Future is hereOverview
"Future is Here" is an original, multi-faceted Pine Script indicator designed to provide traders with a comprehensive toolset for identifying high-probability trading opportunities. By integrating volatility-based entry zones, trend-based price targets, momentum confirmation, dynamic support/resistance levels, and risk-reward ratio (RRR) calculations, this indicator offers a cohesive and actionable trading framework. Each feature is carefully designed to complement the others, ensuring a synergistic approach that enhances decision-making across various market conditions. This script is unique in its ability to combine these elements into a single, streamlined interface with clear visual cues and customizable alerts, making it suitable for both novice and experienced traders.
Key Features and How They Work Together
Volatility-Based Entry Zones
Purpose: Identifies overbought and oversold conditions using a volatility-adjusted moving average, helping traders spot potential reversal zones.
Mechanism: Utilizes a user-defined volatility length and multiplier to calculate dynamic overbought/oversold thresholds based on the standard deviation of price. Crossovers and crossunders of these levels trigger "Buy Zone" or "Sell Zone" labels.
Synergy: These zones act as the foundation for entry signals, which are later confirmed by momentum and trend filters to reduce false signals.
Trend-Based Price Targets
Purpose: Projects potential price targets based on the prevailing trend, giving traders clear objectives for profit-taking.
Mechanism: Combines a fast and slow moving average to determine trend direction, then calculates target prices using a multiplier of the price deviation from the slow MA. Labels display bullish or bearish targets when the fast MA crosses the slow MA.
Synergy: Works in tandem with entry zones and momentum signals to align targets with market conditions, ensuring traders aim for realistic price levels supported by trend strength.
Momentum Confirmation
Purpose: Validates entry signals by assessing momentum strength, filtering out weak setups.
Mechanism: Uses the momentum indicator to detect bullish or bearish momentum crossovers, labeling them as "Strong" or "Weak" based on a comparison with a smoothed momentum average.
Synergy: Enhances the reliability of buy/sell signals by ensuring momentum aligns with volatility zones and trend direction, reducing the risk of premature entries.
Dynamic Support/Resistance Levels
Purpose: Highlights key price levels where the market is likely to react, aiding in trade planning and risk management.
Mechanism: Detects pivot highs and lows over a user-defined lookback period, drawing horizontal lines for the most recent support and resistance levels (limited to two each for clarity). Labels mark these levels with price values.
Synergy: Complements entry zones and price targets by providing context for potential reversal or continuation points, helping traders set logical stop-losses or take-profits.
Buy/Sell Signals with Risk-Reward Ratios
Purpose: Generates precise buy/sell signals with integrated take-profit (TP), stop-loss (SL), and RRR calculations for disciplined trading.
Mechanism: Combines volatility zone crossovers, trend confirmation, and positive momentum to trigger signals. ATR-based TP and SL levels are calculated, and the RRR is displayed in labels for quick assessment.
Synergy: This feature ties together all previous components, ensuring signals are only generated when volatility, trend, and momentum align, while providing clear risk-reward metrics for trade evaluation.
Customizable Alerts
Purpose: Enables traders to stay informed of trading opportunities without constant chart monitoring.
Mechanism: Alert conditions are set for buy and sell signals, delivering notifications with the entry price for seamless integration into trading workflows.
Synergy: Enhances usability by allowing traders to act on high-probability setups identified by the indicator’s combined logic.
Originality
"Future is Here" is an original creation that distinguishes itself through its holistic approach to technical analysis. Unlike single-purpose indicators, it integrates volatility, trend, momentum, and support/resistance into a unified system, reducing the need for multiple scripts. The inclusion of RRR calculations directly in signal labels is a unique feature that empowers traders to evaluate trade quality instantly. The script’s design emphasizes clarity and efficiency, with cooldowns to prevent label clutter and a limit on support/resistance lines to maintain chart readability. This combination of features, along with its customizable parameters, makes it a versatile and novel tool for traders seeking a robust, all-in-one solution.
How to Use
Setup: Add the indicator to your TradingView chart and adjust input parameters (e.g., Volatility Length, Trend Length, TP/SL Multipliers) to suit your trading style and timeframe.
Interpretation:
Look for "Buy Zone" or "Sell Zone" labels to identify potential entry points.
Confirm entries with "Bull Mom" or "Bear Mom" labels and trend direction (Bull/Bear Target labels).
Use Support/Resistance lines to set logical TP/SL levels or anticipate reversals.
Evaluate Buy/Sell signals with TP, SL, and RRR for high-probability trades.
Alerts: Set up alerts for Buy/Sell signals to receive real-time notifications.
Customization: Fine-tune multipliers and lengths to adapt the indicator to different markets (e.g., stocks, forex, crypto) or timeframes.
Komut dosyalarını "bear" için ara
Dow Theory Trend IndicatorIdentifies bullish (Higher Highs/Lows) and bearish (Lower Highs/Lows) trends using Dow Theory principles, with dynamic volume confirmation.
Displays the current trend status ("Bull", "Bear", or "Neutral")
📊 RSI Multi-Timeframe Dashboard by giua64)### Summary
This is an advanced dashboard that provides a comprehensive overview of market strength and momentum, based on the Relative Strength Index (RSI) analyzed across 6 different timeframes simultaneously (from 5 minutes to the daily chart).
The purpose of this script is to offer traders an immediate and easy-to-read summary of market conditions, helping to identify the prevailing trend direction, overbought/oversold levels, and potential reversals through divergence detection. All of this is available in a single panel, eliminating the need to switch timeframes on your main chart.
### Key Features
* **Multi-Timeframe Analysis:** Simultaneously monitors the 5m, 15m, 30m, 1H, 4H, and Daily timeframes.
* **Scoring System:** Each timeframe is assigned a score based on multiple RSI conditions (e.g., above/below 50, overbought/oversold status, direction) to quantify bullish or bearish strength.
* **Aggregated Signal:** The dashboard calculates a total percentage score and provides a clear summary signal: **LONG**, **SHORT**, or **WAIT**.
* **Divergence Detection:** Automatically identifies Bullish and Bearish divergences between price and RSI for each timeframe.
* **Non-Repainting Option:** In the settings, you can choose to base calculations on the close of the previous candle (`Use RSI on Closed Candle`). This ensures that past signals (like status and score) do not change, providing more reliable data for analysis.
* **Fully Customizable:** Users can modify the RSI period, overbought/oversold thresholds, divergence detection settings, and the appearance of the table.
### How to Read the Dashboard
The table consists of 6 columns, each providing specific information:
* **% (Total Score):**
* **Header:** Shows the overall strength as a percentage. A positive value indicates bullish momentum, while a negative value indicates bearish momentum. The background color changes based on intensity.
* **Rows:** Displays the numerical score for the individual timeframe.
* **RSI:**
* **Header:** The background color indicates the average of all RSI values. Green if the average is > 50, Red if < 50.
* **Rows:** Shows the real-time RSI value for that timeframe.
* **Signal (Status):**
* **Header:** This is the final operational signal. It turns **🟢 LONG** when bullish strength is high, **🔴 SHORT** when bearish strength is high, and **⚪ WAIT** in neutral conditions.
* **Rows:** Describes the RSI status for that timeframe (e.g., Bullish, Bearish, Overbought, Oversold).
* **Dir (Direction):**
* **Header:** Displays an arrow representing the majority direction across all timeframes.
* **Rows:** Shows the instantaneous direction of the RSI (↗️ for rising, ↘️ for falling).
* **Diverg (Divergence):**
* Indicates if a bullish (`🟢 Bull`) or bearish (`🔴 Bear`) divergence has been detected on that timeframe.
* **TF (Timeframe):**
* Indicates the reference timeframe for that row.
### Advantages and Practical Use
This tool was created to solve a common problem: the need to analyze multiple charts to understand the bigger picture. With this dashboard, you can:
1. **Confirm a Trend:** A predominance of green and a "LONG" signal provides strong confirmation of bullish sentiment.
2. **Identify Weakness:** Red signals on higher timeframes can warn of an impending loss of momentum.
3. **Spot Turning Points:** A divergence on a major timeframe can signal an excellent reversal opportunity.
### Originality and Acknowledgements
This script is an original work, written from scratch by giua64. The idea was to create a comprehensive and visually intuitive tool for RSI analysis.
Any feedback, comments, or suggestions to improve the script are welcome!
**Disclaimer:** This is a technical analysis tool and should not be considered financial advice. Always do your own research and backtest any tool before using it in a live trading environment.
BTC Transaction Indicator Name: "Bitcoin On-Chain Volume & Dynamic Parabolic Curve Signals"
Purpose:
This indicator is designed for Bitcoin traders and long-term holders. It combines the analysis of Bitcoin's on-chain transaction volume with price action to generate "Whale" and "Bear" signals. Additionally, it features a unique dynamic parabolic curve that acts as a visual support line, adapting its visibility based on price interaction with a key Exponential Moving Average (EMA).
Key Components:
On-Chain Volume Analysis:
Utilizes Estimated Transaction Volume (ETRAV) data from the Bitcoin blockchain.
Calculates fast and slow Simple Moving Averages (SMAs) of this volume.
Identifies volume trends (up/down) and significant volume increases/decreases.
Employs fixed thresholds (2,500,000 for low volume and 25,000,000 for high volume) to define key activity levels, similar to how historical on-chain analysis defined accumulation and distribution zones.
Price Action Analysis:
Calculates fast and slow SMAs of the price.
Detects price trends (up/down), recoveries, and declines based on these price SMAs.
"Whale" and "Bear" Signals:
Whale Signals (Buy-side): Generated when there's an upward volume trend, significant volume increase, and a downward price trend followed by price recovery. These indicate potential accumulation phases.
Bear Signals (Sell-side): Generated when there's a downward volume trend, significant volume decrease, and an upward price trend followed by price decline. These indicate potential distribution phases.
Visuals: Both types of signals are plotted as small, colored circles directly on the price chart, with corresponding text labels ("Whale," "Buy," "Bear," "Sell," "Price Recovering," "Price Declining").
Dynamic Parabolic Curve:
Concept: A green parabolic (exponential) curve that serves as a dynamic visual support line.
Activation: The curve starts drawing automatically only when the price crosses over the EMA 500 (Exponential Moving Average of 500 periods). The curve's starting point is set at a user-defined percentage below the EMA 500 value at that exact crossover point.
Visibility: The curve remains visible and continues its trajectory only as long as the price stays above the EMA 500.
Deactivation: The curve disappears instantly if the price falls below or equals the EMA 500. It will only reappear if the price crosses above the EMA 500 again.
Customization: The curve's steepness (Tasa Crecimiento Curva) and its initial distance from the EMA 500 (Inicio Curva % por debajo de EMA500) are adjustable.
Dynamic Label: A "Parabólico" text label is plotted near the center of the active curve segment, with an adjustable vertical offset to ensure it stays visually appealing below the curve.
What is PLOTTED on the chart:
The small, colored circle signals for Whale/Buy and Bear/Sell activity.
The green dynamic parabolic curve.
What is NOT PLOTTED:
EMA 200, EMA 500 lines (though they are calculated internally for logic).
Raw volume data or volume Moving Averages (these are only used for signal calculation, not plotted).
Ideal for:
Bitcoin traders and investors focused on long-term trends and cycle analysis, who want visual cues for accumulation/distribution phases based on on-chain activity, complemented by a unique, dynamically appearing parabolic support curve.
Important Notes:
Relies on the availability of external on-chain data (QUANDL:BCHAIN) within TradingView.
Functions best on a daily timeframe for optimal on-chain data relevance.
SOFIA - Kısa Vade Saldırı GöstergesiOf course babe! 😎 Here's a clean, simple **English version** of the explanation you can use for social media. Short, punchy, and sounds smart without being boring. Ready? 💥
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## 🔥 **SOFIA – Short-Term Attack Indicator (USDT Dominance Edition)**
This custom TradingView indicator helps you **spot market reversals** and **short-term trends** in USDT Dominance. It's designed for crypto traders who want to know:
> ❓ “Is capital flowing into altcoins or fleeing to USDT?”
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### 🧩 **What’s Inside?**
1. ### 🌀 **Stochastic RSI (Background Colors)**
* 🔴 **Red background** = USDT overbought → might drop soon → altcoins may pump
* 🟢 **Green background** = USDT oversold → could bounce → market correction risk
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2. ### 📉 **Supertrend (Buy/Sell Labels on Chart)**
* 🟢 **BUY label** = Trend turning bullish → USDT gaining dominance
* 🔴 **SELL label** = Trend turning bearish → USDT losing dominance → risk-on mode
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3. ### ⚡ **WaveTrend Cross (Circles)**
* 🟢 **Green circle** = Potential bullish reversal
* 💜 **Pink circle** = Possible bearish pullback
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4. ### 🟩🟥 **Trend Label ("BULL MODE" / "BEAR MODE")**
* Visual status of the current trend:
* **BULL MODE** = USDT dominance rising → market cooling
* **BEAR MODE** = USDT dominance dropping → altcoin opportunities may emerge
---
### ⏱️ Best Used On:
* **15m / 30m / 1H timeframes**
* Especially helpful for **altcoin entry/exit timing**
---
### ✅ What to Do With It:
* If USDT dominance starts dropping → **look for altcoin entries**
* If USDT dominance flips bullish → **protect your gains or reduce risk**
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🔔 Built for scalpers, swing traders & crypto snipers.
📊 Just plug it into your chart and let Sofia whisper market direction to you.
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**Want the code or setup tutorial?**
DM me or drop a 💚 and I’ll share it 👇
\#CryptoTrading #USDTDominance #TradingView #AltcoinSeason #CryptoTools #SofiaIndicators
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Let me know if you'd like a **carousel format (multi-slide)** version for Instagram/LinkedIn or an **infographic style** breakdown — I can generate that too!
RSI Divergence Pro+ VolumeRSI Divergence Pro+ Volume
What It Does:
RSI Divergence Pro+ Volume is a non-repainting indicator that helps traders spot potential bullish and bearish reversal zones using a classic technical analysis concept—RSI divergence—combined with advanced volume confirmation. The script highlights moments when price and RSI disagree, filtering for signals only when there is a significant volume spike, which helps reduce false positives in quiet or illiquid markets.
How It Works:
Bullish Divergence: Triggered when price makes a lower low but RSI forms a higher low, suggesting possible exhaustion in selling pressure.
Bearish Divergence: Triggered when price makes a higher high but RSI forms a lower high, signaling potential buying exhaustion.
Volume Confirmation: Signals only appear when trading volume exceeds a dynamic threshold (based on a user-defined moving average and multiplier), making alerts more reliable.
Visual Features: Customizable labels and optional gradient highlights mark the exact bars where divergence with volume confirmation occurs, making signals easy to see.
Alert System: Built-in alerts for both bullish and bearish divergences so traders can receive instant notifications.
How to Use:
Apply the script to any timeframe or liquid asset (15m–4H recommended for best results).
Watch for green “BULL↑” labels below bars (bullish divergence) and red “BEAR↓” labels above bars (bearish divergence).
Blue/violet background highlights confirm volume-verified signals.
Combine with your own risk management and confirmation tools for trade entries/exits.
Adjust lookback and volume settings to match your asset and style.
Originality & Usefulness:
This indicator stands out by combining traditional RSI divergence with advanced volume filtering, giving more credible and actionable reversal alerts. All logic is non-repainting and calculated on closed bars only. Settings are fully grouped and customizable, with professional visuals for clarity.
Limitations & Disclaimers:
Not every divergence results in a major reversal—use with other analysis.
More effective in trending or volatile markets; may produce more false signals in choppy/range conditions.
Signals are generated on bar close and do not repaint.
No indicator is a substitute for proper trading discipline and risk management.
