TimeMapTimeMap is a visual price-reference indicator designed to help traders rapidly visualize how current price levels relate to significant historical closing prices. It overlays your chart with reference lines representing past weekly, monthly, quarterly (3-month), semi-annual (6-month), and annual closing prices. By clearly plotting these historical price references, TimeMap helps traders quickly gauge price position relative to historical market structure, aiding in the identification of trends, support/resistance levels, and potential reversals.
How it Works:
The indicator calculates the precise number of historical bars corresponding to weekly, monthly, quarterly, semi-annual, and annual intervals, dynamically adjusting according to your chart’s timeframe (intraday, daily, weekly, monthly) and chosen market type (Stocks US, Crypto, Forex, or Futures). Historical closing prices from these periods are plotted directly on your chart as horizontal reference lines.
For intraday traders, the script accurately calculates historical offsets considering regular and extended trading sessions (e.g., pre-market and after-hours sessions for US stocks), ensuring correct positioning of historical lines.
User-Configurable Inputs Explained in Detail:
Market Type:
Allows you to specify your trading instrument type, automatically adjusting calculations for:
- Stocks US (default): 390 minutes per regular session (780 minutes if extended hours enabled), 5 trading days/week.
- Crypto: 1440 minutes/day, 7 trading days/week.
- Forex: 1440 minutes/day, 5 trading days/week.
- Futures: 1320 minutes/day, 5 trading days/week.
Show Weekly Close:
When enabled, plots a line at the exact closing price from one week ago. Provides short-term context and helps identify recent price momentum.
Show Monthly Close:
When enabled, plots a line at the exact closing price from one month ago. Helpful for evaluating medium-term price positioning and monthly trend strength.
Show 3-Month Close:
When enabled, plots a line at the exact closing price from three months ago. Useful for assessing quarterly market shifts, intermediate trend changes, and broader market sentiment.
Show 6-Month Close:
When enabled, plots a line at the exact closing price from six months ago. Useful for identifying semi-annual trends, significant price pivots, and longer-term support/resistance levels.
Show 1-Year Close:
When enabled, plots a line at the exact closing price from one year ago. Excellent for assessing long-term market direction and key annual price levels.
Enable Smoothing:
Activates a Simple Moving Average (SMA) smoothing of historical reference lines, reducing volatility and providing clearer visual references. Recommended for traders preferring less volatile reference levels.
Smoothing Length:
Determines the number of bars used in calculating the SMA smoothing of historical lines. Higher values result in smoother but slightly delayed reference lines; lower values offer more immediate yet more volatile levels.
Use Extended Hours (Intraday Only):
When enabled (only applicable for Stocks US), it accounts for pre-market and after-hours trading sessions, providing accurate intraday historical line calculations based on extended sessions (typically 780 minutes/day total).
Important Notes and Compliance:
- This indicator does not provide trading signals, recommendations, or predictions. It serves purely as a visual analytical tool to supplement traders’ existing methods.
- Historical lines plotted are strictly based on past available price data; the indicator never accesses future data or data outside the scope of Pine Script’s standard capabilities.
- The script incorporates built-in logic to avoid runtime errors if insufficient historical data exists for a selected timeframe, ensuring robustness even with limited historical bars.
- TimeMap is original work developed exclusively by Julien Eche (@Julien_Eche). It does not reuse or replicate third-party or existing open-source scripts.
Recommended Best Practices:
- Use TimeMap as a complementary analytical reference, not as a standalone strategy or trade decision-making tool.
- Adapt displayed historical periods and smoothing settings based on your trading style and market approach.
- Default plot colors are optimized for readability on dark-background charts; adjust as necessary according to your preference and chart color scheme.
This script is published open-source to benefit the entire TradingView community and fully complies with all TradingView script publishing rules and guidelines.
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Globex, Extended, Daily, Weekly, Monthly, Yearly Range* Adds Right Side Only Price Line & Labels for Tracking without Extending Both Sides
* Tracks Current, Previous, and Two Previous Globex Sessions/ Futures:
* Tracks Current, Previous, and Two Previous Extended Session/ Stocks:
* Tracks Current, Previous, Two, & Three Previous Day Session/ Equities:
* Tracks Current, Last, Two, Three, Four, & Five Week Session/ Equities:
* Tracks Current, Last, Two, Three, Four, & Five Month Session/ Equities:
* Tracks Current, Last, Two, Three, Four, & Five Year Session/ Equities:
* Allows Custom Range on Globex, Extended, & Daily Sessions
* Allows Custom Range on Weekly, Monthly, & Yearly Sessions
* Lines & Labels Are Not Visible on Chart Scales
* Reversible Text & Background Color
* Lines Extend Accordingly with Range
* Labels show Price & Percent Change
* Background Colors should match Chart Color to avoid Overlapping Text & Labels
* Lines have Offset Extension
* Labels have Offset Extension
* Globex Session is only visible on Futures & if Current Timeframe is Intraday
* Extended Session is only visible on Stocks & if Current Timeframe is Intraday
* Daily, Weekly, Monthly, & Yearly Sessions are visible on All Symbols & All Timeframes
* Globex, Extended, & Regular use their Default Time Sessions but allow Customization
* For Back Testing Default Sessions, switch over on the Menu to Style and Turn On/Off their Background Color; Any Area on the Chart Without Background Color is Regular Session
Dominant Cycle Tuned RsiIntroduction
Adaptive technical indicators are importants in a non stationary market, the ability to adapt to a situation can boost the efficiency of your strategy. A lot of methods have been proposed to make technical indicators "smarters" , from the use of variable smoothing constant for exponential smoothing to artificial intelligence.
The dominant cycle tuned rsi depend on the dominant cycle period of the market, such method allow the rsi to return accurate peaks and valleys levels. This indicator is an estimation of the cycle finder tuned rsi proposed by Lars von Thienen published in Decoding the Hidden Market Rhythm/Fine-tuning technical indicators using the dominant market vibration/2010 using the cycle measurement method described by John F.Ehlers in Cybernetic Analysis for Stocks and Futures .
The following section is for information purpose only, it can be technical so you can skip directly to the The Indicator section.
Frequency Estimation and Maximum Entropy Spectral Analysis
“Looks like rain,” said Tom precipitously.
Tom would have been a great weather forecaster, but market patterns are more complex than weather ones. The ability to measure dominant cycles in a complex signal is hard, also a method able to estimate it really fast add even more challenge to the task. First lets talk about the term dominant cycle , signals can be decomposed in a sum of various sine waves of different frequencies and amplitudes, the dominant cycle is considered to be the frequency of the sine wave with the highest amplitude. In general the highest frequencies are those who form the trend (often called fundamentals) , so detrending is used to eliminate those frequencies in order to keep only mid/mid - highs ones.
A lot of methods have been introduced but not that many target market price, Lars von Thienen proposed a method relying on the following processing chain :
Lars von Thienen Method = Input -> Filtering and Detrending -> Discrete Fourier Transform of the result -> Selection using Bartels statistical test -> Output
Thienen said that his method is better than the one proposed by Elhers. The method from Elhers called MESA was originally developed to interpret seismographic information. This method in short involve the estimation of the phase using low amount of information which divided by 360 return the frequency. At first sight there are no relations with the Maximum entropy spectral estimation proposed by Burg J.P. (1967). Maximum Entropy Spectral Analysis. Proceedings of 37th Meeting, Society of Exploration Geophysics, Oklahoma City.
You may also notice that these methods are plotted in the time domain where more classic method such as : power spectrum, spectrogram or FFT are not. The method from Elhers is the one used to tune our rsi.
The Indicator
Our indicator use the dominant cycle frequency to calculate the period of the rsi thus producing an adaptive rsi . When our adaptive rsi cross under 70, price might start a downtrend, else when our adaptive rsi crossover 30, price might start an uptrend. The alpha parameter is a parameter set to be always lower than 1 and greater than 0. Lower values of alpha minimize the number of detected peaks/valleys while higher ones increase the number of those. 0.07 for alpha seems like a great parameter but it can sometimes need to be changed.
The adaptive indicator can also detect small top/bottoms of small periods
Of course the indicator is subject to failures
At the end it is totally dependent of the dominant cycle estimation, which is still a rough method subject to uncertainty.
Conclusion
Tuning your indicator is a great way to make it adapt to the market, but its also a complex way to do so and i'm not that convinced about the complexity/result ratio. The version using chart background will be published separately.
Feel free to tune your indicators with the estimator from elhers and see if it provide a great enhancement :)
Thanks for reading !
References
for the calculation of the dominant cycle estimator originally from www.davenewberg.com
Decoding the Hidden Market Rhythm (2010) Lars von Thienen
Ehlers , J. F. 2004 . Cybernetic Analysis for Stocks and Futures: Cutting-Edge DSP Technology to Improve Your Trading . Wiley
Universal Sentiment Oscillator with Trade RecommendationsUniversal Sentiment Oscillator & Strategy Guide
Summary
This all-in-one indicator is designed to be a comprehensive co-pilot for your trading journey. It moves beyond simple buy/sell signals by analyzing the underlying market sentiment and providing a dynamic, risk-assessed guide of potential trading strategies. Whether you're a novice learning the ropes or an expert seeking confirmation, this tool provides a structured framework for making smarter, more informed decisions in stocks, options, and futures.
How It Works
The core of the indicator is the Sentiment Oscillator, which calculates a score from -5 (Extremely Bearish) to +5 (Extremely Bullish) on every bar. This isn't just a single measurement; it's a weighted aggregate of several key technical conditions:
Trend Analysis: Price position relative to the 20, 50, and 200 EMAs.
Momentum Analysis: The current RSI value.
Hybrid Analysis: The state of the MACD and its signal line.
These factors are intelligently combined and normalized to produce a single, intuitive sentiment score, giving you an at-a-glance understanding of the market's pulse.
Core Features
Dynamic Trade Recommendation Table:
The informational heart of the indicator. This on-chart table provides a list of potential trades perfectly aligned with the current sentiment score.
Risk-Ranked Strategies:
All suggested trades are logically ordered by risk, helping you quickly identify strategies that match your comfort level.
Adjusted Trade Suggestions:
The indicator analyzes sentiment momentum (the score vs. its signal line) to provide proactive, forward-looking trade ideas based on where the market might be heading next.
Customizable Trading Styles:
Tell the indicator if you are a Conservative, Neutral, or Aggressive trader, and the "Adjusted Trade Suggestion" will automatically tailor its recommendations to your personal risk preference.
Context-Aware Futures Mode:
When viewing a futures contract, enable this mode to switch all recommendations from stock/options to futures-specific actions (e.g., "Cautious Long," "Monitor Range").
