DEMA MACD BUY signal confirmationDEMA MACD – Trend Continuation Signals
Okay I made this script and wrote this description using AI. I was inspired by the HAP MACD indicator so I made signal confirmation indicator based on that.
This indicator is a momentum-based signal tool built around a DEMA MACD model.
It is designed to help identify potential continuation entries within an existing trend.
Important notes
This indicator works best in clear uptrend conditions.
It is not suitable for consolidation or downtrend markets.
Higher timeframes (Daily / Weekly) generally provide more reliable signals than lower timeframes.
Signals
BUY
Indicates a potential entry in the direction of the current trend.
SELL
Indicates an exit from the previous BUY.
This is not a short or sell-to-open signal.
Usage
Use this tool as a confirmation, not as a standalone decision maker.
Always consider overall market context and basic price structure.
Risk management is essential.
This indicator is shared for educational purposes and reflects one possible approach to trend continuation trading.
M-oscillator
HOHO Oscillator Squeeze With Goldilocks Pivot FractalsDESCRIPTION:
HOHO Oscillator Squeeze With Goldilocks Pivot Fractals combines three powerful technical analysis methodologies into one comprehensive momentum indicator designed for identifying high-probability trading opportunities.
Core Components:
1. HOHO (Hump Oscillator)
Multi-timeframe momentum analysis using dual oscillators (fast and slow) to identify market momentum shifts. The histogram colors change based on momentum direction and strength, providing clear visual cues for trend changes.
2. Squeeze Detection
Bollinger Bands and Keltner Channel compression analysis identifies periods of low volatility (squeeze conditions) that often precede significant price moves. Yellow dots on the zero line indicate active squeeze conditions.
3. Goldilocks Pivot Fractals
Williams Fractals-based reversal detection identifies significant swing highs and lows. BUY and SELL signals are dynamically positioned to "hug" the histogram, providing clear entry and exit signals at major turning points.
Key Features:
- Dynamic Signal Positioning: Arrows and text automatically adjust to histogram height for optimal visibility
- Customizable Visual Elements: Full control over colors for arrows, text, squeeze dots, and histogram
- Multiple Alert Options: Configurable alerts for fractals, squeeze events, and momentum shifts
- Adjustable Sensitivity: Fractal periods can be tuned for different trading styles (lower = more signals, higher = fewer signals)
- Paint Bars Option: Optional bar coloring based on fast or slow oscillator momentum
- Non-Repainting: All signals are based on confirmed price action
- Independent Spacing Controls: Separate BUY and SELL text spacing for perfect visual balance
How to Use:
Entry Signals:
- BUY arrows appear below histogram at swing lows (bullish fractals)
- SELL arrows appear above histogram at swing highs (bearish fractals)
- Best entries occur when squeeze releases coincide with fractal signals
Momentum Confirmation:
- Green histogram = bullish momentum
- Red histogram = bearish momentum
- Lighter shades indicate weakening momentum
- Darker shades indicate strengthening momentum
Squeeze Conditions:
-Yellow dots = Volatility compression (squeeze active)
- Gray dots = Normal volatility (no squeeze)
- Watch for squeeze release followed by directional move
HOHO Settings:
- Adjustable MA lengths and types (EMA/SMA)
- Customizable smoothing parameters
Goldilocks Fractals:
- Fractal Periods: Sensitivity control (default: 2)
- Arrow Spacing: Distance from histogram (default: 2.0)
- BUY Text Spacing: Distance from BUY arrow (default: 1.7)
- SELL Text Spacing: Distance from SELL arrow (default: 0.8)
- Toggle arrows and text independently
Visual Customization:
- Arrow colors (bullish/bearish)
- Text colors (BUY/SELL)
- Squeeze dot colors (ON/OFF)
- Dot size adjustment
Alerts:
- Bullish/Bearish fractal detection
- Squeeze start/release
- Momentum shift crossovers
Best Practices:
- Trend Alignment: Use on higher timeframes (15m+) for more reliable signals
- Confluence: Combine fractal signals with momentum direction for higher probability trades
- Risk Management: Place stops beyond the fractal high/low that triggered the signal
- Squeeze Strategy: Wait for squeeze release before taking directional positions
- Filter Signals: Increase fractal periods (10-20) to focus only on major turning points
Recommended Timeframes:
- Scalping: 5m-15m (fractal periods 2-5)
- Day Trading: 15m-1H (fractal periods 5-10)
- Swing Trading: 4H-Daily (fractal periods 10-20)
Important Notes:
This indicator is provided for educational and informational purposes only. It is not financial advice. Past performance does not guarantee future results. Always perform your own analysis and use proper risk management. Trading involves substantial risk of loss.
Old Glory Exhaustion Detector / In Chart Oscillator SignalsThis custom oscillator-based indicator detects potential trend exhaustion and reversal points through overextension thresholds. It highlights overextended candle bodies in gold and plots diamonds for buy/sell signals (red/blue), regular divergences (yellow), and hidden divergences (silver). Customize lengths, thresholds, and all colors via inputs for flexible analysis across timeframes.
Vix FIX dotsDescription
Vix FIX Dots is a momentum and volatility-based trend-following tool. It combines the classic Williams VIX Fix logic with Stochastic and RSI filters to identify high-probability reversal points and trend exhaustion.
Unlike the standard VIX Fix which is often displayed in a separate pane, this script overlays signals directly onto your chart as colorful dots to simplify the decision-making process.
How it Works
The script calculates the "Synthetic VIX" (Williams VIX Fix) to find market bottoms and volatility peaks. To reduce noise and false signals, it incorporates price action filters and trend-strength lookbacks.
Signal Guide
The indicator plots four distinct types of dots:
Green Circle (Below Bar): Filtered Long Entry. This represents a standard buy signal where volatility has peaked and price action confirms a move up.
Blue Circle (Below Bar): Aggressive Long Entry. A faster signal for traders looking to catch a move earlier, based on multi-candle lookbacks.
Red Circle (Above Bar): Filtered Exit/Short. Indicates a standard trend exhaustion point.
Orange Circle (Above Bar): Aggressive Exit/Short. A faster signal indicating the trend may be rolling over.
