NQ-VIX Expected Move LevelsNQ -VIX Daily Price Bands
This indicator plots dynamic intraday price bands for NQ 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 Open + (NQ Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily Open - (NQ 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 open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's open
Lower band (red) contracts from the current day's open
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
Information table displaying:
Current NQ price and VIX level
Daily Open
Expected move
Cerca negli script per "100万新币等于多少人民币"
NQ-VIX Expected Move LTF LevelsNQ -VIX LTF Price Bands
This indicator plots dynamic intraday price bands for NQ 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 = (Input TF Open) + (NQ Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
Lower Band = Daily Open - (NQ Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
The calculation uses the square root of Input TF ÷ (23h in min) to convert annualized VIX volatility into an expected TF move, then scales it as a percentage adjustment from the current TF input's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current TF's open
Lower band (red) contracts from the current TF's open
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
Information table displaying:
Current input TF
Current NQ price and VIX level
Current input TF Open
Expected move
Viprasol Elite Advanced Pattern Scanner# 🚀 Viprasol Elite Advanced Pattern Scanner
## Overview
The **Viprasol Elite Advanced Pattern Scanner** is a sophisticated technical analysis tool designed to identify high-probability double bottom (DISCOUNT) and double top (PREMIUM) patterns with unprecedented accuracy. Unlike basic pattern detectors, this elite scanner employs an AI-powered quality scoring system to filter out false signals and highlight only the most reliable trading opportunities.
## 🎯 Key Features
### Advanced Pattern Detection
- **DISCOUNT Patterns** (Double Bottoms): Identifies bullish reversal zones where price may bounce
- **PREMIUM Patterns** (Double Tops): Detects bearish reversal zones where price may decline
- Multi-point validation system (5-point structure)
- Symmetry analysis with customizable tolerance
### 🤖 AI Quality Scoring System
Each pattern receives a quality score (0-100) based on:
- **Symmetry Analysis** (32% weight): How closely the two bottoms/tops match
- **Trend Context** (22% weight): Strength of the preceding trend using ADX
- **Volume Profile** (22% weight): Volume confirmation at key points
- **Pattern Depth** (16% weight): Significance of the pattern's price range
- **Structure Quality** (16% weight): Overall pattern formation quality
Quality Grades:
- ⭐ **ELITE** (88-100): Highest probability setups
- ✨ **VERY STRONG** (77-87): Strong trade opportunities
- ✓ **STRONG** (67-76): Valid patterns with good potential
- ○ **VALID** (65-66): Acceptable patterns meeting minimum criteria
### 🎯 Intelligent Target System
Three target modes per pattern direction:
- **Conservative**: 0.618 Fibonacci extension (safer, closer targets)
- **Balanced**: 1.0 extension (moderate risk/reward)
- **Aggressive**: 1.618 extension (higher risk/reward)
Targets automatically adjust based on pattern quality score.
### 🔧 Advanced Filtering Options
- **Volatility Filter (ATR)**: Excludes patterns during extreme volatility
- **Momentum Filter (ADX)**: Ensures sufficient trend strength
- **Liquidity Filter (Volume)**: Confirms adequate trading volume
### 📊 Pattern Lifecycle Management
- Real-time neckline tracking with extension multiplier
- Pattern invalidation after extended wait period
- Breakout/breakdown confirmation
- Reversal detection (pattern failure scenarios)
- Target achievement tracking
### 🌈 Premium Visual System
- Color-coded quality levels
- Cyber-themed color scheme (Neon Green/Hot Pink/Purple/Cyan)
- Transparent fills for pattern zones
- Dynamic labels with pattern information
- Elite dashboard showing live pattern stats
## 📈 How To Use
### Basic Setup
1. Add indicator to your chart
2. Enable desired patterns (DISCOUNT and/or PREMIUM)
3. Adjust quality threshold (default: 65) - higher = fewer but better signals
4. Set your preferred target mode
### Trading DISCOUNT Patterns (Bullish)
1. Wait for pattern detection (labeled points 1-4)
2. Check quality score on dashboard
3. Entry on breakout above neckline (point 5)
4. Stop loss below the lowest bottom
5. Target shown automatically based on your mode
6. ⚠️ Watch for pattern failure (break below bottoms = SHORT signal)
### Trading PREMIUM Patterns (Bearish)
1. Wait for pattern detection (labeled points 1-4)
2. Check quality score on dashboard
3. Entry on breakdown below neckline (point 5)