Predictive Sentiment Cone:
Visualize the potential short-term path of sentiment based on current momentum, helping you anticipate future conditions.
Fully Customizable:
Every parameter—from EMA lengths to trade filters—can be adjusted, allowing you to fine-tune the indicator to your exact specifications.
How to Use This Indicator
This tool is flexible and can be integrated into many trading systems. Here is a powerful, professional approach:
Top-Down Analysis (for Swing or Position Trading):
Establish the Trend: Start on the higher timeframes (Monthly, Weekly, Daily). Use the oscillator's color and score to define the dominant, long-term market sentiment. You only want to look for trades that align with this macro trend.
Refine the Entry: Drop down to the medium timeframes (4-Hour, 1-Hour). Wait for the sentiment on these charts to come into alignment with the higher-timeframe trend. This pullback or consolidation is your "zone of interest."
Pinpoint the Execution: Move to a lower timeframe (e.g., 15-Minute). Use the Adjusted Trade Suggestion and Sentiment Momentum to find a precise entry as momentum begins to shift back in the direction of the primary trend. You can set alerts on the oscillator's zero-line for early warnings of a sentiment shift.
As a Confirmation Tool: If you have an existing trade idea, use the indicator to validate it. Does the sentiment score align with your bullish or bearish thesis? Does the momentum confirm that now is a good time to enter?
As an Idea Generation Tool: Unsure what to trade? Browse different assets and let the indicator's "Primary Trades" and "Adjusted Trade Suggestion" present you with a list of risk-assessed ideas that you can then investigate further.
Disclaimer: This is an analysis tool and should not be considered financial advice. All forms of trading involve substantial risk. You should not trade with money you cannot afford to lose. Always perform your own due diligence and use this indicator as one component of a complete trading plan.
Risk TrackerThis Risk Tracker Pine Script provides traders with a customizable tool for tracking and managing trade risk directly on their chart. The script is designed to accommodate both futures and crypto trades, allowing you to monitor risk and reward parameters, adjust contract sizes, and manage leverage in real-time.
Key Features:
1. Trade Direction and Risk-Reward Ratio:
• Select between Long or Short trade directions.
• Set a custom Risk-Reward Ratio (RRR) to calculate potential profit and loss levels based on your trade setup.
2. Customizable Parameters:
• Input fields for contract size, leverage, margin, and maximum drawdown allow you to adjust the risk settings depending on the market you are trading.
• You can toggle between using a dollar-based or percentage-based risk calculation depending on whether you’re trading futures (USD-based) or crypto.
3. Real-time Stop-Loss and Take-Profit Calculation:
• The script automatically calculates and draws the Stop-Loss (SL) and Take-Profit (TP) levels on the chart based on your entry price and selected risk settings.
• The color of the SL and TP lines is customizable, allowing you to visually distinguish profit and loss levels.
4. Historical Price Levels:
• If there is no active trade, the script scans historical price data to find the last instances when the price hit the predefined stop-loss or take-profit levels, helping you understand past price behavior.
5. Risk Management Table:
• A summary table is displayed on the chart, showing the key metrics of your trade, including:
• Tick value and Dollar value for futures.
• Margin and Leverage for crypto.
• Risk-Reward Ratio, Entry price, Risk and Profit in USD or percentage terms.
• The table dynamically updates based on the current trade status.
6. Extended Chart Visualization:
• Option to extend the SL and TP lines to the left of the chart, allowing you to easily view these levels across multiple timeframes and bars.
This script helps ensure you are always aware of your trade’s risk profile, providing a clear and visual representation of potential profit and loss, both in terms of percentage and dollar value. Ideal for futures and crypto traders who rely on precise risk management to maintain profitability.
LibraryCOT_NZLibrary "LibraryCOT_NZ"
This library provides tools to help Pine programmers fetch Commitment of Traders (COT) data for futures.
rootToCFTCCode(root)
Accepts a futures root and returns the relevant CFTC code.
Parameters:
root (simple string) : Root prefix of the future's symbol, e.g. "ZC" for "ZC1!"" or "ZCU2021".
Returns: The part of a COT ticker corresponding to `root`, or "" if no CFTC code exists for the `root`.
currencyToCFTCCode(currency)
Converts a currency string to its corresponding CFTC code.
Parameters:
currency (simple string)
Returns: The corresponding to the currency, if one exists.
optionsToTicker(includeOptions)
Returns the part of a COT ticker using the `includeOptions` value supplied, which determines whether options data is to be included.
Parameters:
includeOptions (simple bool) : A "bool" value: 'true' if the symbol should include options and 'false' otherwise.
Returns: The part of a COT ticker: "FO" for data that includes options and "F" for data that doesn't.
metricNameAndDirectionToTicker(metricName, metricDirection)
Returns a string corresponding to a metric name and direction, which is one component required to build a valid COT ticker ID.
Parameters:
metricName (simple string) : One of the metric names listed in this library's chart. Invalid values will cause a runtime error.
metricDirection (simple string) : Metric direction. Possible values are: "Long", "Short", "Spreading", and "No direction". Valid values vary with metrics. Invalid values will cause a runtime error.
Returns: The part of a COT ticker ID string, e.g., "OI_OLD" for "Open Interest" and "No direction", or "TC_L" for "Traders Commercial" and "Long".
typeToTicker(metricType)
Converts a metric type into one component required to build a valid COT ticker ID. See the "Old and Other Futures" section of the CFTC's Explanatory Notes for details on types.
Parameters:
metricType (simple string) : Metric type. Accepted values are: "All", "Old", "Other".
Returns: The part of a COT ticker.
convertRootToCOTCode(mode, convertToCOT)
Depending on the `mode`, returns a CFTC code using the chart's symbol or its currency information when `convertToCOT = true`. Otherwise, returns the symbol's root or currency information. If no COT data exists, a runtime error is generated.
Parameters:
mode (simple string) : A string determining how the function will work. Valid values are:
"Root": the function extracts the futures symbol root (e.g. "ES" in "ESH2020") and looks for its CFTC code.
"Base currency": the function extracts the first currency in a pair (e.g. "EUR" in "EURUSD") and looks for its CFTC code.
"Currency": the function extracts the quote currency ("JPY" for "TSE:9984" or "USDJPY") and looks for its CFTC code.
"Auto": the function tries the first three modes (Root -> Base Currency -> Currency) until a match is found.
convertToCOT (simple bool) : "bool" value that, when `true`, causes the function to return a CFTC code. Otherwise, the root or currency information is returned. Optional. The default is `true`.
Returns: If `convertToCOT` is `true`, the part of a COT ticker ID string. If `convertToCOT` is `false`, the root or currency extracted from the current symbol.
COTTickerid(COTType, CFTCCode, includeOptions, metricName, metricDirection, metricType)
Returns a valid TradingView ticker for the COT symbol with specified parameters.
Parameters:
COTType (simple string) : A string with the type of the report requested with the ticker, one of the following: "Legacy", "Disaggregated", "Financial".
CFTCCode (simple string)
includeOptions (simple bool) : A boolean value. 'true' if the symbol should include options and 'false' otherwise.
metricName (simple string) : One of the metric names listed in this library's chart.
metricDirection (simple string) : Direction of the metric, one of the following: "Long", "Short", "Spreading", "No direction".
metricType (simple string) : Type of the metric. Possible values: "All", "Old", and "Other".
Returns: A ticker ID string usable with `request.security()` to fetch the specified Commitment of Traders data.
Weis V5 zigzag jayySomehow, I deleted version 5 of the zigzag script. Same name. I have added some older notes describing how the Weis Wave works.
I have also changed the date restriction that stopped the script from working after Dec 31, 2022.
What you see here is the Weis zigzag wave plotted directly on the price chart. This script is the companion to the Weis cumulative wave volume script.
What is a Weis wave? David Weis has been recognized as a Wyckoff method analyst he has written two books one of which, Trades About to Happen, describes the evolution of the now-popular Weis wave. The method employed by Weis is to identify waves of price action and to compare the strength of the waves on characteristics of wave strength. Chief among the characteristics of strength is the cumulative volume of the wave. There are other markers that Weis uses as well for example how the actual price difference between the start of the Weis wave from start to finish. Weis also uses time, particularly when using a Renko chart
David Weis did a futures io video which is a popular source of information about his method. (Search David Weis and futures.io. I strongly suggest you also read “Trades About to Happen” by David Weis.
This will get you up and running more quickly when studying charts. However, you should choose the Traditional method to be true to David Weis technique as described in his book "Trades About to Happen" and in the Futures IO Webcast featuring David Weis
. The Weis pip zigzag wave shows how far in terms of bar close price a Weis wave has traveled through the duration of a Weis wave. The Weis zigzag wave is used in combination with the Weis cumulative volume wave. The two waves should be set to the same "wave size".
To use this script, you must set the wave size: Using the traditional Weis method simply enter the desired wave size in the box "How should wave size be calculated", in this example I am using a traditional wave size of .25. Each wave for each security and each timeframe requires its own wave size. Although not the traditional method devised by David Weis a more automatic way to set wave size would be to use Average True Range (ATR). Using ATR is not the true Weis method but it does give you similar waves and, importantly, without the hassle described above. Once the Weis wave size is set then the zigzag wave will be shown with volume. Because Weis used the closing price of a wave to define waves a line Bar highs and bar lows are not captured by the Weis Wave. The default script setting is now cumulative volume waves using an ATR of 7 and a multiplication factor of .5.
To display volume in a way that does not crowd out neighbouring volumes Weis displayed volume as a maximum of 3 digits (usually). Consider two Weis Wave volumes 176,895,570 and 2,654,763,889. To display wave volume as three digits it is necessary to take a number such as 176,895,570 and truncate it. 176,895,570 can be represented as 177 X 10 to the power of 6. The number displayed must also be relative to other numbers in the field. If the highest volume on the page is: 2,654,763,889 and with only three numbers available to display the result the value shown must be 265 (265 X 10 to the power of 7). Since 176,895,570 is an order of magnitude smaller than 2,654,763,889 therefore 175,895,570 must be shown as 18 instead of 177. In this way, the relative magnitudes of the two volumes can be understood. All numbers in the field of view must be truncated by the same order of magnitude to make the relative volumes understandable. The script attempts to calculate the order of magnitude value automatically. If you see a red number in the field of view it means the script has failed to do the calculation automatically and you should use the manual method – use the dialogue box “Calculate truncated wave value automatically or manually”. Scroll down from the automatic method and select manual. Once "manual" is selected the values displayed become the power values or multipliers for each wave.
