CRR Birgua HUD (HH-HL / LL-LH)CRR Birgua HUD (HH-HL / LL-LH) essentially does three things:
Detects price structure using pivots.
Marks highs as:
HH = Higher High
LH = Lower High
Marks lows as:
HL = Higher Low
LL = Lower Low
It uses a pivot length (pivotLen, default 3) to find these turning points.
Measures the “Birgua” (impulse correction).
In a downtrend:
When an LH appears, it measures how much the retracement rose from the last low to that LH.
In an uptrend:
When an HL appears, it measures how much the retracement fell from the last high to that HL.
It calculates two things:
% correction (birgua_lastPct)
ATR multiples (birgua_lastAtrMult)
It only considers it “valid” if:
% correction ≥ birgua_minBirguaPc (e.g., 25%)
ATR multiple ≥ birgua_minAtrMult (e.g., 0.5)
If valid: it labels it with OK; otherwise: SMALL.
Creates a HUD and a “Birgua Score.”
Calculates a Birgua Score (0–100):
Starts at 50.
If the last Birgua was at an HL (strong bullish), it increases from 50.
If it was at an LH (strong bearish), it decreases from 50.
It can draw a line at the bottom with this score if you enable Show Birgua Score.
At the top of the screen, it displays a HUD with:
Direction: BULL (HL), BEAR (LH), or NEUTRAL.
B: XX.X% (Birgua percentage).
ATR: X.XX (ATR multiples).
Strength: Strong / Weak / N/A based on the minimums you defined.
🧠 Quick Use:
HL + strong Birgua → probable bullish continuation.
LH + strong Birgua → probable bearish continuation.
The HUD summarizes whether the last correction was strong or weak and on which side (bull or bear).
Regressions
80% EDGE Rule - TPO Based═════════════════════════════════════════════════════════════
80% EDGE RULE - TPO BASED
═════════════════════════════════════════════════════════════
█ OVERVIEW
The 80% Edge Rule is a high-probability Market Profile concept that identifies when price is likely to traverse the prior session's Value Area. This indicator automates the detection, confirmation, and tracking of 80% EDGE Rule setups using true TPO (Time Price Opportunity) calculations—not volume profile.
When price opens outside the previous day's Value Area and then re-enters and is "accepted" back inside, there is an 80% statistical probability that price will travel to the opposite side of the Value Area. This indicator does all the heavy lifting: calculating the prior session's Value Area, detecting valid setups, confirming acceptance, and tracking progress toward the target.
█ THE 80% EDGE RULE EXPLAINED
The 80% Edge Rule is based on Market Profile theory developed by J. Peter Steidlmayer at the Chicago Board of Trade. The rule states:
❶ If price OPENS OUTSIDE the prior day's Value Area...
❷ And then ENTERS and is ACCEPTED back into the Value Area...
❸ There is an 80% chance price will rotate to the OTHER SIDE of the Value Area.
"Acceptance" is defined as price spending TWO OR MORE TPO periods (typically 30-minute blocks) inside the Value Area. This indicates that the market has accepted these prices as fair value, and the auction process will likely continue through to the opposite boundary.
BULLISH SETUP: Price opens BELOW the prior VAL → Enters and is accepted → Target is VAH
BEARISH SETUP: Price opens ABOVE the prior VAH → Enters and is accepted → Target is VAL
█ HOW THIS INDICATOR WORKS
This indicator performs several automated functions:
1. TPO VALUE AREA CALCULATION
• Analyzes the prior RTH (Regular Trading Hours) session
• Builds a true TPO distribution using 30-minute time blocks
• Each price level receives +1 TPO for each period it was touched
• Calculates POC (Point of Control) as the price with highest TPO count
• Expands from POC using the CME/CBOT standard "two-price" method until 70% of TPOs are captured
• This defines VAH (Value Area High) and VAL (Value Area Low)
2. SETUP DETECTION
• Monitors the RTH open (default 9:30 AM ET)
• Detects if price opened outside the prior Value Area
• Determines setup direction (Bullish or Bearish)
3. ACCEPTANCE MONITORING
• Tracks TPO blocks where price remains inside the Value Area
• Confirms setup when required number of blocks is reached (default: 2)
• Resets count if price exits VA before confirmation
4. TARGET & INVALIDATION TRACKING
• Monitors for target completion (opposite VA boundary)
• Monitors for invalidation (price moves beyond entry VA boundary + buffer)
• Visual feedback on outcome
█ VISUAL ELEMENTS
PRIOR VALUE AREA LINES (Dashed)
• RED DASHED LINE: Prior Day VAH (Value Area High)
• GREEN DASHED LINE: Prior Day VAL (Value Area Low)
• PURPLE DOTTED LINE: Prior Day POC (Point of Control)
TRADE LINES (Solid)
• YELLOW LINE: Entry price (where setup was confirmed)
• CYAN LINE: Target price (opposite VA boundary)
• GREEN LINE: Entry line turns green when target is hit
• GRAY LINES: Both lines turn gray if setup is invalidated
STATUS LABEL
• Floating label showing current setup state
• ORANGE "WATCHING": Setup detected, monitoring for acceptance
• YELLOW "CONFIRMED": Setup confirmed, tracking toward target
• GREEN "TARGET HIT ✓": Target successfully reached
• RED "INVALIDATED ✗": Setup failed, price moved against
DASHBOARD (Top Right Corner)
• Prior VAH: Yesterday's Value Area High
• Prior VAL: Yesterday's Value Area Low
• Prior POC: Yesterday's Point of Control
• Open Price: Today's RTH opening price
• Direction: BULLISH ↑ or BEARISH ↓
• Status: Current setup state
█ CONFIGURABLE SETTINGS
┌────────────────────────────────────────────────────────────
│ TPO SETTINGS
├────────────────────────────────────────────────────────────
│ Tick Size (Default: 0.25) │ • Price increment for TPO calculations
│ • ES/MES: 0.25
│ • NQ/MNQ: 0.25
│ • YM/MYM: 1.0
│ • RTY: 0.1 │ • CL/MCL: 0.01
│ • GC/MGC: 0.1
│
│ Value Area % (Default: 70)
│ • Percentage of TPOs to include in Value Area
│ • Standard is 70% (one standard deviation)
│ • Can adjust 50-90% based on preference
│
│ TPO Block Duration (Default: 30 minutes)
│ • Length of each TPO period
│ • Standard Market Profile uses 30-minute periods
│ • Adjust if using non-standard TPO settings
└────────────────────────────────────────────────────────────
┌────────────────────────────────────────────────────────────
│ 80% EDGE RULE SETTINGS
├────────────────────────────────────────────────────────────
│ TPO Blocks Required for Acceptance (Default: 2)
│ • Number of 30-min periods price must stay inside VA
│ • Standard rule requires 2 periods for acceptance
│ • More conservative: Increase to 3
│ • More aggressive: Reduce to 1 (not recommended)
│
│ Invalidation Distance (Default: 10 points)
│ • Buffer beyond VA boundary before setup is invalidated
│ • Bullish: Invalidates if LOW goes below VAL minus this distance
│ • Bearish: Invalidates if HIGH goes above VAH plus this distance
│ • Adjust based on product volatility and your risk tolerance
│
│ Fade Delay (Default: 5 minutes)
│ • How long entry/target lines stay visible after outcome
│ • Lines and floating label disappear after this delay
│ • Dashboard retains the outcome status until next session
└────────────────────────────────────────────────────────────
┌────────────────────────────────────────────────────────────
│ SESSION SETTINGS
├────────────────────────────────────────────────────────────
│ RTH Session (Default: 0930-1600)
│ • Regular Trading Hours window
│ • This determines which bars are used for TPO calculation
│ • Also determines when RTH "open" is detected
│
│ PRODUCT-SPECIFIC RTH SESSIONS:
│ • Equity Index Futures (ES, NQ, YM, RTY): 0930-1600
│ • Crude Oil (CL): 0900-1430 (pit session)
│ • Gold (GC): 0820-1330 (pit session)
│ • Treasury Bonds/Notes: 0720-1400
│ • Forex Futures: Varies by product
│
│ Timezone (Default: America/New_York)
│ • Timezone for session calculations
│ • Options: New York, Chicago, Los Angeles, UTC
│ • Use exchange timezone for accurate session detection
└────────────────────────────────────────────────────────────
┌────────────────────────────────────────────────────────────
│ VISUAL SETTINGS
├────────────────────────────────────────────────────────────
│ Show Prior VA Lines: Toggle VAH/VAL/POC lines on/off
│ Show Entry/Target Lines: Toggle trade-related lines on/off
│ VAH Color: Color for Value Area High line
│ VAL Color: Color for Value Area Low line
│ POC Color: Color for Point of Control line
│ Entry Line Color: Color for entry price line
│ Target Line Color: Color for target price line
│ Target Hit Color: Color when target is reached (default: green)
│ Line Width: Thickness of all lines (1-5)
└────────────────────────────────────────────────────────────
┌────────────────────────────────────────────────────────────
│ DEBUG SETTINGS
├────────────────────────────────────────────────────────────
│ Show Debug Info: Displays additional diagnostic information
│ • Session High/Low of prior day
│ • Current RTH status
│ • Current TPO block number
│ • Outcome timestamp
│ • Useful for troubleshooting or verifying calculations
└────────────────────────────────────────────────────────────
█ ALERTS
This indicator includes three configurable alerts:
① SETUP CONFIRMED
• Triggers when acceptance criteria is met
• Includes entry price and target price in alert message
② TARGET HIT
• Triggers when price reaches the opposite VA boundary
• Confirms successful completion of the 80% Rule setup
③ INVALIDATED
• Triggers when price moves beyond the invalidation threshold
• Signals that the setup has failed
To enable alerts:
