Daily Volatility Range (DVR) [GIF]VIX as a Volatility Indicator:
The VIX is a measure of the market's expectation of volatility in the S&P 500 over the next 30 days, based on the prices of S&P 500 options.
The Rule of 16:
A VIX of 16 implies that the market expects the SPX to move up or down by roughly 1% on any given day.
If the VIX is 24, the expected daily move is around 1.5%, and with a VIX of 32, the expected move is around 2%.
The rationale for the rule is that the square root of the number of trading days in a year (approximately 252) is roughly 16.
Example:
If the VIX is at 20, the rule suggests that the SPX might see daily moves of around 1.25%.
Practical Application:
The rule of 16 can be used as a quick and easy way to estimate the potential daily volatility of the S&P 500 based on the VIX.
The Daily Volatility Range:
This indicator cross references the ticker on the chart with it's own volatility index (assuming it has one). Below are the indexes and stocks that have their own volatility index:
S&P 500
Nasdaq 100
Russell 2000
Dow Jones
TLT
Bitcoin
Gold
Crude Oil
Apple
Amazon
Google
IBM
Goldman Sachs
How I use the DVR:
Historic probabilities show that you will close the day within the DVR. However, there are times when those probabilities diminish greatly. One of those times is when you open in the RTH session outside of the DVR. If you open outside the DVR, you can look for the DVR to becomes support/resistance and stay extended outside the DVR. These days can often become muted as most of the most has happened before the market open. However, if we open outside the DVR and break back into range, it is highly probable that we will not break back into those extended ranges.
Indicator Options:
There are 2x and 3x DVR levels that can be plotted. During times of extreme volatility, it will become important to have these plotted.
There is the option to plot calculated pivot points. These are fib ranges that have historically been areas of consolidation or trend reversal. These are projections based on my own research and are not as important as the DVR levels themselves.
There is also an option to color the candles a specific color if the candle closes outside the DVR. This is to highlight the fact that price action has exceeded the range and caution should be taken.
If you have suggestions how to make this indicator better, please let me know in the comments and I will look into it. Thank you!
Bantlar ve Kanallar
ATLAS Reversion Bands v2 [EMA % Spread]🧠 About the ATLAS Reversion Bands v2
I created this indicator to answer a simple question:
"When is price extended too far from trend, and likely to revert?"
The ATLAS Reversion Bands measure the percentage spread between a fast and slow EMA (default 25/200) and track how far that spread moves from its historical average using z-score and standard deviation bands—essentially building a Bollinger Band system on top of EMA distance.
Instead of relying on traditional oscillators like RSI or MACD, this tool is purely math-driven and tailored for spotting overextensions across any asset.
🔍 What It Does
Tracks the normalized spread between EMA 25 and EMA 200
Highlights statistically rare zones using ±2 and ±3 standard deviation bands
Plots BUY/SELL triangle markers only on first entry into extreme zones
Helps identify mean reversion opportunities (deep pullbacks or FOMO tops)
📈 How to Use It
Wait for the spread to hit or exceed ±2.5 or ±3 standard deviations
Look for confirmation via price structure, candles, or volume
Best used on spot or perp markets with healthy liquidity
Ideal for swing trading or narrative-based rotational setups
🕐 Recommended Timeframes
1H, 4H, and 1D are optimal
Use MTF mode to apply daily logic on lower timeframes (e.g., see 1D exhaustion while trading 4H)
Works across:
✅ BTC, ETH, Majors
✅ Meme coins (better on 1H/4H)
✅ Market indexes (TOTAL2, BTC.D, etc.)
📌 Pro Tips
Raise the Z-score alert threshold for stricter signals (e.g., 3.0 for only the wildest extensions)
Use with other confluence tools (like S/R, candles, or RSI)
Not designed for chasing trends — this is a fade-the-hype, buy-the-blood kind of tool
Multi-Timeframe Trading SystemOverview
The Multi-Timeframe Trading System is an advanced technical analysis indicator designed to identify high-probability trading opportunities by combining signals from multiple timeframes and trading strategies. This system analyzes market context, identifies optimal setups, and confirms entries with lower timeframe precision, significantly increasing signal reliability.
Key Features
Triple Timeframe Analysis: Combines high, medium, and low timeframe data for comprehensive market analysis
Three Trading Strategies in One: Incorporates trend-following, mean-reversion, and breakout strategies
Adaptive to Market Conditions: Automatically identifies the current market context (trending or ranging) and applies the appropriate strategy
Signal Strength Evaluation: Rates buy/sell signals from weak to strong based on indicator confluence
Visual Alerts: Clear buy/sell signals with on-chart markers and signal labels
Customizable Parameters: Fully adjustable settings for all indicators and timeframes
Technical Indicators Included
-Moving Averages (EMA 50, EMA 200)
-Ichimoku Cloud components
-ADX for trend strength
-RSI for momentum and oversold/overbought conditions
-Stochastic oscillator for entry timing
-MACD for trend confirmation
-Bollinger Bands for volatility and price channels
-ATR for measuring market volatility
Trading Strategies
1. Trend-Following Strategy
Identifies the primary trend direction on higher timeframes
Locates optimal pullback entry points on medium timeframes
Confirms entries with precision using lower timeframe momentum signals
2. Mean-Reversion Strategy
Activates during ranging market conditions
Identifies oversold and overbought conditions using Bollinger Bands and RSI
Confirms reversals with Stochastic crossovers
3. Breakout Strategy
Detects price consolidation periods through Bollinger Band width
Identifies volatility expansion and price breakouts
Confirms breakout direction with momentum indicators
Ideal For
Swing traders looking for high-probability setups
Day traders seeking to align with the larger trend
Traders who want systematic confirmation across multiple timeframes
Those looking to adapt their trading approach to changing market conditions
How To Use
Apply the indicator to your chart and customize the timeframe settings to match your trading style
-Observe the market context information (uptrend, downtrend, or ranging)
-Wait for a setup to form on the medium timeframe
-Enter when the low timeframe confirms the signal
-Use the signal strength rating to prioritize the highest probability trades
The Multi-Timeframe Trading System eliminates the guesswork from your trading by providing clear, objective signals based on professional-grade multi-timeframe analysis techniques.
