Composite Z-Score with Linear Regression Bands [UAlgo]The Composite Z-Score with Linear Regression Bands is a technical indicator designed to provide traders with a comprehensive analysis of price momentum, volatility, and volume. By combining multiple moving averages with slope analysis, volume/volatility compression-expansion metrics, and Z-Score calculations, this indicator aims to highlight potential breakout and breakdown points with high accuracy. The inclusion of linear regression bands further enhances the analysis by providing dynamic support and resistance levels, which adapt to market conditions. This makes the indicator particularly useful in identifying overbought/oversold conditions, volume squeezes, and the overall direction of the trend.
🔶 Key Features
Multi-Length Slope Calculation: The indicator uses multiple Hull Moving Averages (HMA) across various lengths to calculate slope angles, which are then converted into Z-Scores. This helps in capturing both short-term and long-term price momentum.
Volume/Volatility Composite Analysis: By calculating a composite value derived from both volume and volatility, the indicator identifies periods of compression (squeezes) and expansion, which are crucial for detecting potential breakout opportunities.
Linear Regression Bands: The inclusion of dynamic linear regression bands provides traders with adaptive support and resistance levels. These bands are enhanced by the composite value, which adjusts the band width based on market conditions, offering a clearer view of possible price reversals.
Overbought/Oversold Detection: The indicator highlights overbought and oversold conditions by comparing Z-Scores against the upper and lower bounds of the regression bands, which can signal potential reversal points.
Customizable Inputs: Users can customize key parameters such as the lengths of the moving averages, the regression band period, and the number of deviations used for the bands, allowing for flexibility in adapting the indicator to different market environments.
🔶 Interpreting the Indicator
Z-Score Plots: The individual Z-Score plots represent the normalized slope of the Hull Moving Averages over different periods. Positive values indicate upward momentum, while negative values suggest downward momentum. The combined Z-Sum provides a broader view of the overall market momentum.
Composite Value: The composite value is a ratio of volume to volatility, which highlights periods of market compression and expansion. When the composite value rises, it suggests increasing market activity, often preceding a breakout.
Why are we calculating values for multiple lengths?
The Composite Z-Score with Linear Regression Bands indicator employs a multi-timeframe analysis by calculating Z-scores for various moving average lengths. This approach provides a more comprehensive view of market dynamics and helps to identify trends and potential reversals across different timeframes. By considering multiple lengths, we can:
Capture a broader range of market behaviors: Different moving average lengths capture different aspects of price movement. Shorter lengths are more sensitive to recent price changes, while longer lengths provide a smoother representation of the underlying trend.
Reduce the impact of noise: By combining Z-scores from multiple lengths, we can help to filter out some of the noise that can be present in shorter-term data and obtain a more robust signal.
Enhance the reliability of signals: When Z-scores from multiple lengths align, it can increase the confidence in the identified trend or potential reversal. This can help to reduce the likelihood of false signals.
In essence, calculating values for multiple lengths allows the indicator to provide a more nuanced and reliable assessment of market conditions, making it a valuable tool for traders and analysts.
Linear Regression Bands: The central line represents the linear regression of the Z-Sum, while the upper and lower bands represent the dynamic resistance and support levels, respectively. The deviation from the regression line indicates the strength of the current trend. When price moves beyond these bands, it may signal an overbought (above upper band) or oversold (below lower band) condition.
Volume/Volatility Squeeze: When the price moves between the regression bands and the volume/volatility-adjusted bands, the market is in a squeeze. Breakouts from this squeeze can lead to significant price moves, which are indicated by the filling of areas between the Z-Score plots and the bands.
Color Interpretation: The indicator uses color changes to make it easier to interpret the data. Teal colors generally indicate upward momentum or strong conditions, while red suggests downward momentum or weakening conditions. The intensity of the color reflects the strength of the signal.
Overbought/Oversold Signals: The indicator marks potential overbought and oversold conditions when Z-Scores cross above or below the upper and lower regression bands, respectively. These signals are crucial for identifying potential reversal points in the market.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Поиск скриптов по запросу "breakout"
Super IndicatorOverview of the Combined Indicator
This combined indicator leverages three major technical analysis tools:
Bollinger Bands
Linear Regression Channels
Scalping Strategy Indicators (RSI, MACD, SMA)
Each of these tools provides unique insights into market conditions, and their integration offers a comprehensive view of price movements, trends, and potential trading signals.
1. Bollinger Bands
Purpose:
Bollinger Bands are used to measure market volatility and identify overbought or oversold conditions.
Components:
Basis (Middle Band): Typically a 20-period Simple Moving Average (SMA).
Upper Band: Basis + (2 * Standard Deviation).
Lower Band: Basis - (2 * Standard Deviation).
Why They Complement:
Bollinger Bands expand and contract based on market volatility. When the bands are narrow, it indicates low volatility and potential for a significant move. Wide bands indicate high volatility. This helps traders gauge the strength of market moves and potential reversals.
2. Linear Regression Channels
Purpose:
Linear Regression Channels identify the overall trend direction and measure deviation from the mean price over a specific period.
Components:
Middle Line (Linear Regression Line): The line of best fit through the price data over a specified period.
Upper and Lower Lines: Channels created by adding/subtracting a multiple of the standard deviation or another deviation measure from the regression line.
Why They Complement:
Linear Regression Channels provide a clear visual representation of the trend direction and the range within which prices typically fluctuate. This can help traders identify trend continuations and reversals, making it easier to spot entry and exit points.
3. Scalping Strategy Indicators
Purpose:
The RSI, MACD, and SMA are used to generate short-term buy and sell signals, which are essential for scalping strategies aimed at capturing quick profits from small price movements.
Components:
RSI (Relative Strength Index): Measures the speed and change of price movements, typically over 14 periods. It helps identify overbought and oversold conditions.
MACD (Moving Average Convergence Divergence): Consists of the MACD line, Signal line, and histogram. It helps identify changes in the strength, direction, momentum, and duration of a trend.
SMA (Simple Moving Average): The average price over a specified period, used to smooth out price data and identify trends.
Why They Complement:
These indicators provide short-term signals that can confirm or refute the signals given by Bollinger Bands and Linear Regression Channels. For example, a buy signal might be more reliable if the price is near the lower Bollinger Band and the MACD crosses above its signal line.
How They Work Together
Scenario 1: Confirming Trend Continuations
Bollinger Bands: Price staying near the upper band suggests a strong uptrend.
Linear Regression Channels: Price staying above the middle line confirms the uptrend.
5-Minute Scalping Strategy: RSI not in overbought territory, and MACD showing bullish momentum confirms continuation.
Scenario 2: Identifying Reversals
Bollinger Bands: Price touching or moving outside the lower band suggests oversold conditions.
Linear Regression Channels: Price at the lower channel line indicates potential support.
5-Minute Scalping Strategy: RSI in oversold territory, and MACD showing a bullish crossover indicates a reversal.
Scenario 3: Volatility Breakouts
Bollinger Bands: Bands contracting indicates low volatility and potential breakout.
Linear Regression Channels: Price moving away from the middle line signals potential breakout direction.
Scalping Strategy: MACD and RSI confirming the breakout direction for entry.
Input Parameters:
Define settings for Bollinger Bands, Linear Regression Channels, and the scalping strategy.
Allow users to customize lengths, multipliers, and colors.
Bollinger Bands Calculation:
Calculate the basis (SMA) and standard deviation.
Derive the upper and lower bands from the basis and standard deviation.
Linear Regression Channel Calculation:
Compute the slope, average, and intercept of the linear regression line.
Calculate deviations to plot upper and lower channel lines.
5-Minute Scalping Strategy:
Calculate RSI, MACD, and SMA for short-term trend analysis.
Define buy and sell conditions based on these indicators.
Plotting and Alerts:
Plot Bollinger Bands and Linear Regression Channels on the chart.
Plot buy and sell signals with shapes.
Set alerts for key conditions like exiting the regression channel bounds and trend switches.
Conclusion
By combining Bollinger Bands, Linear Regression Channels, and a 5-minute scalping strategy, this indicator offers a robust tool for traders. Bollinger Bands provide volatility insights, Linear Regression Channels highlight trend direction and potential reversals, and the scalping strategy offers precise entry and exit points. Together, these tools can enhance a trader's ability to make informed decisions in various market conditions.
Liquidity Grab Zones | Flux Charts💎 GENERAL OVERVIEW
Introducing our new Liquidity Grab Zones Indicator! This indicator finds liquidity grabs in the current ticker and renders buyside & sellside liquidity grab zones. The retests and breakout of the zones are labeled, and you can set up alerts to get notified. For more information, please check the "HOW DOES IT WORK" section.
Features of the new Liquidity Grab Zones Indicator :
Renders Buyside & Sellside Liquidity Grab Zones
Retests & Breaks
Inverse Zones After Broken Feature
Alerts For All Features
Customizable Algorithm
Customizable Styles
🚩UNIQUENESS
Liquidity grabs can be useful when determining candles that have executed a lot of market orders, so you can plann your trades accordingly. This indicator lets you customize the pivot length and the wick-body ratio for liquidity grabs, provide retest & breakout labels, with customized styling and alerts.
📌 HOW DOES IT WORK ?
Liquidity grabs occur when one of the latest pivots has a false breakout. Then, if the wick to body ratio of the bar is higher than 0.5 (can be changed from the settings) a zone is plotted.
These zones usually indicate areas of high market interest where price action may reverse or accelerate. Identifying these zones can provide traders with critical levels for entering or exiting trades. A breakout of these zones generally mean strong movements are inbound, while failing breakouts make these zones act like support / resistance zones.
The indicator also reverses the type of the zone after an invalidation (can be turned off from the settings). This feature helps traders identify potential reversals more accurately.
The zone width is set to the area from the wick to the body of the candlestick, which can be seen here :
⚙️SETTINGS
1. General Configuration
Pivot Length -> This setting determines the range of the pivots. This means a candle has to have the highest / lowest wick of the previous X bars and the next X bars to become a high / low pivot.
Wick-Body Ratio -> After a pivot has a false breakout, the wick-body ratio of the latest candle is tested. The resulting ratio must be higher than this setting for it to be considered as a liquidity grab.
Zone Invalidation -> Select between Wick & Close price for Liquidity Grab Zone Invalidation.
Use these customizable settings to fine-tune the indicator according to your trading strategy and preferences.
Dynamic Price Oscillator (Zeiierman)█ Overview
The Dynamic Price Oscillator (DPO) by Zeiierman is designed to gauge the momentum and volatility of asset prices in trading markets. By integrating elements of traditional oscillators with volatility adjustments and Bollinger Bands, the DPO offers a unique approach to understanding market dynamics. This indicator is particularly useful for identifying overbought and oversold conditions, capturing price trends, and detecting potential reversal points.
█ How It Works
The DPO operates by calculating the difference between the current closing price and a moving average of the closing price, adjusted for volatility using the True Range method. This difference is then smoothed over a user-defined period to create the oscillator. Additionally, Bollinger Bands are applied to the oscillator itself, providing visual cues for volatility and potential breakout signals.
█ How to Use
⚪ Trend Confirmation
The DPO can serve as a confirmation tool for existing trends. Traders might look for the oscillator to maintain above or below its mean line to confirm bullish or bearish trends, respectively. A consistent direction in the oscillator's movement alongside price trend can provide additional confidence in the strength and sustainability of the trend.
⚪ Overbought/Oversold Conditions
With the application of Bollinger Bands directly on the oscillator, the DPO can highlight overbought or oversold conditions in a unique manner. When the oscillator moves outside the Bollinger Bands, it signifies an extreme condition.
⚪ Volatility Breakouts
The width of the Bollinger Bands on the oscillator reflects market volatility. Sudden expansions in the bands can indicate a breakout from a consolidation phase, which traders can use to enter trades in the direction of the breakout. Conversely, a contraction suggests a quieter market, which might be a signal for traders to wait or to look for range-bound strategies.
⚪ Momentum Trading
Momentum traders can use the DPO to spot moments when the market momentum is picking up. A sharp move of the oscillator towards either direction, especially when crossing the Bollinger Bands, can indicate the start of a strong price movement.
⚪ Mean Reversion
The DPO is also useful for mean reversion strategies, especially considering its volatility adjustment feature. When the oscillator touches or breaches the Bollinger Bands, it indicates a deviation from the normal price range. Traders might look for opportunities to enter trades anticipating a reversion to the mean.
⚪ Divergence Trading
Divergences between the oscillator and price action can be a powerful signal for reversals. For instance, if the price makes a new high but the oscillator fails to make a corresponding high, it may indicate weakening momentum and a potential reversal. Traders can use these divergence signals to initiate counter-trend moves.
█ Settings
Length: Determines the lookback period for the oscillator and Bollinger Bands calculation. Increasing this value smooths the oscillator and widens the Bollinger Bands, leading to fewer, more significant signals. Decreasing this value makes the oscillator more sensitive to recent price changes, offering more frequent signals but with increased noise.
Smoothing Factor: Adjusts the degree of smoothing applied to the oscillator's calculation. A higher smoothing factor reduces noise, offering clearer trend identification at the cost of signal timeliness. Conversely, a lower smoothing factor increases the oscillator's responsiveness to price movements, which may be useful for short-term trading but at the risk of false signals.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Liquidity Trendline With Signals [BigBeluga]The Liquidity Trendline is an indicator designed to identify potential breakouts by utilizing pivot points. These pivotal moments can trigger significant market reactions, either by breaking out or by serving as breakout and retest signals.
🔶 FEATURES
The indicator contains the following features:
Period of the calculation
Padding (spacing between the 2 lines)
Signal for breakouts
🔶 USAGE
As shown in the example, breakouts can be powerful points to see reversions in the market and can lead to a lot of volatility in the market.
When a trendline is broken, a signal will be plotted; the user can disable/enable those signals.
A trendline is formed when 2 consecutive pivot points are found, each of them lower or higher than the previous one. this is the anchor point for our trend line that we will use to spot rejection or breakouts
The delay in the creation of those trend lines will be the period input used to find the pivot point on the chart.
Another good example is using these trendlines as simple retests.
Prices bouncing on top of them will suggest a possible continuation of the current trend.
We can filter out stronger breakouts by looking at how many times the price has rejected the trendline, more rejections will result in more liquidity once the price breaks it.
Signals are plotted on the chart for every breakout that happens.
Another good utility is simply using them as retest once the price breaks those levels and holding above/below them, indicating a possible support or resistance area used for confluence
Here is another good example of how we can correctly spot price deviating from our trendline and spotting powerful continuation in price.
As said before we can filter out bad and good breakouts simply by looking at how many times rejected from those levels.
More rejection will result in a stronger reaction
🔶 CONCLUSION
This script is as simple as that and can be used in a few ways to spot reversals, price continuation, or even sentiment in price (bullish or bearish).
Smart Money Concepts (SMC) [LuxAlgo]This all-in-one indicator displays real-time market structure (internal & swing BOS / CHoCH), order blocks, premium & discount zones, equal highs & lows, and much more...allowing traders to automatically mark up their charts with widely used price action methodologies. Following the release of our Fair Value Gap script, we received numerous requests from our community to release more features in the same category.
"Smart Money Concepts" (SMC) is a fairly new yet widely used term amongst price action traders looking to more accurately navigate liquidity & find more optimal points of interest in the market. Trying to determine where institutional market participants have orders placed (buy or sell side liquidity) can be a very reasonable approach to finding more practical entries & exits based on price action.
The indicator includes alerts for the presence of swing structures and many other relevant conditions.