1. Ensure "Enable Alerts" is checked in indicator settings
2. Right-click on the indicator → "Add Alert"
3. Select the condition you want to be alerted on
4. Configure notification method (popup, email, webhook, etc.)
█ RECOMMENDED USAGE
TIMEFRAME:
• Best used on 5-minute, 15-minute, or 30-minute charts
• The chart timeframe should divide evenly into 30 minutes
• Ensure sufficient historical bars are loaded for prior session calculation
BEST PRACTICES:
• Wait for full confirmation (2 TPO blocks inside VA) before considering entry
• Use the target line as your profit objective
• Consider the invalidation level for stop-loss placement
• Monitor the dashboard for real-time setup status
• Combine with other confluence factors (order flow, support/resistance, etc.)
IMPORTANT NOTES:
• This indicator calculates TRUE TPO-based Value Area, not volume profile
• Prior day VA is recalculated at each new session
• The 80% Rule is a statistical tendency, not a guarantee
• Always use proper risk management
█ ADJUSTING FOR DIFFERENT PRODUCTS
This indicator defaults to Equity Index Futures (ES, NQ, etc.) with:
• RTH Session: 0930-1600
• Timezone: America/New_York
• Tick Size: 0.25
FOR OTHER PRODUCTS, ADJUST:
CRUDE OIL (CL/MCL):
• RTH Session: 0900-1430
• Tick Size: 0.01
GOLD (GC/MGC):
• RTH Session: 0820-1330
• Tick Size: 0.10
TREASURY FUTURES (ZB, ZN):
• RTH Session: 0720-1400
• Tick Size: 0.03125 (ZB) or 0.015625 (ZN)
E-MINI DOW (YM/MYM):
• RTH Session: 0930-1600
• Tick Size: 1.0
RUSSELL 2000 (RTY):
• RTH Session: 0930-1600
• Tick Size: 0.10
Always verify the RTH session times and tick sizes for your specific product and exchange.
█ DISCLAIMER
This indicator is provided for educational and informational purposes only. It is not financial advice and should not be construed as a recommendation to buy or sell any financial instrument. Trading futures and other leveraged products involves substantial risk of loss and is not suitable for all investors.
Past performance is not indicative of future results. The 80% Edge Rule is a statistical observation based on Market Profile theory and does not guarantee any specific outcome. Always conduct your own analysis and use proper risk management.
Linear Regression CVDHere is the complete user manual and introduction for the Linear Regression CVD indicator in English. You can save this as your documentation for your trading system.
📊 Linear Regression CVD – Trader’s Manual
1. Introduction
Core Concept:
Standard Cumulative Volume Delta (CVD) indicators are often noisy and jagged, making it difficult to decipher the true direction of capital flow. This indicator applies a Linear Regression algorithm to smooth out the CVD data and adds a Standard Deviation Channel. It is designed to answer two critical questions:
What is the "True Trend" of the money flow? (Filtering out noise)
Is the market sentiment currently overheated? (Using the channel to spot extremes)
Best Markets:
Crypto Perpetual Futures (e.g., BTCUSDT.P) — Highly Recommended.
Stocks & Forex (Must have volume data).
Timeframes:
Scalping: 1m, 5m, 15m (To catch rapid capital inflows/outflows).
Swing Trading: 1H, 4H (To identify the dominant direction of "Smart Money").
2. Visual Guide
When you load the indicator, you will see the following elements:
A. The Main Line (Linear Regression)
Appearance: A smooth, thick line.
Meaning: The average trend of capital flow.
Color Logic:
🟢 Green: Money flow is trending UP (Buyers are dominant).
🔴 Red: Money flow is trending DOWN (Sellers are dominant).
B. The Raw Line (Gray Hairline)
Appearance: A thin, jagged gray line fluctuating around the main line.
Meaning: The Raw, Real-time CVD. It calculates the volume delta (Close vs. Open) for every single candle without smoothing.
C. The Channel (Blue Background)
Appearance: A blue shaded area around the main line.
Meaning: The "Normal Volatility Range."
Calculated based on 2 Standard Deviations (2σ) from the Linear Regression.
If the Gray Line stays inside this channel, the market is stable/balanced.
D. The Signal Dots
🟢 Green Dot (Upside Extension): The Raw CVD has broken above the upper channel.
Meaning: Extreme Greed / Aggressive Buying / FOMO.
🔴 Red Dot (Downside Extension): The Raw CVD has broken below the lower channel.
Meaning: Extreme Fear / Panic Selling / Capitulation.
3. Trading Strategies
Strategy 1: Trend Confirmation
The basic "Follow the Money" approach.
Bullish Signal (Long):
Price is making Higher Highs.
CVD Main Line turns Green and slopes upward.
Action: Confirms that the price rise is backed by real volume. Hold or Add to Longs.
Bearish Signal (Short):
Price is making Lower Lows.
CVD Main Line turns Red and slopes downward.
Action: Confirms that sellers are in control. Hold Shorts.
Strategy 2: Divergence (High Win Rate)
Finding disagreements between "Price" and "Money Flow".
Bearish Divergence (Top Signal):
Price makes a Higher High.
CVD Main Line makes a Lower High (or fails to break out).
Meaning: Price is rising, but buying effort is fading (Exhaustion) or Limit Sellers are absorbing the buy orders (Absorption).
Action: Look for Short entries.
Bullish Divergence (Bottom Signal):
Price makes a Lower Low.
CVD Main Line makes a Higher Low.
Meaning: Price is dropping, but selling pressure is drying up, or Smart Money is absorbing sell orders via limit buy orders.
Action: Look for Long entries.
Strategy 3: Mean Reversion (Extreme Extensions)
Using the Red/Green dots to fade extremes.
Long Opportunity (Bounce):
Price crashes rapidly.
Cluster of Red Dots appears at the bottom.
Meaning: Panic selling has peaked (Capitulation). The market is oversold on a volume basis.
Action: Wait for a candle reversal pattern, then Long for a bounce.
Short Opportunity (Pullback):
Price pumps vertically.
Cluster of Green Dots appears at the bottom.
Meaning: Retail traders are chasing the pump (FOMO). Buying power is overextended.
Action: Wait for momentum to stall, then Short.
4. Important Limitations & Notes
Data Source Accuracy:
TradingView Standard Volume is an approximation (Close vs. Open logic).
It is not perfect "Tick Data" (like professional Orderflow software), but it is 90% accurate for trend analysis on 1H/4H charts.
Tip: Always use Perpetual Contract charts (e.g., BTCUSDT.P) for Crypto, not Spot charts, to get the correct volume data.
The "Extension" Trap:
Do not Short just because you see a Green Dot. In a strong parabolic bull run, you will see many Green Dots in a row while price keeps flying.
These dots indicate velocity, not necessarily a reversal. Always look for resistance levels or divergence before fading the move.