DD Keltner Channels (1-3 ATR)This indicator creates Keltner Channels with 1, 2, and 3 ATR multipliers, allowing you to visualize different volatility levels around a moving average.
It's specifically created for people taking the "Deep Dip Buy" stock trading course, and attempts to provide a ready-to-go solution for those struggling with configuring the default Keltner indicator on TradingView to suit their needs for the course.
Any input from students or the instructor is welcome to improve this indicator so it offers more value to those looking to learn how to trade.
Features:
- Uses SMA or EMA as the base (20-period default)
- Displays 6 lines: +3, +2, +1, -1, -2, and -3 ATR levels
- Color-coded for easy identification:
• +/-1 ATR: Green
• +/-2 ATR: Light Gray (thin)
• +/-3 ATR: Dark Gray (thick)
DT_KEY_LEVELSDT_Key_Levels: Powerful Market Structure Analysis Indicator
DT_Key_Levels is an advanced indicator for fundamental market structure analysis, optimized for higher timeframes (D1, W, M). The indicator combines three powerful technical analysis tools — fractals, Fair Value Gaps (FVG), and psychological levels — in one comprehensive solution.
Three Components of the Indicator
1. Enhanced Fractal System
The indicator uses an improved version of Bill Williams' classic fractals, allowing for deeper market structure analysis:
Dual Identification System:
Standard 5-bar fractals (displayed with thick lines) for analyzing reliable support/resistance levels
Light 3-bar fractals (displayed with thin lines) for early identification of potential reversal points
Intelligent Tracking System:
Automatic detection and filtering of completed fractals
Marking fractals with corresponding timeframe designation (HTF-1D, HTF-1W, HTF-1M)
Tracking and marking the All-Time High (ATH)
2. Fair Value Gaps (FVG) System
The indicator identifies and visualizes price gaps in market structure — zones that often act as magnets for future price movements:
Precise Identification of Inefficient Zones:
Bullish FVG: when the current candle's low is above the -2 candle's high
Bearish FVG: when the current candle's high is below the -2 candle's low
Detailed Visualization:
Clear display of upper and lower boundaries of each FVG
Midline (0.5 FVG) for determining key reaction levels within the gap
Marking each FVG with "FF" (Fair value Fill) label for quick identification
Dynamic Management:
Automatic removal of FVGs when they are filled by price movement
Customizable line extension for improved tracking of target zones
3. Intelligent Psychological Levels
The indicator automatically determines key psychological levels with adaptation to the type of instrument being traded:
Specialized Calibration for Various Assets:
Forex (EUR/USD, GBP/USD, USD/JPY): optimization for standard figures and round values
Precious metals (XAUUSD): adaptation to typical gold reaction zones with a $50 step
Cryptocurrencies (BTC, ETH): dynamic step adjustment depending on current price zone
Stock indices (NASDAQ, S&P500, DAX): accounting for the movement characteristics of each index
Smart Adaptation System:
Automatic determination of the optimal step for any instrument
Generation of up to 24 key levels, evenly distributed around the current price
Intelligent filtering to display only significant levels
Practical Application
Strategic Analysis
Identifying Key Structural Levels:
Use monthly and weekly fractals to determine strategic support/resistance zones
Look for coincidences of fractals with psychological levels to identify particularly strong zones of interest
Determine long-term barriers using type 5 fractals on higher timeframes
Analysis of Market Inefficiencies:
Track the formation of FVGs as potential targets for future movements
Use FVG midlines (0.5) as important internal reaction levels
Analyze the speed of FVG filling to understand trend strength
Tactical Trading Decisions
Entry Points and Risk Management:
Use bounces from fractals in the direction of the larger trend as a signal for entry
Place stop-losses behind fractal levels or key psychological levels
Monitor the formation of new fractals as a signal of potential reversal
Determining Target Levels:
Use unfilled FVGs as natural price targets
Apply nearby psychological levels for partial position closing
Project higher timeframe fractals to determine long-term goals
Indicator Advantages
Comprehensive Approach: combining three methodologies for a complete understanding of market structure
Intelligent Adaptation: automatic adjustment to the characteristics of different types of assets
Clean Visual Presentation: despite the abundance of information, the indicator maintains clarity of display
Effective Signal Filtering: automatic removal of completed levels to reduce visual noise
Higher Timeframe Optimization: specifically designed for daily, weekly and monthly charts
Usage Recommendations
Use the indicator only on D1, W, and M timeframes for the most reliable signals
Pay special attention to areas where different types of signals coincide (e.g., fractal + psychological level)
Use higher timeframe fractals as key zones for medium and long-term trading
Track FVGs as potential target zones and focus on their filling
Transient Impact Model [ScorsoneEnterprises]This indicator is an implementation of the Transient Impact Model. This tool is designed to show the strength the current trades have on where price goes before they decay.
Here are links to more sophisticated research articles about Transient Impact Models than this post arxiv.org and arxiv.org
The way this tool is supposed to work in a simple way, is when impact is high price is sensitive to past volume, past trades being placed. When impact is low, it moves in a way that is more independent from past volume. In a more sophisticated system, perhaps transient impact should be calculated for each trade that is placed, not just the total volume of a past bar. I didn't do it to ensure parameters exist and aren’t na, as well as to have more iterations for optimization. Note that the value will change as volume does, as soon as a new candle occurs with no volume, the values could be dramatically different.
How it works
There are a few components to this script, so we’ll go into the equation and then the other functions used in this script.
// Transient Impact Model
transient_impact(params, price_change, lkb) =>
alpha = array.get(params, 0)
beta = array.get(params, 1)
lambda_ = array.get(params, 2)
instantaneous = alpha * volume
transient = 0.0
for t = 1 to lkb - 1
if na(volume )
break
transient := transient + beta * volume * math.exp(-lambda_ * t)
predicted_change = instantaneous + transient
math.pow(price_change - predicted_change, 2)
The parameters alpha, beta, and lambda all represent a different real thing.