Key Features
Volatility Bands: Uses Bollinger Bands and Percentile calculations on the VIX Fix to identify extreme exhaustion.
Price Action Filter: Signals only trigger if the current close outperforms a user-defined number of previous bars.
Customizable Lookbacks: Fully adjustable settings for Stochastic and RSI filters to match your specific timeframe (M5, H1, D1, etc.).
MACD-V (ATR Normalized)Per Financial Wisdom (YT):
Adjusted MACD = (EMA 12 - EMA 26 / ATR 26) x 100
Objective:
Mathematical definitions work universally across all markets and all timeframes
Improves readability and usability (values resemble RSI/MACD ranges instead of tiny decimals)
Makes threshold-based rules cleaner (e.g., ±50, ±100).
No change to signal quality — purely a scaling transformation.
RSI MTF Table (Threshold Colors + Direction Arrows) [v6]Sometimes I want to know what other timeframes are indicating for the RSI so I borrowed from another indicator and created this script. Since I swing trade, I have the timeframes set higher, but you can adjust them to your needs in the settings.
Each pane is color coded light green below 50, and pink above 50. Then you can define your own thresholds but the defaults are Red above 70, and Dark Green below 30. The colors can be adjusted to your needs.
The top of each pane is its timeframe, then the RSI value for that timeframe. Then I check the current bar against the prior bar to see if the current value is higher (Up Arrow) or lower (Down Arrow) so that you know which way the RSI is moving. The position on your chart can be changed to your needs.
This keeps the momentum in perspective for me. I hope it helps you. Good luck in your trading.
Multi Cycles Slope-Fit System MLMulti Cycles Predictive System : A Slope-Adaptive Ensemble
Executive Summary:
The MCPS-Slope (Multi Cycles Slope-Fit System) represents a paradigm shift from static technical analysis to adaptive, probabilistic market modeling. Unlike traditional indicators that rely on a single algorithm with fixed settings, this system deploys a "Mixture of Experts" (MoE) ensemble comprising 13 distinct cycle and trend algorithms.
Using a Gradient-Based Memory (GBM) learning engine, the system dynamically solves the "Cycle Mode" problem by real-time weighting. It aggressively curve-fits the Slope of component cycles to the Slope of the price action, rewarding algorithms that successfully predict direction while suppressing those that fail.
This is a non-repainting, adaptive oscillator designed to identify market regimes, pinpoint high-probability reversals via OB/OS logic, and visualize the aggregate consensus of advanced signal processing mathematics.
1. The Core Philosophy: Why "Slope" Matters:
In technical analysis, most traders focus on Levels (Price is above X) or Values (RSI is at 70). However, the primary driver of price action is Momentum, which is mathematically defined as the Rate of Change, or the Slope.
This script introduces a novel approach: Slope Fitting.
Instead of asking "Is the cycle high or low?", this system asks: "Is the trajectory (Slope) of this cycle matching the trajectory of the price?"
The Dual-Functionality of the Normalized Oscillator
The final output is a normalized oscillator bounded between -1.0 and +1.0. This structure serves two critical functions simultaneously:
Directional Bias (The Slope):
When the Combined Cycle line is rising (Positive Slope), the aggregate consensus of the 13 algorithms suggests bullish momentum. When falling (Negative Slope), it suggests bearish momentum. The script measures how well these slopes correlate with price action over a rolling lookback window to assign confidence weights.
Overbought / Oversold (OB/OS) Identification:
Because the output is mathematically clipped and normalized:
Approaching +1.0 (Overbought): Indicates that the top-weighted algorithms have reached their theoretical maximum amplitude. This is a statistical extreme, often preceding a mean reversion or trend exhaustion.
Approaching -1.0 (Oversold): Indicates the aggregate cycle has reached maximum bearish extension, signaling a potential accumulation zone.
Zero Line (0.0): The equilibrium point. A cross of the Zero Line is the most traditional signal of a trend shift.
2. The "Mixture of Experts" (MoE) Architecture:
Markets are dynamic. Sometimes they trend (Trend Following works), sometimes they chop (Mean Reversion works), and sometimes they cycle cleanly (Signal Processing works). No single indicator works in all regimes.
This system solves that problem by running 13 Algorithms simultaneously and voting on the outcome.
The 13 "Experts" Inside the Code:
All algorithms have been engineered to be Non-Repainting.
Ehlers Bandpass Filter: Extracts cycle components within a specific frequency bandwidth.
Schaff Trend Cycle: A double-smoothed stochastic of the MACD, excellent for cycle turning points.
Fisher Transform: Normalizes prices into a Gaussian distribution to pinpoint turning points.
Zero-Lag EMA (ZLEMA): Reduces lag to track price changes faster than standard MAs.
Coppock Curve: A momentum indicator originally designed for long-term market bottoms.
Detrended Price Oscillator (DPO): Removes trend to isolate short-term cycles.
MESA Adaptive (Sine Wave): Uses Phase accumulation to detect cycle turns.
Goertzel Algorithm: Uses Digital Signal Processing (DSP) to detect the magnitude of specific frequencies.
Hilbert Transform: Measures the instantaneous position of the cycle.
Autocorrelation: measures the correlation of the current price series with a lagged version of itself.
SSA (Simplified): Singular Spectrum Analysis approximation (Lag-compensated, non-repainting).
Wavelet (Simplified): Decomposes price into approximation and detail coefficients.
EMD (Simplified): Empirical Mode Decomposition approximation using envelope theory.
3. The Adaptive "GBM" Learning Engine
This is the "Machine Learning" component of the script. It does not use pre-trained weights; it learns live on your chart.
How it works:
Fitting Window: On every bar, the system looks back 20 days (configurable).
Slope Correlation: It calculates the correlation between the Slope of each of the 13 algorithms and the Slope of the Price.
Directional Bonus: It checks if the algorithm is pointing in the same direction as the price.
Weight Optimization:
Algorithms that match the price direction and correlation receive a higher "Fit Score."
Algorithms that diverge from price action are penalized.
A "Softmax" style temperature function and memory decay allow the weights to shift smoothly but aggressively.
