ADX Forecast Colorful [DiFlip]ADX Forecast Colorful
Introducing one of the most advanced ADX indicators available — a fully customizable analytical tool that integrates forward-looking forecasting capabilities. ADX Forecast Colorful is a scientific evolution of the classic ADX, designed to anticipate future trend strength using linear regression. Instead of merely reacting to historical data, this indicator projects the future behavior of the ADX, giving traders a strategic edge in trend analysis.
⯁ Real-Time ADX Forecasting
For the first time, a public ADX indicator incorporates linear regression (least squares method) to forecast the future behavior of ADX. This breakthrough approach enables traders to anticipate trend strength changes based on historical momentum. By applying linear regression to the ADX, the indicator plots a projected trendline n periods ahead — helping users make more accurate and timely trading decisions.
⯁ Highly Customizable
The indicator adapts seamlessly to any trading style. It offers a total of 26 long entry conditions and 26 short entry conditions, making it one of the most configurable ADX tools on TradingView. Each condition is fully adjustable, enabling the creation of statistical, quantitative, and automated strategies. You maintain full control over the signals to align perfectly with your system.
⯁ Innovative and Science-Based
This is the first public ADX indicator to apply least-squares predictive modeling to ADX dynamics. Technically, it embeds machine learning logic into a traditional trend-strength indicator. Using linear regression as a predictive engine adds powerful statistical rigor to the ADX, turning it into an intelligent, forward-looking signal generator.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental method in statistics and machine learning used to model the relationship between a dependent variable y and one or more independent variables x. The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted value (e.g., future ADX)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept
β₁ = slope (rate of change)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the ADX projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error, the regression coefficients are calculated as:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ = summation
x̄ and ȳ = means of x and y
i ranges from 1 to n (number of data points)
These formulas provide the best linear unbiased estimator under Gauss-Markov conditions — assuming constant variance and linearity.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational algorithm in supervised learning. Its power in producing quantitative predictions makes it essential in AI systems, predictive analytics, time-series forecasting, and automated trading. Applying it to the ADX essentially places an intelligent forecasting engine inside a classic trend tool.
⯁ Visual Interpretation
Imagine an ADX time series like this:
Time →
ADX →
The regression line smooths these values and projects them n periods forward, creating a predictive trajectory. This forecasted ADX line can intersect with the actual ADX, offering smarter buy and sell signals.
⯁ Summary of Scientific Concepts
Linear Regression: Models variable relationships with a straight line.
Least Squares: Minimizes prediction errors for best fit.
Time-Series Forecasting: Predicts future values using historical data.
Supervised Learning: Trains models to predict outcomes from inputs.
Statistical Smoothing: Reduces noise and highlights underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Based on rigorous statistical theory.
Unprecedented: First public ADX using least-squares forecast modeling.
Smart: Uses machine learning logic.
Forward-Looking: Generates predictive, not just reactive, signals.
Customizable: Flexible for any strategy or timeframe.
⯁ Conclusion
By merging ADX and linear regression, this indicator enables traders to predict market momentum rather than merely follow it. ADX Forecast Colorful is not just another indicator — it’s a scientific leap forward in technical analysis. With 26 fully configurable entry conditions and smart forecasting, this open-source tool is built for creating cutting-edge quantitative strategies.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the ADX?
The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down.
The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength.
⯁ How to use the ADX?
The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones:
Strong Trend: When the ADX is above 25, indicating a strong trend.
Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend.
Neutral Zone: Between 20 and 25, where the trend strength is unclear.
⯁ Entry Conditions
Each condition below is fully configurable and can be combined to build precise trading logic.
📈 BUY
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
📉 SELL
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
🤖 Automation
All BUY and SELL conditions are compatible with TradingView alerts, making them ideal for fully or semi-automated systems.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Поиск скриптов по запросу "马斯克+100万"
Superior-Range Bound Renko - Strategy - 11-29-25 - SignalLynxSuperior-Range Bound Renko Strategy with Advanced Risk Management Template
Signal Lynx | Free Scripts supporting Automation for the Night-Shift Nation 🌙
1. Overview
Welcome to Superior-Range Bound Renko (RBR) — a volatility-aware, structure-respecting swing-trading system built on top of a full Risk Management (RM) Template from Signal Lynx.
Instead of relying on static lookbacks (like “14-period RSI”) or plain MA crosses, Superior RBR:
Adapts its range definition to market volatility in real time
Emulates Renko Bricks on a standard, time-based chart (no Renko chart type required)
Uses a stack of Laguerre Filters to detect genuine impulse vs. noise
Adds an Adaptive SuperTrend powered by a small k-means-style clustering routine on volatility
Under the hood, this script also includes the full Signal Lynx Risk Management Engine:
A state machine that separates “Signal” from “Execution”
Layered exit tools: Stop Loss, Trailing Stop, Staged Take Profit, Advanced Adaptive Trailing Stop (AATS), and an RSI-style stop (RSIS)
Designed for non-repainting behavior on closed candles by basing execution-critical logic on previous-bar data
We are publishing this as an open-source template so traders and developers can leverage a professional-grade RM engine while integrating their own signal logic if they wish.
2. Quick Action Guide (TL;DR)
Best Timeframe:
4 Hours (H4) and above. This is a high-conviction swing-trading system, not a scalper.
Best Assets:
Volatile instruments that still respect market structure:
Bitcoin, Ethereum, Gold (XAUUSD), high-volatility Forex pairs (e.g., GBPJPY), indices with clean ranges.
Strategy Type:
Volatility-Adaptive Trend Following + Impulse Detection.
It hunts for genuine expansion out of ranges, not tiny mean-reversion nibbles.
Key Feature:
Renko Emulation on time-based candles.
We mathematically model Renko Bricks and overlay them on your standard chart to define:
“Equilibrium” zones (inside the brick structure)
“Breakout / impulse” zones (when price AND the impulse line depart from the bricks)
Repainting:
Designed to be non-repainting on closed candles.
All RM execution logic uses confirmed historical data (no future bars, no security() lookahead). Intrabar flicker during formation is allowed, but once a bar closes the engine’s decisions are stable.
Core Toggles & Filters:
Enable Longs and Shorts independently
Optional Weekend filter (block trades on Saturday/Sunday)
Per-module toggles: Stop Loss, Trailing Stop, Staged Take Profits, AATS, RSIS
3. Detailed Report: How It Works
A. The Strategy Logic: Superior RBR
Superior RBR builds its entry signal from multiple mathematical layers working together.
1) Adaptive Lookback (Volatility Normalization)
Instead of a fixed 100-bar or 200-bar range, the script:
Computes ATR-based volatility over a user-defined period.
Normalizes that volatility relative to its recent min/max.
Maps the normalized value into a dynamic lookback window between a minimum and maximum (e.g., 4 to 100 bars).
High Volatility:
The lookback shrinks, so the system reacts faster to explosive moves.
Low Volatility:
The lookback expands, so the system sees a “bigger picture” and filters out chop.
All the core “Range High/Low” and “Range Close High/Low” boundaries are built on top of this adaptive window.
2) Range Construction & Quick Ranges
The engine constructs several nested ranges:
Outer Range:
rangeHighFinal – dynamic highest high
rangeLowFinal – dynamic lowest low
Inner Close Range:
rangeCloseHighFinal – highest close
rangeCloseLowFinal – lowest close
Quick Ranges:
“Half-length” variants of those, used to detect more responsive changes in structure and volatility.
These ranges define:
The macro box price is trading inside
Shorter-term “pressure zones” where price is coiling before expansion
3) Renko Emulation (The Bricks)
Rather than using the Renko chart type (which discards time), this script emulates Renko behavior on your normal candles:
A “brick size” is defined either:
As a standard percentage move, or
As a volatility-driven (ATR) brick, optionally inhibited by a minimum standard size
The engine tracks a base value and derives:
brickUpper – top of the emulated brick
brickLower – bottom of the emulated brick
When price moves sufficiently beyond those levels, the brick “shifts”, and the directional memory (renkoDir) updates:
renkoDir = +2 when bricks are advancing upward
renkoDir = -2 when bricks are stepping downward
You can think of this as a synthetic Renko tape overlaid on time-based candles:
Inside the brick: equilibrium / consolidation
Breaking away from the brick: momentum / expansion
4) Impulse Tracking with Laguerre Filters
The script uses multiple Laguerre Filters to smooth price and brick-derived data without traditional lag.
Key filters include:
LagF_1 / LagF_W: Based on brick upper/lower baselines
LagF_Q: Based on HLCC4 (high + low + 2×close)/4
LagF_Y / LagF_P: Complex averages combining brick structures and range averages
LagF_V (Primary Impulse Line):
A smooth, high-level impulse line derived from a blend of the above plus the outer ranges
Conceptually:
When the impulse line pushes away from the brick structure and continues in one direction, an impulse move is underway.
When its direction flips and begins to roll over, the impulse is fading, hinting at mean reversion back into the range.
5) Fib-Based Structure & Swaps
The system also layers in Fib levels derived from the adaptive ranges:
Standard levels (12%, 23.6%, 38.2%, 50%, 61%, 76.8%, 88%) from the main range
A secondary “swap” set derived from close-range dynamics (fib12Swap, fib23Swap, etc.)
These Fibs are used to:
Bucket price into structural zones (below 12, between 23–38, etc.)
Detect breakouts when price and Laguerre move beyond key Fib thresholds
Drive zSwap logic (where a secondary Fib set becomes the active structure once certain conditions are met)
6) Adaptive SuperTrend with K-Means-Style Volatility Clustering
Under the hood, the script uses a small k-means-style clustering routine on ATR:
ATR is measured over a fixed period
The range of ATR values is split into Low, Medium, High volatility centroids
Current ATR is assigned to the nearest centroid (cluster)
From that, a SuperTrend variant (STK) is computed with dynamic sensitivity:
In quiet markets, SuperTrend can afford to be tighter
In wild markets, it widens appropriately to avoid constant whipsaw
This SuperTrend-based oscillator (LagF_K and its signals) is then combined with the brick and Laguerre stack to confirm valid trend regimes.
7) Final Baseline Signals (+2 / -2)
The “brain” of Superior RBR lives in the Baseline & Signal Generation block:
Two composite signals are built: B1 and B2:
They combine:
Fib breakouts
Renko direction (renkoDir)
Expansion direction (expansionQuickDir)
Multiple Laguerre alignments (LagF_Q, LagF_W, LagF_Y, LagF_Z, LagF_P, LagF_V)
They also factor in whether Fib structures are expanding or contracting.
A user toggle selects the “Baseline” signal:
finalSig = B2 (default) or B1 (alternate baseline)
finalSig is then filtered through the RM state machine and only when everything aligns, we emit:
+2 = Long / Buy signal
-2 = Short / Sell signal
0 = No new trade
Those +2 / -2 values are what feed the Risk Management Engine.
B. The Risk Management (RM) Engine
This script features the Signal Lynx Risk Management Engine, a proprietary state machine built to separate Signal from Execution.
Instead of firing orders directly on indicator conditions, we:
Convert the raw signal into a clean integer (Fin = +2 / -2 / 0)
Feed it into a Trade State Machine that understands:
Are we flat?
Are we in a long or short?
Are we in a closing sequence?
Should we permit re-entry now or wait?
Logic Injection / Template Concept:
The RM engine expects a simple integer:
+2 → Buy
-2 → Sell
Everything else (0) is “no new trade”
This makes the script a template:
You can remove the Superior RBR block
Drop in your own logic (RSI, MACD, price action, etc.)
As long as you output +2 or -2 into the same signal channel, the RM engine can drive all exits and state transitions.
