulfing() =>
isBull() and isBear() and
open >= close and close <= open
//===== SR PROXIMITY =====
tol = priceTolPct * 0.01 * close
nearSupport = not na(srSupport) and math.abs(close - srSupport) <= tol
nearResistance = not na(srResistance) and math.abs(close - srResistance) <= tol
//===== SIGNAL CONDITIONS =====
bullCandle = bullHammer() or isDoji() or bullEngulfing()
bearCandle = bearShootingStar() or isDoji() or bearEngulfing()
longTrendOK = uptrend1 and uptrend15
shortTrendOK = downtrend1 and downtrend15
longSignal = longTrendOK and nearSupport and bullCandle
shortSignal = shortTrendOK and nearResistance and bearCandle
//===== POSITION SIZING (IN RISK UNITS) =====
var float lastEquity = strategy.equity
riskCapital = strategy.equity * (capitalRiskPct * 0.01)
//===== ENTRY / EXIT PRICES =====
longStop = math.min(low, nz(srSupport, low))
longRisk = close - longStop
longTP = close + risk_RR * longRisk
shortStop = math.max(high, nz(srResistance, high))
shortRisk = shortStop - close
shortTP = close - risk_RR * shortRisk
// qty in contracts (approx; assumes price * qty ≈ capital used)
longQty = longRisk > 0 ? riskCapital / longRisk : 0.0
shortQty = shortRisk > 0 ? riskCapital / shortRisk : 0.0
//===== EXECUTION =====
if longSignal and longRisk > 0 and longQty > 0
strategy.entry("Long", strategy.long, qty=longQty)
strategy.exit("Long TP/SL", from_entry="Long", stop=longStop, limit=longTP)
if shortSignal and shortRisk > 0 and shortQty > 0
strategy.entry("Short", strategy.short, qty=shortQty)
strategy.exit("Short TP/SL", from_entry="Short", stop=shortStop, limit=shortTP)
//===== PLOTS =====
plot(sma1, color=color.orange, title="SMA 1m")
plot(sma15, color=color.blue, title="HTF SMA (15m)")
plot(srSupport, "SR Support", color=color.new(color.green, 50), style=plot.style_linebr)
plot(srResistance,"SR Resistance",color=color.new(color.red, 50), style=plot.style_linebr)
// Visual debug for signals
plotshape(longSignal, title="Long Signal", style=shape.triangleup, location=location.belowbar, color=color.lime, size=size.tiny)
plotshape(shortSignal, title="Short Signal", style=shape.triangledown, location=location.abovebar, color=color.red, size=size.tiny)