Alpha (α):
Represents the instantaneous impact coefficient. It quantifies the immediate effect of the current volume on the price change. In the equation, instantaneous = alpha * volume , alpha scales the current bar's volume (volume ) to determine how much of the price change is due to immediate market impact. A larger alpha suggests that current volume has a stronger instantaneous influence on price.
Beta (β):
Represents the transient impact coefficient.It measures the lingering effect of past volumes on the current price change. In the loop calculating transient, beta * volume * math.exp(-lambda_ * t) shows that beta scales the volume from previous bars (volume ), contributing to a decaying effect over time. A higher beta indicates a stronger influence from past volumes, though this effect diminishes with time due to the exponential decay factor.
Lambda (λ):
Represents the decay rate of the transient impact.It controls how quickly the influence of past volumes fades over time in the transient component. In the term math.exp(-lambda_ * t), lambda determines the rate of exponential decay, where t is the time lag (in bars). A larger lambda means the impact of past volumes decays faster, while a smaller lambda implies a longer-lasting effect.
So in full.
The instantaneous term, alpha * volume , captures the immediate price impact from the current volume.
The transient term, sum of beta * volume * math.exp(-lambda_ * t) over the lookback period, models the cumulative, decaying effect of past volumes.
The total predicted_change combines these two components and is compared to the actual price change to compute an error term, math.pow(price_change - predicted_change, 2), which the script minimizes to optimize alpha, beta, and lambda.
Other parts of the script.
Objective function:
This is a wrapper function with a function to minimize so we get the best alpha, beta, and lambda values. In this case it is the Transient Impact Function, not something like a log-likelihood function, helps with efficiency for a high iteration count.
Finite Difference Gradient:
This function calculates the gradient of the objective function we spoke about. The gradient is like a directional derivative. Which is like the direction of the rate of change. Which is like the direction of the slope of a hill, we can go up or down a hill. It nudges around the parameter, and calculates the derivative of the parameter. The array of these nudged around parameters is what is returned after they are optimized.
Minimize:
This is the function that actually has the loop and calls the Finite Difference Gradient each time. Here is where the minimizing happens, how we go down the hill. If we are below a tolerance, we are at the bottom of the hill.
Applied
After an initial guess, we optimize the parameters and get the transient impact value. This number is huge, so we apply a log to it to make it more readable. From here we need some way to tell if the value is low or high. We shouldn’t use standard deviation because returns are not normally distributed, an IQR is similar and better for non normal data. We store past transient impact values in an array, so that way we can see the 25th and 90th percentiles of the data as a rolling value. If the current transient impact is above the 90th percentile, it is notably high. If below the 25th percentile, notably low. All of these values are plotted so we can use it as a tool.
Tool examples:
The idea around it is that when impact is low, there is room for big money to get size quickly and move prices around.
Here we see the price reacting in the IQR Bands. We see multiple examples where the value above the 90th percentile, the red line, corresponds to continuations in the trend, and below the 25th percentile, the purple line, corresponds to reversals. There is no guarantee these tools will be perfect, that is outlined in these situations, however there is clearly a correlation in this tool and trend.
This tool works on any timeframe, daily as we saw before, or lower like a two minute. The bands don’t represent a direction, like bullish or bearish, we need to determine that by interpreting price action. We see at open and at close there are the highest values for the transient impact. This is to be expected as these are the times with the highest volume of the trading day.
This works on futures as well as equities with the same context. Volume can be attributed to volatility as well. In volatile situations, more volatility comes in, and we can perceive it through the transient impact value.
Inputs
Users can enter the lookback value.
No tool is perfect, the transient impact value is also not perfect and should not be followed blindly. It is good to use any tool along with discretion and price action.
ICT & SMC Multi-Timeframe by [KhedrFX]Transform your trading experience with the ICT & SMC Multi-Timeframe by indicator. This innovative tool is designed for traders who want to harness the power of multi-timeframe analysis, enabling them to make informed trading decisions based on key market insights. By integrating concepts from the Inner Circle Trader (ICT) and Smart Money Concepts (SMC), this indicator provides a comprehensive view of market dynamics, helping you identify potential trading opportunities with precision.
Key Features
- Multi-Timeframe Analysis: Effortlessly switch between various timeframes (5 minutes, 15 minutes, 30 minutes, 1 hour, 4 hours, daily, and weekly) to capture the full spectrum of market movements.
- High and Low Levels: Automatically calculates and displays the highest and lowest price levels over the last 20 bars, highlighting critical support and resistance zones.
- Market Structure Visualization: Identifies the last swing high and swing low, allowing you to recognize current market trends and potential reversal points.
- Order Block Detection: Detects significant order blocks, pinpointing areas of strong buying or selling pressure that can indicate potential market reversals.
- Custom Alerts: Set alerts for when the price crosses above or below identified order block levels, enabling you to act swiftly on trading opportunities.
How to Use the Indicator
1. Add the Indicator to Your Chart
- Open TradingView.
- Click on the "Indicators" button at the top of the screen.
- Search for "ICT & SMC Multi-Timeframe by " in the search bar.
- Click on the indicator to add it to your chart.
2. Select Your Timeframe
- Use the dropdown menu to choose your preferred timeframe (5, 15, 30, 60, 240, D, W) for analysis.
3. Interpret the Signals
- High Level (Green Line): Represents the highest price level over the last 20 bars, acting as a potential resistance level.
- Low Level (Red Line): Represents the lowest price level over the last 20 bars, acting as a potential support level.
- Last Swing High (Blue Cross): Indicates the most recent significant high, useful for identifying potential reversal points.
- Last Swing Low (Orange Cross): Indicates the most recent significant low, providing insight into market structure.
- Order Block High (Purple Line): Marks the upper boundary of a detected order block, suggesting potential selling pressure.
- Order Block Low (Yellow Line): Marks the lower boundary of a detected order block, indicating potential buying pressure.
4. Set Alerts
- Utilize the alert conditions to receive notifications when the price crosses above or below the order block levels, allowing you to stay informed about potential trading opportunities.
5. Implement Risk Management
- Always use proper risk management techniques. Consider setting stop-loss orders based on the identified swing highs and lows or the order block levels to protect your capital.
