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Volume profile [Signals] - By Leviathan [Mindyourbuisness]Market Sessions and Volume Profile with Sweep Signals - Based on Leviathan's Volume Profile This indicator is an enhanced version of Leviathan's Volume Profile indicator, adding session-based value area analysis and sweep detection signals. It combines volume profile analysis with market structure concepts to identify potential reversal opportunities. Features - Session-based volume profiles (Daily, Weekly, Monthly, Quarterly, Yearly) - Forex sessions support (Tokyo, London, New York) - Value Area analysis with POC, VAH, and VAL levels - Extended level visualization for the last completed session - Sweep detection signals for key value area levels Sweep Signals Explanation The indicator detects two types of sweeps at VAH, VAL, and POC levels: Bearish Sweeps (Red Triangle Down) Conditions: - Price makes a high above the level (VAH/VAL/POC) - Closes below the level - Closes below the previous candle's low - Previous candle must be bullish Trading Implication: Suggests a failed breakout and potential reversal to the downside. These sweeps often indicate stop-loss hunting above key levels followed by institutional selling. Bullish Sweeps (Green Triangle Up) Conditions: - Price makes a low below the level (VAH/VAL/POC) - Closes above the level - Closes above the previous candle's high - Previous candle must be bearish Trading Implication: Suggests a failed breakdown and potential reversal to the upside. These sweeps often indicate stop-loss hunting below key levels followed by institutional buying. Trading Guidelines 1. Use sweep signals in conjunction with the overall trend 2. Look for additional confirmation like: - Volume surge during the sweep - Price action patterns - Support/resistance levels 3. Consider the session's volatility and time of day 4. More reliable signals often occur at VAH and VAL levels 5. POC sweeps might indicate stronger reversals due to their significance as fair value levels Notes - The indicator works best on higher timeframes (1H and above) - Sweep signals are more reliable during active market hours - Consider using multiple timeframe analysis for better confirmation - Past performance is not indicative of future results Credits: Original Volume Profile indicator by Leviathan
Indicateur Pine Script™
par mindyourbuisness
2
Trendilo ARTrendilo AR is a custom trading indicator designed to identify market trends using advanced techniques such as the Arnaud Legoux Moving Average (ALMA), volume confirmations, and dynamic volatility bands. This indicator provides a clear visualization of trends, including significant changes and custom alerts. Review of Indicators Used 1. ALMA Description: ALMA is a moving average that applies an advanced filter to smooth price data, reducing noise and focusing on actual trends. Usage in the Indicator: Used to calculate the smoothed percentage price change and determine trend direction. Customizable parameters include: - Length: Defines the number of bars to consider. - Offset: Adjusts sensitivity toward recent prices. - Sigma: Controls the degree of smoothing. Advantages: - Reduced lag in trend detection. - Resistance to market noise. 2. ATR Description: ATR measures the market’s average volatility by considering the range between high and low prices over a given period. Usage in the Indicator: ATR is used to calculate "dynamic smoothing", adjusting the indicator’s sensitivity based on current market volatility. Advantages: - Adapts to high or low volatility conditions. - Helps define dynamic support and resistance levels. 3. SMA Description: SMA calculates the average of prices or volume over a specific time period. Usage in the Indicator: Used to calculate the volume moving average (Volume SMA) to confirm whether the current volume supports the detected trend. Advantages: - Easy to understand and calculate. - Provides volume-based trend confirmation. 4. RMS Bands Description: RMS Bands calculate the standard deviation of percentage price changes, creating upper and lower levels that act as overbought and oversold indicators. Usage in the Indicator: - Define the range within which the market is considered neutral. - Crosses above or below the bands indicate trend changes. Advantages: - Visual identification of strong trends. - Helps filter false signals. Colors and Visuals Used in the Indicator 1. ALMA Line Colors: - Green: Indicates a confirmed uptrend (with sufficient volume). - Red: Indicates a confirmed downtrend (with sufficient volume). - Gray: Indicates a neutral phase or insufficient volume to confirm a trend. 2. RMS Bands - Upper and Lower Lines: - Purple (with transparency): These lines represent the RMS bands (upper and lower) and adjust opacity based on trend strength. - Stronger trends result in less transparency (more solid colors). 