RVOL Effort Matrix💪🏻 RVOL Effort Matrix is a tiered volume framework that translates crowd participation into structure-aware visual zones. Rather than simply flagging spikes, it measures each bar’s volume as a ratio of its historical average and assigns to that effort dynamic tiers, creating a real-time map of conviction , exhaustion , and imbalance —before price even confirms.
⚖️ At its core, the tool builds a histogram of relative volume (RVOL). When enabled, a second layer overlays directional effort by estimating buy vs sell volume using candle body logic. If the candle closes higher, green (buy) volume dominates. If it closes lower, red (sell) volume leads. These components are stacked proportionally and inset beneath a colored cap line—a small but powerful layer that maintains visibility of the true effort tier even when split bars are active. The cap matches the original zone color, preserving context at all times.
Coloration communicates rhythm, tempo, and potential turning points:
• 🔴 = structurally weak effort, i.e. failed moves, fake-outs or trend exhaustion
• 🟡 = neutral volume, as seen in consolidations or pullbacks
• 🟢 = genuine commitment, good for continuation, breakout filters, or early rotation signals
• 🟣 = explosive volume signaling either climax or institutional entry—beware!
Background shading (optional) mirrors these zones across the pane for structural scanning at a glance. Volume bars can be toggled between full-stack mode or clean column view. Every layer is modular—built for composability with tools like ZVOL or OBVX Conviction Bias.
🧐 Ideal Use-Cases:
• 🕰 HTF bias anchoring → LTF execution
• 🧭 Identifying when structure is being driven by real crowd pressure
• 🚫 Fading green/fuchsia bars that fail to break structure
• ✅ Riding green/fuchsia follow-through in directional moves
🍷 Recommended Pairings:
• ZVOL for statistically significant volume anomaly detection
• OBVX Conviction Bias ↔️ for directional confirmation of effort zones
• SUPeR TReND 2.718 for structure-congruent entry filtering
• ATR Turbulence Ribbon to distinguish expansion pressure from churn
🥁 RVOL Effort Matrix is all about seeing—how much pressure is behind a move, whether that pressure is sustainable, and whether the crowd is aligned with price. It's volume, but readable. It’s structure, but dynamic. It’s the difference between obeying noise and trading to the beat of the market.
지표 및 전략
OBVX Conviction Bias🧮 The OBVX Conviction Bias overlay tracks the flow of directional volume using the classic On-Balance Volume calculation, then filters it through a layered moving average system to expose crowd commitment , pressure transitions , and momentum fatigue . The tool applies two smoothed averages to the OBV line—a fast curve and a longer-term baseline scaled using Euler’s constant (2.718)—and visualizes their relationship using a color-coded crossover ribbon and pressure fills. When used correctly, it reveals whether a move is being supported by meaningful volume, or whether the crowd is starting to disengage.
🚦 The core signal compares OBV to its fast moving average. When OBV climbs above the short average, it fills green—suggesting real directional effort. When OBV sinks below, the fill turns maroon—flagging fading conviction or pullback potential. A second fill between the short and long OBV moving averages captures the broader trend of volume intention. If the short is above the long, this space fills greenish, showing constructive pressure. If it flips, the fill fades red, signaling crowd hesitation, rotation, or early exhaustion.
⚖️ All smoothing is user-selectable, defaulting to VWMA for effort-sensitive structure. The long-term average is auto-scaled using the natural exponential multiplier (2.718), offering rhythm that reflects the curve of participation. OBVX Conviction Bias isn’t trying to predict—it’s trying to show you where the crowd is leaning , and whether that lean is gaining traction or losing strength.
🧐 Ideal Use-Cases:
• Detect divergence between volume flow and price action
• Confirm breakout validity with volume alignment
• Fade breakouts where OBV fails to follow through
• Time pullback entries when OBV pressure resumes in trend direction
🍷 Recommended Pairings:
• ZVOL to measure whether volume is statistically significant or just noise (as shown)
• RVOL Effort Matrix to validate crowd effort by tier and structure zone
• SUPeR TReND 2.718 and/or MA Ribbons for directional confluence
• ATR Turbulence to track volatility-phase alignment with volume intention
Gann Percentage of High & Low Prices for Options - Keanu_RiTzThis Indicator is based on the text from Chapter 4 "Percentage of High & Low Prices" page number "30" from the book "WD Gann 45 years in Wall Street".
This Indicator is to be used on Intraday Timeframes and only on Options Charts (CALL & PUT) and not on any other chart.
The following is the text from that page :-
One of the greatest discoveries I ever made was how to figure the percentage of high and low prices on the averages and individual stocks.
The percentages of extreme high and low levels indicate future resistance levels.
There is a relation between every low price to some future high price and a percentage of the low price indicates what levels to expect the next high price.
At this price you can sell out long stocks and sell short with a limited risk.
The extreme high price or any minor tops are related to future bottoms er low levels.
The percentage of the high price tells where to expect low prices in the future and gives you resistance levels where you can buy with a limited risk.
The most important resistance level is 50% of any high or low price.
Second in importance is 100% on the lowest selling price on the averages or individual stocks.
You must also use 200%, 300%, 400%, 500%, 600% or more, depending upon the price and the Time Periods from High and Low.
Third in importance is 25% of the Lowest price or the Highest price.
Fourth in importance is 121/2% of the extreme Low or extreme High price.
Fifth in importance is 61/4% of the Highest price, but this is only to be used when the averages or individual stocks are selling at very high levels.
Sixth in importance is 33 1/3 and 66 2/3%. These percentages should be calculated and watched for resistance next after 25% and after 50%.
You should always have percentage tables made up on the Industrial Averages and on the individual stocks you trade in in order to know where these important resistance levels are located.
Description :
It plots the Intraday % levels from the highest high and lowest low of that day.
The calculation of these levels is based on the text from Chapter 4 "Percentage of High & Low Prices" page number "30" from the book "WD Gann 45 years in Wall Street".