Conclusion
The ICT & SMC Multi-Timeframe by indicator is an essential tool for traders looking to enhance their market analysis and decision-making process. By leveraging multi-timeframe insights, market structure visualization, and order block detection, you can navigate the complexities of the market with confidence. Start using this powerful indicator today and take your trading to the next level.
⚠️ Trade Responsibly
This tool helps you analyze the market, but it’s not a guarantee of profits. Always do your own research, manage risk, and trade with caution.
Nifty Range % and Points by Time BlocksPine Script that gives you day-wise intraday range percentage for these 3 time blocks (9:16–10:45, 10:45–1:15, 1:15–3:15), we can:
Detect time blocks during the day
Track High/Low for each block
Calculate range % for each block:
\text{Range %} = \frac{(High - Low)}{\text{Previous Day Close}} \times 100
Plot / Label it on the chart at the end of each block
Nifty 1m EMA Pullback Scalper Signals
### **Master the Market with the Sniper Scalping Strategy for Nifty (1-Minute Timeframe)**
Unlock the power of precision trading with this expertly crafted **Sniper Scalping Strategy**, designed specifically for the Nifty index on a lightning-fast 1-minute timeframe. Perfect for traders who thrive on quick decisions and small, consistent profits, this strategy combines multiple indicators to deliver razor-sharp entries and exits—ideal for India’s dynamic market.
#### **Why This Strategy Stands Out**
- **Pinpoint Accuracy**: Harness the synergy of the **5 EMA and 10 EMA crossover** to lock onto the short-term trend, while the **Stochastic Oscillator (14,3,3)** times your entries and exits with surgical precision.
- **Fast and Effective**: Tailored for the 1-minute chart, this strategy capitalizes on Nifty’s volatility, targeting **10-point profits** with a tight **5-point stop-loss**—keeping your risk low and rewards high.
- **Trend + Momentum**: Blend trend-following (EMAs) with momentum signals (Stochastic) for a robust, multi-dimensional approach that cuts through market noise.
#### **How It Works**
- **Buy Signal**: Enter long when the 5 EMA crosses above the 10 EMA and the Stochastic rises above 20—catching the uptrend at its sweet spot.
- **Sell Signal**: Go short when the 5 EMA dips below the 10 EMA and the Stochastic falls below 80—riding the downtrend with confidence.
- **Exit Like a Pro**: Take profits at 10 points or when the Stochastic hits overbought/oversold extremes, ensuring you’re in and out before the market shifts.
#### **Perfect for Nifty Scalpers**
Built for the fast-paced world of Nifty trading, this strategy shines during high-volatility sessions like the market open or global overlaps. Whether you’re a beginner honing your skills or a seasoned trader seeking consistency, the Sniper Scalping Strategy offers a clear, actionable framework to scalp profits with discipline and precision.
#### **Get Started**
Test it in a demo account, refine it to your style, and watch your scalping game soar. Trade smart, stay focused, and let the Sniper Scalping Strategy turn Nifty’s 1-minute moves into your edge!
Log Regression Oscillator Channel [BigBeluga]
This unique overlay tool blends logarithmic trend analysis with dynamic oscillator behavior. It projects RSI, MFI, or Stochastic lines directly into a log regression channel on the price chart — offering an intuitive way to detect overbought/oversold momentum within the broader price structure.
🔵Key Features:
Logarithmic Regression Channel:
➣ Draws a trend-based channel using logarithmic regression, adapting to price growth curvature over time.
➣ Features upper, lower, and optional midline boundaries to visualize trend flow and range extremes.
Oscillator Overlay (RSI / MFI / Stochastic):
➣ Projects your chosen oscillator inside the channel using dynamic polylines.
➣ Allows switching between RSI, Money Flow Index, or Stochastic for versatile momentum insight.
Threshold-Based Scaling:
➣ The top and bottom of the channel represent traditional oscillator thresholds (e.g., RSI 70/30).
➣ Users can modify the scale in settings to customize what "overbought" or "oversold" means visually.
Signal Line Integration:
➣ Adds a yellow moving average (signal line) for smoother confirmation of oscillator turns.
➣ Helps identify divergence, momentum shifts, and fakeouts with better clarity.
Live Oscillator Readout:
➣ Displays the real-time oscillator value at the right edge of the chart.
➣ Ensures traders stay aware of current momentum levels without switching panels.
🔵Usage:
Momentum Context:
➣ When the oscillator touches the upper regression band, it may signal local overbought pressure.
➣ Touching the lower band may indicate oversold conditions within the current log trend.
Divergence Detection:
➣ Use the oscillator’s behavior relative to the channel slope to spot divergence from price.
➣ For example, RSI rising inside a falling channel can flag early trend shifts.
Trend-Sensitive Entries:
➣ Combine oscillator signals with log channel direction to filter trades in trend alignment.
➣ Signal line crossovers inside the channel act as early warning for momentum turns.
The Log Regression Oscillator Channel transforms how traders view classic momentum tools. By embedding oscillators into a logarithmic trend structure, it offers unmatched clarity on momentum positioning relative to price expansion. Ideal for swing traders, mean-reverters, or trend followers looking to sharpen entries and exits with style.
Volume Flow with Bollinger Bands and EMA Cross SignalsThe Volume Flow with Bollinger Bands and EMA Cross Signals indicator is a custom technical analysis tool designed to identify potential buy and sell signals based on several key components:
Volume Flow: This component combines price movement and trading volume to create a signal that indicates the strength or weakness of price movements. When the price is rising with increasing volume, it suggests strong buying activity, whereas falling prices with increasing volume indicate strong selling pressure.
Bollinger Bands: Bollinger Bands consist of three lines:
The Basis (middle line), which is a Simple Moving Average (SMA) of the price over a set period.
The Upper Band, which is the Basis plus a multiple of the standard deviation (typically 2).
The Lower Band, which is the Basis minus a multiple of the standard deviation. Bollinger Bands help identify periods of high volatility and potential overbought/oversold conditions. When the price touches the upper band, it might indicate that the market is overbought, while touching the lower band might indicate oversold conditions.
EMA Crossovers: The script includes two Exponential Moving Averages (EMAs):
Fast EMA: A shorter-term EMA, typically more sensitive to price changes.