4. Stop loss above the highest top
5. Target shown automatically based on your mode
6. ⚠️ Watch for pattern failure (break above tops = LONG signal)
## ⚙️ Input Settings Guide
### 🔍 Detection Engine
- **Left/Right Pivots**: Higher = fewer but cleaner patterns (default: 6/4)
- **Min Pattern Width**: Minimum bars between bottoms/tops (default: 12)
- **Symmetry Tolerance**: Max % difference allowed between levels (default: 1.8%)
- **Extension Multiplier**: How long to wait for breakout (default: 2.2x pattern width)
### ⭐ Quality AI
- **Min Quality Score**: Only show patterns above this score (default: 65)
- **Weight Distribution**: Customize what matters most (symmetry/trend/volume/depth/structure)
### 🔧 Filters
- **Volatility Filter**: Avoid choppy markets (recommended: ON)
- **Momentum Filter**: Ensure trend strength (recommended: ON)
- **Liquidity Filter**: Volume confirmation (recommended: ON)
### 💎 Target System
- Choose target aggression for each pattern type and direction
- Higher quality patterns get adjusted targets automatically
## 🎨 Visual Customization
- Adjust colors for DISCOUNT/PREMIUM patterns
- Set quality-based color coding
- Customize label sizes
- Toggle dashboard visibility and position
- Show/hide historical patterns
## 🚨 Alert System
Set up TradingView alerts for:
- 🚀 **LONG Signals**: DISCOUNT breakout, PREMIUM failure
- 📉 **SHORT Signals**: PREMIUM breakdown, DISCOUNT failure
- ✅ **Target Achievement**: When price hits your target
## 💡 Pro Tips
1. **Higher Timeframes = Better Signals**: Patterns on 4H, Daily, Weekly are more reliable
2. **Quality Over Quantity**: Focus on ELITE and VERY STRONG grades
3. **Combine with Trend**: DISCOUNT in uptrend, PREMIUM in downtrend = best results
4. **Watch Pattern Failures**: Failed patterns often provide strong counter-trend signals
5. **Adjust for Your Style**: Intraday traders use Conservative, swing traders use Aggressive
## 🔒 Pattern Invalidation
Patterns become invalid if:
- No breakout/breakdown within extension period
- Support/resistance levels are broken prematurely
- Pattern shown in faded colors = no longer active
## ⚠️ Risk Disclaimer
This indicator is a tool for technical analysis and does not guarantee profitable trades. Always:
- Use proper risk management
- Combine with other analysis methods
- Never risk more than you can afford to lose
- Past performance does not indicate future results
ES-VIX Expected Move LTF LevelsES-VIX LTF 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 = (Input TF Open) + (ES Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
Lower Band = Daily Open - (ES Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
The calculation uses the square root of Input TF ÷ (23h in min) to convert annualized VIX volatility into an expected TF move, then scales it as a percentage adjustment from the current TF input's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current TF's open
Lower band (red) contracts from the current TF's open
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
Information table displaying:
Current input TF
Current ES price and VIX level
Current input TF Open
Expected move
Hurst Exponent - Detrended Fluctuation AnalysisIn stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analyzing time series that appear to be long-memory processes and noise.
█ OVERVIEW
We have introduced the concept of Hurst Exponent in our previous open indicator Hurst Exponent (Simple). It is an indicator that measures market state from autocorrelation. However, we apply a more advanced and accurate way to calculate Hurst Exponent rather than simple approximation. Therefore, we recommend using this version of Hurst Exponent over our previous publication going forward. The method we used here is called detrended fluctuation analysis. (For folks that are not interested in the math behind the calculation, feel free to skip to "features" and "how to use" section. However, it is recommended that you read it all to gain a better understanding of the mathematical reasoning).
█ Detrend Fluctuation Analysis
Detrended Fluctuation Analysis was first introduced by by Peng, C.K. (Original Paper) in order to measure the long-range power-law correlations in DNA sequences . DFA measures the scaling-behavior of the second moment-fluctuations, the scaling exponent is a generalization of Hurst exponent.
The traditional way of measuring Hurst exponent is the rescaled range method. However DFA provides the following benefits over the traditional rescaled range method (RS) method:
• Can be applied to non-stationary time series. While asset returns are generally stationary, DFA can measure Hurst more accurately in the instances where they are non-stationary.