Using the manual method you will select a “Multiplier” in the next dialogue box. Scan the field and select the largest value in the field of view (visible chart) is the multiplier of interest. If you select a lower number than the maximum value will see at least one red “up”. If you are too high you will see at least one red “down”. Scroll in the direction recommended or the values on the screen will be totally incorrect. With volume truncated to the highest order values, the eye can quickly get a feel for relative volumes. It also reduces the crowding and overlapping of values on the screen. You can opt to show the full volume to help get a sense of the magnitude of the true volumes.
How does the script determine if a Weis wave is continuing to grow or not?
The script evaluates the closing price of each new bar relative to the "Weis wave size". Suppose the current bar closes at a new low close, within the current down wave, at $30.00. If the Weis wave size is $0.10 then the algorithm will remember the $30.00 close and compare it to the close of the next bar. If the bar close price does not close equal to or lower than $30.00 or close equal to or higher than $30.10 then the wave is still a down wave with a current low of $30.00. This is true even if the bar low is less than $30.00 or the bar high is greater than 30.10 – only the bar’s closing price matters. If a bar's closing price climbs back up to a close of $30.11 then because the closing price has moved more than $0.10 (the Weis wave size) then that is a wave reversal with a new up-trending wave. In the above example if there was currently a downward trending wave and the bar closes were as follows $30.00, $30.09, $30.01, $30.05, $30.10 The wave direction would continue to stay downward trending until the close of $30.10 was achieved. As such $30.00 would be the low and the following closes $30.09, $30.01, $30.05 would be allocated to the new upward-trending wave. If however There was a series of bar closes like this $30.00, $30.09, $30.01, $30.05, $29.99 since none of the closes was equal to above the 10-cent reversal target of $30.10 but instead, a new Weis wave low was achieved ($29.99). As such the closes of $30.09, $30.01, $30.05 would all be attributed to the continued down-trending wave with a current low of $29.99, even though the closing price for the interim bars was above $30.00. Now that the Weis Wave low is now 429.99 then, in order to reverse this continued downtrend price will need to close at or above $30.09 on subsequent bar closes assuming now new low bar close is achieved. With large wave sizes, wave direction can be in limbo for many bars before a close either renews wave direction or reverses it and confirms wave direction as either a reversal or a continuation. On the zig-zag, a wave line and its volume will not be "printed" until a wave reversal is confirmed.
The wave attribution is similar when using other methods to define wave size. If ATR is used for wave size instead of a traditional wave constant size such as $0.10 or $2 or 2000 pips or ... then the wave size is calculated based on current ATR instead of the Weis wave constant (Traditional selected value).
I have the option to display pseudo-Ord volume. In truth, Ord used more traditional zig-zag pivots of bar highs and lows. Waves using closes as pivots can have some significant differences. This difference can be lessened by using smaller time frames and larger wave sizes.
There are other options such to display the delta price or pip size of a Weis Wave, the number of bars in a wave, and a few other options.
Globex Trap w/ percentage [SLICKRICK]Globex Trap w/ Percentage
Overview
The Globex Trap w/ Percentage indicator is a powerful tool designed to help traders identify high-probability trading opportunities by analyzing price action during the Globex (overnight) session and regular trading hours. By combining Globex session ranges with Supply & Demand zones, this indicator highlights potential "trap" areas where significant price reactions may occur. Additionally, it calculates the Globex session range as a percentage of the daily Average True Range (ATR), providing valuable context for assessing market volatility.
This indicator is ideal for traders in futures markets or other instruments traded during Globex sessions, offering a visual and analytical edge for spotting key price levels and potential reversals or breakouts.
Key Features
Globex Session Tracking:
Visualizes the high and low of the Globex session (default: 3:00 PM to 6:30 AM PST) with customizable time settings.
Displays a semi-transparent box to mark the Globex range, with labels for "Globex High" and "Globex Low."
Calculates the Globex range as a percentage of the daily ATR, displayed as a label for quick reference.
Supply & Demand Zones:
Identifies Supply & Demand zones during regular trading hours (default: 6:00 AM to 8:00 AM PST) with customizable time settings.
Draws semi-transparent boxes to highlight these zones, aiding in the identification of key support and resistance areas.
Trap Area Identification:
Highlights potential trap zones where Globex ranges and Supply & Demand zones overlap, indicating areas where price may reverse or consolidate due to trapped traders.
Customizable Settings:
Adjust Globex and Supply & Demand session times to suit your trading preferences.
Toggle visibility of Globex and Supply & Demand zones independently.
Customize box colors for better chart readability.
Set the lookback period (default: 10 days) to control how many historical zones are displayed.
Configure the ATR length (default: 14) for the percentage calculation.
PST Timezone Default:
All times are based on Pacific Standard Time (PST) by default, ensuring accurate session tracking for users in this timezone or those aligning with U.S. West Coast market hours.
Recommended Usage
Timeframes: Best used on 1-hour charts or lower (e.g., 15-minute, 5-minute) for precise entry and exit points.
Markets: Optimized for futures (e.g., ES, NQ, CL) and other instruments traded during Globex sessions.
Historical Data: Ensure at least 10 days of historical data for optimal visualization of zones.
Strategy Integration: Use the indicator to identify potential reversals or breakouts at Globex highs/lows or Supply & Demand zones. The ATR percentage provides context for whether the Globex range is significant relative to typical daily volatility.
How It Works
Globex Session:
Tracks the high and low prices during the user-defined Globex session (default: 3:00 PM to 6:30 AM PST).
When the session ends, a box is drawn from the start to the end of the session, capturing the high and low prices.
Labels are placed at the midpoint of the session, showing "Globex High," "Globex Low," and the range as a percentage of the daily ATR (e.g., "75.23% of Daily ATR").
Supply & Demand Zones:
Tracks the high and low prices during the user-defined regular trading hours (default: 6:00 AM to 8:00 AM PST).
Draws a box to mark these zones, which often act as key support or resistance levels.
ATR Percentage:
Calculates the Globex range (high minus low) and divides it by the daily ATR to express it as a percentage.
This metric helps traders gauge whether the overnight price movement is significant compared to the instrument’s typical volatility.
Time Handling:
Uses PST (UTC-8) for all time calculations, ensuring accurate session timing for users aligning with this timezone.
Properly handles overnight sessions that cross midnight, ensuring seamless tracking.
Input Settings
Globex Session Settings:
Show Globex Session: Enable/disable Globex session visualization (default: true).
Globex Start/End Time: Set the start and end times for the Globex session (default: 3:00 PM to 6:30 AM PST).
Globex Box Color: Customize the color of the Globex session box (default: semi-transparent gray).
Supply & Demand Zone Settings:
Show Supply & Demand Zone: Enable/disable zone visualization (default: true).
Zone Start/End Time: Set the start and end times for Supply & Demand zones (default: 6:00 AM to 8:00 AM PST).
Zone Box Color: Customize the color of the zone box (default: semi-transparent aqua).
General Settings:
Days to Look Back: Number of historical days to display zones (default: 10).
ATR Length: Period for calculating the daily ATR (default: 14).
Notes
All times are in Pacific Standard Time (PST). Adjust the start and end times if your market operates in a different timezone or if you prefer different session windows.
The indicator is optimized for instruments with active Globex sessions, such as futures. Results may vary for non-24/5 markets.
A typo in the label "Globe Low" (should be "Globex Low") will be corrected in future updates.
Ensure your TradingView chart is set to display sufficient historical data to view the full lookback period.
Why Use This Indicator?
The Globex Trap w/ Percentage indicator provides a unique combination of session-based range analysis, Supply & Demand zone identification, and volatility context via the ATR percentage. Whether you’re a day trader, swing trader, or scalper, this tool helps you:
Pinpoint key price levels where institutional traders may act.
Assess the significance of overnight price movements relative to daily volatility.
Identify potential trap zones for high-probability setups.
Customize the indicator to fit your trading style and market preferences.
UB Short Signal (10Y Yield Future Spike)"This indicator identifies short opportunities on UB futures based on inverse correlation with 10Y Yield Futures. A macro trading tool to be used with additional confirmations."
🎯 Indicator Strategy
This tool generates sell signals for Ultra Bond (UB) futures when:
The Micro 10-Year Yield Future shows an upward spike (> adjustable threshold)
Trading volume is significant (false signal filter)
Inverse correlation is confirmed (UB falls when 10Y rises)
⚙️ Parameters
Spike Threshold: Sensitivity adjustment (e.g., 0.08% for swing trading)
Minimum Volume: Default 100 (optimized for Micro 10Y contracts)
📊 Recent Backtest
06/15/2024: +0.10% spike → UB dropped -0.3% within 15 minutes
06/18/2024: Valid signal post-CPI release
⚠️ Disclaimer
Analytical tool only – not financial advice
Must be combined with proper risk management
Open Interest Auto OverrideWhat does this “Open Interest Auto Override” Indicator
do?
Open Interest data is not supplied by every exchange to TradingView, however it is available on Binance Perpetual Futures. This script helps the crypto trader to identify the equivalent Binance Perpetual Futures Chart that has Open Interest Data available and automatically displays this on the traders chart.
How can a trader use this indicator?
This helps the trader to identify if there is Open Interest Data available in Binance and automatically displays it, making it easier to switch Coins whilst viewing the market.
What is Open Interest and how can I trade using this indicator?
Open Interest (OI) is the number of open futures contracts held by traders in active positions. The higher the value the Higher the number of open positions which indicates an increase in interest by traders in the asset.
If OI is increasing an equal number of longs and short positions are being opened.
If OI Decreases both longs and shorts are exiting the market.
If OI remains unchanged, no new contracts are entering or exiting, or an equal number of positions are being opened as there are being closed.
Open Interest can help traders by giving us a hint that a breakout may occur. If Open Interest is increasing whilst price is consolidating it may indicate that a breakout is imminent. If Open Interest is decreasing whilst price is consolidating it is likely that a false move in the form of a stop hunt may be issued prior to the actual breakout.
Usage of the Indicator:
By default the indicator will automatically use the Equivalent Binance Perpetual Chart for the Data
You can override the symbol manually if you what to view another exchanges data.
GG - LevelsThe GG Levels indicator is a tool designed for day trading U.S. equity futures. It highlights key levels intraday, overnight, intermediate-swing levels that are relevant for intraday futures trading.
Terminology
RTH (Regular Trading Hours): Represents the New York session from 09:30 to 17:00 EST.
ON Session (Overnight Session): Represents the trading activity from 17:00 to 09:29 EST.
IB (Initial Balance): The first hour of the New York session, from 09:30 to 10:30 EST.
Open: The opening price of the RTH session.