The Result: If the market enters a clean sine-wave cycle, the Ehlers and Goertzel weights will spike. If the market explodes into a linear trend, ZLEMA and Schaff will take over, suppressing the cycle indicators that would otherwise call for a premature top.
4. How to Read the Interface:
The visual interface is designed for maximum information density without clutter.
The Dashboard (Bottom Left - GBM Stats)
Combined Fit: A percentage score (0-100%). High values (>70%) mean the system is "Locked In" and tracking price accurately. Low values suggest market chaos/noise.
Entropy: A measure of disorder. High entropy means the algorithms disagree (Neutral/Chop). Low entropy means the algorithms are unanimous (Strong Trend).
Top 1 / Top 3 Weight: Shows how concentrated the decision is. If Top 1 Weight is 50%, one algorithm is dominating the decision.
The Matrix (Bottom Right - Weight Table)
This table lifts the hood on the engine.
Fit Score: How well this specific algo is performing right now.
Corr/Dir: Raw correlation and Direction Match stats.
Weight: The actual percentage influence this algorithm has on the final line.
Cycle: The current value of that specific algorithm.
Regime: Identifies if the consensus is Bullish, Bearish, or Neutral.
The Chart Overlay
The Line: The Gradient-Colored line is the Weighted Ensemble Prediction.
Green: Bullish Slope.
Red: Bearish Slope.
Triangles: Zero-Cross signals (Bullish/Bearish).
"STRONG" Labels: Appears when the cycle sustains a value above +0.5 or below -0.5, indicating strong momentum.
Background Color: Changes subtly to reflect the aggregate Regime (Strong Up, Bullish, Neutral, Bearish, Strong Down).
5. Trading Strategies:
A. The Slope Reversal (OB/OS Fade)
Concept: Catching tops and bottoms using the -1/+1 normalization.
Signal: Wait for the Combined Cycle to reach extreme values (>0.8 or <-0.8).
Trigger: The entry is taken not when it hits the level, but when the Slope flips.
Short: Cycle hits +0.9, color turns from Green to Red (Slope becomes negative).
Long: Cycle hits -0.9, color turns from Red to Green (Slope becomes positive).
B. The Zero-Line Trend Join
Concept: Joining an established trend after a correction.
Signal: Price is trending, but the Cycle pulls back to the Zero line.
Trigger: A "Triangle" signal appears as the cycle crosses Zero in the direction of the higher timeframe trend.
C. Divergence Analysis
Concept: Using the "Fit Score" to identify weak moves.
Signal: Price makes a Higher High, but the Combined Cycle makes a Lower High.
Confirmation: Check the GBM Stats table. If "Combined Fit" is dropping while price is rising, the trend is decoupling from the cycle logic. This is a high-probability reversal warning.
6. Technical Configuration:
Fitting Window (Default: 20): The number of bars the ML engine looks back to judge algorithm performance. Lower (10-15) for scalping/quick adaptation. Higher (30-50) for swing trading and stability.
GBM Learning Rate (Default: 0.25): Controls how fast weights change.
High (>0.3): The system reacts instantly to new behaviors but may be "jumpy."
Low (<0.15): The system is very smooth but may lag in regime changes.
Max Single Weight (Default: 0.55): Prevents one single algorithm from completely hijacking the system, ensuring an ensemble effect remains.
Slope Lookback: The period over which the slope (velocity) is calculated.
7. Disclaimer & Notes:
Repainting: This indicator utilizes closed bar data for calculations and employs non-repainting approximations of SSA, EMD, and Wavelets. It does not repaint historical signals.
Calculations: The "ML" label refers to the adaptive weighting algorithm (Gradient-based optimization), not a neural network black box.
Risk: No indicator guarantees future performance. The "Fit Score" is a backward-looking metric of recent performance; market regimes can shift instantly. Always use proper risk management.
Author's Note
The MCPS-Slope was built to solve the frustration of "indicator shopping." Instead of switching between an RSI, a MACD, and a Stochastic depending on the day, this system mathematically determines which one is working best right now and presents you with a single, synthesized data stream.
If you find this tool useful, please leave a Boost and a Comment below!
Keltner-Aroon-EFI FlowKeltner-Aroon-EFI Flow - |K| |A| |E| |F|
KAE Flow is a quantitative trend-aggregation engine designed to determine the dominant market bias by fusing three distinct market dimensions: Volatility, Trend Strength, and Volume.
This script does not rely on a single metric. Instead, it creates a composite "Flow" score derived from the Daily timeframe to act as a high-level bias filter for intraday or swing trading.
1. The Quantitative Logic (The Engine)
The core of this indicator is the KAE Engine, which polls data from the Daily timeframe (by default) to ensure you are always trading in alignment with the macro trend. It aggregates three logical components:
K (Keltner Channels): Measures Volatility Breakouts.
Logic: Returns bullish if price closes above the Upper Channel, bearish if below the Lower Channel. This captures the expansion phase of price action.
A (Aroon): Measures Trend Age & Strength.
Logic: Returns bullish only if the Aroon Up is > 70 and dominating the Aroon Down. This ensures the trend is not just present, but mathematically strong.
E (Elder’s Force Index): Measures Volume-Weighted Momentum.
Logic: Uses volume pressure to confirm price moves. Positive smoothed force indicates bullish accumulation.
2. Signal Processing (ALMA)
Raw data is noisy. The KAE Flow takes the aggregated raw score from the components above and runs it through an ALMA (Arnaud Legoux Moving Average).
Why ALMA? It offers the best balance between smoothness and responsiveness, removing "false flips" in the trend bias while reacting quickly to genuine reversals.
The Color (The Bias):
Deep Blue: Strong Bullish Flow (KAE Score > 0.1). Look for Long entries .
White: Strong Bearish Flow (KAE Score < -0.1). Look for Short entries.
Gray: Neutral/Transition. Volatility is contracting or the trend is conflicting.
5. Settings & Configuration
Keltner/Aroon/EFI Lengths: Fully customizable to fit different asset classes (Crypto vs. Forex).
Active Smoothing: Toggle ALMA on/off.
