Bitcoin Polynomial Regression OscillatorThis is the oscillator version of the script. Click here for the other part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Tìm kiếm tập lệnh với "Cycle"
GUSI ProGUSI — Adaptive Bitcoin Cycle Risk Model
Most on-chain metrics published on TradingView — such as NUPL, MVRV, or Puell Multiple — were once reliable in past cycles but have lost accuracy. The reason is simple: their trigger levels are static, while Bitcoin’s market structure changes over time. Tops have formed lower each cycle, yet the traditional horizontal thresholds remain unchanged.
What GUSI does differently:
It introduces sloped trigger functions that decrease over time, adapting each metric to Bitcoin’s maturing market.
It applies long-term normalization methods (smoothing and z-score lookups) to reduce distortion from short-term volatility and extreme outliers.
It only includes signals that remain valid across all Bitcoin cycles since 2011, discarding dozens of popular on-chain ideas that fail even after adjustment.
How GUSI is built:
GUSI is not just a mashup of indicators. Each component is a proprietary, modified version of a known on-chain signal:
Logarithmic MACD with declining trigger bands
MVRV-Z Score Regression with cycle-aware slopes
Net Unrealized Profit/Loss Ratio normalized with dynamic z-scores
Puell Multiple with logarithmic decay
Weekly RSI momentum filter for bottoms
Optional Pi Cycle Top logic with sloped moving averages
These are combined into a composite risk scoring system (0–100). Every signal contributes to the score according to user-defined weights, and each can be toggled on/off. The end result is a flexible model that adapts to long-term changes in Bitcoin’s cycles while staying transparent in its logic.
How to use it:
Scores near 97 indicate historically high-risk conditions (cycle tops).
Scores near 2.5 highlight deep accumulation zones (cycle bottoms).
Background colors and labels make the conditions clear, and built-in alerts let you automate your strategy.
GUSI is designed for the INDEX:BTCUSD 1D chart and works best when viewed in that context.
In short: GUSI makes classic on-chain indicators relevant again by adapting them to Bitcoin’s evolving market cycles. Instead of relying on static thresholds that stop working over time, it introduces dynamic slopes, normalization, and a weighted composite framework that traders can adjust themselves.
ETH/SOL 1D Dynamic Trend Core - Indicator v46🚀 Dynamic Trend Core
The Dynamic Trend Core is a sophisticated, multi-layer trading engine designed to identify high-probability, trend-following opportunities. It offers both a quantitative backtesting engine and a rich, intuitive visual interface.
Its core philosophy is simple: confirmation. The system seeks to filter out market noise by requiring a confluence of conditions—trend, momentum, price action, and volume—to be in alignment before a signal is considered valid.
⚙️ Core Logic Components
Primary Trend Analysis (SAMA): The foundation is a Self-Adjusting Moving Average (SAMA) that determines the underlying market trend (Bullish, Bearish, or Consolidation).
Confirmation & Momentum: Signals are confirmed with a blend of the Natural Market Slope and a Cyclic RSI to ensure momentum aligns with the primary trend.
Advanced Filtering Layers: A suite of optional filters allows for robust customization:
Volume & ADX: Ensure sufficient market participation and trend strength.
Market Regime: Uses the total crypto market cap to gauge broad market health.
Multi-Timeframe (MTF): Aligns signals with the dominant weekly trend.
BTC Cycle Analysis: Uses Halving or Mayer Multiple models to position trades within historical macro cycles.
Delta Zones: An additional filter to confirm signals with recent buy or sell pressure detected in candle wicks.
📊 The On-Chart Command Center
The strategy's real power comes from its on-chart visual feedback system, which provides full transparency into the engine's decision-making process.
Note: For the dashboard to update in real-time, you must enable "Recalculate on every tick" in the script's settings.
Power Core Gauge: Located at the bottom-center, this gauge is the heart of the system. It displays the number of active filter conditions met (e.g., 6/7) and "powers up" by glowing brightly as a signal becomes fully confirmed.
Live Conditions Panel: In the bottom-right corner, this panel acts as a detailed pre-flight checklist. It shows the real-time status of every single filter, helping you understand exactly why a trade is (or is not) being triggered.
Energized Trendline: The main SAMA trendline changes color and brightness based on the strength and direction of the trend, providing immediate visual context.
Halving Cycle Visualization: An optional visual guide to the phases of the Bitcoin halving cycle.
Delta Zone Pressure Boxes: A visual guide that draws boxes around candles exhibiting significant buying or selling pressure.
🛠️ How to Use
Operation Mode: "Alerts-Only Mode" for generating live signals.
Configure Strategy: Start with the default filters. If a potential trade setup is missed, check the Live Conditions Panel to see exactly which filter blocked the signal. Adjust the filters to suit your specific asset and timeframe.
Manage Risk: Adjust the Risk & Exit settings to match your personal risk tolerance.
Ehlers Autocorrelation Periodogram [Loxx]Ehlers Autocorrelation Periodogram contains two versions of Ehlers Autocorrelation Periodogram Algorithm. This indicator is meant to supplement adaptive cycle indicators that myself and others have published on Trading View, will continue to publish on Trading View. These are fast-loading, low-overhead, streamlined, exact replicas of Ehlers' work without any other adjustments or inputs.
Versions:
- 2013, Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers
- 2016, TASC September, "Measuring Market Cycles"
Description
The Ehlers Autocorrelation study is a technical indicator used in the calculation of John F. Ehlers’s Autocorrelation Periodogram. Its main purpose is to eliminate noise from the price data, reduce effects of the “spectral dilation” phenomenon, and reveal dominant cycle periods. The spectral dilation has been discussed in several studies by John F. Ehlers; for more information on this, refer to sources in the "Further Reading" section.
As the first step, Autocorrelation uses Mr. Ehlers’s previous installment, Ehlers Roofing Filter, in order to enhance the signal-to-noise ratio and neutralize the spectral dilation. This filter is based on aerospace analog filters and when applied to market data, it attempts to only pass spectral components whose periods are between 10 and 48 bars.
Autocorrelation is then applied to the filtered data: as its name implies, this function correlates the data with itself a certain period back. As with other correlation techniques, the value of +1 would signify the perfect correlation and -1, the perfect anti-correlation.
Using values of Autocorrelation in Thermo Mode may help you reveal the cycle periods within which the data is best correlated (or anti-correlated) with itself. Those periods are displayed in the extreme colors (orange) while areas of intermediate colors mark periods of less useful cycles.
What is an adaptive cycle, and what is the Autocorrelation Periodogram Algorithm?