Features
This indicator includes many features relevant to SMC, these are highlighted below:
Full internal & swing market structure labeling in real-time
Break of Structure (BOS)
Change of Character (CHoCH)
Order Blocks (bullish & bearish)
Equal Highs & Lows
Fair Value Gap Detection
Previous Highs & Lows
Premium & Discount Zones as a range
Options to style the indicator to more easily display these concepts
Settings
Mode: Allows the user to select Historical (default) or Present, which displays only recent data on the chart.
Style: Allows the user to select different styling for the entire indicator between Colored (default) and Monochrome.
Color Candles: Plots candles based on the internal & swing structures from within the indicator on the chart.
Internal Structure: Displays the internal structure labels & dashed lines to represent them. (BOS & CHoCH).
Confluence Filter: Filter non-significant internal structure breakouts.
Swing Structure: Displays the swing structure labels & solid lines on the chart (larger BOS & CHoCH labels).
Swing Points: Displays swing points labels on chart such as HH, HL, LH, LL.
Internal Order Blocks: Enables Internal Order Blocks & allows the user to select how many most recent Internal Order Blocks appear on the chart.
Swing Order Blocks: Enables Swing Order Blocks & allows the user to select how many most recent Swing Order Blocks appear on the chart.
Equal Highs & Lows: Displays EQH/EQL labels on chart for detecting equal highs & lows.
Bars Confirmation: Allows the user to select how many bars are needed to confirm an EQH/EQL symbol on chart.
Fair Value Gaps: Displays boxes to highlight imbalance areas on the chart.
Auto Threshold: Filter out non-significant fair value gaps.
Timeframe: Allows the user to select the timeframe for the Fair Value Gap detection.
Extend FVG: Allows the user to choose how many bars to extend the Fair Value Gap boxes on the chart.
Highs & Lows MTF: Allows the user to display previous highs & lows from daily, weekly, & monthly timeframes as significant levels.
Premium/Discount Zones: Allows the user to display Premium, Discount, and Equilibrium zones on the chart
Usage
Users can see automatic CHoCH and BOS labels to highlight breakouts of market structure, which allows to determine the market trend. In the chart below we can see the internal structure which displays more frequent labels within larger structures. We can also see equal highs & lows (EQH/EQL) labels plotted alongside the internal structure to frequently give indications of potential reversals.
In the chart below we can see the swing market structure labels. These are also labeled as BOS and CHoCH but with a solid line & larger text to show larger market structure breakouts & trend reversals. Users can be mindful of these larger structure labels while trading internal structures as displayed in the previous chart.
Order blocks highlight areas where institutional market participants open positions, one can use order blocks to determine confirmation entries or potential targets as we can expect there is a large amount of liquidity at these order blocks. In the chart below we can see 2 potential trade setups with confirmation entries. The path outlined in red would be a potential short entry targeting the blue order block below, and the path outlined in green would be a potential long entry, targeting the red order blocks above.
As we can see in the chart below, the bullish confirmation entry played out in this scenario with the green path outlined in hindsight. As price breaks though the order blocks above, the indicator will consider them mitigated causing them to disappear, and as per the logic of these order blocks they will always display 5 (by default) on the chart so we can now see more actionable levels.
The Smart Money Concepts indicator has many other features and here we can see how they can also help a user find potential levels for price action trading. In the screenshot below we can see a trade setup using the Previous Monthly High, Strong High, and a Swing Order Block as a stop loss. Accompanied by the Premium from the Discount/Premium zones feature being used as a potential entry. A potential take profit level for this trade setup that a user could easily identify would be the 50% mark labeled with the Fair Value Gap & the Equilibrium all displayed automatically by the indicator.
Conclusion
This indicator highlights all relevant components of Smart Money Concepts which can be a very useful interpretation of market structure, liquidity, & more simply put, price action. The term was coined & popularized primarily within the forex community & by ICT while making its way to become a part of many traders' analysis. These concepts, with or without this indicator do not guarantee a trader to be trading within the presence of institutional or "bank-level" liquidity, there is no supporting data regarding the validity of these teachings.
MA total distance on chartNOTE:
The name I used for this indicator was created by me and I’m not sure if it has been used or created by any other trader/creator in the past or not!
Motivation to create:
One of the most important uses of “moving averages” is indicating the trend! There are different ways you can distinguish trend by using moving averages and one of the most popular type of it is comparing closing price to a MA. In this case if close is higher than the MA, trend is bullish and if close is lower than MA, it’s bearish. This method is really useful and I see great results in my long-term back-tests, especially SMA-100 in 1H chart filter so many fake signals in many different indicator-based strategies (Personal experience). There are so many problems with using indicators that sometimes have difficult solutions but one of them is fake breakout!
Looking at the top picture, you’ll get a breakout has happened but trend did not change!
A super bearish trend is obviously visible in the chart and we know a small break out might be a fake one, but what if we have an indicator make conditions of a trend change a little harder?
Introduction:
I was careful about how I used moving averages and I got that I will take not only the last candle close price into consideration, so in these kind of false breakouts I will not fall into trap of them, On the contrary, I find a good opportunity to enter the market opposite of the MA break! (In this case short trade). I calculate the total distance of last 40 candles and divide them to 40, to get the average distance, to each a mathematical score for power of our trend comparing to the MA!
Number are just default you can change them.
In the picture below you can see how well it filtered the false breakout.
As it is obvious, Timeframe, MA length, MA source and MA type are editable.
Since I do not tested this indicator enough (for me enough means more than 5000 trades and 10 years) I can’t suggest any settings as the best one.
The distance length, which means number of candles that their distance to MA is considered in our calculations, the distance source and also smoothing of the MATD is editable too.
And without editing it will look like something like this!
Turtle Trade Channels Indicator TUTCILegendary trade system which proved that great traders can be made, not born.
Turtle Trade Experiment made 80% annual return for 4 years and made 150 million $
Turtle Trade trend following system is a complete opposite to the "buy low and sell high" approach.
This trend following system was taught to a group of average and normal individuals, and almost everyone turned into a profitable trader.
They used the basis logic of well known DONCHIAN CHANNELS which developed by Richard Donchian.
The main rule is "Trade an 20-day breakout and take profits when an 10-day high or low is breached ". Examples:
Buy a 20-day breakout and close the trade when price action reaches a 10-day low.
Go short a 20-day breakout and close the trade when price action reaches a 10-day high.
In this indicator,
The red line is the trading line which indicates the trend directio n:
Price bars over the trend line indicates uptrend
Price bars under the trend line means downtrend
The dotted blue line is the exit line.
Original system is:
Go long when the price High is equal to or above previous 20 day Highest price.
Go short when the price Low is equal to or below previous 20 day Lowest price.
Exit long positions when the price touches the exit line
Exit short positions when the price touches the exit line
Recommended initial stop-loss is ATR * 2 from the opening price.
Default system parameters were 20,10 and 55,20.
Original Turtle Rules:
To trade exactly like the turtles did, you need to set up two indicators representing the main and the failsafe system.
Set up the main indicator with EntryPeriod = 20 and ExitPeriod = 10 (A.k.a S1)
Set up the failsafe indicator with EntryPeriod = 55 and ExitPeriod = 20 using a different color. (A.k.a S2)
The entry strategy using S1 is as follows
Buy 20-day breakouts using S1 only if last signaled trade was a loss.
Sell 20-day breakouts using S1 only if last signaled trade was a loss.
If last signaled trade by S1 was a win, you shouldn't trade -Irregardless of the direction or if you traded last signal it or not-
The entry strategy using S2 is as follows:
Buy 55-day breakouts only if you ignored last S1 signal and the market is rallying without you
Sell 55-day breakouts only if you ignored last S1 signal and the market is pluging without you
You can Highlight the chart with provided trade signals:
Green background color when Long
Red background color when Short
No background color when flat
WARNING: TURTLE TRADE STOP or ADDING more UNITS RULES ARE NOT INCLUDED.
Author: Kıvanç Özbilgiç
Also you can show or hide trade signals with the button on the settings menu
CBG Key Numbers v6Here is my opening range, key numbers indicator. It takes the Opening Range (5 minutes by default) and then plots the opening range and up to 7 extensions of that range above and below.
It's amazing how the OR is stamped up on the rest of the day's price movements.
2 strategies (at least) are to play the OR range breakout and to fade when price hits an extreme range.
You have total control over how you set up the various lines and colors.
If you start overlaying the trading day with the OR and it's extensions, you will see amazing patterns become clear. For example, the pump and reverse. This is where price pumps right out of the opening and then reverses later in the morning.
I have the opening price set to big circles as this is one of the most important reference points during the day.
Important: For some reason, the 9:30 am time Opening acts differently for equities and futures . For equities, you can set the time values to 0930. But for futures , to capture the Open at 9:30, you have to set the time values to start at 0830. I haven't been able to find a better solution but setting the times manually works. Make sure to set all the time values on the Options screen.
There is one more setting of interest. It is called IB Target Amount. This is a number above and below the opening range that I have observed price to hit whenever there's a breakout. This will allow you to predict a price target on breakouts. For SPY , I have found that price usually breaks out to at least 50 cents. On ES futures , it's 6 dollars. This can help you lock in 10% and 20% when trading options and is a great tool. That's why I have it so prominent in red. You will also see price return to this level during the day and act as support or resistance.
Please disregard the red and green shaded rectangles. They are my own support and resistance zones and TV wouldn't let me hide them from the picture. :-)
I mostly use this on a 5 minute chart but any timeframe will work.
Turtle Trade Channels by KıvanÇ fr3762his trend following system was designed by Dennis Gartman and Bill Eckhart, and relies on breakouts of historical highs and lows to take and close trades: it is the complete opposite to the "buy low and sell high" approach. This trend following system was taught to a group of average and normal individuals, and almost everyone turned into a profitable trader.
The main rule is "Trade an N-day breakout and take profits when an M-day high or low is breached (N must me above M)". Examples:
Buy a 10-day breakout and close the trade when price action reaches a 5-day low.
Go short a 20-day breakout and close the trade when price action reaches a 10-day high.
In this indicator, the red line is the trading line, and the dotted blue line is the exit line. Original system is:
Go long when the trading line crosses below close price
Go short when the trading line rosses above close price
Exit long positions when the price touches the exit line
Exit short positions when the price touches the exit line
Recommended initial stop-loss is ATR * 2 from the opening price. Default system parameters were 20,10 and 55,20.
Original Turtle Rules:
To trade exactly like the turtles did, you need to set up two indicators representing the main and the failsafe system.
Set up the main indicator with TradePeriod = 20 and StopPeriod = 10 (A.k.a S1)
Set up the failsafe indicator with TradePeriod = 55 and StopPeriod = 20 using a different color. (A.k.a S2)
The entry strategy using S1 is as follows
Buy 20-day breakouts using S1 only if last signaled trade was a loss.
Sell 20-day breakouts using S1 only if last signaled trade was a loss.
If last signaled trade by S1 was a win, you shouldn't trade -Irregardless of the direction or if you traded last signal it or not-
The entry strategy using S2 is as follows:
Buy 55-day breakouts only if you ignored last S1 signal and the market is rallying without you
Sell 55-day breakouts only if you ignored last S1 signal and the market is pluging without you
The turtles had a progressive position sizing approach that boosted their winnings. Once a trading decision has been made you should...
Developers: Dennis Gartman and Bill Eckhart
İndikatörü geliştiren: Dennis Gartman and Bill Eckhart
Volume Surge by MashrabThe "Volume Surge" indicator is like a simple market health checkup. It looks at how much of an asset (like a stock or crypto) is being traded right now and compares it to the recent past. Think of it as a way to quickly see if interest in that asset is suddenly spiking, fading, or staying the same.
The indicator shows this information in an easy-to-read table right on your chart.
How it works:
The indicator keeps track of two main things for you:
Current Volume: The total trading volume over the last "N" days (or whatever time period you choose).
Previous Volume: The total trading volume over the period right before that
Then, it gives you a summary:
The "Ratio" tells you how many times bigger or smaller the current volume is.
The "Percent Change" shows the percentage jump in volume.
How to use it:
This indicator helps you see when something interesting might be happening. Here are a few ways traders use it:
Confirm Breakouts: If a stock breaks above a key price level and the indicator shows a huge volume surge, it’s a stronger signal that the move is real and not a false alarm.
Spot Reversals: If a stock has been trending up but the volume starts to drop off, it could mean the trend is losing steam. A sudden, massive volume surge on a down day might indicate panic selling, which can sometimes happen right before the price turns around.
Check Trend Strength: A healthy trend usually has increasing volume going in the same direction. For example, if a stock is in an uptrend, you want to see lots of volume on the days it goes up.
This indicator isn't a crystal ball, but it's a great tool for understanding the "who" and "how much" behind a price move. It helps you see when a price change is backed by a lot of market activity, which often makes the move more trustworthy.
Bollinger Band Spread (Dunk)Bollinger Band Width measures the distance between the upper and lower Bollinger Bands. It reflects market volatility—wider bands mean higher volatility, narrower bands mean lower volatility.
When the width contracts to low levels, it can signal price consolidation and potential breakouts. When the width expands, it indicates active markets or strong trends.
Traders use it to spot volatility squeezes, confirm breakouts, and compare relative volatility across assets or timeframes.
COT IndexTHE HIDDEN INTELLIGENCE IN FUTURES MARKETS
What if you could see what the smartest players in the futures markets are doing before the crowd catches on? While retail traders chase momentum indicators and moving averages, obsess over Japanese candlestick patterns, and debate whether the RSI should be set to fourteen or twenty-one periods, institutional players leave footprints in the sand through their mandatory reporting to the Commodity Futures Trading Commission. These footprints, published weekly in the Commitment of Traders reports, have been hiding in plain sight for decades, available to anyone with an internet connection, yet remarkably few traders understand how to interpret them correctly. The COT Index indicator transforms this raw institutional positioning data into actionable trading signals, bringing Wall Street intelligence to your trading screen without requiring expensive Bloomberg terminals or insider connections.
The uncomfortable truth is this: Most retail traders operate in a binary world. Long or short. Buy or sell. They apply technical analysis to individual positions, constrained by limited capital that forces them to concentrate risk in single directional bets. Meanwhile, institutional traders operate in an entirely different dimension. They manage portfolios dynamically weighted across multiple markets, adjusting exposure based on evolving market conditions, correlation shifts, and risk assessments that retail traders never see. A hedge fund might be simultaneously long gold, short oil, neutral on copper, and overweight agricultural commodities, with position sizes calibrated to volatility and portfolio Greeks. When they increase gold exposure from five percent to eight percent of portfolio allocation, this rebalancing decision reflects sophisticated analysis of opportunity cost, risk parity, and cross-market dynamics that no individual chart pattern can capture.
This portfolio reweighting activity, multiplied across hundreds of institutional participants, manifests in the aggregate positioning data published weekly by the CFTC. The Commitment of Traders report does not show individual trades or strategies. It shows the collective footprint of how actual commercial hedgers and large speculators have allocated their capital across different markets. When mining companies collectively increase forward gold sales to hedge thirty percent more production than last quarter, they are not reacting to a moving average crossover. They are making strategic allocation decisions based on production forecasts, cost structures, and price expectations derived from operational realities invisible to outside observers. This is portfolio management in action, revealed through positioning data rather than price charts.
If you want to understand how institutional capital actually flows, how sophisticated traders genuinely position themselves across market cycles, the COT report provides a rare window into that hidden world. But understand what you are getting into. This is not a tool for scalpers seeking confirmation of the next five-minute move. This is not an oscillator that flashes oversold at market bottoms with convenient precision. COT analysis operates on a timescale measured in weeks and months, revealing positioning shifts that precede major market turns but offer no precision timing. The data arrives three days stale, published only once per week, capturing strategic positioning rather than tactical entries.