Active Components: You can toggle specific engines (K, A, or E) on or off. Default uses Keltner + Aroon for a pure Price/Time analysis.
Risk Warning: This indicator pulls higher-timeframe data (Daily) to color lower-timeframes. While this provides a powerful macro view, be aware that closed candle data is used to prevent repainting issues in real-time.
D_Quant --- Trade With Discipline
Precision Trend Signal V5Strategy Logic OverviewThis indicator is a "Triple-Confirmation" trend-following system. It combines volume-weighted smoothing, immediate price action, and momentum filtering.1. Core ComponentsEMA 1 (The Trigger): Since the period is set to 1, this represents the raw price action. It acts as the fastest possible trigger to capture entries at the exact moment a trend shifts.SALMA (The Baseline): This is a double-smoothed moving average. It provides a stabilized support/resistance line that filters out market noise better than a standard SMA.Tillson T3 (The Trend Filter): Known for its low lag and extreme smoothness. We use this as a "Guardrail." We only take BUY signals when price is above the T3 and SELL signals when price is below it.RSI (The Momentum Filter): Ensures that we only enter a trade when there is sufficient strength ($> 50$ for Long, $< 50$ for Short).2. Signal Rules🚀 BUY SignalA green BUY label appears when:Crossover: EMA 1 crosses above the SALMA line.Trend: The current price is trading above the Tillson T3 line.Momentum: RSI is greater than 50.🔻 SELL SignalA red SELL label appears when:Crossunder: EMA 1 crosses below the SALMA line.Trend: The current price is trading below the Tillson T3 line.Momentum: RSI is less than 50.3. Execution & ManagementTake Profit (TP): Based on your preference, the suggested target is 2%.Alerts: The script includes alertcondition functions. You can set up TradingView alerts to send Webhooks to your quant infrastructure or bot, solving the "manual execution" problem you mentioned.
EDUVEST Lorentzian ClassificationEDUVEST Lorentzian Classification - Machine Learning Signal Detection
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█ ORIGINALITY
This indicator enhances the original Lorentzian Classification concept by jdehorty with EduVest's visual modifications and alert system integration. The core innovation is using Lorentzian distance instead of Euclidean distance for k-NN classification, providing more robust pattern recognition in financial markets.
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█ WHAT IT DOES
- Generates BUY/SELL signals using machine learning classification
- Displays kernel regression estimate for trend visualization
- Shows prediction values on each bar
- Provides trade statistics (Win Rate, W/L Ratio)
- Includes multiple filter options (Volatility, Regime, ADX, EMA, SMA)
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█ HOW IT WORKS
【Lorentzian Distance Calculation】
Unlike Euclidean distance, Lorentzian distance uses logarithmic transformation:
d = Σ log(1 + |xi - yi|)
This provides:
- Better handling of outliers
- More stable distance measurements
- Reduced sensitivity to extreme values
【Feature Engineering】
The classifier uses up to 5 configurable features:
- RSI (Relative Strength Index)
- WT (WaveTrend)
- CCI (Commodity Channel Index)
- ADX (Average Directional Index)
Each feature is normalized using the n_rsi, n_wt, n_cci, or n_adx functions.
【k-Nearest Neighbors Classification】
1. Calculate Lorentzian distance between current bar and historical bars
2. Find k nearest neighbors (default: 8)
3. Sum predictions from neighbors
4. Generate signal based on prediction sum (>0 = Long, <0 = Short)
【Kernel Regression】
Uses Rational Quadratic kernel for smooth trend estimation:
- Lookback Window: 8
- Relative Weighting: 8
- Regression Level: 25
【Filters】
- Volatility Filter: Filters signals during extreme volatility
- Regime Filter: Identifies market regime using threshold
- ADX Filter: Confirms trend strength
- EMA/SMA Filter: Trend direction confirmation
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█ HOW TO USE
【Recommended Settings】
- Timeframe: 15M, 1H, 4H, Daily
- Neighbors Count: 8 (default)
- Feature Count: 5 for comprehensive analysis
【Signal Interpretation】
- Green BUY label: Long entry signal
- Red SELL label: Short entry signal
- Bar colors: Green (bullish) / Red (bearish) prediction strength
【Trade Statistics Panel】
- Winrate: Historical win percentage
- Trades: Total (Wins|Losses)
- WL Ratio: Win/Loss ratio
- Early Signal Flips: Premature signal changes
【Filter Recommendations】
- Enable Volatility Filter for ranging markets
- Enable Regime Filter for trend confirmation
- Use EMA Filter (200) for higher timeframes
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█ CREDITS
Original Lorentzian Classification concept and MLExtensions library by jdehorty.
Enhanced with visual modifications and alert integration by EduVest.
License: Mozilla Public License 2.0
EMA Angle Average by Eric ValerianoThis indicator determines market direction by calculating the angle of an exponential moving average and smoothing that angle over several bars. By averaging the EMA’s slope, it reduces noise and clearly classifies the market as bullish, bearish, or neutral based on trend strength rather than short term price fluctuations.
It is best used as a trend filter to confirm direction, avoid choppy conditions, and add context to entries based on other signals such as pullbacks, breakouts, or momentum setups.
[RoyalNeuron] RSI-SMA-PIVOT [WidowMaker v2.0]Hey guys 👋
Spent a bit of time tinkering with the original WidowMaker and figured the next logical step was adding price pivot, and honestly, it’s made a decent difference when you use it right.
Thinking out of the box here, and it looks good.
The core is still the same clean, zero-lag smoothed RSI (pick SMA or EMA) with green/red momentum histogram that helps you see real strength or weakness without all the usual rubbish.
What’s new in v2.0:
- Price pivots (high/low) now show up, but only when RSI is in the right zone
- Pivot High only appears when RSI > 65 (overbought area)
- Pivot Low only when RSI < 35 (oversold area)
- This filters out a ton of fake pivots and keeps things useful
Quick way I’ve been using it:
Look for overbought/oversold areas first (faint red/green shading helps spot them fast).
Then wait for the pivot to print in that zone.
If you time it correctly (with price action or structure), the combo works really nicely – especially on 1H and above.
It’s still 100% free, open-source, colours fully customisable, and I’m using it myself every day.
