Trend Filter (2-pole) [BigBeluga]Trend Filter (2-pole)
The Trend Filter (2-pole) is an advanced trend-following indicator based on a two-pole filter, which smooths out market noise while effectively highlighting trends and their strength. It incorporates color gradients and support/resistance dots to enhance trend visualization and decision-making for traders.
SP500:
🔵What is a Two-Pole Filter?
A two-pole filter is a digital signal processing technique widely used in electronics, control systems, and time series data analysis to smooth data and reduce noise.
//@function Two-pole filter
//@param src (series float) Source data (e.g., price)
//@param length (float) Length of the filter (higher value means smoother output)
//@param damping (float) Damping factor for the filter
//@returns (series float) Filtered value
method two_pole_filter(float src, int length, float damping) =>
// Calculate filter coefficients
float omega = 2.0 * math.pi / length
float alpha = damping * omega
float beta = math.pow(omega, 2)
// Initialize the filter variables
var float f1 = na
var float f2 = na
// Update the filter
f1 := nz(f1 ) + alpha * (src - nz(f1 ))
f2 := nz(f2 ) + beta * (f1 - nz(f2 ))
f2
It operates using two cascaded smoothing stages (poles), allowing for a more refined and responsive output compared to simple moving averages or other basic filters.
Two-pole filters are particularly valued for their ability to maintain smooth transitions while reducing lag, making them ideal for applications where precision and responsiveness are critical.
In trading, this filter helps detect trends by smoothing price data while preserving significant directional changes.
🔵Key Features of the Indicator:
Gradient-Colored Trend Filter Line: The main filter line dynamically changes color based on trend strength and direction:
- Green: Strong uptrend.
- Red: Strong downtrend.
- Yellow: Indicates a transition phase, signaling potential trend shifts.
Support and Resistance Dots with Signals:
- Dots are plotted below the filter line during uptrends and above it during downtrends.
- These dots represent consecutive rising or falling conditions of the filter line, which traders can set in the settings (e.g., the number of consecutive rises or falls required).
- The dots often act as dynamic support or resistance levels, providing valuable guidance during trends.
- Trend Signals:
Customizable Sensitivity: The indicator allows traders to adjust the filter length, damping factor, and the threshold for rising/falling conditions, enabling it to adapt to different trading styles and timeframes.
Bar Color Option: The indicator can optionally color bars to match the gradient of the filter line, enhancing visual clarity of trends directly on the price chart.
🔵How It Works:
The Trend Filter (2-pole) smooths price data using a two-pole filter, which reduces noise and highlights the underlying trend.
The gradient coloring of the filter line helps traders visually assess the strength and direction of trends.
Rising and falling conditions of the filter line are tracked, and dots are plotted when consecutive conditions meet the threshold, acting as potential support or resistance levels during trends.
The yellow transition color signals periods of indecision, helping traders anticipate potential reversals or consolidations.
🔵Use Cases:
Identify and follow strong uptrends and downtrends with gradient-based visual cues.
Use the yellow transition color to anticipate trend shifts or consolidation zones.
Leverage the plotted dots as dynamic support and resistance levels to refine entry and exit strategies.
Combine with other indicators for confirmation of trends and reversals.
This indicator is perfect for traders who want a visually intuitive and highly customizable tool to spot trends, gauge their strength, and make informed trading decisions.
Полосы и каналы
G. Santostasi's Bimodal Regimes Power Law G. Santostasi's Bimodal Regimes Power Law Model
Invite-Only TradingView Indicator
The Bimodal Power Law Model is a powerful TradingView indicator that provides a detailed visualization of Bitcoin's price behavior relative to its long-term power law trend. By leveraging volatility-normalized deviations, this model uncovers critical upper and lower bounds that govern Bitcoin’s price dynamics.
Key Features:
Power Law Support Line:
The model highlights the power law support line, a natural lower bound that has consistently defined Bitcoin's price floor over time. This line provides a crucial reference point for identifying accumulation zones.
Volatility-Normalized Upper Bound:
The indicator introduces a volatility-normalized upper channel, dynamically defined by the deviations from the power law. This bound represents the natural ceiling for Bitcoin’s price action and adjusts in real time to reflect changes in market volatility.
Color-Shaded Volatility Bounds:
The upper and lower bounds are visualized as color-shaded regions that represent the range of current volatility relative to the power law trend. These shaded regions dynamically expand or contract based on the level of market volatility, providing an intuitive view of Bitcoin’s expected price behavior under normalized conditions.
Two Regime Analysis:
Using a Gaussian Hidden Markov Model (HMM), the indicator separates Bitcoin's price action into two distinct regimes:
Above the power law:
Bullish phases characterized by overextensions.
Below the power law:
Bearish or accumulation phases where price consolidates below the trend.
Dynamic Bounds with Standard Deviations:
The model plots 2 standard deviation bands for both regimes, offering precise insights into the natural limits of Bitcoin’s price fluctuations. Peaks exceeding these bounds are contextualized as anomalies caused by historically higher volatility, emphasizing the consistency of normalized deviations.
Enhanced Visualization and Analysis:
The indicator integrates running averages calculated using deviations from the power law trend and smoothed volatility data to ensure a visually intuitive representation of Bitcoin’s price behavior. These insights help traders and researchers identify when price action is approaching statistically significant levels.
Use Cases:
Support and Resistance Identification:
Use the power law support line and upper volatility bounds to identify critical levels for buying or taking profit.
Cycle Analysis:
Distinguish between sustainable trends and speculative bubbles based on deviations from the power law.
Risk Management:
The shaded volatility regions provide a dynamic measure of risk, helping traders gauge when Bitcoin is overbought or oversold relative to its historical norms.
Market Timing: Understand Bitcoin’s cyclical behavior to time entries and exits based on its position within the shaded bounds.
Note:
This indicator is designed for long-term Bitcoin investors, researchers, and advanced traders who seek to leverage statistical regularities in Bitcoin’s price behavior. Available by invitation only.
Multi-Band Comparison Strategy (CRYPTO)Multi-Band Comparison Strategy (CRYPTO)
Optimized for Cryptocurrency Trading
This Pine Script strategy is built from the ground up for traders who want to take advantage of cryptocurrency volatility using a confluence of advanced statistical bands. The strategy layers Bollinger Bands, Quantile Bands, and a unique Power-Law Band to map out crucial support/resistance zones. It then focuses on a Trigger Line—the lower standard deviation band of the upper quantile—to pinpoint precise entry and exit signals.
Key Features
Bollinger Band Overlay
The upper Bollinger Band visually shifts to yellow when price exceeds it, turning black otherwise. This offers a straightforward way to gauge heightened momentum or potential market slowdowns.
Quantile & Power-Law Integration
The script calculates upper and lower quantile bands to assess probabilistic price extremes.
A Power-Law Band is also available to measure historically significant return levels, providing further insight into overbought or oversold conditions in fast-moving crypto markets.
Standard Deviation Trigger
The lower standard deviation band of the upper quantile acts as the strategy’s trigger. If price consistently holds above this line, the strategy interprets it as a strong bullish signal (“green” zone). Conversely, dipping below indicates a “red” zone, signaling potential reversals or exits.
Consecutive Bar Confirmation
To reduce choppy signals, you can fine-tune the number of consecutive bars required to confirm an entry or exit. This helps filter out noise and false breaks—critical in the often-volatile crypto realm.
Adaptive for Multiple Timeframes
Whether you’re scalping on a 5-minute chart or swing trading on daily candles, the strategy’s flexible confirmation and overlay options cater to different market conditions and trading styles.
Complete Plot Customization
Easily toggle visibility of each band or line—Bollinger, Quantile, Power-Law, and more.