I developed this indicator to see if those percentages work on Options prices or not,
and from my observation I found that it works wonderfully in Options as well.
These % levels work like magic and act as great Dynamic Support and Resistance levels.
Don't trust my words blindly and see for yourself.
This indicator is for educational and research purpose only.
It does not provide any BUY/SELL signals.
Institutional Quantum Momentum Impulse [BullByte]## Overview
The Institutional Quantum Momentum Impulse (IQMI) is a sophisticated momentum oscillator designed to detect institutional-level trend strength, volatility conditions, and market regime shifts. It combines multiple advanced technical concepts, including:
- Quantum Momentum Engine (Hilbert Transform + MACD Divergence + Stochastic Energy)
- Fractal Volatility Scoring (GARCH + Keltner-based volatility)
- Dynamic Adaptive Bands (Self-adjusting thresholds based on efficiency)
- Market Phase Detection (Volume + Momentum alignment)
- Liquidity & Cumulative Delta Analysis
The indicator provides a Z-score normalized momentum reading, making it ideal for mean-reversion and trend-following strategies.
---
## Key Features
### 1. Quantum Momentum Core
- Combines Hilbert Transform, MACD divergence, and Stochastic Energy into a single composite momentum score.
- Normalized using a Z-score for statistical significance.
- Smoothed with EMA/WMA/HMA for cleaner signals.
### 2. Dynamic Adaptive Bands
- Upper/Lower bands adjust based on volatility and efficiency ratio .
- Acts as overbought/oversold zones when momentum reaches extremes.
### 3. Market Phase Detection
- Identifies bullish , bearish , or neutral phases using:
- Volume-Weighted MA alignment
- Fractal momentum extremes
### 4. Volatility & Liquidity Filters
- Fractal Volatility Score (0-100 scale) shows market instability.
- Liquidity Check ensures trades are taken in favorable spread conditions.
### 5. Dashboard & Visuals
- Real-time dashboard with key metrics:
- Momentum strength, volatility, efficiency, cumulative delta, and market regime.
- Gradient coloring for intuitive momentum visualization .
---
## Best Trade Setups
### 1. Trend-Following Entries
- Signal :
- QM crosses above zero + Market Phase = Bullish + ADX > 25
- Cumulative Delta rising (buying pressure)
- Confirmation :
- Efficiency > 0.5 (strong momentum quality)
- Liquidity = High (tight spreads)
### 2. Mean-Reversion Entries
- Signal :
- QM touches upper band + Volatility expanding
- Market Regime = Ranging (ADX < 25)
- Confirmation :
- Efficiency < 0.3 (weak momentum follow-through)
- Cumulative Delta divergence (price high but delta declining)
### 3. Breakout Confirmation
- Signal :
- QM holds above zero after a pullback
- Market Phase shifts to Bullish/Bearish
- Confirmation :
- Volatility rising (expansion phase)
- Liquidity remains high
---
## Recommended Timeframes
- Intraday (5M - 1H): Works well for scalping & swing trades.
- Swing Trading (4H - Daily): Best for trend-following setups.
- Position Trading (Weekly+): Useful for macro trend confirmation.
---
## Input Customization
- Resonance Factor (1.0 - 3.618 ): Adjusts MACD divergence sensitivity.
- Entropy Filter (0.382/0.50/0.618) : Controls stochastic damping.
- Smoothing Type (EMA/WMA/HMA) : Changes momentum responsiveness.
- Normalization Period : Adjusts Z-score lookback.
---
The IQMI is a professional-grade momentum indicator that combines institutional-level concepts into a single, easy-to-read oscillator. It works across all markets (stocks, forex, crypto) and is ideal for traders who want:
✅ Early trend detection
✅ Volatility-adjusted signals
✅ Institutional liquidity insights
✅ Clear dashboard for quick analysis
Try it on TradingView and enhance your trading edge! 🚀
Happy Trading!
- BullByte
DB - Global M2 Money Index (Pro Version)This professional-grade TradingView indicator plots a composite Global M2 Money Supply Index by aggregating the money supply (M2) of 21+ global economies—adjusted to USD via currency conversion. It helps traders and investors assess the global liquidity cycle and its potential leading relationship with asset classes like Bitcoin, equities, and gold.
This Pro version includes an optional 5-color dynamic heatmap gradient, manual color override, correlation table, and preset time offsets for popular assets to easily visualize delayed macro correlations.
✅ Key Features:
1. 💰 Global M2 Aggregation
Sources daily M2 data from:
US, EU, UK, China, Japan, Canada, Brazil, India, Russia, and more (21 total).
Adjusts all foreign M2 values to USD via FX pairs.
Normalizes total M2 data to trillions (divided by 1e12).
2. 🔀 Time Offset Options
Easily offset the M2 curve to match lagging or leading macro behavior.
Preset Offsets for key assets like:
Bitcoin (108 days), SOL (82), SUI (84), ETH (78), TSLA (77), QQQ (50), Gold (7), etc.
Or use Manual Offset (custom range: ±1000 days).
3. 🌈 Visual Heatmap (Optional)
Enable 5-Color Heatmap Gradient for dynamic insight:
Blue to Green to Yellow to Orange to Red.
Indicates relative M2 supply pressure over a 90-day range.
Or use a Manual Line Color for static visualizations.
4. 📈 Overlay Plotting
Plots the M2 curve directly on the chart (right-scale overlay).
Adjustable offset aligns the plot visually with price action trends.
5. 📋 Built-In Correlation Table
Auto-calculates and displays correlations between the asset’s price (HLC3) and the M2 index (with offset applied) across:
30, 60, 90, 180, 360, 540, and 720-day lookbacks.
Dynamic table includes:
Correlation % (color-coded: red for negative, green for strong positive).
Adjustable position (top/bottom/left/right).