Would love your honest take: does the pivot filter help you? Any pairs/timeframes it shines on? Anything you’d change?
Cheers for checking it out – more updates coming soon!
© RoyalNeuron 2025 | Open Source (CC BY-NC-SA 4.0)
Trade Decision MatrixTrade Decision Matrix (TDM)
Trade Decision Matrix (TDM) is a professional-grade, multi-phase market intelligence indicator designed to assist traders in understanding market structure, regime behavior, capital confidence, and execution readiness using a systematic, probabilistic framework.
This indicator does not generate trade signals. Instead, it provides a structured decision matrix similar to institutional trading desks, combining regime analytics, entropy confidence, Bayesian reliability, capital allocation logic, and scenario interpretation.
🔹 Core Architecture
TDM is built using a nine-phase institutional decision pipeline:
Phase 1 — Market Context
Spot–future basis, volatility normalization, and structural slope detection.
Phase 2 — Regime Engine
Probabilistic classification of Trend, Breakout, Range, or Mean Reversion environments.
Phase 3 — Orthogonal Model Cores
Independent statistical, trend, breakout, and mean-reversion cores.
Phase 4 — Bayesian Reliability Engine
Adaptive reliability scoring for each core using Bayesian reinforcement.
Phase 5 — Capital Engine
Capital confidence and capital mode based on opportunity quality, regime clarity, entropy confidence, and risk filters.
Phase 6 — Decision Matrix
Bias, participation level, and trade quality grading.
Phase 7 — Scenario Engine
Contextual scenario interpretation such as Trend Expansion, Breakout Failure, Range Compression, etc.
Phase 8 — Execution Gate
Execution readiness filter based on capital and model alignment.
Phase 9 — Reversal Engine
Probabilistic reversal risk estimation using multi-factor logic.
🔹 Regime Entropy Confidence
TDM uses Shannon entropy to measure regime uncertainty and converts it into a confidence score.
Lower entropy = higher regime confidence.
Higher entropy = unstable or transitional market state.
This prevents over-confidence in noisy conditions.
🔹 Institutional Commentary Engine
A professional commentary layer interprets all internal engines and outputs institutional-style guidance such as:
• Institutional Alignment
• Capital Protection Mode
• Regime Uncertainty
• Momentum Continuation
• Structural Breakout
• Volatility Coiling
• Reversal Risk Elevated
This commentary is designed for situational awareness, not signal generation.
🔹 Dashboard
The dark-theme dashboard provides a compact institutional decision panel:
• Regime
• Entropy Confidence
• Scenario
• Bias
• Strength
• Capital Confidence
• Capital Mode
• Trade Quality
• Execution State
• Commentary
• Reversal Risk
All values are color-coded with heat shading for instant visual interpretation.
🔹 How To Use
TDM is best used as a decision support layer alongside your own trading strategy.
Typical workflow:
Identify regime and entropy confidence.
Observe capital confidence and capital mode.
Check scenario and bias alignment.
Confirm execution readiness.
Monitor reversal risk before entering or holding positions.
This tool is ideal for:
• Intraday traders
• Swing traders
• Options traders
• Index traders
• Systematic discretionary traders
🔹 Important Notes
• This indicator does NOT produce buy/sell signals.
• It is a decision intelligence framework.
• It should not be used as a standalone trading system.
• Always apply personal risk management.
🔹 Disclaimer
This indicator is provided for educational and informational purposes only.It does not constitute financial advice or investment recommendations.Trading involves risk. Users are responsible for their own trading decisions.
Guac's MAs, BBs, and ADX (SMA/EMA/BB + ADX/DI + Daily ATR)As someone who browses through numerous TradingView scripts, I find many ideas/functions that I find useful. However, sometimes I find certain features that I don't find useful or that could be added to make something more useful. Because of this I designed this script to collectively encompass functionality of the items/indicators I find useful when looking at an index/equity chart.
This script was desgined/inspired to keep the chart clean while providing signal context for trend, volatility, price action, and regime conditions.
Summary of what this script does:
Plots a compact, customizable set of SMAs + EMAs for structure and trend layering.
Adds Bollinger Bands with expansion/contraction coloring to visualize volatility state.
Optionally overlays ADX/DI regime context, including:
• an ADX-based “regime fill” (temperature-style colors) on the BB fill
• optional DI+ / DI- cross markers for directional shift awareness
• expanded ADX regime labels (Dead Chop → Very Strong/Extended)
• optional “ADX momentum” (smoothed ADX slope) in the status label to show regime acceleration/decay
Provides a small corner “Regime Status Label” that summarizes ADX regime (with numeric ADX) when enabled.
Optionally appends Daily ATR (value + momentum) to the same label for range/volatility context that is consistent across intraday timeframes.
I always find it frustrating when I am testing or playing with someones indicator and they don't have tooltips implemented so that I can understand the purpose of their parameters and the inputs. I have specifically tried to implement tooltip info bubbles next to every parameter input to give a short explanation of the parameter and it's purpose
TGIF RSI MIDWhen RSI crosses 50, shows a vertical line green for bullish and red for bearish will appear..
EDUVEST QQE Grade System - S/A/B/C Signal ClassificationEDUVEST QQE Grade System - S/A/B/C Signal Classification
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█ ORIGINALITY
This indicator introduces a unique grading system (S/A/B/C) for QQE signals, combining traditional QQE analysis with SMC (Smart Money Concepts) price zones and trading session filters. Unlike standard QQE indicators that show all signals equally, this version classifies signals by quality to help traders focus on the highest probability setups.