YH (Yesterday's High): The highest price during the RTH session of the previous day.
YL (Yesterday's Low): The lowest price during the RTH session of the previous day.
YC (Yesterday's Close): The daily bar close which for futures gets updated to settlement.
IBH (Initial Balance High): The highest price during the IB session.
IBL (Initial Balance Low): The lowest price during the IB session.
ONH (Overnight High): The highest price during the ON session.
ONL (Overnight Low): The lowest price during the ON session.
VWAP (Volume-Weighted Average Price): The volume-weighted average price that resets each day.
Why is RTH Important?
Tracking the RTH session is important because often times the overnight session can be filled with "lies". It is thought that because the overnight session is lower volume price can be pushed or "manipulated" to extremes that would not happen during higher volume times.
Why is the ON Session Important Then?
Just because the ON session can be thought as a "lie" doesn't mean it is relevant to know. For example, if price is stuck inside the ON range then you can think of the market as rotational or range-bound. If price is above the ON range then it can be thought of as bullish. If price is below the ON range then it can be thought as bearish.
What is IB?
IB or initial balance is the first hour of the New York Session. Typically the market sets the tone for the day in the first hour. This tone is similarly a map like the ON session. If we are above the IBH then it is bullish and likely a trend day to the upside. If we are below the IBL then it is bearish and likely a trend day to the downside. If we are in IB then we want to avoid conducting business in the middle of IBH and IBL to avoid getting chopped up in a range bound market.
These levels are not a holy grail
You should use this indicator as guide or map for context about the instrument you are trading. You need to combine your own technical analysis with this indicator. You want as much context confirming your trade thesis in order to enter a trade. Simply buying or selling because we are above or below a level is not recommended in any circumstance. If it were that easy I would not publish this indicator.
Adjustments
In the indicator settings you can adjust the RTH, ON, and IB session-time settings. All of the times entered must be in EST (Eastern Standard Time). You may want to do this to apply the levels to a foreign market.
Examples
PriceCatch-Intraday VolumeHi TV Community,
Greetings to you.
This is a script that may be of use to intra-day traders. Knowing how much volume is getting traded and in which direction can help with decision-making in trading - especially when trading Futures.
So, this script, displays volume, number of candles and trades on intra-day time-frames.
FUTURES CHART
NOTE: The instrument must contain volume information for this script to work.
Number of trades will be accurate on Futures Chart because Volume / lot-size will give number of trades on a specific time-interval. For cash chart, please ignore this value.
Please use this script on Intra-day time-frame only.
Hope this script may be of use to you. All the best.
Comments/queries welcome.
PriceCatch
PS: As always with trading you and you alone are responsible for your actions and the profits/losses resulting from your trading activity.
Ether (Ethereum) CME Gaps [NeoButane]Detects gaps in trading for CME's "Ether" cash-settled futures. This will show gaps as they happen on the 24/7 charts that crypto exchanges use. It is not usable on CME's tickers themselves, as gaps in trading are not displayed.
This indicator will only display if viewing an ETH chart.
More information on the CME ETH futures here:
www.cmegroup.com
Based on:
What's different: CME's BTC and ETH markets trade the same hours, but one may hit a limit breaker while there may be a case where the other does not.
EMP Probabilistic [CHE]Part 1 — For Traders (Practical Overview, no formulas)
What this tool does
EMP Probabilistic \ turns raw price action into a clean, probability-aware map. It builds two adaptive bands around the session open of a higher timeframe you choose (called the S-timeframe) and highlights a robust median threshold. At a glance you know:
Where price has recently tended to stay,
Whether current momentum sits above or below the median, and
A live Long vs. Short probability based on recent outcomes.
Why it improves decisions
Objective context in any regime: The nonparametric band comes straight from recent market behavior, without assuming a particular distribution.
Volatility-aware risk lens: The parametric band adapts to current volatility, helping you judge stretch and room for continuation or snap-back.
No lookahead: All stats update only after an S-bar is finished. That means the panel reflects information you truly had at that time.
How to read the chart
Orange band = empirical, distribution-free range derived from recent session returns (nonparametric).
Teal band = volatility-scaled range around the session open (parametric).
Median dots: green when close is above the median threshold, red when below.
Info panel: shows the active S-timeframe, window sizes, live coverage for both bands, the internal width parameter and volatility estimate, plus a one-line summary.
Probability label: “Long XX% • Short YY%” — a simple read on the recent balance of up vs. down S-bars.
How to use it (quick start)
1. Choose S-timeframe with Auto, Multiplier, or Manual. “Auto” scales your chart TF up to a sensible higher step.
2. Set alpha to control how tight the inner band should be. A typical value gives you a comfortable center zone without cutting off healthy trends.
3. Trade the context:
Trend-following: Prefer longs when price holds above the median; prefer shorts when it stays below.
Mean-reversion: Fade moves near the outer edges during ranges; look for reversion back toward the median.
Breakout filter: Require closes that push and hold beyond the volatility band for momentum plays; avoid noise when price chops inside the middle of the orange band.
Risk management made practical
Size positions relative to the teal band width to keep risk consistent across instruments and regimes.
For stops, many traders set them just beyond the opposite orange bound or use a fraction of the teal band.
Watch the panel’s coverage readouts and Brier score; when they deteriorate, the market may be shifting — reduce size or demand stronger confirmation.
Suggested presets
Scalping (Crypto/FX): Auto S-TF, alpha around a fifth, calibration window near two hundred, RS volatility, metrics window near two hundred.
Intraday Futures: Multiplier 3–5× your chart TF; similar alpha and window sizes; RS volatility is a solid default.
Swing/Equities: S-TF at least daily; test both RS and GK volatility modes; keep windows on the larger side for stability.
What makes it different
Two complementary lenses: a distribution-free read of recent behavior and a volatility-scaled read for risk and stretch.
Self-calibrating width: the parametric band quietly nudges its internal multiplier so actual coverage tracks your target.
Clean UX: grouped inputs, tooltips, an info panel that tells you what’s going on, and a simple median bias you can act on.
Repainting & timing
The logic updates only when the S-bar closes. On lower-timeframe charts you’ll see intrabar flips of the dot color — that’s just live price moving around. For strict signals, confirm on S-bar close.
Friendly note (not financial advice)
Use this as a context engine. It won’t predict the future, but it will keep you on the right side of probability and volatility more often, which is exactly where consistency starts.
Part 2 — Under the Hood (Conceptual, no formulas)
Data and timeframe design
The script works on a higher S-timeframe you select. It fetches the open, high, low, close, and time of that S-bar. Internally, it only updates its rolling windows after an S-bar has finished. It then pushes the previous S-bar’s statistics into its arrays. That design removes lookahead and keeps the metrics out-of-sample relative to the current S-bar.
Nonparametric band (distribution-free)
The orange band comes from the empirical distribution of recent session-level close-minus-open moves. The script keeps a rolling window, sorts a safe copy, and reads three key points: a lower bound, a median, and an upper bound. Because it’s based purely on observed outcomes, it adapts naturally to skew, fat tails, and regime shifts without assuming any particular shape. The orange range shows “where price has tended to live” lately on the chosen S-timeframe.
Parametric band (volatility-scaled)
The teal band models log-space variability around the session open using one of two well-known OHLC volatility estimators: Rogers–Satchell or Garman–Klass. Each estimator contributes a per-bar variance figure; the script averages these across the rolling window to form a current volatility scale. It then builds a symmetric band around the session open in price space. This gives you a volatility-aware notion of stretch that complements the distribution-free orange band.
Self-calibration of band width
The teal band has an internal width multiplier. After each completed S-bar the script checks whether the realized move stayed inside that band. If the band was too tight, the multiplier is nudged upward; if it was too loose, it’s eased downward. A simple learning rate governs how quickly it adapts. Over time this keeps the realized inside-coverage close to the target implied by your alpha setting, without you having to hand-tune anything.
Long/Short probability and calibration quality
The Long vs. Short probability is a transparent statistic: it’s just the recent fraction of up sessions in the rolling window. It is not a complex model — and that’s the point. You get an honest, intuitive read on directional tendency.
To monitor how well this simple probability lines up with reality, the script tracks a Brier-style score over a separate metrics window. Lower is better: it means your recent probability read has matched outcomes more closely.
Coverage tracking for both bands
The panel reports coverage for the orange band (nonparametric) and the teal band (parametric). These are rolling averages of how often recent S-bar moves landed inside each band. Watching these two numbers tells you whether market behavior still aligns with the recent distribution and with the current volatility model.
Why it doesn’t repaint
Because the arrays update only when an S-bar closes and only push the previous bar’s stats, the panel and metrics reflect information you had at the time. Intrabar visuals can change while a bar is forming — that’s expected — but the decision framework itself is anchored to completed S-bars.
Performance and practicality
The heaviest step is sorting a copy of the window for the nonparametric band. With typical window sizes this stays responsive on TradingView. The volatility estimators and rolling averages are lightweight. Inputs are grouped with clear tooltips so you can tune without hunting.
Limitations and good practice
In thin or gappy markets the bands can jump; consider a larger window or a higher S-timeframe.
During violent regime shifts, shorten the window and increase the learning rate slightly so the teal band catches up faster — but don’t overdo it, or you’ll chase noise.
The Long/Short probability is intentionally simple; it’s a context indicator, not a standalone signal factory. Combine it with structure, volume, or your execution rules.
Takeaway
Under the hood, the script blends empirical behavior and volatility scaling, then self-calibrates so the teal band’s real-world coverage stays near your target. You get clarity, consistency, and a dashboard that tells you when its own assumptions are holding up — exactly what you need to trade with confidence.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Best regards and happy trading
Chervolino
Weighted Multi-Mode Oscillator [BackQuant]Weighted Multi‑Mode Oscillator
1. What Is It?
The Weighted Multi‑Mode Oscillator (WMMO) is a next‑generation momentum tool that turns a dynamically‑weighted moving average into a 0‑100 bounded oscillator.
It lets you decide how each bar is weighted (by volume, volatility, momentum or a hybrid blend) and how the result is normalised (Percentile, Z‑Score or Min‑Max).
The outcome is a self‑adapting gauge that delivers crystal‑clear overbought / oversold zones, divergence clues and regime shifts on any market or timeframe.
2. How It Works
• Dynamic Weight Engine
▪ Volume – emphasises bars with exceptional participation.
▪ Volatility – inverse ATR weighting filters noisy spikes.
▪ Momentum – amplifies strong directional ROC bursts.
▪ Hybrid – equal‑weight blend of the three dimensions.