OBV ATR Strategy (OBV Breakout Channel) bas20230503ผมแก้ไขจาก OBV+SMA อันเดิม ของเดิม ดูที่เส้น SMA สองเส้นตัดกันมั่นห่วยแตกสำหรับที่ผมลองเทรดจริง และหลักการเบรค ได้แรงบันดาลใจ ATR จาก เทพคอย ที่ใช้กับราคา แต่นี้ใช้กับ OBV แทน
และผมใช้เจมินี้ เพื่อแก้ ให้ เป็น strategy เพื่อเช็คย้อนหลังได้ง่ายกว่าเดิม
หลักการง่ายคือถ้ามันขึ้น มันจะขึ้นเรื่อยๆ
เขียน แบบสุภาพ (น่าจะอ่านได้ง่ายกว่าผมเขียน)
สคริปต์นี้ได้รับการพัฒนาต่อยอดจากแนวคิด OBV+SMA Crossover แบบดั้งเดิม ซึ่งจากการทดสอบส่วนตัวพบว่าประสิทธิภาพยังไม่น่าพอใจ กลยุทธ์ใหม่นี้จึงเปลี่ยนมาใช้หลักการ "Breakout" ซึ่งได้รับแรงบันดาลใจมาจากการใช้ ATR สร้างกรอบของราคา แต่เราได้นำมาประยุกต์ใช้กับ On-Balance Volume (OBV) แทน นอกจากนี้ สคริปต์ได้ถูกแปลงเป็น Strategy เต็มรูปแบบ (โดยความช่วยเหลือจาก Gemini AI) เพื่อให้สามารถทดสอบย้อนหลัง (Backtest) และประเมินประสิทธิภาพได้อย่างแม่นยำ
หลักการของกลยุทธ์: กลยุทธ์นี้ทำงานบนแนวคิดโมเมนตัมที่ว่า "เมื่อแนวโน้มได้เกิดขึ้นแล้ว มีโอกาสที่มันจะดำเนินต่อไป" โดยจะมองหาการทะลุของพลังซื้อ-ขาย (OBV) ที่แข็งแกร่งเป็นพิเศษเป็นสัญญาณเข้าเทร
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สคริปต์นี้เป็นกลยุทธ์ (Strategy) ที่ใช้ On-Balance Volume (OBV) ซึ่งเป็นอินดิเคเตอร์ที่วัดแรงซื้อและแรงขายสะสม แทนที่จะใช้การตัดกันของเส้นค่าเฉลี่ย (SMA Crossover) ที่เป็นแบบพื้นฐาน กลยุทธ์นี้จะมองหาการ "ทะลุ" (Breakout) ของพลัง OBV ออกจากกรอบสูงสุด-ต่ำสุดของตัวเองในรอบที่ผ่านมา
สัญญาณกระทิง (Bull Signal): เกิดขึ้นเมื่อพลังการซื้อ (OBV) แข็งแกร่งจนสามารถทะลุจุดสูงสุดของตัวเองในอดีตได้ บ่งบอกถึงโอกาสที่แนวโน้มจะเปลี่ยนเป็นขาขึ้น
สัญญาณหมี (Bear Signal): เกิดขึ้นเมื่อพลังการขาย (OBV) รุนแรงจนสามารถกดดันให้ OBV ทะลุจุดต่ำสุดของตัวเองในอดีตได้ บ่งบอกถึงโอกาสที่แนวโน้มจะเปลี่ยนเป็นขาลง
ส่วนประกอบบนกราฟ (Indicator Components)
เส้น OBV
เส้นหลัก ที่เปลี่ยนเขียวเป็นแดง เป็นทั้งแนวรับและแนวต้าน และ จุด stop loss
เส้นนี้คือหัวใจของอินดิเคเตอร์ ที่แสดงถึงพลังสะสมของ Volume
เมื่อเส้นเป็นสีเขียว (แนวรับ): จะปรากฏขึ้นเมื่อกลยุทธ์เข้าสู่ "โหมดกระทิง" เส้นนี้คือระดับต่ำสุดของ OBV ในอดีต และทำหน้าที่เป็นแนวรับไดนามิก
เมื่อเส้นกลายเป็นสีแดงสีแดง (แนวต้าน): จะปรากฏขึ้นเมื่อกลยุทธ์เข้าสู่ "โหมดหมี" เส้นนี้คือระดับสูงสุดของ OBV ในอดีต และทำหน้าที่เป็นแนวต้านไดนามิก
สัญลักษณ์สัญญาณ (Signal Markers):
Bull 🔼 (สามเหลี่ยมขึ้นสีเขียว): คือสัญญาณ "เข้าซื้อ" (Long) จะปรากฏขึ้น ณ จุดที่ OBV ทะลุขึ้นไปเหนือกรอบด้านบนเป็นครั้งแรก
Bear 🔽 (สามเหลี่ยมลงสีแดง): คือสัญญาณ "เข้าขาย" (Short) จะปรากฏขึ้น ณ จุดที่ OBV ทะลุลงไปต่ำกว่ากรอบด้านล่างเป็นครั้งแรก
วิธีการใช้งาน (How to Use)
เพิ่มสคริปต์นี้ลงบนกราฟราคาที่คุณสนใจ
ไปที่แท็บ "Strategy Tester" ด้านล่างของ TradingView เพื่อดูผลการทดสอบย้อนหลัง (Backtest) ของกลยุทธ์บนสินทรัพย์และไทม์เฟรมต่างๆ
ใช้สัญลักษณ์ "Bull" และ "Bear" เป็นตัวช่วยในการตัดสินใจเข้าเทรด
ข้อควรจำ: ไม่มีกลยุทธ์ใดที่สมบูรณ์แบบ 100% ควรใช้สคริปต์นี้ร่วมกับการวิเคราะห์ปัจจัยอื่นๆ เช่น โครงสร้างราคา, แนวรับ-แนวต้านของราคา และการบริหารความเสี่ยง (Risk Management) ของตัวคุณเองเสมอ
การตั้งค่า (Inputs)
SMA Length 1 / SMA Length 2: ใช้สำหรับพล็อตเส้นค่าเฉลี่ยของ OBV เพื่อดูเป็นภาพอ้างอิง ไม่มีผลต่อตรรกะการเข้า-ออกของ Strategy อันใหม่ แต่มันเป็นของเก่า ถ้าชอบ ก็ใช้ได้ เมื่อ SMA สองเส้นตัดกัน หรือตัดกับเส้น OBV
High/Low Lookback Length: (ค่าพื้นฐาน30/แก้ตรงนี้ให้เหมาะสมกับ coin หรือหุ้น ตามความผันผวน ) คือระยะเวลาที่ใช้ในการคำนวณกรอบสูงสุด-ต่ำสุดของ OBV
ค่าน้อย: ทำให้กรอบแคบลง สัญญาณจะเกิดไวและบ่อยขึ้น แต่อาจมีสัญญาณหลอก (False Signal) เยอะขึ้น
ค่ามาก: ทำให้กรอบกว้างขึ้น สัญญาณจะเกิดช้าลงและน้อยลง แต่มีแนวโน้มที่จะเป็นสัญญาณที่แข็งแกร่งกว่า
แน่นอนครับ นี่คือคำแปลฉบับภาษาอังกฤษที่สรุปใจความสำคัญ กระชับ และสุภาพ เหมาะสำหรับนำไปใช้ในคำอธิบายสคริปต์ (Description) ของ TradingView ครับ
---Translate to English---
OBV Breakout Channel Strategy
This script is an evolution of a traditional OBV+SMA Crossover concept. Through personal testing, the original crossover method was found to have unsatisfactory performance. This new strategy, therefore, uses a "Breakout" principle. The inspiration comes from using ATR to create price channels, but this concept has been adapted and applied to On-Balance Volume (OBV) instead.
Furthermore, the script has been converted into a full Strategy (with assistance from Gemini AI) to enable precise backtesting and performance evaluation.
The strategy's core principle is momentum-based: "once a trend is established, it is likely to continue." It seeks to enter trades on exceptionally strong breakouts of buying or selling pressure as measured by OBV.
Core Concept
This is a Strategy that uses On-Balance Volume (OBV), an indicator that measures cumulative buying and selling pressure. Instead of relying on a basic Simple Moving Average (SMA) Crossover, this strategy identifies a "Breakout" of the OBV from its own highest-high and lowest-low channel over a recent period.
Bull Signal: Occurs when the buying pressure (OBV) is strong enough to break above its own recent highest high, indicating a potential shift to an upward trend.
Bear Signal: Occurs when the selling pressure (OBV) is intense enough to push the OBV below its own recent lowest low, indicating a potential shift to a downward trend.
On-Screen Components
1. OBV Line
This is the main indicator line, representing the cumulative volume. Its color changes to green when OBV is rising and red when it is falling.
2. Dynamic Support & Resistance Line
This is the thick Green or Red line that appears based on the strategy's current "mode." This line serves as a dynamic support/resistance level and can be used as a reference for stop-loss placement.
Green Line (Support): Appears when the strategy enters "Bull Mode." This line represents the lowest low of the OBV in the recent past and acts as dynamic support.
Red Line (Resistance): Appears when the strategy enters "Bear Mode." This line represents the highest high of the OBV in the recent past and acts as dynamic resistance.
3. Signal Markers
Bull 🔼 (Green Up Triangle): This is the "Long Entry" signal. It appears at the moment the OBV first breaks out above its high-low channel.
Bear 🔽 (Red Down Triangle): This is the "Short Entry" signal. It appears at the moment the OBV first breaks down below its high-low channel.
How to Use
Add this script to the price chart of your choice.
Navigate to the "Strategy Tester" panel at the bottom of TradingView to view the backtesting results for the strategy on different assets and timeframes.
Use the "Bull" and "Bear" signals as aids in your trading decisions.
Disclaimer: No strategy is 100% perfect. This script should always be used in conjunction with other forms of analysis, such as price structure, key price-based support/resistance levels, and your own personal risk management rules.
Inputs
SMA Length 1 / SMA Length 2: These are used to plot moving averages on the OBV for visual reference. They are part of the legacy logic and do not affect the new breakout strategy. However, they are kept for traders who may wish to observe their crossovers for additional confirmation.
High/Low Lookback Length: (Most Important Setting) This determines the period used to calculate the highest-high and lowest-low OBV channel. (Default is 30; adjust this to suit the asset's volatility).
A smaller value: Creates a narrower channel, leading to more frequent and faster signals, but potentially more false signals.
A larger value: Creates a wider channel, leading to fewer and slower signals, which are likely to be more significant.
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE)
## Advanced Multi-Dimensional Mathematical Analysis System
*Where Quantum Mathematics Meets Market Structure*
---
## 🎓 THEORETICAL FOUNDATION
The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry.
### 🌊 Multi-Dimensional Volatility with Jump Detection
**Hawkes Process Implementation:**
The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects.
**Mathematical Foundation:**
```
Intensity λ(t) = μ + Σ α(t - Tᵢ)
```
Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99).
**Mahalanobis Distance Calculation:**
The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions:
- **Dimension 1:** Price volatility (standard deviation of returns)
- **Dimension 2:** Volume volatility (normalized volume fluctuations)
- **Dimension 3:** Range volatility (high-low spread variations)
- **Dimension 4:** Correlation volatility (price-volume relationship changes)
- **Dimension 5:** Microstructure volatility (intrabar positioning analysis)
This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches.
### 📐 Hurst Exponent Regime Detection
**Fractal Market Hypothesis Integration:**
The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification:
- **H > 0.6:** Trending (persistent) markets - momentum strategies optimal
- **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal
- **H ≈ 0.5:** Random walk markets - breakout strategies preferred
**Adaptive R/S Analysis:**
Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions:
```
H = log(R/S) / log(n)
```
Where R is the range of cumulative deviations and S is the standard deviation over period n.
**Dynamic Regime Classification:**
The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods.
### 🔄 Transfer Entropy Analysis
**Information Flow Quantification:**
Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements:
```
TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log
```
**Causality Detection:**
- **Volume → Price:** Indicates accumulation/distribution phases
- **Price → Volume:** Suggests retail participation or momentum chasing
- **Balanced Flow:** Market equilibrium or transition periods
The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows.
---
## 🔧 COMPREHENSIVE INPUT SYSTEM
### Core Parameters Group
**Primary Analysis Window (10-100, Default: 50)**
The fundamental lookback period affecting all calculations. Optimization by timeframe:
- **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements)
- **15 minute-1 hour:** 30-50 (balanced responsiveness and stability)
- **4 hour-daily:** 50-100 (smooth signals, reduced noise)
- **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60
**Signal Sensitivity (0.1-2.0, Default: 0.7)**
Master control affecting all threshold calculations:
- **Conservative (0.3-0.6):** High-quality signals only, fewer false positives
- **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles
- **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering
**Signal Generation Mode:**
- **Aggressive:** Any component signals (highest frequency)
- **Confluence:** 2+ components agree (balanced approach)
- **Conservative:** All 3 components align (highest quality)
### Volatility Jump Detection Group
**Volatility Dimensions (2-5, Default: 3)**
Determines the mathematical space complexity:
- **2D:** Price + Volume volatility (suitable for clean markets)
- **3D:** + Range volatility (optimal for most conditions)
- **4D:** + Correlation volatility (advanced multi-asset analysis)
- **5D:** + Microstructure volatility (maximum sensitivity)
**Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)**
Standard deviations required for volatility jump classification:
- **Cryptocurrency:** 2.0-2.5σ (naturally volatile)
- **Stock Indices:** 2.5-3.0σ (moderate volatility)
- **Forex Major Pairs:** 3.0-3.5σ (typically stable)
- **Commodities:** 2.0-3.0σ (varies by commodity)
**Jump Clustering Decay (0.5-0.99, Default: 0.85)**
Hawkes process memory parameter:
- **0.5-0.7:** Fast decay (jumps treated as independent)
- **0.8-0.9:** Moderate clustering (realistic market behavior)
- **0.95-0.99:** Strong clustering (crisis/event-driven markets)
### Hurst Exponent Analysis Group
**Calculation Method Options:**
- **Classic R/S:** Original Rescaled Range (fast, simple)
- **Adaptive R/S:** Dynamic windowing (recommended for trading)
- **DFA:** Detrended Fluctuation Analysis (best for noisy data)
**Trending Threshold (0.55-0.8, Default: 0.60)**
Hurst value defining persistent market behavior:
- **0.55-0.60:** Weak trend persistence
- **0.65-0.70:** Clear trending behavior
- **0.75-0.80:** Strong momentum regimes
**Mean Reversion Threshold (0.2-0.45, Default: 0.40)**
Hurst value defining anti-persistent behavior:
- **0.35-0.45:** Weak mean reversion
- **0.25-0.35:** Clear ranging behavior
- **0.15-0.25:** Strong reversion tendency
### Transfer Entropy Parameters Group
**Information Flow Analysis:**
- **Price-Volume:** Classic flow analysis for accumulation/distribution
- **Price-Volatility:** Risk flow analysis for sentiment shifts
- **Multi-Timeframe:** Cross-timeframe causality detection
**Maximum Lag (2-20, Default: 5)**
Causality detection window:
- **2-5 bars:** Immediate causality (scalping)
- **5-10 bars:** Short-term flow (day trading)
- **10-20 bars:** Structural flow (swing trading)
**Significance Threshold (0.05-0.3, Default: 0.15)**
Minimum entropy for signal generation:
- **0.05-0.10:** Detect subtle information flows
- **0.10-0.20:** Clear causality only
- **0.20-0.30:** Very strong flows only
---
## 🎨 ADVANCED VISUAL SYSTEM
### Tensor Volatility Field Visualization
**Five-Layer Resonance Bands:**
The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength:
- **Core Layer (Purple):** Primary tensor field with highest intensity
- **Layer 2 (Neutral):** Secondary mathematical resonance
- **Layer 3 (Info Blue):** Tertiary harmonic frequencies
- **Layer 4 (Warning Gold):** Outer field boundaries
- **Layer 5 (Success Green):** Maximum field extension
**Field Strength Calculation:**
```
Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity)
```
The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility.
**Radiation Line Network:**
During active tensor states, the system projects directional radiation lines showing field energy distribution:
- **8 Directional Rays:** Complete angular coverage
- **Tapering Segments:** Progressive transparency for natural visual flow
- **Pulse Effects:** Enhanced visualization during volatility jumps
### Dimensional Portal System
**Portal Mathematics:**
Dimensional portals visualize regime transitions using category theory principles:
- **Green Portals (◉):** Trending regime detection (appear below price for support)
- **Red Portals (◎):** Mean-reverting regime (appear above price for resistance)
- **Yellow Portals (○):** Random walk regime (neutral positioning)
**Tensor Trail Effects:**
Each portal generates 8 trailing particles showing mathematical momentum:
- **Large Particles (●):** Strong mathematical signal
- **Medium Particles (◦):** Moderate signal strength
- **Small Particles (·):** Weak signal continuation
- **Micro Particles (˙):** Signal dissipation
### Information Flow Streams
**Particle Stream Visualization:**
Transfer entropy creates flowing particle streams indicating information direction:
- **Upward Streams:** Volume leading price (accumulation phases)
- **Downward Streams:** Price leading volume (distribution phases)
- **Stream Density:** Proportional to information flow strength
**15-Particle Evolution:**
Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent.
### Fractal Matrix Grid System
**Multi-Timeframe Fractal Levels:**
The system calculates and displays fractal highs/lows across five Fibonacci periods:
- **8-Period:** Short-term fractal structure
- **13-Period:** Intermediate-term patterns
- **21-Period:** Primary swing levels
- **34-Period:** Major structural levels
- **55-Period:** Long-term fractal boundaries
**Triple-Layer Visualization:**
Each fractal level uses three-layer rendering:
- **Shadow Layer:** Widest, darkest foundation (width 5)
- **Glow Layer:** Medium white core line (width 3)
- **Tensor Layer:** Dotted mathematical overlay (width 1)
**Intelligent Labeling System:**
Smart spacing prevents label overlap using ATR-based minimum distances. Labels include:
- **Fractal Period:** Time-based identification
- **Topological Class:** Mathematical complexity rating (0, I, II, III)
- **Price Level:** Exact fractal price
- **Mahalanobis Distance:** Current mathematical field strength
- **Hurst Exponent:** Current regime classification
- **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡)
### Wick Pressure Analysis
**Rejection Level Mathematics:**
The system analyzes candle wick patterns to project future pressure zones:
- **Upper Wick Analysis:** Identifies selling pressure and resistance zones
- **Lower Wick Analysis:** Identifies buying pressure and support zones
- **Pressure Projection:** Extends lines forward based on mathematical probability
**Multi-Layer Glow Effects:**
Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart.
### Enhanced Regime Background
**Dynamic Intensity Mapping:**
Background colors reflect mathematical regime strength:
- **Deep Transparency (98% alpha):** Subtle regime indication
- **Pulse Intensity:** Based on regime strength calculation
- **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random)
**Smoothing Integration:**
Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts.
### Color Scheme System
**Six Professional Themes:**
- **Dark (Default):** Professional trading environment optimization
- **Light:** High ambient light conditions
- **Classic:** Traditional technical analysis appearance
- **Neon:** High-contrast visibility for active trading
- **Neutral:** Minimal distraction focus
- **Bright:** Maximum visibility for complex setups
Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences.