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█ WHAT IT DOES
- Generates BUY/SELL signals with S/A/B/C grade classification
- Automatically detects asset type and applies optimized QQE factors
- Integrates SMC price zones (support/resistance) for grade enhancement
- Filters signals by trading session time
- Displays real-time session and market status
Grade Hierarchy:
- S (Gold/Orange): Signal near SMC zone + active trading hours - Highest quality
- A (Green/Red): Score 70+ during trading hours - High quality
- B (Darker): Score 50-69 during trading hours - Medium quality
- C (Gray, small): Outside trading hours or weak signal - Low quality
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█ HOW IT WORKS
【QQE Core Calculation】
The QQE (Quantitative Qualitative Estimation) is calculated as:
1. RSI with configurable period (default: 14)
2. EMA smoothing of RSI (Smoothing Factor: 5)
3. Dynamic bands using Wilder's smoothing: RSI ± (ATR of RSI × QQE Factor)
QQE Factor is auto-adjusted per asset:
- USD/JPY: 4.238
- EUR/USD: 3.8
- Gold (XAU/USD): 8.0
- NASDAQ/US100: 9.0
【Signal Generation】
- BUY: QQE line crosses above its trailing stop (QQExlong == 1)
- SELL: QQE line crosses below its trailing stop (QQExshort == 1)
【Internal Scoring System】
Score components (0-100):
- Signal Base: +25 points when signal occurs
- QQE Strength: +10 to +20 based on RSI distance from 50
- Volatility: +15 (optimal ATR ratio 1.1-2.0), -10 (low volatility)
- Volume Confirmation: +10 (high volume), -5 (low volume)
- Session Bonus: +5 during London/NY sessions
- Base: +20 points
【Grade Assignment】
- Grade S: Signal near user-defined SMC price zone (within tolerance %) AND during trading hours
- Grade A: Internal score >= 70 AND during trading hours
- Grade B: Internal score >= 50 AND during trading hours
- Grade C: Outside trading hours OR score < 50
【SMC Price Zone Integration】
Users can set support/resistance levels for each asset. When price is within the tolerance percentage of these levels, signals are upgraded to S-grade, indicating confluence with institutional price levels.
【Trading Session Filter】
Configurable active trading hours (JST timezone):
- Default: 15:00 - 01:00 JST (London + NY overlap)
- Signals outside this window receive C-grade
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█ HOW TO USE
【Recommended Settings】
- Timeframe: 15M, 1H, 4H
- Best on: USD/JPY, EUR/USD, Gold, NASDAQ
- Focus on: S and A grade signals
【Trading Strategy】
- S-Grade (Gold/Orange): Highest conviction - consider larger position
- A-Grade (Green/Red): Strong signal - standard position
- B-Grade: Valid but use additional confirmation
- C-Grade: Avoid or use minimal size
【Setting Up SMC Zones】
1. Identify key support/resistance on higher timeframe
2. Input prices in SMC Price Settings
3. Adjust tolerance % (default: 0.15%)
4. S-grade appears when signal occurs near these levels
【Info Panel】
Top-right panel shows:
- Asset name and detection mode (Auto/Manual)
- Current session (Tokyo/London/NY)
- Trading hours status
- SMC zone proximity
【Alert Setup】
1. Enable alerts in settings
2. Create alert with "Any alert() function call"
3. Alerts include grade, price, and session info
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█ SETTINGS
Basic Settings:
- Enable Alerts: Turn on/off notifications
- Time Filter: Activate trading hour filter
- Start/End Hour: Define active trading window (JST)
QQE Settings:
- RSI Period: RSI calculation period
- RSI Smoothing: EMA smoothing factor
- Auto QQE Factor: Auto-detect optimal factor per asset
- Manual QQE Factor: Override when auto is disabled
SMC Price Settings:
- Support/Resistance levels for each asset
- Tolerance %: How close to SMC line for S-grade
Display Settings:
- Grade Only: Hide QQE lines, show only signals
- Show SMC Lines: Display support/resistance on chart
- Show Debug: Display asset detection info
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█ CREDITS
QQE concept originally developed by John Ehlers.
SMC (Smart Money Concepts) integration and grading system by EduVest.
License: Mozilla Public License 2.0
EDUVEST QQE Signal v3.0 - Multi-Timeframe Scoring SystemEDUVEST QQE Signal v3.0 - Multi-Timeframe Scoring System
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█ ORIGINALITY
This indicator combines QQE (Quantitative Qualitative Estimation) with HMA (Hull Moving Average) and introduces a unique AI-based scoring system that rates signal quality from 0-100. Unlike traditional QQE indicators that show simple buy/sell signals, this version categorizes signals into four strength levels: BIG CHANCE, SUPER, POWER, and STRONG.
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█ WHAT IT DOES
- Generates scored BUY/SELL signals with quality ratings (60-100 points)
- Categorizes signals into 4 strength levels for easy decision making
- Supports Multi-Timeframe (MTF) analysis
- Auto-detects asset type and applies optimized QQE factors
- Provides customizable alerts based on score thresholds
Signal Hierarchy:
- 💰 BIG CHANCE (90-100): Highest probability setups
- ⚡ SUPER (80-89): Very strong signals
- 🚀 POWER (70-79): Strong signals with HMA confluence
- 💪 STRONG (60-69): Standard quality signals
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█ HOW IT WORKS
【QQE Calculation】
QQE is based on a smoothed RSI with dynamic bands:
1. Calculate RSI with specified period (default: 14)
2. Apply EMA smoothing to RSI (Smoothing Factor, default: 5)
3. Calculate ATR of the smoothed RSI
4. Create dynamic bands: RSI ± (ATR × QQE Factor)
The QQE Factor is automatically adjusted per asset:
- Forex (USDJPY, EURUSD): 3.8 - 4.238
- Gold (XAUUSD): 8.0
- Crypto (BTC): 12.0, (ETH): 10.0
- Indices (NASDAQ): 4.238
【HMA Calculation】
Hull Moving Average for trend confirmation:
HMA = WMA(2 × WMA(price, n/2) - WMA(price, n), √n)
【Signal Generation】
- BUY: QQE crosses above its band (QQExlong == 1)
- SELL: QQE crosses below its band (QQExshort == 1)
【AI Scoring System】
The score is calculated from multiple factors:
Signal Base (0-35 points):
- QQE + HMA confluence: +35
- QQE or HMA alone: +25
QQE Strength (10-25 points):
- RSI distance from 50 (momentum strength)
- >30 distance: +25, >20: +20, >10: +15, else: +10
Volatility Score (-10 to +15 points):
- ATR ratio 1.1-2.0: +15 (optimal volatility)
- ATR ratio <0.8: -10 (low volatility warning)
Volume Confirmation (-5 to +15 points):
- Volume > 120% of average: +15
- Volume < 80% of average: -5
Base Points: +15
Final Score = Clamped(0, 100, sum of all factors)
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█ HOW TO USE
【Recommended Settings】
- Timeframe: 5M, 15M, 1H, 4H
- Best on: Forex, Gold, NASDAQ, BTC/ETH
- Minimum Score: 60 (adjustable)
【Reading Signals】
- BIG CHANCE (Gold label, 90+): Highest conviction - consider larger position
- SUPER (Yellow label, 80-89): Very strong - standard position
- POWER (Cyan/Magenta label, 70-79): Strong with trend confirmation
- STRONG (Green/Red label, 60-69): Valid but use additional confirmation
【MTF Feature】
Enable MTF to analyze signals from a higher timeframe while viewing lower timeframe charts. The indicator auto-selects 5-minute as the analysis timeframe, or you can set it manually.