Aggressive vs Conservative Modes:
The input AgressiveRM (Aggressive RM) governs how we interpret signals:
Conservative Mode (Aggressive RM = false):
Uses a more filtered internal signal (AF) to open trades
Effectively waits for a clean trend flip / confirmation before new entries
Minimizes whipsaw at the cost of fewer trades
Aggressive Mode (Aggressive RM = true):
Reacts directly to the fresh alert (AO) pulses
Allows faster re-entries in the same direction after RM-based exits
Still respects your pyramiding setting; this script ships with pyramiding = 0 by default, so it will not stack multiple positions unless you change that parameter in the strategy() call.
The state machine enforces discipline on top of your signal logic, reducing double-fires and signal spam.
C. Advanced Exit Protocols (Layered Defense)
The exit side is where this template really shines. Instead of a single “take profit or stop loss,” it uses multiple, cooperating layers.
1) Hard Stop Loss
A classic percentage-based Stop Loss (SL) relative to the entry price.
Acts as a final “catastrophic protection” layer for unexpected moves.
2) Standard Trailing Stop
A percentage-based Trailing Stop (TS) that:
Activates only after price has moved a certain percentage in your favor (tsActivation)
Then trails price by a configurable percentage (ts)
This is a straightforward, battle-tested trailing mechanism.
3) Staged Take Profits (Three Levels)
The script supports three staged Take Profit levels (TP1, TP2, TP3):
Each stage has:
Activation percentage (how far price must move in your favor)
Trailing amount for that stage
Position percentage to close
Example setup:
TP1:
Activate at +10%
Trailing 5%
Close 10% of the position
TP2:
Activate at +20%
Trailing 10%
Close another 10%
TP3:
Activate at +30%
Trailing 5%
Close the remaining 80% (“runner”)
You can tailor these quantities for partial scaling out vs. letting a core position ride.
4) Advanced Adaptive Trailing Stop (AATS)
AATS is a sophisticated volatility- and structure-aware stop:
Uses Hirashima Sugita style levels (HSRS) to model “floors” and “ceilings” of price:
Dungeon → Lower floors → Mid → Upper floors → Penthouse
These levels classify where current price sits within a long-term distribution.
Combines HSRS with Bollinger-style envelopes and EMAs to determine:
Is price extended far into the upper structure?
Is it compressed near the lower ranges?
From this, it computes an adaptive factor that controls how tight or loose the trailing level (aATS / bATS) should be:
High Volatility / Penthouse areas:
Stop loosens to avoid getting wicked out by inevitable spikes.
Low Volatility / compressed structure:
Stop tightens to lock in and protect profit.
AATS is designed to be the “smart last line” that responds to context instead of a single fixed percentage.
5) RSI-Style Stop (RSIS)
On top of AATS, the script includes a RSI-like regime filter:
A McGinley Dynamic mean of price plus ATR bands creates a dynamic channel.
Crosses above the top band and below the lower band change a directional state.
When enabled (UseRSIS):
RSIS can confirm or veto AATS closes:
For longs: A shift to bearish RSIS can force exits sooner.
For shorts: A shift to bullish RSIS can do the same.
This extra layer helps avoid over-reactive stops in strong trends while still respecting a regime change when it happens.
D. Repainting Protection
Many strategies look incredible in the Strategy Tester but fail in live trading because they rely on intrabar values or future-knowledge functions.
This template is built with closed-candle realism in mind:
The Risk Management logic explicitly uses previous bar data (open , high , low , close ) for the key decisions on:
Trailing stop updates
TP triggers
SL hits
RM state transitions
No security() lookahead or future-bar access is used.
This means:
Backtest behavior is designed to match what you can actually get with TradingView alerts and live automation.
Signals may “flicker” intrabar while the candle is forming (as with any strategy), but on closed candles, the RM decisions are stable and non-repainting.
4. For Developers & Modders
We strongly encourage you to mod this script.
To plug your own strategy into the RM engine:
Look for the section titled:
// BASELINE & SIGNAL GENERATION
You will see composite logic building B1 and B2, and then selecting:
baseSig = B2
altSig = B1
finalSig = sigSwap ? baseSig : altSig
You can replace the content used to generate baseSig / altSig with your own logic, for example:
RSI crosses
MACD histogram flips
Candle pattern detectors
External condition flags
Requirements are simple:
Your final logic must output:
2 → Buy signal
-2 → Sell signal
0 → No new trade
That output flows into the RM engine via finalSig → AlertOpen → state machine → Fin.
Once you wire your signals into finalSig, the entire Risk Management system (Stops, TPs, AATS, RSIS, re-entry logic, weekend filters, long/short toggles) becomes available for your custom strategy without re-inventing the wheel.
This makes Superior RBR not just a strategy, but a reference architecture for serious Pine dev work.
5. About Signal Lynx
Automation for the Night-Shift Nation 🌙
Signal Lynx focuses on helping traders and developers bridge the gap between indicator logic and real-world automation. The same RM engine you see here powers multiple internal systems and templates, including other public scripts like the Super-AO Strategy with Advanced Risk Management.
We provide this code open source under the Mozilla Public License 2.0 (MPL-2.0) to:
Demonstrate how Adaptive Logic and structured Risk Management can outperform static, one-layer indicators
Give Pine Script users a battle-tested RM backbone they can reuse, remix, and extend
If you are looking to automate your TradingView strategies, route signals to exchanges, or simply want safer, smarter strategy structures, please keep Signal Lynx in your search.
License: Mozilla Public License 2.0 (Open Source).
If you make beneficial modifications, please consider releasing them back to the community so everyone can benefit.
Single AHR DCA (HM) — AHR Pane (customized quantile)Customized note
The log-regression window LR length controls how long a long-term fair value path is estimated from historical data.
The AHR window AHR window length controls over which historical regime you measure whether the coin is “cheap / expensive”.
When you choose a log-regression window of length L (years) and an AHR window of length A (years), you can intuitively read the indicator as:
“Within the last A years of this regime, relative to the long-term trend estimated over the same A years, the current price is cheap / neutral / expensive.”
Guidelines:
In general, set the AHR window equal to or slightly longer than the LR window:
If the AHR window is much longer than LR, you mix different baselines (different LR regimes) into one distribution.
If the AHR window is much shorter than LR, quantiles mostly reflect a very local slice of history.
For BTC / ETH and other BTC-like assets, you can use relatively long horizons (e.g. LR ≈ 3–5 years, AHR window ≈ 3–8 years).
For major altcoins (BNB / SOL / XRP and similar high-beta assets), it is recommended to use equal or slightly shorter horizons, e.g. LR ≈ 2–3 years, AHR window ≈ 2–3 years.
1. Price series & windows
Working timeframe: daily (1D).
Let the daily close of the current symbol on day t be P_t .
Main length parameters:
HM window: L_HM = maLen (default 200 days)
Log-regression window: L_LR = lrLen (default 1095 days ≈ 3 years)
AHR window (regime window): W = windowLen (default 1095 days ≈ 3 years)
2. Harmonic moving average (HM)
On a window of length L_HM, define the harmonic mean:
HM_t = ^(-1)
Here eps = 1e-10 is used to avoid division by zero.
Intuition: HM is more sensitive to low prices – an extremely low price inside the window will drag HM down significantly.
3. Log-regression baseline (LR)
On a window of length L_LR, perform a linear regression on log price:
Over the last L_LR bars, build the series
x_k = log( max(P_k, eps) ), for k = t-L_LR+1 ... t, and fit
x_k ≈ a + b * k.
The fitted value at the current index t is
log_P_hat_t = a + b * t.
Exponentiate to get the log-regression baseline:
LR_t = exp( log_P_hat_t ).
Interpretation: LR_t is the long-term trend / fair value path of the current regime over the past L_LR days.
4. HM-based AHR (valuation ratio)
At each time t, build an HM-based AHR (valuation multiple):
AHR_t = ( P_t / HM_t ) * ( P_t / LR_t )
Interpretation:
P_t / HM_t : deviation of price from the mid-term HM (e.g. 200-day harmonic mean).
P_t / LR_t : deviation of price from the long-term log-regression trend.
Multiplying them means:
if price is above both HM and LR, “expensiveness” is amplified;
if price is below both, “cheapness” is amplified.
Typical reading:
AHR_t < 1 : price is below both mid-term mean and long-term trend → statistically cheaper.
AHR_t > 1 : price is above both mid-term mean and long-term trend → statistically more expensive.
5. Empirical quantile thresholds (Opp / Risk)
On each new day, whenever AHR_t is valid, add it into a rolling array:
A_t_window = { AHR_{t-W+1}, ..., AHR_t } (at most W = windowLen elements)
On this empirical distribution, define two quantiles:
Opportunity quantile: q_opp (default 15%)
Risk quantile: q_risk (default 65%)
Using standard percentile computation (order statistics + linear interpolation), we get:
Opp threshold:
theta_opp = Percentile( A_t_window, q_opp )
Risk threshold:
theta_risk = Percentile( A_t_window, q_risk )
We also compute the percentile rank of the current AHR inside the same history:
q_now = PercentileRank( A_t_window, AHR_t ) ∈
This yields three valuation zones:
Opportunity zone: AHR_t <= theta_opp
(corresponds to roughly the cheapest ~q_opp% of historical states in the last W days.)
Neutral zone: theta_opp < AHR_t < theta_risk
Risk zone: AHR_t >= theta_risk
(corresponds to roughly the most expensive ~(100 - q_risk)% of historical states in the last W days.)
All quantiles are purely empirical and symbol-specific: they are computed only from the current asset’s own history, without reusing BTC thresholds or assuming cross-asset similarity.
6. DCA simulation (lightweight, rolling window)
Given:
a daily budget B (input: budgetPerDay), and
a DCA simulation window H (input: dcaWindowLen, default 900 days ≈ 2.5 years),
The script applies the following rule on each new day t:
If thresholds are unavailable or AHR_t > theta_risk
→ classify as Risk zone → buy = 0
If AHR_t <= theta_opp
→ classify as Opportunity zone → buy = 2B (double size)
Otherwise (Neutral zone)
→ buy = B (normal DCA)
Daily invested cash:
C_t ∈ {0, B, 2B}
Daily bought quantity:
DeltaQ_t = C_t / P_t
The script keeps rolling sums over the last H days:
Cumulative position:
Q_H = sum_{k=t-H+1..t} DeltaQ_k
Cumulative invested cash:
C_H = sum_{k=t-H+1..t} C_k
Current portfolio value:
PortVal_t = Q_H * P_t
Cumulative P&L:
PnL_t = PortVal_t - C_H
Active days:
number of days in the last H with C_k > 0.