From his Ehlers' book mentioned above, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator.This look-back period is commonly a fixed value. However, since the measured cycle period is changing, as we have seen in previous chapters, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
How to use this indicator
The point of the Ehlers Autocorrelation Periodogram Algorithm is to dynamically set a period between a minimum and a maximum period length. While I leave the exact explanation of the mechanic to Dr. Ehlers’s book, for all practical intents and purposes, in my opinion, the punchline of this method is to attempt to remove a massive source of overfitting from trading system creation–namely specifying a look-back period. SMA of 50 days? 100 days? 200 days? Well, theoretically, this algorithm takes that possibility of overfitting out of your hands. Simply, specify an upper and lower bound for your look-back, and it does the rest. In addition, this indicator tells you when its best to use adaptive cycle inputs for your other indicators.
Usage Example 1
Let's say you're using "Adaptive Qualitative Quantitative Estimation (QQE) ". This indicator has the option of adaptive cycle inputs. When the "Ehlers Autocorrelation Periodogram " shows a period of high correlation that adaptive cycle inputs work best during that period.
Usage Example 2
Check where the dominant cycle line lines, grab that output number and inject it into your other standard indicators for the length input.
Sequential SMT (QT)Sequential SMT (Quarterly Theory)
Price Divergences Between Correlated Asset Pairs Across Time Quarters
This indicator identifies Sequential SMT patterns - divergences between correlated assets across consecutive time periods. When price action diverges between traditionally correlated pairs, it may signal potential reversals or distribution phases.
How It Works
The indicator divides the trading day into specific time quarters and analyzes price extremes within each period. It compares consecutive quarters to detect divergences:
Bullish Pattern: One asset makes a lower low while its correlated pair makes a higher/equal low
Bearish Pattern: One asset makes a higher high while its correlated pair makes a lower/equal high
This implementation enhances standard divergence detection by:
Analyzing multiple timeframe cycles simultaneously (dual-cycle approach)
Using both wick and body-based analysis for hidden divergences
Incorporating True Open levels as confluence filters
Providing visual quarter/cycle boundaries for context
Key Features
Dual-Cycle Detection
M5 Timeframe: Tracks Daily Cycles (6h) AND 90-minute quarters simultaneously
M1 Timeframe: Tracks 90-minute cycles AND 22.5-minute quarters simultaneously
Both cycle types run concurrently for multiple confluence levels
Divergence Analysis
Standard Patterns: Identifies divergences using full candle ranges
Hidden Patterns: Body-only analysis for concealed divergence detection
5 Configurable Correlation Pairs
Pre-configured with major correlations:
BTC/ETH (Cryptocurrency pairs)
NQ/ES (Index futures)
EUR/GBP (Forex majors)
Gold/Silver (Precious metals)
Custom pair slot
Visual Components
Quarter Boxes: Color-coded Q1-Q4 periods showing price ranges
Cycle Frames: Larger timeframe boundaries for context
SSMT Lines: Connect divergence points between quarters
True Opens: TDO (daily) and TSO (session) reference levels
Dual Labels: Period identification for each timeframe
Trading Application
This indicator is designed to identify divergence patterns that may precede reversals:
Signals are strongest when divergences occur near True Open levels
Multiple timeframe confluence increases signal reliability
Best used in conjunction with other technical analysis methods
The indicator is particularly useful for traders who:
Trade correlated asset pairs
Focus on intraday reversals
Use time-based market structure analysis
Combine multiple confluence factors for entries
Customization
Toggle individual components, adjust colors, control visual density. Configure correlation pairs to match your trading instruments. Debug panel available for detailed analysis.
Important Note
This indicator identifies divergence patterns based on mathematical relationships between correlated assets. Like all technical indicators, it should be used as part of a comprehensive trading approach with proper risk management.
---
Based on time-quarter analysis and correlation divergence concepts. Designed to help identify potential reversal zones through systematic divergence detection across multiple time cycles.
Muzyorae - Quarterly TheoryQuarterly Theory — NY Session Macro Model
The Quarterly Theory Model is a structured framework for analyzing intraday market behavior based on institutional activity and macro-level cycles.
It divides the New York trading session into four sequential “quarters” (Q1–Q4), each representing distinct phases of market participation, liquidity accumulation, and directional bias.
This model is designed for professional traders who aim to align their strategies with institutional flows, key liquidity zones, and market structure shifts.
It accommodates both AMDX (Accumulation → Manipulation → Distribution → Expansion) and XAMD (reversal sequences) fractal patterns, allowing traders to adapt to varying market conditions.
Price action may expand early during Q1 in an XAMD sequence, representing an initial breakout or early liquidity sweep before the typical Q2 manipulation phase. Traders should be aware that Q1 can occasionally produce unexpected volatility or directional bias in such sequences.
Session Breakdown (New York Time)
Q1 – Accumulation
Time: 9:30 – 10:00 AM
Phase Characteristics: Early session positioning, initial liquidity sweeps, and false moves. Institutions build positions while retail participants often react to gaps and premarket activity.
Note: Price may expand early in an XAMD sequence, creating a short-term directional move before Q2.
Q2 – Manipulation / Expansion
Time: 10:00 – 11:30 AM
Phase Characteristics: The main directional move develops, often characterized by breaks of structure, fair value gaps, and liquidity sweeps. This is a prime area for trend initiation.
Q3 – Distribution / Retracement
Time: 11:30 AM – 1:30 PM
Phase Characteristics: Price consolidates and retraces into prior accumulation zones, reflecting profit-taking or redistribution by institutions. Market chop and sideways movement are common.
Q4 – Final Expansion / Repricing
Time: 1:30 – 4:00 PM
Phase Characteristics: The afternoon session often produces final liquidity sweeps, trend continuation, or reversals, setting the high or low of the day and completing the daily macro cycle.
Key Features of the Model
Fractal-Based Structure: Q1–Q4 cycles reflect institutional behavior at a macro level, scalable to other intraday or multi-day fractals.
Supports AMDX & XAMD: Allows for both standard accumulation → manipulation → distribution → expansion sequences and reversal patterns depending on market behavior.
Early Expansion in Q1: Recognizes that in XAMD sequences, Q1 may produce early directional moves or breakout activity.
True Open Q2 Line: Highlights the opening price of Q2 as a reference for trend validation and potential entry zones.
Dynamic Time Alignment: Fully synchronized with New York (ET) time zone, ensuring accurate representation of market cycles.
Professional Visualization: Optional labels and vertical markers for each quarter, supporting quick visual analysis and pattern recognition.
Integration with ICT Concepts: Compatible with Smart Money Techniques (SMT), Fair Value Gaps (FVGs), Order Blocks (OBs), and Break of Structure (BOS) for enhanced trade planning.
Purpose and Application
Anticipates areas of liquidity accumulation and manipulation.
Identifies optimal entry and exit zones within institutional cycles.
Structures trades around probable trend initiation and continuation periods.