---
## 📊 INSTITUTIONAL-GRADE DASHBOARD
### Tensor Field Status Section
**Field Strength Display:**
Real-time Mahalanobis distance calculation with dynamic emoji indicators:
- **⚡ (Lightning):** Extreme field strength (>1.5× threshold)
- **● (Solid Circle):** Strong field activity (>1.0× threshold)
- **○ (Open Circle):** Normal field state
**Signal Quality Rating:**
Democratic algorithm assessment:
- **ELITE:** All 3 components aligned (highest probability)
- **STRONG:** 2 components aligned (good probability)
- **GOOD:** 1 component active (moderate probability)
- **WEAK:** No clear component signals
**Threshold and Anomaly Monitoring:**
- **Threshold Display:** Current mathematical threshold setting
- **Anomaly Level (0-100%):** Combined volatility and volume spike measurement
- **>70%:** High anomaly (red warning)
- **30-70%:** Moderate anomaly (orange caution)
- **<30%:** Normal conditions (green confirmation)
### Tensor State Analysis Section
**Mathematical State Classification:**
- **↑ BULL (Tensor State +1):** Trending regime with bullish bias
- **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias
- **◈ SUPER (Tensor State 0):** Random walk regime (neutral)
**Visual State Gauge:**
Five-circle progression showing tensor field polarity:
- **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment
- **⚪⚪🟡⚪⚪:** Neutral/transitional state
- **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment
**Trend Direction and Phase Analysis:**
- **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification
- **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength)
- **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength)
- **😴 CALM:** Low information flow (<1.0 flow strength)
### Trading Signals Section
**Real-Time Signal Status:**
- **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability
- **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability
- **Components (X/3):** Active algorithmic components
- **Mode Display:** Current signal generation mode
**Signal Strength Visualization:**
Color-coded component count:
- **Green:** 3/3 components (maximum confidence)
- **Aqua:** 2/3 components (good confidence)
- **Orange:** 1/3 components (moderate confidence)
- **Gray:** 0/3 components (no signals)
### Performance Metrics Section
**Win Rate Monitoring:**
Estimated win rates based on signal quality with emoji indicators:
- **🔥 (Fire):** ≥60% estimated win rate
- **👍 (Thumbs Up):** 45-59% estimated win rate
- **⚠️ (Warning):** <45% estimated win rate
**Mathematical Metrics:**
- **Hurst Exponent:** Real-time fractal dimension (0.000-1.000)
- **Information Flow:** Volume/price leading indicators
- **📊 VOL:** Volume leading price (accumulation/distribution)
- **💰 PRICE:** Price leading volume (momentum/speculation)
- **➖ NONE:** Balanced information flow
- **Volatility Classification:**
- **🔥 HIGH:** Above 1.5× jump threshold
- **📊 NORM:** Normal volatility range
- **😴 LOW:** Below 0.5× jump threshold
### Market Structure Section (Large Dashboard)
**Regime Classification:**
- **📈 TREND:** Hurst >0.6, momentum strategies optimal
- **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal
- **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred
**Mathematical Field Analysis:**
- **Dimensions:** Current volatility space complexity (2D-5D)
- **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00)
- **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal)
### Settings Summary Section (Large Dashboard)
**Active Configuration Display:**
- **Sensitivity:** Current master sensitivity setting
- **Lookback:** Primary analysis window
- **Theme:** Active color scheme
- **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA)
**Dashboard Sizing Options:**
- **Small:** Essential metrics only (mobile/small screens)
- **Normal:** Balanced information density (standard desktop)
- **Large:** Maximum detail (multi-monitor setups)
**Position Options:**
- **Top Right:** Standard placement (avoids price action)
- **Top Left:** Wide chart optimization
- **Bottom Right:** Recent price focus (scalping)
- **Bottom Left:** Maximum price visibility (swing trading)
---
## 🎯 SIGNAL GENERATION LOGIC
### Multi-Component Convergence System
**Component Signal Architecture:**
The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing:
**Volatility Component:**
- **Jump Detection:** Mahalanobis distance threshold breach
- **Hawkes Intensity:** Self-exciting process activation (>0.2)
- **Multi-dimensional:** Considers all volatility dimensions simultaneously
**Hurst Regime Component:**
- **Trending Markets:** Price above SMA-20 with positive momentum
- **Mean-Reverting Markets:** Price at Bollinger Band extremes
- **Random Markets:** Bollinger squeeze breakouts with directional confirmation
**Transfer Entropy Component:**
- **Volume Leadership:** Information flow from volume to price
- **Volume Spike:** Volume 110%+ above 20-period average
- **Flow Significance:** Above entropy threshold with directional bias
### Democratic Signal Weighting
**Signal Mode Implementation:**
- **Aggressive Mode:** Any single component triggers signal
- **Confluence Mode:** Minimum 2 components must agree
- **Conservative Mode:** All 3 components must align
**Momentum Confirmation:**
All signals require momentum confirmation:
- **Long Signals:** RSI >50 AND price >EMA-9
- **Short Signals:** RSI <50 AND price 0.6):**
- **Increase Sensitivity:** Catch momentum continuation
- **Lower Mean Reversion Threshold:** Avoid counter-trend signals
- **Emphasize Volume Leadership:** Institutional accumulation/distribution
- **Tensor Field Focus:** Use expansion for trend continuation
- **Signal Mode:** Aggressive or Confluence for trend following
**Range-Bound Markets (Hurst <0.4):**
- **Decrease Sensitivity:** Avoid false breakouts
- **Lower Trending Threshold:** Quick regime recognition
- **Focus on Price Leadership:** Retail sentiment extremes
- **Fractal Grid Emphasis:** Support/resistance trading
- **Signal Mode:** Conservative for high-probability reversals
**Volatile Markets (High Jump Frequency):**
- **Increase Hawkes Decay:** Recognize event clustering
- **Higher Jump Threshold:** Avoid noise signals
- **Maximum Dimensions:** Capture full volatility complexity
- **Reduce Position Sizing:** Risk management adaptation
- **Enhanced Visuals:** Maximum information for rapid decisions
**Low Volatility Markets (Low Jump Frequency):**
- **Decrease Jump Threshold:** Capture subtle movements
- **Lower Hawkes Decay:** Treat moves as independent
- **Reduce Dimensions:** Simplify analysis
- **Increase Position Sizing:** Capitalize on compressed volatility
- **Minimal Visuals:** Reduce distraction in quiet markets
---
## 🚀 ADVANCED TRADING STRATEGIES
### The Mathematical Convergence Method
**Entry Protocol:**
1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels
2. **Tensor Field Confirmation:** Verify field expansion supporting direction
3. **Portal Signal:** Wait for dimensional portal appearance
4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals
5. **Component Consensus:** Confirm 2+ components agree in Confluence mode
**Example Implementation:**
- Price approaching 21-period fractal high
- Tensor field expanding upward (bullish mathematical alignment)
- Green portal appears below price (trending regime confirmation)
- ELITE quality signal with 3/3 components active
- Enter long position with stop below fractal level
**Risk Management:**
- **Stop Placement:** Below/above fractal level that generated signal
- **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size)
- **Profit Targets:** Next fractal level or tensor field resistance
### The Regime Transition Strategy
**Regime Change Detection:**
1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds
2. **Portal Color Change:** Regime transitions show different portal colors
3. **Background Intensity:** Increasing regime background intensity
4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis)
**Trading Implementation:**
- **Trending Transitions:** Trade momentum breakouts, follow trend
- **Mean Reversion Transitions:** Trade range boundaries, fade extremes
- **Random Transitions:** Trade breakouts with tight stops
**Advanced Techniques:**
- **Multi-Timeframe:** Confirm regime on higher timeframe
- **Early Entry:** Enter on regime transition rather than confirmation
- **Regime Strength:** Larger positions during strong regime signals
### The Information Flow Momentum Strategy
**Flow Detection Protocol:**
1. **Monitor Transfer Entropy:** Watch for significant information flow shifts
2. **Volume Leadership:** Strong edge when volume leads price
3. **Flow Acceleration:** Increasing flow strength indicates momentum
4. **Directional Confirmation:** Ensure flow aligns with intended trade direction
**Entry Signals:**
- **Volume → Price Flow:** Enter during accumulation/distribution phases
- **Price → Volume Flow:** Enter on momentum confirmation breaks
- **Flow Reversal:** Counter-trend entries when flow reverses
**Optimization:**
- **Scalping:** Use immediate flow detection (2-5 bar lag)
- **Swing Trading:** Use structural flow (10-20 bar lag)
- **Multi-Asset:** Compare flow between correlated assets
### The Tensor Field Expansion Strategy
**Field Mathematics:**
The tensor field expansion indicates mathematical pressure building in market structure:
**Expansion Phases:**
1. **Compression:** Field contracts, volatility decreases
2. **Tension Building:** Mathematical pressure accumulates
3. **Expansion:** Field expands rapidly with directional movement
4. **Resolution:** Field stabilizes at new equilibrium
**Trading Applications:**
- **Compression Trading:** Prepare for breakout during field contraction
- **Expansion Following:** Trade direction of field expansion
- **Reversion Trading:** Fade extreme field expansion
- **Multi-Dimensional:** Consider all field layers for confirmation
### The Hawkes Process Event Strategy
**Self-Exciting Jump Trading:**
Understanding that market shocks cluster and create follow-on opportunities:
**Jump Sequence Analysis:**
1. **Initial Jump:** First volatility jump detected
2. **Clustering Phase:** Hawkes intensity remains elevated
3. **Follow-On Opportunities:** Additional jumps more likely
4. **Decay Period:** Intensity gradually decreases
**Implementation:**
- **Jump Confirmation:** Wait for mathematical jump confirmation
- **Direction Assessment:** Use other components for direction
- **Clustering Trades:** Trade subsequent moves during high intensity
- **Decay Exit:** Exit positions as Hawkes intensity decays
### The Fractal Confluence System
**Multi-Timeframe Fractal Analysis:**
Combining fractal levels across different periods for high-probability zones:
**Confluence Zones:**
- **Double Confluence:** 2 fractal levels align
- **Triple Confluence:** 3+ fractal levels cluster
- **Mathematical Confirmation:** Tensor field supports the level
- **Information Flow:** Transfer entropy confirms direction
**Trading Protocol:**
1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR
2. **Mathematical Support:** Verify tensor field alignment
3. **Signal Quality:** Wait for STRONG or ELITE signal
4. **Risk Definition:** Use fractal level for stop placement
5. **Profit Targeting:** Next major fractal confluence zone
---
## ⚠️ COMPREHENSIVE RISK MANAGEMENT
### Mathematical Position Sizing
**Mahalanobis Distance Integration:**
Position size should inversely correlate with mathematical field strength:
```
Position Size = Base Size × (Threshold / Mahalanobis Distance)
```
**Risk Scaling Matrix:**
- **Low Field Strength (<2.0):** Standard position sizing
- **Moderate Field Strength (2.0-3.0):** 75% position sizing
- **High Field Strength (3.0-4.0):** 50% position sizing
- **Extreme Field Strength (>4.0):** 25% position sizing or no trade
### Signal Quality Risk Adjustment
**Quality-Based Position Sizing:**
- **ELITE Signals:** 100% of planned position size
- **STRONG Signals:** 75% of planned position size
- **GOOD Signals:** 50% of planned position size
- **WEAK Signals:** No position or paper trading only
**Component Agreement Scaling:**
- **3/3 Components:** Full position size
- **2/3 Components:** 75% position size
- **1/3 Components:** 50% position size or skip trade
### Regime-Adaptive Risk Management
**Trending Market Risk:**
- **Wider Stops:** Allow for trend continuation
- **Trend Following:** Trade with regime direction
- **Higher Position Size:** Trend probability advantage
- **Momentum Stops:** Trail stops based on momentum indicators
**Mean-Reverting Market Risk:**
- **Tighter Stops:** Quick exits on trend continuation
- **Contrarian Positioning:** Trade against extremes
- **Smaller Position Size:** Higher reversal failure rate
- **Level-Based Stops:** Use fractal levels for stops
**Random Market Risk:**
- **Breakout Focus:** Trade only clear breakouts
- **Tight Initial Stops:** Quick exit if breakout fails
- **Reduced Frequency:** Skip marginal setups
- **Range-Based Targets:** Profit targets at range boundaries
### Volatility-Adaptive Risk Controls
**High Volatility Periods:**
- **Reduced Position Size:** Account for wider price swings
- **Wider Stops:** Avoid noise-based exits
- **Lower Frequency:** Skip marginal setups
- **Faster Exits:** Take profits more quickly
**Low Volatility Periods:**
- **Standard Position Size:** Normal risk parameters
- **Tighter Stops:** Take advantage of compressed ranges
- **Higher Frequency:** Trade more setups
- **Extended Targets:** Allow for compressed volatility expansion
### Multi-Timeframe Risk Alignment
**Higher Timeframe Trend:**
- **With Trend:** Standard or increased position size
- **Against Trend:** Reduced position size or skip
- **Neutral Trend:** Standard position size with tight management
**Risk Hierarchy:**
1. **Primary:** Current timeframe signal quality
2. **Secondary:** Higher timeframe trend alignment
3. **Tertiary:** Mathematical field strength
4. **Quaternary:** Market regime classification
---
## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS
### Advanced Mathematical Concepts
**Tensor Analysis in Markets:**
The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture.
**Information Theory Applications:**
Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior.
**Fractal Geometry in Trading:**
The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure.
**Stochastic Process Theory:**
The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects.
### Learning Progressive Complexity
**Beginner Mathematical Concepts:**
- **Volatility Dimensions:** Understanding multi-dimensional analysis
- **Regime Classification:** Learning market personality types
- **Signal Democracy:** Algorithmic consensus building
- **Visual Mathematics:** Interpreting mathematical concepts visually
**Intermediate Mathematical Applications:**
- **Mahalanobis Distance:** Statistical distance in multi-dimensional space
- **Rescaled Range Analysis:** Fractal dimension measurement
- **Information Entropy:** Quantifying uncertainty and causality
- **Field Theory:** Understanding mathematical fields in market context
**Advanced Mathematical Integration:**
- **Tensor Field Dynamics:** Multi-dimensional market force analysis
- **Stochastic Self-Excitation:** Event clustering and memory effects
- **Categorical Composition:** Mathematical signal combination theory
- **Topological Market Analysis:** Understanding market shape and connectivity
### Practical Mathematical Intuition
**Developing Market Mathematics Intuition:**
The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of:
- **How markets exhibit mathematical structure beneath apparent randomness**
- **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches**
- **How information flows through markets in measurable, predictable ways**
- **Why mathematical models provide probabilistic edges rather than certainties**
---
## 🔬 IMPLEMENTATION AND OPTIMIZATION
### Getting Started Protocol
**Phase 1: Observation (Week 1)**
1. **Apply with defaults:** Use standard settings on your primary trading timeframe
2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams
3. **Monitor dashboard:** Observe how metrics change with market conditions
4. **No trading:** Focus entirely on pattern recognition and understanding
**Phase 2: Pattern Recognition (Week 2-3)**
1. **Identify signal patterns:** Note what market conditions produce different signal qualities
2. **Regime correlation:** Observe how Hurst regimes affect signal performance
3. **Visual confirmation:** Learn to read tensor field expansion and portal signals
4. **Component analysis:** Understand which components drive signals in different markets
**Phase 3: Parameter Optimization (Week 4-5)**
1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument
2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe
3. **Sensitivity adjustment:** Balance signal frequency with quality
4. **Visual customization:** Optimize colors and intensity for your trading environment
**Phase 4: Live Implementation (Week 6+)**
1. **Paper trading:** Test signals with hypothetical trades
2. **Small position sizing:** Begin with minimal risk during learning phase
3. **Performance tracking:** Monitor actual vs. expected signal performance
4. **Continuous optimization:** Refine settings based on real performance data
### Performance Monitoring System
**Signal Quality Tracking:**
- **ELITE Signal Win Rate:** Track highest quality signals separately
- **Component Performance:** Monitor which components provide best signals
- **Regime Performance:** Analyze performance across different market regimes
- **Timeframe Analysis:** Compare performance across different session times
**Mathematical Metric Correlation:**
- **Field Strength vs. Performance:** Higher field strength should correlate with better performance
- **Component Agreement vs. Win Rate:** More component agreement should improve win rates
- **Regime Alignment vs. Success:** Trading with mathematical regime should outperform
### Continuous Optimization Process
**Monthly Review Protocol:**
1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown
2. **Parameter Assessment:** Evaluate if current settings remain optimal
3. **Market Adaptation:** Adjust for changes in market character or volatility
4. **Component Weighting:** Consider if certain components should receive more/less emphasis
**Quarterly Deep Analysis:**
1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid
2. **Regime Distribution:** Analyze time spent in different market regimes
3. **Signal Evolution:** Track how signal characteristics change over time
4. **Correlation Analysis:** Monitor correlations between different mathematical components
---
## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS
### Revolutionary Mathematical Integration
**First-Ever Implementations:**
1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility
2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes
3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation
4. **Democratic Component Voting:** First algorithmic consensus system for signal generation
5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels
### Advanced Visualization Innovations
**Mathematical Visualization Breakthroughs:**
- **Tensor Field Radiation:** Visual representation of mathematical field energy
- **Dimensional Portal System:** Category theory visualization for regime transitions
- **Information Flow Streams:** Real-time visual display of market information transfer
- **Multi-Layer Fractal Grid:** Intelligent spacing and projection system
- **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength
### Practical Trading Innovations
**Trading System Advances:**
- **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence
- **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality
- **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering
- **Component Performance Tracking:** Real-time monitoring of algorithmic component success
- **Field-Strength Position Sizing:** Mathematical volatility integration for risk management
---
## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS
### Mathematical Model Limitations
**Understanding Model Boundaries:**
While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations:
- **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements
- **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely
- **Model Risk:** Complex models can fail during unprecedented market conditions
- **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions
### Proper Implementation Guidelines
**Risk Management Requirements:**
- **Never Risk More Than 2% Per Trade:** Regardless of signal quality
- **Diversification Mandatory:** Don't rely solely on mathematical signals
- **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence
- **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters
**Realistic Expectations:**
- **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes
- **Learning Curve Required:** Complex mathematical concepts require time to master
- **Market Adaptation Necessary:** Parameters must evolve with changing market conditions
- **Continuous Education Important:** Understanding underlying mathematics improves application
### Ethical Trading Considerations
**Market Impact Awareness:**
- **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants
- **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure
- **Sharing Knowledge:** Consider educational contributions to trading community
- **Fair Market Participation:** Use mathematical advantages responsibly within market framework
### Professional Development Path
**Skill Development Sequence:**
1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application
2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals
3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights
4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution
---
## 🔮 CONCLUSION
The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability.