These results are only used to visualize how this AHR-quantile-driven DCA rule would have behaved over the recent regime, and do not constitute financial advice.
new_youtube_strategy//@version=5
strategy("Dow + Homma 1m Scalper (15m filter)", overlay=true, margin_long=100, margin_short=100, initial_capital=10000)
//===== INPUTS =====
maLen = input.int(50, "Trend SMA Length", minval=5)
htf_tf = input.timeframe("15", "Higher TF")
priceTolPct = input.float(0.05, "SR tolerance %", step=0.01)
wickFactor = input.float(2.0, "Hammer/ShootingStar wick factor", step=0.1)
dojiThresh = input.float(0.1, "Doji body % of range", step=0.01)
risk_RR = input.float(2.0, "Reward:Risk", step=0.1)
capitalRiskPct = input.float(1.0, "Risk % of equity per trade", step=0.1)
//===== 1m TREND (SMA) =====
sma1 = ta.sma(close, maLen)
sma1Up = sma1 > sma1
sma1Down = sma1 < sma1
uptrend1 = close > sma1 and sma1Up
downtrend1 = close < sma1 and sma1Down
//===== 15m TREND VIA request.security =====
sma15 = request.security(syminfo.tickerid, htf_tf, ta.sma(close, maLen), lookahead=barmerge.lookahead_off)
sma15Up = sma15 > sma15
sma15Down = sma15 < sma15
uptrend15 = close > sma15 and sma15Up
downtrend15 = close < sma15 and sma15Down
//===== SWING HIGHS/LOWS (LOCAL EXTREMA) =====
var int left = 3
var int right = 3
swHigh = ta.pivothigh(high, left, right)
swLow = ta.pivotlow(low, left, right)
//===== SR FLIP LEVELS =====
var float srSupport = na
var float srResistance = na
// when a swing high is broken -> new support
if not na(swHigh)
if close > swHigh
srSupport := swHigh
// when a swing low is broken -> new resistance
if not na(swLow)
if close < swLow
srResistance := swLow
//===== CANDLE METRICS =====
body = math.abs(close - open)
cRange = high - low
upperW = high - math.max(open, close)
lowerW = math.min(open, close) - low
isBull() => close > open
isBear() => close < open
bullHammer() =>
cRange > 0 and
isBull() and
lowerW >= wickFactor * body and
upperW <= body
bearShootingStar() =>
cRange > 0 and
isBear() and
upperW >= wickFactor * body and
lowerW <= body
isDoji() =>
cRange > 0 and body <= dojiThresh * cRange
bullEngulfing() =>
isBear() and isBull() and
open <= close and close >= open
bearEngulfing() =>
isBull() and isBear() and
open >= close and close <= open
//===== SR PROXIMITY =====
tol = priceTolPct * 0.01 * close
nearSupport = not na(srSupport) and math.abs(close - srSupport) <= tol
nearResistance = not na(srResistance) and math.abs(close - srResistance) <= tol
//===== SIGNAL CONDITIONS =====
bullCandle = bullHammer() or isDoji() or bullEngulfing()
bearCandle = bearShootingStar() or isDoji() or bearEngulfing()
longTrendOK = uptrend1 and uptrend15
shortTrendOK = downtrend1 and downtrend15
longSignal = longTrendOK and nearSupport and bullCandle
shortSignal = shortTrendOK and nearResistance and bearCandle
//===== POSITION SIZING (IN RISK UNITS) =====
var float lastEquity = strategy.equity
riskCapital = strategy.equity * (capitalRiskPct * 0.01)
//===== ENTRY / EXIT PRICES =====
longStop = math.min(low, nz(srSupport, low))
longRisk = close - longStop
longTP = close + risk_RR * longRisk
shortStop = math.max(high, nz(srResistance, high))
shortRisk = shortStop - close
shortTP = close - risk_RR * shortRisk
// qty in contracts (approx; assumes price * qty ≈ capital used)
longQty = longRisk > 0 ? riskCapital / longRisk : 0.0
shortQty = shortRisk > 0 ? riskCapital / shortRisk : 0.0
//===== EXECUTION =====
if longSignal and longRisk > 0 and longQty > 0
strategy.entry("Long", strategy.long, qty=longQty)
strategy.exit("Long TP/SL", from_entry="Long", stop=longStop, limit=longTP)
if shortSignal and shortRisk > 0 and shortQty > 0
strategy.entry("Short", strategy.short, qty=shortQty)
strategy.exit("Short TP/SL", from_entry="Short", stop=shortStop, limit=shortTP)
//===== PLOTS =====
plot(sma1, color=color.orange, title="SMA 1m")
plot(sma15, color=color.blue, title="HTF SMA (15m)")
plot(srSupport, "SR Support", color=color.new(color.green, 50), style=plot.style_linebr)
plot(srResistance,"SR Resistance",color=color.new(color.red, 50), style=plot.style_linebr)
// Visual debug for signals
plotshape(longSignal, title="Long Signal", style=shape.triangleup, location=location.belowbar, color=color.lime, size=size.tiny)
plotshape(shortSignal, title="Short Signal", style=shape.triangledown, location=location.abovebar, color=color.red, size=size.tiny)
ES-VIX Daily Price Bands - Inner and OuterES-VIX Daily Price Bands
This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = Daily Low + (ES Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily High - (ES Price × VIX ÷ √252 ÷ 100)
The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's extremes.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's low
Lower band (red) contracts from the current day's high
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Outer upper band (green) at 150% of expected move
Outer lower band (red) at 150% of expected move
Shaded zone between bands for visual clarity
Information table displaying:
Current ES price and VIX level
Running daily high and low
Current upper and lower band values
Psychological levels [Kodologic] Psychological levels
Markets are not random, they are driven by human psychology and algorithmic order flow. A well-known phenomenon in trading is the "Whole Number Bias" — the tendency for price to react significantly at clean, round numbers (e.g., Bitcoin at $95,000 or EURUSD at 1.0500).
Manually drawing horizontal lines at every round number is tedious, clutters your object tree, and distracts you from analyzing price action.
Psychological levels Numbers is a workflow utility designed to solve this problem. It automatically projects a clean, customizable grid of key price levels onto your chart, helping you instantly identify areas where liquidity and orders are likely to cluster.
Why This Indicator Helps Traders :
Professional traders know that "00" and "50" levels act as magnets for price. Here is how this tool assists in your analysis:
1. Institutional Footprints : Large institutions and bank algorithms often execute orders at whole numbers to simplify accounting. This script highlights these potential liquidity zones automatically.
2. Support & Resistance Discovery: You will often notice price wicking or reversing exactly on these grid lines. This helps in spotting natural support and resistance without needing complex technical analysis.
3. Cognitive Load Reduction: Instead of calculating where the next "major level" is, the grid is visually present, allowing you to focus on candlestick patterns and market structure.
Features :
Dynamic Calculation : The grid updates automatically as price moves, you never have to redraw lines.
Zero Clutter : The lines are drawn using code, meaning they do not appear in your manual drawing tools list or clutter your object tree.
Fully Customizable Step : You define what constitutes a "Round Number" for your specific asset class (Forex, Crypto, Indices, or Stocks).
Visual Control : Adjust line styles (Solid, Dotted, Dashed), colors, and transparency to keep your chart aesthetic and readable.
How to Use in Your Strategy :
1. Target Setting (Take Profit)
If you are in a long position, use the next upper grid line as a logical Take Profit area. Price often gravitates toward these whole numbers before reversing or consolidating.
2. Stop Loss Placement
Avoid placing Stop Losses exactly on a round number, as these are often "stop hunted." Instead, use the grid to visualize the level and place your stop slightly *below* or *above* the round number for better protection.
3. Confluence Trading
Do not use these lines in isolation. Look for Confluence :
Example: If a Fibonacci 61.8% level lines up exactly with a Round Number grid line, that level becomes a high-probability reversal zone.
Settings Guide (Important)
Since every asset is priced differently, you must adjust the "levels Step Size" to match your instrument:
Forex (e.g., EURUSD, GBPUSD): Set Step Size to `0.0050` (50 pips) or `0.0100` (100 pips).
Crypto (e.g., BTCUSD): Set Step Size to `500` or `1000`.
Indices (e.g., US30, SPX500): Set Step Size to `100` or `500`.
Gold (XAUUSD):** Set Step Size to `10`.
Disclaimer: This tool is for educational and visual aid purposes only. It does not provide buy or sell signals. Always manage your risk.
ULTRA PRO SCALPING V6//@version=6
indicator("ULTRA PRO SCALPING V6", overlay=true, max_lines_count=500, max_labels_count=500)
// SETTINGS
lengthEMA = input.int(21, "EMA Trend")
riskRR = input.float(1.5, "Ratio TP/SL", step=0.1)
sl_pips = input.float(0.15, "Stop Loss (%)", step=0.01)
showTP_SL = input.bool(true, "Afficher TP & SL")
showSignals = input.bool(true, "Afficher Signaux")
// TREND FILTER
ema = ta.ema(close, lengthEMA)
plot(ema, "EMA", color=color.new(color.yellow, 0), linewidth=2)
// ENTRY SIGNALS
longSignal = ta.crossover(close, ema)
shortSignal = ta.crossunder(close, ema)
// TP/SL SYSTEM
var float lastSL = na
var float lastTP = na
if longSignal
lastSL := close * (1 - sl_pips/100)
lastTP := close + (close - lastSL) * riskRR
if shortSignal
lastSL := close * (1 + sl_pips/100)
lastTP := close - (lastSL - close) * riskRR
// DISPLAY
if showTP_SL and not na(lastSL)
line.new(bar_index-1, lastSL, bar_index, lastSL, color=color.red)
label.new(bar_index, lastSL, "SL", color=color.red)
if showTP_SL and not na(lastTP)
line.new(bar_index-1, lastTP, bar_index, lastTP, color=color.green)
label.new(bar_index, lastTP, "TP", color=color.green)
if showSignals and longSignal
label.new(bar_index, low, "BUY", color=color.green, style=label.style_label_up)
if showSignals and shortSignal
label.new(bar_index, high, "SELL", color=color.red, style=label.style_label_down)
// ALERTS
alertcondition(longSignal, "BUY Signal", "Signal d’achat détecté")
alertcondition(shortSignal, "SELL Signal", "Signal de vente détecté")
ES-VIX Daily Price Bands - Inner bands (80% and 50%)ES-VIX Daily Price Bands
This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = Daily Low + (ES Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily High - (ES Price × VIX ÷ √252 ÷ 100)
The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's extremes.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's low
Lower band (red) contracts from the current day's high
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Shaded zone between bands for visual clarity
Information table displaying:
Current ES price and VIX level
Running daily high and low
Current upper and lower band values
ES-VIX Daily Price Bands - Inner bandsES-VIX Daily Price Bands
This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = Daily Low + (ES Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily High - (ES Price × VIX ÷ √252 ÷ 100)
The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's extremes.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's low
Lower band (red) contracts from the current day's high
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Shaded zone between bands for visual clarity
Information table displaying:
Current ES price and VIX level
Running daily high and low
Current upper and lower band values
ES-VIX Daily Price BandsES-VIX Daily Price Bands
This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = Daily Low + (ES Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily High - (ES Price × VIX ÷ √252 ÷ 100)
The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's extremes.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's low
Lower band (red) contracts from the current day's high
Shaded zone between bands for visual clarity
Information table displaying:
Current ES price and VIX level
Running daily high and low
Current upper and lower band values
Dynamic Ratchet Trend Strategy [VIX Filter]Overview This strategy is a long-only trend-following system designed to capture major market moves while strictly managing downside risk through a state-machine based "Ratchet" exit logic. It incorporates a volatility filter using the CBOE VIX index to stay out of (or exit) the market during high-stress environments.
Key Features
1. Multi-Condition Entries The strategy looks for momentum shifts and trend breakouts using four Simple Moving Averages (25, 50, 100, 200).
Momentum Cross: SMA 25 crossover above SMA 50.
Trend Breakouts: A specific "3-Bar Breakout" logic above the SMA 50, 100, or 200. This requires the price to hold above the SMA for 3 consecutive bars after being below it, reducing false signals compared to simple closes.
2. VIX Volatility Filter Before entering any trade, the script checks the CBOE:VIX.
Filter: If VIX is above the threshold (default 32), new entries are blocked.
Panic Exit: If you are in a position and the VIX spikes above the threshold, the strategy executes an immediate "Panic Exit" to preserve capital during market crashes.
3. The "Ratchet" Exit System (3 Stages) Unlike a standard trailing stop, this strategy uses a 3-stage dynamic exit mechanism that tightens as profits grow:
Stage 0 (Initial Risk): Standard percentage-based Stop Loss from the entry price.
Stage 1 (The Lock-In): Triggered when profit hits 10% (configurable).
Unique Logic: Instead of trailing from the highest high, the stop is calculated based on the price at the exact moment this stage was triggered. It "steps up" once and holds, securing the initial move without being prematurely stopped out by normal volatility.