Aligns retail activity with institutional flow for higher probability setups.
Adapts to market variability through AMDX and XAMD fractal patterns.
Accounts for early expansions or breakout activity during Q1 in XAMD sequences.
By using the Quarterly Theory Model, traders gain a systematic, time-based framework to interpret market structure and maximize alignment with institutional participants.
Mikula's Master 360° Square of 12Mikula’s Master 360° Square of 12
An educational W. D. Gann study indicator for price and time. Anchor a compact Square of 12 table to a start point you choose. Begin from a bar’s High or Low (or set a manual start price). From that anchor you can progress or regress the table to study how price steps through cycles in either direction.
What you’re looking at :
Zodiac rail (far left): the twelve signs.
Degree rail: 24 rows in 15° steps from 15° up to 360°/0°.
Transit rail and Natal rail: track one planet per rail. Each planet is placed at its current row (℞ shown when retrograde). As longitude advances, the planet climbs bottom → top, then wraps to the bottom at the next sign; during retrograde it steps downward.
Hover a planet’s cell to see a tooltip with its exact longitude and sign (e.g., 152.4° ♌︎). The linked price cell in the grid moves with the planet’s row so you can follow a planet’s path through the zodiac as a path through price.
Price grid (right): the 12×24 Square of 12. Each column is a cycle; cells are stepped price levels from your start price using your increment.
Bottom rail: shows the current square number and labels the twelve columns in that square.
How the square is read
The square always begins at the bottom left. Read each column bottom → top. At the top, return to the bottom of the next column and read up again. One square contains twelve cycles. Because the anchor can be a High or a Low, you can progress the table upward from the anchor or regress it downward while keeping the same bottom-to-top reading order.
Iterate Square (shifting)
Iterate Square shifts the entire 12×24 grid to the next set of twelve cycles.
Square 1 shows cycles 1–12; Square 2 shows 13–24; Square 3 shows 25–36, etc.
Visibility rules
Pivot cells are table-bound. If you shift the square beyond those prices, their highlights won’t appear in the table.
A/B levels and Transit/Natal planetary lines are chart overlays and can remain visible on the table as you shift the square.
Quick use
Choose an anchor (date/time + High/Low) or enable a manual start price .
Set the increment. If you anchored with a Low and want the table to step downward from there, use a negative value.
Optional: pick Transit and Natal planets (one per rail), toggle their plots, and hover their cells for longitude/sign.
Optional: turn on A/B levels to display repeating bands from the start price.
Optional: enable swing pivots to tint matching cells after the anchor.
Use Iterate Square to shift to later squares of twelve cycles.
Examples
These are exploratory examples to spark ideas:
Overview layout (zodiac & degree rails, Transit/Natal rails, price grid)
A-levels plotted, pivots tinted on the table, real-time price highlighted
Drawing angles from the anchor using price & time read from the table
Using a TradingView Gann box along the A-levels to study reactions
Attribution & originality
This script is an original implementation (no external code copied). Conceptual credit to Patrick Mikula, whose discussion of the Master 360° Square of 12 inspired this study’s presentation.
Further reading (neutral pointers)
Patrick Mikula, Gann’s Scientific Methods Unveiled, Vol. 2, “W. D. Gann’s Use of the Circle Chart.”
W. D. Gann’s Original Commodity Course (as provided by WDGAN.com).
No affiliation implied.
License CC BY-NC-SA 4.0 (non-commercial; please attribute @Javonnii and link the original).
Dependency AstroLib by @BarefootJoey
Disclaimer Educational use only; not financial advice.
Aeon FluxAeon Flux visualizes rolling cumulative realized volatility, as a signal-generating leading indicator.
'Realized volatility' is shorthand for the metric's true output: entropy . The uniformity (or lack of uniformity) of price and volume distributions over a rolling cumulative period, normalized across the asset's full history.
Entropy = x⋅log2(x)−(1−x)⋅log2(1−x)
AEON FLUX VISUALIZES TIME CYCLES
Aeon Flux distills any asset's cyclical pendulum-like behavior, from bull to bear and vice versa, in a visualization that surfaces and isolates the pendulum shift.
As such, Aeon Flux may be the first metric to automate visualization of time cycles.
Time cycles are a soft science and esoteric concept in markets: an opinion, hard to prove or disprove.
They're ultimately just cycles of accumulation & distribution, that tend to recur at rough consistent intervals.
(Aeon Flux does not measure accumulation & distribution directly, those forces are merely implied.)
ENTROPY AS A LEADING INDICATOR
The transitions between state (from bullish to bearish & vice versa) are often good swing entries & exits, across a wide range of high cap risk markets.
ENTROPY AS A DISTRIBUTION MONITOR
Aeon Flux has a track record of detecting higher timeframe macro distribution on the BTC Index.
The signal: two cycles in a row of lower highs, where the cycle high (the highest oscillator print achieved that cycle) is lower than the previous cycle's high.
Invalidation: if the second cycle in a row of lower highs touches the green AND red target areas on its way up, that demonstrates robust volatility, and the distribution signal is invalidated.
ALERTS & NOTIFICATIONS
Alerts are enabled for swing long & short signals. Automating alerts to monitor distribution are a potential enhancement for future iterations of the script.
Bitcoin Macro Trend Map [Ox_kali]
## Introduction
__________________________________________________________________________________
The “Bitcoin Macro Trend Map” script is designed to provide a comprehensive analysis of Bitcoin’s macroeconomic trends. By leveraging a unique combination of Bitcoin-specific macroeconomic indicators, this script helps traders identify potential market peaks and troughs with greater accuracy. It synthesizes data from multiple sources to offer a probabilistic view of market excesses, whether overbought or oversold conditions.
This script offers significant value for the following reasons:
1. Holistic Market Analysis : It integrates a diverse set of indicators that cover various aspects of the Bitcoin market, from investor sentiment and market liquidity to mining profitability and network health. This multi-faceted approach provides a more complete picture of the market than relying on a single indicator.
2. Customization and Flexibility : Users can customize the script to suit their specific trading strategies and preferences. The script offers configurable parameters for each indicator, allowing traders to adjust settings based on their analysis needs.
3. Visual Clarity : The script plots all indicators on a single chart with clear visual cues. This includes color-coded indicators and background changes based on market conditions, making it easy for traders to quickly interpret complex data.
4. Proven Indicators : The script utilizes well-established indicators like the EMA, NUPL, PUELL Multiple, and Hash Ribbons, which are widely recognized in the trading community for their effectiveness in predicting market movements.
5. A New Comprehensive Indicator : By integrating background color changes based on the aggregate signals of various indicators, this script essentially creates a new, comprehensive indicator tailored specifically for Bitcoin. This visual representation provides an immediate overview of market conditions, enhancing the ability to spot potential market reversals.