-----------------------------------------------------
🔧 Customization Inputs:
Manual Time Offset (days)
Asset Offset: dropdown with preset lags
Enable 5-Color Heatmap
Manual Line Color
Table Position: full placement control
-----------------------------------------------------
📊 Use Case:
Perfect for macro-focused traders who want to:
Track global liquidity trends.
Time lagged macro correlations (e.g., M2 expansion → crypto rallies).
Confirm or dismiss liquidity-driven market regimes.
Intrinsic Event (Multi DC OS)Overview
This indicator implements an event-based approach to analyze price movements in the foreign exchange market, inspired by the intrinsic time framework introduced in Fractals and Intrinsic Time - A Challenge to Econometricians by U. A. Müller et al. (1995). It identifies significant price events using an intrinsic time perspective and supports multi-agent analysis to reflect the heterogeneous nature of financial markets. The script plots these events as lines and labels on the chart, offering a visual tool for traders to understand market dynamics at different scales.
Key Features
Intrinsic Events : The indicator detects directional change (DC) and overshoot (OS) events based on user-defined thresholds (delta), aligning with the paper’s concept of intrinsic time (Section 6). Intrinsic time redefines time based on market activity, expanding during volatile periods and contracting during inactive ones, rather than relying on a physical clock.
Multi-Agent Analysis : Supports up to five agents, each with its own threshold and color settings, reflecting the heterogeneous market hypothesis (Section 5). This allows the indicator to capture the perspectives of market participants with different time horizons, such as short-term FX dealers and long-term central banks.
How It Works
Intrinsic Events Detection : The script identifies two types of events using intrinsic time principles:
Directional Change (DC) : Triggered when the price reverses by the threshold (delta) against the current trend (e.g., a drop by delta in an uptrend signals a "Down DC").
Overshoot (OS) : Occurs when the price continues in the trend direction by the threshold (e.g., a rise by delta in an uptrend signals an "Up OS").
DC events are plotted as solid lines, and OS events as dashed lines, with labels like "Up DC" or "OS Down" for clarity. The label style adjusts based on the trend to ensure visibility.
Multi-Agent Setup : Each agent operates independently with its own threshold, mimicking market participants with varying time horizons (Section 5). Smaller thresholds detect frequent, short-term events, while larger thresholds capture broader, long-term movements.
Settings
Each agent can be configured with:
Enable Agent : Toggle the agent on or off.
Threshold (%) : The percentage threshold (delta) for detecting DC and OS events (default values: 0.1%, 0.2%, 0.5%, 1%, 2% for agents 1–5).
Up Mode Color : Color for lines and labels in up mode (DC events).
Down Mode Color : Color for lines and labels in down mode (OS events).
Usage Notes
This indicator is designed for the foreign exchange market, leveraging its high liquidity, as noted in the paper (Section 1). Adjust the threshold values based on the instrument’s volatility—higher volatility leads to more intrinsic events (Section 4). It can be adapted to other markets where event-based analysis applies.
Reference
The methodology is based on:
Fractals and Intrinsic Time - A Challenge to Econometricians by U. A. Müller, M. M. Dacorogna, R. D. Davé, O. V. Pictet, R. B. Olsen, and J. R. Ward (June 28, 1995). Olsen & Associates Preprint.
Dynamic Support & Resistance ZonesThis indicator dynamically detects and plots support and resistance levels by analyzing recent price pivots. It works as follows:
Inputs:
Set the number of bars to look back, pivot strength (left/right), minimum touches for a major level, and a tolerance percentage that is converted to decimal.
Note:
• Pivot Left is the number of bars before the candidate bar that must be lower (for a swing high) or higher (for a swing low) than the candidate. It ensures the candidate is higher (or lower) than recent previous bars.
• Pivot Right is the number of bars after the candidate bar that are also required to be lower (for a swing high) or higher (for a swing low) than the candidate. This parameter is crucial because it verifies that the candidate bar wasn’t just a temporary spike—price needs to remain below (or above) the candidate for a number of subsequent bars.
Suggested default settings for a daily chart: Bars to Look Back: 60; Pivot Left: 2; Pivot Right: 2; Min Touches for Major Level: 2; Tolerance (%): 1
Pivot Detection:
It uses built-in pivot functions to identify swing highs (potential resistance) and swing lows (potential support) based on a user-defined lookback window and pivot strength.
Clustering:
Detected levels that are close (within a set tolerance) are grouped into clusters. Each cluster averages the levels and counts the number of touches. This helps determine if a zone is repeatedly tested.
Visual Display:
It plots horizontal lines on the chart:
Red lines indicate “major” zones that have been tested a number of times (as defined by the user).
Blue lines represent secondary levels that have been touched less often.
Dynamic Cleanup:
Clusters outside the user-defined lookback period are removed so that only recent, relevant levels remain visible.
In short, the indicator creates a dynamic, visually updated map of key support and resistance zones based on recent price action and pivot behavior.
DD ATR ReadingsThe DD ATR Readings indicator displays customizable Average True Range (ATR) multiplier values directly on your chart. Unlike standard ATR indicators that only show a line, this indicator calculates and displays the exact numeric values for three different ATR multipliers, giving you precise volatility measurements for your trading decisions.
It's specifically created for people taking the "Deep Dip Buy" stock trading course, and attempts to provide a ready-to-go solution to allow easy position size calculations as per the course, with the required ATR values visible at a glance.
The default values of 2.0, 1.5 and 0.45 are the same values used by the course instructor in his charting software, but you can change these values to any multiplier you choose.
Any input from students or the instructor is welcome to improve this indicator so it offers more value to those looking to learn how to trade.