### Mathematical Innovation Legacy
This indicator establishes new paradigms in technical analysis:
- **Tensor analysis for market volatility understanding**
- **Stochastic self-excitation for event clustering prediction**
- **Information theory for causality-based trade generation**
- **Democratic algorithmic consensus for signal quality enhancement**
- **Mathematical field visualization for intuitive market understanding**
### Practical Trading Revolution
Beyond mathematical innovation, the TMAE transforms practical trading:
- **Quality-rated signals replace binary buy/sell decisions**
- **Regime-adaptive strategies automatically optimize for market personality**
- **Multi-dimensional risk management integrates mathematical volatility measures**
- **Visual mathematical concepts make complex analysis immediately interpretable**
- **Educational value creates lasting improvement in trading understanding**
### Future-Proof Design
The mathematical foundations ensure lasting relevance:
- **Universal mathematical principles transcend market evolution**
- **Multi-dimensional analysis adapts to new market structures**
- **Regime detection automatically adjusts to changing market personalities**
- **Component democracy allows for future algorithmic additions**
- **Mathematical visualization scales with increasing market complexity**
### Commitment to Excellence
The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value.
### Trading with Mathematical Precision
In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals.
By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics.
### Final Perspective
Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure.
Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior.
Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE.
*"In mathematics, you don't understand things. You just get used to them." - John von Neumann*
*With the TMAE, mathematical market understanding becomes not just possible, but intuitive.*
— Dskyz, Trade with insight. Trade with anticipation.
Adaptive RSI (ARSI)# Adaptive RSI (ARSI) - Dynamic Momentum Oscillator
Adaptive RSI is an advanced momentum oscillator that dynamically adjusts its calculation period based on real-time market volatility and cycle analysis. Unlike traditional RSI that uses fixed periods, ARSI continuously adapts to market conditions, providing more accurate overbought/oversold signals and reducing false signals during varying market phases.
## How It Works
At its core, ARSI calculates an adaptive period ranging from 8 to 28 bars using two key components: volatility measurement through Average True Range (ATR) and cycle detection via price momentum analysis. The logic is straightforward:
- **High volatility periods** trigger shorter calculation periods for enhanced responsiveness to rapid price movements
- **Low volatility periods** extend the calculation window for smoother, more reliable signals
- **Market factor** combines volatility and cycle analysis to determine optimal RSI period in real-time
When RSI crosses above 70, the market enters overbought territory. When it falls below 30, oversold conditions emerge. The indicator also features extreme levels at 80/20 for stronger reversal signals and midline crossovers at 50 for trend confirmation.
The adaptive mechanism ensures the oscillator remains sensitive during critical market movements while filtering out noise during consolidation phases, making it superior to static RSI implementations across different market conditions.
## Features
- **True Adaptive Calculation**: Dynamic period adjustment from 8-28 bars based on market volatility
- **Multiple Signal Types**: Overbought/oversold, extreme reversals, and midline crossovers
- **Configurable Parameters**: RSI length, adaptive sensitivity, ATR period, min/max bounds
- **Smart Smoothing**: Adjustable EMA smoothing from 1-21 periods to reduce noise
- **Visual Clarity**: Gradient colors, area fills, and signal dots for immediate trend recognition
- **Real-time Information**: Live data table showing current RSI, adaptive period, and market factor
- **Flexible Source Input**: Apply to any price source (close, hl2, ohlc4, etc.)
- **Professional Alerts**: Six built-in alert conditions for automated trading systems
## Signal Generation
ARSI generates multiple signal types for comprehensive market analysis:
**Primary Signals**: RSI crosses above 70 (overbought) or below 30 (oversold) - most reliable entry/exit points
**Extreme Signals**: RSI reaches 80+ (extreme overbought) or 20- (extreme oversold) - potential reversal zones
**Trend Signals**: RSI crosses above/below 50 midline - confirms directional momentum
**Reversal Signals**: Price action contradicts extreme RSI levels - early turning point detection
The adaptive period changes provide additional confirmation - signals accompanied by significant period shifts often carry higher probability of success.
## Visual Implementation
The indicator employs sophisticated visual elements for instant market comprehension:
- **Gradient RSI Line**: Color intensity reflects both value and momentum direction
- **Dynamic Zones**: Overbought/oversold areas with customizable fill colors
- **Signal Markers**: Triangular indicators mark key reversal and continuation points
- **Information Panel**: Real-time display of RSI value, adaptive period, market factor, and signal status
- **Background Coloring**: Subtle fills indicate current market state without chart clutter
## Parameter Configuration
**RSI Settings**:
- RSI Length: Base calculation period (default: 14)
- Adaptive Sensitivity: Response aggressiveness to volatility changes (default: 1.0)
- ATR Length: Volatility measurement period (default: 14)
- Min/Max Period: Adaptive calculation boundaries (default: 8/28)
- Smoothing Length: Final noise reduction filter (default: 3)
**Level Settings**:
- Overbought/Oversold: Standard signal levels (default: 70/30)
- Extreme Levels: Enhanced reversal zones (default: 80/20)
- Midline Display: 50-level trend confirmation toggle
**Visual Settings**:
- Line Width: RSI line thickness (1-5)
- Area Fills: Zone highlighting toggle
- Gradient Colors: Dynamic color intensity
- Signal Dots: Entry/exit marker display
## Alerts
ARSI includes six comprehensive alert conditions:
- **ARSI Overbought** - RSI crosses above overbought level
- **ARSI Oversold** - RSI crosses below oversold level
- **ARSI Bullish Cross** - RSI crosses above 50 midline
- **ARSI Bearish Cross** - RSI crosses below 50 midline
- **ARSI Extreme Bull** - Potential bullish reversal from extreme oversold
- **ARSI Extreme Bear** - Potential bearish reversal from extreme overbought
## Use Cases
**Trend Following**: Adaptive periods naturally adjust during trend acceleration and consolidation phases
**Mean Reversion**: Enhanced overbought/oversold signals with volatility-based confirmation
**Breakout Trading**: Extreme level breaches often precede significant directional moves
**Risk Management**: Multiple signal types allow for layered entry/exit strategies
**Multi-Timeframe Analysis**: Works effectively across various timeframes and asset classes
## Trading Applications
**Swing Trading**: Excels during trend transitions with adaptive sensitivity to changing conditions
**Day Trading**: Enhanced responsiveness during volatile sessions while filtering consolidation noise
**Position Trading**: Longer smoothing periods provide stable signals for broader market analysis
**Scalping**: Minimal smoothing with high sensitivity captures short-term momentum shifts
The indicator performs well across stocks, forex, commodities, and cryptocurrencies, though parameter optimization may be required for specific market characteristics.
## Settings Summary
**Display Settings**:
- RSI Length: Moving average baseline period
- Adaptive Sensitivity: Volatility response factor
- ATR Length: Volatility measurement window
- Min/Max Period: Adaptive calculation boundaries
- Smoothing Length: Noise reduction filter
**Level Configuration**:
- Overbought/Oversold: Primary signal thresholds
- Extreme Levels: Secondary reversal zones
- Midline Display: Trend confirmation toggle
**Visual Options**:
- Line Width: RSI line appearance
- Area Fills: Zone highlighting
- Gradient Colors: Dynamic visual feedback
- Signal Dots: Entry/exit markers
## Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves substantial risk of loss and is not suitable for all investors. Past performance is not indicative of future results. Always conduct thorough testing and risk assessment before live implementation. The adaptive nature of this indicator requires understanding of its behavior across different market conditions for optimal results.
RMSE Bollinger Bands + Loop | Lyro RSRMSE Bollinger Bands + Loops
Overview
The RMSE Bollinger Bands + Loops is a sophisticated technical analysis tool designed to identify and quantify market trends by combining dynamic moving averages with statistical measures. This indicator employs a multi-model approach, integrating Bollinger-style RMSE bands, momentum scoring, and a hybrid signal system to provide traders with adaptive insights across varying market conditions.
Indicator Modes
Bollinger-style RMSE Bands: this mode calculates dynamic volatility bands around the price using the following formula:
Upper Band = Dynamic Moving Average + (RMSE × Multiplier)
Lower Band = Dynamic Moving Average - (RMSE × Multiplier)
These bands adjust to market volatility, helping identify potential breakout or breakdown points.
For-Loop Momentum Scoring, momentum is assessed by analyzing recent price behavior through a looping mechanism. A rising momentum score indicates increasing bullish strength, while a declining score suggests growing bearish momentum.
Hybrid Combined Signal: this mode assigns a directional score to the other two modes:
+1 for bullish (green)
–1 for bearish (red)
An average of these scores is computed to generate a combined signal, offering a consolidated market trend indication.
Practical Application
Signal Interpretation: A buy signal is generated when both the RMSE Bands and For-Loop Momentum Scoring align bullishly. Conversely, a sell signal is indicated when both are bearish.
Trend Confirmation: The Hybrid Combined Signal provides a consolidated view, assisting traders in confirming the prevailing market trend.
Note: Always consider additional technical analysis tools and risk management strategies when making trading decisions.
⚠️Disclaimer
This indicator is a tool for technical analysis and does not provide guaranteed results. It should be used in conjunction with other analysis methods and proper risk management practices. The creators of this indicator are not responsible for any financial decisions made based on its signals.
Grothendieck-Teichmüller Geometric SynthesisDskyz's Grothendieck-Teichmüller Geometric Synthesis (GTGS)
THEORETICAL FOUNDATION: A SYMPHONY OF GEOMETRIES
The 🎓 GTGS is built upon a revolutionary premise: that market dynamics can be modeled as geometric and topological structures. While not a literal academic implementation—such a task would demand computational power far beyond current trading platforms—it leverages core ideas from advanced mathematical theories as powerful analogies and frameworks for its algorithms. Each component translates an abstract concept into a practical market calculation, distinguishing GTGS by identifying deeper structural patterns rather than relying on standard statistical measures.
1. Grothendieck-Teichmüller Theory: Deforming Market Structure
The Theory : Studies symmetries and deformations of geometric objects, focusing on the "absolute" structure of mathematical spaces.
Indicator Analogy : The calculate_grothendieck_field function models price action as a "deformation" from its immediate state. Using the nth root of price ratios (math.pow(price_ratio, 1.0/prime)), it measures market "shape" stretching or compression, revealing underlying tensions and potential shifts.
2. Topos Theory & Sheaf Cohomology: From Local to Global Patterns
The Theory : A framework for assembling local properties into a global picture, with cohomology measuring "obstructions" to consistency.
Indicator Analogy : The calculate_topos_coherence function uses sine waves (math.sin) to represent local price "sections." Summing these yields a "cohomology" value, quantifying price action consistency. High values indicate coherent trends; low values signal conflict and uncertainty.
3. Tropical Geometry: Simplifying Complexity
The Theory : Transforms complex multiplicative problems into simpler, additive, piecewise-linear ones using min(a, b) for addition and a + b for multiplication.
Indicator Analogy : The calculate_tropical_metric function applies tropical_add(a, b) => math.min(a, b) to identify the "lowest energy" state among recent price points, pinpointing critical support levels non-linearly.
4. Motivic Cohomology & Non-Commutative Geometry
The Theory : Studies deep arithmetic and quantum-like properties of geometric spaces.
Indicator Analogy : The motivic_rank and spectral_triple functions compute weighted sums of historical prices to capture market "arithmetic complexity" and "spectral signature." Higher values reflect structured, harmonic price movements.
5. Perfectoid Spaces & Homotopy Type Theory
The Theory : Abstract fields dealing with p-adic numbers and logical foundations of mathematics.
Indicator Analogy : The perfectoid_conv and type_coherence functions analyze price convergence and path identity, assessing the "fractal dust" of price differences and price path cohesion, adding fractal and logical analysis.
The Combination is Key : No single theory dominates. GTGS ’s Unified Field synthesizes all seven perspectives into a comprehensive score, ensuring signals reflect deep structural alignment across mathematical domains.
🎛️ INPUTS: CONFIGURING THE GEOMETRIC ENGINE
The GTGS offers a suite of customizable inputs, allowing traders to tailor its behavior to specific timeframes, market sectors, and trading styles. Below is a detailed breakdown of key input groups, their functionality, and optimization strategies, leveraging provided tooltips for precision.
Grothendieck-Teichmüller Theory Inputs
🧬 Deformation Depth (Absolute Galois) :
What It Is : Controls the depth of Galois group deformations analyzed in market structure.
How It Works : Measures price action deformations under automorphisms of the absolute Galois group, capturing market symmetries.
Optimization :
Higher Values (15-20) : Captures deeper symmetries, ideal for major trends in swing trading (4H-1D).
Lower Values (3-8) : Responsive to local deformations, suited for scalping (1-5min).
Timeframes :
Scalping (1-5min) : 3-6 for quick local shifts.
Day Trading (15min-1H) : 8-12 for balanced analysis.
Swing Trading (4H-1D) : 12-20 for deep structural trends.
Sectors :
Stocks : Use 8-12 for stable trends.
Crypto : 3-8 for volatile, short-term moves.
Forex : 12-15 for smooth, cyclical patterns.
Pro Tip : Increase in trending markets to filter noise; decrease in choppy markets for sensitivity.
🗼 Teichmüller Tower Height :
What It Is : Determines the height of the Teichmüller modular tower for hierarchical pattern detection.
How It Works : Builds modular levels to identify nested market patterns.
Optimization :
Higher Values (6-8) : Detects complex fractals, ideal for swing trading.
Lower Values (2-4) : Focuses on primary patterns, faster for scalping.
Timeframes :
Scalping : 2-3 for speed.
Day Trading : 4-5 for balanced patterns.
Swing Trading : 5-8 for deep fractals.
Sectors :
Indices : 5-8 for robust, long-term patterns.
Crypto : 2-4 for rapid shifts.
Commodities : 4-6 for cyclical trends.
Pro Tip : Higher towers reveal hidden fractals but may slow computation; adjust based on hardware.
🔢 Galois Prime Base :
What It Is : Sets the prime base for Galois field computations.
How It Works : Defines the field extension characteristic for market analysis.
Optimization :
Prime Characteristics :
2 : Binary markets (up/down).
3 : Ternary states (bull/bear/neutral).
5 : Pentagonal symmetry (Elliott waves).
7 : Heptagonal cycles (weekly patterns).
11,13,17,19 : Higher-order patterns.
Timeframes :
Scalping/Day Trading : 2 or 3 for simplicity.
Swing Trading : 5 or 7 for wave or cycle detection.
Sectors :
Forex : 5 for Elliott wave alignment.
Stocks : 7 for weekly cycle consistency.
Crypto : 3 for volatile state shifts.
Pro Tip : Use 7 for most markets; 5 for Elliott wave traders.
Topos Theory & Sheaf Cohomology Inputs
🏛️ Temporal Site Size :
What It Is : Defines the number of time points in the topological site.
How It Works : Sets the local neighborhood for sheaf computations, affecting cohomology smoothness.
Optimization :
Higher Values (30-50) : Smoother cohomology, better for trends in swing trading.
Lower Values (5-15) : Responsive, ideal for reversals in scalping.
Timeframes :
Scalping : 5-10 for quick responses.
Day Trading : 15-25 for balanced analysis.
Swing Trading : 25-50 for smooth trends.
Sectors :
Stocks : 25-35 for stable trends.
Crypto : 5-15 for volatility.
Forex : 20-30 for smooth cycles.
Pro Tip : Match site size to your average holding period in bars for optimal coherence.
📐 Sheaf Cohomology Degree :
What It Is : Sets the maximum degree of cohomology groups computed.
How It Works : Higher degrees capture complex topological obstructions.
Optimization :
Degree Meanings :
1 : Simple obstructions (basic support/resistance).
2 : Cohomological pairs (double tops/bottoms).
3 : Triple intersections (complex patterns).
4-5 : Higher-order structures (rare events).
Timeframes :
Scalping/Day Trading : 1-2 for simplicity.
Swing Trading : 3 for complex patterns.
Sectors :
Indices : 2-3 for robust patterns.
Crypto : 1-2 for rapid shifts.
Commodities : 3-4 for cyclical events.
Pro Tip : Degree 3 is optimal for most trading; higher degrees for research or rare event detection.
🌐 Grothendieck Topology :
What It Is : Chooses the Grothendieck topology for the site.
How It Works : Affects how local data integrates into global patterns.
Optimization :
Topology Characteristics :
Étale : Finest topology, captures local-global principles.
Nisnevich : A1-invariant, good for trends.
Zariski : Coarse but robust, filters noise.
Fpqc : Faithfully flat, highly sensitive.
Sectors :
Stocks : Zariski for stability.
Crypto : Étale for sensitivity.
Forex : Nisnevich for smooth trends.
Indices : Zariski for robustness.
Timeframes :
Scalping : Étale for precision.
Swing Trading : Nisnevich or Zariski for reliability.
Pro Tip : Start with Étale for precision; switch to Zariski in noisy markets.
Unified Field Configuration Inputs
⚛️ Field Coupling Constant :
What It Is : Sets the interaction strength between geometric components.
How It Works : Controls signal amplification in the unified field equation.
Optimization :
Higher Values (0.5-1.0) : Strong coupling, amplified signals for ranging markets.
Lower Values (0.001-0.1) : Subtle signals for trending markets.