Slow EMA: A longer-term EMA, responding slower to price changes. The crossover of the Fast EMA crossing above the Slow EMA (bullish crossover) signals a potential buy opportunity, while the Fast EMA crossing below the Slow EMA (bearish crossover) signals a potential sell opportunity.
Background Color and Candle Color: The indicator highlights the chart's background with specific colors based on the signals:
Green background for buy signals.
Yellow background for sell signals. Additionally, the candles are colored green for buy signals and yellow for sell signals to visually reinforce the trade opportunities.
Buy/Sell Labels: Small labels are placed on the chart:
"BUY" label in green is placed below the bar when a buy signal is generated.
"SELL" label in yellow is placed above the bar when a sell signal is generated.
Working of the Indicator:
Volume Flow Calculation: The Volume Flow is calculated by multiplying the price change (current close minus the previous close) with the volume. This product is then smoothed with a Simple Moving Average (SMA) over a user-defined period (length). The result is then multiplied by a multiplier to adjust its sensitivity.
Price Change = close - close
Volume Flow = Price Change * Volume
Smoothed Volume Flow = SMA(Volume Flow, length)
The Volume Flow Signal is then: Smooth Volume Flow * Multiplier
This calculation represents the buying or selling pressure in the market.
Bollinger Bands: Bollinger Bands are calculated using the Simple Moving Average (SMA) of the closing price (basis) and the Standard Deviation (stdev) of the price over a period defined by the user (bb_length).
Basis (Middle Band) = SMA(close, bb_length)
Upper Band = Basis + (bb_std_dev * Stdev)
Lower Band = Basis - (bb_std_dev * Stdev)
The upper and lower bands are plotted alongside the price to identify the price's volatility. When the price is near the upper band, it could be overbought, and near the lower band, it could be oversold.
EMA Crossovers: The Fast EMA and Slow EMA are calculated using the Exponential Moving Average (EMA) function. The crossovers are detected by checking:
Buy Signal (Bullish Crossover): When the Fast EMA crosses above the Slow EMA.
Sell Signal (Bearish Crossover): When the Fast EMA crosses below the Slow EMA.
The long_condition variable checks if the Fast EMA crosses above the Slow EMA, and the short_condition checks if it crosses below.
Visual Signals:
Background Color: The background is colored green for a buy signal and yellow for a sell signal. This gives an immediate visual cue to the trader.
Bar Color: The candles are colored green for buy signals and yellow for sell signals.
Labels:
A "BUY" label in green appears below the bar when the Fast EMA crosses above the Slow EMA.
A "SELL" label in yellow appears above the bar when the Fast EMA crosses below the Slow EMA.
Summary of Buy/Sell Logic:
Buy Signal:
The Fast EMA crosses above the Slow EMA (bullish crossover).
Volume flow is positive, indicating buying pressure.
Background turns green and candles are colored green.
A "BUY" label appears below the bar.
Sell Signal:
The Fast EMA crosses below the Slow EMA (bearish crossover).
Volume flow is negative, indicating selling pressure.
Background turns yellow and candles are colored yellow.
A "SELL" label appears above the bar.
Usage of the Indicator:
This indicator is designed to help traders identify potential entry (buy) and exit (sell) points based on:
The interaction of Exponential Moving Averages (EMAs).
The strength and direction of Volume Flow.
Price volatility using Bollinger Bands.
By combining these components, the indicator provides a comprehensive view of market conditions, helping traders make informed decisions on when to enter and exit trades.
DT_Sessions TOPDT_Sessions TOP - Powerful Trading Sessions and Key Levels Indicator
Description
DT_Sessions is a versatile TradingView indicator that displays major trading sessions and important price levels on your chart. It's ideal for traders working in forex, cryptocurrency, and stock markets, helping to visualize critical market information directly on the chart.
Key Features:
Visualization of major trading sessions: Asian, Frankfurt, London, New York (AM and PM)
Previous day high and low (PDH/PDL) tracking
Display of key psychological levels for major trading instruments
Customizable colors and styles for all indicator elements
Flexible timezone management for accurate session synchronization
Benefits of Use
Enhanced market analysis: Understanding the activity of different trading sessions helps better interpret price movements
Trading time optimization: Visual display of the most volatile market periods
Key resistance and support levels: Automatic display of psychologically significant price levels
Daily extreme monitoring: PDH/PDL help in determining the trading range
Supported Instruments
The indicator automatically recognizes popular instruments, including:
Forex pairs (EUR/USD, GBP/USD, USD/JPY)
Cryptocurrencies (Bitcoin, Ethereum)
Stock indices (DAX, NASDAQ, S&P 500, EuroStoxx50)
Precious metals (XAU/USD)
How to Use
Add the indicator to your favorite asset's chart
Observe the trading session ranges highlighted in different colors
Use PDH/PDL lines to identify significant daily levels
Pay attention to key psychological levels for your instrument
Advanced Settings
The indicator offers numerous settings for each session:
Enable/disable individual sessions
Adjust start and end times for each session
Change colors and transparency
Configure PDH/PDL display
Manage timezones and UTC offset
Effective For
Scalpers and day traders
Long-term investors tracking key levels
Algorithmic traders needing session data visualization
Beginners studying the impact of trading sessions on market activity
DT_Sessions is an essential tool for traders of all levels, providing valuable information about market dynamics and key levels directly on your TradingView chart.
Range Filter Buy and Sell 5min## **Enhanced Range Filter Strategy: A Comprehensive Overview**
### **1. Introduction**
The **Enhanced Range Filter Strategy** is a powerful technical trading system designed to identify high-probability trading opportunities while filtering out market noise. It utilizes **range-based trend filtering**, **momentum confirmation**, and **volatility-based risk management** to generate precise entry and exit signals. This strategy is particularly useful for traders who aim to capitalize on trend-following setups while avoiding choppy, ranging market conditions.
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### **2. Key Components of the Strategy**
#### **A. Range Filter (Trend Determination)**
- The **Range Filter** smooths price fluctuations and helps identify clear trends.
- It calculates an **adjusted price range** based on a **sampling period** and a **multiplier**, ensuring a dynamic trend-following approach.