Settings:
Default Length: 20.
For faster signals: Try 10 or 14.
For smoother trends: Try 50.
5. Pre-Trade Checklist
Before entering a trade, check the Linear CVD:
Color: Is the CVD Line Green or Red? Does it match my trade direction?
Slope: Is the CVD accelerating or flattening out?
Divergence: Did price break a level, but CVD failed to follow? (Fakeout warning).
Extremes: Are there Red/Green dots appearing? If yes, am I chasing a trade too late?
这是一套完整的线性回归 CVD (Linear Regression CVD) 指标的使用说明书和简介。你可以把它保存下来，作为你的交易系统参考文档。
📊 线性回归 CVD (Linear Regression CVD) —— 交易员手册
1. 指标简介 (Introduction)
核心理念：
普通的 CVD（累积成交量差）往往噪音很大，线条锯齿状严重，导致交易者难以看清真正的资金流向趋势。本指标通过线性回归算法 (Linear Regression) 对 CVD 进行平滑处理，并结合标准差通道 (Standard Deviation Channel)，试图解决两个核心问题：
资金流向的真实趋势是什么？（排除噪音）
当前的情绪是否过热？（通过通道判定）
适用市场：
加密货币合约 (BTC, ETH 等永续合约) —— 效果最佳
股票、外汇 (需有成交量数据)
适用周期：
日内短线：1分钟、5分钟、15分钟（捕捉快速的资金进出）。
趋势波段：1小时、4小时（判断主力资金的大方向）。
2. 视觉元素说明 (Visual Guide)
当你加载指标后，你会看到以下几个部分：
A. 彩色主线 (The LinReg Line)
形态：一条平滑的粗线。
含义：资金流向的**“平均趋势”**。
颜色：
🟢 绿色：资金流向趋势向上（买盘主导）。
🔴 红色：资金流向趋势向下（卖盘主导）。
B. 灰色背景细线 (Raw CVD)
形态：一条充满锯齿的灰色细线，在主线周围波动。
含义：原始的、实时的累积成交量。它反应了当下的每一根K线的实际买卖差额。
C. 蓝色背景通道 (The Channel)
形态：包裹在主线周围的深蓝色带状区域。
含义：“正常波动范围”。
基于线性回归的 2倍标准差计算。
如果灰色细线在通道内运行，说明市场情绪稳定，多空力量均衡。
D. 信号点 (The Dots)
🟢 绿点 (底部出现)：原始 CVD 向上突破了通道上轨。代表极度贪婪 / 抢筹。
🔴 红点 (底部出现)：原始 CVD 向下跌破了通道下轨。代表极度恐慌 / 抛售。
3. 实战交易策略 (Trading Strategies)
策略一：趋势确认 (Trend Following)
这是最基础的顺势用法。
做多信号：
价格处于上升趋势（如在均线之上）。
CVD 主线由红变绿，且持续向上倾斜。
操作：这确认了价格的上涨有真金白银的买盘支持，可以持有或加仓。
做空信号：
价格处于下降趋势。
CVD 主线由绿变红，且持续向下倾斜。
操作：确认卖盘主导，价格下跌是健康的。
策略二：背离交易 (Divergence) —— 胜率最高的用法
寻找“主力资金”与“价格”不一致的地方。
顶背离 (看跌)：
价格创出了新高 (Higher High)。
CVD 主线却没有创新高，或者形成更低的高点 (Lower High)。
含义：价格在涨，但买入的资金在减少。这通常是主力在通过限价单悄悄出货，或者是买盘枯竭。
操作：准备做空，或多单止盈。
底背离 (看涨)：
价格创出了新低 (Lower Low)。
CVD 主线却形成了更高的低点 (Higher Low)。
含义：价格在跌，但卖出的资金在减少，或者有大资金在底部通过挂单吸筹 (Absorption)。
操作：准备做多，或空单止盈。
策略三：极端情绪反转 (Mean Reversion)
利用红绿点判断短期的超买超卖。
做多机会 (反弹)：
价格快速下跌，甚至暴跌。
指标底部出现密集的红点 (Downside Extension)。
含义：恐慌盘被杀出来了 (Capitulation)，市场短期内无可再卖。
操作：等待K线出现反转形态（如长下影线）后尝试博反弹。
做空机会 (回调)：
价格快速拉升（垂直上涨）。
指标底部出现密集的绿点 (Upside Extension)。
含义：大量的散户在追涨 (FOMO)，透支了买盘动能。
操作：等待上涨停滞后尝试做空。
4. 关键注意事项 (Limitations)
数据源区别：
TradingView 的普通 Volume 是基于 K 线的近似计算（Close > Open 算买，Close < Open 算卖）。
这与专业的 Orderflow 软件（如 Exocharts）使用的逐笔 Tick 数据有一定误差，但在 1小时/4小时 级别上，趋势方向基本一致。
建议：如果你是做合约，请务必加载 合约图表（如 BTCUSDT.P），不要用现货图表看 CVD。
红绿点的陷阱：
不要一看到绿点就做空！ 在超级大单边行情（比如牛市主升浪）中，绿点会连续出现，价格会一直涨。
红绿点必须配合 关键支撑/阻力位 使用。如果在“半空中”出现绿点，往往意味着趋势加速，而不是反转。
参数调整：
默认 LinReg Length = 20。
如果你觉得反应太慢，可以改为 10 或 14。
如果你觉得假信号太多，可以改为 50，但这会牺牲灵敏度。
5. 快速检查清单 (Checklist)
在开单前，看一眼 CVD：
颜色：CVD 是绿的还是红的？和我想做的方向一致吗？
斜率：CVD 是在加速上升/下降，还是开始变平了？
背离：价格破位了，CVD 跟着破位了吗？如果没跟，就是假突破。
极值：有没有出现红点/绿点？如果出现了，是不是应该等回调再进场？
Vietnamese Stock: Discount Linear Regression Liquidity GrabThe Discount Linear Regression Liquidity Grab is a sophisticated technical analysis tool that combines statistical trend analysis with Premium/Discount Zone and Price Action logic. Unlike standard Linear Regression Channels that repaint or stretch indefinitely, this indicator is dynamic: it automatically detects volatility breakouts to "reset" the channel, creating distinct market "Sections."
This tool is designed to help traders identify trend exhaustion, fair value gaps (FVGs), and high-probability reversal or continuation zones using two distinct built-in strategies.
Key Features
1. Dynamic Channel Resets
The core engine calculates a Linear Regression Channel based on a Pearson R coefficient and Deviation multipliers.
- How it works: When price breaks out of the Upper or Lower Deviation bands, the script recognizes a shift in momentum. It "locks" the previous channel and begins calculating a new one from the breakout point.
- Benefit: This creates a historical map of market structure, showing you exactly where previous trends began and ended.
2. Smart Money Concepts (SMC) Integration
For every completed section (channel), the indicator automatically highlights:
Highest High & Lowest Low Boxes: Identifies the structural range of the previous move.
- Gaps & FVGs: Automatically draws boxes for Fair Value Gaps and Price Gaps within the channel, acting as potential magnets for price.
3. The Discount Zone (New Feature)
The indicator projects a Discount Area (Red Box) from the previous section's midline down to its lowest low.
- Logic: This box represents the "Discount" pricing relative to the previous move.
- Behavior: The box extends to the right until price successfully "grabs liquidity" (closes below the midline/red line). Once the grab occurs, the box stops extending, marking that the liquidity event is complete.
Built-In Strategies
This indicator includes two automated strategy signals based on the interaction between current price and historical sections.
Strategy 1: Breakout & Retest (Trend Continuation)
This strategy looks for a classic resistance-turned-support setup.
- Breakout: Price closes above the Highest High of a previous section (Triangle Up).
- Retest: Price pulls back and closes at or below that breakout level (Triangle Down).
- Confirmation: Price breaks above the high of the initial breakout candle (Green Background).
Strategy 2: Midline Reclaim (Mean Reversion / Discount Buy)
This strategy focuses on buying from the "Discount" zone.
- Liquidity Grab: Price drops below the Midline (Red Line) of a previous section, entering the Discount Zone.
- Reclaim: Price closes back above the Midline, signaling that the dip was bought up.
Signal: A Diamond shape and Teal Background appear.
How to Use
- Trend Trading: Use the Dynamic Channels to visualize the current slope. If the channel is angling up, look for long setups.