Stage 2 (Trailing Mode): Triggered when profit hits 15% (configurable).
The strategy switches to a classic Trailing Stop, following the percentage distance from the Highest High.
4. Emergency Backup A "Dead Cross" (SMA 25 crossing under SMA 50) acts as a final fail-safe to close positions if the trend reverses completely before hitting a stop.
Settings & Inputs
SMAs: Customize the lengths for all four moving averages.
VIX Filter: Toggle the filter on/off and set the panic threshold.
Exit Logic: Fully customizable percentages for Initial SL, Stage 1 Trigger/Distance, and Stage 2 Trigger/Trailing Distance.
Disclaimer This script is for educational purposes only. Past performance is not indicative of future results. Always manage your risk appropriately.
Ratchet Exit Trend Strategy with VIX FilterThis strategy is a trend-following system designed specifically for volatile markets. Instead of focusing solely on the "perfect entry," this script emphasizes intelligent trade management using a custom **"Ratchet Exit System."**
Additionally, it integrates a volatility filter based on the CBOE Volatility Index (VIX) to minimize risk during extreme market phases.
### 🎯 The Concept: Ratchet Exit
The "Ratchet" system operates like a mechanical ratchet tool: the Stop Loss can only move in one direction (up, for long trades) and "locks" into specific stages. The goal is to give the trade "room to breathe" initially to avoid being stopped out by noise, then aggressively reduce risk as the trade moves into profit.
The exit logic moves through 3 distinct phases:
1. **Phase 0 (Initial Risk):** At the start of the trade, a wide Stop Loss is set (Default: 10%) to tolerate normal market volatility.
2. **Phase 1 (Risk Reduction):** Once the trade reaches a specific floating profit (Default: +10%), the Stop Loss is raised and "pinned" to a fixed value (Default: -8% from entry). This drastically reduces risk while keeping the trade alive.
3. **Phase 2 (Trailing Mode):** If the trend extends to a higher profit zone (Default: +15%), the Stop switches to a dynamic Trailing Mode. It follows the **Highest High** at a fixed percentage distance (Default: 8%).
### 🛡️ VIX Filter & Panic Exit
High volatility is often the enemy of trend-following strategies.
* **Entry Filter:** The system will not enter new positions if the VIX is above a user-defined threshold (Default: 32). This helps avoid entering "falling knife" markets.
* **Panic Exit:** If the VIX spikes above the threshold (32) while a trade is open, the position is closed immediately to protect capital (Emergency Exit).
### 📈 Entry Signals
The strategy trades **LONG only** and uses Simple Moving Averages (SMAs) to identify trends:
* **Golden Cross:** SMA 25 crosses over SMA 50.
* **3-Bar Breakouts:** A confirmation logic where the price must close above the SMA 50, 100, or 200 for 3 consecutive bars.
### ⚙️ Settings (Inputs)
All parameters are fully customizable via the settings menu:
* **SMAs:** Lengths for the trend indicators (Default: 25, 50, 100, 200).
* **VIX Filter:** Toggle the filter on/off and adjust the panic threshold.
* **Ratchet Settings:** Percentages for Initial Stop, Trigger Levels for Stages 1 & 2, and the Trailing Distance.
### ⚠️ Technical Note & Risk Warning
This script uses `request.security` to fetch VIX data. Please ensure you understand the risks associated with trading leveraged or volatile assets. Past performance is not indicative of future results.
Minervini VCP Pattern -Indian ContextThis script implements Mark Minervini's Trend Template and VCP (Volatility Contraction Pattern) pattern, specifically adapted for Indian stock markets (NSE). It helps identify stocks that are in strong uptrends and ready to break out.
Core Concepts Explained
1. What is the Minervini Trend Template?
Mark Minervini's method identifies stocks in Stage 2 uptrends - the sweet spot where institutional money is accumulating and stocks show the strongest momentum. Think of it as finding stocks that are "leaders" rather than "laggards."
2. What is VCP (Volatility Contraction Pattern)?
A VCP occurs when:
Stock price consolidates (moves sideways) after an uptrend
Price swings get tighter and tighter (like a coiled spring)
Volume dries up (fewer people trading)
Then it breaks out with force.
You can customize the strategy settings without editing code.
Key Settings:
Minimum Price (₹50): Filters out penny stocks that are too volatile
Min Distance from 52W Low (30%): Stock should be at least 30% above its yearly low
Max Distance from 52W High (25%): Stock should be within 25% of its yearly high (showing strength)
Moving Average Periods: 10, 50, 150, 200 days (industry standard)
Minimum Volume (100,000 shares): Ensures the stock is liquid enough to trade
Indian Market Adaptation: The default values (₹50 minimum, volume thresholds) are adjusted for NSE stocks, which behave differently than US markets.
The script pulls weekly chart data even when you're viewing daily charts.
Why it matters: Weekly trends are more reliable than daily noise. Professional traders use weekly charts to confirm the bigger picture.
What are Moving Averages (MAs)?
Simple averages of closing prices over X days
They smooth out price action to show trends
Think of them as the "average cost" of buyers over different time periods
The 4 Key MAs:
10 MA (Fast): Very short-term trend
50 MA: Short to medium-term trend
150 MA: Medium to long-term trend
200 MA: Long-term trend (the "grandfather" of all MAs)
Why Weekly MAs?
The script also calculates 10 and 50 MAs on weekly data for additional confirmation of the bigger trend.
The script Finds the highest and lowest prices over the past 52 weeks (1 year).
Why it matters:
Stocks near 52-week highs are showing strength (institutions buying)
Stocks far from 52-week lows have "room to run" upward
This is a psychological level that influences trader behaviour.
What is Volume here ?
The number of shares traded each day
High volume = many traders interested (conviction)
Low volume = lack of interest (weakness or consolidation)
Volume in VCP:
During consolidation (sideways movement), volume should dry up - this shows sellers are exhausted and buyers are holding. When volume spikes on a breakout, it confirms the move.
NSE Context: Indian stocks often have different volume patterns than US stocks, so the 50-day average is used as a baseline.
Relative Strength vs Nifty:
Example:
If your stock is up 20% and Nifty is up 10%, your stock has strong RS
If your stock is up 5% and Nifty is up 15%, your stock has weak RS (avoid it!)
Why it matters: The best performing stocks almost always have strong relative strength before major moves.
The 13 Minervini Conditions:-
Condition 1: Price > 50/150/200 MA
Meaning: Current price must be above ALL three major moving averages.
Why: This confirms the stock is in a clear uptrend. If price is below these MAs, the stock is weak or in a downtrend.
Condition 2: MA 50 > 150 > 200
Meaning: The moving averages themselves must be in proper order.
Analogy: Think of this like layers in a cake - short-term on top, long-term at bottom. If they're tangled, the trend is unclear.
Condition 3: 200 MA Rising (1 Month)
Meaning: The 200 MA today must be higher than it was 20 days ago.
Why: This confirms the long-term trend is UP, not flat or down. The means "20 bars ago."
Condition 4: 50 MA Rising
Meaning: The 50 MA today must be higher than 5 days ago.
Why: Confirms short-term momentum is accelerating upward.
Condition 5: Within 25% of 52-Week High
Meaning: Current price should be within 25% of its 1-year high.
Example:
52-week high = ₹1000
Current price must be above ₹750 (within 25%)
Why: Strong stocks stay near their highs. Weak stocks fall far from highs.
Condition 6: 30%+ Above 52-Week Low (OPTIONAL)
Meaning: Stock should be at least 30% above its yearly low.
Note: The script marks this as "SECONDARY - Optional" because the other conditions are more important. However, it's still a good confirmation.
Condition 7: Price > 10 MA
Meaning: Very short-term strength - price above the 10-day moving average.
Why: Ensures the stock hasn't just rolled over in the immediate term.
Condition 8: Price >= ₹50
Meaning: Filters out stocks below ₹50.
Why: In Indian markets, stocks below ₹50 tend to be penny stocks with poor liquidity and higher manipulation risk.
Condition 9: Weekly Uptrend
Meaning: On the weekly chart, price must be above both weekly MAs, and they must be properly aligned.
Why: Confirms the bigger picture trend, not just daily fluctuations.
Condition 10: 150 MA Rising
Meaning: The 150 MA is trending upward over the past 10 days.
Why: Another confirmation of medium-term trend health.
Condition 11: Sufficient Volume
Meaning: Average volume must exceed 100,000 shares (or your custom setting).
Why: Ensures you can actually buy/sell the stock without moving the price too much (liquidity).
Condition 12: RS vs Nifty Strong
Meaning: The stock's relative strength vs Nifty must be improving.
Why: You want stocks that are outperforming the market, not underperforming.
Condition 13: Nifty in Uptrend
Meaning: The Nifty 50 index itself must be above its 50 MA.
Why: "A rising tide lifts all boats." It's easier to make money in individual stocks when the overall market is bullish.
VCP Requirements:
Volatility Contracting: Price swings getting tighter (coiling spring)
Volume Drying Up: Fewer shares trading + trending lower
The Setup: When volatility contracts and volume dries up WHILE all 13 trend conditions are met, you have a VCP setup ready to explode.
What You See on Chart:
Colored Lines: 10 MA (green), 50 MA (blue), 150 MA (orange), 200 MA (red)
Blue Background: Trend template conditions met (watch zone)
Green Background: Full VCP setup detected (buy zone)
↟ Symbol Below Price: New VCP buy signal just triggered
Information Table:
What it does: Creates a checklist table on your chart showing the status of all conditions.
Table Structure:
Column 1: Condition name
Column 2: Status (✓ green = met, ✗ red = not met)
Final Row: Shows "BUY" (green) or "WAIT" (red) based on full VCP setup status.
Dos:
Example:
Account size: ₹5,00,000
Risk per trade: 1% = ₹5,000
Entry: ₹1000
Stop loss: ₹920 (8% below)
Distance to stop: ₹80
Shares to buy: ₹5,000 / ₹80 = 62 shares
Exit Strategy:
Sell 1/3 at +20% profit
Sell another 1/3 at +40% profit
Let the final 1/3 run with a trailing stop
Always exit if price closes below 10 MA on heavy volume
What This Script Does NOT Do:
Guarantee profits - No strategy works 100% of the time
Account for news events - Earnings, regulatory changes, etc.
Consider fundamentals - Company financials, debt, management quality
Adapt to market crashes - Works best in bull markets
Best Market Conditions:
✅ Nifty in uptrend (above 50 MA)
✅ Market breadth positive (more stocks advancing)
✅ Sector rotation happening
❌ Avoid in bear markets or high volatility periods
References:
Trade Like a Stock Market Wizard by Mark Minervini
Think & Trade Like a Champion by Mark Minervini
Chart attached: AU Small Finance Bank as on EoD dated 28/11/25
This script is a powerful tool for educational purpose only, remember: It's a tool, not a crystal ball. Use it to find high-probability setups, then apply proper risk management and patience. Good luck!
RSI adaptive zones [AdaptiveRSI]This script introduces a unified mathematical framework that auto-scales oversold/overbought and support/resistance zones for any period length. It also adds true RSI candles for spotting intrabar signals.
Built on the Logit RSI foundation, this indicator converts RSI into a statistically normalized space, allowing all RSI lengths to share the same mathematical footing.
What was once based on experience and observation is now grounded in math.