Built-in Simple and Exponential Moving Averages can be enabled to further contextualize market trends.
Why It Excels at Crypto
Cryptocurrencies are known for rapid price swings, and this strategy addresses exactly that by combining multiple statistical methods. The quantile-based confirmation reduces noise, while Bollinger and Power-Law bands help highlight breakout regions in trending markets. Traders have reported that it works seamlessly across various coins and tokens, adapting its triggers to each asset’s unique volatility profile.
Give it a try on your favorite cryptocurrency pairs. With advanced data handling, crisp visual cues, and adjustable confirmation logic, the Multi-Band Comparison Strategy provides a robust framework to capture profitable moves and mitigate risk in the ever-evolving crypto space.
AMG Supply and Demand ZonesSupply and Demand Zones Indicator
This indicator identifies and visualizes supply and demand zones on the chart to help traders spot key areas of potential price reversals or continuations. The indicator uses historical price data to calculate zones based on high/low ranges and a customizable ATR-based fuzz factor.
Key Features:
Back Limit: Configurable look-back period to identify zones.
Zone Types: Options to display weak, untested, and turncoat zones.
Customizable Parameters: Adjust fuzz factor and visualization settings.
Usage:
Use this indicator to enhance your trading strategy by identifying key supply and demand areas where price is likely to react.
You can customize this further based on how you envision users benefiting from your indicator. Let me know if you'd like to add or adjust anything!
Falcon Liquidity Grab StrategyHow to Use This Script for Commodities and Indices
Best Timeframes: Start with 15-minute charts but test on higher timeframes like 1 hour for indices.
Risk Settings: Adjust the stop_loss_points and take_profit_multiplier to match the volatility of the chosen instrument.
Adaptive Fourier Transform Supertrend [QuantAlgo]Discover a brand new way to analyze trend with Adaptive Fourier Transform Supertrend by QuantAlgo , an innovative technical indicator that combines the power of Fourier analysis with dynamic Supertrend methodology. In essence, it utilizes the frequency domain mathematics and the adaptive volatility control technique to transform complex wave patterns into clear and high probability signals—ideal for both sophisticated traders seeking mathematical precision and investors who appreciate robust trend confirmation!
🟢 Core Architecture
At its core, this indicator employs an adaptive Fourier Transform framework with dynamic volatility-controlled Supertrend bands. It utilizes multiple harmonic components that let you fine-tune how market frequencies influence trend detection. By combining wave analysis with adaptive volatility bands, the indicator creates a sophisticated yet clear framework for trend identification that dynamically adjusts to changing market conditions.
🟢 Technical Foundation
The indicator builds on three innovative components:
Fourier Wave Analysis: Decomposes price action into primary and harmonic components for precise trend detection
Adaptive Volatility Control: Dynamically adjusts Supertrend bands using combined ATR and standard deviation
Harmonic Integration: Merges multiple frequency components with decreasing weights for comprehensive trend analysis
🟢 Key Features & Signals
The Adaptive Fourier Transform Supertrend transforms complex wave calculations into clear visual signals with:
Dynamic trend bands that adapt to market volatility
Sophisticated cloud-fill visualization system
Strategic L/S markers at key trend reversals
Customizable bar coloring based on trend direction
Comprehensive alert system for trend shifts
🟢 Practical Usage Tips
Here's how you can get the most out of the Adaptive Fourier Transform Supertrend :
1/ Setup:
Add the indicator to your favorites, then apply it to your chart ⭐️
Start with close price as your base source
Use standard Fourier period (14) for balanced wave detection
Begin with default harmonic weight (0.5) for balanced sensitivity
Start with standard Supertrend multiplier (2.0) for reliable band width
2/ Signal Interpretation:
Monitor trend band crossovers for potential signals
Watch for convergence of price with Fourier trend
Use L/S markers for trade entry points
Monitor bar colors for trend confirmation
Configure alerts for significant trend reversals
🟢 Pro Tips
Fine-tune Fourier parameters for optimal sensitivity:
→ Lower Base Period (8-12) for more reactive analysis
→ Higher Base Period (15-30) to filter out noise
→ Adjust Harmonic Weight (0.3-0.7) to control shorter trend influence
Customize Supertrend settings:
→ Lower multiplier (1.5-2.0) for tighter bands
→ Higher multiplier (2.0-3.0) for wider bands
→ Adjust ATR length based on market volatility
Strategy Enhancement:
→ Compare signals across multiple timeframes
→ Combine with volume analysis
→ Use with support/resistance levels
→ Integrate with other momentum indicators
Accurate Bollinger Bands mcbw_ [True Volatility Distribution]The Bollinger Bands have become a very important technical tool for discretionary and algorithmic traders alike over the last decades. It was designed to give traders an edge on the markets by setting probabilistic values to different levels of volatility. However, some of the assumptions that go into its calculations make it unusable for traders who want to get a correct understanding of the volatility that the bands are trying to be used for. Let's go through what the Bollinger Bands are said to show, how their calculations work, the problems in the calculations, and how the current indicator I am presenting today fixes these.
--> If you just want to know how the settings work then skip straight to the end or click on the little (i) symbol next to the values in the indicator settings window when its on your chart <--
--------------------------- What Are Bollinger Bands ---------------------------
The Bollinger Bands were formed in the 1980's, a time when many retail traders interacted with their symbols via physically printed charts and computer memory for personal computer memory was measured in Kb (about a factor of 1 million smaller than today). Bollinger Bands are designed to help a trader or algorithm see the likelihood of price expanding outside of its typical range, the further the lines are from the current price implies the less often they will get hit. With a hands on understanding many strategies use these levels for designated levels of breakout trades or to assist in defining price ranges.
--------------------------- How Bollinger Bands Work ---------------------------
The calculations that go into Bollinger Bands are rather simple. There is a moving average that centers the indicator and an equidistant top band and bottom band are drawn at a fixed width away. The moving average is just a typical moving average (or common variant) that tracks the price action, while the distance to the top and bottom bands is a direct function of recent price volatility. The way that the distance to the bands is calculated is inspired by formulas from statistics. The standard deviation is taken from the candles that go into the moving average and then this is multiplied by a user defined value to set the bands position, I will call this value 'the multiple'. When discussing Bollinger Bands, that trading community at large normally discusses 'the multiple' as a multiplier of the standard deviation as it applies to a normal distribution (gaußian probability). On a normal distribution the number of standard deviations away (which trades directly use as 'the multiple') you are directly corresponds to how likely/unlikely something is to happen:
1 standard deviation equals 68.3%, meaning that the price should stay inside the 1 standard deviation 68.3% of the time and be outside of it 31.7% of the time;
2 standard deviation equals 95.5%, meaning that the price should stay inside the 2 standard deviation 95.5% of the time and be outside of it 4.5% of the time;
3 standard deviation equals 99.7%, meaning that the price should stay inside the 3 standard deviation 99.7% of the time and be outside of it 0.3% of the time.
Therefore when traders set 'the multiple' to 2, they interpret this as meaning that price will not reach there 95.5% of the time.
---------------- The Problem With The Math of Bollinger Bands ----------------
In and of themselves the Bollinger Bands are a great tool, but they have become misconstrued with some incorrect sense of statistical meaning, when they should really just be taken at face value without any further interpretation or implication.
In order to explain this it is going to get a bit technical so I will give a little math background and try to simplify things. First let's review some statistics topics (distributions, percentiles, standard deviations) and then with that understanding explore the incorrect logic of how Bollinger Bands have been interpreted/employed.
---------------- Quick Stats Review ----------------
.