- Confluence: Use the Discount Zones and FVG boxes as areas of interest. If price enters a Red Discount Box and forms a reversal pattern, it is a high-probability entry.
- Stop Loss Placement: The Lowest Low boxes of previous sections serve as excellent invalidation points for long positions.
Alerts
The indicator comes with pre-configured alerts for:
- Strategy 1 Confirmation.
- Strategy 2 Midline Reclaim.
- New Channel Formation (Trend Reset).
- Liquidity Grab Events.
Ratio with Lag• Ratio = X(T) / Y(T-lag)
• Auto-detects “X/Y” typed in chart search bar
• Plots ratio directly on main chart
• Adds 30-week MA (weekly SMA of the ratio)
• Adds 150-day SMA (daily SMA of the ratio)
BTC Spot vs Perpetual CVD Divergence + Delta Confirm + Band FillThis indicator detects real market turning points by comparing Spot vs Perpetual CVD flows to identify forced positioning changes, leverage clean-ups, and true spot absorption.
It tracks normalized CVD for both Spot and Perps, calculates the divergence between them, and applies a dynamic volatility-based threshold to filter noise. Signals only trigger at confirmed pivot points, ensuring accuracy over early false reversals. An optional Delta confirmation layer further validates setups by requiring aggressive market flow in the direction of the pivot reversal.
This tool is not designed for blind entries — it highlights high-probability reversal zones. Best used in combination with VWAP, HTF structure, OI, and funding rate analysis to time optimal entries via pullbacks and momentum confirmation.
✅ Ideal for:
• Identifying local tops & bottoms
• Tracking spot vs leverage dominance
• Trading mean reversion and squeeze setups
• Flow-based scalping
❌ Not intended for:
• Chasing breakouts
• Standalone entry signals without price structure
Trend Flip Exhaustion SignalsThis Pine Script is designed to generate buy and short trading signals based on a combination of technical indicators. It calculates fast and slow EMAs, RSI, a linear regression channel, and a simplified TTM squeeze histogram to measure momentum.
- Short signals trigger when price is above both EMAs, near the upper regression channel, momentum is weakening, volume is fading, and RSI is overbought.
- Buy signals trigger when price is below both EMAs, near the lower regression channel, momentum is strengthening, volume is surging, and RSI is oversold.
- Signals are displayed as labels anchored to price bars (with optional plotshape arrows for backup).
- The script also plots the EMAs and regression channel for visual context.
In short - it’s a trend‑following entry tool that highlights potential exhaustion points for shorts and potential reversals for buys, with clear on‑chart markers to guide decision‑making.
Physics of PricePhysics of Price is a non-repainting kinematic reversal and volatility overlay. It models price as a physical object with position, velocity, and acceleration, then builds adaptive bands and a short-term predictive “ghost cone” to highlight where reversals are statistically more likely.
CONCEPT
Instead of using only moving averages, the core engine tracks a smoothed price (position), trend speed (velocity), and change in trend speed (acceleration). Standard deviation of the model error defines probabilistic bands around this kinematic centerline. When price stretches too far away and snaps back, the move is treated as a potential exhaustion event.
CORE COMPONENTS
– Kinematic centerline (Alpha–Beta–Gamma style filter) that bends with trend instead of lagging like a simple MA.
– Inner and outer bands based on the standard deviation of residuals between price and the kinematic model.
– Regime filter using R² and band width to avoid signals in chaotic or ultra-wide regimes.
– Optional RSI “hook” filter that waits for momentum to actually turn instead of buying into a falling RSI.
– Optional divergence add-on using kinematic velocity, so a marginal new price extreme with weaker velocity is recognized as a possible exhaustion pattern.
REVERSAL EVENTS AND SCORING
Raw events are detected when price wicks through the outer band and closes back inside (band hit with snap). These are plotted as diamonds and treated as candidates, not automatic trades.
Each event is then scored from 0 to 100 using several factors:
– How far price overshot the outer band.
– How strongly it snapped back inside.
– Whether an RSI hook is present (if enabled).
– Regime quality from the kinematic model.
– Basic kinematic safety to avoid the most aggressive “knife-catch” situations.
– Optional divergence bonus when price makes a new extreme but velocity does not.
Only events with a score above the chosen threshold become confirmed signals (triangles labeled PHYSICS REV).
GHOST CONE (PREDICTIVE BAND)
On the latest bar, the script projects a short-horizon “ghost cone” into the future using position, velocity, and a damped acceleration term. This creates a curved predictive band that visualizes a plausible short-term path and range, rather than a simple straight line. The cone is meant as context for trade management and risk, not as a hard target.
FILTERS AND OPTIONS
– Regime filter (R² and band width) can be tightened or relaxed depending on how selective you want the engine to be.
– RSI and volume filters can be toggled on for extra confirmation or off to see the raw kinematic behavior.
– An optional trend baseline (EMA) can be enabled to bias or restrict reversals relative to a higher-timeframe trend.
– Dynamic cooldown scales with volatility so the script does not spam signals in fast environments.
HOW TO USE
Physics of Price is primarily a mean-reversion and exhaustion tool. It works best in markets that respect ranges, swings, and two-sided order flow. Confirmed PHYSICS REV signals near the outer bands, with decent model health and a clean RSI hook, are the core use case. The bands and ghost cone can also be used as a context overlay alongside your own entries, exits, and risk framework.
This is an indicator, not a complete trading system. It does not use lookahead or higher-timeframe security calls and is designed for “once per bar close” alerts. Always combine it with your own risk management and confluence.
Syntropy - System v4Syntropy System v4 – La Estrategia de Acumulación Profesional que Todos Están UsandoEDICIÓN LIMITADA – SOLO 10 PLAZAS DISPONIBLES EN TODO EL MUNDOPor primera (y única) vez, libero mi estrategia privada más potente:
La misma que uso personalmente y que ha cambiado por completo la forma en que acumulo en Bitcoin, Ethereum y altcoins de alto potencial.¿Qué incluye Syntropy v4?8 motores de entrada independientes (PG Solo, PG+FA, RZ1/RZ2, SFP, Liquidity Sweep, STE Bottom + reentradas inteligentes)
Piramidación hasta 20 niveles con control total de riesgo
Medias móviles dinámicas + proyecciones extendidas
Tabla en tiempo real con P&L total, capital invertido y operaciones abiertas/cerradas
Señales 100% visuales y sin repintado
Optimizada para cripto, pero funciona perfecto en forex y acciones
OFERTA EXCLUSIVA Y POR TIEMPO MUY LIMITADOPrecio normal: 499 USD (pago único de por vida + todas las futuras actualizaciones) PRECIO LANZAMIENTO SOLO PARA LOS PRIMEROS 10 COMPRADORES:
50 USD DE POR VIDA
(Sí, leíste bien: cincuenta dólares una sola vez y el indicador es tuyo para siempre)Una vez que se vendan las 10 primeras licencias, este precio desaparece para siempre y vuelve al valor real de 499 USD.Ya van 7/10 vendidas en las últimas horas…¿Quieres ser uno de los últimos 3 que se lleven Syntropy v4 a precio de lanzamiento?Envíame YA un mensaje privado con la palabra “SYNTROPY 50” y te mando el enlace de pago + acceso inmediato al script protegido.No hay prueba gratis esta vez porque a este precio es literalmente un regalo… pero sí te doy mi palabra: si en 30 días no estás 100% convencido de que es la mejor estrategia que has usado jamás, te devuelvo hasta el último centavo.Quedan muy pocas horas antes de que suba el precio para siempre.Los primeros 10 que escriban ahora se llevan el indicador de por vida por solo 50 USD.
El resto pagará 10 veces más.Tú decides si estás dentro del grupo élite o te quedas mirando desde afuera.Te espero del otro lado.Aviso importante (reglas de TradingView):
Este es un script privado de pago. No constituye asesoramiento financiero. Operar implica riesgo de pérdida de capital. Los resultados pasados no garantizan resultados futuros. Uso bajo tu propia responsabilidad.