✦ ✦ ✦ ✦ ✦
💡 Example Use Cases
RSI(14): Classic overbought/oversold signals + divergence
Support in an uptrend using RSI(14)
Range breakouts using RSI(21)
Short-term pullbacks using RSI(5)
✦ ✦ ✦ ✦ ✦
THE PAST: RSI Interpretation Required Multiple Rulebooks
Over decades, RSI practitioners discovered that RSI behaves differently depending on trend and lookback length:
• In uptrends, RSI tends to hold higher support zones (40–50)
• In downtrends, RSI tends to resist below 50–60
• Short RSIs (e.g., RSI(2)) require far more extreme threshold values
• Longer RSIs cluster near the center and rarely reach 70/30
These observations were correct — but lacked a unifying mathematical explanation.
✦ ✦ ✦ ✦ ✦
THE PRESENT: One Framework Handles RSI(2) to RSI(200)
Instead of using fixed thresholds (70/30, 90/10, etc.), this indicator maps RSI into a normalized statistical space using:
• The Logit transformation to remove 0–100 scale distortion
• A universal scaling based on 2/√(n−1) scaling factor to equalize distribution shapes
As a result, RSI values become directly comparable across all lookback periods.
✦ ✦ ✦ ✦ ✦
💡 How the Adaptive Zones Are Calculated
The adaptive framework defines RSI zones as statistical regimes derived from the Logit-transformed RSI .
Each boundary corresponds to a standard deviation (σ) threshold, scaled by 2/√(n−1), making RSI distributions comparable across periods.
This structure was inspired by Nassim Nicholas Taleb’s body–shoulders–tails regime model:
Body (±0.66σ) — consolidation / equilibrium
Shoulders (±1σ to ±2.14σ) — trending region
Tails (outside of ±2.14σ) — rare, high-volatility behavior
Transitions between these regimes are defined by the derivatives of the position (CDF) function :
• ±1σ → shift from consolidation to trend
• ±√3σ → shift from trend to exhaustion
Adaptive Zone Summary
Consolidation: −0.66σ to +0.66σ
Support/Resistance: ±0.66σ to ±1σ
Uptrend/Downtrend: ±1σ to ±√3σ
Overbought/Oversold: ±√3σ to ±2.14σ
Tails: outside of ±2.14σ
✦ ✦ ✦ ✦ ✦
📌 Inverse Transformation: From σ-Space Back to RSI
A final step is required to return these statistically normalized boundaries back into the familiar 0–100 RSI scale. Because the Logit transform maps RSI into an unbounded real-number domain, the inverse operation uses the hyperbolic tangent function to compress σ-space back into the bounded RSI range.
RSI(n) = 50 + 50 · tanh(z / √(n − 1))
The result is a smooth, mathematically consistent conversion where the same statistical thresholds maintain identical meaning across all RSI lengths, while still expressing themselves as intuitive RSI values traders already understand.
✦ ✦ ✦ ✦ ✦
Key Features
Mathematically derived adaptive zones for any RSI period
Support/resistance zone identification for trend-aligned reversals
Optional OHLC RSI bars/candles for intrabar zone interactions
Fully customizable zone visibility and colors
Statistically consistent interpretation across all markets and timeframes
Inputs
RSI Length — core parameter controlling zone scaling
RSI Display : Line / Bar / Candle visualization modes
✦ ✦ ✦ ✦ ✦
💡 How to Use
This indicator is a framework , not a binary signal generator.
Start by defining the question you want answered, e.g.:
• Where is the breakout?
• Is price overextended or still trending?
• Is the correction ending, or is trend reversing?
Then:
Choose the RSI length that matches your timeframe
Observe which adaptive zone price is interacting with
Interpret market behavior accordingly
Example: Long-Term Trend Assesment using RSI(200)
A trader may ask: "Is this a long term top?"
Unlikely, because RSI(200) holds above Resistance zone , therefore the trend remains strong.
✦ ✦ ✦ ✦ ✦
👉 Practical tip:
If you used to overlay weekly RSI(14) on a daily chart (getting a line that waits 5 sessions to recalculate), you can now read the same long-horizon state continuously : set RSI(70) on the daily chart (~14 weeks × 5 days/week = 70 days) and let the adaptive zones update every bar .
Note: It won’t be numerically identical to the weekly RSI due to lookback period used, but it tracks the same regime on a standardized scale with bar-by-bar updates.
✦ ✦ ✦ ✦ ✦
Note: This framework describes statistical structure, not prediction. Use as part of a complete trading approach. Past behavior does not guarantee future outcomes.
framework ≠ guaranteed signal
---
Attribution & License
This indicator incorporates:
• Logit transformation of RSI
• Variance scaling using 2/√(n−1)
• Zone placement derived from Taleb’s body–shoulders–tails regime model and CDF derivatives
• Inverse TANH(z) transform for mapping z-scores back into bounded RSI space
Released under CC BY-NC-SA 4.0 — free for non-commercial use with credit.
© AdaptiveRSI
DarkPool's Dashboard v2 DarkPool's Dashboard v2 is a comprehensive "Heads-Up Display" (HUD) designed to aggregate critical market data into a single, customizable table overlaid on the price chart. Its primary goal is to declutter the trading workspace by removing the need for multiple separate indicator panes (like RSI, MACD, and Volume below the chart).
The core of the system is a composite Momentum Score, which calculates a value between -100 and +100 based on a weighted average of RSI, MACD, Stochastic, and Rate of Change (ROC). This score drives the main "Signal" output (e.g., STRONG BUY, HOLD, SELL). Additionally, the dashboard integrates a suite of volume analysis tools—including VWAP, OBV, and Volume Delta—alongside volatility and trend filters to provide a complete market health check at a glance.
Key Features
Composite Momentum Score: A unified metric combining four oscillators to gauge the true strength of the move.
Volume Intelligence: Monitors Relative Volume (RVOL), On-Balance Volume (OBV), Volume Delta, and VWAP status.
Trend & Filter Engine: Visualizes trend direction using EMAs and filters signals based on Volatility (ATR) and Trend Strength (EMA Separation).
Dynamic UI: A fully scalable and customizable table that can be positioned anywhere on the screen, with options to toggle specific data rows on or off.
Alert System: Integrated alerts for Volume Spikes, Divergences, and VWAP crossovers.
How to Use
1. Reading the Main Signal The top rows of the dashboard provide the immediate trade bias:
Signal: Displays text such as "STRONG BUY," "BUY," "HOLD," "SELL," or "STRONG SELL."
Momentum Score: A numeric value next to the signal.
> 50: Strong Bullish Momentum.
20 to 50: Moderate Bullish Momentum.
-20 to 20: Neutral / Hold (Chop).
<-20: Bearish Momentum.
2. Volume Analysis
Volume Bar: Visualizes the current volume relative to the Moving Average.
Spike: If the bar turns Orange/Yellow, a Volume Spike (default 2x average) has occurred.
VWAP: Indicates if the price is trading "Above" or "Below" the Volume Weighted Average Price.
Money Flow (MFI): Checks for institutional buying/selling pressure. "OB" means Overbought, "OS" means Oversold.
3. Trend & Volatility
Trend: Shows "UP" or "DOWN" based on Fast/Slow EMA crossovers.
Volatility: Measures the daily range. "HIGH" volatility suggests expansion, while "LOW" suggests compression (potential breakout pending).
4. Filtering Bad Signals The dashboard includes an "ATR Filter" and "Trend Confirmation" logic.
If the market is moving sideways (low ATR), the dashboard may default to "HOLD" or "NEUTRAL" even if oscillators are crossing, preventing false entries during consolidation.
Configuration Settings
Dashboard Settings
Table Position/Width/Scale: adjust the size and location of the table to fit your screen resolution (e.g., increase scale for 4K monitors).
Colors/Transparency: Customize the background and text colors to match your chart theme.
Indicator Settings
Oscillators: Adjust lengths for RSI, MACD, and Stochastic to tune sensitivity.
Volume: Enable or disable specific volume metrics like OBV or Delta.
Display Options: You can toggle specific rows off (e.g., turn off "ADX" or "SMA" if you do not use them) to compact the table.
Filter Settings
Enable ATR Filter: Toggles volatility filtering.
Trend Confirmation Bars: How many bars the trend must persist before the dashboard flips its bias (helps avoid fake-outs).
Disclaimer This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a guarantee of future results. Trading cryptocurrencies and financial markets involves a high level of risk. Always perform your own due diligence before making any trading decisions.
لbsm15// This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) creativecommons.org
// © LuxAlgo
//@version=5
indicator("لbsm15", overlay = true, max_lines_count = 500, max_boxes_count = 500, max_bars_back = 3000)
//------------------------------------------------------------------------------
//Settings
//-----------------------------------------------------------------------------{
liqGrp = 'Liquidity Detection'
liqLen = input.int (7, title = 'Detection Length', minval = 3, maxval = 13, inline = 'LIQ', group = liqGrp)
liqMar = 10 / input.float (6.9, 'Margin', minval = 4, maxval = 9, step = 0.1, inline = 'LIQ', group = liqGrp)
liqBuy = input.bool (true, 'Buyside Liquidity Zones, Margin', inline = 'Buyside', group = liqGrp)
marBuy = input.float(2.3, '', minval = 1.5, maxval = 10, step = .1, inline = 'Buyside', group = liqGrp)
cLIQ_B = input.color (color.new(#4caf50, 0), '', inline = 'Buyside', group = liqGrp)
liqSel = input.bool (true, 'Sellside Liquidity Zones, Margin', inline = 'Sellside', group = liqGrp)
marSel = input.float(2.3, '', minval = 1.5, maxval = 10, step = .1, inline = 'Sellside', group = liqGrp)
cLIQ_S = input.color (color.new(#f23645, 0), '', inline = 'Sellside', group = liqGrp)
lqVoid = input.bool (false, 'Liquidity Voids, Bullish', inline = 'void', group = liqGrp)
cLQV_B = input.color (color.new(#4caf50, 0), '', inline = 'void', group = liqGrp)
cLQV_S = input.color (color.new(#f23645, 0), 'Bearish', inline = 'void', group = liqGrp)
lqText = input.bool (false, 'Label', inline = 'void', group = liqGrp)
mode = input.string('Present', title = 'Mode', options = , inline = 'MOD', group = liqGrp)
visLiq = input.int (3, ' # Visible Levels', minval = 1, maxval = 50, inline = 'MOD', group = liqGrp)
//-----------------------------------------------------------------------------}
//General Calculations
//-----------------------------------------------------------------------------{
maxSize = 50
atr = ta.atr(10)
atr200 = ta.atr(200)
per = mode == 'Present' ? last_bar_index - bar_index <= 500 : true
//-----------------------------------------------------------------------------}
//User Defined Types
//-----------------------------------------------------------------------------{
// @type used to store pivot high/low data
//
// @field d (array) The array where the trend direction is to be maintained
// @field x (array) The array where the bar index value of pivot high/low is to be maintained
// @field y (array) The array where the price value of pivot high/low is to be maintained
type ZZ
int d
int x
float y
// @type bar properties with their values
//
// @field o (float) open price of the bar
// @field h (float) high price of the bar
// @field l (float) low price of the bar
// @field c (float) close price of the bar
// @field i (int) index of the bar
type bar
float o = open
float h = high
float l = low
float c = close
int i = bar_index
// @type liquidity object definition
//
// @field bx (box) box maitaing the liquity level margin extreme levels
// @field bxz (box) box maitaing the liquity zone margin extreme levels
// @field bxt (box) box maitaing the labels
// @field brZ (bool) mainains broken zone status
// @field brL (bool) mainains broken level status
// @field ln (line) maitaing the liquity level line
// @field lne (line) maitaing the liquity extended level line
type liq
box bx
box bxz
box bxt
bool brZ
bool brL
line ln
line lne
//-----------------------------------------------------------------------------}
//Variables
//-----------------------------------------------------------------------------{
var ZZ aZZ = ZZ.new(
array.new (maxSize, 0),
array.new (maxSize, 0),
array.new (maxSize, na)
)
bar b = bar.new()
var liq b_liq_B = array.new (1, liq.new(box(na), box(na), box(na), false, false, line(na), line(na)))
var liq b_liq_S = array.new (1, liq.new(box(na), box(na), box(na), false, false, line(na), line(na)))
var b_liq_V = array.new_box()
var int dir = na, var int x1 = na, var float y1 = na, var int x2 = na, var float y2 = na
//-----------------------------------------------------------------------------}
//Functions/methods
//-----------------------------------------------------------------------------{
// @function maintains arrays
// it prepends a `value` to the arrays and removes their oldest element at last position
// @param aZZ (UDT, array, array>) The UDT obejct of arrays
// @param _d (array) The array where the trend direction is maintained
// @param _x (array) The array where the bar index value of pivot high/low is maintained
// @param _y (array) The array where the price value of pivot high/low is maintained
//
// @returns none
method in_out(ZZ aZZ, int _d, int _x, float _y) =>
aZZ.d.unshift(_d), aZZ.x.unshift(_x), aZZ.y.unshift(_y), aZZ.d.pop(), aZZ.x.pop(), aZZ.y.pop()
// @function (build-in) sets the maximum number of bars that is available for historical reference
max_bars_back(time, 1000)
//-----------------------------------------------------------------------------}
//Calculations
//-----------------------------------------------------------------------------{
x2 := b.i - 1
ph = ta.pivothigh(liqLen, 1)
pl = ta.pivotlow (liqLen, 1)
if ph
dir := aZZ.d.get(0)
x1 := aZZ.x.get(0)
y1 := aZZ.y.get(0)
y2 := nz(b.h )
if dir < 1
aZZ.in_out(1, x2, y2)
else
if dir == 1 and ph > y1
aZZ.x.set(0, x2), aZZ.y.set(0, y2)
if per
count = 0
st_P = 0.