- **Uptrends:** When the current price is above the range filter and the trend is strengthening.
- **Downtrends:** When the price falls below the range filter and momentum confirms the move.
#### **B. RSI (Relative Strength Index) as Momentum Confirmation**
- RSI is used to **filter out weak trades** and prevent entries during overbought/oversold conditions.
- **Buy Signals:** RSI is above a certain threshold (e.g., 50) in an uptrend.
- **Sell Signals:** RSI is below a certain threshold (e.g., 50) in a downtrend.
#### **C. ADX (Average Directional Index) for Trend Strength Confirmation**
- ADX ensures that trades are only taken when the trend has **sufficient strength**.
- Avoids trading in low-volatility, ranging markets.
- **Threshold (e.g., 25):** Only trade when ADX is above this value, indicating a strong trend.
#### **D. ATR (Average True Range) for Risk Management**
- **Stop Loss (SL):** Placed **one ATR below** (for long trades) or **one ATR above** (for short trades).
- **Take Profit (TP):** Set at a **3:1 reward-to-risk ratio**, using ATR to determine realistic price targets.
- Ensures volatility-adjusted risk management.
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### **3. Entry and Exit Conditions**
#### **📈 Buy (Long) Entry Conditions:**
1. **Price is above the Range Filter** → Indicates an uptrend.
2. **Upward trend strength is positive** (confirmed via trend counter).
3. **RSI is above the buy threshold** (e.g., 50, to confirm momentum).
4. **ADX confirms trend strength** (e.g., above 25).
5. **Volatility is supportive** (using ATR analysis).
#### **📉 Sell (Short) Entry Conditions:**
1. **Price is below the Range Filter** → Indicates a downtrend.
2. **Downward trend strength is positive** (confirmed via trend counter).
3. **RSI is below the sell threshold** (e.g., 50, to confirm momentum).
4. **ADX confirms trend strength** (e.g., above 25).
5. **Volatility is supportive** (using ATR analysis).
#### **🚪 Exit Conditions:**
- **Stop Loss (SL):**
- **Long Trades:** 1 ATR below entry price.
- **Short Trades:** 1 ATR above entry price.
- **Take Profit (TP):**
- Set at **3x the risk distance** to achieve a favorable risk-reward ratio.
- **Ranging Market Exit:**
- If ADX falls below the threshold, indicating a weakening trend.
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### **4. Visualization & Alerts**
- **Colored range filter line** changes based on trend direction.
- **Buy and Sell signals** appear as labels on the chart.
- **Stop Loss and Take Profit levels** are plotted as dashed lines.
- **Gray background highlights ranging markets** where trading is avoided.
- **Alerts trigger on Buy, Sell, and Ranging Market conditions** for automation.
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### **5. Advantages of the Enhanced Range Filter Strategy**
✅ **Trend-Following with Noise Reduction** → Helps avoid false signals by filtering out weak trends.
✅ **Momentum Confirmation with RSI & ADX** → Ensures that only strong, valid trades are executed.
✅ **Volatility-Based Risk Management** → ATR ensures adaptive stop loss and take profit placements.
✅ **Works on Multiple Timeframes** → Effective for day trading, swing trading, and scalping.
✅ **Visually Intuitive** → Clearly displays trade signals, SL/TP levels, and trend conditions.
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### **6. Who Should Use This Strategy?**
✔ **Trend Traders** who want to enter trades with momentum confirmation.
✔ **Swing Traders** looking for medium-term opportunities with a solid risk-reward ratio.
✔ **Scalpers** who need precise entries and exits to minimize false signals.
✔ **Algorithmic Traders** using alerts for automated execution.
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### **7. Conclusion**
The **Enhanced Range Filter Strategy** is a powerful trading tool that combines **trend-following techniques, momentum indicators, and risk management** into a structured, rule-based system. By leveraging **Range Filters, RSI, ADX, and ATR**, traders can improve trade accuracy, manage risk effectively, and filter out unfavorable market conditions.
This strategy is **ideal for traders looking for a systematic, disciplined approach** to capturing trends while **avoiding market noise and false breakouts**. 🚀
MACD Z-ScoreMACD Z-Score Indicator Description
This indicator takes the traditional MACD and converts its histogram into a standardized z‑score. It does so by first calculating the MACD using a fast and a slow moving average (which you can choose to compute with either SMA or EMA). The MACD histogram is then derived as the difference between the MACD line and a signal line (again, with your choice of smoothing method).
Next, the indicator computes a z‑score of that histogram over a user‑defined lookback period. In simple terms, it measures how far (in terms of standard deviations) the current histogram value deviates from its average. This standardization makes it easier to compare the MACD’s momentum across different assets or timeframes. There’s also an option to further smooth the z‑score with an EMA to reduce noise.
Finally, the indicator plots the resulting z‑score along with horizontal reference lines at key levels (such as 1, -1, 2, -2, 3, and -3) and changes the background color when the z‑score exceeds a high threshold (above 2) or drops below a low threshold (below -2), providing a visual cue for potential long or short conditions.
This indicator is ideal for traders looking for a normalized way to assess momentum, helping them to easily spot when the MACD histogram deviates significantly from its typical range.
Sigma Expected Movement)Okay, here's a brief description of what the final Pine Script code achieves:
Indicator Description:
This indicator calculates and plots expected price movement ranges based on the VIX index for daily, weekly, or monthly periods. It uses user-selectable VIX data (Today's Open / Previous Close) and a center price source (Today's Open / Previous Close).
Key features include:
Up to three customizable deviation levels, based on user-defined percentages of the calculated expected move.
Configurable visibility, color, opacity (default 50%), line style, and width (default 1) for each deviation level.
Optional filled area boxes between the 1st and 2nd deviation levels (enabled by default), with customizable fill color/opacity.
An optional center price line with configurable visibility (disabled by default), color, opacity, style, and width.
All drawings appear only within a user-defined time window (e.g., specific market hours).
Does not display price labels on the lines.
Optional rounding of calculated price levels.
Kase Permission StochasticOverview
The Kase Permission Stochastic indicator is an advanced momentum oscillator developed from Kase's trading methodology. It offers enhanced signal smoothing and filtering compared to traditional stochastic oscillators, providing clearer entry and exit signals with fewer false triggers.