Syntropy System v4 – The Most Powerful Accumulation Strategy Ever ReleasedWORLDWIDE LIMITED EDITION – ONLY 10 LIFETIME SEATSFor the first and last time ever, I’m opening my personal, private strategy that I use every single day to stack Bitcoin, Ethereum, Ethereum and high-conviction altcoins.What you get with Syntropy v48 independent & complementary entry engines (PG Solo, PG+FA, RZ1/RZ2, SFP, Liquidity Sweep, STE Bottom + smart reentries)
Up to 20 pyramiding levels with perfect risk scaling
Dynamic moving averages + extended visual projections
Real-time dashboard (total P&L, invested capital, open/closed trades)
100% visual, non-repainting signals
Built for crypto, but works flawlessly on forex and stocks too
INSANE LAUNCH PRICE – ONLY FOR THE FIRST 10 PEOPLENormal lifetime price: $499 (one-time payment + all future updates forever)LAUNCH PRICE – FIRST 10 BUYERS ONLY:
$50 USD LIFETIME
(Yes, you read that right: fifty dollars one time and the indicator is yours forever)Once these 10 licenses are gone, the price jumps permanently to $499 and will never come back down.7 out of 10 already sold in the last few hours…That leaves only 3 seats at this ridiculous price.Want to be one of the last 3 people on Earth to grab Syntropy v4 for $50 lifetime?Send me a private message RIGHT NOW with the words
“SYNTROPY 50”
and I’ll instantly send you the payment link + immediate access to the protected script.There is no free trial at this price (it would be insane), but I give you my personal word:
If within 30 days you’re not 100% blown away and convinced this is the best strategy you’ve ever used, I’ll refund every single penny — no questions asked.The clock is ticking. In a few hours this $50 offer disappears forever.The first 10 who message me now get lifetime access for only $50.
Everyone else will pay 10× more.Your move: be part of the elite 10 or watch from the sidelines.I’ll see you inside.TradingView Required Disclaimer
This is a paid private script. Not financial advice. Trading involves substantial risk of loss. Past performance is no guarantee of future results. Use only capital you can afford to lose. You are solely responsible for your trading decisions.
Bitcoin Power-Law Bands + Quantile OscillatorDescription
This indicator visualizes a set of statistically derived Power-Law bands for the Bitcoin price.
The model is based on a log–log regression of the Bitcoin price over time and a weighted quantile regression that captures the distributional structure of the price across several long-term quantiles.
It provides a historical context for where the price currently lies relative to these mathematically estimated zones.
This indicator does not perform any new model fitting; it only displays the pre-computed band structure derived from the full historical dataset.
How the model works
This indicator is based on a statistical Power-Law model of the Bitcoin price.
A long-term trend was estimated using a log–log OLS regression, and the deviations from this trend were analyzed through a rolling multi-year volatility measure.
The inverse of this volatility served as the weight for several quantile regression fits, producing robust long-term bands at multiple distribution levels (0.1%, 15%, 50%, 85%, 95%, 99.9%).
These quantile curves represent the historical valuation zones of the Bitcoin price.
All final regression coefficients are fixed and embedded into the Pine script, which reconstructs the bands directly on the chart.
The extension of the bands into the future is based solely on the mathematical form of each curve and does not use any future market data.
What the indicator displays
• Six Power-Law quantile bands (0.1%, 15%, 50%, 85%, 95%, 99.9%) displayed as stacked colored zones
• Future-offset projection curves (mathematical extrapolation of the fitted Power-Laws, not based on future prices)
• Quantile Oscillator: A normalized representation of where the current price lies relative to the quantile structure.
How to use it
This indicator is not a timing tool.
It provides a structural, long-term statistical context for the Bitcoin price, showing:
• how extreme a current valuation is relative to long-term history
• where the price sits within the Power-Law quantile spectrum
• long-term distribution zones derived from the quantile regressions
• a volatility-weighted representation of historical deviations
It may be useful for long-term cycle studies or valuation comparisons, but there is no guarantee that this historical relationship will persist.
Important notes
• This indicator does not repaint.
• All projections are non-predictive mathematical extrapolations.
• This script is designed only for the symbol: INDEX:BTCUSD
• It does not provide trading signals, recommendations, or financial advice.
Why closed-source?
The underlying regression model, weighting logic, and quantile estimations were produced externally using Python and constitute the core intellectual component of the study. The Pine version contains only the pre-calculated parameters and the visualization logic.
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"
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.
BTC/Gold Power-Law Bands + Quantile OscillatorDescription
This indicator visualizes a set of statistically derived Power-Law bands for the BTC/Gold ratio.
The model is based on a log–log regression of the BTC/Gold ratio over time and a weighted quantile regression that captures the distributional structure of the ratio across several long-term quantiles.
It provides a historical context for where the ratio currently lies relative to these mathematically estimated zones.
This indicator does not perform any new model fitting; it only displays the pre-computed band structure derived from the full historical dataset.
How the model works
This indicator is based on a statistical Power-Law model of the BTC/Gold ratio.
A long-term trend was estimated using a log–log OLS regression, and the deviations from this trend were analyzed through a rolling multi-year volatility measure.
The inverse of this volatility served as the weight for several quantile regression fits, producing robust long-term bands at multiple distribution levels (0.1%, 12.5%, 50%, 80%, 95%, 99.5%).
These quantile curves represent the historical valuation zones of the BTC/Gold ratio.
All final regression coefficients are fixed and embedded into the Pine script, which reconstructs the bands directly on the chart.
The extension of the bands into the future is based solely on the mathematical form of each curve and does not use any future market data.
What the indicator displays
• Six Power-Law quantile bands (0.1%, 12.5%, 50%, 80%, 95%, 99.5%) displayed as stacked colored zones
• BTC/Gold Ratio line
• Future-offset projection curves (mathematical extrapolation of the fitted Power-Laws, not based on future prices)
• Quantile Oscillator: A normalized representation of where the current ratio lies relative to the quantile structure.
How to use it
This indicator is not a timing tool.
It provides a structural, long-term statistical context for the BTC/Gold ratio, showing:
• how extreme a current valuation is relative to long-term history
• where the ratio sits within the Power-Law quantile spectrum
• long-term distribution zones derived from the quantile regressions
• a volatility-weighted representation of historical deviations
It may be useful for long-term cycle studies or ratio-based valuation comparisons, but there is no guarantee that this historical relationship will persist.
Important notes
• This indicator does not repaint.
• All projections are non-predictive mathematical extrapolations.
• This script is designed only for the symbol: BTCUSD/TVC:GOLD (BTC/Gold Ratio)
• It does not provide trading signals, recommendations, or financial advice.
Why closed-source?
The underlying regression model, weighting logic, and quantile estimations were produced externally using Python and constitute the core intellectual component of the study. The Pine version contains only the pre-calculated parameters and the visualization logic.
Omega Correlation [OmegaTools]Omega Correlation (Ω CRR) is a cross-asset analytics tool designed to quantify both the strength of the relationship between two instruments and the tendency of one to move ahead of the other. It is intended for traders who work with indices, futures, FX, commodities, equities and ETFs, and who require something more robust than a simple linear correlation line.
The indicator operates in two distinct modes, selected via the “Show” parameter: Correlation and Anticipation. In Correlation mode, the script focuses on how tightly the current chart and the chosen second asset move together. In Anticipation mode, it shifts to a lead–lag perspective and estimates whether the second asset tends to behave as a leader or a follower relative to the symbol on the chart.
In both modes, the core inputs are the chart symbol and a user-selected second symbol. Internally, both assets are transformed into normalized log-returns: the script computes logarithmic returns, removes short-term mean and scales by realized volatility, then clips extreme values. This normalisation allows the tool to compare behaviour across assets with different price levels and volatility profiles.
In Correlation mode, the indicator computes a composite correlation score that typically ranges between –1 and +1. Values near +1 indicate strong and persistent positive co-movement, values near zero indicate an unstable or weak link, and values near –1 indicate a stable anti-correlation regime. The composite score is constructed from three components.
The first component is a normalized return co-movement measure. After transforming both instruments into normalized returns, the script evaluates how similar those returns are bar by bar. When the two assets consistently deliver returns of similar sign and magnitude, this component is high and positive. When they frequently diverge or move in opposite directions, it becomes negative. This captures short-term co-movement in a volatility-adjusted way.