• According the the asymptotic distribution value of DFA and RS, the latter usually overestimates Hurst exponent (even after Anis- Llyod correction) resulting in the expected value of RS Hurst being close to 0.54, instead of the 0.5 that it should be. Therefore it's harder to determine the autocorrelation based on the expected value. The expected value is significantly closer to 0.5 making that threshold much more useful, using the DFA method on the Hurst Exponent (HE).
• Lastly, DFA requires lower sample size relative to the RS method. While the RS method generally requires thousands of observations to reduce the variance of HE, DFA only needs a sample size greater than a hundred to accomplish the above mentioned.
█ Calculation
DFA is a modified root-mean-squares (RMS) analysis of a random walk. In short, DFA computes the RMS error of linear fits over progressively larger bins (non-overlapped “boxes” of similar size) of an integrated time series.
Our signal time series is the log returns. First we subtract the mean from the log return to calculate the demeaned returns. Then, we calculate the cumulative sum of demeaned returns resulting in the cumulative sum being mean centered and we can use the DFA method on this. The subtraction of the mean eliminates the “global trend” of the signal. The advantage of applying scaling analysis to the signal profile instead of the signal, allows the original signal to be non-stationary when needed. (For example, this process converts an i.i.d. white noise process into a random walk.)
We slice the cumulative sum into windows of equal space and run linear regression on each window to measure the linear trend. After we conduct each linear regression. We detrend the series by deducting the linear regression line from the cumulative sum in each windows. The fluctuation is the difference between cumulative sum and regression.
We use different windows sizes on the same cumulative sum series. The window sizes scales are log spaced. Eg: powers of 2, 2,4,8,16... This is where the scale free measurements come in, how we measure the fractal nature and self similarity of the time series, as well as how the well smaller scale represent the larger scale.
As the window size decreases, we uses more regression lines to measure the trend. Therefore, the fitness of regression should be better with smaller fluctuation. It allows one to zoom into the “picture” to see the details. The linear regression is like rulers. If you use more rulers to measure the smaller scale details you will get a more precise measurement.
The exponent we are measuring here is to determine the relationship between the window size and fitness of regression (the rate of change). The more complex the time series are the more it will depend on decreasing window sizes (using more linear regression lines to measure). The less complex or the more trend in the time series, it will depend less. The fitness is calculated by the average of root mean square errors (RMS) of regression from each window.
Root mean Square error is calculated by square root of the sum of the difference between cumulative sum and regression. The following chart displays average RMS of different window sizes. As the chart shows, values for smaller window sizes shows more details due to higher complexity of measurements.
The last step is to measure the exponent. In order to measure the power law exponent. We measure the slope on the log-log plot chart. The x axis is the log of the size of windows, the y axis is the log of the average RMS. We run a linear regression through the plotted points. The slope of regression is the exponent. It's easy to see the relationship between RMS and window size on the chart. Larger RMS equals less fitness of the regression. We know the RMS will increase (fitness will decrease) as we increases window size (use less regressions to measure), we focus on the rate of RMS increasing (how fast) as window size increases.
If the slope is < 0.5, It means the rate of of increase in RMS is small when window size increases. Therefore the fit is much better when it's measured by a large number of linear regression lines. So the series is more complex. (Mean reversion, negative autocorrelation).
If the slope is > 0.5, It means the rate of increase in RMS is larger when window sizes increases. Therefore even when window size is large, the larger trend can be measured well by a small number of regression lines. Therefore the series has a trend with positive autocorrelation.
If the slope = 0.5, It means the series follows a random walk.
█ FEATURES
• Sample Size is the lookback period for calculation. Even though DFA requires a lower sample size than RS, a sample size larger > 50 is recommended for accurate measurement.
• When a larger sample size is used (for example = 1000 lookback length), the loading speed may be slower due to a longer calculation. Date Range is used to limit numbers of historical calculation bars. When loading speed is too slow, change the data range "all" into numbers of weeks/days/hours to reduce loading time. (Credit to allanster)
• “show filter” option applies a smoothing moving average to smooth the exponent.
• Log scale is my work around for dynamic log space scaling. Traditionally the smallest log space for bars is power of 2. It requires at least 10 points for an accurate regression, resulting in the minimum lookback to be 1024. I made some changes to round the fractional log space into integer bars requiring the said log space to be less than 2.