• Multi‑Mode Smoothing
Choose from 8 MA types (EMA, DEMA, HMA, LINREG, TEMA, RMA, SMA, WMA) plus a secondary smoothing factor to fine‑tune lag vs. responsiveness.
• Normalization Suite
▪ Percentile – rank vs. recent history (context aware).
▪ Z‑Score – standard deviations from mean (statistical extremes).
▪ Min‑Max – scale between rolling high/low (trend friendly).
3. Reading the Oscillator
Zone Default Level Interpretation
Bull > 80 Acceleration; momentum buyers in control
Neutral 20 – 80 Consolidation / no edge
Bear < 20 Exhaustion; sellers dominate
Gradient line/area automatically shades from bright green (strong bull) to deep red (strong bear).
Optional bar‑painting colours price bars the same way for rapid chart scanning.
4. Typical Use‑Cases
Trend Confirmation – Set Weight = Hybrid, Smoothing = EMA. Enter pullbacks only when WMMO > 50 and rising.
Mean Reversion – Weight = Volatility, reduce upper / lower bands to 70 / 30 and fade extremes.
Volume Pulse – Intraday futures: Weight = Volume to catch participation surges before breakout candles.
Divergence Spotting – Compare price highs/lows to WMMO peaks for early reversal clues.
5. Inputs & Styling
Calculation: Source, MA Length, MA Type, Smoothing
Weighting: Volume period & factor, Volatility length, Momentum period
Normalisation: Method, Look‑back, Upper / Lower thresholds
Display: Gradient fills, Threshold lines, Bar‑colouring toggle, Line width & colours
All thresholds, colours and fills are fully customisable inside the settings panel.
6. Built‑In Alerts
WMMO Long – oscillator crosses up through upper threshold.
WMMO Short – oscillator crosses down through lower threshold.
Attach them once and receive push / e‑mail notifications the moment momentum flips.
7. Best Practices
Percentile mode is self‑adaptive and works well across assets; Z‑Score excels in ranges; Min‑Max shines in persistent trends.
Very short MA lengths (< 10) may produce jitter; compensate with higher “Smoothing” or longer look‑backs.
Pair WMMO with structure‑based tools (S/R, trend lines) for higher‑probability trade confluence.
Disclaimer
This script is provided for educational purposes only. It is not financial advice. Always back‑test thoroughly and manage risk before trading live capital.
Rolling VWAP LevelsRolling VWAP Levels Indicator
Overview
Dynamic horizontal lines showing rolling Volume Weighted Average Price (VWAP) levels for multiple timeframes (7D, 30D, 90D, 365D) that update in real-time as new bars form.
Who This Is For
Day traders using VWAP as support/resistance
Swing traders analyzing multi-timeframe price structure
Scalpers looking for mean reversion entries
Options traders needing volatility bands for strike selection
Institutional traders tracking volume-weighted fair value
Risk managers requiring dynamic stop levels
How To Trade With It
Mean Reversion Strategies:
Buy when price is below VWAP and showing bullish divergence
Sell when price is above VWAP and showing bearish signals
Use multiple timeframes - enter on shorter, confirm on longer
Target opposite VWAP level for profit taking
Breakout Trading:
Watch for price breaking above/below key VWAP levels with volume
Use 7D VWAP for intraday breakouts
Use 30D/90D VWAP for swing trade breakouts
Confirm breakout with move beyond first standard deviation band
Support/Resistance Trading:
VWAP levels act as dynamic support in uptrends
VWAP levels act as dynamic resistance in downtrends
Multiple timeframe VWAP confluence creates stronger levels
Use standard deviation bands as additional S/R zones
Risk Management:
Place stops beyond next VWAP level
Use standard deviation bands for position sizing
Exit partial positions at VWAP levels
Monitor distance table for overextended moves
Key Features
Real-time Updates: Lines move and extend as new bars form
Individual Styling: Custom colors, widths, styles for each timeframe
Standard Deviation Bands: Optional volatility bands with custom multipliers
Smart Labels: Positioned above, below, or diagonally relative to lines
Distance Table: Shows percentage distance from each VWAP level
Alert System: Get notified when price crosses VWAP levels
Memory Efficient: Automatically cleans up old drawing objects
Settings Explained
Display Group: Show/hide labels, font size, line transparency, positioning
Individual VWAP Groups: Color, line width (1-5), line style for each timeframe
Standard Deviation Bands: Enable bands with custom multipliers (0.5, 1.0, 1.5, 2.0, etc.)
Labels Group: Position (8 options including diagonal), custom text, price display
Additional Info: Distance table, alert conditions
Technical Implementation
Uses rolling arrays to maintain sliding windows of price*volume data. The core calculation function processes both VWAP and standard deviation efficiently. Lines are created dynamically and updated every bar. Memory management prevents object accumulation through automatic cleanup.
Best Practices
Start with 7D and 30D VWAP for most strategies
Add 90D/365D for longer-term context
Use standard deviation bands when volatility matters
Position labels to avoid chart clutter
Enable distance table during high volatility periods
Set alerts for key VWAP level breaks
Market Applications
Forex: Major pairs during London/NY sessions
Stocks: Large cap names with good volume
Crypto: Bitcoin, Ethereum, major altcoins
Futures: ES, NQ, CL, GC with continuous volume
Options: Use SD bands for strike selection and volatility assessment
Advanced Fed Decision Forecast Model (AFDFM)The Advanced Fed Decision Forecast Model (AFDFM) represents a novel quantitative framework for predicting Federal Reserve monetary policy decisions through multi-factor fundamental analysis. This model synthesizes established monetary policy rules with real-time economic indicators to generate probabilistic forecasts of Federal Open Market Committee (FOMC) decisions. Building upon seminal work by Taylor (1993) and incorporating recent advances in data-dependent monetary policy analysis, the AFDFM provides institutional-grade decision support for monetary policy analysis.
## 1. Introduction
Central bank communication and policy predictability have become increasingly important in modern monetary economics (Blinder et al., 2008). The Federal Reserve's dual mandate of price stability and maximum employment, coupled with evolving economic conditions, creates complex decision-making environments that traditional models struggle to capture comprehensively (Yellen, 2017).
The AFDFM addresses this challenge by implementing a multi-dimensional approach that combines:
- Classical monetary policy rules (Taylor Rule framework)
- Real-time macroeconomic indicators from FRED database
- Financial market conditions and term structure analysis
- Labor market dynamics and inflation expectations
- Regime-dependent parameter adjustments
This methodology builds upon extensive academic literature while incorporating practical insights from Federal Reserve communications and FOMC meeting minutes.
## 2. Literature Review and Theoretical Foundation
### 2.1 Taylor Rule Framework
The foundational work of Taylor (1993) established the empirical relationship between federal funds rate decisions and economic fundamentals:
rt = r + πt + α(πt - π) + β(yt - y)
Where:
- rt = nominal federal funds rate
- r = equilibrium real interest rate
- πt = inflation rate
- π = inflation target
- yt - y = output gap
- α, β = policy response coefficients
Extensive empirical validation has demonstrated the Taylor Rule's explanatory power across different monetary policy regimes (Clarida et al., 1999; Orphanides, 2003). Recent research by Bernanke (2015) emphasizes the rule's continued relevance while acknowledging the need for dynamic adjustments based on financial conditions.
### 2.2 Data-Dependent Monetary Policy
The evolution toward data-dependent monetary policy, as articulated by Fed Chair Powell (2024), requires sophisticated frameworks that can process multiple economic indicators simultaneously. Clarida (2019) demonstrates that modern monetary policy transcends simple rules, incorporating forward-looking assessments of economic conditions.
### 2.3 Financial Conditions and Monetary Transmission
The Chicago Fed's National Financial Conditions Index (NFCI) research demonstrates the critical role of financial conditions in monetary policy transmission (Brave & Butters, 2011). Goldman Sachs Financial Conditions Index studies similarly show how credit markets, term structure, and volatility measures influence Fed decision-making (Hatzius et al., 2010).
### 2.4 Labor Market Indicators
The dual mandate framework requires sophisticated analysis of labor market conditions beyond simple unemployment rates. Daly et al. (2012) demonstrate the importance of job openings data (JOLTS) and wage growth indicators in Fed communications. Recent research by Aaronson et al. (2019) shows how the Beveridge curve relationship influences FOMC assessments.
## 3. Methodology
### 3.1 Model Architecture
The AFDFM employs a six-component scoring system that aggregates fundamental indicators into a composite Fed decision index:
#### Component 1: Taylor Rule Analysis (Weight: 25%)
Implements real-time Taylor Rule calculation using FRED data:
- Core PCE inflation (Fed's preferred measure)
- Unemployment gap proxy for output gap
- Dynamic neutral rate estimation
- Regime-dependent parameter adjustments
#### Component 2: Employment Conditions (Weight: 20%)
Multi-dimensional labor market assessment:
- Unemployment gap relative to NAIRU estimates
- JOLTS job openings momentum
- Average hourly earnings growth
- Beveridge curve position analysis
#### Component 3: Financial Conditions (Weight: 18%)
Comprehensive financial market evaluation:
- Chicago Fed NFCI real-time data
- Yield curve shape and term structure
- Credit growth and lending conditions
- Market volatility and risk premia
#### Component 4: Inflation Expectations (Weight: 15%)
Forward-looking inflation analysis:
- TIPS breakeven inflation rates (5Y, 10Y)
- Market-based inflation expectations
- Inflation momentum and persistence measures
- Phillips curve relationship dynamics
#### Component 5: Growth Momentum (Weight: 12%)
Real economic activity assessment:
- Real GDP growth trends
- Economic momentum indicators
- Business cycle position analysis
- Sectoral growth distribution
#### Component 6: Liquidity Conditions (Weight: 10%)
Monetary aggregates and credit analysis:
- M2 money supply growth
- Commercial and industrial lending
- Bank lending standards surveys
- Quantitative easing effects assessment
### 3.2 Normalization and Scaling
Each component undergoes robust statistical normalization using rolling z-score methodology:
Zi,t = (Xi,t - μi,t-n) / σi,t-n
Where:
- Xi,t = raw indicator value
- μi,t-n = rolling mean over n periods
- σi,t-n = rolling standard deviation over n periods
- Z-scores bounded at ±3 to prevent outlier distortion
### 3.3 Regime Detection and Adaptation
The model incorporates dynamic regime detection based on:
- Policy volatility measures
- Market stress indicators (VIX-based)
- Fed communication tone analysis
- Crisis sensitivity parameters
Regime classifications:
1. Crisis: Emergency policy measures likely
2. Tightening: Restrictive monetary policy cycle
3. Easing: Accommodative monetary policy cycle
4. Neutral: Stable policy maintenance
### 3.4 Composite Index Construction
The final AFDFM index combines weighted components:
AFDFMt = Σ wi × Zi,t × Rt
Where:
- wi = component weights (research-calibrated)
- Zi,t = normalized component scores
- Rt = regime multiplier (1.0-1.5)
Index scaled to range for intuitive interpretation.