The second component focuses on high–low swing alignment. Rather than looking only at closes, it examines the direction of changes in highs and lows for each bar. If both instruments are printing higher highs and higher lows together, or lower highs and lower lows together, the swing structure is considered aligned. Persistent alignment contributes positively to the correlation score, while repeated mismatches between the swing directions reduce it. This helps differentiate between superficial price noise and structural similarity in trend behaviour.
The third component is a classical Pearson correlation on closing prices, computed over a longer lookback. This serves as a stabilising backbone that summarises general co-movement over a broader window. By combining normalized return co-movement, swing alignment and standard price correlation with calibrated weights, the Correlation mode provides a richer view than a single linear measure, capturing both short-term dynamic interaction and longer-term structural linkage.
In Anticipation mode, Omega Correlation estimates whether the second asset tends to lead or lag the current chart. The output is again a continuous score around the range. Positive values suggest that the second asset is acting more as a leader, with its past moves bearing informative value for subsequent moves of the chart symbol. Negative values indicate that the second asset behaves more like a laggard or follower. Values near zero suggest that no stable lead–lag structure can be identified.
The anticipation score is built from four elements inspired by quantitative lead–lag and price discovery analysis. The first element is a residual lead correlation, conceptually similar to Granger-style logic. The script first measures how much of the chart symbol’s normalized returns can be explained by its own lagged values. It then removes that component and studies the correlation between the residuals and lagged returns of the second asset. If the second asset’s past returns consistently explain what the chart symbol does beyond its own autoregressive behaviour, this residual correlation becomes significantly positive.
The second element is an asymmetric lead–lag structure measure. It compares the strength of relationships in both directions across multiple lags: the correlation of the current symbol with lagged versions of the second asset (candidate leader) versus the correlation of lagged values of the current symbol with the present values of the second asset. If the forward direction (second asset leading the first) is systematically stronger than the backward direction, the structure is skewed toward genuine leadership of the second asset.
The third element is a relative price discovery score, constructed by building a dynamic hedge ratio between the two prices and defining a spread. The indicator looks at how changes in each asset contribute to correcting deviations in this spread over time. When the chart symbol tends to do most of the adjustment while the second asset remains relatively stable, it suggests that the second asset is taking a greater role in determining the equilibrium price and the chart symbol is adjusting to it. The difference in adjustment intensity between the two instruments is summarised into a single score.
The fourth element is a breakout follow-through causality component. The script scans for breakout events on the second asset, where its price breaks out of a recent high or low range while the chart symbol has not yet done so. It then evaluates whether the chart symbol subsequently confirms the breakout direction, remains neutral, or moves against it. Events where the second asset breaks and the first asset later follows in the same direction add positive contribution, while failed or contrarian follow-through reduce this component. The contribution is also lightly modulated by the strength of the breakout, via the underlying normalized return.
The four elements of the Anticipation mode are combined into a single leading correlation score, providing a compact and interpretable measure of whether the second asset currently behaves as an effective early signal for the symbol you trade.
To aid interpretation, Omega Correlation builds dynamic bands around the active series (correlation or anticipation). It estimates a long-term central tendency and a typical deviation around it, plotting upper and lower bands that highlight unusually high or low values relative to recent history. These bands can be used to distinguish routine fluctuations from genuinely extreme regimes.
The script also computes percentile-based levels for the correlation series and uses them to track two special price levels on the main chart: lost correlation levels and gained correlation levels. When the correlation drops below an upper percentile threshold, the current price is stored as a lost correlation level and plotted as a horizontal line. When the correlation rises above a lower percentile threshold, the current price is stored as a gained correlation level. These levels mark zones where a historically strong relationship between the two markets broke down or re-emerged, and can be used to frame divergence, convergence and spread opportunities.
An information panel summarises, in real time, whether the second asset is behaving more as a leading, lagging or independent instrument according to the anticipation score, and suggests whether the current environment is more conducive to de-alignment, re-alignment or classic spread behaviour based on the correlation regime. This makes the tool directly interpretable even for users who are not familiar with all the underlying statistical details.
Typical applications for Omega Correlation include intermarket analysis (for example, index vs index, commodity vs related equity sector, FX vs bonds), dynamic hedge sizing, regime detection for algorithmic strategies, and the identification of lead–lag structures where a macro driver or benchmark can be monitored as an early signal for the instrument actually traded. The indicator can be applied across intraday and higher timeframes, with the understanding that the strength and nature of relationships will differ across horizons.
Omega Correlation is designed as an advanced analytical framework, not as a standalone trading system. Correlation and lead–lag relationships are statistical in nature and can change abruptly, especially around macro events, regime shifts or liquidity shocks. A positive anticipation reading does not guarantee that the second asset will always move first, and a high correlation regime can break without warning. All outputs of this tool should be combined with independent analysis, sound risk management and, when appropriate, backtesting or forward testing on the user’s specific instruments and timeframes.
The intention behind Omega Correlation is to bring techniques inspired by quantitative research, such as normalized return analysis, residual correlation, asymmetric lead–lag structure, price discovery logic and breakout event studies, into an accessible TradingView indicator. It is intended for traders who want a structured, professional way to understand how markets interact and to incorporate that information into their discretionary or systematic decision-making processes.
Multivariate Kalman Filter🙏🏻 I see no1 ever posted an open source Multivariate Kalman filter on TV, so here it is, for you. Tested and mathematically correct implementation, with numerical safeties in place that do not affect the final results at all. That’s the main purpose of this drop, just to make the tool available here. Linear algebra everywhere, Neo would approve 4 sure.
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Personally I haven't found any real use case of it for myself, aside from a very specific one I will explain later, but others usually do…
Almost every1 in the quant industry who uses Kalman is in fact misusing it, because by its real definition, it should be applied to Not the exact known values (e.g as real ticks provided by transparent audited regulated exchange), but “measurements” of those ‘with errors’.
If your measurements don’t have errors or you have real precise data, by its internal formulas Kalman will output the exact inputs. So most who use it come up with some imaginary errors of sorts, like from some kind of imaginary fair price etc. The important easy to miss point, the errors of your measurements have to be symmetric around its mean ‘ at least ’, if errors are biased, Kalman won’t provide.
For most tasks there are better tools, including other state space models , but still Multivariate Kalman is a very powerful instrument, you can make it do all kinds of stuff. Also as a state space model it can also provide confidence & prediction intervals without explicit calculations of dem.
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In this script I included 2 example use cases, the first one is the classic tho perfectly working misuse, the second one is what I do with it:
One
Naive, estimates “hidden” adaptive moving regression endpoint. The result you can see on the chart above. You can imagine that your real datapoints are in fact non perfect measures of some hidden state, and by defining measurement noise and process noise, and by constructing the input matrixes in certain ways, you can express what that state should be.
Two
Upscaling tick lattice, aka modelling prices as if native tick size would’ve been lower. Kinda very specific task, mostly needed in HFT or just for analytical purposes. Some like ZN have huge tick sizes, they are traded a lot but barely do more than 20 ticks range in a session. The idea is to model raw data as AR2 process , learn the phi1 and phi2, make one point forecasts based on dem, and the process noise would be the variance of errors from these forecasts. The measurement noise here is legit, it’s quantization noise based on tick size, no need in olympic gold in mental gymnastics xd
^^ artificially upscaling ZN futures tick lattice
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I really made it available there so You guys can take it and some crazy ish with it, just let state space models abduct your conciseness and never look back
∞
Final_CDVCumulative Delta volume using Heikin-Ashi calculation. I don't own the idea behind it, but I updated the calculation to smoothen the oscillation
ArithmaReg Candles [NeuraAlgo]ArithmaReg Candles
ArimaReg Candles provide a quantitative approach toward the visualization of price by rebuilding each candle using an adaptive regression model. This indicator eliminates much of the noise and micro-spikes and consolidates irregular volatility of raw OHLC data, which typically characterizes candles, into a much cleaner and more stable representation that better reflects the true directional intent of the market.
The algorithm applies a dynamic state-space filter to track the equilibrium price, truePrice, while suppressing high-frequency fluctuations. Noise in the price is extracted by comparing the raw close to the filtered state and removed from the candle body and wick structure through controlled adjustment logic. Finally, a volatility-based spread model rebuilds the candle's range to maintain realistic price geometry.
The direction of trends is given by comparing the truePrice against a smoothing baseline, permitting ArithmaReg Candles to underline the bullish and bearish phases with more clarity and much-reduced distortion. This yields a chart where transitions within trends, pullbacks, and momentum shifts are much easier to comprehend than their representation via traditional candles.