If you need instant gratification, if you trade intraday moves, if you demand mechanical signals with ninety percent accuracy, close this document now. COT analysis rewards patience, position sizing discipline, and tolerance for being early. It punishes impatience, overleveraging, and the expectation that any single indicator can substitute for market understanding.
The premise is deceptively simple. Every Tuesday, large traders in futures markets must report their positions to the CFTC. By Friday afternoon, this data becomes public. Academic research spanning three decades has consistently shown that not all market participants are created equal. Some traders consistently profit while others consistently lose. Some anticipate major turning points while others chase trends into exhaustion. Bessembinder and Chan (1992) demonstrated in their seminal study that commercial hedgers, those with actual exposure to the underlying commodity or financial instrument, possess superior forecasting ability compared to speculators. Their research, published in the Journal of Finance, found statistically significant predictive power in commercial positioning, particularly at extreme levels. This finding challenged the efficient market hypothesis and opened the door to a new approach to market analysis based on positioning rather than price alone.
Think about what this means. Every week, the government publishes a report showing you exactly how the most informed market participants are positioned. Not their opinions. Not their predictions. Their actual money at risk. When agricultural producers collectively hold their largest short hedge in five years, they are not making idle speculation. They are locking in prices for crops they will harvest, informed by private knowledge of weather conditions, soil quality, inventory levels, and demand expectations invisible to outside observers. When energy companies aggressively hedge forward production at current prices, they reveal information about expected supply that no analyst report can capture. This is not technical analysis based on past prices. This is not fundamental analysis based on publicly available data. This is behavioral analysis based on how the smartest money is actually positioned, how institutions allocate capital across portfolios, and how those allocation decisions shift as market conditions evolve.
WHY SOME TRADERS KNOW MORE THAN OTHERS
Building on this foundation, Sanders, Boris and Manfredo (2004) conducted extensive research examining the behaviour patterns of different trader categories. Their work, which analyzed over a decade of COT data across multiple commodity markets, revealed a fascinating dynamic that challenges much of what retail traders are taught. Commercial hedgers consistently positioned themselves against market extremes, buying when speculators were most bearish and selling when speculators reached peak bullishness. The contrarian positioning of commercials was not random noise but rather reflected their superior information about supply and demand fundamentals. Meanwhile, large speculators, primarily hedge funds and commodity trading advisors, exhibited strong trend-following behaviour that often amplified market moves beyond fundamental values. Small traders, the retail participants, consistently entered positions late in trends, frequently near turning points, making them reliable contrary indicators.
Wang (2003) extended this research by demonstrating that the predictive power of commercial positioning varies significantly across different commodity sectors. His analysis of agricultural commodities showed particularly strong forecasting ability, with commercial net positions explaining up to fifteen percent of return variance in subsequent weeks. This finding suggests that the informational advantages of hedgers are most pronounced in markets where physical supply and demand fundamentals dominate, as opposed to purely financial markets where information asymmetries are smaller. When a corn farmer hedges six months of expected harvest, that decision incorporates private observations about rainfall patterns, crop health, pest pressure, and local storage capacity that no distant analyst can match. When an oil refinery hedges crude oil purchases and gasoline sales simultaneously, the spread relationships reveal expectations about refining margins that reflect operational realities invisible in public data.
The theoretical mechanism underlying these empirical patterns relates to information asymmetry and different participant motivations. Commercial hedgers engage in futures markets not for speculative profit but to manage business risks. An agricultural producer selling forward six months of expected harvest is not making a bet on price direction but rather locking in revenue to facilitate financial planning and ensure business viability. However, this hedging activity necessarily incorporates private information about expected supply, inventory levels, weather conditions, and demand trends that the hedger observes through their commercial operations (Irwin and Sanders, 2012). When aggregated across many participants, this private information manifests in collective positioning.
Consider a gold mining company deciding how much forward production to hedge. Management must estimate ore grades, recovery rates, production costs, equipment reliability, labor availability, and dozens of other operational variables that determine whether locking in prices at current levels makes business sense. If the industry collectively hedges more aggressively than usual, it suggests either exceptional production expectations or concern about sustaining current price levels or combination of both. Either way, this positioning reveals information unavailable to speculators analyzing price charts and economic data. The hedger sees the physical reality behind the financial abstraction.
Large speculators operate under entirely different incentives and constraints. Commodity Trading Advisors managing billions in assets typically employ systematic, trend-following strategies that respond to price momentum rather than fundamental supply and demand. When crude oil rallies from sixty dollars to seventy dollars per barrel, these systems generate buy signals. As the rally continues to eighty dollars, position sizes increase. The strategy works brilliantly during sustained trends but becomes a liability at reversals. By the time oil reaches ninety dollars, trend-following funds are maximally long, having accumulated positions progressively throughout the rally. At this point, they represent not smart money anticipating further gains but rather crowded money vulnerable to reversal. Sanders, Boris and Manfredo (2004) documented this pattern across multiple energy markets, showing that extreme speculator positioning typically marked late-stage trend exhaustion rather than early-stage trend development.
Small traders, the retail participants who fall below reporting thresholds, display the weakest forecasting ability. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns, meaning their aggregate positioning served as a reliable contrary indicator. The explanation combines several factors. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, entering trends after mainstream media coverage when institutional participants are preparing to exit. Perhaps most importantly, they trade with emotion, buying into euphoria and selling into panic at precisely the wrong times.
At major turning points, the three groups often position opposite each other with commercials extremely bearish, large speculators extremely bullish, and small traders piling into longs at the last moment. These high-divergence environments frequently precede increased volatility and trend reversals. The insiders with business exposure quietly exit as the momentum traders hit maximum capacity and retail enthusiasm peaks. Within weeks, the reversal begins, and positions unwind in the opposite sequence.
FROM RAW DATA TO ACTIONABLE SIGNALS
The COT Index indicator operationalizes these academic findings into a practical trading tool accessible through TradingView. At its core, the indicator normalizes net positioning data onto a zero to one hundred scale, creating what we call the COT Index. This normalization is critical because absolute position sizes vary dramatically across different futures contracts and over time. A commercial trader holding fifty thousand contracts net long in crude oil might be extremely bullish by historical standards, or it might be quite neutral depending on the context of total market size and historical ranges. Raw position numbers mean nothing without context. The COT Index solves this problem by calculating where current positioning stands relative to its range over a specified lookback period, typically two hundred fifty-two weeks or approximately five years of weekly data.
The mathematical transformation follows the methodology originally popularized by legendary trader Larry Williams, though the underlying concept appears in statistical normalization techniques across many fields. For any given trader category, we calculate the highest and lowest net position values over the lookback period, establishing the historical range for that specific market and trader group. Current positioning is then expressed as a percentage of this range, where zero represents the most bearish positioning ever seen in the lookback window and one hundred represents the most bullish extreme. A reading of fifty indicates positioning exactly in the middle of the historical range, suggesting neither extreme optimism nor pessimism relative to recent history (Williams and Noseworthy, 2009).
This index-based approach allows for meaningful comparison across different markets and time periods, overcoming the scaling problems inherent in analyzing raw position data. A commercial index reading of eighty-five in gold carries the same interpretive meaning as an eighty-five reading in wheat or crude oil, even though the absolute position sizes differ by orders of magnitude. This standardization enables systematic analysis across entire futures portfolios rather than requiring market-specific expertise for each contract.
The lookback period selection involves a fundamental tradeoff between responsiveness and stability. Shorter lookback periods, perhaps one hundred twenty-six weeks or approximately two and a half years, make the index more sensitive to recent positioning changes. However, it also increases noise and produces more false signals. Longer lookback periods, perhaps five hundred weeks or approximately ten years, create smoother readings that filter short-term noise but become slower to recognize regime changes. The indicator settings allow users to adjust this parameter based on their trading timeframe, risk tolerance, and market characteristics.
UNDERSTANDING CFTC DATA STRUCTURES
The indicator supports both Legacy and Disaggregated COT report formats, reflecting the evolution of CFTC reporting standards over decades of market development. Legacy reports categorize market participants into three broad groups: commercial traders (hedgers with underlying business exposure), non-commercial traders (large speculators seeking profit without commercial interest), and non-reportable traders (small speculators below reporting thresholds). Each category brings distinct motivations and information advantages to the market (CFTC, 2020).
The Disaggregated reports, introduced in September 2009 for physical commodity markets, provide finer granularity by splitting participants into five categories (CFTC, 2009). Producer and merchant positions capture those actually producing, processing, or merchandising the physical commodity. Swap dealers represent financial intermediaries facilitating derivative transactions for clients. Managed money includes commodity trading advisors and hedge funds executing systematic or discretionary strategies. Other reportables encompasses diverse participants not fitting the main categories. Small traders remain as the fifth group, representing retail participation.
This enhanced categorization reveals nuances invisible in Legacy reports, particularly distinguishing between different types of institutional capital and their distinct behavioural patterns. The indicator automatically detects which report type is appropriate for each futures contract and adjusts the display accordingly.
Importantly, Disaggregated reports exist only for physical commodity futures. Agricultural commodities like corn, wheat, and soybeans have Disaggregated reports because clear producer, merchant, and swap dealer categories exist. Energy commodities like crude oil and natural gas similarly have well-defined commercial hedger categories. Metals including gold, silver, and copper also receive Disaggregated treatment (CFTC, 2009). However, financial futures such as equity index futures, Treasury bond futures, and currency futures remain available only in Legacy format. The CFTC has indicated no plans to extend Disaggregated reporting to financial futures due to different market structures and participant categories in these instruments (CFTC, 2020).
THE BEHAVIORAL FOUNDATION
Understanding which trader perspective to follow requires appreciation of their distinct trading styles, success rates, and psychological profiles. Commercial hedgers exhibit anticyclical behaviour rooted in their fundamental knowledge and business imperatives. When agricultural producers hedge forward sales during harvest season, they are not speculating on price direction but rather locking in revenue for crops they will harvest. Their business requires converting volatile commodity exposure into predictable cash flows to facilitate planning and ensure survival through difficult periods. Yet their aggregate positioning reveals valuable information because these hedging decisions incorporate private information about supply conditions, inventory levels, weather observations, and demand expectations that hedgers observe through their commercial operations (Bessembinder and Chan, 1992).
Consider a practical example from energy markets. Major oil companies continuously hedge portions of forward production based on price levels, operational costs, and financial planning needs. When crude oil trades at ninety dollars per barrel, they might aggressively hedge the next twelve months of production, locking in prices that provide comfortable profit margins above their extraction costs. This hedging appears as short positioning in COT reports. If oil rallies further to one hundred dollars, they hedge even more aggressively, viewing these prices as exceptional opportunities to secure revenue. Their short positioning grows increasingly extreme. To an outside observer watching only price charts, the rally suggests bullishness. But the commercial positioning reveals that the actual producers of oil find these prices attractive enough to lock in years of sales, suggesting skepticism about sustaining even higher levels. When the eventual reversal occurs and oil declines back to eighty dollars, the commercials who hedged at ninety and one hundred dollars profit while speculators who chased the rally suffer losses.
Large speculators or managed money traders operate under entirely different incentives and constraints. Their systematic, momentum-driven strategies mean they amplify existing trends rather than anticipate reversals. Trend-following systems, the most common approach among large speculators, by definition require confirmation of trend through price momentum before entering positions (Sanders, Boris and Manfredo, 2004). When crude oil rallies from sixty dollars to eighty dollars per barrel over several months, trend-following algorithms generate buy signals based on moving average crossovers, breakouts, and other momentum indicators. As the rally continues, position sizes increase according to the systematic rules.
However, this approach becomes a liability at turning points. By the time oil reaches ninety dollars after a sustained rally, trend-following funds are maximally long, having accumulated positions progressively throughout the move. At this point, their positioning does not predict continued strength. Rather, it often marks late-stage trend exhaustion. The psychological and mechanical explanation is straightforward. Trend followers by definition chase price momentum, entering positions after trends establish rather than anticipating them. Eventually, they become fully invested just as the trend nears completion, leaving no incremental buying power to sustain the rally. When the first signs of reversal appear, systematic stops trigger, creating a cascade of selling that accelerates the downturn.
Small traders consistently display the weakest track record across academic studies. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns in his analysis across multiple commodity markets. This result means that whatever small traders collectively do, the opposite typically proves profitable. The explanation for small trader underperformance combines several factors documented in behavioral finance literature. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, learning about commodity trends through mainstream media coverage that arrives after institutional participants have already positioned. Perhaps most importantly, retail traders are more susceptible to emotional decision-making, buying into euphoria and selling into panic at precisely the wrong times (Tharp, 2008).
SETTINGS, THRESHOLDS, AND SIGNAL GENERATION
The practical implementation of the COT Index requires understanding several key features and settings that users can adjust to match their trading style, timeframe, and risk tolerance. The lookback period determines the time window for calculating historical ranges. The default setting of two hundred fifty-two bars represents approximately one year on daily charts or five years on weekly charts, balancing responsiveness with stability. Conservative traders seeking only the most extreme, highest-probability signals might extend the lookback to five hundred bars or more. Aggressive traders seeking earlier entry and willing to accept more false positives might reduce it to one hundred twenty-six bars or even less for shorter-term applications.
The bullish and bearish thresholds define signal generation levels. Default settings of eighty and twenty respectively reflect academic research suggesting meaningful information content at these extremes. Readings above eighty indicate positioning in the top quintile of the historical range, representing genuine extremes rather than temporary fluctuations. Conversely, readings below twenty occupy the bottom quintile, indicating unusually bearish positioning (Briese, 2008).
However, traders must recognize that appropriate thresholds vary by market, trader category, and personal risk tolerance. Some futures markets exhibit wider positioning swings than others due to seasonal patterns, volatility characteristics, or participant behavior. Conservative traders seeking high-probability setups with fewer signals might raise thresholds to eighty-five and fifteen. Aggressive traders willing to accept more false positives for earlier entry could lower them to seventy-five and twenty-five.
The key is maintaining meaningful differentiation between bullish, neutral, and bearish zones. The default settings of eighty and twenty create a clear three-zone structure. Readings from zero to twenty represent bearish territory where the selected trader group holds unusually bearish positions. Readings from twenty to eighty represent neutral territory where positioning falls within normal historical ranges. Readings from eighty to one hundred represent bullish territory where the selected trader group holds unusually bullish positions.
The trading perspective selection determines which participant group the indicator follows, fundamentally shaping interpretation and signal meaning. For counter-trend traders seeking reversal opportunities, monitoring commercial positioning makes intuitive sense based on the academic research discussed earlier. When commercials reach extreme bearish readings below twenty, indicating unprecedented short positioning relative to recent history, they are effectively betting against the crowd. Given their informational advantages demonstrated by Bessembinder and Chan (1992), this contrarian stance often precedes major bottoms.
Trend followers might instead monitor large speculator positioning, but with inverted logic compared to commercials. When managed money reaches extreme bullish readings above eighty, the trend may be exhausting rather than accelerating. This seeming paradox reflects their late-cycle participation documented by Sanders, Boris and Manfredo (2004). Sophisticated traders thus use speculator extremes as fade signals, entering positions opposite to speculator consensus.
Small trader monitoring serves primarily as a contrary indicator for all trading styles. Extreme small trader bullishness above seventy-five or eighty typically warns of retail FOMO at market tops. Extreme small trader bearishness below twenty or twenty-five often marks capitulation bottoms where the last weak hands have sold.
VISUALIZATION AND USER INTERFACE
The visual design incorporates multiple elements working together to facilitate decision-making and maintain situational awareness during active trading. The primary COT Index line plots in bold with adjustable line width, defaulting to two pixels for clear visibility against busy price charts. An optional glow effect, controlled by a simple toggle, adds additional visual prominence through multiple plot layers with progressively increasing transparency and width.