Optimal for use on timeframes ranging from 1 day to 1 week , the “Bitcoin Macro Trend Map” provides traders with actionable insights, enhancing their ability to make informed decisions in the highly volatile Bitcoin market. By combining these indicators, the script delivers a robust tool for identifying market extremes and potential reversal points.
## Key Indicators
__________________________________________________________________________________
Macroeconomic Data: The script combines several relevant macroeconomic indicators for Bitcoin, such as the 10-month EMA, M2 money supply, CVDD, Pi Cycle, NUPL, PUELL, MRVR Z-Scores, and Hash Ribbons (Full description bellow).
Open Source Sources: Most of the scripts used are sourced from open-source projects that I have modified to meet the specific needs of this script.
Recommended Timeframes: For optimal performance, it is recommended to use this script on timeframes ranging from 1 day to 1 week.
Objective: The primary goal is to provide a probabilistic solution to identify market excesses, whether overbought or oversold points.
## Originality and Purpose
__________________________________________________________________________________
This script stands out by integrating multiple macroeconomic indicators into a single comprehensive tool. Each indicator is carefully selected and customized to provide insights into different aspects of the Bitcoin market. By combining these indicators, the script offers a holistic view of market conditions, helping traders identify potential tops and bottoms with greater accuracy. This is the first version of the script, and additional macroeconomic indicators will be added in the future based on user feedback and other inputs.
## How It Works
__________________________________________________________________________________
The script works by plotting each macroeconomic indicator on a single chart, allowing users to visualize and interpret the data easily. Here’s a detailed look at how each indicator contributes to the analysis:
EMA 10 Monthly: Uses an exponential moving average over 10 monthly periods to signal bullish and bearish trends. This indicator helps identify long-term trends in the Bitcoin market by smoothing out price fluctuations to reveal the underlying trend direction.Moving Averages w/ 18 day/week/month.
Credit to @ryanman0
M2 Money Supply: Analyzes the evolution of global money supply, indicating market liquidity conditions. This indicator tracks the changes in the total amount of money available in the economy, which can impact Bitcoin’s value as a hedge against inflation or economic instability.
Credit to @dylanleclair
CVDD (Cumulative Value Days Destroyed): An indicator based on the cumulative value of days destroyed, useful for identifying market turning points. This metric helps assess the Bitcoin market’s health by evaluating the age and value of coins that are moved, indicating potential shifts in market sentiment.
Credit to @Da_Prof
Pi Cycle: Uses simple and exponential moving averages to detect potential sell points. This indicator aims to identify cyclical peaks in Bitcoin’s price, providing signals for potential market tops.
Credit to @NoCreditsLeft
NUPL (Net Unrealized Profit/Loss): Measures investors’ unrealized profit or loss to signal extreme market levels. This indicator shows the net profit or loss of Bitcoin holders as a percentage of the market cap, helping to identify periods of significant market optimism or pessimism.
Credit to @Da_Prof
PUELL Multiple: Assesses mining profitability relative to historical averages to indicate buying or selling opportunities. This indicator compares the daily issuance value of Bitcoin to its yearly average, providing insights into when the market is overbought or oversold based on miner behavior.
Credit to @Da_Prof
MRVR Z-Scores: Compares market value to realized value to identify overbought or oversold conditions. This metric helps gauge the overall market sentiment by comparing Bitcoin’s market value to its realized value, identifying potential reversal points.
Credit to @Pinnacle_Investor
Hash Ribbons: Uses hash rate variations to signal buying opportunities based on miner capitulation and recovery. This indicator tracks the health of the Bitcoin network by analyzing hash rate trends, helping to identify periods of miner capitulation and subsequent recoveries as potential buying opportunities.
Credit to @ROBO_Trading
## Indicator Visualization and Interpretation
__________________________________________________________________________________
For each horizontal line representing an indicator, a legend is displayed on the right side of the chart. If the conditions are positive for an indicator, it will turn green, indicating the end of a bearish trend. Conversely, if the conditions are negative, the indicator will turn red, signaling the end of a bullish trend.
The background color of the chart changes based on the average of green or red indicators. This parameter is configurable, allowing adjustment of the threshold at which the background color changes, providing a clear visual indication of overall market conditions.
## Script Parameters
__________________________________________________________________________________
The script includes several configurable parameters to customize the display and behavior of the indicators:
Color Style:
Normal: Default colors.
Modern: Modern color style.
Monochrome: Monochrome style.
User: User-customized colors.
Custom color settings for up trends (Up Trend Color), down trends (Down Trend Color), and NaN (NaN Color)
Background Color Thresholds:
Thresholds: Settings to define the thresholds for background color change.
Low/High Red Threshold: Low and high thresholds for bearish trends.
Low/High Green Threshold: Low and high thresholds for bullish trends.
Indicator Display:
Options to show or hide specific indicators such as EMA 10 Monthly, CVDD, Pi Cycle, M2 Money, NUPL, PUELL, MRVR Z-Scores, and Hash Ribbons.
Specific Indicator Settings:
EMA 10 Monthly: Options to customize the period for the exponential moving average calculation.
M2 Money: Aggregation of global money supply data.
CVDD: Adjustments for value normalization.
Pi Cycle: Settings for simple and exponential moving averages.
NUPL: Thresholds for unrealized profit/loss values.
PUELL: Adjustments for mining profitability multiples.
MRVR Z-Scores: Settings for overbought/oversold values.
Hash Ribbons: Options for hash rate moving averages and capitulation/recovery signals.
## Conclusion
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The “Bitcoin Macro Trend Map” by Ox_kali is a tool designed to analyze the Bitcoin market. By combining several macroeconomic indicators, this script helps identify market peaks and troughs. It is recommended to use it on timeframes from 1 day to 1 week for optimal trend analysis. The scripts used are sourced from open-source projects, modified to suit the specific needs of this analysis.
## Notes
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This is the first version of the script and it is still in development. More indicators will likely be added in the future. Feedback and comments are welcome to improve this tool.
## Disclaimer:
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Please note that the Open Interest liquidation map is not a guarantee of future market performance and should be used in conjunction with proper risk management. Always ensure that you have a thorough understanding of the indicator’s methodology and its limitations before making any investment decisions. Additionally, past performance is not indicative of future results.
Wyckoff Phases OscillatorThe "Wyckoff Phases Oscillator" is a script designed for the TradingView platform. It's an indicator that provides traders with an oscillator-based visual representation of the Wyckoff Market Cycle. The oscillator doesn't overlay the price chart but instead appears in a separate panel beneath it.