### 3.5 Decision Probability Calculation
Fed decision probabilities derived through empirical mapping:
P(Cut) = max(0, (Tdovish - AFDFMt) / |Tdovish| × 100)
P(Hike) = max(0, (AFDFMt - Thawkish) / Thawkish × 100)
P(Hold) = 100 - |AFDFMt| × 15
Where Thawkish = +2.0 and Tdovish = -2.0 (empirically calibrated thresholds).
## 4. Data Sources and Real-Time Implementation
### 4.1 FRED Database Integration
- Core PCE Price Index (CPILFESL): Monthly, seasonally adjusted
- Unemployment Rate (UNRATE): Monthly, seasonally adjusted
- Real GDP (GDPC1): Quarterly, seasonally adjusted annual rate
- Federal Funds Rate (FEDFUNDS): Monthly average
- Treasury Yields (GS2, GS10): Daily constant maturity
- TIPS Breakeven Rates (T5YIE, T10YIE): Daily market data
### 4.2 High-Frequency Financial Data
- Chicago Fed NFCI: Weekly financial conditions
- JOLTS Job Openings (JTSJOL): Monthly labor market data
- Average Hourly Earnings (AHETPI): Monthly wage data
- M2 Money Supply (M2SL): Monthly monetary aggregates
- Commercial Loans (BUSLOANS): Weekly credit data
### 4.3 Market-Based Indicators
- VIX Index: Real-time volatility measure
- S&P; 500: Market sentiment proxy
- DXY Index: Dollar strength indicator
## 5. Model Validation and Performance
### 5.1 Historical Backtesting (2017-2024)
Comprehensive backtesting across multiple Fed policy cycles demonstrates:
- Signal Accuracy: 78% correct directional predictions
- Timing Precision: 2.3 meetings average lead time
- Crisis Detection: 100% accuracy in identifying emergency measures
- False Signal Rate: 12% (within acceptable research parameters)
### 5.2 Regime-Specific Performance
Tightening Cycles (2017-2018, 2022-2023):
- Hawkish signal accuracy: 82%
- Average prediction lead: 1.8 meetings
- False positive rate: 8%
Easing Cycles (2019, 2020, 2024):
- Dovish signal accuracy: 85%
- Average prediction lead: 2.1 meetings
- Crisis mode detection: 100%
Neutral Periods:
- Hold prediction accuracy: 73%
- Regime stability detection: 89%
### 5.3 Comparative Analysis
AFDFM performance compared to alternative methods:
- Fed Funds Futures: Similar accuracy, lower lead time
- Economic Surveys: Higher accuracy, comparable timing
- Simple Taylor Rule: Lower accuracy, insufficient complexity
- Market-Based Models: Similar performance, higher volatility
## 6. Practical Applications and Use Cases
### 6.1 Institutional Investment Management
- Fixed Income Portfolio Positioning: Duration and curve strategies
- Currency Trading: Dollar-based carry trade optimization
- Risk Management: Interest rate exposure hedging
- Asset Allocation: Regime-based tactical allocation
### 6.2 Corporate Treasury Management
- Debt Issuance Timing: Optimal financing windows
- Interest Rate Hedging: Derivative strategy implementation
- Cash Management: Short-term investment decisions
- Capital Structure Planning: Long-term financing optimization
### 6.3 Academic Research Applications
- Monetary Policy Analysis: Fed behavior studies
- Market Efficiency Research: Information incorporation speed
- Economic Forecasting: Multi-factor model validation
- Policy Impact Assessment: Transmission mechanism analysis
## 7. Model Limitations and Risk Factors
### 7.1 Data Dependency
- Revision Risk: Economic data subject to subsequent revisions
- Availability Lag: Some indicators released with delays
- Quality Variations: Market disruptions affect data reliability
- Structural Breaks: Economic relationship changes over time
### 7.2 Model Assumptions
- Linear Relationships: Complex non-linear dynamics simplified
- Parameter Stability: Component weights may require recalibration
- Regime Classification: Subjective threshold determinations
- Market Efficiency: Assumes rational information processing
### 7.3 Implementation Risks
- Technology Dependence: Real-time data feed requirements
- Complexity Management: Multi-component coordination challenges
- User Interpretation: Requires sophisticated economic understanding
- Regulatory Changes: Fed framework evolution may require updates
## 8. Future Research Directions
### 8.1 Machine Learning Integration
- Neural Network Enhancement: Deep learning pattern recognition
- Natural Language Processing: Fed communication sentiment analysis
- Ensemble Methods: Multiple model combination strategies
- Adaptive Learning: Dynamic parameter optimization
### 8.2 International Expansion
- Multi-Central Bank Models: ECB, BOJ, BOE integration
- Cross-Border Spillovers: International policy coordination
- Currency Impact Analysis: Global monetary policy effects
- Emerging Market Extensions: Developing economy applications
### 8.3 Alternative Data Sources
- Satellite Economic Data: Real-time activity measurement
- Social Media Sentiment: Public opinion incorporation
- Corporate Earnings Calls: Forward-looking indicator extraction
- High-Frequency Transaction Data: Market microstructure analysis
## References
Aaronson, S., Daly, M. C., Wascher, W. L., & Wilcox, D. W. (2019). Okun revisited: Who benefits most from a strong economy? Brookings Papers on Economic Activity, 2019(1), 333-404.
Bernanke, B. S. (2015). The Taylor rule: A benchmark for monetary policy? Brookings Institution Blog. Retrieved from www.brookings.edu
Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910-945.
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43.
Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661-1707.
Clarida, R. H. (2019). The Federal Reserve's monetary policy response to COVID-19. Brookings Papers on Economic Activity, 2020(2), 1-52.
Clarida, R. H. (2025). Modern monetary policy rules and Fed decision-making. American Economic Review, 115(2), 445-478.
Daly, M. C., Hobijn, B., Şahin, A., & Valletta, R. G. (2012). A search and matching approach to labor markets: Did the natural rate of unemployment rise? Journal of Economic Perspectives, 26(3), 3-26.
Federal Reserve. (2024). Monetary Policy Report. Washington, DC: Board of Governors of the Federal Reserve System.
Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. National Bureau of Economic Research Working Paper, No. 16150.
Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of Monetary Economics, 50(5), 983-1022.
Powell, J. H. (2024). Data-dependent monetary policy in practice. Federal Reserve Board Speech. Jackson Hole Economic Symposium, Federal Reserve Bank of Kansas City.
Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214.
Yellen, J. L. (2017). The goals of monetary policy and how we pursue them. Federal Reserve Board Speech. University of California, Berkeley.
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Disclaimer: This model is designed for educational and research purposes only. Past performance does not guarantee future results. The academic research cited provides theoretical foundation but does not constitute investment advice. Federal Reserve policy decisions involve complex considerations beyond the scope of any quantitative model.
Citation: EdgeTools Research Team. (2025). Advanced Fed Decision Forecast Model (AFDFM) - Scientific Documentation. EdgeTools Quantitative Research Series
UT Bot + Hull MA Confirmed Signal DelayOverview
This indicator is designed to detect high-probability reversal entry signals by combining "UT Bot Alerts" (UT Bot Alerts script adapted from QuantNomad - Originally developed by Yo_adriiiiaan and idea of original code for "UT Bot Alerts" from HPotter ) with confirmation from a Hull Moving Average (HMA) Developed by Alan Hull . It focuses on capturing momentum shifts that often precede trend reversals, helping traders identify potential entry points while filtering out false signals.
🔍 How It Works
This strategy operates in two stages:
1. UT Bot Momentum Trigger
The foundation of this script is the "UT Bot Alerts" , which uses an ATR-based trailing stop to detect momentum changes. Specifically:
The script calculates a dynamic stop level based on the Average True Range (ATR) multiplied by a user-defined sensitivity factor (Key Value).
When price closes above this trailing stop and the short-term EMA crosses above the stop, a potential buy setup is triggered.
Conversely, when price closes below the trailing stop and the short-term EMA crosses below, a potential sell setup is triggered.
These UT Bot alerts are designed to identify the initial shift in market direction, acting as the first filter in the signal process.
2. Hull MA Confirmation
To reduce noise and false triggers from the UT Bot alone, this script delays the entry signal until price confirms the move by crossing the Hull Moving Average (or its variants: HMA, THMA, EHMA) in the same direction as the UT Bot trigger:
A Buy Signal is generated only when:
A UT Bot Buy condition is active, and
The price closes above the Hull MA.
Or, if a UT Bot Buy condition was recently triggered but price hadn’t yet crossed above the Hull MA, a delayed buy is signaled when price finally breaks above it.
A Sell Signal is generated only when:
A UT Bot Sell condition is active, and
The price closes below the Hull MA.
Similarly, a delayed sell signal can occur if price breaks below the Hull MA shortly after a UT Bot Sell trigger.
This dual-confirmation process helps traders avoid premature entries and improves the reliability of reversal signals.
📈 Best Use Cases
Reversal Trading: This strategy is particularly well-suited for catching early trend reversals rather than trend continuations. It excels at identifying momentum pivots that occur after pullbacks or exhaustion moves.
Heikin Ashi Charts Recommended: The script offers a Heikin Ashi mode for smoothing out noise and enhancing visual clarity. Using Heikin Ashi candles can further reduce whipsaws and highlight cleaner shifts in trend direction.
MACD Alignment: For best results, trade in the direction of the MACD trend or use it as a filter to avoid counter-trend trades.
⚠️ Important Notes
Entry Signals Only: This indicator only plots entry points (Buy and Sell signals). It does not define exit strategies, so users should manage trades manually using trailing stops, profit targets, or other exit indicators.
No Signal = No Confirmation: You may see a UT Bot trigger without a corresponding Buy/Sell signal. This means the price did not confirm the move by crossing the Hull MA, and therefore the setup was considered too weak or incomplete.
⚙️ Customization
UT Bot Sensitivity: Adjust the “Key Value” and “ATR Period” to make the UT Bot more or less reactive to price action.
Use Heikin Ashi: Toggle between standard candles or Heikin Ashi in the indicator settings for a smoother trading experience.
The HMA length may also be modified in the indicator settings from its standard 55 length to increase or decrease the sensitivity of signal.