Features
Displays three customizable ATR multiplier values (default: 2.0, 1.5, and 0.45 from the course)
Uses either SMA or EMA for ATR calculation (20-period default)
Fully customizable label appearance (position, color, size)
Real-time value updates as you move through the chart
Clean, unobtrusive display that doesn't clutter your chart with additional lines
Customization Options
ATR Length: Number of bars used in the ATR calculation (default: 20)
ATR Multipliers: Three customizable multiplier values
SMA/EMA: Choose your preferred moving average type for ATR calculation
Label Style: Multiple positioning options for the text display
Colors and Size: Fully customizable appearance
Multi-Timeframe Trading SystemOverview
The Multi-Timeframe Trading System is an advanced technical analysis indicator designed to identify high-probability trading opportunities by combining signals from multiple timeframes and trading strategies. This system analyzes market context, identifies optimal setups, and confirms entries with lower timeframe precision, significantly increasing signal reliability.
Key Features
Triple Timeframe Analysis: Combines high, medium, and low timeframe data for comprehensive market analysis
Three Trading Strategies in One: Incorporates trend-following, mean-reversion, and breakout strategies
Adaptive to Market Conditions: Automatically identifies the current market context (trending or ranging) and applies the appropriate strategy
Signal Strength Evaluation: Rates buy/sell signals from weak to strong based on indicator confluence
Visual Alerts: Clear buy/sell signals with on-chart markers and signal labels
Customizable Parameters: Fully adjustable settings for all indicators and timeframes
Technical Indicators Included
-Moving Averages (EMA 50, EMA 200)
-Ichimoku Cloud components
-ADX for trend strength
-RSI for momentum and oversold/overbought conditions
-Stochastic oscillator for entry timing
-MACD for trend confirmation
-Bollinger Bands for volatility and price channels
-ATR for measuring market volatility
Trading Strategies
1. Trend-Following Strategy
Identifies the primary trend direction on higher timeframes
Locates optimal pullback entry points on medium timeframes
Confirms entries with precision using lower timeframe momentum signals
2. Mean-Reversion Strategy
Activates during ranging market conditions
Identifies oversold and overbought conditions using Bollinger Bands and RSI
Confirms reversals with Stochastic crossovers
3. Breakout Strategy
Detects price consolidation periods through Bollinger Band width
Identifies volatility expansion and price breakouts
Confirms breakout direction with momentum indicators
Ideal For
Swing traders looking for high-probability setups
Day traders seeking to align with the larger trend
Traders who want systematic confirmation across multiple timeframes
Those looking to adapt their trading approach to changing market conditions
How To Use
Apply the indicator to your chart and customize the timeframe settings to match your trading style
-Observe the market context information (uptrend, downtrend, or ranging)
-Wait for a setup to form on the medium timeframe
-Enter when the low timeframe confirms the signal
-Use the signal strength rating to prioritize the highest probability trades
The Multi-Timeframe Trading System eliminates the guesswork from your trading by providing clear, objective signals based on professional-grade multi-timeframe analysis techniques.
Failed Breakout DetectionThis indicator is a reverse-engineered copy of the FBD Detection indicator published by xfuturesgod. The original indicator aimed at detecting "Failed Breakdowns". This version tracks the opposite signals, "Failed Breakouts". It was coded with the ES Futures 15 minute chart in mind but may be useful on other instruments and time frames.
The original description, with terminology reversed to explain this version:
'Failed Breakouts' are a popular set up for short entries.
In short, the set up requires:
1) A significant high is made ('initial high')
2) Initial high is undercut with a new high
3) Price action then 'reclaims' the initial high by moving +8-10 points from the initial high
This script aims at detecting such set ups. It was coded with the ES Futures 15 minute chart in mind but may be useful on other instruments and time frames.
Business Logic:
1) Uses pivot highs to detect 'significant' initial highs
2) Uses amplitude threshold to detect a new high above the initial high; used /u/ben_zen script for this
3) Looks for a valid reclaim - a red candle that occurs within 10 bars of the new high
4) Price must reclaim at least 8 points for the set up to be valid
5) If a signal is detected, the initial high value (pivot high) is stored in array that prevents duplicate signals from being generated.
6) FBO Signal is plotted on the chart with "X"
7) Pivot high detection is plotted on the chart with "P" and a label
8) New highs are plotted on the chart with a red triangle
Notes:
User input
- My preference is to use the defaults as is, but as always feel free to experiment
- Can modify pivot length but in my experience 10/10 work best for pivot highs
- New high detection - 55 bars and 0.05 amplitude work well based on visual checks of signals
- Can modify the number of points needed to reclaim a high, and the # of bars limit over which this must occur.
Alerts:
- Alerts are available for detection of new highs and detection of failed breakouts
- Alerts are also available for these signals but only during 7:30PM-4PM EST - 'prime time' US trading hours
Limitations:
- Current version of the script only compares new highs to the most recent pivot high, does not look at anything prior to that
- Best used as a discretionary signal
DD Keltner Channels (1-3 ATR)This indicator creates Keltner Channels with 1, 2, and 3 ATR multipliers, allowing you to visualize different volatility levels around a moving average.
It's specifically created for people taking the "Deep Dip Buy" stock trading course, and attempts to provide a ready-to-go solution for those struggling with configuring the default Keltner indicator on TradingView to suit their needs for the course.
Any input from students or the instructor is welcome to improve this indicator so it offers more value to those looking to learn how to trade.
Features:
- Uses SMA or EMA as the base (20-period default)
- Displays 6 lines: +3, +2, +1, -1, -2, and -3 ATR levels
- Color-coded for easy identification:
• +/-1 ATR: Green
• +/-2 ATR: Light Gray (thin)
• +/-3 ATR: Dark Gray (thick)
Fibonacci Counter-Trend TradingOverview:
The Fibonacci Counter-Trend Trading strategy is designed to capitalize on price reversals by utilizing Fibonacci levels calculated from the standard deviation of price movements. This strategy opens a sell order when the closing price crosses above a specified upper Fibonacci level and a buy order when the closing price crosses below a specified lower Fibonacci level. By leveraging the principles of Fibonacci retracement and volatility, this strategy aims to identify potential reversal points in the market.
How It Works:
Fibonacci Levels Calculation:
The strategy calculates upper and lower Fibonacci levels based on the standard deviation of the price over a specified moving average length. These levels are derived from the Fibonacci sequence, which is widely used in technical analysis to identify potential support and resistance levels.