How it works:
The script operates based on two input parameters: length and timeFrame. The length parameter, set by default to 21, determines the period used for various calculations within the script. On the other hand, timeFrame, set by default to "1", specifies the timeframe for which the script will gather and analyze data.
The script requests security information such as closing prices (higherClose), volume (higherVolume), highest prices (higherHigh), and lowest prices (higherLow) from the ticker symbol (syminfo.tickerid) within the defined timeframe.
Two exponential moving averages (ema1 and ema2) are calculated based on the closing prices, with lengths of 5 and 9 respectively.
A Rate of Change (ROC) is calculated based on the closing prices and the defined length.
An average volume (avgVolume) is calculated using a simple moving average (SMA) based on the volume and the defined length.
The script defines conditions for institutional buying and selling.
Institutional buying is determined when the closing price is greater than the lowest price and the volume is greater than the average volume.
Institutional selling is determined when the closing price is less than the highest price and the volume is greater than the average volume.
The script also defines conditions for the four phases of the Wyckoff Market Cycle: Accumulation, Markup, Distribution, and Markdown. Each phase has specific conditions based on the closing prices, EMA values, ROC, and institutional buying or selling conditions.
The script then assigns oscillator values based on the Wyckoff phase:
Accumulation is assigned a value of 1
Markup is assigned a value of 2
Distribution is assigned a value of 3
Markdown is assigned a value of 4
These oscillator values are plotted as colored circles, with different colors representing different phases. The color values are specified in RGB format.
Finally, the script plots horizontal lines as references for each of the four phases using the hline function. These lines are labeled and color-coded to match the corresponding oscillator circles. The lines have a linewidth of 1 and are solid in style.
If the oscillator moves from level 1 (Accumulation) to level 2 (Markup), this could indicate a potential bullish trend, as the market moves from a phase of accumulation to a phase of increasing prices.
Conversely, if the oscillator moves from level 3 (Distribution) to level 4 (Markdown), this could signal a potential bearish trend, signaling that the market has moved from a phase of distribution to a phase of declining prices.
While the Wyckoff Phases Oscillator can provide valuable insights on its own, it can also be used in conjunction with other technical analysis tools and indicators. For example, you might use it alongside a volume indicator to confirm signals, or with support and resistance levels to identify potential entry and exit points.
[GYTS-Pro] Market Regime Detector🧊 Market Regime Detector (Professional Edition)
🌸 Part of GoemonYae Trading System (GYTS) 🌸
🌸 --------- INTRODUCTION --------- 🌸
💮 What is the Market Regime Detector?
The Market Regime Detector (Pro) is an elite, consensus-based market state analyzer designed to filter noise and identify the true underlying market structure. By distinguishing between trending (bullish or bearish) and cyclic (range-bound) market conditions with high precision, this detector acts as the "brain" of your trading system. Instead of forcing a single strategy across incompatible market conditions, the detector empowers you to deploy the right tactic at exactly the right time.
💮 The Importance of Market Regimes
Markets constantly shift between different behavioural states or "regimes":
• Bullish trending markets - characterised by sustained upward price movement
• Bearish trending markets - characterised by sustained downward price movement
• Cyclic markets - characterised by range-bound, oscillating behaviour
Each regime requires fundamentally different trading approaches. Trend-following strategies excel in trending markets but fail in cyclic ones, while mean-reversion strategies shine in cyclic markets but underperform in trending conditions. However, detecting these regimes is easier said than done, and we have gone through hundreds of hours of testing to create the Market Regime Detector, using multiple very sophisticated methods in an easy-to-use indicator.
💮 Professional vs Community Edition
The Market Regime Detector comes in two versions: a comprehensive Professional Edition and a streamlined Community Edition.
Key advantages of the Professional Edition:
• Enhanced detection accuracy - Utilises 5 advanced detection methods (compared to only 2 in the CE version)
• Proprietary cycle measurement - Automatically detects the market's dominant cycle instead of requiring manual input
• Superior consensus mechanism - Includes a unique "strength-weighted decision" mode that gives more influence to stronger signals
• Reduced false signals - Multiple complementary methods working together provide more reliable regime identification
• Advanced DSP algorithms - Implements sophisticated digital signal processing techniques for superior market analysis
The Professional Edition delivers significant improvements in detection accuracy, signal stability, and overall trading performance.
🌸 --------- KEY FEATURES --------- 🌸
💮 Consensus-Based Detection
Rather than relying on a single method, our detector employs multiple complementary detection methodologies that analyse different aspects of market behaviour:
• Advanced digital signal processing techniques
• Volatility and momentum analysis
• Adaptive filters and mathematical transformations
• Cycle identification
• Channel breakout detection
These diverse perspectives are synthesised into a robust consensus that minimises false signals while maintaining responsiveness to genuine regime changes.
💮 Proprietary Dominant Cycle Measurement ( Pro Edition only )
At the heart of our Professional Edition detector is a proprietary dominant cycle measurement system that automatically and adaptively identifies the market's natural rhythm. This system provides a stable reference framework that continuously adapts to changing market conditions while avoiding the erratic behaviour of typical cycle-finding algorithms like Hilbert Transforms, Discrete Fourier Transforms, or autocorrelation measurements.
Unlike the Community Edition which requires manual input of a single, constant dominant cycle period, the Professional Edition automatically detects and continuously adapts this critical parameter. This automated and adaptive approach ensures optimal detection accuracy across different markets and timeframes without requiring user expertise in cycle analysis, and provides significantly better responsiveness to evolving market conditions.
💮 Intuitive Parameter System
We've distilled complex technical parameters into intuitive controls that traders can easily understand:
• Adaptability - how quickly the detector responds to changing market conditions
• Sensitivity - how readily the detector identifies transitions between regimes
• Consensus requirement - how much agreement is needed among detection methods
This approach makes the detector accessible to traders of all experience levels while preserving the power of the underlying algorithms.
💮 Visual Market Feedback
The detector provides clear visual feedback about the current market regime through:
• Colour-coded chart backgrounds (purple shades for bullish, pink for bearish, yellow for cyclic)
• Colour-coded price bars
• Strength indicators showing the degree of consensus
• Customisable color schemes to match your preferences or trading system
💮 Integration in the GYTS suite
What is of paramount importance, is that the Market Regime Detector is compatible with the GYTS Suite , i.e. it passes the regime into the Order Orchestrator where you can set how to trade the trending and cyclic regime. The intention is to integrate it with more indicators.
🌸 --------- CONFIGURATION SETTINGS --------- 🌸
💮 Adaptability
Controls how quickly the Market Regime detector adapts to changing market conditions. You can see it as a low-frequency, long-term change parameter:
• Very Low: Very slow adaptation, most stable but may miss regime changes
• Low: Slower adaptation, more stability but less responsiveness
• Normal: Balanced between stability and responsiveness
• High: Faster adaptation, more responsive but less stable
• Very High: Very fast adaptation, highly responsive but may generate false signals
This setting affects lookback periods and filter parameters across all detection methods.