This strategy is best used by traders looking for a structured, logic-based way to enter early into reversals with added confirmation to reduce risk. By combining two independent systems—momentum detection (UT Bot) and trend confirmation (Hull MA)—it aims to provide high-confidence entries without overwhelming complexity.
Let the indicator guide your entries—you manage the exits.
Examples of use:
Futures:
Stock:
Crypto:
As shown in the snapshots this strategy, like most, works the best when price action has a sizeable ATR and works the least when price is choppy. Therefore it is always best to use this system when price is coming off known support or resistance levels and when it is seen to respect short term EMA's like the 9 or 15.
My personal preference to use this system is for day trading on a 3 or 5 minute chart. But it is valid for all timeframes and simply marks a high probability for a new trend to form.
Sources:
Quant Nomad - www.tradingview.com
Yo_adriiiiaan - www.tradingview.com
HPotter - www.tradingview.com
Hull Moving Average - alanhull.com
Enhanced Volume Trend Indicator with BB SqueezeEnhanced Volume Trend Indicator with BB Squeeze: Comprehensive Explanation
The visualization system allows traders to quickly scan multiple securities to identify high-probability setups without detailed analysis of each chart. The progression from squeeze to breakout, supported by volume trend confirmation, offers a systematic approach to identifying trading opportunities.
The script combines multiple technical analysis approaches into a comprehensive dashboard that helps traders make informed decisions by identifying high-probability setups while filtering out noise through its sophisticated confirmation requirements. It combines multiple technical analysis approaches into an integrated visual system that helps traders identify potential trading opportunities while filtering out false signals.
Core Features
1. Volume Analysis Dashboard
The indicator displays various volume-related metrics in customizable tables:
AVOL (After Hours + Pre-Market Volume): Shows extended hours volume as a percentage of the 21-day average volume with color coding for buying/selling pressure. Green indicates buying pressure and red indicates selling pressure.
Volume Metrics: Includes regular volume (VOL), dollar volume ($VOL), relative volume compared to 21-day average (RVOL), and relative volume compared to 90-day average (RVOL90D).
Pre-Market Data: Optional display of pre-market volume (PVOL), pre-market dollar volume (P$VOL), pre-market relative volume (PRVOL), and pre-market price change percentage (PCHG%).
2. Enhanced Volume Trend (VTR) Analysis
The Volume Trend indicator uses adaptive analysis to evaluate buying and selling pressure, combining multiple factors:
MACD (Moving Average Convergence Divergence) components
Volume-to-SMA (Simple Moving Average) ratio
Price direction and market conditions
Volume change rates and momentum
EMA (Exponential Moving Average) alignment and crossovers
Volatility filtering
VTR Visual Indicators
The VTR score ranges from 0-100, with values above 50 indicating bullish conditions and below 50 indicating bearish conditions. This is visually represented by colored circles:
"●" (Filled Circle):
Green: Strong bullish trend (VTR ≥ 80)
Red: Strong bearish trend (VTR ≤ 20)
"◯" (Hollow Circle):
Green: Moderate bullish trend (VTR 65-79)
Red: Moderate bearish trend (VTR 21-35)
"·" (Small Dot):
Green: Weak bullish trend (VTR 55-64)
Red: Weak bearish trend (VTR 36-45)
"○" (Medium Hollow Circle): Neutral conditions (VTR 46-54), shown in gray
In "Both" display mode, the VTR shows both the numerical score (0-100) alongside the appropriate circle symbol.
Enhanced VTR Settings
The Enhanced Volume Trend component offers several advanced customization options:
Adaptive Volume Analysis (volTrendAdaptive):
When enabled, dynamically adjusts volume thresholds based on recent market volatility
Higher volatility periods require proportionally higher volume to generate significant signals
Helps prevent false signals during highly volatile markets
Keep enabled for most trading conditions, especially in volatile markets
Speed of Change Weight (volTrendSpeedWeight, range 0-1):
Controls emphasis on volume acceleration/deceleration rather than absolute levels
Higher values (0.7-1.0): More responsive to new volume trends, better for momentum trading
Lower values (0.2-0.5): Less responsive, better for trend following
Helps identify early volume trends before they fully develop
Momentum Period (volTrendMomentumPeriod, range 2-10):
Defines lookback period for volume change rate calculations
Lower values (2-3): More responsive to recent changes, better for short timeframes
Higher values (7-10): Smoother, better for daily/weekly charts
Directly affects how quickly the indicator responds to new volume patterns
Volatility Filter (volTrendVolatilityFilter):
Adjusts significance of volume by factoring in current price volatility
High volume during high volatility receives less weight
High volume during low volatility receives more weight
Helps distinguish between genuine volume-driven moves and volatility-driven moves
EMA Alignment Weight (volTrendEmaWeight, range 0-1):
Controls importance of EMA alignments in final VTR calculation
Analyzes multiple EMA relationships (5, 10, 21 period)
Higher values (0.7-1.0): Greater emphasis on trend structure
Lower values (0.2-0.5): More focus on pure volume patterns
Display Mode (volTrendDisplayMode):
"Value": Shows only numerical score (0-100)
"Strength": Shows only symbolic representation
"Both": Shows numerical score and symbol together
3. Bollinger Band Squeeze Detection (SQZ)
The BB Squeeze indicator identifies periods of low volatility when Bollinger Bands contract inside Keltner Channels, often preceding significant price movements.
SQZ Visual Indicators
"●" (Filled Circle): Strong squeeze - high probability setup for an impending breakout
Green: Strong squeeze with bullish bias (likely upward breakout)
Red: Strong squeeze with bearish bias (likely downward breakout)
Orange: Strong squeeze with unclear direction
"◯" (Hollow Circle): Moderate squeeze - medium probability setup
Green: With bullish EMA alignment
Red: With bearish EMA alignment
Orange: Without clear directional bias
"-" (Dash): Gray dash indicates no squeeze condition (normal volatility)
The script identifies squeeze conditions through multiple methods:
Bollinger Bands contracting inside Keltner Channels
BB width falling to bottom 20% of recent range (BB width percentile)
Very narrow Keltner Channel (less than 5% of basis price)
Tracking squeeze duration in consecutive bars
Different squeeze strengths are detected:
Strong Squeeze: BB inside KC with tight BB width and narrow KC
Moderate Squeeze: BB inside KC with either tight BB width or narrow KC
No Squeeze: Normal market conditions
4. Breakout Detection System
The script includes two breakout indicators working in sequence:
4.1 Pre-Breakout (PBK) Indicator
Detects potential upcoming breakouts by analyzing multiple factors:
Squeeze conditions lasting 2-3 bars or more
Significant price ranges
Strong volume confirmation
EMA/MACD crossovers
Consistent price direction
PBK Visual Indicators
"●" (Filled Circle): Detected pre-breakout condition
Green: Likely upward breakout (bullish)
Red: Likely downward breakout (bearish)
Orange: Direction not yet clear, but breakout likely
"-" (Dash): Gray dash indicates no pre-breakout condition
The PBK uses sophisticated conditions to reduce false signals including minimum squeeze length, significant price movement, and technical confirmations.
4.2 Breakout (BK) Indicator
Confirms actual breakouts in progress by identifying:
End of squeeze or strong expansion of Bollinger Bands
Volume expansion
Price moving outside Bollinger Bands
EMA crossovers with volume confirmation
MACD crossovers with significant price range
BK Visual Indicators
"●" (Filled Circle): Confirmed breakout in progress
Green: Upward breakout (bullish)
Red: Downward breakout (bearish)
Orange: Unusual breakout pattern without clear direction
"◆" (Diamond): Special breakout conditions (meets some but not all criteria)
"-" (Dash): Gray dash indicates no breakout detected
The BK indicator uses advanced filters for confirmation:
Requires consecutive breakout signals to reduce false positives
Strong volume confirmation requirements (40% above average)
Significant price movement thresholds
Consistency checks between price action and indicators
5. Market Metrics and Analysis
Price Change Percentage (CHG%)
Displays the current percentage change relative to the previous day's close, color-coded green for positive changes and red for negative changes.
Average Daily Range (ADR%)
Calculates the average daily percentage range over a specified period (default 20 days), helping traders gauge volatility and set appropriate price targets.
Average True Range (ATR)
Shows the Average True Range value, a volatility indicator developed by J. Welles Wilder that measures market volatility by decomposing the entire range of an asset price for that period.
Relative Strength Index (RSI)
Displays the standard 14-period RSI, a momentum oscillator that measures the speed and change of price movements on a scale from 0 to 100.
6. External Market Indicators
QQQ Change
Shows the percentage change in the Invesco QQQ Trust (tracking the Nasdaq-100 Index), useful for understanding broader tech market trends.
UVIX Change
Displays the percentage change in UVIX, a volatility index, providing insight into market fear and potential hedging activity.
BTC-USD
Shows the current Bitcoin price from Coinbase, useful for traders monitoring crypto correlation with equities.
Market Breadth (BRD)
Calculates the percentage difference between ATHI.US and ATLO.US (high vs. low securities), indicating overall market direction and strength.
7. Session Analysis and Volume Direction
Session Detection
The script accurately identifies different market sessions:
Pre-market: 4:00 AM to 9:30 AM
Regular market: 9:30 AM to 4:00 PM
After-hours: 4:00 PM to 8:00 PM
Closed: Outside trading hours
This detection works on any timeframe through careful calculation of current time in seconds.
Buy/Sell Volume Direction
The script analyzes buying and selling pressure by:
Counting up volume when close > open
Counting down volume when close < open
Tracking accumulated volume within the day
Calculating intraday pressure (up volume minus down volume)
Enhanced AVOL Calculation
The improved AVOL calculation works in all timeframes by:
Estimating typical pre-market and after-hours volume percentages
Combining yesterday's after-hours with today's pre-market volume
Calculating this as a percentage of the 21-day average volume
Determining buying/selling pressure by analyzing after-hours and pre-market price changes
Color-coding results: green for buying pressure, red for selling pressure
This calculation is particularly valuable because it works consistently across any timeframe.
Customization Options
Display Settings
The dashboard has two customizable tables: Volume Table and Metrics Table, with positions selectable as bottom_left or bottom_right.
All metrics can be individually toggled on/off:
Pre-market data (PVOL, P$VOL, PRVOL, PCHG%)
Volume data (AVOL, RVOL Day, RVOL 90D, Volume, SEED_YASHALGO_NSE_BREADTH:VOLUME )
Price metrics (ADR%, ATR, RSI, Price Change%)
Market indicators (QQQ, UVIX, Breadth, BTC-USD)
Analysis indicators (Volume Trend, BB Squeeze, Pre-Breakout, Breakout)
These toggle options allow traders to customize the dashboard to show only the metrics they find most valuable for their trading style.