The upper levels are calculated by adding specific Fibonacci ratios (0.236, 0.382, 0.5, 0.618, 0.764, and 1.0) multiplied by the standard deviation to the basis (the volume-weighted moving average).
The lower levels are calculated by subtracting the same Fibonacci ratios multiplied by the standard deviation from the basis.
Trade Entry Rules:
Sell Order: A sell order is triggered when the closing price crosses above the selected upper Fibonacci level. This indicates a potential reversal point where the price may start to decline.
Buy Order: A buy order is initiated when the closing price crosses below the selected lower Fibonacci level. This suggests a potential reversal point where the price may begin to rise.
Trade Management:
The strategy includes stop-losses based on the Fibonacci levels to protect against adverse price movements.
How to Use:
Users can customize the moving average length and the multiplier for the standard deviation to suit their trading preferences and market conditions.
The strategy can be applied to various financial instruments, including stocks, forex, and cryptocurrencies, making it versatile for different trading environments.
Pros:
The Fibonacci Counter-Trend Trading strategy combines the mathematical principles of the Fibonacci sequence with the statistical measure of standard deviation, providing a unique approach to identifying potential market reversals.
This strategy is particularly useful in volatile markets where price swings can lead to significant trading opportunities.
The use of Fibonacci levels can help traders identify key support and resistance areas, enhancing decision-making.
Cons:
The strategy may generate false signals in choppy or sideways markets, leading to potential losses if the price does not reverse as anticipated.
Relying solely on Fibonacci levels without considering other technical indicators or market conditions may result in missed opportunities or increased risk.
The effectiveness of the strategy can vary depending on the chosen parameters (e.g., moving average length and standard deviation multiplier), requiring users to spend time optimizing these settings for different market conditions.
As with any counter-trend strategy, there is a risk of significant drawdowns during strong trending markets, where the price continues to move in one direction without reversing.
By understanding the mechanics of the Fibonacci Counter-Trend Trading strategy, along with its pros and cons, traders can effectively implement it in their trading routines and potentially enhance their trading performance.
Quantify [Trading Model] | FractalystNote: In this description, "TM" refers to Trading Model (not trademark) and "EM" refers to Entry Model
What’s the indicator’s purpose and functionality?
You know how to identify market bias but always struggle with figuring out the best exit method, or even hesitating to take your trades?
I've been there. That's why I built this solution—once and for all—to help traders who know the market bias but need a systematic and quantitative approach for their entries and trade management.
A model that shows you real-time market probabilities and insights, so you can focus on execution with confidence—not doubt or FOMO.
How does this Quantify differentiate from Quantify ?
Have you managed to code or even found an indicator that identifies the market bias for you, so you don’t have to manually spend time analyzing the market and trend?
Then that’s exactly why you might need the Quantify Trading Model.
With the Trading Model (TM) version, the script automatically uses your given bias identification method to determine the trend (bull vs bear and neutral), detect the bias, and provide instant insight into the trades you could’ve taken.
To avoid complications from consecutive signals, it uses a kNN machine learning algorithm that processes market structure and probabilities to predict the best future patterns.
(You don’t have to deal with any complexity—it’s all taken care of for you.)
Quantify TM uses the k-Nearest Neighbors (kNN) machine learning algorithm to learn from historical market patterns and adapt to changing market structures. This means it can recognize similar market conditions from the past and apply those lessons to current trading decisions.
On the other hand, Quantify EM requires you to manually select your directional bias. It then focuses solely on generating entry signals based on that pre-determined bias.
While the entry model version (EM) uses your manual bias selection to determine the trend, it then provides insights into trades you could’ve taken and should be taking.
Trading Model (TM)
- Uses `input.source()` to incorporate your personal methodology for identifying market bias
- Automates everything—from bias detection to entry and exit decisions
- Adapts to market bias changes through kNN machine learning optimization
- Reduces human intervention in trading decisions, limiting emotional interference
Entry Model (EM)
- Focuses specifically on optimizing entry points within your pre-selected directional bias
- Requires manual input for determining market bias
- Provides entry signals without automating alerts or bias rules
Can the indicator be applied to any market approach/trading strategy?
Yes, if you have clear rules for identifying the market bias, then you can code your bias detection and then use the input.source() user input to retrieve the direction from your own indicator, then the Quantify uses machine-learning identify the best setups for you.
Here's an example:
//@version=6
indicator('Moving Averages Bias', overlay = true)
// Input lengths for moving averages
ma10_length = input.int(10, title = 'MA 10 Length')
ma20_length = input.int(20, title = 'MA 20 Length')
ma50_length = input.int(50, title = 'MA 50 Length')
// Calculate moving averages
ma10 = ta.sma(close, ma10_length)
ma20 = ta.sma(close, ma20_length)
ma50 = ta.sma(close, ma50_length)
// Identify bias
var bias = 0
if close > ma10 and close > ma20 and close > ma50 and ma10 > ma20 and ma20 > ma50
bias := 1 // Bullish
bias
else if close < ma10 and close < ma20 and close < ma50 and ma10 < ma20 and ma20 < ma50
bias := -1 // Bearish
bias
else
bias := 0 // Neutral
bias
// Plot the bias
plot(bias, title = 'Identified Bias', color = color.blue,display = display.none)
Once you've created your custom bias indicator, you can integrate it with Quantify :
- Add your bias indicator to your chart
- Open the Quantify settings
- Set the Bias option to "Auto"
- Select your custom indicator as the bias source
The machine learning algorithms will then analyze historical price action and identify optimal setups based on your defined bias parameters. Performance statistics are displayed in summary tables, allowing you to evaluate effectiveness across different timeframes.
Can the indicator be used for different timeframes or trading styles?
Yes, regardless of the timeframe you’d like to take your entries, the indicator adapts to your trading style.