💮 Sensitivity
Controls the conviction threshold required to trigger a regime change. This acts as a high-frequency, short-term filter for market noise:
• Very Low: Requires overwhelming evidence to identify a regime change.
• Low: Prioritizes stability; reduces false signals but may delay transition detection.
• Normal: Balanced sensitivity suitable for most liquid markets.
• High: Highly responsive; detects subtle regime changes early but may react to market noise.
• Very High: Extremely sensitive; detects minor fluctuations immediately.
Pro Feature Note: In the Strength-Weighted Decision mode, this setting acts as a dynamic calibrator. It not only adjusts individual method thresholds but also scales the global consensus threshold . A 'High' sensitivity lowers the barrier for the weighted consensus, allowing the system to react to early-stage breakouts even if not all methods fully agree yet.
💮 Consensus Mode
Determines how the signals from all detection methods are combined to produce the final market regime:
• Any Method (OR) : Signals bullish/bearish if any method detects that regime. If methods conflict, the stronger signal wins. More sensitive, catches more regime changes but may produce more false signals.
• All Methods (AND) : Signals only when all methods agree on the regime. More conservative, reduces false signals but might miss some legitimate regime changes.
• Weighted Decision : Balances all methods with equal voting rights. Signals bullish/bearish when the weighted consensus reaches a fixed majority (0.5). Provides a middle ground between sensitivity and stability.
• Strength-Weighted Decision ( Pro Edition only ): A "meritocratic" approach where methods reporting extreme confidence (high signal strength) are given proportionally more weight than those reporting weak signals. Unlike standard voting, a single clear signal from a highly reliable method can override indecision from others.
Note: The threshold for this decision is dynamically calibrated by your 'Sensitivity' setting, ensuring the logic adapts to your desired risk profile.
Each mode also calculates a continuous regime strength value that drives the color intensity in the 'unconstrained' display mode, giving you a visual heatmap of trend conviction.
💮 Display Mode
Choose how to display the market regime colours:
• Unconstrained regime: Shows the regime strength as a continuous gradient. This provides more nuanced visualisation where the intensity of the color indicates the strength of the trend.
• Consensus only: Shows only the final consensus regime with fixed colours based on the detected regime type.
The background and bar colours will change to indicate the current market regime:
• Purple shades : Bullish trending market. In 'unconstrained' mode, darker purple indicates a stronger bullish trend.
• Pink shades : Bearish trending market. In 'unconstrained' mode, darker pink indicates a stronger bearish trend.
• Yellow : Cyclic (range-bound) market.
💮 Custom Color Options
The Market Regime Detector allows you to customize the color scheme to match your personal preferences or to coordinate with other indicators:
• Use custom colours: Toggle to enable your own color choices instead of the default scheme
• Transparency: Adjust the transparency level of all regime colours
• Bullish colours: Define custom colours for strong, medium, weak, and very weak bullish trends
• Bearish colours: Define custom colours for strong, medium, weak, and very weak bearish trends
• Cyclic color: Define a custom color for cyclic (range-bound) market conditions
🌸 --------- DETECTION METHODS --------- 🌸
💮 Five-Method Consensus Architecture
The Professional Edition employs a sophisticated multi-stage architecture to determine market regimes with high precision.
The detection process flows through four logical stages:
1. Market Data & Cycle Detection
Price data flows into the system where the Dominant Cycle Detector automatically identifies the market's natural rhythm. This adaptive cycle length calibrates all subsequent calculations, ensuring the detector remains in sync with changing market conditions without manual adjustments.
2. Five Detection Methods
Using the detected cycle, five complementary algorithms independently evaluate the market state:
• Cyclic Centroid Analysis : Calculates the market's 'centre point' over its dominant cycle and measures price displacement to determine trend or equilibrium.
• Spectral Momentum : Measures momentum across the market's frequency spectrum to identify trend concentration.
• Energy Distribution Gauge : Gauges how price movement energy is distributed to flag cyclic or trending states.
• Volatility Channel : Models the market's volatility state, using band breakouts to indicate a trend.
• Phase Coherence Detector : Analyses phase relationships between adaptive low-pass filters to detect trend stability and identify early regime shifts.
3. Consensus Engine
The signals from all five methods are fed into the Consensus Engine. Depending on your configuration, it aggregates these votes using one of four logic modes (Any, All, Weighted, or Strength-Weighted) to filter out noise and confirm the true market regime.
4. Regime Output
The final result is broadcast as a clear market state:
• Bullish (1) : Trending upwards
• Bearish (-1) : Trending downwards
• Cyclic (0) : Range-bound or oscillating
This output drives the visual feedback on your chart and can be streamed directly to the Order Orchestrator for automated strategy switching.
💮 Synergy & Complementarity
What makes these methods powerful is not just their individual sophistication, but how they complement one another:
• Some excel at early detection while others provide confirmation
• Some analyse time-domain behaviour while others work in the frequency domain
• Some focus on momentum characteristics while others assess volatility patterns
• Some respond quickly to changes while others filter out market noise
This creates a comprehensive analytical framework that can detect regime changes more accurately than any single method. All methods utilize the automatically detected and continuously adaptive dominant cycle period, ensuring they remain precisely calibrated to current market conditions without manual intervention.
🌸 --------- USAGE GUIDE --------- 🌸
💮 Starting with Default Settings
The default settings (Normal for Adaptability, Sensitivity, and Consensus) provide a balanced starting point suitable for most markets and timeframes. Begin by observing how these settings identify regimes in your preferred instruments.
💮 Adjusting Parameters
• If you notice too many regime changes → Decrease Sensitivity or increase Consensus requirement
• If regime changes seem delayed → Increase Adaptability
• If a trending regime is not detected, the market is automatically assigned to be in a cyclic state. The majority of methods actually measure this explicitly.
• If you want to see more nuanced regime transitions → Try the "unconstrained" display mode (note that this will not affect the output to other indicators)
💮 Trading Applications
Regime-Specific Strategies:
• Bullish Trending Regime - Use trend-following strategies, trail stops wider, focus on breakouts, consider holding positions longer, and emphasise buying dips
• Bearish Trending Regime - Consider shorts, tighter stops, focus on breakdown points, sell rallies, implement downside protection, and reduce position sizes
• Cyclic Regime - Apply mean-reversion strategies, trade range boundaries, apply oscillators, target definable support/resistance levels, and use profit-taking at extremes
Strategy Switching:
Create a set of rules for each market regime and switch between them based on the detector's signal. This approach can significantly improve performance compared to applying a single strategy across all market conditions. The Pro Edition's multiple detection methods and advanced consensus mechanisms provide more reliable regime transitions, leading to better strategy switching decisions.