A twenty-one period exponential moving average overlays the index line, providing trend context for positioning changes. When the index crosses above its moving average, it signals accelerating bullish sentiment among the selected trader group regardless of whether absolute positioning is extreme. Conversely, when the index crosses below its moving average, it signals deteriorating sentiment and potentially the beginning of a reversal in positioning trends.
The EMA provides a dynamic reference line for assessing positioning momentum. When the index trades far above its EMA, positioning is not only extreme in absolute terms but also building with momentum. When the index trades far below its EMA, positioning is contracting or reversing, which may indicate weakening conviction even if absolute levels remain elevated.
The data table positioned at the top right of the chart displays eleven metrics for each trader category, transforming the indicator from a simple index calculation into an analytical dashboard providing multidimensional market intelligence. Beyond the COT Index itself, users can monitor positioning extremity, which measures how unusual current levels are compared to historical norms using statistical techniques. The extremity metric clarifies whether a reading represents the ninety-fifth or ninety-ninth percentile, with values above two standard deviations indicating genuinely exceptional positioning.
Market power quantifies each group's influence on total open interest. This metric expresses each trader category's net position as a percentage of total market open interest. A commercial entity holding forty percent of total open interest commands significantly more influence than one holding five percent, making their positioning signals more meaningful.
Momentum and rate of change metrics reveal whether positions are building or contracting, providing early warning of potential regime shifts. Position velocity measures the rate of change in positioning changes, effectively a second derivative providing even earlier insight into inflection points.
Sentiment divergence highlights disagreements between commercial and speculative positioning. This metric calculates the absolute difference between normalized commercial and large speculator index values. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals.
The table also displays concentration metrics when available, showing how positioning is distributed among the largest handful of traders in each category. High concentration indicates a few dominant players controlling most of the positioning, while low concentration suggests broad-based participation across many traders.
THE ALERT SYSTEM AND MONITORING
The alert system, comprising five distinct alert conditions, enables systematic monitoring of dozens of futures markets without constant screen watching. The bullish and bearish COT signal alerts trigger when the index crosses user-defined thresholds, indicating the selected trader group has reached extreme positioning worthy of attention. These alerts fire in real-time as new weekly COT data publishes, typically Friday afternoon following the Tuesday measurement date.
Extreme positioning alerts fire at ninety and ten index levels, representing the top and bottom ten percent of the historical range, warning of particularly stretched readings that historically precede reversals with high probability. When commercials reach a COT Index reading below ten, they are expressing their most bearish stance in the entire lookback period.
The data staleness alert notifies users when COT reports have not updated for more than ten days, preventing reliance on outdated information for trading decisions. Government shutdowns or federal holidays can interrupt the normal Friday publication schedule. Using stale signals while believing them current creates dangerous false confidence.
The indicator's watermark information display positioned in the bottom right corner provides essential context at a glance. This persistent display shows the symbol and timeframe, the COT report date timestamp, days since last update, and the current signal state. A trader analyzing a potential short entry in crude oil can glance at the watermark to instantly confirm positioning context without interrupting analysis flow.
LIMITATIONS AND REALISTIC EXPECTATIONS
Practical application requires understanding both the indicator's considerable strengths and inherent limitations. COT data inherently lags price action by three days, as Tuesday positions are not published until Friday afternoon. This delay means the indicator cannot catch rapid intraday reversals or respond to surprise news events. Traders using the COT Index for timing entries must accept this latency and focus on swing trading and position trading timeframes where three-day lags matter less than in day trading or scalping.
The weekly publication schedule similarly makes the indicator unsuitable for short-term trading strategies requiring immediate feedback. The COT Index works best for traders operating on weekly or longer timeframes, where positioning shifts measured in weeks and months align with trading horizon.
Extreme COT readings can persist far longer than typical technical indicators suggest, testing the patience and capital reserves of traders attempting to fade them. When crude oil enters a sustained bull market driven by genuine supply disruptions, commercial hedgers may maintain bearish positioning for many months as prices grind higher. A commercial COT Index reading of fifteen indicating extreme bearishness might persist for three months while prices continue rallying before finally reversing. Traders without sufficient capital and risk tolerance to weather such drawdowns will exit prematurely, precisely when the signal is about to work (Irwin and Sanders, 2012).
Position sizing discipline becomes paramount when implementing COT-based strategies. Rather than risking large percentages of capital on individual signals, successful COT traders typically allocate modest position sizes across multiple signals, allowing some to take time to mature while others work more quickly.
The indicator also cannot overcome fundamental regime changes that alter the structural drivers of markets. If gold enters a true secular bull market driven by monetary debasement, commercial hedgers may remain persistently bearish as mining companies sell forward years of production at what they perceive as favorable prices. Their positioning indicates valuation concerns from a production cost perspective, but cannot stop prices from rising if investment demand overwhelms physical supply-demand balance.
Similarly, structural changes in market participation can alter the meaning of positioning extremes. The growth of commodity index investing in the two thousands brought massive passive long-only capital into futures markets, fundamentally changing typical positioning ranges. Traders relying on COT signals without recognizing this regime change would have generated numerous false bearish signals during the commodity supercycle from 2003 to 2008.
The research foundation supporting COT analysis derives primarily from commodity markets where the commercial hedger information advantage is most pronounced. Studies specifically examining financial futures like equity indices and bonds show weaker but still present effects. Traders should calibrate expectations accordingly, recognizing that COT analysis likely works better for crude oil, natural gas, corn, and wheat than for the S&P 500, Treasury bonds, or currency futures.
Another important limitation involves the reporting threshold structure. Not all market participants appear in COT data, only those holding positions above specified minimums. In markets dominated by a few large players, concentration metrics become critical for proper interpretation. A single large trader accounting for thirty percent of commercial positioning might skew the entire category if their individual circumstances are idiosyncratic rather than representative.
GOLD FUTURES DURING A HYPOTHETICAL MARKET CYCLE
Consider a practical example using gold futures during a hypothetical but realistic market scenario that illustrates how the COT Index indicator guides trading decisions through a complete market cycle. Suppose gold has rallied from fifteen hundred to nineteen hundred dollars per ounce over six months, driven by inflation concerns following aggressive monetary expansion, geopolitical uncertainty, and sustained buying by Asian central banks for reserve diversification.
Large speculators, operating primarily trend-following strategies, have accumulated increasingly bullish positions throughout this rally. Their COT Index has climbed progressively from forty-five to eighty-five. The table display shows that large speculators now hold net long positions representing thirty-two percent of total open interest, their highest in four years. Momentum indicators show positive readings, indicating positions are still building though at a decelerating rate. Position velocity has turned negative, suggesting the pace of position building is slowing.
Meanwhile, commercial hedgers have responded to the rally by aggressively selling forward production and inventory. Their COT Index has moved inversely to price, declining from fifty-five to twenty. This bearish commercial positioning represents mining companies locking in forward sales at prices they view as attractive relative to production costs. The table shows commercials now hold net short positions representing twenty-nine percent of total open interest, their most bearish stance in five years. Concentration metrics indicate this positioning is broadly distributed across many commercial entities, suggesting the bearish stance reflects collective industry view rather than idiosyncratic positioning by a single firm.
Small traders, attracted by mainstream financial media coverage of gold's impressive rally, have recently piled into long positions. Their COT Index has jumped from forty-five to seventy-eight as retail investors chase the trend. Television financial networks feature frequent segments on gold with bullish guests. Internet forums and social media show surging retail interest. This retail enthusiasm historically marks late-stage trend development rather than early opportunity.
The COT Index indicator, configured to monitor commercial positioning from a contrarian perspective, displays a clear bearish signal given the extreme commercial short positioning. The table displays multiple confirming metrics: positioning extremity shows commercials at the ninety-sixth percentile of bearishness, market power indicates they control twenty-nine percent of open interest, and sentiment divergence registers sixty-five, indicating massive disagreement between commercial hedgers and large speculators. This divergence, the highest in three years, places the market in the historically high-risk category for reversals.
The interpretation requires nuance and consideration of context beyond just COT data. Commercials are not necessarily predicting an imminent crash. Rather, they are hedging business operations at what they collectively view as favorable price levels. However, the data reveals they have sold unusually large quantities of forward production, suggesting either exceptional production expectations for the year ahead or concern about sustaining current price levels or combination of both. Combined with extreme speculator positioning indicating a crowded long trade, and small trader enthusiasm confirming retail FOMO, the confluence suggests elevated reversal risk even if the precise timing remains uncertain.
A prudent trader analyzing this situation might take several actions based on COT Index signals. Existing long positions could be tightened with closer stop losses. Profit-taking on a portion of long exposure could lock in gains while maintaining some participation. Some traders might initiate modest short positions as portfolio hedges, sizing them appropriately for the inherent uncertainty in timing reversals. Others might simply move to the sidelines, avoiding new long entries until positioning normalizes.
The key lesson from case study analysis is that COT signals provide probabilistic edges rather than deterministic predictions. They work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five percent win rate with proper risk management produces substantial profits over time, yet still means forty-five percent of signals will be premature or wrong. Traders must embrace this probabilistic reality rather than seeking the impossible goal of perfect accuracy.
INTEGRATION WITH TRADING SYSTEMS
Integration with existing trading systems represents a natural and powerful use case for COT analysis, adding a positioning dimension to price-based technical approaches or fundamental analytical frameworks. Few traders rely exclusively on a single indicator or methodology. Rather, they build systems that synthesize multiple information sources, with each component addressing different aspects of market behavior.
Trend followers might use COT extremes as regime filters, modifying position sizing or avoiding new trend entries when positioning reaches levels historically associated with reversals. Consider a classic trend-following system based on moving average crossovers and momentum breakouts. Integration of COT analysis adds nuance. When large speculator positioning exceeds ninety or commercial positioning falls below ten, the regime filter recognizes elevated reversal risk. The system might reduce position sizing by fifty percent for new signals during these high-risk periods (Kaufman, 2013).
Mean reversion traders might require COT signal confluence before fading extended moves. When crude oil becomes technically overbought and large speculators show extreme long positioning above eighty-five, both signals confirm. If only technical indicators show extremes while positioning remains neutral, the potential short signal is rejected, avoiding fades of trends with underlying institutional support (Kaufman, 2013).
Discretionary traders can monitor the indicator as a continuous awareness tool, informing bias and position sizing without dictating mechanical entries and exits. A discretionary trader might notice commercial positioning shifting from neutral to progressively more bullish over several months. This trend informs growing positive bias even without triggering mechanical signals.
Multi-timeframe analysis represents another powerful integration approach. A trader might use daily charts for trade execution and timing while monitoring weekly COT positioning for strategic context. When both timeframes align, highest-probability opportunities emerge.
Portfolio construction for futures traders can incorporate COT signals as an additional selection criterion. Markets showing strong technical setups AND favorable COT positioning receive highest allocations. Markets with strong technicals but neutral or unfavorable positioning receive reduced allocations.
ADVANCED METRICS AND INTERPRETATION
The metrics table transforms simple positioning data into multidimensional market intelligence. Position extremity, calculated as the absolute deviation from the historical mean normalized by standard deviation, helps identify truly unusual readings versus routine fluctuations. A reading above two standard deviations indicates ninety-fifth percentile or higher extremity. Above three standard deviations indicates ninety-ninth percentile or higher, genuinely rare positioning that historically precedes major events with high probability.
Market power, expressed as a percentage of total open interest, reveals whose positioning matters most from a mechanical market impact perspective. Consider two scenarios in gold futures. In scenario one, commercials show a COT Index reading of fifteen while their market power metric shows they hold net shorts representing thirty-five percent of open interest. This is a high-confidence bearish signal. In scenario two, commercials also show a reading of fifteen, but market power shows only eight percent. While positioning is extreme relative to this category's normal range, their limited market share means less mechanical influence on price.
The rate of change and momentum metrics highlight whether positions are accelerating or decelerating, often providing earlier warnings than absolute levels alone. A COT Index reading of seventy-five with rapidly building momentum suggests continued movement toward extremes. Conversely, a reading of eighty-five with decelerating or negative momentum indicates the positioning trend is exhausting.
Position velocity measures the rate of change in positioning changes, effectively a second derivative. When velocity shifts from positive to negative, it indicates that while positioning may still be growing, the pace of growth is slowing. This deceleration often precedes actual reversal in positioning direction by several weeks.
Sentiment divergence calculates the absolute difference between normalized commercial and large speculator index values. When commercials show extreme bearish positioning at twenty while large speculators show extreme bullish positioning at eighty, the divergence reaches sixty, representing near-maximum disagreement. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The mechanism is intuitive. Extreme divergence indicates the informed hedgers and momentum-following speculators have positioned opposite each other with conviction. One group will prove correct and profit while the other proves incorrect and suffers losses. The resolution of this disagreement through price movement often involves volatility.
The table also displays concentration metrics when available. High concentration indicates a few dominant players controlling most of the positioning within a category, while low concentration suggests broad-based participation. Broad-based positioning more reliably reflects collective market intelligence and industry consensus. If mining companies globally all independently decide to hedge aggressively at similar price levels, it suggests genuine industry-wide view about price valuations rather than circumstances specific to one firm.
DATA QUALITY AND RELIABILITY
The CFTC has maintained COT reporting in various forms since the nineteen twenties, providing nearly a century of positioning data across multiple market cycles. However, data quality and reporting standards have evolved substantially over this long period. Modern electronic reporting implemented in the late nineteen nineties and early two thousands significantly improved accuracy and timeliness compared to earlier paper-based systems.
Traders should understand that COT reports capture positions as of Tuesday's close each week. Markets remain open three additional days before publication on Friday afternoon, meaning the reported data is three days stale when received. During periods of rapid market movement or major news events, this lag can be significant. The indicator addresses this limitation by including timestamp information and staleness warnings.
The three-day lag creates particular challenges during extreme volatility episodes. Flash crashes, surprise central bank interventions, geopolitical shocks, and other high-impact events can completely transform market positioning within hours. Traders must exercise judgment about whether reported positioning remains relevant given intervening events.
Reporting thresholds also mean that not all market participants appear in disaggregated COT data. Traders holding positions below specified minimums aggregate into the non-reportable or small trader category. This aggregation affects different markets differently. In highly liquid contracts like crude oil with thousands of participants, reportable traders might represent seventy to eighty percent of open interest. In thinly traded contracts with only dozens of active participants, a few large reportable positions might represent ninety-five percent of open interest.
Another data quality consideration involves trader classification into categories. The CFTC assigns traders to commercial or non-commercial categories based on reported business purpose and activities. However, this process is not perfect. Some entities engage in both commercial and speculative activities, creating ambiguity about proper classification. The transition to Disaggregated reports attempted to address some of these ambiguities by creating more granular categories.
COMPARISON WITH ALTERNATIVE APPROACHES
Several alternative approaches to COT analysis exist in the trading community beyond the normalization methodology employed by this indicator. Some analysts focus on absolute position changes week-over-week rather than index-based normalization. This approach calculates the change in net positioning from one week to the next. The emphasis falls on momentum in positioning changes rather than absolute levels relative to history. This method potentially identifies regime shifts earlier but sacrifices cross-market comparability (Briese, 2008).
Other practitioners employ more complex statistical transformations including percentile rankings, z-score standardization, and machine learning classification algorithms. Ruan and Zhang (2018) demonstrated that machine learning models applied to COT data could achieve modest improvements in forecasting accuracy compared to simple threshold-based approaches. However, these gains came at the cost of interpretability and implementation complexity.