Whether you’re a swing trader, scalper, or even a position trader, the algorithm dynamically evaluates market conditions across your chosen timeframe.
How Quantify Helps You Trade Profitably?
The Quantify Trading Model offers several powerful features that can significantly improve your trading profitability when used correctly:
Real-Time Edge Assessment
It displays real-time probability of price moving in your favor versus hitting your stoploss
This gives you immediate insight into risk/reward dynamics before entering trades
You can make more informed decisions by knowing the statistical likelihood of success
Historical Edge Validation
Instantly shows whether your trading approach has demonstrated an edge in historical data
Prevents you from trading setups that historically haven't performed well
Gives confidence when entering trades that have proven statistical advantages
Optimized Position Sizing
Analyzes each setup's success rate to determine the adjusted Kelly criterion formula
Customizes position sizing based on your selected maximum drawdown tolerance
Helps prevent account-destroying losses while maximizing growth potential
Advanced Exit Management
Utilizes market structure-based trailing stop-loss mechanisms
Maximizes the average risk-reward ratio profit per winning trade
Helps capture larger moves while protecting gains during market reversals
Emotional Discipline Enforcement
Eliminates emotional bias by adhering to your pre-defined rules for market direction
Prevents impulsive decisions by providing objective entry and exit signals
Creates psychological distance between your emotions and trading decisions
Overtrading Prevention
Highlights only setups that demonstrate positive expectancy
Reduces frequency of low-probability trades
Conserves capital for higher-quality opportunities
Systematic Approach Benefits
By combining machine learning algorithms with your personal bias identification methods, Quantify helps transform discretionary trading approaches into more systematic, probability-based strategies.
What Entry Models are used in Quantify Trading Model version?
The Quantify Trading Model utilizes two primary entry models to identify high-probability trade setups:
Breakout Entry Model
- Identifies potential trade entries when price breaks through significant swing highs and swing lows
- Captures momentum as price moves beyond established trading ranges
- Particularly effective in trending markets when combined with the appropriate bias detection
- Optimized by machine learning to filter false breakouts based on historical performance
Fractals Entry Model
- Utilizes fractal patterns to identify potential reversal or continuation points
- Also uses swing levels to determine optimal entry locations
- Based on the concept that market structure repeats across different timeframes
- Identifies local highs and lows that form natural entry points
- Enhanced by machine learning to recognize the most profitable fractal formations
- These entry models work in conjunction with your custom bias indicator to ensure trades are taken in the direction of the overall market trend. The machine learning component analyzes historical performance of these entry types across different market conditions to optimize entry timing and signal quality.
How Does This Indicator Identify Market Structure?
1. Swing Detection
• The indicator identifies key swing points on the chart. These are local highs or lows where the price reverses direction, forming the foundation of market structure.
2. Structural Break Validation
• A structural break is flagged when a candle closes above a previous swing high (bullish) or below a previous swing low (bearish).
• Break Confirmation Process:
To confirm the break, the indicator applies the following rules:
• Valid Swing Preceding the Break: There must be at least one valid swing point before the break.
3. Numeric Labeling
• Each confirmed structural break is assigned a unique numeric ID starting from 1.
• This helps traders track breaks sequentially and analyze how the market structure evolves over time.
4. Liquidity and Invalidation Zones
• For every confirmed structural break, the indicator highlights two critical zones:
1. Liquidity Zone (LIQ): Represents the structural liquidity level.
2. Invalidation Zone (INV): Acts as Invalidation point if the structure fails to hold.
How does the trailing stop-loss work? what are the underlying calculations?
A trailing stoploss is a dynamic risk management tool that moves with the price as the market trend continues in the trader’s favor. Unlike a fixed take profit, which stays at a set level, the trailing stoploss automatically adjusts itself as the market moves, locking in profits as the price advances.
In Quantify, the trailing stoploss is enhanced by incorporating market structure liquidity levels (explain above). This ensures that the stoploss adjusts intelligently based on key price levels, allowing the trader to stay in the trade as long as the trend remains intact, while also protecting profits if the market reverses.
What is the Kelly Criterion, and how does it work in Quantify?
The Kelly Criterion is a mathematical formula used to determine the optimal position size for each trade, maximizing long-term growth while minimizing the risk of large drawdowns. It calculates the percentage of your portfolio to risk on a trade based on the probability of winning and the expected payoff.
Quantify integrates this with user-defined inputs to dynamically calculate the most effective position size in percentage, aligning with the trader’s risk tolerance and desired exposure.
How does Quantify use the Kelly Criterion in practice?
Quantify uses the Kelly Criterion to optimize position sizing based on the following factors:
1. Confidence Level: The model assesses the confidence level in the trade setup based on historical data and sample size. A higher confidence level increases the suggested position size because the trade has a higher probability of success.
2. Max Allowed Drawdown (User-Defined): Traders can set their preferred maximum allowed drawdown, which dictates how much loss is acceptable before reducing position size or stopping trading. Quantify uses this input to ensure that risk exposure aligns with the trader’s risk tolerance.
3. Probabilities: Quantify calculates the probabilities of success for each trade setup. The higher the probability of a successful trade (based on historical price action and liquidity levels), the larger the position size suggested by the Kelly Criterion.
How can I get started to use the indicator?
1. Set Your Market Bias
• Choose Auto.
• Select the source you want Quantify to use as for bias identification method (explained above)
2. Choose Your Entry Timeframes
• Specify the timeframes you want to focus on for trade entries.
• The indicator will dynamically analyze these timeframes to provide optimal setups.
3. Choose Your Entry Model and BE/TP Levels
• Choose a model that suits your personality
• Choose a level where you'd like the script to take profit or move stop-loss to BE
4. Set and activate the alerts
What tables are used in the Quantify?