• For a more accurate calculation a larger "Base Scale" and "Max Scale" should be selected. However, when the sample size is small, a larger value would cause issues. Therefore, a general rule to be followed is: A larger "Base Scale" and "Max Scale" should be selected for a larger the sample size. It is recommended for the user to try and choose a larger scale if increasing the value doesn't cause issues.
The following chart shows the change in value using various scales. As shown, sometimes increasing the value makes the value itself messy and overshoot.
When using the lowest scale (4,2), the value seems stable. When we increase the scale to (8,2), the value is still alright. However, when we increase it to (8,4), it begins to look messy. And when we increase it to (16,4), it starts overshooting. Therefore, (8,2) seems to be optimal for our use.
█ How to Use
Similar to Hurst Exponent (Simple). 0.5 is a level for determine long term memory.
• In the efficient market hypothesis, market follows a random walk and Hurst exponent should be 0.5. When Hurst Exponent is significantly different from 0.5, the market is inefficient.
• When Hurst Exponent is > 0.5. Positive Autocorrelation. Market is Trending. Positive returns tend to be followed by positive returns and vice versa.
• Hurst Exponent is < 0.5. Negative Autocorrelation. Market is Mean reverting. Positive returns trends to follow by negative return and vice versa.
However, we can't really tell if the Hurst exponent value is generated by random chance by only looking at the 0.5 level. Even if we measure a pure random walk, the Hurst Exponent will never be exactly 0.5, it will be close like 0.506 but not equal to 0.5. That's why we need a level to tell us if Hurst Exponent is significant.
So we also computed the 95% confidence interval according to Monte Carlo simulation. The confidence level adjusts itself by sample size. When Hurst Exponent is above the top or below the bottom confidence level, the value of Hurst exponent has statistical significance. The efficient market hypothesis is rejected and market has significant inefficiency.
The state of market is painted in different color as the following chart shows. The users can also tell the state from the table displayed on the right.
An important point is that Hurst Value only represents the market state according to the past value measurement. Which means it only tells you the market state now and in the past. If Hurst Exponent on sample size 100 shows significant trend, it means according to the past 100 bars, the market is trending significantly. It doesn't mean the market will continue to trend. It's not forecasting market state in the future.
However, this is also another way to use it. The market is not always random and it is not always inefficient, the state switches around from time to time. But there's one pattern, when the market stays inefficient for too long, the market participants see this and will try to take advantage of it. Therefore, the inefficiency will be traded away. That's why Hurst exponent won't stay in significant trend or mean reversion too long. When it's significant the market participants see that as well and the market adjusts itself back to normal.
The Hurst Exponent can be used as a mean reverting oscillator itself. In a liquid market, the value tends to return back inside the confidence interval after significant moves(In smaller markets, it could stay inefficient for a long time). So when Hurst Exponent shows significant values, the market has just entered significant trend or mean reversion state. However, when it stays outside of confidence interval for too long, it would suggest the market might be closer to the end of trend or mean reversion instead.
Larger sample size makes the Hurst Exponent Statistics more reliable. Therefore, if the user want to know if long term memory exist in general on the selected ticker, they can use a large sample size and maximize the log scale. Eg: 1024 sample size, scale (16,4).
Following Chart is Bitcoin on Daily timeframe with 1024 lookback. It suggests the market for bitcoin tends to have long term memory in general. It generally has significant trend and is more inefficient at it's early stage.
Expected Move BandsExpected move is the amount that an asset is predicted to increase or decrease from its current price, based on the current levels of volatility.
In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution.
Note: Normal distribution is just an assumption, it's not the real distribution of return
Settings:
"Estimation Period Selection" is for selecting the period we want to construct the prediction interval.
For "Current Bar", the interval is calculated based on the data of the previous bar close. Therefore changes in the current price will have little effect on the range. What current bar means is that the estimated range is for when this bar close. E.g., If the Timeframe on 4 hours and 1 hour has passed, the interval is for how much time this bar has left, in this case, 3 hours.
For "Future Bars", the interval is calculated based on the current close. Therefore the range will be very much affected by the change in the current price. If the current price moves up, the range will also move up, vice versa. Future Bars is estimating the range for the period at least one bar ahead.
There are also other source selections based on high low.