How It Works
This indicator calculates a specialized stochastic using a multi-stage smoothing process:
Initial stochastic calculation based on high, low, and close prices
Application of weighted moving averages (WMA) for short-term smoothing
Progressive smoothing through differential factors
Final smoothing to reduce noise and highlight significant trend changes
The indicator oscillates between 0 and 100, with two main components:
Main Line (Green): The smoothed stochastic value
Signal Line (Yellow): A further smoothed version of the main line
Signal Generation
Trading signals are generated when the main line crosses the signal line:
Buy Signal (Green Triangle): When the main line crosses above the signal line
Sell Signal (Red Triangle): When the main line crosses below the signal line
Key Features
Multiple Smoothing Algorithms: Uses a combination of weighted and exponential moving averages for superior noise reduction
Clear Visualization: Color-coded lines and background filling
Reference Levels: Horizontal lines at 25, 50, and 75 for context
Customizable Colors: All visual elements can be color-customized
Customization Options
PST Length: Base period for the stochastic calculation (default: 9)
PST X: Multiplier for the lookback period (default: 5)
PST Smooth: Smoothing factor for progressive calculations (default: 3)
Smooth Period: Final smoothing period (default: 10)
Trading Applications
Trend Confirmation: Use crossovers to confirm entries in the direction of the prevailing trend
Reversal Detection: Identify potential market reversals when crossovers occur at extreme levels
Range-Bound Markets: Look for oscillations between overbought and oversold levels
Filter for Other Indicators: Use as a confirmation tool alongside other technical indicators
Best Practices
Most effective in trending markets or during well-defined ranges
Combine with price action analysis for better context
Consider the overall market environment before taking signals
Use longer settings for fewer but higher-quality signals
The Kase Permission Stochastic delivers a sophisticated approach to momentum analysis, offering a refined perspective on market conditions while filtering out much of the noise that affects standard oscillators.
HTF Support & Resistance Zones📌 English Description:
HTF Support & Resistance Zones is a powerful indicator designed to auto-detect key support and resistance levels from higher timeframes (Daily, Weekly, Monthly, Yearly).
It displays the number of touches for each level and automatically classifies its strength (Weak – Strong – Very Strong) with full customization options.
✅ Features:
Auto-detection of support/resistance from HTFs
Strength calculation based on touch count
Clean visual display with color, size, and label customization
Ideal for scalping and intraday trading
📌 الوصف العربي:
مؤشر "HTF Support & Resistance Zones" يساعد المتداولين على تحديد أهم مناطق الدعم والمقاومة المستخرجة تلقائيًا من الفريمات الكبيرة (اليومي، الأسبوعي، الشهري، السنوي).
يعرض المؤشر عدد اللمسات لكل مستوى ويقيّم قوته تلقائيًا (ضعيف – قوي – قوي جدًا)، مع خيارات تخصيص كاملة للعرض.
✅ ميزات المؤشر:
دعم/مقاومة تلقائية من الفريمات الكبيرة
تقييم تلقائي لقوة المستويات بناءً على عدد اللمسات
عرض مرئي مرن مع تحكم بالألوان، الحجم، الشكل، والخلفية
مناسب للتداولات اليومية والسكالبينج
EMA Channel Key K-LinesEMA Channel Setup :
Three 32-period EMAs (high, low, close prices)
Visually distinct colors (red, blue, green)
Gray background between high and low EMAs
Key K-line Identification :
For buy signals: Close > highest EMA, K-line height ≥ channel height, body ≥ 2/3 of range
For sell signals: Close < lowest EMA, K-line height ≥ channel height, body ≥ 2/3 of range
Alternating signals only (no consecutive buy/sell signals)
Visual Markers :
Green "BUY" labels below key buy K-lines
Red "SELL" labels above key sell K-lines
Clear channel visualization
Logic Flow :
Tracks last signal direction to prevent consecutive same-type signals
Strict conditions ensure only significant breakouts are marked
All calculations based on your exact specifications
Fibonacci Sequence NumbersThe "Fibonacci Sequence Numbers" indicator overlays horizontal lines on a trading chart based on Fibonacci sequence values (0, 34, 55, 89, 144, 233, 377, 610) relative to a user-defined reference price and time. Users can specify the direction ("Above," "Below," or "Both") to plot these levels above and/or below the reference price, with customizable line length, width, and colors for each level (e.g., red for Level 0, blue for 34). A vertical dashed line marks the reference time, while horizontal lines extend rightward, accompanied by labeled annotations shifted slightly left for clear view.
[TABLE] Moving Average Stage Indicator Table📈 MA Stage Indicator Table
🧠 Overview:
This script analyzes market phases based on moving average (MA) crossovers, classifying them into 6 distinct stages and displaying statistical summaries for each.
🔍 Key Features:
• Classifies market condition into Stage 1 to Stage 6 based on the relationship between MA1 (short), MA2 (mid), and MA3 (long)
• Provides detailed stats for each stage:
• Average Duration
• Average Width (MA distance)
• Slope (Angle) - High / Low / Average
• Shows current stage details in real-time
• Supports custom date range filtering
• Choose MA type: SMA or EMA
• Optional background coloring for stages
• Clean summary table displayed on the chart
VVV AB+The VVV AB+ is an indicator that displays information on the chart based on higher time frame candles and their characteristics. The indicator performs the following functions:
Candle Analysis: The indicator calculates the direction and colors (bullish or bearish) of the selected time frame candles and draws a box around the candle body, depending on whether the closing price is higher or lower than the previous candle’s opening price.
Lines: The indicator calculates and displays the median value of the selected candles (the average of the high and low), as well as a simple moving average (SMA), providing additional technical information to the user.
This indicator can be useful for those who focus on analyzing higher time frame candles while visually displaying the candle's direction and other key information, such as the previous day's high/low.