(If you are comfortable with statistics feel free to skip ahead to the next section)
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-------- I: Probability distributions --------
When you have a lot of data it is helpful to see how many times different results appear in your dataset. To visualize this people use "histograms", which just shows how many times each element appears in the dataset by stacking each of the same elements on top of each other to form a graph. You may be familiar with the bell curve (also called the "normal distribution", which we will be calling it by). The normal distribution histogram looks like a big hump around zero and then drops off super quickly the further you get from it. This shape (the bell curve) is very nice because it has a lot of very nifty mathematical properties and seems to show up in nature all the time. Since it pops up in so many places, society has developed many different shortcuts related to it that speed up all kinds of calculations, including the shortcut that 1 standard deviation = 68.3%, 2 standard deviations = 95.5%, and 3 standard deviations = 99.7% (these only apply to the normal distribution). Despite how handy the normal distribution is and all the shortcuts we have for it are, and how much it shows up in the natural world, there is nothing that forces your specific dataset to look like it. In fact, your data can actually have any possible shape. As we will explore later, economic and financial datasets *rarely* follow the normal distribution.
-------- II: Percentiles --------
After you have made the histogram of your dataset you have built the "probability distribution" of your own dataset that is specific to all the data you have collected. There is a whole complicated framework for how to accurately calculate percentiles but we will dramatically simplify it for our use. The 'percentile' in our case is just the number of data points we are away from the "middle" of the data set (normally just 0). Lets say I took the difference of the daily close of a symbol for the last two weeks, green candles would be positive and red would be negative. In this example my dataset of day by day closing price difference is:
week 1:
week 2:
sorting all of these value into a single dataset I have:
I can separate the positive and negative returns and explore their distributions separately:
negative return distribution =
positive return distribution =
Taking the 25th% percentile of these would just be taking the value that is 25% towards the end of the end of these returns. Or akin the 100%th percentile would just be taking the vale that is 100% at the end of those:
negative return distribution (50%) = -5
positive return distribution (50%) = +4
negative return distribution (100%) = -10
positive return distribution (100%) = +20
Or instead of separating the positive and negative returns we can also look at all of the differences in the daily close as just pure price movement and not account for the direction, in this case we would pool all of the data together by ignoring the negative signs of the negative reruns
combined return distribution =
In this case the 50%th and 100%th percentile of the combined return distribution would be:
combined return distribution (50%) = 4
combined return distribution (100%) = 10
Sometimes taking the positive and negative distributions separately is better than pooling them into a combined distribution for some purposes. Other times the combined distribution is better.
Most financial data has very different distributions for negative returns and positive returns. This is encapsulated in sayings like "Price takes the stairs up and the elevator down".
-------- III: Standard Deviation --------
The formula for the standard deviation (refereed to here by its shorthand 'STDEV') can be intimidating, but going through each of its elements will illuminate what it does. The formula for STDEV is equal to:
square root ( (sum ) / N )
Going back the the dataset that you might have, the variables in the formula above are:
'mean' is the average of your entire dataset
'x' is just representative of a single point in your dataset (one point at a time)
'N' is the total number of things in your dataset.
Going back to the STDEV formula above we can see how each part of it works. Starting with the '(x - mean)' part. What this does is it takes every single point of the dataset and measure how far away it is from the mean of the entire dataset. Taking this value to the power of two: '(x - mean) ^ 2', means that points that are very far away from the dataset mean get 'penalized' twice as much. Points that are very close to the dataset mean are not impacted as much. In practice, this would mean that if your dataset had a bunch of values that were in a wide range but always stayed in that range, this value ('(x - mean) ^ 2') would end up being small. On the other hand, if your dataset was full of the exact same number, but had a couple outliers very far away, this would have a much larger value since the square par of '(x - mean) ^ 2' make them grow massive. Now including the sum part of 'sum ', this just adds up all the of the squared distanced from the dataset mean. Then this is divided by the number of values in the dataset ('N'), and then the square root of that value is taken.
There is nothing inherently special or definitive about the STDEV formula, it is just a tool with extremely widespread use and adoption. As we saw here, all the STDEV formula is really doing is measuring the intensity of the outliers.
--------------------------- Flaws of Bollinger Bands ---------------------------
The largest problem with Bollinger Bands is the assumption that price has a normal distribution. This is assumption is massively incorrect for many reasons that I will try to encapsulate into two points:
Price return do not follow a normal distribution, every single symbol on every single timeframe has is own unique distribution that is specific to only itself. Therefore all the tools, shortcuts, and ideas that we use for normal distributions do not apply to price returns, and since they do not apply here they should not be used. A more general approach is needed that allows each specific symbol on every specific timeframe to be treated uniquely.
The distributions of price returns on the positive and negative side are almost never the same. A more general approach is needed that allows positive and negative returns to be calculated separately.
In addition to the issues of the normal distribution assumption, the standard deviation formula (as shown above in the quick stats review) is essentially just a tame measurement of outliers (a more aggressive form of outlier measurement might be taking the differences to the power of 3 rather than 2). Despite this being a bit of a philosophical question, does the measurement of outlier intensity as defined by the STDEV formula really measure what we want to know as traders when we're experiencing volatility? Or would adjustments to that formula better reflect what we *experience* as volatility when we are actively trading? This is an open ended question that I will leave here, but I wanted to pose this question because it is a key part of what how the Bollinger Bands work that we all assume as a given.
Circling back on the normal distribution assumption, the standard deviation formula used in the calculation of the bands only encompasses the deviation of the candles that go into the moving average and have no knowledge of the historical price action. Therefore the level of the bands may not really reflect how the price action behaves over a longer period of time.
------------ Delivering Factually Accurate Data That Traders Need------------
In light of the problems identified above, this indicator fixes all of these issue and delivers statistically correct information that discretionary and algorithmic traders can use, with truly accurate probabilities. It takes the price action of the last 2,000 candles and builds a huge dataset of distributions that you can directly select your percentiles from. It also allows you to have the positive and negative distributions calculated separately, or if you would like, you can pool all of them together in a combined distribution. In addition to this, there is a wide selection of moving averages directly available in the indicator to choose from.
Hedge funds, quant shops, algo prop firms, and advanced mechanical groups all employ the true return distributions in their work. Now you have access to the same type of data with this indicator, wherein it's doing all the lifting for you.
------------------------------ Indicator Settings ------------------------------
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---- Moving average ----
Select the type of moving average you would like and its length
---- Bands ----
The percentiles that you enter here will be pulled directly from the return distribution of the last 2,000 candles. With the typical Bollinger Bands, traders would select 2 standard deviations and incorrectly think that the levels it highlights are the 95.5% levels. Now, if you want the true 95.5% level, you can just enter 95.5 into the percentile value here. Each of the three available bands takes the true percentile you enter here.
---- Separate Positive & Negative Distributions----
If this box is checked the positive and negative distributions are treated indecently, completely separate from each other. You will see that the width of the top and bottom bands will be different for each of the percentiles you enter.
If this box is unchecked then all the negative and positive distributions are pooled together. You will notice that the width of the top and bottom bands will be the exact same.
---- Distribution Size ----
This is the number of candles that the price return is calculated over. EG: to collect the price return over the last 33 candles, the difference of price from now to 33 candles ago is calculated for the last 2,000 candles, to build a return distribution of 2000 points of price differences over 33 candles.
NEGATIVE NUMBERS(<0) == exact number of candles to include;
EG: setting this value to -20 will always collect volatility distributions of 20 candles
POSITIVE NUMBERS(>0) == number of candles to include as a multiple of the Moving Average Length value set above;
EG: if the Moving Average Length value is set to 22, setting this value to 2 will use the last 22*2 = 44 candles for the collection of volatility distributions
MORE candles being include will generally make the bands WIDER and their size will change SLOWER over time.
I wish you focus, dedication, and earnest success on your journey.