st_B = 0
minP = 0.
maxP = 10e6
for i = 0 to maxSize - 1
if aZZ.d.get(i) == 1
if aZZ.y.get(i) > ph + (atr / liqMar)
break
else
if aZZ.y.get(i) > ph - (atr / liqMar) and aZZ.y.get(i) < ph + (atr / liqMar)
count += 1
st_B := aZZ.x.get(i)
st_P := aZZ.y.get(i)
if aZZ.y.get(i) > minP
minP := aZZ.y.get(i)
if aZZ.y.get(i) < maxP
maxP := aZZ.y.get(i)
if count > 2
getB = b_liq_B.get(0)
if st_B == getB.bx.get_left()
getB.bx.set_top(math.avg(minP, maxP) + (atr / liqMar))
getB.bx.set_rightbottom(b.i + 10, math.avg(minP, maxP) - (atr / liqMar))
else
b_liq_B.unshift(
liq.new(
box.new(st_B, math.avg(minP, maxP) + (atr / liqMar), b.i + 10, math.avg(minP, maxP) - (atr / liqMar), bgcolor=color(na), border_color=color(na)),
box.new(na, na, na, na, bgcolor = color(na), border_color = color(na)),
box.new(st_B, st_P, b.i + 10, st_P, text = 'Buyside liquidity', text_size = size.tiny, text_halign = text.align_left, text_valign = text.align_bottom, text_color = color.new(cLIQ_B, 25), bgcolor = color(na), border_color = color(na)),
false,
false,
line.new(st_B , st_P, b.i - 1, st_P, color = color.new(cLIQ_B, 0)),
line.new(b.i - 1, st_P, na , st_P, color = color.new(cLIQ_B, 0), style = line.style_dotted))
)
alert('buyside liquidity level detected/updated for ' + syminfo.ticker)
if b_liq_B.size() > visLiq
getLast = b_liq_B.pop()
getLast.bx.delete()
getLast.bxz.delete()
getLast.bxt.delete()
getLast.ln.delete()
getLast.lne.delete()
if pl
dir := aZZ.d.get (0)
x1 := aZZ.x.get (0)
y1 := aZZ.y.get (0)
y2 := nz(b.l )
if dir > -1
aZZ.in_out(-1, x2, y2)
else
if dir == -1 and pl < y1
aZZ.x.set(0, x2), aZZ.y.set(0, y2)
if per
count = 0
st_P = 0.
st_B = 0
minP = 0.
maxP = 10e6
for i = 0 to maxSize - 1
if aZZ.d.get(i) == -1
if aZZ.y.get(i) < pl - (atr / liqMar)
break
else
if aZZ.y.get(i) > pl - (atr / liqMar) and aZZ.y.get(i) < pl + (atr / liqMar)
count += 1
st_B := aZZ.x.get(i)
st_P := aZZ.y.get(i)
if aZZ.y.get(i) > minP
minP := aZZ.y.get(i)
if aZZ.y.get(i) < maxP
maxP := aZZ.y.get(i)
if count > 2
getB = b_liq_S.get(0)
if st_B == getB.bx.get_left()
getB.bx.set_top(math.avg(minP, maxP) + (atr / liqMar))
getB.bx.set_rightbottom(b.i + 10, math.avg(minP, maxP) - (atr / liqMar))
else
b_liq_S.unshift(
liq.new(
box.new(st_B, math.avg(minP, maxP) + (atr / liqMar), b.i + 10, math.avg(minP, maxP) - (atr / liqMar), bgcolor=color(na), border_color=color(na)),
box.new(na, na, na, na, bgcolor=color(na), border_color=color(na)),
box.new(st_B, st_P, b.i + 10, st_P, text = 'Sellside liquidity', text_size = size.tiny, text_halign = text.align_left, text_valign = text.align_top, text_color = color.new(cLIQ_S, 25), bgcolor=color(na), border_color=color(na)),
false,
false,
line.new(st_B , st_P, b.i - 1, st_P, color = color.new(cLIQ_S, 0)),
line.new(b.i - 1, st_P, na , st_P, color = color.new(cLIQ_S, 0), style = line.style_dotted))
)
alert('sellside liquidity level detected/updated for ' + syminfo.ticker)
if b_liq_S.size() > visLiq
getLast = b_liq_S.pop()
getLast.bx.delete()
getLast.bxz.delete()
getLast.bxt.delete()
getLast.ln.delete()
getLast.lne.delete()
for i = 0 to b_liq_B.size() - 1
x = b_liq_B.get(i)
if not x.brL
x.lne.set_x2(b.i)
if b.h > x.bx.get_top()
x.brL := true
x.brZ := true
alert('buyside liquidity level breached for ' + syminfo.ticker)
x.bxz.set_lefttop(b.i - 1, math.min(x.ln.get_y1() + marBuy * (atr), b.h))
x.bxz.set_rightbottom(b.i + 1, x.ln.get_y1())
x.bxz.set_bgcolor(color.new(cLIQ_B, liqBuy ? 73 : 100))
else if x.brZ
if b.l > x.ln.get_y1() - marBuy * (atr) and b.h < x.ln.get_y1() + marBuy * (atr)
x.bxz.set_right(b.i + 1)
x.bxz.set_top(math.max(b.h, x.bxz.get_top()))
if liqBuy
x.lne.set_x2(b.i + 1)
else
x.brZ := false
for i = 0 to b_liq_S.size() - 1
x = b_liq_S.get(i)
if not x.brL
x.lne.set_x2(b.i)
if b.l < x.bx.get_bottom()
x.brL := true
x.brZ := true
alert('sellside liquidity level breached for ' + syminfo.ticker)
x.bxz.set_lefttop(b.i - 1, x.ln.get_y1())
x.bxz.set_rightbottom(b.i + 1, math.max(x.ln.get_y1() - marSel * (atr), b.l))
x.bxz.set_bgcolor(color.new(cLIQ_S, liqSel ? 73 : 100))
else if x.brZ
if b.l > x.ln.get_y1() - marSel * (atr) and b.h < x.ln.get_y1() + marSel * (atr)
x.bxz.set_rightbottom(b.i + 1, math.min(b.l, x.bxz.get_bottom()))
if liqSel
x.lne.set_x2(b.i + 1)
else
x.brZ := false
if lqVoid and per
bull = b.l - b.h > atr200 and b.l > b.h and b.c > b.h
bear = b.l - b.h > atr200 and b.h < b.l and b.c < b.l
if bull
l = 13
if bull
st = math.abs(b.l - b.l ) / l
for i = 0 to l - 1
array.push(b_liq_V, box.new(b.i - 2, b.l + i * st, b.i, b.l + (i + 1) * st, border_color = na, bgcolor = color.new(cLQV_B, 90) ))
else
st = math.abs(b.l - b.h ) / l
for i = 0 to l - 1
if lqText and i == 0
array.push(b_liq_V, box.new(b.i - 2, b.h + i * st, b.i, b.h + (i + 1) * st, text = 'Liquidity Void ', text_size = size.tiny, text_halign = text.align_right, text_valign = text.align_bottom, text_color = na, border_color = na, bgcolor = color.new(cLQV_B, 90) ))
else
array.push(b_liq_V, box.new(b.i - 2, b.h + i * st, b.i, b.h + (i + 1) * st, border_color = na, bgcolor = color.new(cLQV_B, 90) ))
if bear
l = 13
if bear
st = math.abs(b.h - b.h) / l
for i = 0 to l - 1
array.push(b_liq_V, box.new(b.i - 2, b.h + i * st, b.i, b.h + (i + 1) * st, border_color = na, bgcolor = color.new(cLQV_S, 90) ))
else
st = math.abs(b.l - b.h) / l
for i = 0 to l - 1
if lqText and i == l - 1
array.push(b_liq_V, box.new(b.i - 2, b.h + i * st, b.i, b.h + (i + 1) * st, text = 'Liquidity Void ', text_size = size.tiny, text_halign = text.align_right, text_valign = text.align_top, text_color = na, border_color = na, bgcolor = color.new(cLQV_S, 90) ))
else
array.push(b_liq_V, box.new(b.i - 2, b.h + i * st, b.i, b.h + (i + 1) * st, border_color = na, bgcolor = color.new(cLQV_S, 90) ))
if b_liq_V.size() > 0
qt = b_liq_V.size()
for bn = qt - 1 to 0
if bn < b_liq_V.size()
cb = b_liq_V.get(bn)
ba = math.avg(cb.get_bottom(), cb.get_top())
if math.sign(b.c - ba) != math.sign(b.c - ba) or math.sign(b.c - ba) != math.sign(b.l - ba) or math.sign(b.c - ba) != math.sign(b.h - ba)
b_liq_V.remove(bn)
else
cb.set_right(b.i + 1)
if b.i - cb.get_left() > 21
cb.set_text_color(color.new(color.gray, 25))
//-----------------------------------------------------------------------------}
🤖 DarkPool's Omni-MA APEX v3 🤖DarkPool's Omni-MA APEX v3 is an all-encompassing technical analysis suite designed to replace multiple indicators with a single, highly optimized tool. At its core, it features five independently customizable "Omni-MAs" capable of running various calculation models (SMA, EMA, HMA, LSMA, etc.) across multiple timeframes.
Beyond standard trend lines, the APEX v3 integrates a sophisticated "Market Structure Engine" that automatically plots Support & Resistance zones based on pivot points and volatility (ATR). It also features a "Trend Cloud" to visualize macro sentiment and a professional-grade Dashboard that aggregates data from over 10 different sources (RSI, MACD, OBV, Volume, etc.) to provide a real-time health check of the asset.
Key Features
5-Layer Omni-MA System: Five distinct moving averages with "Smart Coloring" that detects trends, consolidations (flat markets), and reversals.
Auto Support & Resistance: A dynamic algorithm that draws, updates, and prunes liquidity zones on the chart automatically.
Macro Trend Cloud: A visual background fill comparing Daily and Weekly momentum to keep you aligned with the higher timeframe.