Time setting is used when "Future Bars" is chosen for the period. The value in time means how many bars ahead of the current bar the range is estimating. When time = 1, it means the interval is constructing for 1 bar head. E.g., If the timeframe is on 4 hours, then it's estimating the next 4 hours range no matter how much time has passed in the current bar.
Note: It's probably better to use "probability cone" for visual presentation when time > 1
Volatility Models :
Sample SD: traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias
Parkinson: Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close
Garman Klass: Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient
Yangzhang Garman Klass Extension: Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps.
about 8 x efficient
Rogers: Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient
EWMA: Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster.
YangZhang: Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance.
Median absolute deviation: It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator.
Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on a larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters.
Standard deviations:
Standard Deviation One shows the estimated range where the closing price will be about 68% of the time.
Standard Deviation two shows the estimated range where the closing price will be about 95% of the time.
Standard Deviation three shows the estimated range where the closing price will be about 99.7% of the time.
Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability.
Manually Entered Standard Deviation shows the range of any entered standard deviation. The probability of that range will be presented on the panel.
People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended. Assuming zero mean is recommended when time is not greater than 1.
When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation.
Note: The significance of autocorrelation can be checked on an ACF indicator.
ACF
The multimeframe option enables people to use higher period expected move on the lower time frame. People should only use time frame higher than the current time frame for the input. An error warning will appear when input Tf is lower. The input format is multiplier * time unit. E.g. : 1D
Unit: M for months, W for Weeks, D for Days, integers with no unit for minutes (E.g. 240 = 240 minutes). S for Seconds.
Smoothing option is using a filter to smooth out the range. The filter used here is John Ehler's supersmoother. It's an advance smoothing technique that gets rid of aliasing noise. It affects is similar to a simple moving average with half the lookback length but smoother and has less lag.
Note: The range here after smooth no long represent the probability
Panel positions can be adjusted in the settings.
X position adjusts the horizontal position of the panel. Higher X moves panel to the right and lower X moves panel to the left.
Y position adjusts the vertical position of the panel. Higher Y moves panel up and lower Y moves panel down.
Step line display changes the style of the bands from line to step line. Step line is recommended because it gets rid of the directional bias of slope of expected move when displaying the bands.
Warnings:
People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails.
Different volatility models provide different properties if people are interested in the accuracy and the fit of expected move, they can try expected move occurrence indicator. (The result also demonstrate the previous point about the drawback of using normal distribution assumption).
Expected move Occurrence Test
The prediction interval is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between. E.g., If 1 SD range is 100 - 200, the price can go to 80 or 230 intrabar, but if the bar close within 100 - 200 in the end. It's still considered a 68% one standard deviation move.
ES-VIX Expected Move - Open basedES-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 Open + (ES Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily Open - (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 open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's open
Lower band (red) contracts from the current day's open
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
Information table displaying:
Current ES price and VIX level
Daily Open
Expected move
AlphaRank MA Lens – Multi-Timeframe Moving Average MapAlphaRank MA Lens – Multi-Timeframe Moving Average Map
AlphaRank MA Lens is a clean, open-source moving-average overlay that turns price action into an easy-to-read trend map. It focuses on structure and context only — no signals, no backtest, no hype — just a clear view of where price sits relative to key moving averages.
The script plots the 10 / 20 / 50 / 100 / 150 / 200 / 730 moving averages with full color control and a single “MA Type” switch, so you can flip the whole stack between SMA and EMA in one click. Instead of loading multiple separate MA indicators, this puts the full trend stack in one tool.
An optional background highlight lets you choose a reference MA (for example the 200 MA) and softly shade the chart:
Green when price is above that MA
Red when price is below it
This makes trend regime changes easy to see at a glance.
How traders typically use it (education only):
10/20/50 MAs → short-term trend and momentum.
100/150/200/730 MAs → bigger structural trend and “where price lives” in the long-term range.
Many traders consider conditions healthier when price and the short MAs are stacked above the longer MAs, and weaker when price trades below them.
Follow my work: AlphaRank
This script is for educational and analytical purposes only and does not provide trading advice or performance promises. Always combine it with your own judgment, testing, and risk management.