Happy trading :)
GANN Level (Salil Sir)GANN Level Indicator Description
This Pine Script calculates and plots Gann Levels based on a user-defined price input. It creates horizontal lines at key support and resistance levels derived from the input price, applying Gann's theory of market structure. The levels are dynamically calculated and squared for enhanced precision.
Key Features:
Manual Price Input:
The user inputs a round off of square root of base price (Manual_Input), which serves as the foundation for calculations.
Support and Resistance Levels:
Six resistance levels (R1 to R6) and six support levels (S1 to S6) are calculated by incrementing or decrementing the base price in steps of 0.25.
Squared Levels:
Each level is squared (level^2) to align with Gann's mathematical principles.
Visualization:
All levels, including the base price squared (GANN), are plotted as horizontal dotted lines:
Black Line: Base price squared (Gann Level).
Green Lines: Resistance levels.
Red Lines: Support levels.
Purpose:
The indicator helps traders identify potential support and resistance zones based on Gann's methodology, providing a mathematical framework for decision-making.
Usage:
Adjust the Manual Price in the settings to the desired value.
Observe the plotted levels for key support and resistance zones on the chart.
Use these levels to make informed trading decisions or to validate other indicators.
Silver Bullet SessionsThe Silver Bullet Sessions indicator is a specialized timing tool designed to highlight key market sessions throughout the trading day. By marking specific hours with vertical lines, it helps traders identify potentially significant market moments that often coincide with increased volatility and trading opportunities.
This indicator plots vertical lines at six strategic times during the trading day: 3:00 AM, 4:00 AM, 10:00 AM, 11:00 AM, 2:00 PM, and 3:00 PM. These times are carefully selected to correspond with important market events and session overlaps in the global trading cycle. The early morning hours (3-4 AM) often capture significant Asian market movements and the European market opening. The mid-morning period (10-11 AM) typically corresponds with peak European trading hours and the pre-US market dynamics. The afternoon times (2-3 PM) coincide with key US market activities and the European market close.
The indicator is implemented using Pine Script version 6, ensuring compatibility with the latest TradingView platform features. It employs a clean, efficient coding structure that minimizes resource usage while maintaining reliable performance. The vertical lines are rendered in blue for clear visibility against any chart background, and their width is optimized for easy identification without obscuring price action.
Traders can use these visual markers to:
Plan their entries and exits around these key time periods
Anticipate potential market volatility
Structure their trading sessions around these significant market hours
Identify session-based trading patterns
Comprehensive RSI, MACD & Stochastic Table
RSI, MACD, and Stochastic Multi-Asset Indicator for TradingView
Introduction
The RSI, MACD, and Stochastic Multi-Asset Indicator is a comprehensive tool designed for traders who want to analyze multiple assets simultaneously while utilizing some of the most popular technical indicators. This indicator is tailored for all market types—whether you're trading cryptocurrencies, stocks, forex, or commodities—and provides a consolidated dashboard for faster and more informed decision-making.
This tool combines Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Stochastic Oscillator, three of the most effective momentum and trend-following indicators. It provides a visual, color-coded table for quick insights and alerts for significant buy or sell opportunities.
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What Does This Indicator Do?
This indicator performs the following key functions:
1. Multi-Asset Analysis: Analyze two assets side by side, allowing you to monitor their momentum, trends, and overbought/oversold conditions simultaneously.
2. Combines Three Powerful Indicators:
RSI: Tracks market momentum and identifies overbought/oversold zones.
MACD: Highlights trend direction and momentum shifts.
Stochastic Oscillator: Provides insights into overbought/oversold zones with smoothing for better accuracy.
3. Color-Coded Dashboard: Displays all indicator values in an easy-to-read table with color coding for quick identification of market conditions.
4. Real-Time Alerts: Generates alerts when strong bullish or bearish conditions are met across multiple indicators.
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Key Features
1. Customizable Inputs
You can adjust RSI periods, MACD parameters, Stochastic settings, and timeframes to suit your trading style.
Analyze default or custom assets (e.g., BTC/USDT, ETH/USDT).
2. Multi-Timeframe Support
Use this indicator on any timeframe (e.g., 1-minute, 1-hour, daily) to suit your trading strategy.
3. Comprehensive Dashboard
Displays values for RSI, MACD, and Stochastic for two assets in one clean, compact table.
Automatically highlights overbought (red), oversold (green), and neutral (gray) conditions.
4. Buy/Sell Signals
Plots buy/sell signals on the chart when all indicators align in strong bullish or bearish zones.
Example:
Strong Buy: RSI above 50, Stochastic %K above 80, and MACD histogram positive.
Strong Sell: RSI below 50, Stochastic %K below 20, and MACD histogram negative.
5. Real-Time Alerts
Alerts notify you when a strong buy or sell condition is detected, so you don't miss critical trading opportunities.
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Who Is This Indicator For?
This indicator is perfect for:
Day Traders who need real-time insights across multiple assets.
Swing Traders who want to identify mid-term trends and momentum shifts.
Crypto, Stock, and Forex Traders looking for a consolidated tool that works across all asset classes.
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How It Works
1. RSI (Relative Strength Index):
Tracks momentum by measuring the speed and change of price movements.
Overbought: RSI > 70 (Red).
Oversold: RSI < 30 (Green).
2. MACD (Moving Average Convergence Divergence):
Combines two exponential moving averages (EMA) to track momentum and trend direction.
Positive Histogram: Bullish momentum.
Negative Histogram: Bearish momentum.
3. Stochastic Oscillator:
Tracks price relative to its high-low range over a specific period.
Overbought: %K > 80.
Oversold: %K < 20.
4. Table View:
Displays indicator values for both assets in an intuitive table format.
Highlights critical zones with color coding.
5. Alerts:
Alerts are triggered when:
RSI, MACD, and Stochastic align in strong bullish or bearish conditions.
These conditions are based on customizable thresholds.
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How to Use the Indicator
1. Add the Indicator to Your Chart:
After publishing, search for the indicator by its name in TradingView's Indicators tab.
2. Customize Inputs:
Adjust settings for RSI periods, MACD parameters, and Stochastic smoothing to suit your strategy.
3. Interpret the Table:
Check the table for highlighted zones (red for overbought, green for oversold).
Look for bullish or bearish signals in the "Signal" column.
4. Act on Alerts:
Use the real-time alerts to take action when strong conditions are met.
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Example Use Cases
1. Crypto Day Trading:
Monitor BTC/USDT and ETH/USDT simultaneously for strong bullish or bearish conditions.
Receive alerts when RSI, MACD, and Stochastic align for a potential reversal.
2. Swing Trading Stocks:
Track a stock (e.g., AAPL) and its sector ETF (e.g., QQQ) to find momentum-based opportunities.
3. Forex Scalping:
Identify overbought/oversold conditions across multiple currency pairs.
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Conclusion
The RSI, MACD, and Stochastic Multi-Asset Indicator simplifies your trading workflow by consolidating multiple technical indicators into one powerful tool. With real-time insights, color-coded visuals, and customizable alerts, this indicator is designed to help you stay ahead in any market.
Whether you're a beginner or an experienced trader, this indicator provides everything you need to make confident trading decisions. Add it to your TradingView chart today and take your analysis to the next level!
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Make sure to leave your feedback and suggestions so I can continue improving the tool for the community. Happy trading!
Multi-Band Comparison (Uptrend)Multi-Band Comparison
Overview:
The Multi-Band Comparison indicator is engineered to reveal critical levels of support and resistance in strong uptrends. In a healthy upward market, the price action will adhere closely to the 95th percentile line (the Upper Quantile Band), effectively “riding” it. This indicator combines a modified Bollinger Band (set at one standard deviation), quantile analysis (95% and 5% levels), and power‑law math to display a dynamic picture of market structure—highlighting a “golden channel” and robust support areas.