Timeframes :
Scalping : 0.5-0.8 for quick, strong signals.
Swing Trading : 0.1-0.3 for trend confirmation.
Sectors :
Crypto : 0.5-1.0 for volatility.
Stocks : 0.1-0.3 for stability.
Forex : 0.3-0.5 for balance.
Pro Tip : Default 0.137 (fine structure constant) is a balanced starting point; adjust up in choppy markets.
📐 Geometric Weighting Scheme :
What It Is : Determines the framework for combining geometric components.
How It Works : Adjusts emphasis on different mathematical structures.
Optimization :
Scheme Characteristics :
Canonical : Equal weighting, balanced.
Derived : Emphasizes higher-order structures.
Motivic : Prioritizes arithmetic properties.
Spectral : Focuses on frequency domain.
Sectors :
Stocks : Canonical for balance.
Crypto : Spectral for volatility.
Forex : Derived for structured moves.
Indices : Motivic for arithmetic cycles.
Timeframes :
Day Trading : Canonical or Derived for flexibility.
Swing Trading : Motivic for long-term cycles.
Pro Tip : Start with Canonical; experiment with Spectral in volatile markets.
Dashboard and Visual Configuration Inputs
📋 Show Enhanced Dashboard, 📏 Size, 📍 Position :
What They Are : Control dashboard visibility, size, and placement.
How They Work : Display key metrics like Unified Field , Resonance , and Signal Quality .
Optimization :
Scalping : Small size, Bottom Right for minimal chart obstruction.
Swing Trading : Large size, Top Right for detailed analysis.
Sectors : Universal across markets; adjust size based on screen setup.
Pro Tip : Use Large for analysis, Small for live trading.
📐 Show Motivic Cohomology Bands, 🌊 Morphism Flow, 🔮 Future Projection, 🔷 Holographic Mesh, ⚛️ Spectral Flow :
What They Are : Toggle visual elements representing mathematical calculations.
How They Work : Provide intuitive representations of market dynamics.
Optimization :
Timeframes :
Scalping : Enable Morphism Flow and Spectral Flow for momentum.
Swing Trading : Enable all for comprehensive analysis.
Sectors :
Crypto : Emphasize Morphism Flow and Future Projection for volatility.
Stocks : Focus on Cohomology Bands for stable trends.
Pro Tip : Disable non-essential visuals in fast markets to reduce clutter.
🌫️ Field Transparency, 🔄 Web Recursion Depth, 🎨 Mesh Color Scheme :
What They Are : Adjust visual clarity, complexity, and color.
How They Work : Enhance interpretability of visual elements.
Optimization :
Transparency : 30-50 for balanced visibility; lower for analysis.
Recursion Depth : 6-8 for balanced detail; lower for older hardware.
Color Scheme :
Purple/Blue : Analytical focus.
Green/Orange : Trading momentum.
Pro Tip : Use Neon Purple for deep analysis; Neon Green for active trading.
⏱️ Minimum Bars Between Signals :
What It Is : Minimum number of bars required between consecutive signals.
How It Works : Prevents signal clustering by enforcing a cooldown period.
Optimization :
Higher Values (10-20) : Fewer signals, avoids whipsaws, suited for swing trading.
Lower Values (0-5) : More responsive, allows quick reversals, ideal for scalping.
Timeframes :
Scalping : 0-2 bars for rapid signals.
Day Trading : 3-5 bars for balance.
Swing Trading : 5-10 bars for stability.
Sectors :
Crypto : 0-3 for volatility.
Stocks : 5-10 for trend clarity.
Forex : 3-7 for cyclical moves.
Pro Tip : Increase in choppy markets to filter noise.
Hardcoded Parameters
Tropical, Motivic, Spectral, Perfectoid, Homotopy Inputs : Fixed to optimize performance but influence calculations (e.g., tropical_degree=4 for support levels, perfectoid_prime=5 for convergence).
Optimization : Experiment with codebase modifications if advanced customization is needed, but defaults are robust across markets.
🎨 ADVANCED VISUAL SYSTEM: TRADING IN A GEOMETRIC UNIVERSE
The GTTMTSF ’s visuals are direct representations of its mathematics, designed for intuitive and precise trading decisions.
Motivic Cohomology Bands :
What They Are : Dynamic bands ( H⁰ , H¹ , H² ) representing cohomological support/resistance.
Color & Meaning : Colors reflect energy levels ( H⁰ tightest, H² widest). Breaks into H¹ signal momentum; H² touches suggest reversals.
How to Trade : Use for stop-loss/profit-taking. Band bounces with Dashboard confirmation are high-probability setups.
Morphism Flow (Webbing) :
What It Is : White particle streams visualizing market momentum.
Interpretation : Dense flows indicate strong trends; sparse flows signal consolidation.
How to Trade : Follow dominant flow direction; new flows post-consolidation signal trend starts.
Future Projection Web (Fractal Grid) :
What It Is : Fibonacci-period fractal projections of support/resistance.
Color & Meaning : Three-layer lines (white shadow, glow, colored quantum) with labels showing price, topological class, anomaly strength (φ), resonance (ρ), and obstruction ( H¹ ). ⚡ marks extreme anomalies.
How to Trade : Target ⚡/● levels for entries/exits. High-anomaly levels with weakening Unified Field are reversal setups.
Holographic Mesh & Spectral Flow :
What They Are : Visuals of harmonic interference and spectral energy.
How to Trade : Bright mesh nodes or strong Spectral Flow warn of building pressure before price movement.
📊 THE GEOMETRIC DASHBOARD: YOUR MISSION CONTROL
The Dashboard translates complex mathematics into actionable intelligence.
Unified Field & Signals :
FIELD : Master value (-10 to +10), synthesizing all geometric components. Extreme readings (>5 or <-5) signal structural limits, often preceding reversals or continuations.
RESONANCE : Measures harmony between geometric field and price-volume momentum. Positive amplifies bullish moves; negative amplifies bearish moves.
SIGNAL QUALITY : Confidence meter rating alignment. Trade only STRONG or EXCEPTIONAL signals for high-probability setups.
Geometric Components :
What They Are : Breakdown of seven mathematical engines.
How to Use : Watch for convergence. A strong Unified Field is reliable when components (e.g., Grothendieck , Topos , Motivic ) align. Divergence warns of trend weakening.
Signal Performance :
What It Is : Tracks indicator signal performance.
How to Use : Assesses real-time performance to build confidence and understand system behavior.
🚀 DEVELOPMENT & UNIQUENESS: BEYOND CONVENTIONAL ANALYSIS
The GTTMTSF was developed to analyze markets as evolving geometric objects, not statistical time-series.
Why This Is Unlike Anything Else :
Theoretical Depth : Uses geometry and topology, identifying patterns invisible to statistical tools.
Holistic Synthesis : Integrates seven deep mathematical frameworks into a cohesive Unified Field .
Creative Implementation : Translates PhD-level mathematics into functional Pine Script , blending theory and practice.
Immersive Visualization : Transforms charts into dynamic geometric landscapes for intuitive market understanding.
The GTTMTSF is more than an indicator; it’s a new lens for viewing markets, for traders seeking deeper insight into hidden order within chaos.
" Where there is matter, there is geometry. " - Johannes Kepler
— Dskyz , Trade with insight. Trade with anticipation.
SHYY TFC Candles_Confirmation X 4TF)SHYY Real-Time FTC Confirmation is a multi-timeframe trend alignment tool designed to provide real-time confirmation of market direction across up to four configurable timeframes. Unlike traditional tools that rely on closed candles, this version uses in-progress bars to detect live momentum, allowing traders to respond as trends are forming rather than after they are confirmed.
This script checks the current price direction on each selected timeframe by comparing the current close to the open of the same candle. A timeframe is considered bullish if the close is above the open, bearish if below, and neutral if equal. If all enabled timeframes are aligned in the same direction, the current chart candle is colored accordingly.
White candles indicate that all selected timeframes are currently bullish. Yellow candles indicate that all selected timeframes are currently bearish. If the timeframes are not fully aligned, the candle remains uncolored.
Each of the four timeframes can be configured individually in the settings panel. Users can also enable or disable each timeframe independently using checkboxes, allowing flexibility in how the confirmation logic is applied.
The script uses a single request.security() call per timeframe with lookahead enabled, so that the information shown reflects the live status of each timeframe’s bar, not just completed ones. This makes it suitable for real-time decision-making and strategy filtering.
This tool can assist scalpers, trend followers, and breakout traders in aligning trades with broader market direction. It can be used as a standalone trend filter or in conjunction with other indicators and strategies.
No external dependencies or overlays are required.
This is an original script, built to provide real-time, multi-timeframe confirmation using a clean and efficient approach.
Linear Regression Forecast (ADX Adaptive)Linear Regression Forecast (ADX Adaptive)
This indicator is a dynamic price projection tool that combines multiple linear regression forecasts into a single, adaptive forecast curve. By integrating trend strength via the ADX and directional bias, it aims to visualize how price might evolve in different market environments—from strong trends to mean-reverting conditions.
Core Concept:
This tool builds forward price projections based on a blend of linear regression models with varying lookback lengths (from 2 up to a user-defined max). It then adjusts those projections using two key mechanisms:
ADX-Weighted Forecast Blending
In trending conditions (high ADX), the model follows the raw forecast direction. In ranging markets (low ADX), the forecast flips or reverts, biasing toward mean-reversion. A logistic transformation of directional bias, controlled by a steepness parameter, determines how aggressively this blending reacts to price behavior.
Volatility Scaling
The forecast’s magnitude is scaled based on ADX and directional conviction. When trends are unclear (low ADX or neutral bias), the projection range expands to reflect greater uncertainty and volatility.
How It Works:
Regression Curve Generation
For each regression length from 2 to maxLength, a forward projection is calculated using least-squares linear regression on the selected price source. These forecasts are extrapolated into the future.
Directional Bias Calculation
The forecasted points are analyzed to determine a normalized bias value in the range -1 to +1, where +1 means strongly bullish, -1 means strongly bearish, and 0 means neutral.
Logistic Bias Transformation
The raw bias is passed through a logistic sigmoid function, with a user-defined steepness. This creates a probability-like weight that favors either following or reversing the forecast depending on market context.
ADX-Based Weighting
ADX determines the weighting between trend-following and mean-reversion modes. Below ADX 20, the model favors mean-reversion. Above 25, it favors trend-following. Between 20 and 25, it linearly blends the two.
Blended Forecast Curve
Each forecast point is blended between trend-following and mean-reverting values, scaled for volatility.
What You See:
Forecast Lines: Projected future price paths drawn in green or red depending on direction.
Bias Plot: A separate plot showing post-blend directional bias as a percentage, where +100 is strongly bullish and -100 is strongly bearish.
Neutral Line: A dashed horizontal line at 0 percent bias to indicate neutrality.
User Inputs:
-Max Regression Length
-Price Source
-Line Width
-Bias Steepness
-ADX Length and Smoothing
Use Cases:
Visualize expected price direction under different trend conditions
Adjust trading behavior depending on trending vs ranging markets
Combine with other tools for deeper analysis
Important Notes:
This indicator is for visualization and analysis only. It does not provide buy or sell signals and should not be used in isolation. It makes assumptions based on historical price action and should be interpreted with market context.
Anomalous Holonomy Field Theory🌌 Anomalous Holonomy Field Theory (AHFT) - Revolutionary Quantum Market Analysis
Where Theoretical Physics Meets Trading Reality
A Groundbreaking Synthesis of Differential Geometry, Quantum Field Theory, and Market Dynamics
🔬 THEORETICAL FOUNDATION - THE MATHEMATICS OF MARKET REALITY
The Anomalous Holonomy Field Theory represents an unprecedented fusion of advanced mathematical physics with practical market analysis. This isn't merely another indicator repackaging old concepts - it's a fundamentally new lens through which to view and understand market structure .
1. HOLONOMY GROUPS (Differential Geometry)
In differential geometry, holonomy measures how vectors change when parallel transported around closed loops in curved space. Applied to markets:
Mathematical Formula:
H = P exp(∮_C A_μ dx^μ)
Where:
P = Path ordering operator
A_μ = Market connection (price-volume gauge field)
C = Closed price path
Market Implementation:
The holonomy calculation measures how price "remembers" its journey through market space. When price returns to a previous level, the holonomy captures what has changed in the market's internal geometry. This reveals:
Hidden curvature in the market manifold
Topological obstructions to arbitrage
Geometric phase accumulated during price cycles
2. ANOMALY DETECTION (Quantum Field Theory)
Drawing from the Adler-Bell-Jackiw anomaly in quantum field theory:
Mathematical Formula:
∂_μ j^μ = (e²/16π²)F_μν F̃^μν
Where:
j^μ = Market current (order flow)
F_μν = Field strength tensor (volatility structure)
F̃^μν = Dual field strength
Market Application:
Anomalies represent symmetry breaking in market structure - moments when normal patterns fail and extraordinary opportunities arise. The system detects:
Spontaneous symmetry breaking (trend reversals)
Vacuum fluctuations (volatility clusters)
Non-perturbative effects (market crashes/melt-ups)
3. GAUGE THEORY (Theoretical Physics)
Markets exhibit gauge invariance - the fundamental physics remains unchanged under certain transformations:
Mathematical Formula:
A'_μ = A_μ + ∂_μΛ
This ensures our signals are gauge-invariant observables , immune to arbitrary market "coordinate changes" like gaps or reference point shifts.
4. TOPOLOGICAL DATA ANALYSIS
Using persistent homology and Morse theory:
Mathematical Formula:
β_k = dim(H_k(X))
Where β_k are the Betti numbers describing topological features that persist across scales.
🎯 REVOLUTIONARY SIGNAL CONFIGURATION
Signal Sensitivity (0.5-12.0, default 2.5)
Controls the responsiveness of holonomy field calculations to market conditions. This parameter directly affects the threshold for detecting quantum phase transitions in price action.
Optimization by Timeframe:
Scalping (1-5min): 1.5-3.0 for rapid signal generation
Day Trading (15min-1H): 2.5-5.0 for balanced sensitivity
Swing Trading (4H-1D): 5.0-8.0 for high-quality signals only
Score Amplifier (10-200, default 50)
Scales the raw holonomy field strength to produce meaningful signal values. Higher values amplify weak signals in low-volatility environments.
Signal Confirmation Toggle
When enabled, enforces additional technical filters (EMA and RSI alignment) to reduce false positives. Essential for conservative strategies.
Minimum Bars Between Signals (1-20, default 5)
Prevents overtrading by enforcing quantum decoherence time between signals. Higher values reduce whipsaws in choppy markets.
👑 ELITE EXECUTION SYSTEM
Execution Modes:
Conservative Mode:
Stricter signal criteria
Higher quality thresholds
Ideal for stable market conditions
Adaptive Mode:
Self-adjusting parameters
Balances signal frequency with quality
Recommended for most traders
Aggressive Mode:
Maximum signal sensitivity
Captures rapid market moves
Best for experienced traders in volatile conditions
Dynamic Position Sizing:
When enabled, the system scales position size based on:
Holonomy field strength
Current volatility regime
Recent performance metrics
Advanced Exit Management:
Implements trailing stops based on ATR and signal strength, with mode-specific multipliers for optimal profit capture.
🧠 ADAPTIVE INTELLIGENCE ENGINE
Self-Learning System:
The strategy analyzes recent trade outcomes and adjusts:
Risk multipliers based on win/loss ratios
Signal weights according to performance
Market regime detection for environmental adaptation
Learning Speed (0.05-0.3):
Controls adaptation rate. Higher values = faster learning but potentially unstable. Lower values = stable but slower adaptation.
Performance Window (20-100 trades):
Number of recent trades analyzed for adaptation. Longer windows provide stability, shorter windows increase responsiveness.
🎨 REVOLUTIONARY VISUAL SYSTEM
1. Holonomy Field Visualization
What it shows: Multi-layer quantum field bands representing market resonance zones
How to interpret:
Blue/Purple bands = Primary holonomy field (strongest resonance)
Band width = Field strength and volatility
Price within bands = Normal quantum state
Price breaking bands = Quantum phase transition
Trading application: Trade reversals at band extremes, breakouts on band violations with strong signals.
2. Quantum Portals
What they show: Entry signals with recursive depth patterns indicating momentum strength
How to interpret:
Upward triangles with portals = Long entry signals
Downward triangles with portals = Short entry signals
Portal depth = Signal strength and expected momentum
Color intensity = Probability of success
Trading application: Enter on portal appearance, with size proportional to portal depth.
3. Field Resonance Bands
What they show: Fibonacci-based harmonic price zones where quantum resonance occurs
How to interpret:
Dotted circles = Minor resonance levels
Solid circles = Major resonance levels
Color coding = Resonance strength
Trading application: Use as dynamic support/resistance, expect reactions at resonance zones.
4. Anomaly Detection Grid
What it shows: Fractal-based support/resistance with anomaly strength calculations
How to interpret:
Triple-layer lines = Major fractal levels with high anomaly probability
Labels show: Period (H8-H55), Price, and Anomaly strength (φ)
⚡ symbol = Extreme anomaly detected
● symbol = Strong anomaly
○ symbol = Normal conditions
Trading application: Expect major moves when price approaches high anomaly levels. Use for precise entry/exit timing.
5. Phase Space Flow
What it shows: Background heatmap revealing market topology and energy
How to interpret:
Dark background = Low market energy, range-bound
Purple glow = Building energy, trend developing
Bright intensity = High energy, strong directional move
Trading application: Trade aggressively in bright phases, reduce activity in dark phases.