【Alert Setup】
1. Enable alerts in settings
2. Set minimum score threshold (default: 60)
3. Create alert with "Any alert() function call"
【Important Notes】
- Signals are confirmed at bar close (no repainting)
- Higher scores = higher probability, not guaranteed profits
- Always use proper risk management
- Consider market context and support/resistance levels
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█ SETTINGS
⏱️ MTF Settings
- MTF Use: Enable multi-timeframe analysis
- Manual Timeframe: Override auto-detection
- Show Panel: Display info panel (default: OFF)
🎨 Design
- Neon Colors: Vibrant color scheme
- Show HMA Line: Display HMA on chart
- Minimum Score: Filter weak signals
- Label Transparency: Adjust label opacity
- Large Labels: Mobile-friendly sizing
🔧 QQE Settings
- RSI Period: RSI calculation period
- Smoothing: EMA smoothing factor
- AI Score: Enable scoring system
🔔 Alerts
- Enable Alerts: Turn on/off notifications
- Minimum Score: Alert threshold
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█ CREDITS
QQE concept originally developed by John Ehlers.
HMA (Hull Moving Average) by Alan Hull.
Enhanced with scoring system and MTF support by EduVest.
License: Mozilla Public License 2.0
S&P 500 Momentum Coiling Tracker [20/200 MA]This indicator measures the absolute point distance between the 20-period SMA and the 200-period SMA, specifically optimized for the S&P 500 (ES/MES) index.
In the style of institutional trend following, it identifies the "Narrow State"—a period of low volatility where a major breakout is imminent.
How to read the Histogram:
🟢 GREEN (< 8 pts): Ultra-Narrow/Coiled State. Stored energy is high. Watch for an explosive breakout.
🟡 YELLOW (8-15 pts): Narrow/Transition. The averages are converging or just starting to fan out.
⚪ GRAY (15-30 pts): Neutral trending zone.
🔴 RED (> 30 pts): Extended State. Price is stretched far from the long-term mean; avoid chasing the move.
Simple RSI Strategy - Rule Based Higher Timeframe Trading
HOW IT WORKS
With the default settings, the strategy buys when RSI reaches 30 and closes when RSI reaches 40 .
That’s it.
A simple, rule-based mean reversion strategy designed for higher timeframes , where market noise is lower and trading becomes easier to manage.
Core logic:
Long when RSI moves into oversold territory
Exit when RSI mean-reverts upward
Optional short trades from overbought levels
One position at a time (no pyramiding)
No filters.
No discretion.
Just clear, testable rules.
MARKETS & TIMEFRAMES
This strategy is intended for:
Indices (Nasdaq, S&P 500, DAX, etc.)
Liquid futures and CFDs
Higher timeframes: 2H, 4H and Daily
The published example is Nasdaq (NDX) on the 2-hour timeframe .
Higher timeframes are strongly recommended.
HOW TO USE IT
Apply the strategy on a higher timeframe
Adjust RSI levels per market if needed
Use TradingView alerts to avoid constant screen-watching
Focus on execution, risk control, and consistency
This strategy is meant to be a building block , not a complete trading business on its own.
For long-term consistency, it works best when combined with other uncorrelated, rule-based systems.
IMPORTANT
This is not financial advice
All results are historical and not indicative of future performance
Always forward-test and apply proper risk management
For additional notes, setups and related systems, visit my TradingView profile page .
DERYA Dynamic Efficiency Regime Yield AnalyzerDERYA: Dynamic Efficiency Regime Yield Analyzer
Mathematical Concept and Problem Statement
Most traditional trend and momentum indicators (e.g., RSI, ADX, MACD) focus on price displacement across a series of bars. However, they are mathematically "blind" to the internal structure of each individual bar. The DERYA indicator solves the "Velocity Trap" and "Lagging Confirmation" issues by shifting the measurement space from price displacement to intrabar efficiency. It quantifies the ratio between net price progress and the total effort (range) expended within the bar.
Logic and Components
The script does not reuse any existing open-source library logic; the methodology is derived from original research. However, it utilizes standard built-in Pine Script functions for structural stabilization:
Efficiency Metaphor: The core logic calculates a proxy for microstructural health using the formula |Close - Close | / (High - Low).
Use of Exponential Moving Average (EMA): A standard ta.ema is applied to the raw efficiency data. Reason for use: Raw microstructural data is inherently noisy due to high-frequency fluctuations. The EMA is used here specifically as a low-pass filter to extract the underlying structural trend of efficiency without the overhead of more complex digital filters.
Use of Min-Max Normalization: The script utilizes ta.highest and ta.lowest over a lookback period. Reason for use: To convert an absolute efficiency metric into a bounded state variable (0-100). This ensures the indicator is adaptive to different volatility regimes, preventing the signal from becoming obsolete as market conditions change.
Interpretation
Expansion Regime (>60): Indicates a high-efficiency environment where price movement is achieved with minimal internal friction.
Collapse Regime (<40): Indicates a structural deterioration where price effort (range) significantly outweighs price progress (displacement), often signaling an imminent trend break.
Visual Integration: The script includes a barcolor feature that highlights bars where DERYA falls below 30, visually flagging points of extreme structural inefficiency directly on the price chart.