Key Components & Calculations:
The Golden Channel: Upper Bollinger Band & Upper Std Dev Band of the Upper Quantile
Upper Bollinger Band:
Calculation:
boll_upper=SMA(close,length)+(boll_mult×stdev)
boll_upper=SMA(close,length)+(boll_mult×stdev) Here, the 20-period SMA is used along with one standard deviation of the close, where the multiplier (boll_mult) is 1.0.
Role in an Uptrend:
In a healthy uptrend, price rides near the 95th percentile line. When price crosses above this Upper Bollinger Band, it confirms strong bullish momentum.
Upper Std Dev Band of the Upper Quantile (95th Percentile) Band:
Calculation:
quant_upper_std_up=quant_upper+stdev
quant_upper_std_up=quant_upper+stdev The Upper Quantile Band, quant_upperquant_upper, is calculated as the 95th percentile of recent price data. Adding one standard deviation creates an extension that accounts for normal volatility around this extreme level.
The Golden Channel:
When the price crosses above the Upper Bollinger Band, the Upper Std Dev Band of the Upper Quantile immediately shifts to gold (yellow) and remains gold until price falls below the Bollinger level. Together, these two lines form the “golden channel”—a visual hallmark of a healthy uptrend where the price reliably hugs the 95th percentile level.
Upper Power‑Law Band
Calculation:
The Upper Power‑Law Band is derived in two steps:
Determine the Extreme Return Factor:
power_upper=Percentile(returns,95%)
power_upper=Percentile(returns,95%) where returns are computed as:
returns=closeclose −1.
returns=close close−1.
Scale the Current Price:
power_upper_band=close×(1+power_upper)
power_upper_band=close×(1+power_upper)
Rationale and Correlation:
By focusing on the upper 5% of returns (reflecting “fat tails”), the Upper Power‑Law Band captures extreme but statistically expected movements. In an uptrend, its value often converges with the Upper Std Dev Band of the Upper Quantile because both measures reflect heightened volatility and extreme price levels. When the Upper Power‑Law Band exceeds the Upper Std Dev Band, it can signal a temporary overextension.
Upper Quantile Band (95% Percentile)
Calculation:
quant_upper=Percentile(price,95%)
quant_upper=Percentile(price,95%) This level represents where 95% of past price data falls below, and in a robust uptrend the price action practically rides this line.
Color Logic:
Its color shifts from a neutral (blackish) tone to a vibrant, bullish hue when the Upper Power‑Law Band crosses above it—signaling extra strength in the trend.
Lower Quantile and Its Support
Lower Quantile Band (5% Percentile):
Calculation:
quant_lower=Percentile(price,5%)
quant_lower=Percentile(price,5%)
Behavior:
In a healthy uptrend, price remains well above the Lower Quantile Band. It turns red only when price touches or crosses it, serving as a warning signal. Under normal conditions it remains bright green, indicating the market is not nearing these extreme lows.
Lower Std Dev Band of the Lower Quantile:
This line is calculated by subtracting one standard deviation from quant_lowerquant_lower and typically serves as absolute support in nearly all conditions (except during gap or near-gap moves). Its consistent role as support provides traders with a robust level to monitor.
How to Use the Indicator:
Golden Channel and Trend Confirmation:
As price rides the Upper Quantile (95th percentile) perfectly in a healthy uptrend, the Upper Bollinger Band (1 stdev above SMA) and the Upper Std Dev Band of the Upper Quantile form a “golden channel” once price crosses above the Bollinger level. When this occurs, the Upper Std Dev Band remains gold until price dips back below the Bollinger Band. This visual cue reinforces trend strength.
Power‑Law Insights:
The Upper Power‑Law Band, which is based on extreme (95th percentile) returns, tends to align with the Upper Std Dev Band. This convergence reinforces that extreme, yet statistically expected, price moves are occurring—indicating that even though the price rides the 95th percentile, it can only stretch so far before a correction or consolidation.
Support Indicators:
Primary and Secondary Support in Uptrends:
The Upper Bollinger Band and the Lower Std Dev Band of the Upper Quantile act as support zones for minor retracements in the uptrend.
Absolute Support:
The Lower Std Dev Band of the Lower Quantile serves as an almost invariable support area under most market conditions.
Conclusion:
The Multi-Band Comparison indicator unifies advanced statistical techniques to offer a clear view of uptrend structure. In a healthy bull market, price action rides the 95th percentile line with precision, and when the Upper Bollinger Band is breached, the corresponding Upper Std Dev Band turns gold to form a “golden channel.” This, combined with the Power‑Law analysis that captures extreme moves, and the robust lower support levels, provides traders with powerful, multi-dimensional insights for managing entries, exits, and risk.
Disclaimer:
Trading involves risk. This indicator is for educational purposes only and does not constitute financial advice. Always perform your own analysis before making trading decisions.
Buy/Sell Signals for CM_Williams_Vix_FixThis script in Pine Script is designed to create an indicator that generates buy and sell signals based on the Williams VIX Fix (WVF) indicator. Here’s a brief explanation of how this script works:
Main Components:
Williams VIX Fix (WVF) – This volatility indicator is calculated using the formula:
WVF
=
(
highest(close, pd)
−
low
highest(close, pd)
)
×
100
WVF=(
highest(close, pd)
highest(close, pd)−low
)×100
where highest(close, pd) represents the highest closing price over the period pd, and low represents the lowest price over the same period.
Bollinger Bands are used to determine levels of overbought and oversold conditions. They are constructed around the moving average (SMA) of the WVF value using standard deviation (SD).
Ranges based on percentiles help identify extreme levels of WVF values to spot entry and exit points.
Buy and sell signals are generated when the WVF crosses the Bollinger Bands lines or reaches the ranges based on percentiles.
Adjustable Parameters:
LookBack Period Standard Deviation High (pd): The lookback period for calculating the highest closing price.
Bolinger Band Length (bbl): The length of the period for constructing the Bollinger Bands.
Bollinger Band Standard Devaition Up (mult): The multiplier for the standard deviation used for the upper Bollinger Band.
Look Back Period Percentile High (lb): The lookback period for calculating maximum and minimum WVF values.
Highest Percentile (ph): The percentile threshold for determining the high level.
Lowest Percentile (pl): The percentile threshold for determining the low level.
Show High Range (hp): Option to display the range based on percentiles.
Show Standard Deviation Line (sd): Option to display the standard deviation line.
Signals:
Buy Signal: Generated when the WVF crosses above the lower Bollinger Band or falls below the lower boundary of the percentile-based range.
Sell Signal: Generated when the WVF crosses below the upper Bollinger Band or rises above the upper boundary of the percentile-based range.
These signals are displayed as triangles below or above the candles respectively.
Application:
The script can be used by traders to analyze market conditions and make buying or selling decisions based on volatility and price behavior.
Cross Alert with Configurable Rectangles**Description:**
This TradingView script, **"Cross Alert with Configurable Rectangles"**, is a technical analysis tool designed to help traders visualize and analyze market trends effectively. It combines configurable moving averages with customizable timeframe-based rectangles for highlighting price ranges.
### Features:
1. **Moving Averages:**
- Calculates and plots an Exponential Moving Average (EMA) and a Simple Moving Average (SMA) based on user-defined lengths.
- Provides both short and long moving averages to identify potential trend reversals or confirmations.
2. **Customizable Timeframe Rectangles:**
- Dynamically draws rectangles around price action based on user-selected timeframes: **Hourly (60 minutes), Daily, Weekly, or Monthly.**
- Automatically updates the rectangles to reflect high and low price levels within the selected timeframe.
- Customizable rectangle color and transparency for better chart visibility.