The COT Index indicator intentionally employs a relatively straightforward normalization methodology for several important reasons. First, transparency enhances user understanding and trust. Traders can verify calculations manually and develop intuitive feel for what different readings mean. Second, academic research suggests that most of the predictive power in COT data comes from extreme positioning levels rather than subtle patterns requiring complex statistical methods to detect. Third, robust methods that work consistently across many markets and time periods tend to be simpler rather than more complex, reducing the risk of overfitting to historical data. Fourth, the complexity costs of implementation matter for retail traders without programming teams or computational infrastructure.
PSYCHOLOGICAL ASPECTS OF COT TRADING
Trading based on COT data requires psychological fortitude that differs from momentum-based approaches. Contrarian positioning signals inherently mean betting against prevailing market sentiment and recent price action. When commercials reach extreme bearish positioning, prices have typically been rising, sometimes for extended periods. The price chart looks bullish, momentum indicators confirm strength, moving averages align positively. The COT signal says bet against all of this. This psychological difficulty explains why COT analysis remains underutilized relative to trend-following methods.
Human psychology strongly predisposes us toward extrapolation and recency bias. When prices rally for months, our pattern-matching brains naturally expect continued rally. The recent price action dominates our perception, overwhelming rational analysis about positioning extremes and historical probabilities. The COT signal asking us to sell requires overriding these powerful psychological impulses.
The indicator design attempts to support the required psychological discipline through several features. Clear threshold markers and signal states reduce ambiguity about when signals trigger. When the commercial index crosses below twenty, the signal is explicit and unambiguous. The background shifts to red, the signal label displays bearish, and alerts fire. This explicitness helps traders act on signals rather than waiting for additional confirmation that may never arrive.
The metrics table provides analytical justification for contrarian positions, helping traders maintain conviction during inevitable periods of adverse price movement. When a trader enters short positions based on extreme commercial bearish positioning but prices continue rallying for several weeks, doubt naturally emerges. The table display provides reassurance. Commercial positioning remains extremely bearish. Divergence remains high. The positioning thesis remains intact even though price action has not yet confirmed.
Alert functionality ensures traders do not miss signals due to inattention while also not requiring constant monitoring that can lead to emotional decision-making. Setting alerts for COT extremes enables a healthier relationship with markets. When meaningful signals occur, alerts notify them. They can then calmly assess the situation and execute planned responses.
However, no indicator design can completely overcome the psychological difficulty of contrarian trading. Some traders simply cannot maintain short positions while prices rally. For these traders, COT analysis might be better employed as an exit signal for long positions rather than an entry signal for shorts.
Ultimately, successful COT trading requires developing comfort with probabilistic thinking rather than certainty-seeking. The signals work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five or sixty percent win rate with proper risk management produces substantial profits over years, yet still means forty to forty-five percent of signals will be premature or wrong. COT analysis provides genuine edge, but edge means probability advantage, not elimination of losing trades.
EDUCATIONAL RESOURCES AND CONTINUOUS LEARNING
The indicator provides extensive built-in educational resources through its documentation, detailed tooltips, and transparent calculations. However, mastering COT analysis requires study beyond any single tool or resource. Several excellent resources provide valuable extensions of the concepts covered in this guide.
Books and practitioner-focused monographs offer accessible entry points. Stephen Briese published The Commitments of Traders Bible in two thousand eight, offering detailed breakdowns of how different markets and trader categories behave (Briese, 2008). Briese's work stands out for its empirical focus and market-specific insights. Jack Schwager includes discussion of COT analysis within the broader context of market behavior in his book Market Sense and Nonsense (Schwager, 2012). Perry Kaufman's Trading Systems and Methods represents perhaps the most rigorous practitioner-focused text on systematic trading approaches including COT analysis (Kaufman, 2013).
Academic journal articles provide the rigorous statistical foundation underlying COT analysis. The Journal of Futures Markets regularly publishes research on positioning data and its predictive properties. Bessembinder and Chan's earlier work on systematic risk, hedging pressure, and risk premiums in futures markets provides theoretical foundation (Bessembinder, 1992). Chang's examination of speculator returns provides historical context (Chang, 1985). Irwin and Sanders provide essential skeptical perspective in their two thousand twelve article (Irwin and Sanders, 2012). Wang's two thousand three article provides one of the most empirical analyses of COT data across multiple commodity markets (Wang, 2003).
Online resources extend beyond academic and book-length treatments. The CFTC website provides free access to current and historical COT reports in multiple formats. The explanatory materials section offers detailed documentation of report construction, category definitions, and historical methodology changes. Traders serious about COT analysis should read these official CFTC documents to understand exactly what they are analyzing.
Commercial COT data services such as Barchart provide enhanced visualization and analysis tools beyond raw CFTC data. TradingView's educational materials, published scripts library, and user community provide additional resources for exploring different approaches to COT analysis.
The key to mastering COT analysis lies not in finding a single definitive source but rather in building understanding through multiple perspectives and information sources. Academic research provides rigorous empirical foundation. Practitioner-focused books offer practical implementation insights. Direct engagement with data through systematic backtesting develops intuition about how positioning dynamics manifest across different market conditions.
SYNTHESIZING KNOWLEDGE INTO PRACTICE
The COT Index indicator represents the synthesis of academic research, trading experience, and software engineering into a practical tool accessible to retail traders equipped with nothing more than a TradingView account and willingness to learn. What once required expensive data subscriptions, custom programming capabilities, statistical software, and institutional resources now appears as a straightforward indicator requiring only basic parameter selection and modest study to understand. This democratization of institutional-grade analysis tools represents a broader trend in financial markets over recent decades.
Yet technology and data access alone provide no edge without understanding and discipline. Markets remain relentlessly efficient at eliminating edges that become too widely known and mechanically exploited. The COT Index indicator succeeds only when users invest time learning the underlying concepts, understand the limitations and probability distributions involved, and integrate signals thoughtfully into trading plans rather than applying them mechanically.
The academic research demonstrates conclusively that institutional positioning contains genuine information about future price movements, particularly at extremes where commercial hedgers are maximally bearish or bullish relative to historical norms. This informational content is neither perfect nor deterministic but rather probabilistic, providing edge over many observations through identification of higher-probability configurations. Bessembinder and Chan's finding that commercial positioning explained modest but significant variance in future returns illustrates this probabilistic nature perfectly (Bessembinder and Chan, 1992). The effect is real and statistically significant, yet it explains perhaps ten to fifteen percent of return variance rather than most variance. Much of price movement remains unpredictable even with positioning intelligence.
The practical implication is that COT analysis works best as one component of a trading system rather than a standalone oracle. It provides the positioning dimension, revealing where the smart money has positioned and where the crowd has followed, but price action analysis provides the timing dimension. Fundamental analysis provides the catalyst dimension. Risk management provides the survival dimension. These components work together synergistically.
The indicator's design philosophy prioritizes transparency and education over black-box complexity, empowering traders to understand exactly what they are analyzing and why. Every calculation is documented and user-adjustable. The threshold markers, background coloring, tables, and clear signal states provide multiple reinforcing channels for conveying the same information.
This educational approach reflects a conviction that sustainable trading success comes from genuine understanding rather than mechanical system-following. Traders who understand why commercial positioning matters, how different trader categories behave, what positioning extremes signify, and where signals fit within probability distributions can adapt when market conditions change. Traders mechanically following black-box signals without comprehension abandon systems after normal losing streaks.
The research foundation supporting COT analysis comes primarily from commodity markets where commercial hedger informational advantages are most pronounced. Agricultural producers hedging crops know more about supply conditions than distant speculators. Energy companies hedging production know more about operating costs than financial traders. Metals miners hedging output know more about ore grades than index funds. Financial futures markets show weaker but still present effects.
The journey from reading this documentation to profitable trading based on COT analysis involves several stages that cannot be rushed. Initial reading and basic understanding represents the first stage. Historical study represents the second stage, reviewing past market cycles to observe how positioning extremes preceded major turning points. Paper trading or small-size real trading represents the third stage to experience the psychological challenges. Refinement based on results and personal psychology represents the fourth stage.
Markets will continue evolving. New participant categories will emerge. Regulatory structures will change. Technology will advance. Yet the fundamental dynamics driving COT analysis, that different market participants have different information, different motivations, and different forecasting abilities that manifest in their positioning, will persist as long as futures markets exist. While specific thresholds or optimal parameters may shift over time, the core logic remains sound and adaptable.
The trader equipped with this indicator, understanding of the theory and evidence behind COT analysis, realistic expectations about probability rather than certainty, discipline to maintain positions through adverse volatility, and patience to allow signals time to develop possesses genuine edge in markets. The edge is not enormous, markets cannot allow large persistent inefficiencies without arbitraging them away, but it is real, measurable, and exploitable by those willing to invest in learning and disciplined application.
REFERENCES
Bessembinder, H. (1992) Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies, 5(4), pp. 637-667.
Bessembinder, H. and Chan, K. (1992) The profitability of technical trading rules in the Asian stock markets, Pacific-Basin Finance Journal, 3(2-3), pp. 257-284.
Briese, S. (2008) The Commitments of Traders Bible: How to Profit from Insider Market Intelligence. Hoboken: John Wiley & Sons.
Chang, E.C. (1985) Returns to speculators and the theory of normal backwardation, Journal of Finance, 40(1), pp. 193-208.
Commodity Futures Trading Commission (CFTC) (2009) Explanatory Notes: Disaggregated Commitments of Traders Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Commodity Futures Trading Commission (CFTC) (2020) Commitments of Traders: About the Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Irwin, S.H. and Sanders, D.R. (2012) Testing the Masters Hypothesis in commodity futures markets, Energy Economics, 34(1), pp. 256-269.
Kaufman, P.J. (2013) Trading Systems and Methods. 5th edn. Hoboken: John Wiley & Sons.
Ruan, Y. and Zhang, Y. (2018) Forecasting commodity futures prices using machine learning: Evidence from the Chinese commodity futures market, Applied Economics Letters, 25(12), pp. 845-849.
Sanders, D.R., Boris, K. and Manfredo, M. (2004) Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports, Energy Economics, 26(3), pp. 425-445.
Schwager, J.D. (2012) Market Sense and Nonsense: How the Markets Really Work and How They Don't. Hoboken: John Wiley & Sons.
Tharp, V.K. (2008) Super Trader: Make Consistent Profits in Good and Bad Markets. New York: McGraw-Hill.
Wang, C. (2003) The behavior and performance of major types of futures traders, Journal of Futures Markets, 23(1), pp. 1-31.
Williams, L.R. and Noseworthy, M. (2009) The Right Stock at the Right Time: Prospering in the Coming Good Years. Hoboken: John Wiley & Sons.
FURTHER READING
For traders seeking to deepen their understanding of COT analysis and futures market positioning beyond this documentation, the following resources provide valuable extensions:
Academic Journal Articles:
Fishe, R.P.H. and Smith, A. (2012) Do speculators drive commodity prices away from supply and demand fundamentals?, Journal of Commodity Markets, 1(1), pp. 1-16.
Haigh, M.S., Hranaiova, J. and Overdahl, J.A. (2007) Hedge funds, volatility, and liquidity provision in energy futures markets, Journal of Alternative Investments, 9(4), pp. 10-38.
Kocagil, A.E. (1997) Does futures speculation stabilize spot prices? Evidence from metals markets, Applied Financial Economics, 7(1), pp. 115-125.
Sanders, D.R. and Irwin, S.H. (2011) The impact of index funds in commodity futures markets: A systems approach, Journal of Alternative Investments, 14(1), pp. 40-49.
Books and Practitioner Resources:
Murphy, J.J. (1999) Technical Analysis of the Financial Markets: A Guide to Trading Methods and Applications. New York: New York Institute of Finance.
Pring, M.J. (2002) Technical Analysis Explained: The Investor's Guide to Spotting Investment Trends and Turning Points. 4th edn. New York: McGraw-Hill.
Federal Reserve and Research Institution Publications:
Federal Reserve Banks regularly publish working papers examining commodity markets, futures positioning, and price discovery mechanisms. The Federal Reserve Bank of San Francisco and Federal Reserve Bank of Kansas City maintain active research programs in this area.
Online Resources:
The CFTC website provides free access to current and historical COT reports, explanatory materials, and regulatory documentation.
Barchart offers enhanced COT data visualization and screening tools.
TradingView's community library contains numerous published scripts and educational materials exploring different approaches to positioning analysis.
Trappin Previous Timeframe LevelsTrappin Previous Timeframe Levels (Trappin PTL)
Overview
Trappin PTL is a comprehensive multi-timeframe support and resistance indicator that displays key price levels from multiple timeframes on a single chart. This indicator helps traders identify critical price zones where reversals or breakouts are likely to occur, making it ideal for both intraday and swing trading strategies.
💡 Origin Story
I got tired of manually drawing these lines that I learned from watching Wallstreet Trapper on Trappin Tuesdays YouTube live streams. After repeatedly marking the same previous timeframe levels on every chart, I decided to automate the process. Hope it helps you as much as it helps me!
Key Features
📊 Multiple Timeframe Levels
The indicator tracks and displays high/low levels from:
Previous Hour (PHH/PHL) - Purple lines
Previous Day (PDH/PDL) - Green lines
Previous Week (PWH/PWL) - Yellow lines
Previous Month (PMH/PML) - Blue lines
All-Time High (ATH) - Red line
52-Week High - Orange line
🎨 Fully Customizable
Colors - Change the color of each timeframe independently
Line Styles - Choose between Solid, Dashed, or Dotted lines
Line Widths - Adjust thickness from 1-4 pixels
All settings organized in intuitive groups for easy access
📍 Smart Line Extension
Lines extend back to show when the level was established
Lines project forward to show current relevance
Historical context helps identify key support/resistance zones
🏷️ Clear Price Labels
Each level displays its exact price value (no currency symbols)
Labels positioned horizontally to avoid overlap
Adaptive text color for visibility on any chart theme (dark or light mode)
Why "Trappin"?
The name is a tribute to Wallstreet Trapper and his Trappin Tuesdays YouTube live streams, where I learned the importance of marking previous timeframe levels. The name also reflects the indicator's purpose: identifying price levels where traders often get "trapped" - whether it's bulls getting trapped below resistance or bears getting trapped above support. These levels represent zones where significant order flow and liquidity exist, making them prime areas for reversals or breakouts.
Credits
Created by resoh
Inspired by Wallstreet Trapper and Trappin Tuesdays YouTube live streams
This indicator is provided for educational and informational purposes. Always practice proper risk management and conduct your own analysis before making trading decisions.
Version History
v1.0 - Initial Release
Multi-timeframe high/low levels
All-time high tracking
52-week high tracking
Fully customizable colors, styles, and widths
Adaptive labels with price display
Smart line extension showing historical context
Tokyo Session DivisionsTokyo Session Divisions
Description:
Asia Range Breakout is a sophisticated, multi-filter trading tool designed to identify high-probability breakout opportunities during the core Asian trading session. By combining session-based range analysis with advanced confirmation filters like Heiken Ashi momentum, Ichimoku baseline trends, and EMA alignment, this indicator helps traders capture decisive moves while filtering out market noise.
Tired of false breakouts? This system provides a structured framework to trade the Sydney and Tokyo sessions with precision and confidence.
Key Features:
Multi-Session Range Tracking:
Monitors 6 distinct pre-defined Asian sessions (Sydney Box, Tokyo Pre-Open, Tokyo Launch, etc.).
Dynamically plots High and Low boundaries for each session (Teal for Highs, Red for Lows).
Individually toggleable sessions to focus on your preferred trading window.
Smart Alert & Signal System:
Generates alerts based on Heiken Ashi candle closings relative to session ranges.
Dual-Size Signals: Differentiates between "Large" breakouts (outside the range) and "Small" signals (within the range).
Configurable alert timeframe for confirmation candle closure.Built-in Sound Alerts for real-time notifications.
Advanced Confirmation Filters:
Heiken Ashi Momentum Filter: Ensures breakout candles have significant momentum, adjustable via a threshold multiplier.