GYTS Suite Integration:
• In the GYTS 🎼 Order Orchestrator, select the '🔗 STREAM-int 🧊 Market Regime' as the market regime source
• Note that the consensus output (i.e. not the "unconstrained" display) will be used in this stream
• Create different strategies for trending (bullish/bearish) and cyclic regimes. The GYTS 🎼 Order Orchestrator is specifically made for this.
• The output stream is actually very simple, and can possibly be used in indicators and strategies as well. It outputs 1 for bullish, -1 for bearish and 0 for cyclic regime.
🌸 --------- FINAL NOTES --------- 🌸
💮 Development Philosophy
The Market Regime Detector has been developed with several key principles in mind:
1. Robustness - The detection methods have been rigorously tested across diverse markets and timeframes to ensure reliable performance.
2. Adaptability - The detector automatically adjusts to changing market conditions, requiring minimal manual intervention.
3. Complementarity - Each detection method provides a unique perspective, with the collective consensus being more reliable than any individual method.
4. Intuitiveness - Complex technical parameters have been abstracted into easily understood controls.
💮 Ongoing Refinement
The Market Regime Detector is under continuous development. We regularly:
• Fine-tune parameters based on expanded market data using state-of-the-art Machine Learning techniques
• Research and integrate new detection methodologies
• Optimise computational efficiency for real-time analysis
Your feedback and suggestions are very important in this ongoing refinement process!
Gould 10Y + 4Y patternDescription:
Overview This indicator is a comprehensive tool for macro-market analysis, designed to visualize historical market cycles on your chart. It combines Edson Gould’s famous Decennial Pattern with a Customizable 4-Year Cycle (e.g., 2002 base) to help traders identify long-term trends, potential market bottoms, and strong bullish years.
This tool is ideal for long-term investors and analysts looking for cyclical confluence on monthly or yearly timeframes (e.g., SPX, NDX).
Key Concepts
Edson Gould’s Decennial Pattern (10-Year Cycle)
Based on the theory that the stock market follows a psychological cycle determined by the last digit of the year.
5 (Strongest Bull): Historically the strongest performance years.
7 (Panic/Crash): Years often associated with market panic or crashes.
2 (Bottom/Buy): Years that often mark major lows.
Custom 4-Year Cycle (Target Year Strategy)
Identify recurring 4-year opportunities based on a user-defined base year.
Default Setting (Base 2002): Highlights years like 2002, 2006, 2010, 2014, 2018, 2022... which have historically been significant market bottoms or excellent buying opportunities.
When a "Target Year" arrives, the indicator highlights the background and displays a distinct Green "Target Year" Label.
Features
Real-time Dashboard: A table in the top-right corner displays the current year's status for both the 10-Year and 4-Year cycles, including a countdown to the next target year.
Dynamic Labels: Automatically marks every year on the chart with its Decennial status (e.g., "Strong Bull (5)", "Panic (7)").
Visual Highlighting:
Target Years: Distinct green background and labels for easy identification of the 4-year cycle.
Significant Decennial Years: Special small markers for years ending in 5 and 7.
Fully Customizable: You can change the base year for the 4-year cycle, toggle the dashboard, and adjust colors via the settings menu.
How to Use
Apply this indicator to high-timeframe charts (Weekly or Monthly) of major indices like S&P 500 or Nasdaq.
Look for confluence between the 10-Year Pattern (e.g., Year 6 - Bullish) and the 4-Year Cycle (Target Year) to confirm long-term bias.
Disclaimer This tool is for educational and research purposes only based on historical cycle theories. Past performance is not indicative of future results. Always manage your risk.
BTC GOD — DEFINITIVE BTC MULTI INDICATORBTC GOD — The Ultimate Bitcoin Cycle Indicator (2025 Edition)
The one indicator every serious BTC holder and trader has been waiting for.
A single script that perfectly combines the 5 most powerful and accurate Bitcoin indicators ever created — all 100 % official versions:
- Official Pi Cycle Top (LookIntoBitcoin) → in 2013, 2017 & 2021 (3/3 hits)
- Official MVRV Z-Score (Glassnode / LookIntoBitcoin) → every major bottom (2015, 2018–19, 2022)
- Dynamic Bull/Bear background (red bear-market when price drops X % from cycle ATH + monthly RSI filter)
- Monthly Golden/Death Cross (50-month EMA vs 200-week EMA) → huge, unmistakable signals
- SuperTrend + 200-week EMA + 50-month EMA
- Cycle ATH/ATL tracking with flashing alert in the table when new highs/lows are made
- Exact days to/from the next halving + optimal accumulation zone (200–750 days post-halving)
- Fully customizable inputs for experienced traders
Zero repainting. Zero errors. Works on every timeframe.
This is the indicator used by people who truly understand Bitcoin’s 4-year cycles.
If you could only keep ONE Bitcoin indicator for the rest of your life… this would be it.
Save it, test it, and you’ll instantly see why it’s called BTC GOD.
Built with love and obsession for Bitcoin cycles.
Last update: November 2025
MastersCycleSignal(Mastersinnifty)Overview
MastersCycleSignal is a high-precision market timing and projection indicator for trend-following and swing traders.
It combines an adaptive cycle detection algorithm, forward-looking sine wave projections, dynamic momentum confirmation, and Gann Square of 9-based geometric targets into a complete structured trading framework.
The script continuously analyzes price oscillations to detect dominant cycles, projects expected price behavior with future-facing sine approximations, and generates buy/sell signals once confirmed by adaptive momentum filtering.
Upon confirmation, it calculates mathematically consistent Gann-based target levels and risk-managed stop-loss suggestions.
Users also benefit from auto-extending targets as price action unfolds — helping traders anticipate rather than react to market shifts.
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Uniqueness
MastersCycleSignal stands apart through a unique fusion of techniques:
- Dynamic Cycle Detection
- Detects dominant cycles using a cosine correlation maximization method between detrended price (close minus SMA) and theoretical cosine curves, dynamically recalibrated across a sliding window.
- Sine Wave Future Projection
- Smooths and projects future price paths by approximating a forward sine wave based on the real-time detected dominant cycle.
- Adaptive Momentum Filtering
- Volatility is scaled by divergence between normalized returns and a 5-period EMA, further adjusted by an RSI(2) factor.
- This makes buy/sell signal confirmation robust against noise and false breakouts.
- Gann-Based Target Computation
- Uses a square-root transformation of price, incremented by selectable Gann Square of 9 degrees, for calculating progressive and dynamically expanding price targets.
- Auto-Extending Targets
- As price achieves a projected target, the system automatically draws subsequent new targets based on the prior target differential — providing continuous guidance in trending conditions.