3. **Dynamic Line Projections:**
- Projects the trend of the long and short moving averages forward in time to help anticipate price movements.
### Use Case:
This script is ideal for traders who want to:
- Identify key support and resistance levels within different timeframes.
- Analyze price behavior relative to moving averages.
- Spot potential trend changes by observing price interaction with the moving averages and timeframe rectangles.
The script is fully configurable, allowing traders to adapt it to their trading strategy and preferences.
EWMA Volatility Bands
The EWMA Volatility Bands indicator combines an Exponential Moving Average (EMA) and Exponentially Weighted Moving Average (EWMA) of volatility to create dynamic upper and lower price bands. It helps traders identify trends, measure market volatility, and spot extreme conditions. Key features include:
Centerline (EMA): Tracks the trend based on a user-defined period.
Volatility Bands: Adjusted by the square root of volatility, representing potential price ranges.
Percentile Rank: Highlights extreme volatility (e.g., >99% or <1%) with shaded areas between the bands.
This tool is useful for trend-following, risk assessment, and identifying overbought/oversold conditions.
AI indicatorThis script is a trading indicator designed for future trading signals on the TradingView platform. It uses a combination of the Relative Strength Index (RSI) and a Simple Moving Average (SMA) to generate buy and sell signals. Here's a breakdown of its components and logic:
1. Inputs
The script includes configurable inputs to make it adaptable for different market conditions:
RSI Length: Determines the number of periods for calculating RSI. Default is 14.
RSI Overbought Level: Signals when RSI is above this level (default 70), indicating potential overbought conditions.
RSI Oversold Level: Signals when RSI is below this level (default 30), indicating potential oversold conditions.
Moving Average Length: Defines the SMA length used to confirm price trends (default 50).
2. Indicators Used
RSI (Relative Strength Index):
Measures the speed and change of price movements.
A value above 70 typically indicates overbought conditions.
A value below 30 typically indicates oversold conditions.
SMA (Simple Moving Average):
Used to smooth price data and identify trends.
Price above the SMA suggests an uptrend, while price below suggests a downtrend.
3. Buy and Sell Signal Logic
Buy Condition:
The RSI value is below the oversold level (e.g., 30), indicating the market might be undervalued.
The current price is above the SMA, confirming an uptrend.
Sell Condition:
The RSI value is above the overbought level (e.g., 70), indicating the market might be overvalued.
The current price is below the SMA, confirming a downtrend.
These conditions ensure that trades align with market trends, reducing false signals.
4. Visual Features
Buy Signals: Displayed as green labels (plotshape) below the price bars when the buy condition is met.
Sell Signals: Displayed as red labels (plotshape) above the price bars when the sell condition is met.
Moving Average Line: A blue line (plot) added to the chart to visualize the SMA trend.
5. How It Works
When the buy condition is true (RSI < 30 and price > SMA), a green label appears below the corresponding price bar.
When the sell condition is true (RSI > 70 and price < SMA), a red label appears above the corresponding price bar.
The blue SMA line helps to visualize the overall trend and acts as confirmation for signals.
6. Advantages
Combines Momentum and Trend Analysis:
RSI identifies overbought/oversold conditions.
SMA confirms whether the market is trending up or down.
Simple Yet Effective:
Reduces noise by using well-established indicators.
Easy to interpret for beginners and experienced traders alike.
Customizable:
Parameters like RSI length, oversold/overbought levels, and SMA length can be adjusted to fit different assets or timeframes.
7. Limitations
Lagging Indicator: SMA is a lagging indicator, so it may not capture rapid market reversals quickly.
Not Foolproof: No trading indicator can guarantee 100% accuracy. False signals can occur in choppy or sideways markets.
Needs Volume Confirmation: The script does not consider trading volume, which could enhance signal reliability.
8. How to Use It
Copy the script into TradingView's Pine Editor.
Save and add it to your chart.
Adjust the RSI and SMA parameters to suit your preferred asset and timeframe.
Look for buy signals (green labels) in uptrends and sell signals (red labels) in downtrends.
NPT Levels GeneratorNPT Levels Generator
Description:
The NPT Levels Generator is a custom indicator designed to draw horizontal lines at specific price levels on the chart. It helps traders identify key levels of interest, making it easier to analyze price action and plan trades.
The indicator takes a manually defined Base Price as the central reference point and then generates a series of horizontal lines above and below it at equal intervals. The number of lines and the distance between them are fully customizable through the settings panel.
This tool is particularly useful for identifying support and resistance levels, pivot zones, or any other significant price levels for technical analysis.
Features:
-Customizable Base Price: Define the central level manually.
- Adjustable Line Distance: Set the spacing between each horizontal line.
- Flexible Number of Lines: Choose how many lines to display above and below the base price.
- Custom Line Appearance: Configure the color and thickness of the lines.
This indicator is ideal for traders using price levels as a core part of their strategy, offering flexibility and clarity in visualizing key areas of interest.
FON60DK by leventsahThe strategy generates buy and sell signals using the Tillson T3 and TOTT (Twin Optimized Trend Tracker) indicators. Additionally, the Williams %R indicator is used to filter the signals. Below is an explanation of the main components of the code:
1. Input Parameters:
Tillson T3 and TOTT parameters: Separate parameters are defined for both buy (AL) and sell (SAT) conditions. These parameters control the sensitivity and behavior of the indicators.
Williams %R period: The period for the Williams %R indicator is set to determine overbought and oversold levels.
2. Tillson T3 Calculation:
The Tillson T3 indicator is a smoothed moving average that uses an exponential moving average (EMA) with additional smoothing. The formula calculates a weighted average of multiple EMAs to produce a smoother line.
The t3 function computes the Tillson T3 value based on the close price and the input parameters.
3. TOTT Calculation (Twin Optimized Trend Tracker):
The TOTT indicator is a trend-following tool that adjusts its sensitivity based on market conditions. It uses a combination of price action and a volatility coefficient to determine trend direction.
The Var_Func function calculates the TOTT value, which is then used to derive the OTT (Optimized Trend Tracker) levels for both buy and sell conditions.
4. Williams %R Calculation:
Williams %R is a momentum oscillator that measures overbought and oversold levels. It is calculated using the highest high and lowest low over a specified period.
5. Buy and Sell Conditions:
Buy Condition: A buy signal is generated when the Tillson T3 value crosses above the TOTT upper band (OTTup) and the Williams %R is above -20 (indicating an oversold condition).
Sell Condition: A sell signal is generated when the Tillson T3 value crosses below the TOTT lower band (OTTdnS) and the Williams %R is above -70 (used to close long positions).
6. Strategy Execution:
The strategy.entry function is used to open a long position when the buy condition is met.
The strategy.close function is used to close the long position when the sell condition is met.
7. Visualization:
The bars on the chart are colored green when a long position is open.
The Tillson T3, TOTT upper band (OTTup), and TOTT lower band (OTTdn) are plotted on the chart for both buy and sell conditions.
8. Plots:
The Tillson T3 values for buy and sell conditions are plotted in blue.
The TOTT upper and lower bands are plotted in green and red, respectively, for both buy and sell conditions.
Summary:
This strategy combines trend-following indicators (Tillson T3 and TOTT) with a momentum oscillator (Williams %R) to generate buy and sell signals. The use of separate parameters for buy and sell conditions allows for fine-tuning the strategy based on market behavior. The visual elements, such as colored bars and plotted indicators, help traders quickly identify signals and trends on the chart.
Channel Breakout by NatXateThe Channel Breakout by NatXate is a multi-channel technical indicator designed to identify potential breakout opportunities based on a combination of Keltner Channels, Donchian Channels, and Bollinger Bands.
This indicator helps traders pinpoint buy and sell signals by analyzing price behavior around key channel boundaries, while filtering out false signals using volatility and momentum criteria such as the Average True Range (ATR) and Bollinger Bands Width (BBW).