3. Highlighted Background (Strong Trends) - Color: - Light Green (transparent): Highlights a strong trend when the smoothed percentage change (ALMA) exceeds 1.5 times the RMS. 4. Horizontal Lines - Baseline (0): - Dark Gray: Serves as a central reference to identify the directionality of percentage changes. - Additional Line (0.1): - Blue: A customizable line to mark user-defined key levels. 5. Bar Colors - Bar Colors: - Green: When the price is in a confirmed uptrend. - Red: When the price is in a confirmed downtrend. - No color: When there is insufficient volume or no clear trend. How to Use the Indicator 1. Initial Setup 1. Add the Indicator to Your Chart: Copy the code into the Pine Editor on TradingView and apply it to your chart. 2. Customize Parameters: Adjust values based on your trading strategy: - Smoothing: Controls the level of smoothing for percentage changes. - Lookback Length: Defines the observation period for calculations. - Band Multiplier: Adjusts the width of RMS bands. 2. Signal Interpretation 1. Indicator Colors: - Green: Confirmed uptrend. - Red: Confirmed downtrend. - Gray: No clear trend or insufficient volume. 2. RMS Bands: - If the ALMA line (smoothed percentage change) crosses above the upper RMS band, it signals a potential uptrend. - If it crosses below the lower RMS band, it signals a potential downtrend. 3. Volume Confirmation: - The indicator's color activates only if the current volume exceeds the Volume SMA. 3. Alerts and Decisions 1. Trend Change Alerts: - The indicator automatically triggers alerts when an uptrend or downtrend is detected. - Configure these alerts to receive real-time notifications. 2. Strong Trend Signals: - When the magnitude of the percentage change exceeds 1.5 times the RMS, the chart background highlights the strong trend. 4. Trading Strategies 1. Buy: - Enter long positions when: - The indicator turns green. - Volume confirms the trend. - Consider placing a stop-loss just below the lower RMS band. 2. Sell: - Enter short positions when: - The indicator turns red. - Volume confirms the trend. - Consider placing a stop-loss just above the upper RMS band. 3. Neutral: - Avoid trading when the indicator is gray, as no clear trend or insufficient volume is present. Disclaimer: As this is my first published indicator, please use it with caution. Feedback is highly appreciated to improve its performance. Happy Trading!
Indicateur Pine Script™
par galvaroe
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.
Indicateur Pine Script™
par BigBeluga
18
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.
Indicateur Pine Script™
par Quantonomyfund
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.
Stratégie Pine Script™
par garysebastianbrowniii
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!
Indicateur Pine Script™
par Viito_
Order Flow VWAP chatGPTVWAP: El cálculo del VWAP se hace con la función ta.vwap(), que toma el cierre de cada barra para ponderarlo por el volumen de esa barra. Bandas de VWAP: Este script también calcula bandas superiores e inferiores del VWAP usando una desviación estándar. Estas bandas ayudan a visualizar áreas de sobrecompra o sobreventa. Opciones de visualización: Puedes activar o desactivar las bandas de VWAP con el parámetro show_bands. Estilo y color: Puedes personalizar los colores, el ancho de las líneas y las bandas, según lo que prefieras para tu gráfico.
Indicateur Pine Script™
par KARMONYFX
1
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
Indicateur Pine Script™
par QuantAlgo
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) . -------- 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 ------------------------------ . ---- 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 :)
Indicateur Pine Script™
par mcbw_
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.
Indicateur Pine Script™
par prabhat76
25
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
Indicateur Pine Script™
par nadia_hachmi_trading
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. --- 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. --- 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. --- 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. --- 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. --- 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. --- 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. --- 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! --- Make sure to leave your feedback and suggestions so I can continue improving the tool for the community. Happy trading!
Indicateur Pine Script™
par doganayy2
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.
Indicateur Pine Script™
par garysebastianbrowniii