ArithmaReg Candles are designed for traders who require consistent, noise-filtered price structure-ideal for trend analysis, breakout validation, and precision entries. The indicator itself does not generate any signals; it only refines the visual environment so that your existing tools and decision models become more reliable.
How It Works
Micro-Price Extraction
A weighted micro-price is calculated to represent the bar's internal structure and reduce intrabar irregularities.
Adaptive Regression Filter
The state-based regression engine continuously updates price equilibrium, adjusting its confidence level. This gives the filter the ability to remain responsive during strong movements yet be stable during noisy periods.
Noise Removal & Candle Reconstruction
The difference between raw price and truePrice is considered noise. This noise is subtracted from OHLC values, and a volatility-scaled spread restores realistic wick and body proportions. What results is a candle that depicts true directional flow.
Trend Classification
A smoothed trend baseline is computed from the filtered price, and candle color is determined by whether the market is positioned above or below this equilibrium trend.
How to Use It
Identify True Trend Direction
Candles follow the cleaned price path so that you can differentiate valid trend shifts from temporary spikes or wick-driven traps.
Improve Existing Strategies
These candles will complement your existing indicators, be they Supertrend, moving averages, volume tools, or momentum oscillators, by giving you a more sound price basis.
Spot Clean Breakouts & Pullbacks
Reduced noise makes breakout structure, swing highs/lows, and retracements significantly clearer. This is particularly useful in fast markets like crypto and Forex.
Improve Entry & Exit Timing
By highlighting the underlying flow of price, ArithmaReg Candles help traders avoid false signals and pinpoint spots where the price momentum is actually changing.
Adaptable to All Timeframes & Assets
The filter is self-adjusting, so it performs consistently on scalping timeframes, intraday charts, swing setups, and all asset classes. Summary ArithmaReg Candles create a mathematically refined view of market structure by removing noise and reconstructing candles through adaptive regression. The result is a more refined, stable price representation that improves trend recognition and decision-making and enables professional-grade technical analysis.
Sniper BB + VWAP System (with SMT Divergence Arrows)STEP 1: Load two correlated futures charts.
Example: CL + RB/SI+GC/ NQ+ES
STEP 2: Add Bollinger Bands (20, 2.0) on both.
Optional add (20, 3.0).
STEP 3: Watch for a BB tag on one chart but not the other.
STEP 4: Wait for a reclaim candle back inside the band.
STEP 5: Enter with stop below/above the wick + 3.0 BB.
STEP 6: Scale out midline, then opposite band.
STEP 7: Hold partials when both pairs confirm trend.
*You can take the vwap bands off the chart if it is too cluttered.
MagFlow X: @Cissora <--MagFlow Trend is a premium trend model created as a quantitative counterpart to widely used commercial indicators. Its structure draws from exchange-oriented analytical concepts to establish a flexible, noise-resistant framework for directional movement. The design prioritizes clarity, reduced lag, and responsiveness across varying market conditions. Developed from original research and external visual models, MagFlow Trend is engineered to reflect a more mathematically disciplined trend engine.
Universal Scalper Indicator [Crypto/Forex/Gold]Universal Scalper Pro is an all-in-one scalping system designed for the 15-Minute Timeframe. It automates the analysis of trend, volatility, and risk management into a single, high-contrast dashboard.
Unlike standard crossover indicators, this system filters out low-volatility "noise" using a built-in ADX engine and automatically calculates dynamic Stop Loss and Take Profit levels based on market volatility (ATR).
It is engineered to work universally on:
Crypto (BTC, ETH, SOL, Altcoins)
Commodities (Gold, Silver, Oil)
Forex (Major & Minor Pairs)
Stocks (High volume tech stocks like NVDA, TSLA)
📈 How It Works (The Strategy)
1. The Trend Engine (9/21 EMA) The core logic utilizes a Fast (9) and Slow (21) Exponential Moving Average crossover.
Bullish Signal: The 9 EMA crosses above the 21 EMA.
Bearish Signal: The 9 EMA crosses below the 21 EMA. This specific combination is chosen for its responsiveness to 15-minute intraday trends.
2. The Noise Filter (ADX > 15) To prevent "whipsaws" (fake signals during sideways markets), the script includes a Volatility Filter based on the Average Directional Index (ADX).
Signals are rejected if the ADX is below 15.
This ensures you only receive alerts when there is sufficient momentum to sustain a move.
3. Dynamic Risk Management (ATR) The script uses the Average True Range (ATR) to calculate Stop Loss and Take Profit levels that adapt to the specific asset's volatility.
Stop Loss: Placed at 1.5x ATR from the entry. (Tight enough to preserve capital, wide enough to survive standard market noise).
Take Profit: Placed at 2.0x ATR from the entry. (Provides a healthy 1:1.3 Risk/Reward ratio).
🚀 Key Features
Universal Dashboard: A bottom-right panel displays the live Trend Status, Entry Price, Stop Loss, and Take Profit. It automatically formats decimals for any asset (e.g., 2 decimals for Gold, 5 for Forex, 8 for Crypto).
"Sticky" Memory: The dashboard retains the prices of the last valid signal, allowing you to manage your trade even after the signal candle closes.
Trend Cloud: A visual Green/Red zone between the EMAs helps you instantly identify the market bias.
Unified Alerts: A single alert setup ("Any alert() function call") sends the Asset Name, Entry, SL, and TP directly to your phone.
🛠️ How to Use
Timeframe: Set your chart to 15 Minutes (15m).
Wait for the Signal: Look for the "BUY" (Green) or "SELL" (Red) label on the chart.
Check the Dashboard: Ensure the "STATUS" is BULLISH (for buys) or BEARISH (for sells). If the status says "WAIT", do not trade.
Execute: Enter the trade using the exact Stop Loss and Take Profit levels shown on the dashboard.
⚠️ Risk Disclaimer
Trading financial markets involves high risk and may not be suitable for all investors. This indicator is a technical analysis tool and does not constitute financial advice. Past performance is not indicative of future results. Always practice with a demo account before trading real capital.
Total Returns indicator by PtahXPtahX Total Returns – True Total-Return View for Any Symbol
Most charts only show price. This script shows what your position actually did once you include dividends and, optionally, inflation.
What this indicator does
1. Builds a Total Return series
You choose how dividends are treated:
* Reinvest (default): All gross dividends are automatically reinvested into more shares on the ex-dividend bar.
* Cash: Dividends are kept as cash added on top of your initial position.
* Ignore: Price only, like a regular chart.
This answers: “If I bought once at the start and held, how much would that position be worth now, given this dividend policy?”
2. Optional inflation-adjusted (real) returns
You can also plot a real total-return line, which adjusts for inflation using a CPI series.
This answers: “How did my purchasing power change after inflation?”
3. Stats window and exponential trendline
You can pick the time window:
* Since inception (full available history)
* YTD
* Last 1 Year
* Last 5 Years
* Custom start date
For that window, the script:
* Normalizes Total Return to 1.0 at the window start.
* Fits an exponential trendline (pink) to the normalized series.
* Displays a stats table in the bottom-right showing:
• Overall Return (%) over the selected range
• CAGR (compound annual growth rate, % per year)
• Trendline growth (% per year)
• R² of the trendline (fit quality)
• A separate “Since inception” block (overall return and CAGR from the first bar on the chart)
How to use it
1. Add the indicator to your chart.
2. Open the settings:
Total Return & Dividends
* Dividend mode
• Reinvest: closest to a true total-return curve (default).
• Cash: price plus cash dividends.
• Ignore: price only.
* Plot inflation-adjusted TR line
• Turn this on if you want to see a real (CPI-adjusted) total-return line.
Inflation / Real Returns
* Inflation country code and field code
• Leave defaults if you just want a standard CPI series.
* Use real TR for stats & trendline
• On: stats and trendline use the inflation-adjusted curve.
• Off: stats use the nominal (non-adjusted) total return.
Stats Range & Trendline
* Stats range: Since inception, YTD, 1 Year, 5 Years, or Custom date.
* Custom date: set year, month, and day if you choose “Custom date”.
* Plot TR exponential trendline: show or hide the pink curve.
* Show stats table / Show Overall Return / Show Trendline stats: toggle what appears in the table.