EMA 200 Filter: Confirms the breakout's alignment with the broader trend.
Ichimoku Baseline (Kijun-sen) Filter: Uses a dynamic support/resistance level for additional confirmation.
Ichimoku Baseline Divergence Filter: A unique feature that requires the baseline's slope to match the breakout direction (Bullish for buys, Bearish for sells).
ATR Volatility Filter: (Optional) Ensures the breakout candle has sufficient range relative to recent market volatility.
Visual Enhancements:
Take-Profit Lines: Projects profit targets using ATR and connects consecutive alerts with a trendline.
Heiken Ashi Overlay: Displays smoothed Heiken Ashi candles directly on the main chart for cleaner trend visualization.
Divergence Trend Line: Visually plots the slope of the Ichimoku Baseline for quick trend assessment.
Session Background Highlighter: Shades the active sessions for easy time reference.
Comprehensive Debug Info Box: Provides real-time feedback on filter status, perfect for strategy validation and learning.
Usage Instructions & Tips:
1. Initial Setup:
Start simple! Apply the indicator to a 5-minute or 1-minute chart.
Recommended Instruments:
Forex: Major pairs like AUD/USD , USD/JPY or EUR/JPY.
Indices: `NAS100` (Nasdaq), `US30` (Dow Jones), `JP225` (Nikkei 225).
Commodities: `XAUUSD` (Gold).
Initially, enable only the Sydney Box (00:30 - 03:15 UTC) as it is the most robust session, then explore others.
2. Interpreting the Signals:
Large Green Arrow (Above Bar): A strong BUY signal. The Heiken Ashi candle closed above the session's High, and all enabled filters are confirmed.
Small Green Arrow (Above Bar): A moderate BUY signal. The candle closed bullishly but within the session range.
Large Red Arrow (Below Bar): A strong SELL signal. The Heiken Ashi candle closed below the session's Low, with filter confirmation.
Small Red Arrow (Below Bar): A moderate SELL signal. The candle closed bearishly but within the session range.
3. Optimizing Your Strategy:
Filter Tuning: The default filters are balanced. Adjust them based on your risk appetite:
Increase the Heiken Ashi Threshold (e.g., to 0.2) for fewer, but stronger signals.
Disable filters like ATR or Divergence if you find them too restrictive.
The Power of Divergence: The Ichimoku Divergence filter is a powerful trend-confirmation tool. Pay close attention to it for the highest-quality signals.
Use the Debug Box: Enable the "Show Debug Info Box" to see exactly why a signal did or did not trigger. This is invaluable for understanding the indicator's logic and avoiding bad trades.
4. Risk Management:
The TP Lines provide a logical profit target based on market volatility. Consider using them for setting take-profit orders.
Always use a stop-loss. A logical level is the opposite side of the session range (e.g., for a buy signal, place a stop below the session low).
Lodon Session DivisionsLondon Session Divisions - Situational Analysis Mastery
Professional Breakout & Trend-Following Strategy Based on Tom Hougaard's Situational Analysis
🎯 WHAT IS THIS INDICATOR?
A sophisticated multi-timeframe breakout scanner designed for European trading sessions, implementing the powerful "Situational Analysis" methodology from renowned trader Tom Hougaard. This professional tool identifies high-probability breakout opportunities with comprehensive filter systems to ensure quality signals.
⭐ KEY FEATURES
🏛️ SESSION-BASED TRADING
- European Overnight Range
- London Pre-Open & First Breakouts
- Tokyo Box & London Launch Sessions
- Smart Session Detection with Auto-DST
🎯 ENHANCED CONDITIONS
- School Run Strategy (SRS) by Tom Hougaard
- Anti-SRS Filter for counter-trend opportunities
- Session-specific logic for optimal entry timing
🛡️ ADVANCED FILTER SYSTEMS
- Heiken Ashi Momentum Confirmation
- EMA 200 Trend Filter
- Ichimoku Baseline & Divergence
- RSI Threshold Filter
- ATR Volatility Filter
- Multi-timeframe Compatibility
⚡ SMART ALERTS & VISUALS
- Multi-timeframe Alert Confirmation
- Breakout Size Detection (Beyond/Within Range)
- Take Profit Levels with ATR Calculation
- Customizable Visual Markers
- Enhanced Alert Messages with Filter Status
📈 OPTIMIZED FOR
- GERMAN DAX ⚡
- OIL & GOLD 🛢️
- NIKKEI 🇯🇵
- US30 & NASDAQ 🇺🇸
- All Major Indices & Commodities
🔧 CORE STRATEGY PHILOSOPHY
This indicator embodies Tom Hougaard's Situational Analysis approach :
- Identify the Situation : Market context through session analysis
- Define the Action : Clear breakout levels and ranges
- Execute with Precision : Filtered, high-quality signals
- Manage the Trade : Built-in TP levels and size detection
🎨 CUSTOMIZATION OPTIONS
Session Management
- Toggle individual trading sessions
- Smart session auto-disable with SRS/Anti-SRS
- Customizable session times
Filter Controls
- Enable/disable all filter systems independently
- Adjustable timeframe for each filter
- Custom threshold settings
Visual Preferences
- Heiken Ashi overlay display
- Breakout marker styles and colors
- TP line customization
- Debug information panel
📊 HOW TO USE
1. SETUP : Apply to your preferred instrument (DAX, Oil, Gold, etc.)
2. CONFIGURE : Enable your preferred sessions and filters
3. MONITOR : Watch for breakout markers during active sessions
4. EXECUTE : Enter on confirmed breakouts with filter alignment
5. MANAGE : Use built-in TP levels or your own risk management
⚠️ RISK DISCLAIMER
This indicator is for educational and informational purposes only. Trading involves substantial risk and is not suitable for every investor. Always practice proper risk management and backtest strategies before live trading. Past performance is not indicative of future results.
🔒 TECHNICAL SPECIFICATIONS
- Platform : TradingView Pine Script v6
- Compatibility : All timeframes
- Markets : Forex, Indices, Commodities, Stocks
- Updates : Regular improvements and bug fixes
📞 SUPPORT & UPDATES
Regular updates based on user feedback and market changes. For suggestions or issues, please comment on the publication.
⭐ If this indicator helps your trading, please like and follow for more advanced tools! ⭐
Why Traders Love This Indicator:
✅ Comprehensive Filter System reduces false signals
✅ Session-Based Logic aligns with professional trading hours
✅ Multiple Timeframe Analysis for confirmation
✅ Customizable for Any Trading Style
✅ Professional-Grade Risk Management Tools
Boost your breakout trading profitability with institutional-grade session analysis!
Lorentzian Harmonic Flow - Temporal Market Dynamic Lorentzian Harmonic Flow - Temporal Market Dynamic (⚡LHF)
By: DskyzInvestments
What this is
LHF Pro is a research‑grade analytical instrument that models market time as a compressible medium , extracts directional flow in curved time using heavy‑tailed kernels, and consults a history‑based memory bank for context before synthesizing a final, bounded probabilistic score . It is not a mashup; each subsystem is mathematically coupled to a single clock (time dilation via gamma) and a single lens (Lorentzian heavy‑tailed weighting). This script is dense in logic (and therefore heavy) because it prioritizes rigor, interpretability, and visual clarity.
Intended use
Education and research. This tool expresses state recognition and regime context—not guarantees. It does not place orders. It is fully functional as published and contains no placeholders. Nothing herein is financial advice.
Why this is original and useful
Curved time: Markets do not move at a constant pace. LHF Pro computes a Lorentz‑style gamma (γ) from relative speed so its analytical windows contract when the tape accelerates and relax when it slows.
Heavy‑tailed lens: Lorentzian kernels weight information with fat tails to respect rare but consequential extremes (unlike Gaussian decay).
Memory of regimes: A K‑nearest‑neighbors engine works in a multi‑feature space using Lorentz kernels per dimension and exponential age fade , returning a memory bias (directional expectation) and assurance (confidence mass).
One ecosystem: Squeeze, TCI, flow, acceleration, and memory live on the same clock and blend into a single final_score —visualized and documented on the dashboard.
Cognitive map: A 2D heat map projects memory resonance by age and flow regime, making “where the past is speaking” visible.
Shadow portfolio metaphor: Neighbor outcomes act like tiny hypothetical positions whose weighted average forms an educational pressure gauge (no execution, purely didactic).
Mathematical framework (full transparency)
1) Returns, volatility, and speed‑of‑market
Log return: rₜ = ln(closeₜ / closeₜ₋₁)
Realized vol: rv = stdev(r, vol_len); vol‑of‑vol: burst = |rv − rv |
Speed‑of‑market (analog to c): c = c_multiplier × (EMA(rv) + 0.5 × EMA(burst) + ε)
2) Trend velocity and Lorentz gamma (time dilation)
Trend velocity: v = |close − close | / (vel_len × ATR)
Relative speed: v_rel = v / c
Gamma: γ = 1 / √(1 − v_rel²), stabilized by caps (e.g., ≤10)
Interpretation: γ > 1 compresses market time → use shorter effective windows.
3) Adaptive temporal scale
Adaptive length: L = base_len / γ^power (bounded for safety)
Harmonic horizons: Lₛ = L × short_ratio, Lₘ = L × mid_ratio, Lₗ = L × long_ratio
4) Lorentzian smoothing and Harmonic Flow
Kernel weight per lag i: wᵢ = 1 / (1 + (d/γ)²), d = i/L
Horizon baselines: lw_h = Σ wᵢ·price / Σ wᵢ
Z‑deviation: z_h = (close − lw_h)/ATR
Harmonic Flow (HFL): HFL = (w_short·zₛ + w_mid·zₘ + w_long·zₗ) / (w_short + w_mid + w_long)
5) Flow kinematics
Velocity: HFL_vel = HFL − HFL
Acceleration (curvature): HFL_acc = HFL − 2·HFL + HFL
6) Squeeze and temporal compression
Bollinger width vs Keltner width using L
Squeeze: BB_width < KC_width × squeeze_mult
Temporal Compression Index: TCI = base_len / L; TCI > 1 ⇒ compressed time
7) Entropy (regime complexity)
Shannon‑inspired proxy on |log returns| with numerical safeguards and smoothing. Higher entropy → more chaotic regime.
8) Memory bank and Lorentzian k‑NN
Feature vector (5D):
Outcomes stored: forward returns at H5, H13, H34
Per‑dimension similarity: k(Δ) = 1 / (1 + Δ²), weighted by user’s feature weights
Age fading: weight_age = mem_fade^age_bars
Neighbor score: sᵢ = similarityᵢ × weight_ageᵢ
Memory bias: mem_bias = Σ sᵢ·outcomeᵢ / Σ sᵢ
Assurance: mem_assurance = Σ sᵢ (confidence mass)
Normalization: mem_bias normalized by ATR and clamped into band
Shadow portfolio metaphor: neighbors behave like micro‑positions; their weighted net forward return becomes a continuous, adaptive expectation.
9) Blended score and breakout proxy
Blend factor: α_mem = 0.45 + 0.15 × (γ − 1)
Final score: final_score = (1−α_mem)·tanh(HFL / (flow_thr·1.5)) + α_mem·tanh(mem_bias_norm)
Breakout probability (bounded): energy = cap(TCI−1) + |HFL_acc|×k + cap(γ−1)×k + cap(mem_assurance)×k; breakout_prob = sigmoid(energy). Caps avoid runaway “100%” readings.
Inputs — every control, purpose, mechanics, and tuning
🔮 Lorentz Core
Auto‑Adapt (Vol/Entropy): On = L responds to γ and entropy (breathes with regime), Off = static testing.
Base Length: Calm‑market anchor horizon. Lower (21–28) for fast tapes; higher (55–89+) for slow.
Velocity Window (vel_len): Bars used in v. Shorter = more reactive γ; longer = steadier.
Volatility Window (vol_len): Bars used for rv/burst (c). Shorter = more sensitive c.
Speed‑of‑Market Multiplier (c_multiplier): Raises/lowers c. Lower values → easier γ spikes (more adaptation). Aim for strong trends to peak around γ ≈ 2–4.
Gamma Compression Power: Exponent of γ in L. <1 softens; >1 amplifies adaptation swings.
Max Kernel Span: Upper bound on smoothing loop (quality vs CPU).
🎼 Harmonic Flow
Short/Mid/Long Horizon Ratios: Partition L into fast/medium/slow views. Smaller short_ratio → faster reaction; larger long_ratio → sturdier bias.
Weights (w_short/w_mid/w_long): Governs HFL blend. Higher w_short → nimble; higher w_long → stable.
📈 Signals
Squeeze Strictness: Threshold for BB1 = compressed (coiled spring); <1 = dilated.
v/c: Relative speed; near 1 denotes extreme pacing. Diagnostic only.
Entropy: Regime complexity; high entropy suggests caution, smaller size, or waiting for order to return.
HFL: Curved‑time directional flow; sign and magnitude are the instantaneous bias.
HFL_acc: Curvature; spikes often accompany regime ignition post‑squeeze.
Mem Bias: Directional expectation from historical analogs (ATR‑normalized, bounded). Aligns or conflicts with HFL.
Assurance: Confidence mass from neighbors; higher → more reliable memory bias.
Squeeze: ON/RELEASE/OFF from BB
Bollinger Band ToolkitBollinger Band Toolkit
An advanced, adaptive Bollinger Band system for traders who want more context, precision, and edge.
This indicator expands on the classic Bollinger Bands by combining statistical and volatility-based methods with modern divergence and squeeze detection tools. It helps identify volatility regimes, potential breakouts, and early momentum shifts — all within one clean overlay.
🔹 Core Features
1. Adaptive Bollinger Bands (σ + ATR)
Classic 20-period bands enhanced with an ATR-based volatility adjustment, making them more responsive to true market movement rather than just price variance.
Reduces “overreacting” during chop and avoids bands collapsing too tightly during trends.
2. %B & RSI Divergence Detection
🟢 Green dots: Positive %B divergence — price makes a lower low, but %B doesn’t confirm (bullish).
🔴 Red dots: Negative %B divergence — price makes a higher high, but %B doesn’t confirm (bearish).
✚ Red/green crosses: RSI divergence confirmation — momentum fails to confirm the price’s new extreme.
These signals highlight potential reversal or slowdown zones that are often invisible to the naked eye.
3. Bollinger Band Squeeze (with Volume Filter)
Yellow squares (■) show periods when Bollinger Bands are at their narrowest relative to recent history.
Volume confirmation ensures the squeeze only triggers when both volatility and participation contract.
Often marks the “calm before the storm” — breakout potential zones.
4. Multi-Timeframe Breakout Markers
Optionally displays breakouts from higher or lower timeframes using different colors/symbols.
Lets you see when a higher timeframe band break aligns with your current chart — a strong trend continuation signal.
5. Dual- and Triple-Band Visualization (±1σ, ±2σ, ±3σ)
Optional inner (±1σ) and outer (±3σ) bands provide a layered volatility map:
Price holding between ±1σ → stable range / mean-reverting behavior
Price riding near ±2σ → trending phase, sustained momentum
Price touching or exceeding ±3σ → volatility expansion or exhaustion zone
This triple-band layout visually distinguishes normal movement from statistical extremes, helping you read when the market is balanced, expanding, or approaching its limits.
⚙️ Inputs & Customization
Choose band type (SMA/EMA/SMMA/WMA/VWMA)
Adjust deviation multiplier (σ) and ATR multiplier
Toggle individual features (divergence dots, squeeze markers, inner bands, etc.)
Multi-timeframe and colour controls for advanced users
🧠 How to Use
Watch for squeeze markers followed by a breakout bar beyond ±2σ → volatility expansion signal.