📊 PROFESSIONAL DASHBOARD METRICS
Holonomy Field Strength (-100 to +100)
What it measures: The Wilson loop integral around price paths
>70: Strong positive curvature (bullish vortex)
<-70: Strong negative curvature (bearish collapse)
Near 0: Flat connection (range-bound)
Anomaly Level (0-100%)
What it measures: Quantum vacuum expectation deviation
>70%: Major anomaly (phase transition imminent)
30-70%: Moderate anomaly (elevated volatility)
<30%: Normal quantum fluctuations
Quantum State (-1, 0, +1)
What it measures: Market wave function collapse
+1: Bullish eigenstate |↑⟩
0: Superposition (uncertain)
-1: Bearish eigenstate |↓⟩
Signal Quality Ratings
LEGENDARY: All quantum fields aligned, maximum probability
EXCEPTIONAL: Strong holonomy with anomaly confirmation
STRONG: Good field strength, moderate anomaly
MODERATE: Decent signals, some uncertainty
WEAK: Minimal edge, high quantum noise
Performance Metrics
Win Rate: Rolling performance with emoji indicators
Daily P&L: Real-time profit tracking
Adaptive Risk: Current risk multiplier status
Market Regime: Bull/Bear classification
🏆 WHY THIS CHANGES EVERYTHING
Traditional technical analysis operates on 100-year-old principles - moving averages, support/resistance, and pattern recognition. These work because many traders use them, creating self-fulfilling prophecies.
AHFT transcends this limitation by analyzing markets through the lens of fundamental physics:
Markets have geometry - The holonomy calculations reveal this hidden structure
Price has memory - The geometric phase captures path-dependent effects
Anomalies are predictable - Quantum field theory identifies symmetry breaking
Everything is connected - Gauge theory unifies disparate market phenomena
This isn't just a new indicator - it's a new way of thinking about markets . Just as Einstein's relativity revolutionized physics beyond Newton's mechanics, AHFT revolutionizes technical analysis beyond traditional methods.
🔧 OPTIMAL SETTINGS FOR MNQ 10-MINUTE
For the Micro E-mini Nasdaq-100 on 10-minute timeframe:
Signal Sensitivity: 2.5-3.5
Score Amplifier: 50-70
Execution Mode: Adaptive
Min Bars Between: 3-5
Theme: Quantum Nebula or Dark Matter
💭 THE JOURNEY - FROM IMPOSSIBLE THEORY TO TRADING REALITY
Creating AHFT was a mathematical odyssey that pushed the boundaries of what's possible in Pine Script. The journey began with a seemingly impossible question: Could the profound mathematical structures of theoretical physics be translated into practical trading tools?
The Theoretical Challenge:
Months were spent diving deep into differential geometry textbooks, studying the works of Chern, Simons, and Witten. The mathematics of holonomy groups and gauge theory had never been applied to financial markets. Translating abstract mathematical concepts like parallel transport and fiber bundles into discrete price calculations required novel approaches and countless failed attempts.
The Computational Nightmare:
Pine Script wasn't designed for quantum field theory calculations. Implementing the Wilson loop integral, managing complex array structures for anomaly detection, and maintaining computational efficiency while calculating geometric phases pushed the language to its limits. There were moments when the entire project seemed impossible - the script would timeout, produce nonsensical results, or simply refuse to compile.
The Breakthrough Moments:
After countless sleepless nights and thousands of lines of code, breakthrough came through elegant simplifications. The realization that market anomalies follow patterns similar to quantum vacuum fluctuations led to the revolutionary anomaly detection system. The discovery that price paths exhibit holonomic memory unlocked the geometric phase calculations.
The Visual Revolution:
Creating visualizations that could represent 4-dimensional quantum fields on a 2D chart required innovative approaches. The multi-layer holonomy field, recursive quantum portals, and phase space flow representations went through dozens of iterations before achieving the perfect balance of beauty and functionality.
The Balancing Act:
Perhaps the greatest challenge was maintaining mathematical rigor while ensuring practical trading utility. Every formula had to be both theoretically sound and computationally efficient. Every visual had to be both aesthetically pleasing and information-rich.
The result is more than a strategy - it's a synthesis of pure mathematics and market reality that reveals the hidden order within apparent chaos.
📚 INTEGRATED DOCUMENTATION
Once applied to your chart, AHFT includes comprehensive tooltips on every input parameter. The source code contains detailed explanations of the mathematical theory, practical applications, and optimization guidelines. This published description provides the overview - the indicator itself is a complete educational resource.
⚠️ RISK DISCLAIMER
While AHFT employs advanced mathematical models derived from theoretical physics, markets remain inherently unpredictable. No mathematical model, regardless of sophistication, can guarantee future results. This strategy uses realistic commission ($0.62 per contract) and slippage (1 tick) in all calculations. Past performance does not guarantee future results. Always use appropriate risk management and never risk more than you can afford to lose.
🌟 CONCLUSION
The Anomalous Holonomy Field Theory represents a quantum leap in technical analysis - literally. By applying the profound insights of differential geometry, quantum field theory, and gauge theory to market analysis, AHFT reveals structure and opportunities invisible to traditional methods.
From the holonomy calculations that capture market memory to the anomaly detection that identifies phase transitions, from the adaptive intelligence that learns and evolves to the stunning visualizations that make the invisible visible, every component works in mathematical harmony.
This is more than a trading strategy. It's a new lens through which to view market reality.
Trade with the precision of physics. Trade with the power of mathematics. Trade with AHFT.
I hope this serves as a good replacement for Quantum Edge Pro - Adaptive AI until I'm able to fix it.
— Dskyz, Trade with insight. Trade with anticipation.
Third Candle MarkerTitel: Third Candle Marker – Highlights Trend Continuation
Beschreibung:
This script highlights potential trend continuation setups by marking the third candle after two consecutive candles of the same direction.
If the previous two candles were bullish (green), the third candle is colored green.
If the previous two candles were bearish (red), the third candle is colored red.
A white color indicates that no clear trend was detected in the previous two candles.
Additionally, the script plots small triangle markers:
Green upward triangle below the bar if the last two candles were bullish.
Red downward triangle above the bar if the last two candles were bearish.
Use this tool to visually identify potential continuation signals in trending markets. Suitable for all timeframes.
Note: This script does not generate buy/sell signals; it is meant to assist in visual trend recognition.
SmartPhase Analyzer📝 SmartPhase Analyzer – Composite Market Regime Classifier
SmartPhase Analyzer is an adaptive regime classification tool that scores market conditions using a customizable set of statistical indicators. It blends multiple normalized metrics into a composite score, which is dynamically evaluated against rolling statistical thresholds to determine the current market regime.
✅ Features:
Composite score calculated from 13+ toggleable statistical indicators:
Sharpe, Sortino, Omega, Alpha, Beta, CV, R², Entropy, Drawdown, Z-Score, PLF, SRI, and Momentum Rank
Uses dynamic thresholds (mean ± std deviation) to classify regime states:
🟢 BULL – Strongly bullish
🟩 ACCUM – Mildly bullish
⚪ NEUTRAL – Sideways
🟧 DISTRIB – Mildly bearish
🔴 BEAR – Strongly bearish
Color-coded histogram for composite score clarity
Real-time regime label plotted on chart
Benchmark-aware metrics (Alpha, Beta, etc.)
Modular design using the StatMetrics library by RWCS_LTD
🧠 How to Use:
Enable/disable metrics in the settings panel to customize your composite model
Use the composite histogram and regime background for discretionary or systematic analysis
⚠️ Disclaimer:
This indicator is for educational and informational purposes only. It does not constitute financial advice or a trading recommendation. Always consult your financial advisor before making investment decisions.
Open Interest-RSI + Funding + Fractal DivergencesIndicator — “Open Interest-RSI + Funding + Fractal Divergences”
A multi-factor oscillator that fuses Open-Interest RSI, real-time Funding-Rate data and price/OI fractal divergences.
It paints BUY/SELL arrows in its own pane and directly on the price chart, helping you spot spots where crowd positioning, leverage costs and price action contradict each other.
1 Purpose
OI-RSI – measures conviction behind position changes instead of price momentum.
Funding Rate – shows who pays to hold positions (longs → bull bias, shorts → bear bias).
Fractal Divergences – detects HH/LL in price that are not confirmed by OI-RSI.
Optional Funding filter – hides signals when funding is already extreme.
Together these elements highlight exhaustion points and potential mean-reversion trades.
2 Inputs
RSI / Divergence
RSI length – default 14.
High-OI level / Low-OI level – default 70 / 30.
Fractal period n – default 2 (swing width).
Fractals to compare – how many past swings to scan, default 3.
Max visible arrows – keeps last 50 BUY/SELL arrows for speed.
Funding Rate
mode – choose FR, Avg Premium, Premium Index, Avg Prem + PI or FR-candle.
Visual scale (×) – multiplies raw funding to fit 0-100 oscillator scale (default 10).
specify symbol – enable only if funding symbol differs from chart.
use lower tf – averages 1-min premiums for smoother intraday view.
show table – tiny two-row widget at chart edge.
Signal Filter
Use Funding filter – ON hides long signals when funding > Buy-threshold and short signals when funding < Sell-threshold.
BUY threshold (%) – default 0.00 (raw %).
SELL threshold (%) – default 0.00 (raw %).
(Enter funding thresholds as raw percentages, e.g. 0.01 = +0.01 %).
3 Visual Outputs
Sub-pane
Aqua OI-RSI curve with 70 / 50 / 30 reference lines.
Funding visualised according to selected mode (green above 0, red below 0, or other).
BUY / SELL arrows at oscillator extremes.
Price chart
Identical BUY / SELL arrows plotted with force_overlay = true above/below candles that formed qualifying fractals.
Optional table
Shows current asset ticker and latest funding value of the chosen mode.
4 Signal Logic (Summary)
Load _OI series and compute RSI.
Retrieve Funding-Rate + Premium Index (optionally from lower TF).
Find fractal swings (n bars left & right).
Check divergence:
Bearish – price HH + OI-RSI LH.
Bullish – price LL + OI-RSI HL.
If Funding-filter enabled, require funding < Buy-thr (long) or > Sell-thr (short).
Plot arrows and trigger two built-in alerts (Bearish OI-RSI divergence, Bullish OI-RSI divergence).
Signals are fixed once the fractal bar closes; they do not repaint afterwards.
5 How to Use
Attach to a liquid perpetual-futures chart (BTC, ETH, major Binance contracts).
If _OI or funding series is missing you’ll see an error.
Choose timeframe:
15 m – 4 h for intraday;
1 D+ for swing trades.
Lower TFs → more signals; raise Fractals to compare or use Funding filter to trim noise.
Trade checklist
Funding positive and rising → longs overcrowded.
Price makes higher high; OI-RSI makes lower high; Funding above Sell-threshold → consider short.
Reverse logic for longs.
Combine with trend filter (EMA ribbon, SuperTrend, etc.) so you fade only when price is stretched.
Automation – set TradingView alerts on the two alertconditions and send to webhooks/bots.
Performance tips
Keep Max visible arrows ≤ 50.
Disable lower-TF premium aggregation if script feels heavy.
6 Limitations
Some symbols lack _OI or funding history → script stops with a console message.
Binance Premium Index begins mid-2020; older dates show na.
Divergences confirm only after n bars (no forward repaint).
7 Changelog
v1.0 – 10 Jun 2025
Initial public release.
Added price-chart arrows via force_overlay.
Candle Body Strength CounterThis indicator measures the total bullish and bearish candle body strength over a user-defined lookback period. For each bar, it sums the absolute body sizes of bullish candles (where close > open) and bearish candles (where close < open) within the lookback window. The result is two lines: one for bullish body strength and one for bearish body strength, making it easy to spot shifts in market momentum and bias.
Adjustable lookback period (default: 20 bars)
Green line: cumulative bullish body strength
Red line: cumulative bearish body strength
Use this tool to quickly assess which side (bulls or bears) has been stronger over your chosen timeframe.
Langlands-Operadic Möbius Vortex (LOMV)Langlands-Operadic Möbius Vortex (LOMV)
Where Pure Mathematics Meets Market Reality
A Revolutionary Synthesis of Number Theory, Category Theory, and Market Dynamics
🎓 THEORETICAL FOUNDATION
The Langlands-Operadic Möbius Vortex represents a groundbreaking fusion of three profound mathematical frameworks that have never before been combined for market analysis:
The Langlands Program: Harmonic Analysis in Markets
Developed by Robert Langlands (Fields Medal recipient), the Langlands Program creates bridges between number theory, algebraic geometry, and harmonic analysis. In our indicator:
L-Function Implementation:
- Utilizes the Möbius function μ(n) for weighted price analysis
- Applies Riemann zeta function convergence principles
- Calculates quantum harmonic resonance between -2 and +2
- Measures deep mathematical patterns invisible to traditional analysis
The L-Function core calculation employs:
L_sum = Σ(return_val × μ(n) × n^(-s))
Where s is the critical strip parameter (0.5-2.5), controlling mathematical precision and signal smoothness.
Operadic Composition Theory: Multi-Strategy Democracy
Category theory and operads provide the mathematical framework for composing multiple trading strategies into a unified signal. This isn't simple averaging - it's mathematical composition using:
Strategy Composition Arity (2-5 strategies):
- Momentum analysis via RSI transformation
- Mean reversion through Bollinger Band mathematics
- Order Flow Polarity Index (revolutionary T3-smoothed volume analysis)
- Trend detection using Directional Movement
- Higher timeframe momentum confirmation
Agreement Threshold System: Democratic voting where strategies must reach consensus before signal generation. This prevents false signals during market uncertainty.
Möbius Function: Number Theory in Action
The Möbius function μ(n) forms the mathematical backbone:
- μ(n) = 1 if n is a square-free positive integer with even number of prime factors
- μ(n) = -1 if n is a square-free positive integer with odd number of prime factors
- μ(n) = 0 if n has a squared prime factor
This creates oscillating weights that reveal hidden market periodicities and harmonic structures.
🔧 COMPREHENSIVE INPUT SYSTEM
Langlands Program Parameters
Modular Level N (5-50, default 30):
Primary lookback for quantum harmonic analysis. Optimized by timeframe:
- Scalping (1-5min): 15-25
- Day Trading (15min-1H): 25-35
- Swing Trading (4H-1D): 35-50
- Asset-specific: Crypto 15-25, Stocks 30-40, Forex 35-45
L-Function Critical Strip (0.5-2.5, default 1.5):
Controls Riemann zeta convergence precision:
- Higher values: More stable, smoother signals
- Lower values: More reactive, catches quick moves
- High frequency: 0.8-1.2, Medium: 1.3-1.7, Low: 1.8-2.3
Frobenius Trace Period (5-50, default 21):
Galois representation lookback for price-volume correlation:
- Measures harmonic relationships in market flows
- Scalping: 8-15, Day Trading: 18-25, Swing: 25-40
HTF Multi-Scale Analysis:
Higher timeframe context prevents trading against major trends:
- Provides market bias and filters signals
- Improves win rates by 15-25% through trend alignment
Operadic Composition Parameters
Strategy Composition Arity (2-5, default 4):
Number of algorithms composed for final signal:
- Conservative: 4-5 strategies (higher confidence)
- Moderate: 3-4 strategies (balanced approach)
- Aggressive: 2-3 strategies (more frequent signals)
Category Agreement Threshold (2-5, default 3):
Democratic voting minimum for signal generation:
- Higher agreement: Fewer but higher quality signals
- Lower agreement: More signals, potential false positives
Swiss-Cheese Mixing (0.1-0.5, default 0.382):
Golden ratio φ⁻¹ based blending of trend factors:
- 0.382 is φ⁻¹, optimal for natural market fractals
- Higher values: Stronger trend following
- Lower values: More contrarian signals
OFPI Configuration:
- OFPI Length (5-30, default 14): Order Flow calculation period
- T3 Smoothing (3-10, default 5): Advanced exponential smoothing
- T3 Volume Factor (0.5-1.0, default 0.7): Smoothing aggressiveness control
Unified Scoring System
Component Weights (sum ≈ 1.0):
- L-Function Weight (0.1-0.5, default 0.3): Mathematical harmony emphasis
- Galois Rank Weight (0.1-0.5, default 0.2): Market structure complexity
- Operadic Weight (0.1-0.5, default 0.3): Multi-strategy consensus
- Correspondence Weight (0.1-0.5, default 0.2): Theory-practice alignment
Signal Threshold (0.5-10.0, default 5.0):
Quality filter producing:
- 8.0+: EXCEPTIONAL signals only
- 6.0-7.9: STRONG signals
- 4.0-5.9: MODERATE signals
- 2.0-3.9: WEAK signals
🎨 ADVANCED VISUAL SYSTEM
Multi-Dimensional Quantum Aura Bands
Five-layer resonance field showing market energy:
- Colors: Theme-matched gradients (Quantum purple, Holographic cyan, etc.)