3. Zoom and change timeframe as usual. The stats range is based on calendar time (YTD, 1Y, 5Y, etc.), not bar count, so the numbers stay meaningful as you change resolutions.
How to read the outputs
* Teal line: Nominal Total Return (using your chosen dividend mode).
* Orange line (if enabled): Real (inflation-adjusted) Total Return.
* Pink line (if enabled): Exponential trendline for the selected stats window.
On the right edge, small labels show the latest value of each active line.
In the bottom-right stats table:
* Overall Return: total percentage gain or loss over the chosen stats range.
* CAGR: the smoothed annual rate that would turn 1.0 into the current value over that range.
* Exponential Trendline: the average trendline growth per year and the R².
• R² near 1 means prices follow a clean exponential path.
• Lower R² means more noise or sideways movement around the trend.
* Range: which window those stats apply to (YTD, 1Y, 5Y, etc.).
* Since inception: overall return and CAGR from the first bar on the chart up to the latest bar, independent of the current stats range.
Use this when you want to compare true performance, not just price – especially for dividend-heavy ETFs, funds, and income strategies.
Donchian Predictive Channel (Zeiierman)█ Overview
Donchian Predictive Channel (Zeiierman) extends the classic Donchian framework into a predictive structure. It does not just track where the range has been; it projects where the Donchian mid, high, and low boundaries are statistically likely to move based on recent directional bias and volatility regime.
By quantifying the linear drift of the Donchian midline and the expansion or compression rate of the Donchian range, the indicator generates a forward propagation cone that reflects the prevailing trend and volatility state. This produces a cleaner, more analytically grounded projection of future price corridors, and it remains fully aligned with the signal precision of the underlying Donchian logic.
█ How It Works
⚪ Donchian Core
The script first computes a standard Donchian Channel over a configurable Length:
Upper Band (dcHi) – highest high over the lookback.
Lower Band (dcLo) – lowest low over the lookback.
Midline (dcMd) – simple midpoint of upper and lower: (dcHi + dcLo)/ 2.
f_getDonchian(length) =>
hi = ta.highest(high, length)
lo = ta.lowest(low, length)
md = (hi + lo) * 0.5
= f_getDonchian(lenDC)
⚪ Slope Estimation & Range Dynamics
To turn the Donchian Channel into a predictive model, the script measures how both the midline and the range are changing over time:
Midline Slope (mSl) – derived from a 1-bar difference in linear regression of the midline.
Range Slope (rSl) – derived from a 1-bar difference in linear regression of the Donchian range (dcHi − dcLo).
This pair describes both directional drift (uptrend vs. downtrend) and range expansion/compression (volatility regime).
f_getSlopes(midLine, rngVal, length) =>
mSl = ta.linreg(midLine, length, 0) - ta.linreg(midLine, length, 1)
rSl = ta.linreg(rngVal, length, 0) - ta.linreg(rngVal, length, 1)
⚪ Forward Projection Engine
At the last bar, the indicator constructs a set of forward points for the mid, upper, and lower projections over Forecast Bars:
The midline is projected linearly using the midline slope per bar.
The range is adjusted using the range slope per bar, creating either a widening cone (expansion) or a tightening cone (compression).
Upper and lower projections are then anchored around the projected midline, with logic that keeps the structure consistent and prevents pathological flips when slope changes sign.
f_generatePoints(hi0, md0, lo0, steps, midSlp, rngSlp) =>
upPts = array.new()
mdPts = array.new()
dnPts = array.new()
fillPts = array.new()
hi_vals = array.new_float()
md_vals = array.new_float()
lo_vals = array.new_float()
curHiLocal = hi0
curLoLocal = lo0
curMidLocal = md0
segBars = math.floor(steps / 3)
segBars := segBars < 1 ? 1 : segBars
for b = 0 to steps
mdProj = md0 + midSlp * b
prevRange = curHiLocal - curLoLocal
rngProj = prevRange + rngSlp * b
hiTemp = 0.0
loTemp = 0.0
if midSlp >= 0
hiTemp := math.max(curHiLocal, mdProj + rngProj * 0.5)
loTemp := math.max(curLoLocal, mdProj - rngProj * 0.5)
else
hiTemp := math.min(curHiLocal, mdProj + rngProj * 0.5)
loTemp := math.min(curLoLocal, mdProj - rngProj * 0.5)
hiProj = hiTemp < mdProj ? curHiLocal : hiTemp
loProj = loTemp > mdProj ? curLoLocal : loTemp
if b % segBars == 0
curHiLocal := hiProj
curLoLocal := loProj
curMidLocal := mdProj
array.push(hi_vals, curHiLocal)
array.push(md_vals, curMidLocal)
array.push(lo_vals, curLoLocal)
array.push(upPts, chart.point.from_index(bar_index + b, curHiLocal))
array.push(mdPts, chart.point.from_index(bar_index + b, curMidLocal))
array.push(dnPts, chart.point.from_index(bar_index + b, curLoLocal))
ptSet.new(upPts, mdPts, dnPts)
⚪ Rejection Signals
The script also tracks failed Donchian breakouts and marks them as potential reversal/reversion cues:
Signal Down: Triggered when price makes an attempt above the upper Donchian band but then pulls back inside and closes above the midline, provided enough bars have passed since the last signal.
Signal Up: Triggered when price makes an attempt below the lower Donchian band but then snaps back inside and closes below the midline, also requiring sufficient spacing from the previous signal.
// Base signal conditions (unfiltered)
bearCond = high < dcHi and high >= dcHi and close > dcMd and bar_index - lastMarker >= lenDC
bullCond = low > dcLo and low <= dcLo and close < dcMd and bar_index - lastMarker >= lenDC
// Apply MA filter if enabled
if signalfilter
bearCond := bearCond and close < ma // Bearish only below MA
bullCond := bullCond and close > ma // Bullish only above MA
signalUp := false
signalDn := false
if bearCond
lastMarker := bar_index
signalDn := true
if bullCond
lastMarker := bar_index
signalUp := true
█ How to Use
The Donchian Predictive Channel is designed to outline possible future price trajectories. Treat it as a directional guide, not a fixed prediction tool.
⚪ Map Future Support & Resistance
Use the projected upper and lower paths as dynamic future reference levels:
Projected upper band ≈ is likely a resistance corridor if the current trend and volatility persist.
Projected lower band ≈ likely support corridor or expected downside range.
⚪ Trend Path & Volatility Cone
Because the projection is driven by midline and range slopes, the channel behaves like a trend + volatility cone:
Steep positive midline slope + expanding range → accelerating, high-volatility trend.
Flat midline + compressing range → coiling/contracting regime ahead of potential expansion.
This helps you distinguish between a gentle drift and an aggressive move that likely needs more risk buffer.
⚪ Reversion & Rejection Signals
The Donchian-based signals are especially useful for mean-reversion and fade-style trades.
A Signal Down near the upper band can mark a failed breakout and a potential rotation back toward the midline or the lower projected band.
A Signal Up near the lower band can flag a failed breakdown and a potential snap-back up the channel.
When Filter Signals is enabled, these signals are only generated when they align with the chart’s directional bias as defined by the moving average. Bullish signals are allowed only when the price is above the MA, and bearish signals only when the price is below it.
This reduces noise and helps ensure that reversions occur in harmony with the prevailing trend environment.
█ Settings
Length – Donchian lookback length. Higher values produce a smoother channel with fewer but more stable signals. Lower values make the channel more reactive and increase sensitivity at the cost of more noise.
Forecast Bars – Number of bars used for projecting the Donchian channel forward.
Higher values create a broader, longer-term projection. Lower values focus on short-horizon price path scenarios.
Filter Signals – Enables directional filtering of Donchian signals using the selected moving average. When ON, bullish signals only trigger when the price is above the MA, and bearish signals only trigger when the price is below it. This helps reduce noise and aligns reversions with the broader trend context.
Moving Average Type – The type of moving average used for signal filtering and optional plotting.
Choose between SMA, EMA, WMA, or HMA depending on desired responsiveness. Faster averages (EMA, HMA) react quickly, while slower ones (SMA, WMA) smooth out short-term noise.
Moving Average Length – Lookback length of the moving average. Higher values create a slower, more stable trend filter. Lower values track price more tightly and can flip the directional bias more frequently.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