Data Dashboard: A customizable panel displaying real-time metrics for Momentum, Volume, RSI, Divergences, and VWAP status.
Signal Generator: Alerts for MA crossovers, S/R breakouts, and trend shifts.
How to Use
1. The Omni-MAs (The Lines) The indicator plots up to five lines, color-coded for instant trend recognition:
Green/Blue: Price is above the previous value (Uptrend).
Red/Maroon: Price is below the previous value (Downtrend).
Gray: The line is flat (Consolidation/Chop).
MA 1-2 (Fast): Use these for entry triggers and scalping.
MA 3 (Medium): The "Anchor" line, often used as dynamic support.
MA 4-5 (Slow): The macro trend filters. If price is below MA 5, looking for longs is risky.
2. The Trend Cloud
Background Fill: This visualizes the difference between the Daily EMA and Weekly EMA.
Green Cloud: The Daily trend is above the Weekly trend (Strong Bullish Market).
Red Cloud: The Daily trend is below the Weekly trend (Strong Bearish Market).
3. Support & Resistance Zones
The Boxes: The script identifies pivot points and projects them forward as boxes.
Strategy: Watch for price to react at these zones. If a candle closes through a zone, it signals a Breakout (Green triangle) or Breakdown (Red triangle).
4. The Dashboard Located in the corner of your chart, this table provides a "Cockpit View" of the market:
Momentum Score: A composite score (-100 to +100) derived from RSI, MACD, and Stochastic.
Vol Ratio: Compares current volume to the average. A green bar indicates volume is higher than usual.
Market State: Classifies the market into regimes like "Volatile Bull," "Quiet Bear," or "Ranging."
Configuration Settings
Dashboard UI
Compact Mode: Reduces the table to show only the final Buy/Sell signal.
Active Widgets: Toggle individual data points (e.g., turn off "OBV" or "ADX" if you don't use them) to save screen space.
Global Analysis (Strategy Engine)
ATR Filter: Filters out "Weak" trends. If the price movement is too small (low volatility), signals are suppressed.
Volume MA: Sets the lookback period for calculating relative volume.
Support & Resistance
Pivot Sensitivity: Lower numbers find more zones (more noise); higher numbers find fewer, stronger zones.
Zone Width: Multiplies the ATR to determine how thick the S/R boxes should be.
MA Settings (1-5)
Type: Choose from SMA, EMA, WMA, HMA (Hull), VWMA, LSMA, ALMA, and more.
Timeframe: You can set MA 5 to "D" (Daily) while trading on a 15-minute chart to see the daily trend line overlaid.
Disclaimer This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a guarantee of future results.
💀 DarkPool's Moving Averages 💀DarkPool's Moving Averages is a consolidated trend analysis tool that allows traders to plot up to five distinct moving averages (MAs) within a single indicator pane. This script is designed to declutter trading charts by replacing multiple individual indicator instances with one comprehensive solution.
Beyond standard plotting, the indicator features Multi-Timeframe (MTF) capabilities, allowing users to overlay higher-timeframe trends (e.g., Daily or Weekly averages) onto lower-timeframe charts (e.g., 5-minute or 1-hour). It also utilizes dynamic color-coding to visually indicate instantaneous trend direction based on the slope of the moving average.
Key Features
5-in-1 Architecture: Configure and toggle up to five independent moving averages simultaneously.
Multi-Timeframe (MTF) Support: Calculate moving averages based on timeframes different from the current chart (e.g., view a 200-day EMA while trading on a 15-minute chart).
Dynamic Trend Coloring: Lines automatically change color based on their slope (rising vs. falling) to provide immediate visual trend confirmation.
Versatile Calculation Models: Supports major averaging methods including SMA, EMA, WMA, RMA, VWMA, and HMA.
How to Use
1. Trend Identification The primary use of this tool is to identify the market trend direction at a glance.
Bullish Trend: When the Moving Average line is colored in the "Bullish Color" (default: dark/green tones) and sloping upward.
Bearish Trend: When the Moving Average line is colored in the "Bearish Color" (default: light/red tones) and sloping downward.
2. Dynamic Support and Resistance Traders can use specific lengths (e.g., 50, 100, 200) to identify dynamic support and resistance levels.
Entry: In an uptrend, price retracing to a rising MA often presents a buying opportunity.
Exit: In a downtrend, price rallying to a falling MA often presents a selling opportunity.
3. The "Ribbon" Effect By enabling multiple MAs with sequential lengths (e.g., 10, 20, 50, 100, 200), traders can visualize the strength of the trend.
Expansion: When the lines spread apart, the trend is strengthening.
Contraction/Crossover: When the lines converge or cross, the trend is weakening or consolidating.
4. Multi-Timeframe Analysis Use the "Timeframe" input in the General Settings to lock the calculations to a specific period.
Example: Set the Timeframe to "D" (Daily) and the Length to 200. You can now drop down to a 5-minute chart, and the indicator will still display the significant 200-Day Moving Average, acting as a major anchor for intraday price action.
Configuration Guide
General Settings
Timeframe: Determines the data source for all MAs. Leave at default to use the current chart's timeframe, or select a specific higher timeframe for macro analysis.
Price Source: Selects the data point used for calculation (Close, Open, High, Low, etc.).
Moving Average Configurations (MA1 - MA5) Each of the five slots allows for individual customization:
Enable: Toggle the visibility of the specific MA.
Type: Select the calculation method.
SMA: Simple Moving Average (Standard).
EMA: Exponential Moving Average (Weight on recent data).
HMA: Hull Moving Average (Reduced lag).
VWMA: Volume Weighted Moving Average.
WMA/RMA: Weighted and Rolling Moving Averages.
Note: While many types are listed, the script explicitly calculates the types listed above; others may default to standard SMA behavior.
Length: The lookback period (e.g., 20, 50, 200).
Colors (Bull/Bear): Customize the colors used when the line is rising versus falling.
Line Style: Choose between Solid, Dashed, or Dotted lines to differentiate between the five MAs.
Disclaimer: This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a guarantee of future results.
🔥 DarkPool's Fear & Greed v4 🔥DarkPool Fear & Greed v4 is a composite sentiment indicator designed to gauge market psychology in real-time. Unlike standard oscillators that rely on a single metric, this tool aggregates data from four distinct technical sources—RSI, MACD, Bollinger Bands, and Moving Averages—to create a unified "Index Score" ranging from 0 to 100.
Beyond general sentiment, the script employs custom algorithms to detect specific market anomalies, including sustainable buying pressure (FOMO), capitulation events (Panic), and trend reversals (Divergences).
Key Features
Composite Index: A weighted average of Momentum, Trend, Volatility, and Price Location.
Anomaly Detection: Specialized logic to flag high-momentum "FOMO" events and high-volatility "Panic" drops.
Divergences: Automatically spots bearish and bullish discrepancies between the sentiment index and price action.
Live Dashboard: A real-time data table displaying current sentiment zones, intensity scores, and volume ratios.
How to Use
1. The Fear & Greed Index The main oscillator line moves between 0 and 100 to visualize market sentiment:
0-20 (Extreme Fear): Deeply oversold; potential capitulation or buying opportunity.
20-40 (Fear): General bearish sentiment.
40-60 (Neutral): Indecisive market.
60-80 (Greed): General bullish sentiment.
80-100 (Extreme Greed): Overbought conditions; potential for a pullback.
2. Visual Signals
FOMO (Triangle Up): Marks candles with excessive buying volume and RSI momentum.
Panic (Triangle Down): Marks candles with sharp percentage drops and volatility spikes.
Divergences (Circles): distinct markers appear when price action contradicts the sentiment index, often signaling a reversal.
3. The Dashboard Located on the chart, the dashboard provides a snapshot of the current market state, including the specific "Intensity" of FOMO or Panic events and a Volume-to-MA ratio to gauge participation.
4. Alerts The script is fully integrated with the alert system. You can set alerts for "Any alert() function call" to receive dynamic notifications for FOMO detections, Panic drops, Extreme Zone entries, and confirmed Divergences.
Disclaimer This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a guarantee of future results.
Advanced FVG Detector Pro📊 Advanced FVG Detector Pro - Smart Money Analysis Tool
Overview
The Advanced FVG Detector Pro is a sophisticated Pine Script v6 indicator designed to identify and track Fair Value Gaps (FVGs) with institutional-grade precision. This tool goes beyond basic gap detection by incorporating volume analysis, smart money scoring, and adaptive filtering to help traders identify high-probability trading opportunities.
What are Fair Value Gaps?
Fair Value Gaps (FVGs) are price inefficiencies that occur when the market moves so quickly that it leaves behind an imbalance or "gap" in price action. These gaps often act as magnets for future price movement as the market seeks to fill these inefficiencies. Professional traders and institutions closely monitor FVGs as they represent areas of potential support, resistance, and high-probability trade setups.
🎯 Key Features
1. Smart Money Scoring System
Proprietary algorithm that rates each FVG on a 0-100 scale Combines gap size, volume strength, price location, and trend alignment Filter out low-quality setups by setting minimum score thresholdsFocus on institutional-grade opportunities with scores above 70
2. Advanced Volume Validation
Validates FVGs with volume analysis to reduce false signals Only displays gaps formed during significant volume periods Customizable volume multiplier for different market conditions
Visual volume strength indicators on chart
3. Flexible Mitigation Options
Full Fill: Traditional complete gap closure Midpoint Touch: More aggressive entry strategy
Partial Fill: Customizable percentage-based mitigation (10-90%) Choose the strategy that matches your trading style
4. ATR-Based Adaptive Filtering
Automatically adjusts to market volatility using Average True Range Works consistently across any instrument, timeframe, or volatility regime No manual recalibration needed when switching markets Filters out noise while capturing meaningful gaps
5. Real-Time Statistics Dashboard
Live tracking of total active FVGs Bullish vs Bearish gap count Mitigation rate percentage
Average Smart Money Score Toggle on/off based on preference
6. Professional Visual Design
Clean, customizable color schemes Optional midline display for precise entry planning
Labels showing gap type, score, and volume strength Automatic extension of active gaps
Mitigated gaps change color for easy identification
📈 How to Use
For Day Traders:
Use 5-15 minute timeframes
Set ATR Multiplier to 0.15-0.25
Enable volume validation
Focus on FVGs with scores above 65
For Swing Traders:
Use 1H-4H timeframes
Set ATR Multiplier to 0.5-1.0
Use "Midpoint Touch" mitigation
Focus on FVGs with scores above 70
For Position Traders:
Use Daily timeframe
Set ATR Multiplier to 0.75-1.5
Use "Full Fill" mitigation
Focus on FVGs with scores above 75
🔧 Customization Options
Detection Settings:
Minimum FVG size percentage filter
ATR-based size filtering
Maximum number of gaps to display
Smart Money Score minimum threshold
Volume Analysis:
Volume validation toggle
Volume multiplier adjustment
Volume moving average period
Visual volume strength background
Mitigation Control:
Choose mitigation type (Full/Midpoint/Partial)
Set partial fill percentage
Auto-remove mitigated gaps
Control how long mitigated gaps remain visible
Visual Customization:
Bullish/Bearish/Mitigated colors
Show/hide midlines
Show/hide labels
Box extension length
Statistics dashboard toggle
🎓 Trading Strategy Ideas
1. FVG Retest Strategy
Wait for price to create a high-score FVG (70+)
Enter on the first retest of the gap
Place stop loss beyond the gap
Target the opposite side of the gap or next FVG
2. Confluence Trading
Combine FVGs with support/resistance levels
Look for FVGs near key moving averages (20/50 EMA)
Higher probability when FVG aligns with trendlines
Use multiple timeframe analysis
3. Breakout Confirmation
FVGs often form during strong breakouts
High-volume FVGs confirm breakout strength
Enter on mitigation of breakout FVG
Trail stops as new FVGs form in trend direction
⚡ Performance Optimizations
Efficient memory management for smooth chart performance
Optimized calculations run only once per bar
Smart array management prevents memory leaks
Works smoothly even with 100+ active FVGs
🔔 Alert System
Customizable alerts for new bullish FVGs
Customizable alerts for new bearish FVGs
Mitigation alerts for active gaps
Frequency control to avoid alert spam
💡 Pro Tips
Multi-Timeframe Approach: Identify major FVGs on higher timeframes (Daily/4H) and use lower timeframes (15M/5M) for precise entries
Volume Confirmation: The highest probability setups occur when FVGs form with 2x+ average volume
Trend Alignment: Trade FVGs in the direction of the major trend for best results
Patience Pays: Wait for price to return to the FVG rather than chasing breakouts
Risk Management: Always use stop losses beyond the FVG boundaries
📚 Educational Value
This indicator is perfect for:
Learning to identify institutional order flow
Understanding market microstructure
Developing price action trading skills
Recognizing supply and demand imbalances
Improving entry and exit timing
⚠️ Disclaimer
This indicator is a tool for technical analysis and should not be used as the sole basis for trading decisions. Always combine with proper risk management, fundamental analysis, and your own trading plan. Past performance does not guarantee future results.