Key Features:
Keltner Channel:
The Keltner Channel is calculated using an Exponential Moving Average (EMA) and ATR to define upper and lower boundaries.
The upper and lower Keltner boundaries serve as potential breakout levels.
Donchian Channel:
The Donchian Channel tracks the highest high and lowest low over a user-defined period.
Price breaking above or below these boundaries indicates a potential long or short opportunity.
Bollinger Bands:
Bollinger Bands use a Simple Moving Average (SMA) and standard deviation to define dynamic support and resistance levels.
The upper and lower Bollinger boundaries provide an additional layer of confirmation for breakouts.
Bollinger Bands Width (BBW) Filter:
Measures the width of the Bollinger Bands, which reflects market volatility.
A minimum BBW threshold (minBBW) ensures signals are only generated during periods of sufficient volatility, helping to avoid false signals in consolidating markets.
ATR Filter:
The ATR is used to measure market volatility.
Only signals with ATR exceeding a user-defined percentage of the current price (atrThresholdPercent) are considered valid.
Buy and Sell Conditions:
Buy Signal:
Price breaks above the upper boundary of any of the three channels (Keltner, Donchian, or Bollinger Bands).
ATR is above its threshold, indicating sufficient volatility.
BBW is above the minBBW threshold.
Sell Signal:
Price breaks below the lower boundary of any of the three channels.
ATR is above its threshold.
BBW is above the minBBW threshold.
Non-Repainting Logic:
Signals are confirmed only after the bar closes (barstate.isconfirmed), preventing repainting and ensuring reliable signal generation.
Visual Signals:
Buy signals are marked with a green "B" label below the bar.
Sell signals are marked with a red "S" label above the bar.
The upper and lower boundaries of the Keltner Channel, Donchian Channel, and Bollinger Bands are plotted for visual clarity.
Alerts:
Separate alerts are available for Buy and Sell signals:
Buy Signal: "Channel Breakout Buy Signal by NatXate detected!"
Sell Signal: "Channel Breakout Sell Signal by NatXate detected!"
Alerts trigger once per bar close, making it suitable for real-time trading or monitoring.
How It Works:
Trend Identification:
The indicator identifies trends based on price breakouts above or below the channel boundaries.
Volatility Filtering:
Both ATR and BBW filters ensure that only high-probability breakout signals are shown, reducing noise in low-volatility environments.
Signal Confirmation:
Signals are confirmed after the bar closes to prevent false positives or premature triggers.
Parameters:
Keltner Channel Parameters:
lengthKC: Period for the Keltner Channel's EMA.
multKC: ATR multiplier for Keltner Channel boundaries.
Donchian Channel Parameters:
lengthDC: Period for calculating the highest high and lowest low.
Bollinger Bands Parameters:
lengthBB: Period for the Bollinger Bands' SMA.
multBB: Standard deviation multiplier for Bollinger Bands boundaries.
ATR Filter:
atrLength: Period for calculating ATR.
atrThresholdPercent: Minimum ATR as a percentage of the price for valid signals.
BBW Filter:
minBBW: Minimum Bollinger Bands Width required for signal generation.
Use Cases:
Breakout Trading:
Detect potential buy and sell opportunities when price breaks key channel boundaries during high volatility.
Trend Following:
Use the indicator to confirm trends and enter trades in the direction of the breakout.
Avoiding Low-Volatility Periods:
The BBW and ATR filters help avoid false signals in consolidating or choppy markets.
Recommended Usage:
Combine this indicator with additional tools such as volume analysis or momentum oscillators (e.g., MACD, RSI) for further confirmation.
Suitable for various timeframes, from intraday to swing trading.
Backtest thoroughly to adjust parameters for the specific market and timeframe you trade.
Midnight Open RangeMidnight Open Range with Breakouts & Targets
This indicator helps traders identify and analyze the Midnight Open Range (12:00 AM to 12:30 AM ET) for potential trading opportunities. Key features include:
1. Automatic detection and plotting of the Midnight Open Range
2. Display of multiple historical ranges (customizable)
3. Breakout signals for range violations
4. Multiple target levels based on the range size
5. Customizable colors and styles for easy visual analysis
Perfect for traders looking to capitalize on overnight price action and early morning trends. Ideal for forex, futures, and 24-hour markets.
Note: For best results, use on lower timeframes (5-minute or less) with 24-hour chart data.
20-34 Dual Dot Alerts OnlyPine Script that uses dual Donchian Channels (20-period and 34-period) and places tiny blue dots above candles when the highest price touches any upper Donchian Channel and below candles when the lowest price touches any lower Donchian Channel, without displaying the channels themselves, you can use the code.
### Explanation of the Code:
1. **Indicator Declaration**: The script is named "Dual Donchian Channels Dots Only" and overlays on the price chart.
2. **Input for Lengths**: Users can set lengths for two Donchian Channels (20 and 34 periods).
3. **Calculating Bands**: The upper and lower bands are calculated using `ta.highest` and `ta.lowest` functions over the specified periods.
4. **Touch Conditions**:
- `upperTouch`: Checks if the highest price of the current candle touches either of the upper bands.
- `lowerTouch`: Checks if the lowest price of the current candle touches either of the lower bands.
5. **Plotting Dots**:
- A tiny blue dot is plotted above bars where `upperTouch` is true.
- A tiny blue dot is plotted below bars where `lowerTouch` is true.
### How to Use:
1. Copy this script into TradingView’s Pine Script editor.
2. Save it and add it to your chart.
3. You will see tiny blue dots appear above or below candles based on whether they touch any of the upper or lower Donchian Bands.
This setup provides a clear visual indication of price interactions with both Donchian Channels while keeping the chart uncluttered by hiding the channel lines.
EMA/RMA clouds by AlpachinoRE-UPLOAD
The indicator is designed for faster trend determination and also provides hints about whether the trend is strong, weaker, or if a range is expected.
It consists of an exponential moving average (EMA) and a slower smoothed moving average (RMA). I chose these because EMA is the fastest and is respected by the market, while I discovered through practice that the market often respects RMA, and in some cases, even more than EMA. Their combination is necessary because I want to take advantage of the best qualities of both averages. Displaying averages based solely on the close values creates a simple line that the market might respect. However, this is often not the case. Market makers know that many traders still believe in the theory that closing above/below an EMA signals a valid new trend. They commonly apply this belief to EMA200. Traders think that if the market closes below EMA, it signals a downtrend. That’s not necessarily true. This misconception often traps inexperienced traders.
For this reason, my indicator does not include a separate line.
I use what are called envelopes. In other words, for both EMA and RMA, the calculation uses the high and low of the selected period, which can be chosen as an input in the indicator.
Why did I choose high and low?
To stabilize price fluctuations as much as possible, especially to allow enough space for the price to react to the moving average. This reaction occurs precisely between the high and low.
Modes:
EMA Cloud – This is the most common envelope in terms of averages. It shows the best reactions with a period of 50.
What should you observe: the alignment of the envelope or its slope.
Usage:
Breakouts through the entire envelope tend to be strong, which signals that the trend may change. However, what interests you most is that the first test of the envelope after a breakout is the most successful entry point for trades in the breakout direction.
In an uptrend, the first support will be the high of the envelope, and the second (let’s call it the "ultimate support") will be the low of the envelope.
If, during an uptrend, the market closes below the low, be cautious, as the trend may reverse.
If the envelope is broken, trade the retest of the envelope.
In general, if the price is above the envelope, focus on long trades; if it’s below the envelope, focus on short trades.
Double Cloud – Since we already know that highs and lows are more relevant for price respect, I utilized this in the double cloud. Here, I use calculations for EMA and RMA highs and EMA and RMA lows.