Table and Text Customization
The dashboard's appearance can be customized:
Table background color via tableBgColor
Text color (White or Black) via textColorOption
The indicator uses smart formatting for volume and price values, automatically adding appropriate suffixes (K, M, B) for readability.
MACD Configuration for VTR
The Volume Trend calculation incorporates MACD with customizable parameters:
Fast Length: Controls the period for the fast EMA (default 3)
Slow Length: Controls the period for the slow EMA (default 9)
Signal Length: Controls the period for the signal line EMA (default 5)
MACD Weight: Controls how much influence MACD has on the volume trend score (default 0.3)
These settings allow traders to fine-tune how momentum is factored into the volume trend analysis.
Bollinger Bands and Keltner Channel Settings
The Bollinger Bands and Keltner Channels used for squeeze detection have preset (hidden) parameters:
BB Length: 20 periods
BB Multiplier: 2.0 standard deviations
Keltner Length: 20 periods
Keltner Multiplier: 1.5 ATR
These settings follow standard practice for squeeze detection while maintaining simplicity in the user interface.
Practical Trading Applications
Complete Trading Strategies
1. Squeeze Breakout Strategy
This strategy combines multiple components of the indicator:
Wait for a strong squeeze (SQZ showing ●)
Look for pre-breakout confirmation (PBK showing ● in green or red)
Enter when breakout is confirmed (BK showing ● in same direction)
Use VTR to confirm volume supports the move (VTR ≥ 65 for bullish or ≤ 35 for bearish)
Set profit targets based on ADR (Average Daily Range)
Exit when VTR begins to weaken or changes direction
2. Volume Divergence Strategy
This strategy focuses on the volume trend relative to price:
Identify when price makes a new high but VTR fails to confirm (divergence)
Look for VTR to show weakening trend (● changing to ◯ or ·)
Prepare for potential reversal when SQZ begins to form
Enter counter-trend position when PBK confirms reversal direction
Use external indicators (QQQ, BTC, Breadth) to confirm broader market support
3. Pre-Market Edge Strategy
This strategy leverages pre-market data:
Monitor AVOL for unusual pre-market activity (significantly above 100%)
Check pre-market price change direction (PCHG%)
Enter position at market open if VTR confirms direction
Use SQZ to determine if volatility is likely to expand
Exit based on RVOL declining or price reaching +/- ADR for the day
Market Context Integration
The indicator provides valuable context for trading decisions:
QQQ change shows tech market direction
BTC price shows crypto market correlation
UVIX change indicates volatility expectations
Breadth measurement shows market internals
This context helps traders avoid fighting the broader market and align trades with overall market direction.
Timeframe Optimization
The indicator is designed to work across different timeframes:
For day trading: Focus on AVOL, VTR, PBK/BK, and use shorter momentum periods
For swing trading: Focus on SQZ duration, VTR strength, and broader market indicators
For position trading: Focus on larger VTR trends and use EMA alignment weight
Advanced Analytical Components
Enhanced Volume Trend Score Calculation
The VTR score calculation is sophisticated, with the base score starting at 50 and adjusting for:
Price direction (up/down)
Volume relative to average (high/normal/low)
Volume acceleration/deceleration
Market conditions (bull/bear)
Additional factors are then applied, including:
MACD influence weighted by strength and direction
Volume change rate influence (speed)
Price/volume divergence effects
EMA alignment scores
Volatility adjustments
Breakout strength factors
Price action confirmations
The final score is clamped between 0-100, with values above 50 indicating bullish conditions and below 50 indicating bearish conditions.
Anti-False Signal Filters
The indicator employs multiple techniques to reduce false signals:
Requiring significant price range (minimum percentage movement)
Demanding strong volume confirmation (significantly above average)
Checking for consistent direction across multiple indicators
Requiring prior bar consistency (consecutive bars moving in same direction)
Counting consecutive signals to filter out noise
These filters help eliminate noise and focus on high-probability setups.
MACD Enhancement and Integration
The indicator enhances standard MACD analysis:
Calculating MACD relative strength compared to recent history
Normalizing MACD slope relative to volatility
Detecting MACD acceleration for stronger signals
Integrating MACD crossovers with other confirmation factors
EMA Analysis System
The indicator uses a comprehensive EMA analysis system:
Calculating multiple EMAs (5, 10, 21 periods)
Detecting golden cross (10 EMA crosses above 21 EMA)
Detecting death cross (10 EMA crosses below 21 EMA)
Assessing price position relative to EMAs
Measuring EMA separation percentage
Recent Enhancements and Evolution
Version 5.2 includes several improvements:
Enhanced AVOL to show buying/selling direction through color coding
Improved VTR with adaptive analysis based on market conditions
AVOL display now works in all timeframes through sophisticated estimation
Removed animal symbols and streamlined code with bright colors for better visibility
Improved anti-false signal filters throughout the system
Optimizing Indicator Settings
For Different Market Types
Range-Bound Markets:
Lower EMA Alignment Weight (0.2-0.4)
Higher Speed of Change Weight (0.8-1.0)
Focus on SQZ and PBK signals for breakout potential
Trending Markets:
Higher EMA Alignment Weight (0.7-1.0)
Moderate Speed of Change Weight (0.4-0.6)
Focus on VTR strength and BK confirmations
Volatile Markets:
Enable Volatility Filter
Enable Adaptive Volume Analysis
Lower Momentum Period (2-3)
Focus on strong volume confirmation (VTR ≥ 80 or ≤ 20)
For Different Asset Classes
Equities:
Standard settings work well
Pay attention to AVOL for gap potential
Monitor QQQ correlation
Futures:
Consider higher Volume/RVOL weight
Reduce MACD weight slightly
Pay close attention to SQZ duration
Crypto:
Higher volatility thresholds may be needed
Monitor BTC price for correlation
Focus on stronger confirmation signals
Integrated Visual System for Trading Decisions
The colored circle indicators create an intuitive visual system for quick market assessment:
Progression Sequence: SQZ (Squeeze) → PBK (Pre-Breakout) → BK (Breakout)
This sequence often occurs in order, with the squeeze leading to pre-breakout conditions, followed by an actual breakout.
VTR (Volume Trend): Provides context about the volume supporting these movements.
Color Coding: Green for bullish conditions, red for bearish conditions, and orange/gray for neutral or undefined conditions.
Change % Inteligente - NQ / ES / YMTopstep Compliance: Daily Price Change % Alert (NQ / ES / YM)
Script Purpose
This script helps funded traders (especially those using Topstep or similar programs) monitor the real-time percentage change of major equity index futures: Nasdaq (NQ), S&P 500 (ES), and Dow Jones (YM).
⚠️ Why it matters
Topstep prohibits trading within 2% of the daily price limits set by the CME. If a trader holds a position too close to those limits, they risk account disqualification.
📊 How it works
• Detects the instrument: NQ1!, ES1!, YM1!, or M2025 contracts
• Calculates the real-time % change from today’s market open
• Simulates daily CME price limits (+7% / -7%)
• Highlights when price enters the last 2% of the limit range (prohibited zone)
• Displays a clean, floating panel with the current % change and a warning if necessary
• Sends a visual and optional audio alert when in the prohibited zone
🧠 What makes this script unique?
This tool is **not for technical analysis**. It focuses exclusively on **funding program compliance** and **account protection**, which is not covered by other public scripts. It’s lightweight, intuitive, and designed for traders who manage risk like professionals.
✅ Open-source and ready for review.
✅ CHART SETUP FOR PUBLICATION
✔️ Use a clean chart
✔️ Only apply this script
✔️ Make sure the panel is visible (top-right or top-center recommended)
❌ No extra indicators or drawings
✔️ Use NQM2025, ESM2025 or YMM2025 on a volatile day (to show -1% to -3% range)
INSTRUCTIONS
1. Add the script to your chart.
2. Use it with NQ1!, ES1!, or YM1! (or M2025 contracts).
3. The panel will show today’s price change %.
4. If the market is within the last 2% of the CME price limit, a warning will appear.
5. Use this to avoid violating Topstep’s trading rules during volatile days.
Maguila Strategy by Rodrigo CohenREAD BEFORE USE!!!
!!!ALERT!!!! THIS CODE ONLY WORKS WITH WDO AND WIN , BOTH WITH TIMEFRAMES 1 MINUTE AND 5 MINUTE.
This is a test to the Maguila strategy created by Rodrigo Cohen.
This code MUST be validaded by Rodrigo Cohen, use ONLY for tests.
Some results are different from Cohen's videos, so the McGuinley indicator needs some ajustments.
FUTURES: WIN , WDO
TIME FRAME: 1 Minute (also works in 5 minutes)
INDICATORS: McGinley Dynamic accompanied by the Exponential Moving Average coloring rule of 21 and 42 periods
MARKET TYPE: In trend (up or down)
INPUT:
1. When buying (long) = Market in an upward trend, the average of 21 crosses that of 42 upwards. When the price returns to the average of 21, wait for a positive candle in the Maguila's color and buy a break from the maximum of this signal candle.
2. On sale (short) = Downtrend market, the average of 21 crosses that of 42 downwards. When the price returns to the average of 21, wait for a negative candle in the Maguila's color and sell when the minimum of this signal candle breaks.
GAIN and LOSS are technical.
DEFAULT VALUES:
Averages:
- 1 minute - EMA 21 and EMA 42
- 5 minute - EMA 17 and EMA 34
Gains and Loss:
- WDO - 10 points
- WIN - 200 points
(ES, NQ) Trend Checker SB1(ES, NQ) Trend Checker SB1
Stay ahead of the market by tracking whether the E-mini S&P 500 (ES) and the Nasdaq 100 (NQ) are moving in sync.
📊 How it works:
The script checks whether each index is bullish (close > open) or bearish (close < open).
If both are aligned (all bullish or all bearish), conditions are stable.
If they diverge, the indicator instantly flags a mismatch in trend.
🎯 Features:
Background shading to highlight mismatched conditions.
Real-time alerts when ES and NQ fall out of sync.
Works on any timeframe.
🔥 Why it matters:
When ES and NQ move together, market momentum is usually stronger and cleaner.
But when they disagree, expect choppiness, fakeouts, or caution zones — the perfect heads-up before entering trades.
Resistance of VolumeIt is used to detect volume resistors in a personalized way, since it allows the user to enter the volume in which he wishes the resistance to jump automatically.
It does not mark the black line and the graphic that's just to help understand how it works
in the above image can see the indicator works on 30 minutes chart
in the above image can see the indicator works on 5 minutes chart
therefore, it can be configured to go through several temporalities.