Abu Basel IQOption 2m Signals//@version=5
indicator("Abu Basel IQOption 2m Signals", overlay = true, timeframe = "", timeframe_gaps = true)
//========================
// الإعدادات
//========================
emaFastLen = input.int(9, "EMA سريع (9)")
emaSlowLen = input.int(21, "EMA بطيء (21)")
rsiLen = input.int(14, "RSI Length", minval = 2)
rsiBuyLevel = input.float(50.0, "RSI حد الشراء (أعلى من)", minval = 0, maxval = 100)
rsiSellLevel= input.float(50.0, "RSI حد البيع (أقل من)", minval = 0, maxval = 100)
bbLen = input.int(20, "Bollinger Length")
bbMult = input.float(2.0, "Bollinger Deviation")
showSignals = input.bool(true, "إظهار الأسهم (CALL / PUT)")
showBg = input.bool(true, "تلوين الخلفية عند الإشارات")
//========================
// المؤشرات الأساسية
//========================
emaFast = ta.ema(close, emaFastLen)
emaSlow = ta.ema(close, emaSlowLen)
basis = ta.sma(close, bbLen)
dev = bbMult * ta.stdev(close, bbLen)
bbUpper = basis + dev
bbLower = basis - dev
rsi = ta.rsi(close, rsiLen)
// رسم المتوسطات والبولينجر
plot(emaFast, title = "EMA 9", linewidth = 2)
plot(emaSlow, title = "EMA 21", linewidth = 2)
plot(basis, title = "BB Basis", linewidth = 1)
plot(bbUpper, title = "BB Upper", linewidth = 1, style = plot.style_line)
plot(bbLower, title = "BB Lower", linewidth = 1, style = plot.style_line)
//========================
// دوال أشكال الشموع الانعكاسية
//========================
bodySize = math.abs(close - open)
fullRange = high - low
upperWick = high - math.max(open, close)
lowerWick = math.min(open, close) - low
isSmallBody = bodySize <= fullRange * 0.3
// Hammer صاعدة (ذيل سفلي طويل)
bullHammer() =>
lowerWick > bodySize * 2 and upperWick <= bodySize and close > open
// Shooting Star هابطة (ذيل علوي طويل)
bearShootingStar() =>
upperWick > bodySize * 2 and lowerWick <= bodySize and close < open
// Bullish Engulfing
bullEngulfing() =>
close > open and close < open and close > open and open < close
// Bearish Engulfing
bearEngulfing() =>
close < open and close > open and close < open and open > close
// تجميع أنماط صعود/هبوط
bullPattern = bullHammer() or bullEngulfing()
bearPattern = bearShootingStar() or bearEngulfing()
//========================
// شروط الدخول
//========================
// تقاطع المتوسطات
bullCross = ta.crossover(emaFast, emaSlow) // صعود
bearCross = ta.crossunder(emaFast, emaSlow) // هبوط
// شروط شراء CALL:
// 1) تقاطع EMA9 فوق EMA21
// 2) السعر فوق خط وسط البولنجر
// 3) RSI أعلى من 50
// 4) شمعة انعكاسية صاعدة (Hammer أو Engulfing)
callCond = bullCross and close > basis and rsi > rsiBuyLevel and bullPattern
// شروط بيع PUT:
// 1) تقاطع EMA9 تحت EMA21
// 2) السعر تحت خط وسط البولنجر
// 3) RSI أقل من 50
// 4) شمعة انعكاسية هابطة (Shooting Star أو Bearish Engulfing)
putCond = bearCross and close < basis and rsi < rsiSellLevel and bearPattern
//========================
// رسم الإشارات على الشارت
//========================
plotshape(showSignals and callCond, title="CALL 2m",
style=shape.labelup, location=location.belowbar,
text="CALL\n2m", size=size.tiny)
plotshape(showSignals and putCond, title="PUT 2m",
style=shape.labeldown, location=location.abovebar,
text="PUT\n2m", size=size.tiny)
// تلوين الخلفية عند الإشارات
bgcolor(showBg and callCond ? color.new(color.green, 85) :
showBg and putCond ? color.new(color.red, 85) : na)
//========================
// شروط التنبيه (Alerts)
//========================
alertcondition(callCond, title="CALL 2m Signal",
message="Abu Basel Signal: CALL 2m on {{ticker}} at {{close}}")
alertcondition(putCond, title="PUT 2m Signal",
message="Abu Basel Signal: PUT 2m on {{ticker}} at {{close}}")
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"
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