Combine divergence dots with RSI or price structure to anticipate slowdowns or reversals.
Confirm direction using multi-timeframe breakouts and volume expansion.
💬 Why It Works
This toolkit transforms qualitative chart reading (tight bands, hidden divergence) into quantitative, testable conditions — giving you objective insights that can be backtested, coded, or simply trusted in live setups.
DTM 444 BANDS 🚀DTM 444 BANDS 🚀:
The DTM 444 BANDS 🚀 is a powerful, multi-purpose trading indicator combining Supertrend, Dynamic Band Levels, Breakout Signals, and Volume Confirmation to help traders identify high-probability trade setups across different timeframes.
🔧 Key Features
✅ Multi-Timeframe Support
Analyze price action across any timeframe using the Timeframe input.
All band calculations (High, Low, Midline, and Supertrend) are pulled from a higher timeframe for clearer context.
✅ Dynamic Bands Based on Supertrend
High Band: Rolling highest of Supertrend over hiLen period.
Low Band: Rolling lowest of Supertrend over loLen period.
Midline: Midpoint of the above.
Acts like dynamic support/resistance, ideal for trend-following and breakout strategies.
✅ Dual Signal System
Breakout Signals (Buy and Sell): Triggered when price breaks the bands with volume confirmation.
Supertrend Crossover Signals (Buy1 and Sell1): Classic momentum entries with a confirmation twist.
Exit Signals: Optional take-profit/neutral indicators when price reverses.
✅ Volume Confirmation Filter (Optional)
Only triggers signals if the volume exceeds its 20-period SMA.
Helps filter out false breakouts and weak trends in low-liquidity periods.
✅ Visual Enhancements
Color-coded candles based on band positioning (e.g., red = weak, green = strong, etc.)
On-chart labels for each signal for quick reference.
Real-time Signal Dashboard using Pine Script tables showing:
Current signal
Volume filter status
Live volume vs volume SMA
🧪 Practical Use Cases
Trend Traders: Use the Supertrend cross and band breakouts to ride trends early.
Breakout Traders: Catch high-probability moves outside established ranges.
Swing Traders: Time entries and exits using color-coded bars and exit labels.
Volume-Sensitive Traders: Focus on trades with strong volume backing.
📊 Backtest Snapshot
Based on the example chart for Reliance Industries (RELIANCE.NS) on the weekly timeframe:
Several profitable buy and breakout signals during uptrends.
Timely exits and breakdown alerts before reversals.
Volume filter keeps trades clean and avoids noise.
⚙️ Customizable Parameters
High Length and Low Length (default: 19)
Supertrend Multiplier and ATR Length
Volume Filter: Toggle ON/OFF
Volume SMA Length: Default 20
Custom Timeframe: Choose any higher timeframe for multi-timeframe analysis
📢 Alerts Ready
Fully integrated with TradingView alerts:
Breakout & Breakdown
Supertrend crossovers
All alerts respect the volume filter setting
🏁 Final Thoughts
DTM 444 BANDS 🚀 is a versatile and adaptive trading system that blends trend analysis, volatility bands, and volume validation. Whether you're a trend trader, breakout hunter, or swing trader — this tool gives you a structured edge with clear visual cues and real-time alerts.
BOCS Channel Scalper Indicator - Mean Reversion Alert System# BOCS Channel Scalper Indicator - Mean Reversion Alert System
## WHAT THIS INDICATOR DOES:
This is a mean reversion trading indicator that identifies consolidation channels through volatility analysis and generates alert signals when price enters entry zones near channel boundaries. **This indicator version is designed for manual trading with comprehensive alert functionality.** Unlike automated strategies, this tool sends notifications (via popup, email, SMS, or webhook) when trading opportunities occur, allowing you to manually review and execute trades. The system assumes price will revert to the channel mean, identifying scalp opportunities as price reaches extremes and preparing to bounce back toward center.
## INDICATOR VS STRATEGY - KEY DISTINCTION:
**This is an INDICATOR with alerts, not an automated strategy.** It does not execute trades automatically. Instead, it:
- Displays visual signals on your chart when entry conditions are met
- Sends customizable alerts to your device/email when opportunities arise
- Shows TP/SL levels for reference but does not place orders
- Requires you to manually enter and exit positions based on signals
- Works with all TradingView subscription levels (alerts included on all plans)
**For automated trading with backtesting**, use the strategy version. For manual control with notifications, use this indicator version.
## ALERT CAPABILITIES:
This indicator includes four distinct alert conditions that can be configured independently:
**1. New Channel Formation Alert**
- Triggers when a fresh BOCS channel is identified
- Message: "New BOCS channel formed - potential scalp setup ready"
- Use this to prepare for upcoming trading opportunities
**2. Long Scalp Entry Alert**
- Fires when price touches the long entry zone
- Message includes current price, calculated TP, and SL levels
- Notification example: "LONG scalp signal at 24731.75 | TP: 24743.2 | SL: 24716.5"
**3. Short Scalp Entry Alert**
- Fires when price touches the short entry zone
- Message includes current price, calculated TP, and SL levels
- Notification example: "SHORT scalp signal at 24747.50 | TP: 24735.0 | SL: 24762.75"
**4. Any Entry Signal Alert**
- Combined alert for both long and short entries
- Use this if you want a single alert stream for all opportunities
- Message: "BOCS Scalp Entry: at "
**Setting Up Alerts:**
1. Add indicator to chart and configure settings
2. Click the Alert (⏰) button in TradingView toolbar
3. Select "BOCS Channel Scalper" from condition dropdown
4. Choose desired alert type (Long, Short, Any, or Channel Formation)
5. Set "Once Per Bar Close" to avoid false signals during bar formation
6. Configure delivery method (popup, email, webhook for automation platforms)
7. Save alert - it will fire automatically when conditions are met
**Alert Message Placeholders:**
Alerts use TradingView's dynamic placeholder system:
- {{ticker}} = Symbol name (e.g., NQ1!)
- {{close}} = Current price at signal
- {{plot_1}} = Calculated take profit level
- {{plot_2}} = Calculated stop loss level
These placeholders populate automatically, creating detailed notification messages without manual configuration.
## KEY DIFFERENCE FROM ORIGINAL BOCS:
**This indicator is designed for traders seeking higher trade frequency.** The original BOCS indicator trades breakouts OUTSIDE channels, waiting for price to escape consolidation before entering. This scalper version trades mean reversion INSIDE channels, entering when price reaches channel extremes and betting on a bounce back to center. The result is significantly more trading opportunities:
- **Original BOCS**: 1-3 signals per channel (only on breakout)
- **Scalper Indicator**: 5-15+ signals per channel (every touch of entry zones)
- **Trade Style**: Mean reversion vs trend following
- **Hold Time**: Seconds to minutes vs minutes to hours
- **Best Markets**: Ranging/choppy conditions vs trending breakouts
This makes the indicator ideal for active day traders who want continuous alert opportunities within consolidation zones rather than waiting for breakout confirmation. However, increased signal frequency also means higher potential commission costs and requires disciplined trade selection when acting on alerts.
## TECHNICAL METHODOLOGY:
### Price Normalization Process:
The indicator normalizes price data to create consistent volatility measurements across different instruments and price levels. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). Current close price is normalized using: (close - lowest_low) / (highest_high - lowest_low), producing values between 0 and 1 for standardized volatility analysis.
### Volatility Detection:
A 14-period standard deviation is applied to the normalized price series to measure price deviation from the mean. Higher standard deviation values indicate volatility expansion; lower values indicate consolidation. The indicator uses ta.highestbars() and ta.lowestbars() to identify when volatility peaks and troughs occur over the detection period (default 14 bars).
### Channel Formation Logic:
When volatility crosses from a high level to a low level (ta.crossover(upper, lower)), a consolidation phase begins. The indicator tracks the highest and lowest prices during this period, which become the channel boundaries. Minimum duration of 10+ bars is required to filter out brief volatility spikes. Channels are rendered as box objects with defined upper and lower boundaries, with colored zones indicating entry areas.
### Entry Signal Generation:
The indicator uses immediate touch-based entry logic. Entry zones are defined as a percentage from channel edges (default 20%):
- **Long Entry Zone**: Bottom 20% of channel (bottomBound + channelRange × 0.2)
- **Short Entry Zone**: Top 20% of channel (topBound - channelRange × 0.2)
Long signals trigger when candle low touches or enters the long entry zone. Short signals trigger when candle high touches or enters the short entry zone. Visual markers (arrows and labels) appear on chart, and configured alerts fire immediately.
### Cooldown Filter:
An optional cooldown period (measured in bars) prevents alert spam by enforcing minimum spacing between consecutive signals. If cooldown is set to 3 bars, no new long alert will fire until 3 bars after the previous long signal. Long and short cooldowns are tracked independently, allowing both directions to signal within the same period.
### ATR Volatility Filter:
The indicator includes a multi-timeframe ATR filter to avoid alerts during low-volatility conditions. Using request.security(), it fetches ATR values from a specified timeframe (e.g., 1-minute ATR while viewing 5-minute charts). The filter compares current ATR to a user-defined minimum threshold:
- If ATR ≥ threshold: Alerts enabled
- If ATR < threshold: No alerts fire
This prevents notifications during dead zones where mean reversion is unreliable due to insufficient price movement. The ATR status is displayed in the info table with visual confirmation (✓ or ✗).
### Take Profit Calculation:
Two TP methods are available:
**Fixed Points Mode**:
- Long TP = Entry + (TP_Ticks × syminfo.mintick)
- Short TP = Entry - (TP_Ticks × syminfo.mintick)
**Channel Percentage Mode**:
- Long TP = Entry + (ChannelRange × TP_Percent)
- Short TP = Entry - (ChannelRange × TP_Percent)
Default 50% targets the channel midline, a natural mean reversion target. These levels are displayed as visual lines with labels and included in alert messages for reference when manually placing orders.
### Stop Loss Placement:
Stop losses are calculated just outside the channel boundary by a user-defined tick offset:
- Long SL = ChannelBottom - (SL_Offset_Ticks × syminfo.mintick)
- Short SL = ChannelTop + (SL_Offset_Ticks × syminfo.mintick)
This logic assumes channel breaks invalidate the mean reversion thesis. SL levels are displayed on chart and included in alert notifications as suggested stop placement.
### Channel Breakout Management:
Channels are removed when price closes more than 10 ticks outside boundaries. This tolerance prevents premature channel deletion from minor breaks or wicks, allowing the mean reversion setup to persist through small boundary violations.
## INPUT PARAMETERS:
### Channel Settings:
- **Nested Channels**: Allow multiple overlapping channels vs single channel
- **Normalization Length**: Lookback for high/low calculation (1-500, default 100)
- **Box Detection Length**: Period for volatility detection (1-100, default 14)
### Scalping Settings:
- **Enable Long Scalps**: Toggle long alert generation on/off
- **Enable Short Scalps**: Toggle short alert generation on/off
- **Entry Zone % from Edge**: Size of entry zone (5-50%, default 20%)
- **SL Offset (Ticks)**: Distance beyond channel for stop (1+, default 5)
- **Cooldown Period (Bars)**: Minimum spacing between alerts (0 = no cooldown)
### ATR Filter:
- **Enable ATR Filter**: Toggle volatility filter on/off
- **ATR Timeframe**: Source timeframe for ATR (1, 5, 15, 60 min, etc.)
- **ATR Length**: Smoothing period (1-100, default 14)
- **Min ATR Value**: Threshold for alert enablement (0.1+, default 10.0)
### Take Profit Settings:
- **TP Method**: Choose Fixed Points or % of Channel
- **TP Fixed (Ticks)**: Static distance in ticks (1+, default 30)
- **TP % of Channel**: Dynamic target as channel percentage (10-100%, default 50%)
### Appearance:
- **Show Entry Zones**: Toggle zone labels on channels
- **Show Info Table**: Display real-time indicator status
- **Table Position**: Corner placement (Top Left/Right, Bottom Left/Right)
- **Long Color**: Customize long signal color (default: darker green for readability)
- **Short Color**: Customize short signal color (default: red)
- **TP/SL Colors**: Customize take profit and stop loss line colors
- **Line Length**: Visual length of TP/SL reference lines (5-200 bars)
## VISUAL INDICATORS:
- **Channel boxes** with semi-transparent fill showing consolidation zones
- **Colored entry zones** labeled "LONG ZONE ▲" and "SHORT ZONE ▼"
- **Entry signal arrows** below/above bars marking long/short alerts
- **TP/SL reference lines** with emoji labels (⊕ Entry, 🎯 TP, 🛑 SL)
- **Info table** showing channel status, last signal, entry/TP/SL prices, risk/reward ratio, and ATR filter status
- **Visual confirmation** when alerts fire via on-chart markers synchronized with notifications
## HOW TO USE:
### For 1-3 Minute Scalping with Alerts (NQ/ES):
- ATR Timeframe: "1" (1-minute)
- ATR Min Value: 10.0 (for NQ), adjust per instrument
- Entry Zone %: 20-25%
- TP Method: Fixed Points, 20-40 ticks
- SL Offset: 5-10 ticks
- Cooldown: 2-3 bars to reduce alert spam
- **Alert Setup**: Configure "Any Entry Signal" for combined long/short notifications
- **Execution**: When alert fires, verify chart visuals, then manually place limit order at entry zone with provided TP/SL levels
### For 5-15 Minute Day Trading with Alerts:
- ATR Timeframe: "5" or match chart
- ATR Min Value: Adjust to instrument (test 8-15 for NQ)
- Entry Zone %: 20-30%
- TP Method: % of Channel, 40-60%
- SL Offset: 5-10 ticks
- Cooldown: 3-5 bars
- **Alert Setup**: Configure separate "Long Scalp Entry" and "Short Scalp Entry" alerts if you trade directionally based on bias
- **Execution**: Review channel structure on alert, confirm ATR filter shows ✓, then enter manually
### For 30-60 Minute Swing Scalping with Alerts:
- ATR Timeframe: "15" or "30"
- ATR Min Value: Lower threshold for broader market
- Entry Zone %: 25-35%
- TP Method: % of Channel, 50-70%
- SL Offset: 10-15 ticks
- Cooldown: 5+ bars or disable
- **Alert Setup**: Use "New Channel Formation" to prepare for setups, then "Any Entry Signal" for execution alerts
- **Execution**: Larger timeframes allow more analysis time between alert and entry
### Webhook Integration for Semi-Automation:
- Configure alert webhook URL to connect with platforms like TradersPost, TradingView Paper Trading, or custom automation
- Alert message includes all necessary order parameters (direction, entry, TP, SL)
- Webhook receives structured data when signal fires
- External platform can auto-execute based on alert payload
- Still maintains manual oversight vs full strategy automation
## USAGE CONSIDERATIONS:
- **Manual Discipline Required**: Alerts provide opportunities but execution requires judgment. Not all alerts should be taken - consider market context, trend, and channel quality
- **Alert Timing**: Alerts fire on bar close by default. Ensure "Once Per Bar Close" is selected to avoid false signals during bar formation
- **Notification Delivery**: Mobile/email alerts may have 1-3 second delay. For immediate execution, use desktop popups or webhook automation
- **Cooldown Necessity**: Without cooldown, rapidly touching price action can generate excessive alerts. Start with 3-bar cooldown and adjust based on alert volume
- **ATR Filter Impact**: Enabling ATR filter dramatically reduces alert count but improves quality. Track filter status in info table to understand when you're receiving fewer alerts
- **Commission Awareness**: High alert frequency means high potential trade count. Calculate if your commission structure supports frequent scalping before acting on all alerts
## COMPATIBLE MARKETS:
Works on any instrument with price data including stock indices (NQ, ES, YM, RTY), individual stocks, forex pairs (EUR/USD, GBP/USD), cryptocurrency (BTC, ETH), and commodities. Volume-based features are not included in this indicator version. Multi-timeframe ATR requires higher-tier TradingView subscription for request.security() functionality on timeframes below chart timeframe.