The core idea is that since the price often reacts more to RMA than EMA, I wanted to eliminate attempts by market makers to lure you into incorrect directions. By creating more space for the price to react to the highs or lows, I made the cloud fill the area between EMA and RMA highs. This serves as the last zone where the price can hold. If the price breaks above this high cloud during a return, this doesn’t happen randomly—you should pay attention, as it’s likely signaling a range or a trend change.
The same applies to the low cloud for EMA and RMA.
The advantage of the double cloud is that you can see two clouds that may move sideways. This can resemble two walls—and they really act as such.
Usage:
Let’s say we have a downtrend. The market seems to be experiencing a downtrend exhaustion. Here's the behavior you might observe:
The price returns to the EMA/RMA low; the first reaction may still have some strength, but each subsequent return will move higher and higher into the cloud with increasingly smaller rejections downward. This indicates the absorption of selling pressure by bullish pressure. Eventually, the price may close above the cloud, significantly disrupting the downtrend and potentially signaling a reversal.
A confirmation of the reversal is usually seen with a retest of the cloud and a bounce upward into an uptrend.
The second scenario, which you’ll often see, involves sharp and significant moves through both envelopes. This kind of move is the strongest signal of a trend change. However, do not jump into trades immediately—wait for the first retest, which is usually successful. Additional tests may not work, as the breakout might not signify a trend change but rather a range.
When the clouds are far apart, it signals a weak trend or that the market is in a range. You will see that this is generally true. When the clouds cross or overlap, their initial point of contact signals the start of a stronger trend. The steeper the slope, the stronger the trend.
Volume-Based RSI Color Indicator with MAsVolume-Based RSI Color Indicator with MAs
Overview
This script combines the Relative Strength Index (RSI) with volume analysis to provide an enhanced perspective on market conditions. By dynamically coloring the RSI line based on overbought/oversold conditions and volume thresholds, this indicator helps traders quickly identify high-probability reversal zones. Additionally, it incorporates short-term and long-term moving averages (MAs) of the RSI for trend analysis, making it a versatile tool for scalping and swing trading strategies.
Key Features
Dynamic RSI Color Coding:
The RSI line changes color based on two conditions:
Overbought/High Volume: RSI is above the overbought threshold (default: 70) and volume exceeds the average volume by a user-defined multiplier (default: 2.0). The line turns red, indicating potential reversal zones.
Oversold/High Volume: RSI is below the oversold threshold (default: 30) and volume exceeds the average volume by the multiplier. The line turns green, suggesting potential buying opportunities.
Neutral Conditions: Default blue color for all other scenarios.
Volume Integration:
Unlike standard RSI indicators, this script incorporates volume data to refine signals, helping traders avoid false signals in low-volume environments.
RSI Moving Averages:
Two moving averages of the RSI (short-term and long-term) provide trend context:
200-period MA: Highlights the long-term trend in RSI values.
20-period MA: Shows short-term fluctuations for quick decision-making.
Both MAs can be calculated using Simple or Exponential methods, giving users flexibility.
Visual Aids:
Horizontal lines at the overbought (70) and oversold (30) levels help define the boundaries of expected price action extremes.
How It Works
The script calculates the RSI over a user-defined length (default: 14).
Volume data is compared to its moving average to determine if it exceeds the user-defined high-volume threshold.
When RSI and volume conditions align, the RSI line is dynamically colored to indicate potential overbought/oversold zones.
The RSI moving averages provide additional context to confirm trends or reversals.
How to Use
Identify Reversal Zones:
Look for green RSI signals in oversold conditions to identify potential buying opportunities.
Look for red RSI signals in overbought conditions to identify potential selling opportunities.
Use Moving Averages for Confirmation:
When the RSI is above its 200-period MA, the long-term trend is bullish; consider only long trades.
When the RSI is below its 200-period MA, the trend is bearish; consider only short trades.
Combine with Other Tools:
This indicator works best when used alongside price action analysis, candlestick patterns, or support/resistance levels.
Originality
This script is unique in combining volume analysis with RSI and RSI-specific moving averages. While many indicators focus on RSI or volume separately, this script marries these two key metrics to filter out weak signals and improve trade decision accuracy.
Chart Recommendations
Clean Chart: Use this indicator on a clean chart without additional overlays for maximum clarity.
Timeframes: Works well on intraday charts (e.g., 5m, 15m) for scalping and on higher timeframes (e.g., 1H, 4H, Daily) for swing trading.
Disclaimer
This indicator is a tool to aid trading decisions and should not be used in isolation. Always consider other factors such as market conditions, news events, and risk management.
Bollinger Bands CustomThe indicator is a customized version of Bollinger Bands with added trading signals. This indicator is designed to help traders identify potential entry (buy) and exit (sell) points based on the interaction between the price and the Bollinger Bands. Below, I will explain in detail its purpose, how it works, and how to use it.
Purpose of the Indicator
The main purpose of this indicator is:
Identify market volatility: Bollinger Bands expand and contract based on price volatility.
Provide trading signals: The indicator generates buy signals (BUY) when the price crosses the lower band and sell signals (SELL) when the price crosses the upper band.
Help identify dynamic support and resistance levels: The upper and lower bands act as dynamic resistance and support levels.
How the Indicator Works
The indicator is based on three main components:
Moving Average (SMA): It calculates the simple moving average (SMA) of the price over a specified period (length).
Bollinger Bands:
The upper band is calculated as the moving average plus a standard deviation multiplied by a factor (mult).
The lower band is calculated as the moving average minus a standard deviation multiplied by the same factor.
Trading signals:
A BUY signal is generated when the price crosses above the lower band.
A SELL signal is generated when the price crosses below the upper band.
How to Use the Indicator
Here is a step-by-step guide on how to use the indicator on TradingView:
1. Add the Indicator to the Chart
Copy the Pine Script code you created.
Open TradingView and go to the Pine Editor.
Paste the code and click "Add to Chart."
The indicator will be displayed directly on the price chart.
2. Customize the Parameters
You can customize the following parameters:
Moving Average Length (length): Set the period for the moving average (default is 20).
Price Source (source): Choose the price to use (default is the closing price).
Standard Deviation Multiplier (mult): Set the multiplier for the standard deviation (default is 2.0).
3. Interpret the Signals
BUY Signal: When you see a "BUY" label below a candle, it means the price has crossed above the lower band. This could indicate a buying opportunity.
SELL Signal: When you see a "SELL" label above a candle, it means the price has crossed below the upper band. This could indicate a selling opportunity.
4. Use Bollinger Bands as Support and Resistance
If the price approaches the upper band, it might indicate a resistance level.
If the price approaches the lower band, it might indicate a support level.
5. Monitor the Colored Background
The chart background turns light green when there is a BUY signal and light red when there is a SELL signal. This helps you quickly identify signals.
Practical Example
Suppose you are analyzing a daily chart of a stock or cryptocurrency:
If the price crosses above the lower band, the indicator will show a "BUY" label. You might consider this as a signal to open a long position.
If the price crosses below the upper band, the indicator will show a "SELL" label. You might consider this as a signal to close a long position or open a short position.
Limitations and Considerations
False signals: In range-bound markets, Bollinger Bands can generate many false signals. It is advisable to use this indicator in combination with other technical analysis tools.
Extreme volatility: During periods of high volatility, the bands expand, and signals may become less reliable.
Confirmation: It is always good practice to confirm signals with other indicators (e.g., RSI, MACD) or candlestick analysis.
Conclusion
My indicator is a useful tool for identifying potential trading opportunities based on Bollinger Bands. However, as with any indicator, it is important to use it in combination with other forms of analysis and risk management to maximize effectiveness. Happy trading! 🚀