A VVV AB+ egy olyan indikátor, amely a magasabb időkeretű gyertyák és azok jellemzői alapján jelenít meg információkat a grafikonon. Az indikátor az alábbi funkciókat valósítja meg:
Gyertya elemzés: Az indikátor kiszámítja a választható idősíkú gyertyák testének irányát és a színeket (bullish vagy bearish), majd a megfelelő színű dobozt rajzol a gyertya teste köré, attól függően, hogy a záróár magasabb vagy alacsonyabb volt, mint az előző gyertya nyitóára.
Vonalak: Az indikátor kiszámítja és megjeleníti a kiválasztott gyertyák medián értékét (a magas és alacsony átlagát), illetve egy egyszerű mozgóátlagot (SMA), hogy további technikai információkat nyújtson a felhasználónak.
Ez az indikátor hasznos lehet azok számára, akik a magasabb időkeretű gyertyák elemzésére építenek, miközben vizuálisan is megjelenítik a gyertya irányát és más kulcsfontosságú információkat, mint PL előző napi High/Low.
BB LevelsBB Levels — Volatility-Based Weekly Trading Ranges
Overview:
BB Levels is a multi-timeframe indicator that projects weekly trading ranges using historical price behavior and volatility modeling. It combines data from both the 4H and Daily timeframes to provide a dual-perspective view of expected price extremes.
Solid Lines → Based on the Daily timeframe (smoother, broader volatility)
Dashed Lines → Based on the 4H timeframe (finer, more reactive to short-term volatility)
How It Works:
The indicator employs a Markov Switching Model to estimate the prevailing market regime and generate a forecasted directional bias. It then applies average historical volatility to project a weekly range:
The centerline is a forecasted “zero level” based on the prior week’s close, adjusted by regime and trend forecast.
The upper and lower bounds are derived from average weekly volatility, scaled to reflect expected movement.
Two versions are shown:
Daily (solid): Represents the more conservative, long-term expectations.
4H (dashed): Captures short-term momentum and faster volatility shifts.
Important Note:
These levels represent statistical expectations, not fixed boundaries. Price may:
Consolidate within the dashed (4H) range during quiet periods
Break beyond the solid (Daily) range during news events or trending expansions
Designed For:
Swing traders seeking structured, volatility-adjusted weekly levels
Day traders targeting high-probability reversal zones
Strategists combining trend forecasting with expected price behavior
ADR Nivele Horizontale dhe EMAThe user provided a Pine Script code for an indicator called "ADR Nivele Horizontale dhe EMA". The task is to explain how this code works. Hmm, first, I need to understand what this indicator does. The title mentions ADR levels and EMA, so it's probably combining horizontal lines based on Average Daily Range and some Exponential Moving Averages.
Looking at the code, the first part is the indicator declaration with version 6. The settings include an ADR period, levels above and below the mid, and line style. Then there's a section for calculations. They request daily high and low to compute the daily range. The ADR is the SMA of the daily range over the specified period. The mid is the average of daily high and low. The adr_val is half of ADR, maybe to create symmetrical levels around the mid.
Next, they create arrays for lines and labels above and below the mid. The loops go from 1 to levels_above/below, calculate each level, draw lines and labels. The mid line is also drawn with a different color. Then, there are several EMAs plotted: 13, 50, 200, 400, 600, 800, each with different colorsWait, but in Pine Script, when you use arrays to store lines and labels, you have to manage them properly to avoid repainting or memory issues. The code uses array.concat to add new lines and labels each bar. However, since this code is running on each bar, the for loops might be creating multiple lines each time, leading to too many objects. Oh right, but they use var to initialize the arrays, so they are only initialized once. Wait, no. The var keyword initializes the variable once, but in each iteration, they are concatenating new lines. Wait, no: the for loops are inside the main script, which runs on each bar. So every time a new bar is processed, the loops will run again, adding new lines and labels. That's a problem because each bar would create new lines, leading to an ever-increasing number of lines, which could hit the maximum limit (max_lines_count is set to 20, but maybe they have more bars). Oh, the indicator has max_lines_count=20, but the code might be creating multiple lines per bar. So maybe the code isn't handling the line management correctly. For example, if levels_above is 2, then each bar would create 2 new lines above and 2 below, plus the mid line. Over 20 bars, that's way over 20 lines. So this could cause the indicator to stop drawing after 20 lines. The user might not have realized that the lines are being recreated every bar, leading to a buildup. But perhaps the original code intended to have the lines update each bar, extending to the current bar. Let's see: in line.new, they use bar_index as the starting x1 and bar_index as x2. So each line is drawn from the previous bar's index to the current one. But since this is done on every bar, each line is only one bar long. Wait, no. Because on each new bar, the code creates a new line from the previous bar's index (bar_index , which is now the bar before the current one) to the current bar_index. So each line is just a segment from the previous bar to the current bar. But when the next bar comes, a new line is created again. So the result is that each level line is a series of one-bar-length segments, which might not be intended. The user probably wants continuous lines that extend from the left to the right, not just per bar. So this approach is incorrect. The correct way would be to have a line that starts at the first bar of the calculation and extends to the right, updating each bar. But the current code creates a new line each bar, leading to multiple short lines instead of one continuous line. That's a mistake.
So the main issue here is that the code is generating a new line segment for each bar, which is not efficient and would quickly hit the line limit. To fix this, instead of creating a new line every bar, they should create the line once and then update its position each bar. But in Pine Script, you can't directly update a line after it's created; instead, you need to track the line ID and use line.set_xy() to extend it. However, the current code uses array.concat to add new lines each bar, which isn't the right approach.
Therefore, the code as written will create multiple short lines instead of continuous horizontal lines. The correct approach for horizontal levels that extend to the right is to create the line once, starting at a certain bar and extending indefinitely, or to update the line's end point each new bar. But in Pine Script, you can't extend lines infinitely; you have to manage them each bar. Alternatively, using the 'line.extend' style might help, but I think the original code's logic is flawed here.
Additionally, the EMA part is straightforward: plotting multiple EMAs with different periods. That part should work as expected.
So the explanation should mention that the ADR levels are calculated based on the daily high and low, then the code attempts to draw horizontal lines above and below the mid-point. However, the way the lines are drawn may lead to multiple short segments instead of continuous lines, potentially causing performance issues or hitting the maximum line count. The EMAs are plotted correctly with various periods..