🔄 Updates & Support
Regular updates will include:
Additional filtering options
Enhanced multi-timeframe analysis
More customization features
Performance improvements
📊 Best Pairs/Markets
Works excellently on:
Forex pairs (EUR/USD, GBP/USD, etc.)
Cryptocurrency (BTC, ETH, etc.)
Stock indices (SPX, NQ, etc.)
Individual stocks
Commodities (Gold, Oil, etc.)
Version Information
Version: 1.0
Pine Script: Version 6
Type: Overlay Indicator
Max Boxes: 500
Max Lines: 500
My script//@version=5
indicator("LTF Multi-Condition BUY Signal (v5 clean)", overlay=true, max_labels_count=100, max_lines_count=100)
// ───────────────── INPUTS ─────────────────
pivot_len = input.int(4, "Pivot sensitivity (structure)", minval=2, maxval=12)
range_len = input.int(20, "Range lookback for breakout", minval=5)
htf_tf = input.timeframe("480", "HTF timeframe (8H+)")
reclaim_window = input.int(5, "Reclaim window (bars)", minval=1)
ema_fast_len = input.int(9, "EMA fast length")
ema_slow_len = input.int(21, "EMA slow length")
rsi_len = input.int(14, "RSI length")
rsi_pivot_len = input.int(4, "RSI pivot sensitivity")
rsi_div_lookback = input.int(30, "RSI divergence max lookback (bars)")
daily_vol_mult = input.float(1.0, "Daily volume vs SMA multiplier", step=0.1)
htf_vol_sma_len = input.int(20, "HTF volume SMA length")
require_reclaim = input.bool(true, "Require HTF reclaim")
use_aggressive_HL = input.bool(false, "Aggressive HL detection")
// ───────────────── BASE INDICATORS ─────────────────
emaFast = ta.ema(close, ema_fast_len)
emaSlow = ta.ema(close, ema_slow_len)
rsiVal = ta.rsi(close, rsi_len)
// ───────────────── DAILY CHECKS (VOLUME & OBV) ─────────────────
// Daily OBV and previous value
daily_obv = request.security(syminfo.tickerid, "D",
ta.cum(ta.change(close) > 0 ? volume : ta.change(close) < 0 ? -volume : 0))
daily_obv_prev = request.security(syminfo.tickerid, "D",
ta.cum(ta.change(close) > 0 ? volume : ta.change(close) < 0 ? -volume : 0) )
// Daily volume & SMA
daily_vol = request.security(syminfo.tickerid, "D", volume)
daily_vol_sma = request.security(syminfo.tickerid, "D", ta.sma(volume, 20))
daily_vol_ok = not na(daily_vol) and not na(daily_vol_sma) and daily_vol > daily_vol_sma * daily_vol_mult
daily_obv_ok = not na(daily_obv) and not na(daily_obv_prev) and daily_obv > daily_obv_prev
// ───────────────── HTF SUPPORT / RECLAIM ─────────────────
htf_high = request.security(syminfo.tickerid, htf_tf, high)
htf_low = request.security(syminfo.tickerid, htf_tf, low)
htf_close = request.security(syminfo.tickerid, htf_tf, close)
htf_volume = request.security(syminfo.tickerid, htf_tf, volume)
htf_vol_sma = request.security(syminfo.tickerid, htf_tf, ta.sma(volume, htf_vol_sma_len))
htf_bull_reject = not na(htf_high) and not na(htf_low) and not na(htf_close) and (htf_close - htf_low) > (htf_high - htf_close)
htf_vol_confirm = not na(htf_volume) and not na(htf_vol_sma) and htf_volume > htf_vol_sma
htf_support_level = (htf_bull_reject and htf_vol_confirm) ? htf_low : na
// Reclaim: LTF close back above HTF support within N bars
reclaimed_now = not na(htf_support_level) and close > htf_support_level and ta.barssince(close <= htf_support_level) <= reclaim_window
htf_reclaim_ok = require_reclaim ? reclaimed_now : true
// ───────────────── STRUCTURE: BOS & HL (CoC) ─────────────────
swingHighVal = ta.pivothigh(high, pivot_len, pivot_len)
swingLowVal = ta.pivotlow(low, pivot_len, pivot_len)
swingHighCond = not na(swingHighVal)
swingLowCond = not na(swingLowVal)
lastSwingHigh = ta.valuewhen(swingHighCond, swingHighVal, 0)
prevSwingHigh = ta.valuewhen(swingHighCond, swingHighVal, 1)
lastSwingLow = ta.valuewhen(swingLowCond, swingLowVal, 0)
prevSwingLow = ta.valuewhen(swingLowCond, swingLowVal, 1)
bos_bull = not na(prevSwingHigh) and close > prevSwingHigh
hl_confirm = not na(lastSwingLow) and not na(prevSwingLow) and lastSwingLow > prevSwingLow and ta.barssince(swingLowCond) <= 30
if use_aggressive_HL
hl_confirm := hl_confirm or (low > low and ta.barssince(swingLowCond) <= 12)
// ───────────────── RSI BULLISH DIVERGENCE ─────────────────
rsiLowVal = ta.pivotlow(rsiVal, rsi_pivot_len, rsi_pivot_len)
rsiLowCond = not na(rsiLowVal)
priceAtRsiLowA = ta.valuewhen(rsiLowCond, low , 0)
priceAtRsiLowB = ta.valuewhen(rsiLowCond, low , 1)
rsiLowA = ta.valuewhen(rsiLowCond, rsiVal , 0)
rsiLowB = ta.valuewhen(rsiLowCond, rsiVal , 1)
rsi_div_ok = not na(priceAtRsiLowA) and not na(priceAtRsiLowB) and not na(rsiLowA) and not na(rsiLowB) and
(priceAtRsiLowA < priceAtRsiLowB) and (rsiLowA > rsiLowB) and ta.barssince(rsiLowCond) <= rsi_div_lookback
// ───────────────── RANGE BREAKOUT ─────────────────
range_high = ta.highest(high, range_len)
range_breakout = ta.crossover(close, range_high)
// ───────────────── EMA CROSS / TREND ─────────────────
ema_cross_happened = ta.crossover(emaFast, emaSlow)
ema_trend_ok = emaFast > emaSlow
// ───────────────── FINAL BUY CONDITION ─────────────────
all_price_checks = bos_bull and hl_confirm and rsi_div_ok and range_breakout
all_filter_checks = ema_trend_ok and ema_cross_happened and daily_vol_ok and daily_obv_ok and htf_reclaim_ok
buy_condition = all_price_checks and all_filter_checks
// ───────────────── PLOTS & ALERT ─────────────────
plotshape(
buy_condition,
title = "BUY Signal",
location = location.belowbar,
style = shape.labelup,
text = "BUY",
textcolor = color.white,
color = color.green,
size = size.small)
plot(htf_support_level, title="HTF Support", color=color.new(color.green, 0), linewidth=2, style=plot.style_linebr)
alertcondition(buy_condition, title="LTF BUY Signal", message="LTF BUY Signal on {{ticker}} ({{interval}}) — all conditions met")
Thirdeyechart Gold Simulation Final 3The Thirdeyechart Gold Simulation Final Version 3 is the ultimate indicator for traders who want a comprehensive, real-time view of gold market dynamics across multiple XAU pairs. This version tracks 8 gold-related pairs simultaneously (XAUUSD, XAUJPY, XAUGBP, XAUEUR, XAUAUD, XAUCHF, XAUCAD, XAUNZD) and provides a consolidated visual table for weekly, daily, 4-hour, and 1-hour percentage changes.
Core Features
Multi-Timeframe Trend Analysis – Calculates percent change for each XAU pair across W, D, H4, H1 using:
pct_tf = ((close_tf - open_tf) / open_tf) * 100
Positive values are colored blue, negative values red, giving an immediate visual sense of market direction.
Buy & Sell Simulation – Each pair’s positive and negative contributions are summed to produce BuySim and SellSim columns, representing the overall pressure in the market without providing explicit trade signals.
Total Row & Strength Row – Aggregates all pairs to show total weekly, daily, H4, and H1 movements, alongside a Strength row indicating "Strong", "Weak", or "Neutral" trends per timeframe. A trend bias (Buy Bias or Sell Bias) is calculated automatically from total positive vs negative pressure.
Safe / Unsafe Trade Detection – Advanced logic measures the difference between total Buy and Sell pressure. If the distance exceeds 50% of total market activity, the market is labeled as Safe Trade with a reason for dominance (buyers or sellers). If below this threshold, it is labeled Unsafe Trade with a note that one side “can dominate the market.” This allows traders to quickly identify high-confidence vs uncertain market conditions.
Visual Layout – The table is fully boxed, color-coded, and easy to read, displaying all key metrics including per-timeframe percent changes, BuySim/SellSim totals, Strength, Trend Bias, and Trade Status with reasons.
Logic Overview
Percent changes per timeframe: pct_tf = ((close - open) / open) * 100
Positive and negative values split into Buy/Sell contributions.
Sum across all pairs and timeframes to calculate totals and bias.
Safe/Unsafe trade threshold: distance >= totalAll * 0.50
Strength interpretation per timeframe: >0 → Strong, <0 → Weak, 0 → Neutral
This indicator is ideal for fast detection of strong vs weak gold trends, global XAU market pressure simulation, and quick risk assessment through safe/unsafe trade labeling.
Disclaimer
This tool is educational and analytical only. It does not provide financial advice or trade signals. Users are responsible for their own trading decisions, and trading involves risk.
© 2025 Thirdeyechart. All rights reserved. Redistribution or commercial use without permission is prohibited.
Gold Key Level LinesOverview
Gold Horizontal Lines is a visual grid tool that draws automatic horizontal levels around the current price. It’s designed for symbols like Gold (XAUUSD), but works on any market and timeframe.
What It Does
Draws main, mid, and quarter price levels based on user-defined intervals (e.g. 100 / 50 / 25).
Centers the grid around the current close, above and below by a chosen number of levels.
Adds optional price labels to each line on the right side of the chart.
Deletes and redraws lines only on the last bar to keep the chart clean and efficient.
Inputs
Main Line Interval – distance between key levels (e.g. 100).
Mid / Quarter Intervals – optional extra levels between main lines (set to 0 to disable).
Colors, Styles, Widths – separate settings for main, mid, and quarter lines.
Show Price Labels – toggle labels on/off.
Number of Lines Above/Below Price – controls how far the grid extends.