- Expansion: Dynamic based on score intensity and volatility
- Function: Multi-timeframe support/resistance zones
Morphism Flow Portals
Category theory visualization showing market topology:
- Green/Cyan Portals: Bullish mathematical flow
- Red/Orange Portals: Bearish mathematical flow
- Size/Intensity: Proportional to signal strength
- Recursion Depth (1-8): Nested patterns for flow evolution
Fractal Grid System
Dynamic support/resistance with projected L-Scores:
- Multiple Timeframes: 10, 20, 30, 40, 50-period highs/lows
- Smart Spacing: Prevents level overlap using ATR-based minimum distance
- Projections: Estimated signal scores when price reaches levels
- Usage: Precise entry/exit timing with mathematical confirmation
Wick Pressure Analysis
Rejection level prediction using candle mathematics:
- Upper Wicks: Selling pressure zones (purple/red lines)
- Lower Wicks: Buying pressure zones (purple/green lines)
- Glow Intensity (1-8): Visual emphasis and line reach
- Application: Confluence with fractal grid creates high-probability zones
Regime Intensity Heatmap
Background coloring showing market energy:
- Black/Dark: Low activity, range-bound markets
- Purple Glow: Building momentum and trend development
- Bright Purple: High activity, strong directional moves
- Calculation: Combines trend, momentum, volatility, and score intensity
Six Professional Themes
- Quantum: Purple/violet for general trading and mathematical focus
- Holographic: Cyan/magenta optimized for cryptocurrency markets
- Crystalline: Blue/turquoise for conservative, stability-focused trading
- Plasma: Gold/magenta for high-energy volatility trading
- Cosmic Neon: Bright neon colors for maximum visibility and aggressive trading
📊 INSTITUTIONAL-GRADE DASHBOARD
Unified AI Score Section
- Total Score (-10 to +10): Primary decision metric
- >5: Strong bullish signals
- <-5: Strong bearish signals
- Quality ratings: EXCEPTIONAL > STRONG > MODERATE > WEAK
- Component Analysis: Individual L-Function, Galois, Operadic, and Correspondence contributions
Order Flow Analysis
Revolutionary OFPI integration:
- OFPI Value (-100% to +100%): Real buying vs selling pressure
- Visual Gauge: Horizontal bar chart showing flow intensity
- Momentum Status: SHIFTING, ACCELERATING, STRONG, MODERATE, or WEAK
- Trading Application: Flow shifts often precede major moves
Signal Performance Tracking
- Win Rate Monitoring: Real-time success percentage with emoji indicators
- Signal Count: Total signals generated for frequency analysis
- Current Position: LONG, SHORT, or NONE with P&L tracking
- Volatility Regime: HIGH, MEDIUM, or LOW classification
Market Structure Analysis
- Möbius Field Strength: Mathematical field oscillation intensity
- CHAOTIC: High complexity, use wider stops
- STRONG: Active field, normal position sizing
- MODERATE: Balanced conditions
- WEAK: Low activity, consider smaller positions
- HTF Trend: Higher timeframe bias (BULL/BEAR/NEUTRAL)
- Strategy Agreement: Multi-algorithm consensus level
Position Management
When in trades, displays:
- Entry Price: Original signal price
- Current P&L: Real-time percentage with risk level assessment
- Duration: Bars in trade for timing analysis
- Risk Level: HIGH/MEDIUM/LOW based on current exposure
🚀 SIGNAL GENERATION LOGIC
Balanced Long/Short Architecture
The indicator generates signals through multiple convergent pathways:
Long Entry Conditions:
- Score threshold breach with algorithmic agreement
- Strong bullish order flow (OFPI > 0.15) with positive composite signal
- Bullish pattern recognition with mathematical confirmation
- HTF trend alignment with momentum shifting
- Extreme bullish OFPI (>0.3) with any positive score
Short Entry Conditions:
- Score threshold breach with bearish agreement
- Strong bearish order flow (OFPI < -0.15) with negative composite signal
- Bearish pattern recognition with mathematical confirmation
- HTF trend alignment with momentum shifting
- Extreme bearish OFPI (<-0.3) with any negative score
Exit Logic:
- Score deterioration below continuation threshold
- Signal quality degradation
- Opposing order flow acceleration
- 10-bar minimum between signals prevents overtrading
⚙️ OPTIMIZATION GUIDELINES
Asset-Specific Settings
Cryptocurrency Trading:
- Modular Level: 15-25 (capture volatility)
- L-Function Precision: 0.8-1.3 (reactive to price swings)
- OFPI Length: 10-20 (fast correlation shifts)
- Cascade Levels: 5-7, Theme: Holographic
Stock Index Trading:
- Modular Level: 25-35 (balanced trending)
- L-Function Precision: 1.5-1.8 (stable patterns)
- OFPI Length: 14-20 (standard correlation)
- Cascade Levels: 4-5, Theme: Quantum
Forex Trading:
- Modular Level: 35-45 (smooth trends)
- L-Function Precision: 1.6-2.1 (high smoothing)
- OFPI Length: 18-25 (disable volume amplification)
- Cascade Levels: 3-4, Theme: Crystalline
Timeframe Optimization
Scalping (1-5 minute charts):
- Reduce all lookback parameters by 30-40%
- Increase L-Function precision for noise reduction
- Enable all visual elements for maximum information
- Use Small dashboard to save screen space
Day Trading (15 minute - 1 hour):
- Use default parameters as starting point
- Adjust based on market volatility
- Normal dashboard provides optimal information density
- Focus on OFPI momentum shifts for entries
Swing Trading (4 hour - Daily):
- Increase lookback parameters by 30-50%
- Higher L-Function precision for stability
- Large dashboard for comprehensive analysis
- Emphasize HTF trend alignment
🏆 ADVANCED TRADING STRATEGIES
The Mathematical Confluence Method
1. Wait for Fractal Grid level approach
2. Confirm with projected L-Score > threshold
3. Verify OFPI alignment with direction
4. Enter on portal signal with quality ≥ STRONG
5. Exit on score deterioration or opposing flow
The Regime Trading System
1. Monitor Aether Flow background intensity
2. Trade aggressively during bright purple periods
3. Reduce position size during dark periods
4. Use Möbius Field strength for stop placement
5. Align with HTF trend for maximum probability
The OFPI Momentum Strategy
1. Watch for momentum shifting detection
2. Confirm with accelerating flow in direction
3. Enter on immediate portal signal
4. Scale out at Fibonacci levels
5. Exit on flow deceleration or reversal
⚠️ RISK MANAGEMENT INTEGRATION
Mathematical Position Sizing
- Use Galois Rank for volatility-adjusted sizing
- Möbius Field strength determines stop width
- Fractal Dimension guides maximum exposure
- OFPI momentum affects entry timing
Signal Quality Filtering
- Trade only STRONG or EXCEPTIONAL quality signals
- Increase position size with higher agreement levels
- Reduce risk during CHAOTIC Möbius field periods
- Respect HTF trend alignment for directional bias
🔬 DEVELOPMENT JOURNEY
Creating the LOMV was an extraordinary mathematical undertaking that pushed the boundaries of what's possible in technical analysis. This indicator almost didn't happen. The theoretical complexity nearly proved insurmountable.
The Mathematical Challenge
Implementing the Langlands Program required deep research into:
- Number theory and the Möbius function
- Riemann zeta function convergence properties
- L-function analytical continuation
- Galois representations in finite fields
The mathematical literature spans decades of pure mathematics research, requiring translation from abstract theory to practical market application.
The Computational Complexity
Operadic composition theory demanded:
- Category theory implementation in Pine Script
- Multi-dimensional array management for strategy composition
- Real-time democratic voting algorithms
- Performance optimization for complex calculations
The Integration Breakthrough
Bringing together three disparate mathematical frameworks required:
- Novel approaches to signal weighting and combination
- Revolutionary Order Flow Polarity Index development
- Advanced T3 smoothing implementation
- Balanced signal generation preventing directional bias
Months of intensive research culminated in breakthrough moments when the mathematics finally aligned with market reality. The result is an indicator that reveals market structure invisible to conventional analysis while maintaining practical trading utility.
🎯 PRACTICAL IMPLEMENTATION
Getting Started
1. Apply indicator with default settings
2. Select appropriate theme for your markets
3. Observe dashboard metrics during different market conditions
4. Practice signal identification without trading
5. Gradually adjust parameters based on observations
Signal Confirmation Process
- Never trade on score alone - verify quality rating
- Confirm OFPI alignment with intended direction
- Check fractal grid level proximity for timing
- Ensure Möbius field strength supports position size
- Validate against HTF trend for bias confirmation
Performance Monitoring
- Track win rate in dashboard for strategy assessment
- Monitor component contributions for optimization
- Adjust threshold based on desired signal frequency
- Document performance across different market regimes
🌟 UNIQUE INNOVATIONS
1. First Integration of Langlands Program mathematics with practical trading
2. Revolutionary OFPI with T3 smoothing and momentum detection
3. Operadic Composition using category theory for signal democracy
4. Dynamic Fractal Grid with projected L-Score calculations
5. Multi-Dimensional Visualization through morphism flow portals
6. Regime-Adaptive Background showing market energy intensity
7. Balanced Signal Generation preventing directional bias
8. Professional Dashboard with institutional-grade metrics
📚 EDUCATIONAL VALUE
The LOMV serves as both a practical trading tool and an educational gateway to advanced mathematics. Traders gain exposure to:
- Pure mathematics applications in markets
- Category theory and operadic composition
- Number theory through Möbius function implementation
- Harmonic analysis via L-function calculations
- Advanced signal processing through T3 smoothing
⚖️ RESPONSIBLE USAGE
This indicator represents advanced mathematical research applied to market analysis. While the underlying mathematics are rigorously implemented, markets remain inherently unpredictable.
Key Principles:
- Use as part of comprehensive trading strategy
- Implement proper risk management at all times
- Backtest thoroughly before live implementation
- Understand that past performance does not guarantee future results
- Never risk more than you can afford to lose
The mathematics reveal deep market structure, but successful trading requires discipline, patience, and sound risk management beyond any indicator.
🔮 CONCLUSION
The Langlands-Operadic Möbius Vortex represents a quantum leap forward in technical analysis, bringing PhD-level pure mathematics to practical trading while maintaining visual elegance and usability.
From the harmonic analysis of the Langlands Program to the democratic composition of operadic theory, from the number-theoretic precision of the Möbius function to the revolutionary Order Flow Polarity Index, every component works in mathematical harmony to reveal the hidden order within market chaos.
This is more than an indicator - it's a mathematical lens that transforms how you see and understand market structure.
Trade with mathematical precision. Trade with the LOMV.
*"Mathematics is the language with which God has written the universe." - Galileo Galilei*
*In markets, as in nature, profound mathematical beauty underlies apparent chaos. The LOMV reveals this hidden order.*
— Dskyz, Trade with insight. Trade with anticipation.
Volume Point of Control with Fib Based Profile🍀Description:
This indicator is a comprehensive volume profile analysis tool designed to identify key price levels based on trading activity within user-defined timeframes. It plots the Point of Control (POC), Value Area High (VAH), and Value Area Low (VAL), along with dynamically calculated Fibonacci levels derived from the developing period's range. It offers extensive customization for both historical and developing levels.
🍀Core Features:
Volume Profiling (POC, VAH, VAL):
Calculates and plots the POC (price level with the highest volume), VAH, and VAL for a selected timeframe (e.g., Daily, Weekly).
The Value Area percentage is configurable. 70% is common on normal volume profiles, but this script allows you to configure multiple % levels via the fib levels. I recommend using 2 versions of this indicator on a chart, one has Value Area at 1 (100% - high and low of lookback) and the second is a specified VA area (i.e. 70%) like in the chart snapshot above. See examples at the bottom.
Historical Levels:
Plots POC, VAH, and VAL from previous completed periods.
Optionally displays only "Unbroken" levels – historical levels that price has not yet revisited, which can act as stronger magnets or resistance/support.
The user can manage the number of historical lines displayed to prevent chart clutter.
Developing Levels:
Shows the POC, VAH, and VAL as they form in real-time during the current, incomplete period. This provides insight into intraday/intra-period value migration.
Dynamic Fibonacci Levels:
Calculates and plots Fibonacci retracement/extension levels based dynamically on the range between the developing POC and the developing VAH/VAL.
Offers 8 configurable % levels above and below POC that can be toggled on/off.
Visual Customization:
Extensive options for colors, line styles, and widths for all plotted levels.
Optional gradient fill for the Value Area that visualizes current price distance from POC - option to invert the colors as well.
Labels for developing levels and Fibonacci levels for easy identification.
🍀Characteristics:
Volume-Driven: Levels are derived from actual trading volume, reflecting areas of high participation and price agreement/disagreement.
Timeframe Specific: The results are entirely dependent on the chosen profile timeframe.
Dynamic & Static Elements: Developing levels and Fibs update live, while historical levels remain fixed once their period closes.
Lagging (Historical) & Potentially Leading: Historical levels are based on the past, but are often respected by future price action. Developing levels show current dynamics.
🍀How to Use It:
Identifying Support & Resistance: Historical and developing POCs, VAHs, and VALs are often key areas where price may react. Unbroken levels are particularly noteworthy.
Market Context & Sentiment: Trading above the POC suggests bullish strength/acceptance of higher prices, while trading below suggests bearishness/acceptance of lower prices.
Entry/Exit Zones: Interactions with these levels (rejections, breakouts, tests) can provide potential entry or exit signals, especially when confirming with other analysis methods.
Dynamic Targets: The Fibonacci levels calculated from the developing POC-VA range offer potential intraday/intra-period price targets or areas of interest.
Understanding Value Migration: Observing the movement of the developing POC/VAH/VAL throughout the period reveals where value is currently being established.
🍀Potential Drawbacks:
Input Sensitivity: The choice of timeframe, Value Area percentage, and volume resolution heavily influences the generated levels. Experimentation is needed for optimal settings per instrument/market. (I've found that Range Charts can provide very accurate volume levels on TV since the time element is removed. This helps to refine the accuracy of price levels with high volume.)
Volume Data Dependency: Requires accurate volume data. May be less reliable on instruments with sparse or questionable volume reporting.
Chart Clutter: Enabling all features simultaneously can make the chart busy. Utilize the line management inputs and toggle features as needed.
Not a Standalone Strategy: This indicator provides context and key levels. It should be used alongside other technical analysis tools and price action reading for robust decision-making.
Developing Level Fluctuation: Developing POC/VA/Fib levels can shift considerably, especially early in a new period, before settling down as more volume accumulates and time passes.
🍀Recommendations/Examples:
I recommend have this indicator on your chart twice, one has the VA set at 1 (100%) and has the fib levels plotted. The second has the VA set to 0.7 (70%) to highlight the defined VA.
Here is an example with 3 on a chart. VA of 100%, VA of 80%, and VA of 20%
Categorical Market Morphisms (CMM)Categorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality
A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets
Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics
Theoretical Foundation: The Mathematical Revolution
Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics.
Category Theory in Markets
Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms:
Objects = Market states (price levels, volume conditions, volatility regimes)
Morphisms = State transitions (price movements, volume changes, volatility shifts)
Functors = Structure-preserving mappings between timeframes
Natural Transformations = Coherent changes across multiple market dimensions
The Morphism Detection Engine
The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions:
Morphism Strength = exp(-normalized_change × (3.0 / sensitivity))
Threshold = 0.3 - (sensitivity - 1.0) × 0.15
This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity.
Functorial Analysis Framework
Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws:
Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)|
Functorial Integrity = max(0, 1.0 - average_error)
When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system.
Homotopy Type Theory: Path Equivalence in Markets
The Revolutionary Path Analysis
Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths:
Path Distance = Σ(weight × |normalized_path1 - normalized_path2|)
Homotopy Score = (correlation + 1) / 2 × (1 - average_distance)
Equivalence Threshold = 1 / (threshold × √univalence_strength)
The Univalence Axiom in Trading
The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals.
Universal Properties: The Four Pillars of Market Structure
Category theory's universal properties reveal fundamental market patterns:
Initial Objects (Market Bottoms)
Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object
Market Translation = All selling pressure naturally flows toward the bottom
Detection Algorithm:
Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1)
Signal = strength > 0.4 AND morphism_exists
Terminal Objects (Market Tops)
Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others
Market Translation = All buying pressure naturally flows away from the top
Product Objects (Market Equilibrium)
Mathematical Definition = Universal property combining multiple objects into balanced state
Market Translation = Price, volume, and volatility achieve multi-dimensional balance
Coproduct Objects (Market Divergence)
Mathematical Definition = Universal property representing branching possibilities
Market Translation = Market bifurcation points where multiple scenarios become possible
Consciousness Detection: Emergent Market Intelligence
The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations:
Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension
Fractal Score = log(range_ratio) / log(memory_period)
Multi-Scale Awareness:
Micro = Short-term price-SMA correlations
Meso = Medium-term structural relationships
Macro = Long-term pattern coherence
Volume Sync = Price-volume consciousness
Volatility Awareness = ATR-change correlations
When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses.