Compliance Note
This script is an original implementation of the DERYA methodology. It does not contain "copy-pasted" code from other public indicators. Standard functions (ta.ema, ta.highest, ta.lowest) are used only for their intended mathematical smoothing and normalization purposes as described above.
Scientific Documentation & Research Paper
This implementation is based on the following published research:
Title: DERYA: Dynamic Efficiency Regime Yield Analyzer - A New Microstructural State Variable for Financial Markets
Published on: Zenodo (CERN)
zenodo.org
DOI: 10.5281/zenodo.18181902
Author: Bülent Duman (Independent Researcher)
Copyright: (C) 2026 Bülent Duman
Demand Index - Metastock VersionThis script implements the Demand Index, a complex technical indicator originally developed by James Sibbet. This specific version is adapted from the classic MetaStock formula to ensure accuracy and consistency with the original methodology.
The Demand Index combines price and volume data to relate price pressure to volume intensity. It is often used as a leading indicator to predict price trends by assessing the balance between buying pressure (Demand) and selling pressure (Supply).
How It Works
The calculation involves several steps to normalize volume and price changes:
Weighted Close: It calculates a weighted close price giving extra weight to the closing price (High + Low + 2*Close) / 4.
Volatility & Volume Averages: It computes the Average True Range (ATR) proxy and an Exponential Moving Average (EMA) of the volume to establish a baseline.
Buying & Selling Pressure: The core logic compares the current weighted close to the previous one.
If prices rise, the volume is assigned to Buying Pressure.
If prices fall, the volume is assigned to Selling Pressure.
A decay factor (Constant) is applied based on volatility to smooth the reaction to extreme price moves.
The Index: The final oscillator is derived from the ratio of smoothed Buying Pressure to Selling Pressure.
How to Use It
The Demand Index oscillates around a zero line. Traders typically look for the following signals:
Divergence: This is the most common use.
Bullish Divergence: Prices are making new lows, but the Demand Index is making higher lows. This suggests selling pressure is waning and a reversal may be imminent.
Bearish Divergence: Prices are making new highs, but the Demand Index is making lower highs. This suggests buying pressure is drying up.
Zero Line Crossovers:
A cross above zero indicates that Buying Pressure has overtaken Selling Pressure (Bullish).
A cross below zero indicates that Selling Pressure has overtaken Buying Pressure (Bearish).
Trend Confirmation: In a strong trend, the Demand Index should generally move in the same direction as the price.
Settings
Length: The lookback period for the moving averages (Default is 19, consistent with the standard MetaStock setting).
Originality & Credits
This script is a direct translation of the mathematical formula used in MetaStock software. While the Demand Index concept belongs to James Sibbet, this specific Pine Script implementation is provided as open source for the community to study and utilize.
Disclaimer:
This script is for educational and informational purposes only. It DOES NOT constitute financial advice. Trading involves significant risk, and past performance is not indicative of future results. Always do your own research before making investment decisions.
Stochastic RSI with DivergencesStochastic RSI with Divergences - Enhanced Edition
DESCRIPTION
- This is an enhanced version of the classic Stochastic RSI indicator with divergence detection, originally created by @fskrypt (Log RSI), @RicardoSantos (Divergences), @JustUncleL (edits), and @NeoButane (2018 modifications). Full credit to these talented developers for the foundational work.
ENHANCEMENTS & MODIFICATIONS
- This version adds several user-requested features for improved customization and clarity:
- Divergence Signal Labels: Regular divergence signals now display "Buy" (green) and "Sell" (red) instead of generic "R" markers. Hidden divergences show "H-Buy" and "H-Sell" for clearer identification.
- Customizable Colors: User-adjustable colors for both K line (default: blue) and D line (default: orange) allow traders to match their chart themes.
- Adjustable Transparency: Separate opacity controls for the K/D fill shading (default: 70%) and background zones (default: 98%) provide precise visual customization without overwhelming the chart.
- Optional Divergence Lines: Toggle the green and red divergence connecting lines on/off while keeping the Buy/Sell labels visible, reducing visual clutter when desired.
- Organized Settings: All inputs are logically grouped (StochRSI Settings, Divergence Settings, Colors, Opacity) for easier navigation and configuration.
HOW IT WORKS
- The indicator identifies regular and hidden divergences between price action and the Stochastic RSI oscillator:
- Regular Bullish Divergence (Buy): Price makes lower lows while StochRSI makes higher lows - potential reversal signal
- Regular Bearish Divergence (Sell): Price makes higher highs while StochRSI makes lower highs - potential reversal signal
- Hidden Bullish Divergence (H-Buy): Price makes higher lows while StochRSI makes lower lows - trend continuation signal
- Hidden Bearish Divergence (H-Sell): Price makes lower highs while StochRSI makes higher highs - trend continuation signal
- The Stochastic RSI oscillates between 0-100, with readings above 80 indicating overbought conditions and below 20 indicating oversold conditions.
SETTINGS
StochRSI Settings
RSI Length: 14 (default)
Stoch Length: 14 (default)
K Smoothing: 3 (default)
D Smoothing: 3 (default)
Log Scale: Optional logarithmic transformation
Average K & D: Optional blending of both lines
Divergence Settings
Show Divergences: Toggle all divergence signals
Show Hidden Divergences: Toggle H-Buy/H-Sell signals
Show Divergence Lines: Toggle connecting lines between divergence points
Show Divergences Channel: Display fractal channels
Colors
K Line Color: Customize the fast line
D Line Color: Customize the slow line
Opacity
- Background Opacity: Control 20-80 zone shading (0-100)
K/D Fill Opacity: Control area between K and D lines (0-100)
USE CASES
- Momentum trading: Identify overbought/oversold conditions
Divergence trading: Spot potential reversals and trend continuations
Multi-timeframe analysis: Confirm signals across different timeframes
Trend confirmation: Use with other indicators for confluence
CREDITS
- Original concept and code: @fskrypt (Log RSI), @RicardoSantos (Divergence detection), @JustUncleL (modifications), @NeoButane (2018 updates)
Enhanced by: NPR21 (User interface improvements, label modifications, transparency controls)