• Quarterly
• Monthly
• Weekly
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
- By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Order Flow Hawkes Process [ScorsoneEnterprises]This indicator is an implementation of the Hawkes Process. This tool is designed to show the excitability of the different sides of volume, it is an estimation of bid and ask size per bar. The code for the volume delta is from www.tradingview.com
Here’s a link to a more sophisticated research article about Hawkes Process than this post arxiv.org
This tool is designed to show how excitable the different sides are. Excitability refers to how likely that side is to get more activity. Alan Hawkes made Hawkes Process for seismology. A big earthquake happens, lots of little ones follow until it returns to normal. Same for financial markets, big orders come in, causing a lot of little orders to come. Alpha, Beta, and Lambda parameters are estimated by minimizing a negative log likelihood function.
How it works
There are a few components to this script, so we’ll go into the equation and then the other functions used in this script.
hawkes_process(params, events, lkb) =>
alpha = clamp(array.get(params, 0), 0.01, 1.0)
beta = clamp(array.get(params, 1), 0.1, 10.0)
lambda_0 = clamp(array.get(params, 2), 0.01, 0.3)
intensity = array.new_float(lkb, 0.0)
events_array = array.new_float(lkb, 0.0)
for i = 0 to lkb - 1
array.set(events_array, i, array.get(events, i))
for i = 0 to lkb - 1
sum_decay = 0.0
current_event = array.get(events_array, i)
for j = 0 to i - 1
time_diff = i - j
past_event = array.get(events_array, j)
decay = math.exp(-beta * time_diff)
past_event_val = na(past_event) ? 0 : past_event
sum_decay := sum_decay + (past_event_val * decay)
array.set(intensity, i, lambda_0 + alpha * sum_decay)
intensity
The parameters alpha, beta, and lambda all represent a different real thing.
Alpha (α):
Definition: Alpha represents the excitation factor or the magnitude of the influence that past events have on the future intensity of the process. In simpler terms, it measures how much each event "excites" or triggers additional events. It is constrained between 0.01 and 1.0 (e.g., clamp(array.get(params, 0), 0.01, 1.0)). A higher alpha means past events have a stronger influence on increasing the intensity (likelihood) of future events. Initial value is set to 0.1 in init_params. In the hawkes_process function, alpha scales the contribution of past events to the current intensity via the term alpha * sum_decay.
Beta (β):
Definition: Beta controls the rate of exponential decay of the influence of past events over time. It determines how quickly the effect of a past event fades away. It is constrained between 0.1 and 10.0 (e.g., clamp(array.get(params, 1), 0.1, 10.0)). A higher beta means the influence of past events decays faster, while a lower beta means the influence lingers longer. Initial value is set to 0.1 in init_params. In the hawkes_process function, beta appears in the decay term math.exp(-beta * time_diff), which reduces the impact of past events as the time difference (time_diff) increases.
Lambda_0 (λ₀):
Definition: Lambda_0 is the baseline intensity of the process, representing the rate at which events occur in the absence of any excitation from past events. It’s the "background" rate of the process. It is constrained between 0.01 and 0.3 .A higher lambda_0 means a higher natural frequency of events, even without the influence of past events. Initial value is set to 0.1 in init_params. In the hawkes_process function, lambda_0 sets the minimum intensity level, to which the excitation term (alpha * sum_decay) is added: lambda_0 + alpha * sum_decay
Alpha (α): Strength of event excitation (how much past events boost future events).
Beta (β): Rate of decay of past event influence (how fast the effect fades).
Lambda_0 (λ₀): Baseline event rate (background intensity without excitation).
Other parts of the script.
Clamp
The clamping function is a simple way to make sure parameters don’t grow or shrink too much.
ObjectiveFunction
This function defines the objective function (negative log-likelihood) to minimize during parameter optimization.It returns a float representing the negative log-likelihood (to be minimized).
How It Works:
Calls hawkes_process to compute the intensity array based on current parameters.Iterates over the lookback period:lambda_t: Intensity at time i.event: Event magnitude at time i.Handles na values by replacing them with 0.Computes log-likelihood: event_clean * math.log(math.max(lambda_t_clean, 0.001)) - lambda_t_clean.Ensures lambda_t_clean is at least 0.001 to avoid log(0).Accumulates into log_likelihood.Returns -log_likelihood (negative because the goal is to minimize, not maximize).
It is used in the optimization process to evaluate how well the parameters fit the observed event data.
Finite Difference Gradient:
This function calculates the gradient of the objective function we spoke about. The gradient is like a directional derivative. Which is like the direction of the rate of change. Which is like the direction of the slope of a hill, we can go up or down a hill. It nudges around the parameter, and calculates the derivative of the parameter. The array of these nudged around parameters is what is returned after they are optimized.
Minimize:
This is the function that actually has the loop and calls the Finite Difference Gradient each time. Here is where the minimizing happens, how we go down the hill. If we are below a tolerance, we are at the bottom of the hill.
Applied
After an initial guess the parameters are optimized with a mix of bid and ask levels to prevent some over-fitting for each side while keeping some efficiency. We initialize two different arrays to store the bid and ask sizes. After we optimize the parameters we clamp them for the calculations. We then get the array of intensities from the Hawkes Process of bid and ask and plot them both. When the bids are greater than the ask it represents a bullish scenario where there are likely to be more buy than sell orders, pushing up price.
Tool examples:
The idea is that when the bid side is more excitable it is more likely to see a bullish reaction, when the ask is we see a bearish reaction.
We see that there are a lot of crossovers, and I picked two specific spots. The idea of this isn’t to spot crossovers but avoid chop. The values are either close together or far apart. When they are far, it is a classification for us to look for our own opportunities in, when they are close, it signals the market can’t pick a direction just yet.
The value works just as well on a higher timeframe as on a lower one. Hawkes Process is an estimate, so there is a leading value aspect of it.
The value works on equities as well, here is NASDAQ:TSLA on a lower time frame with a lookback of 5.
Inputs
Users can enter the lookback value and timeframe.
No tool is perfect, the Hawkes Process value is also not perfect and should not be followed blindly. It is good to use any tool along with discretion and price action.