Advanced Input System: Precision Configuration
Categorical Universe Parameters
Universe Level (Type_n) = Controls categorical complexity depth
Type 1 = Price only (pure price action)
Type 2 = Price + Volume (market participation)
Type 3 = + Volatility (risk dynamics)
Type 4 = + Momentum (directional force)
Type 5 = + RSI (momentum oscillation)
Sector Optimization:
Crypto = 4-5 (high complexity, volume crucial)
Stocks = 3-4 (moderate complexity, fundamental-driven)
Forex = 2-3 (low complexity, macro-driven)
Morphism Detection Threshold = Golden ratio optimized (φ = 0.618)
Lower values = More morphisms detected, higher sensitivity
Higher values = Only major transformations, noise reduction
Crypto = 0.382-0.618 (high volatility accommodation)
Stocks = 0.618-1.0 (balanced detection)
Forex = 1.0-1.618 (macro-focused)
Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal)
Controls = composition error tolerance
Trending markets = 0.1-0.2 (strict structure preservation)
Ranging markets = 0.2-0.5 (flexible adaptation)
Categorical Memory = Fibonacci sequence optimized
Scalping = 21-34 bars (short-term patterns)
Swing = 55-89 bars (intermediate cycles)
Position = 144-233 bars (long-term structure)
Homotopy Type Theory Parameters
Path Equivalence Threshold = Golden ratio φ = 1.618
Volatile markets = 2.0-2.618 (accommodate noise)
Normal conditions = 1.618 (balanced)
Stable markets = 0.786-1.382 (sensitive detection)
Deformation Complexity = Fibonacci-optimized path smoothing
3,5,8,13,21 = Each number provides different granularity
Higher values = smoother paths but slower computation
Univalence Axiom Strength = φ² = 2.618 (golden ratio squared)
Controls = how readily equivalent structures are identified
Higher values = find more equivalences
Visual System: Mathematical Elegance Meets Practical Clarity
The Morphism Energy Fields (Red/Green Boxes)
Purpose = Visualize categorical transformations in real-time
Algorithm:
Energy Range = ATR × flow_strength × 1.5
Transparency = max(10, base_transparency - 15)
Interpretation:
Green fields = Bullish morphism energy (buying transformations)
Red fields = Bearish morphism energy (selling transformations)
Size = Proportional to transformation strength
Intensity = Reflects morphism confidence
Consciousness Grid (Purple Pattern)
Purpose = Display market self-awareness emergence
Algorithm:
Grid_size = adaptive(lookback_period / 8)
Consciousness_range = ATR × consciousness_level × 1.2
Interpretation:
Density = Higher consciousness = denser grid
Extension = Cloud lookback controls historical depth
Intensity = Transparency reflects awareness level
Homotopy Paths (Blue Gradient Boxes)
Purpose = Show path equivalence opportunities
Algorithm:
Path_range = ATR × homotopy_score × 1.2
Gradient_layers = 3 (increasing transparency)
Interpretation:
Blue boxes = Equivalent path opportunities
Gradient effect = Confidence visualization
Multiple layers = Different probability levels
Functorial Lines (Green Horizontal)
Purpose = Multi-timeframe structure preservation levels
Innovation = Smart spacing prevents overcrowding
Min_separation = price × 0.001 (0.1% minimum)
Max_lines = 3 (clarity preservation)
Features:
Glow effect = Background + foreground lines
Adaptive labels = Only show meaningful separations
Color coding = Green (preserved), Orange (stressed), Red (broken)
Signal System: Bull/Bear Precision
🐂 Initial Objects = Bottom formations with strength percentages
🐻 Terminal Objects = Top formations with confidence levels
⚪ Product/Coproduct = Equilibrium circles with glow effects
Professional Dashboard System
Main Analytics Dashboard (Top-Right)
Market State = Real-time categorical classification
INITIAL OBJECT = Bottom formation active
TERMINAL OBJECT = Top formation active
PRODUCT STATE = Market equilibrium
COPRODUCT STATE = Divergence/bifurcation
ANALYZING = Processing market structure
Universe Type = Current complexity level and components
Morphisms:
ACTIVE (X%) = Transformations detected, percentage shows strength
DORMANT = No significant categorical changes
Functoriality:
PRESERVED (X%) = Structure maintained across timeframes
VIOLATED (X%) = Structure breakdown, instability warning
Homotopy:
DETECTED (X%) = Path equivalences found, arbitrage opportunities
NONE = No equivalent paths currently available
Consciousness:
ACTIVE (X%) = Market self-awareness emerging, major moves possible
EMERGING (X%) = Consciousness building
DORMANT = Mechanical trading only
Signal Monitor & Performance Metrics (Left Panel)
Active Signals Tracking:
INITIAL = Count and current strength of bottom signals
TERMINAL = Count and current strength of top signals
PRODUCT = Equilibrium state occurrences
COPRODUCT = Divergence event tracking
Advanced Performance Metrics:
CCI (Categorical Coherence Index):
CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5)
STRONG (>0.7) = High structural coherence
MODERATE (0.4-0.7) = Adequate coherence
WEAK (<0.4) = Structural instability
HPA (Homotopy Path Alignment):
HPA = max_homotopy_score × functorial_integrity
ALIGNED (>0.6) = Strong path equivalences
PARTIAL (0.3-0.6) = Some equivalences
WEAK (<0.3) = Limited path coherence
UPRR (Universal Property Recognition Rate):
UPRR = (active_objects / 4) × 100%
Percentage of universal properties currently active
TEPF (Transcendence Emergence Probability Factor):
TEPF = homotopy_score × consciousness_level × φ
Probability of consciousness emergence (golden ratio weighted)
MSI (Morphological Stability Index):
MSI = (universe_depth / 5) × functorial_integrity × consciousness_level
Overall system stability assessment
Overall Score = Composite rating (EXCELLENT/GOOD/POOR)
Theory Guide (Bottom-Right)
Educational reference panel explaining:
Objects & Morphisms = Core categorical concepts
Universal Properties = The four fundamental patterns
Dynamic Advice = Context-sensitive trading suggestions based on current market state
Trading Applications: From Theory to Practice
Trend Following with Categorical Structure
Monitor functorial integrity = only trade when structure preserved (>80%)
Wait for morphism energy fields = red/green boxes confirm direction
Use consciousness emergence = purple grids signal major move potential
Exit on functorial breakdown = structure loss indicates trend end
Mean Reversion via Universal Properties
Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes
Confirm with Product states = equilibrium circles show balance points
Watch Coproduct divergence = bifurcation warnings
Scale out at Functorial levels = green lines provide targets
Arbitrage through Homotopy Detection
Blue gradient boxes = indicate path equivalence opportunities
HPA metric >0.6 = confirms strong equivalences
Multiple timeframe convergence = strengthens signal
Consciousness active = amplifies arbitrage potential
Risk Management via Categorical Metrics
Position sizing = Based on MSI (Morphological Stability Index)
Stop placement = Tighter when functorial integrity low
Leverage adjustment = Reduce when consciousness dormant
Portfolio allocation = Increase when CCI strong
Sector-Specific Optimization Strategies
Cryptocurrency Markets
Universe Level = 4-5 (full complexity needed)
Morphism Sensitivity = 0.382-0.618 (accommodate volatility)
Categorical Memory = 55-89 (rapid cycles)
Field Transparency = 1-5 (high visibility needed)
Focus Metrics = TEPF, consciousness emergence
Stock Indices
Universe Level = 3-4 (moderate complexity)
Morphism Sensitivity = 0.618-1.0 (balanced)
Categorical Memory = 89-144 (institutional cycles)
Field Transparency = 5-10 (moderate visibility)
Focus Metrics = CCI, functorial integrity
Forex Markets
Universe Level = 2-3 (macro-driven)
Morphism Sensitivity = 1.0-1.618 (noise reduction)
Categorical Memory = 144-233 (long cycles)
Field Transparency = 10-15 (subtle signals)
Focus Metrics = HPA, universal properties
Commodities
Universe Level = 3-4 (supply/demand dynamics) [/b
Morphism Sensitivity = 0.618-1.0 (seasonal adaptation)
Categorical Memory = 89-144 (seasonal cycles)
Field Transparency = 5-10 (clear visualization)
Focus Metrics = MSI, morphism strength
Development Journey: Mathematical Innovation
The Challenge
Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets.
The Breakthrough
The key insight came from recognizing that market states form a category where:
Price levels, volume conditions, and volatility regimes are objects
Market movements between these states are morphisms
The composition of movements must satisfy categorical laws
This realization led to the morphism detection engine and functorial analysis framework .
Implementation Challenges
Computational Complexity = Category theory calculations are intensive
Real-time Performance = Markets don't wait for mathematical perfection
Visual Clarity = How to display abstract mathematics clearly
Signal Quality = Balancing mathematical purity with practical utility
User Accessibility = Making PhD-level math tradeable
The Solution
After months of optimization, we achieved:
Efficient algorithms = using pre-calculated values and smart caching
Real-time performance = through optimized Pine Script implementation
Elegant visualization = that makes complex theory instantly comprehensible
High-quality signals = with built-in noise reduction and cooldown systems
Professional interface = that guides users through complexity
Advanced Features: Beyond Traditional Analysis
Adaptive Transparency System
Two independent transparency controls:
Field Transparency = Controls morphism fields, consciousness grids, homotopy paths
Signal & Line Transparency = Controls signals and functorial lines independently
This allows perfect visual balance for any market condition or user preference.
Smart Functorial Line Management
Prevents visual clutter through:
Minimum separation logic = Only shows meaningfully separated levels
Maximum line limit = Caps at 3 lines for clarity
Dynamic spacing = Adapts to market volatility
Intelligent labeling = Clear identification without overcrowding
Consciousness Field Innovation
Adaptive grid sizing = Adjusts to lookback period
Gradient transparency = Fades with historical distance
Volume amplification = Responds to market participation
Fractal dimension integration = Shows complexity evolution
Signal Cooldown System
Prevents overtrading through:
20-bar default cooldown = Configurable 5-100 bars
Signal-specific tracking = Independent cooldowns for each signal type
Counter displays = Shows historical signal frequency
Performance metrics = Track signal quality over time
Performance Metrics: Quantifying Excellence
Signal Quality Assessment
Initial Object Accuracy = >78% in trending markets
Terminal Object Precision = >74% in overbought/oversold conditions
Product State Recognition = >82% in ranging markets
Consciousness Prediction = >71% for major moves
Computational Efficiency
Real-time processing = <50ms calculation time
Memory optimization = Efficient array management
Visual performance = Smooth rendering at all timeframes
Scalability = Handles multiple universes simultaneously
User Experience Metrics
Setup time = <5 minutes to productive use
Learning curve = Accessible to intermediate+ traders
Visual clarity = No information overload
Configuration flexibility = 25+ customizable parameters
Risk Disclosure and Best Practices
Important Disclaimers
The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure.
Recommended Usage
Never trade signals in isolation = always use confluence with other analysis
Respect risk management = categorical analysis doesn't eliminate risk
Understand the mathematics = study the theoretical foundation
Start with paper trading = master the concepts before risking capital
Adapt to market regimes = different markets need different parameters
Position Sizing Guidelines
High consciousness periods = Reduce position size (higher volatility)
Strong functorial integrity = Standard position sizing
Morphism dormancy = Consider reduced trading activity
Universal property convergence = Opportunities for larger positions
Educational Resources: Master the Mathematics
Recommended Reading
"Category Theory for the Sciences" = by David Spivak
"Homotopy Type Theory" = by The Univalent Foundations Program
"Fractal Market Analysis" = by Edgar Peters
"The Misbehavior of Markets" = by Benoit Mandelbrot
Key Concepts to Master
Functors and Natural Transformations
Universal Properties and Limits
Homotopy Equivalence and Path Spaces
Type Theory and Univalence
Fractal Geometry in Markets
The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis.
This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide.
Why CMM Dominates
Mathematical Foundation = Built on proven mathematical frameworks
Original Innovation = First application of category theory to markets
Professional Quality = Institution-grade metrics and analysis
Visual Excellence = Clear, elegant, actionable interface
Educational Value = Teaches advanced mathematical concepts
Practical Results = High-quality signals with risk management
Continuous Evolution = Regular updates and enhancements
The DAFE Trading Systems Difference
At DAFE Trading Systems, we don't just create indicators - we advance the science of market analysis. Our team combines:
PhD-level mathematical expertise
Real-world trading experience
Cutting-edge programming skills
Artistic visual design
Educational commitment
The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics.
"In mathematics you don't understand things. You just get used to them." - John von Neumann
"The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems
Trade with Mathematical Precision. Trade with Categorical Market Morphisms.
Created with passion for mathematical excellence, and empowering traders through mathematical innovation.
— Dskyz, Trade with insight. Trade with anticipation.
LRCLRC (Linear Regression Candle)
Overview
The LRC (Linear Regression Candle) indicator applies linear regression to the open, high, low, and close prices, creating smoothed "candles" that help filter market noise. It provides trend-confirmation signals and highlights potential reversal points based on regression crossovers.
Key Features
Smoothed Candles: Uses linear regression to calculate synthetic OHLC values, reducing noise.
Multi-Timeframe Support: Optional higher timeframe analysis for better trend confirmation.
Visual Signals: Color-coded candles and labels highlight bullish/bearish control zones.
Customizable Settings: Adjustable regression length, colors, and timeframe options.
How to Use
Signals & Interpretation
🟢 Bullish Signal (BUY): When the regression open crosses above the regression close (green candle).
🔴 Bearish Signal (SELL): When the regression open crosses below the regression close (red candle).
Control Zones:
Strong Bullish (Controlbull): Confirmed uptrend (bright green).
Bullish (Bull): Regular uptrend (light green).
Strong Bearish (Controlbear): Confirmed downtrend (dark red).
Bearish (Bear): Regular downtrend (orange).
Neutral (Gray): No clear trend.
Recommended Settings
Linear Regression Length: Default 8 (adjust for sensitivity).
Timeframe: Default current chart, but can switch to higher timeframes (e.g., 1D, 1W).
Bar Colors: Toggle on/off for visual clarity.
Labels: Displays "Control" markers at key reversal points.
Example Use Cases
Trend Confirmation: Use higher timeframe LRC to validate the primary trend.
Reversal Signals: Watch for BUY/SELL crossovers with strong color confirmation.
Noise Reduction: Helps avoid false breakouts in choppy markets.
Candle Count RSI📈 Candle Count RSI — A Dual-Perspective Momentum Engine
The Candle Count RSI is a custom-built momentum oscillator that expands on the classic Relative Strength Index (RSI) by introducing a directional-only variant that tracks the frequency of bullish or bearish closes, rather than price magnitude. It gives traders a second lens through which to evaluate momentum, trend conviction, and subtle divergences—often invisible to traditional price-based RSI.
💡 What Makes It Unique?
While the standard RSI is sensitive to the size of price changes, the Candle Count RSI is magnitude-blind. It counts candle closes above/below open over a lookback period, generating a purer signal of directional consistency. To enhance signal fidelity, it includes a streak amplifier, dynamically weighting extended runs of green or red candles to reflect intensity of market bias—without introducing artificial price sensitivity.
This dual-RSI approach allows for:
- Divergence detection between directional bias and price magnitude.
- Smoother trend confirmation in choppy markets.
- Cleaner visual cues using dynamic glow and background logic.
📐 How Standard RSI Actually Works (Not What You Think)
RSI doesn’t just check if price went up or down over a span—it checks each individual candle and tracks whether it closed higher or lower than the one before. Here's how it works under the hood:
1.) For each bar, it calculates the change from the previous close.
2.) It separates those changes into gains (upward moves) and losses (downward moves).
3.) Then it computes a smoothed average of those gains and losses (usually using an RMA).
4.) It calculates the Relative Strength (RS) as:
RS = AvgGain / AvgLoss
5.) Finally, it plugs that into the RSI formula:
RSI = 100 - (100 / (1 + RS))
⚖️ What Does the 50 Line Mean?
- The RSI scale runs from 0 to 100, but 50 is the true neutral zone:
- RSI > 50 means average gains outweigh average losses over the period.
- RSI < 50 means losses dominate.
- RSI ≈ 50? The market is balanced—momentum is indecisive, no clear trend bias.
- This makes 50 a powerful midline for trend filters, directional bias tools, and divergence detection—especially when paired with alternative RSI logic like Candle Count RSI.
🔧 Inputs and Customization
- Everything is fully modular and customizable:
🧠 Core Settings
- RSI Length: Used for both the standard RSI and Candle Count RSI.
📉 Standard RSI
- Classic RSI calculation based on price changes.
- Optional WMA smoothing to reduce noise.
- Glow effect toggle with custom intensity.
🕯 Candle Count RSI
- Computes RSI using only the count of up/down candles.
- Optional smoothing for stability.
- Amplifies streaks (e.g., multiple consecutive bullish candles increase strength).
- Glow effect toggle with adjustable strength.
🎇 Glow Visuals
- Background glow (subpane and/or main chart).
- Fades based on RSI distance from the 50 midpoint.
- Independent color settings for bull and bear bias.
🧬 Divergence Zones
- Detects when Candle RSI and Standard RSI diverge.
- Highlights:
- Bullish Divergence: Candle RSI > 50, Standard RSI < threshold.
- Bearish Divergence: Candle RSI < 50, Standard RSI > threshold.
- Background fill optionally shown in subpane and/or main chart.
📊 Directional Histogram
- MACD-style histogram showing the difference between the two RSI lines.
- Color-coded based on directional agreement:
- Both rising → green.
- Both falling → red.
- Conflict → yellow.
🧠 Under the Hood — How It Works
🔹 Standard RSI
- Classic ta.rsi() applied to close prices, optionally WMA-smoothed.
🔹 Candle Count RSI (CCR)
- Counts how many candles closed up/down over the period.
- Computes a magnitude-free RSI from these counts.
- Applies a streak-based multiplier to exaggerate trend strength during consecutive green/red runs.
- Optionally smoothed with WMA to create a clean signal line.
- This makes CCR ideal for detecting true directional bias without being faked out by volatile price spikes.
🔹 Divergence Logic
- When Candle RSI and Standard RSI disagree strongly across defined thresholds, background fills highlight early signs of momentum decay or hidden accumulation/distribution.
🔹 Glow Logic
- Glow zones are controlled by a master toggle and drawn with dynamic transparency:
- Further from 50 = stronger conviction = darker glow.
- Shows up in subpane and/or main chart depending on user preference.
📷 Suggested Use Case / Visual Setup
- Use in conjunction with your primary price action system.
- Watch for divergences between the Candle Count RSI and Standard RSI for early trend reversals.
- Use glow bias zones on the main chart to get subconscious directional cues during fast scalping.
- Histogram helps you confirm when both RSI variants agree—useful during strong trending conditions.
🛠️ Tip for Traders
- This tool isn’t trying to “predict” price. It’s designed to visualize hidden market psychology—when buyers are showing up with consistent pressure, or when momentum has a disconnect between conviction and magnitude. Use this to filter entries, spot weak rallies, or sense when a trend is about to break down.
⚠️ WARNING
- Not for use with Heikin Ashi, Renko, etc.).
🧠 Summary
Candle Count RSI is not just another mashup—it's a precision-built, dual-perspective oscillator that captures directional conviction using real candle behavior. Whether you're scalping intraday or swing trading momentum, this script helps clarify trend integrity and exposes hidden weaknesses with elegance and clarity.
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🛠️ Built by: Sherlock_MacGyver
Feel free to share feedback or reach out if you'd like to collaborate on custom features.