## KNOWN LIMITATIONS:
- **Indicator does not execute trades** - alerts are informational only; you must manually place all orders
- **Alert delivery depends on TradingView infrastructure** - delays or failures possible during platform issues
- **No position tracking** - indicator doesn't know if you're in a trade; you must manage open positions independently
- **TP/SL levels are reference only** - you must manually set these on your broker platform; they are not live orders
- **Immediate touch entry can generate many alerts** in choppy zones without adequate cooldown
- **Channel deletion at 10-tick breaks** may be too aggressive or lenient depending on instrument tick size
- **ATR filter from lower timeframes** requires TradingView Premium/Pro+ for request.security()
- **Mean reversion logic fails** in strong breakout scenarios - alerts will fire but trades may hit stops
- **No partial closing capability** - full position management is manual; you determine scaling out
- **Alerts do not account for gaps** or overnight price changes; morning alerts may be stale
## RISK DISCLOSURE:
Trading involves substantial risk of loss. This indicator provides signals for educational and informational purposes only and does not constitute financial advice. Past performance does not guarantee future results. Mean reversion strategies can experience extended drawdowns during trending markets. Alerts are not guaranteed to be profitable and should be combined with your own analysis. Stop losses may not fill at intended levels during extreme volatility or gaps. Never trade with capital you cannot afford to lose. Consider consulting a licensed financial advisor before making trading decisions. Always verify alerts against current market conditions before executing trades manually.
## ACKNOWLEDGMENT & CREDITS:
This indicator is built upon the channel detection methodology created by **AlgoAlpha** in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns. The core channel formation logic using normalized price standard deviation is AlgoAlpha's original contribution to the TradingView community.
Enhancements to the original concept include: mean reversion entry logic (vs breakout), immediate touch-based alert generation, comprehensive alert condition system with customizable notifications, multi-timeframe ATR volatility filtering, cooldown period for alert management, dual TP methods (fixed points vs channel percentage), visual TP/SL reference lines, and real-time status monitoring table. This indicator version is specifically designed for manual traders who prefer alert-based decision making over automated execution.
BOCS Channel Scalper Strategy - Automated Mean Reversion System# BOCS Channel Scalper Strategy - Automated Mean Reversion System
## WHAT THIS STRATEGY DOES:
This is an automated mean reversion trading strategy that identifies consolidation channels through volatility analysis and executes scalp trades when price enters entry zones near channel boundaries. Unlike breakout strategies, this system assumes price will revert to the channel mean, taking profits as price bounces back from extremes. Position sizing is fully customizable with three methods: fixed contracts, percentage of equity, or fixed dollar amount. Stop losses are placed just outside channel boundaries with take profits calculated either as fixed points or as a percentage of channel range.
## KEY DIFFERENCE FROM ORIGINAL BOCS:
**This strategy is designed for traders seeking higher trade frequency.** The original BOCS indicator trades breakouts OUTSIDE channels, waiting for price to escape consolidation before entering. This scalper version trades mean reversion INSIDE channels, entering when price reaches channel extremes and betting on a bounce back to center. The result is significantly more trading opportunities:
- **Original BOCS**: 1-3 signals per channel (only on breakout)
- **Scalper Version**: 5-15+ signals per channel (every touch of entry zones)
- **Trade Style**: Mean reversion vs trend following
- **Hold Time**: Seconds to minutes vs minutes to hours
- **Best Markets**: Ranging/choppy conditions vs trending breakouts
This makes the scalper ideal for active day traders who want continuous opportunities within consolidation zones rather than waiting for breakout confirmation. However, increased trade frequency also means higher commission costs and requires tighter risk management.
## TECHNICAL METHODOLOGY:
### Price Normalization Process:
The strategy normalizes price data to create consistent volatility measurements across different instruments and price levels. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). Current close price is normalized using: (close - lowest_low) / (highest_high - lowest_low), producing values between 0 and 1 for standardized volatility analysis.
### Volatility Detection:
A 14-period standard deviation is applied to the normalized price series to measure price deviation from the mean. Higher standard deviation values indicate volatility expansion; lower values indicate consolidation. The strategy uses ta.highestbars() and ta.lowestbars() to identify when volatility peaks and troughs occur over the detection period (default 14 bars).
### Channel Formation Logic:
When volatility crosses from a high level to a low level (ta.crossover(upper, lower)), a consolidation phase begins. The strategy tracks the highest and lowest prices during this period, which become the channel boundaries. Minimum duration of 10+ bars is required to filter out brief volatility spikes. Channels are rendered as box objects with defined upper and lower boundaries, with colored zones indicating entry areas.
### Entry Signal Generation:
The strategy uses immediate touch-based entry logic. Entry zones are defined as a percentage from channel edges (default 20%):
- **Long Entry Zone**: Bottom 20% of channel (bottomBound + channelRange × 0.2)
- **Short Entry Zone**: Top 20% of channel (topBound - channelRange × 0.2)
Long signals trigger when candle low touches or enters the long entry zone. Short signals trigger when candle high touches or enters the short entry zone. This captures mean reversion opportunities as price reaches channel extremes.
### Cooldown Filter:
An optional cooldown period (measured in bars) prevents signal spam by enforcing minimum spacing between consecutive signals. If cooldown is set to 3 bars, no new long signal will fire until 3 bars after the previous long signal. Long and short cooldowns are tracked independently, allowing both directions to signal within the same period.
### ATR Volatility Filter:
The strategy includes a multi-timeframe ATR filter to avoid trading during low-volatility conditions. Using request.security(), it fetches ATR values from a specified timeframe (e.g., 1-minute ATR while trading on 5-minute charts). The filter compares current ATR to a user-defined minimum threshold:
- If ATR ≥ threshold: Trading enabled
- If ATR < threshold: No signals fire
This prevents entries during dead zones where mean reversion is unreliable due to insufficient price movement.
### Take Profit Calculation:
Two TP methods are available:
**Fixed Points Mode**:
- Long TP = Entry + (TP_Ticks × syminfo.mintick)
- Short TP = Entry - (TP_Ticks × syminfo.mintick)
**Channel Percentage Mode**:
- Long TP = Entry + (ChannelRange × TP_Percent)
- Short TP = Entry - (ChannelRange × TP_Percent)
Default 50% targets the channel midline, a natural mean reversion target. Larger percentages aim for opposite channel edge.
### Stop Loss Placement:
Stop losses are placed just outside the channel boundary by a user-defined tick offset:
- Long SL = ChannelBottom - (SL_Offset_Ticks × syminfo.mintick)
- Short SL = ChannelTop + (SL_Offset_Ticks × syminfo.mintick)
This logic assumes channel breaks invalidate the mean reversion thesis. If price breaks through, the range is no longer valid and position exits.
### Trade Execution Logic:
When entry conditions are met (price in zone, cooldown satisfied, ATR filter passed, no existing position):
1. Calculate entry price at zone boundary
2. Calculate TP and SL based on selected method
3. Execute strategy.entry() with calculated position size
4. Place strategy.exit() with TP limit and SL stop orders
5. Update info table with active trade details
The strategy enforces one position at a time by checking strategy.position_size == 0 before entry.
### Channel Breakout Management:
Channels are removed when price closes more than 10 ticks outside boundaries. This tolerance prevents premature channel deletion from minor breaks or wicks, allowing the mean reversion setup to persist through small boundary violations.
### Position Sizing System:
Three methods calculate position size:
**Fixed Contracts**:
- Uses exact contract quantity specified in settings
- Best for futures traders (e.g., "trade 2 NQ contracts")
**Percentage of Equity**:
- position_size = (strategy.equity × equity_pct / 100) / close
- Dynamically scales with account growth
**Cash Amount**:
- position_size = cash_amount / close
- Maintains consistent dollar exposure regardless of price
## INPUT PARAMETERS:
### Position Sizing:
- **Position Size Type**: Choose Fixed Contracts, % of Equity, or Cash Amount
- **Number of Contracts**: Fixed quantity per trade (1-1000)
- **% of Equity**: Percentage of account to allocate (1-100%)
- **Cash Amount**: Dollar value per position ($100+)
### Channel Settings:
- **Nested Channels**: Allow multiple overlapping channels vs single channel
- **Normalization Length**: Lookback for high/low calculation (1-500, default 100)
- **Box Detection Length**: Period for volatility detection (1-100, default 14)
### Scalping Settings:
- **Enable Long Scalps**: Toggle long entries on/off
- **Enable Short Scalps**: Toggle short entries on/off
- **Entry Zone % from Edge**: Size of entry zone (5-50%, default 20%)
- **SL Offset (Ticks)**: Distance beyond channel for stop (1+, default 5)
- **Cooldown Period (Bars)**: Minimum spacing between signals (0 = no cooldown)
### ATR Filter:
- **Enable ATR Filter**: Toggle volatility filter on/off
- **ATR Timeframe**: Source timeframe for ATR (1, 5, 15, 60 min, etc.)
- **ATR Length**: Smoothing period (1-100, default 14)
- **Min ATR Value**: Threshold for trade enablement (0.1+, default 10.0)
### Take Profit Settings:
- **TP Method**: Choose Fixed Points or % of Channel
- **TP Fixed (Ticks)**: Static distance in ticks (1+, default 30)
- **TP % of Channel**: Dynamic target as channel percentage (10-100%, default 50%)
### Appearance:
- **Show Entry Zones**: Toggle zone labels on channels
- **Show Info Table**: Display real-time strategy status
- **Table Position**: Corner placement (Top Left/Right, Bottom Left/Right)
- **Color Settings**: Customize long/short/TP/SL colors
## VISUAL INDICATORS:
- **Channel boxes** with semi-transparent fill showing consolidation zones
- **Colored entry zones** labeled "LONG ZONE ▲" and "SHORT ZONE ▼"
- **Entry signal arrows** below/above bars marking long/short entries
- **Active TP/SL lines** with emoji labels (⊕ Entry, 🎯 TP, 🛑 SL)
- **Info table** showing position status, channel state, last signal, entry/TP/SL prices, and ATR status
## HOW TO USE:
### For 1-3 Minute Scalping (NQ/ES):
- ATR Timeframe: "1" (1-minute)
- ATR Min Value: 10.0 (for NQ), adjust per instrument
- Entry Zone %: 20-25%
- TP Method: Fixed Points, 20-40 ticks
- SL Offset: 5-10 ticks
- Cooldown: 2-3 bars
- Position Size: 1-2 contracts
### For 5-15 Minute Day Trading:
- ATR Timeframe: "5" or match chart
- ATR Min Value: Adjust to instrument (test 8-15 for NQ)
- Entry Zone %: 20-30%
- TP Method: % of Channel, 40-60%
- SL Offset: 5-10 ticks
- Cooldown: 3-5 bars
- Position Size: Fixed contracts or 5-10% equity
### For 30-60 Minute Swing Scalping:
- ATR Timeframe: "15" or "30"
- ATR Min Value: Lower threshold for broader market
- Entry Zone %: 25-35%
- TP Method: % of Channel, 50-70%
- SL Offset: 10-15 ticks
- Cooldown: 5+ bars or disable
- Position Size: % of equity recommended
## BACKTEST CONSIDERATIONS:
- Strategy performs best in ranging, mean-reverting markets
- Strong trending markets produce more stop losses as price breaks channels
- ATR filter significantly reduces trade count but improves quality during low volatility
- Cooldown period trades signal quantity for signal quality
- Commission and slippage materially impact sub-5-minute timeframe performance
- Shorter timeframes require tighter entry zones (15-20%) to catch quick reversions
- % of Channel TP adapts better to varying channel sizes than fixed points
- Fixed contract sizing recommended for consistent risk per trade in futures
**Backtesting Parameters Used**: This strategy was developed and tested using realistic commission and slippage values to provide accurate performance expectations. Recommended settings: Commission of $1.40 per side (typical for NQ futures through discount brokers), slippage of 2 ticks to account for execution delays on fast-moving scalp entries. These values reflect real-world trading costs that active scalpers will encounter. Backtest results without proper cost simulation will significantly overstate profitability.
## COMPATIBLE MARKETS:
Works on any instrument with price data including stock indices (NQ, ES, YM, RTY), individual stocks, forex pairs (EUR/USD, GBP/USD), cryptocurrency (BTC, ETH), and commodities. Volume-based features require data feed with volume information but are optional for core functionality.
## KNOWN LIMITATIONS:
- Immediate touch entry can fire multiple times in choppy zones without adequate cooldown
- Channel deletion at 10-tick breaks may be too aggressive or lenient depending on instrument tick size
- ATR filter from lower timeframes requires higher-tier TradingView subscription (request.security limitation)
- Mean reversion logic fails in strong breakout scenarios leading to stop loss hits
- Position sizing via % of equity or cash amount calculates based on close price, may differ from actual fill price
- No partial closing capability - full position exits at TP or SL only
- Strategy does not account for gap openings or overnight holds
## RISK DISCLOSURE:
Trading involves substantial risk of loss. Past performance does not guarantee future results. This strategy is for educational purposes and backtesting only. Mean reversion strategies can experience extended drawdowns during trending markets. Stop losses may not fill at intended levels during extreme volatility or gaps. Thoroughly test on historical data and paper trade before risking real capital. Use appropriate position sizing and never risk more than you can afford to lose. Consider consulting a licensed financial advisor before making trading decisions. Automated trading systems can malfunction - monitor all live positions actively.
## ACKNOWLEDGMENT & CREDITS:
This strategy is built upon the channel detection methodology created by **AlgoAlpha** in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns. The core channel formation logic using normalized price standard deviation is AlgoAlpha's original contribution to the TradingView community.
Enhancements to the original concept include: mean reversion entry logic (vs breakout), immediate touch-based signals, multi-timeframe ATR volatility filtering, flexible position sizing (fixed/percentage/cash), cooldown period filtering, dual TP methods (fixed points vs channel percentage), automated strategy execution with exit management, and real-time position monitoring table.
Volume-Price Divergence Indicator V3Description:
This indicator helps you identify volume-price divergences and potential trend weakness across any specified timeframe.
Features:
Volume bars with moving average – green for bullish, red for bearish, with orange SMA to detect low-volume situations.
Custom OBV calculation with divergence detection – highlights when price makes new highs/lows but OBV does not.
VWAP deviation alerts – signals when price moves far from VWAP while volume remains low, indicating potential fake breakouts.
Fully configurable – select any reference timeframe, adjust volume MA, OBV period, and VWAP deviation threshold.
Visual markers – easily spot bullish/bearish divergences and volume-price mismatches directly on your chart.
Use case:
Spot early trend exhaustion points.
Identify fake breakouts or weak rallies/drops.
Combine with your existing trading strategy for more informed entries and exits.
Volume-Price Divergence Indicator V2Description:
This indicator helps you identify volume-price divergences and potential trend weakness across any specified timeframe.
Features:
Volume bars with moving average – green for bullish, red for bearish, with orange SMA to detect low-volume situations.
Custom OBV calculation with divergence detection – highlights when price makes new highs/lows but OBV does not.
VWAP deviation alerts – signals when price moves far from VWAP while volume remains low, indicating potential fake breakouts.
Fully configurable – select any reference timeframe, adjust volume MA, OBV period, and VWAP deviation threshold.
Visual markers – easily spot bullish/bearish divergences and volume-price mismatches directly on your chart.
Use case:
Spot early trend exhaustion points.
Identify fake breakouts or weak rallies/drops.
Combine with your existing trading strategy for more informed entries and exits.