DoloresOverview
The "Dolores Trading Assistant" is a sleek and intuitive indicator designed to empower traders during the high-volatile New York trading session. This tool overlays a customizable table on your chart, presenting real-time insights from key market internals—NYSE and Nasdaq Advance/Decline (A/D), Volume Difference (VOLD), and TICK—in a clear, color-coded format. Its streamlined design focuses on delivering essential market data with trend states, making it a practical companion for assessing momentum and sentiment at a glance.
Purpose and Usefulness
Tailored for intraday traders, the Dolores Trading Assistant goes beyond price-based analysis by tapping into broad market internals to uncover the underlying forces driving the New York session. Whether you’re scalping, day trading, or monitoring short-term trends, this indicator helps you quickly gauge market direction, confirm momentum, and identify potential shifts—all from a single, visually accessible table. Its simplicity and focus on real-time data make it a valuable tool for traders seeking clarity in fast-moving markets.
How It Works
The indicator fetches live data from six vital market internals using a 1-second timeframe:
NYSE Advance/Decline (A/D) - Tracks the net balance of advancing versus declining NYSE stocks.
Nasdaq Advance/Decline (A/D) - Monitors the same for Nasdaq stocks.
NYSE VOLD - Measures the net volume difference between buying and selling on the NYSE.
Nasdaq VOLD - Captures the equivalent for Nasdaq.
NYSE TICK - Reflects the net number of NYSE stocks ticking up versus down.
Nasdaq TICK - Provides the same for Nasdaq.
These internals are processed to determine their trend states—such as bullish, bearish, or neutral—displayed in colors and emojis for instant recognition. The table organizes this data into three columns: the condition (e.g., "NYSE A/D"), its current reading (formatted for readability, like "1.2m" or "500k"), and its trend state (e.g., "Trending Bullish" or "Neutral"). This setup offers a snapshot of market health without overwhelming the user with excessive details.
Internal Signals and Their Role
Each internal signal contributes to understanding the market’s current state and the conditions you’re trading into:
NYSE and Nasdaq A/D: Reveals market breadth. Strong positive values suggest widespread buying, while negative readings indicate broad selling, helping you confirm if a move has robust participation.
NYSE and Nasdaq VOLD: Tracks volume momentum. High positive figures point to aggressive buying pressure, while deep negatives signal heavy selling, validating whether price moves are backed by volume.
NYSE and Nasdaq TICK: Captures short-term sentiment. Extreme readings highlight overbought or oversold conditions, offering clues about momentum strength or potential exhaustion.
How Signals Confirm Conditions
Trend States: Each internal’s trend state (e.g., "Bullish," "Bearish," "Trending Bearish") reflects its current momentum. Consistent bullish states across multiple internals confirm a strong upward trend, while bearish alignments suggest selling pressure. Neutral or mixed states indicate indecision or choppiness, guiding you to adjust expectations accordingly.
Visual Cues: Color-coded backgrounds (e.g., green for bullish, red for bearish) and emojis (e.g., 🐂for bullish, 🐻for bearish) make it easy to spot dominant conditions or emerging shifts, enhancing your ability to react quickly.
How to Use It
Add to Chart: Apply the indicator to any symbol (Best W/ SPY, QQQ, IWM and correlated futures securities) on a 1-second or higher timeframe. The table appears in your chosen position (default: bottom right).
Monitor Internals: Check the table for real-time readings and trend states. For example, "NYSE TICK: 800, Strong Overbought Rally" signals short-term bullish momentum.
Assess Conditions: Look for alignment across internals—e.g., multiple "Bullish" states suggest buying strength, while "Neutral" dominance warns of choppy action.
Adapt Your Strategy: Use the trend states to confirm entries, exits, or hold-off decisions. Pair with price tools (e.g., candlestick patterns) for a fuller picture.
Customize: Adjust table position, orientation (vertical/horizontal), text size, colors, and transparency via inputs to match your setup.
Customization
Tailor the experience with options for table placement (e.g., "Top Left"), text size (e.g., "Small" to "Huge"), orientation, and color schemes. Adjust transparency settings to keep the table unobtrusive yet readable.
Limitations
Requires real-time NYSE/Nasdaq data access, which may depend on your TradingView subscription.
Displays current conditions only, not predictive signals—use it as a real-time snapshot, not a crystal ball.
Best paired with price analysis for comprehensive trading decisions.
Requires a TradingView Subscription that supports the 1s Time Frame
Why It’s Original
The Dolores Trading Assistant stands out with its elegant, yet simple table-based presentation of NYSE and Nasdaq internals, distilled into an intuitive format with trend states and visual cues. Unlike cluttered dashboards or generic mashups, it offers a focused, trader-centric view of market momentum, avoiding unnecessary complexity while delivering actionable insights—making it a fresh and practical tool for the New York session.
YY Price LimitsThis Pine Script indicator is designed to visualize potential price limits (e.g., daily price limits used in some markets like commodities) on a TradingView chart. It calculates and plots lines representing percentage-based price limits above and below a reference price (typically the previous day's close). The indicator allows you to customize the displayed price limits, their appearance, and how they extend across the chart. It's particularly useful for intraday traders who need to be aware of potential price ceilings and floors.
Key Features:
Percentage-Based Limits:
Calculates price limits based on percentages (3%, 5%, and 7%) of a reference price.
Customizable Display:
Toggle visibility of reference price and each percentage limit (3%, 5%, 7%).
Customize the color, style (solid, dashed, dotted), and width of the price limit lines.
Extends Lines: Allows you to extend the price limit lines to the left, right, both directions, or not at all.
CME Reference Price: It is designed to plot price limits based on the CME (Chicago Mercantile Exchange) methodology, which uses the last close as the reference price. The tooltip reminds users to verify the actual reference price on the CME Group website.
Intraday Focus: The indicator is specifically designed for intraday timeframes, as it uses the previous day's close as the reference point.
Clear Visuals: Plots horizontal lines with labels indicating the price level and percentage.