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Usefulness
MastersCycleSignal is built to help traders:
- Identify early trend reversals through cycle shifts.
- Forecast probable price paths in advance.
- Plan systematic target and stop-loss zones with geometric accuracy.
- Reduce guesswork in trend-following and swing trading.
- Maintain structured discipline across intraday, swing, and positional strategies.
It works seamlessly across stocks, indices, forex, commodities, and crypto markets — on any timeframe.
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How to Use
- Attach the indicator to your desired chart.
- When a Buy Signal or Sell Signal appears (green or red markers):
- Use the attached stop-loss labels to manage risk.
- Monitor the automatically plotted target lines for partial exits or full profits.
- The orange projected sine wave illustrates the expected future market path.
- Customization Options:
- Cycle Detection Length — adjust to fine-tune cycle sensitivity.
- Projection Length — modify the forward distance of sine wave forecast.
- Gann Square of 9 Degrees — personalize target increments.
- Toggle Signals and Target visibility as needed.
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Disclaimer
- MastersCycleSignal uses no future data or lookahead bias.
- All projections are based on geometric extrapolations from historical price action — not guaranteed predictions.
- Trading involves risks, and historical cycle behavior may differ in future conditions.
Revolution SMA-EMA DivergenceThis is an MACD inspired indicator and it analyzes the difference between the SMA and EMA using the same time period. Unlike the MACD, it can give you a better understanding of the overall trend. Values above 0 is bullish and below 0 bearish. It consists of two cycles: Black histogram - the long-term cycle and orange histogram - the short-term cycle, as well as timing signal (red line).
Bitcoin Halving Rainbow + S2F Model PriceOverview
The rainbow price line:
This script creates a colorful view of Bitcoin's price action, where different colors indicate the time until the next halving date. The color scale in the top right highlights what each main color group represents in terms of days until the next halving. Using historical data, the simple indication of days until the next halving has somewhat accurately predicted potential bottoms and tops of market cycles. Comparing current colors to previous cycles provides a rough view of where BTC is in its current cycle and what to expect going forward until the next halving date.
In addition to the colored price action, I have incorporated the stock-to-flow model price for Bitcoin.
The stock-to-flow (S2F) model price:
The stock-to-flow ratio is a calculation that aims to estimate how many years are required to produce the current stock of an asset, based on the current production rate. When applied to Bitcoin, we simply divide the total amount of bitcoins in circulation by the amount of bitcoins mined in a certain timeframe. Once we have this value, we can calculate a model price based on the stock-to-flow ratio. This S2F model price uses a 463-day moving average. Preston Pysh came up with this number as he believed Bitcoin cycles happen in three phases: bull run, correction, and a reversion to the mean. He estimated there are about 200,000 blocks per cycle, three phases per cycle, and ~144 blocks per day. Dividing all three gets us 463. I have removed 1,000,000 coins from this calculation to account for Satoshi's coins.
The process I took to plot this model price (credit to PlanB for originally creating this calculation):
-Declare constant variables for the halving period, starting block reward, and the number of coins Satoshi owns.
-Fetch the block index by using the request.security() function.
-Determine the number of halvings that have occurred by dividing the block index by the halving period.
-Calculate the current block reward by multiplying the initial block reward by 0.5 raised to the power of the number of halvings.
-Calculate the number of blocks mined per period (day or week) and derive the stock (total bitcoins in circulation minus Satoshi's coins) and flow (annual block rewards) from it.
-Calculate the S2F ratio by dividing the stock by the flow.
-Calculate the S2F model price by applying a mathematical formula (ModelPrice = exp(-1.84) * S2F to the power of 3.36) along with a 463-day moving average.
** Please note, due to the use of the 463-day MA, the first ~400 days of the S2F model price is not entirely accurate.
In addition to the above, I have added vertical lines on each halving date, along with labels that have a tooltip if you hover over them, which will show more information about that particular halving.
Important tips:
-This script has been designed to work on the 1-Day timeframe but can also work on the 1-Week timeframe. Any other timeframe will not accurately plot all the information due to the way I have developed the script.
-This script is best used on the ticker I have posted this on, "INDEX:BTCUSD". It can also work on "BLX" or "BITSTAMP:BTCUSD".
-Hide candles when using the script to just show the halving rainbow (hover over the symbol name in the top left and press the eye icon).
-Right-click the price scale and select "Scale price chart only" to get a better view of the plots.
-Right-click the price scale and select "Logarithmic."
-I will update the script as time goes on to show future halvings along with adjusting the next halving date as we get closer (if it changes).
Settings Menu:
Tooltips are included explaining what the settings do, but here's a quick summary:
-'Show Vertical Halving Lines?': Default is true. This allows the user to remove the vertical lines shown on each halving date.
-'Show Halving Labels?': Default is true. This allows the user to remove the info labels shown on each halving date.
-'Halving Line and Label Color': Default is white. This allows the user to change the color of the halving lines and labels to better fit their chart layout.
-'Show Stock to Flow Model Price?': Default is true. This allows the user to remove the S2F model price.
-'Stock to Flow Model Price Color': Default is white. This allows the user to change the color of the S2F model price to better fit their chart layout.
-'Draw Color Table?': Default is true. This allows the user to remove the color table in the top right of the chart.
-'Distance rainbow is away from actual price action': Default is 0 (Plots over candles). This allows the user to adjust where the halving rainbow is plotted if they would like to also see candles on the chart. (Use any value under 0.9)
Feel free to message me or comment on the post with any questions or issues!
Much more to come!
Thanks for reading, enjoy!
Triangulation : Statistically Approved ReversalsA lot of calculation, but a simple and effective result displayed on the chart.
It automatically identifies a very favorable period for a price reversal, by analyzing the daily and intraday price action statistics from the maximum of the most recent bars from the historical data. No repainting. Alerts can be set.
The statistical study is done in real time for each instrument. The probabilities therefore vary over time and adapt to the latest information collected by the indicator.
The time range of the data study can be changed by simply changing the UT :
- 30m = 3.5 last months feed statistics
- 15m = 52 last days feed statistics
- 5m = 17 last days feed statistics (recommanded)
HOW TO USE
This indicator informs when we are in a time period strongly favorable to reversal.
==> Crossing probabilities of different kinds, in price and in time => Triangulation of top and bottom !
HOW It WORK :
fractal statistics on high and low formation.
hour's probabilities of making the high/low of the day are crossed with day's probabilities of making the high/low of the week.
First for the day, we study:
- value of the probability compared to the average probabilities
- value of the coefficient between the high probability and the low probability
which we then refine for the hour, with the same calculation.
Result: bright color for a day + hour with high probability, weak color if the probability is low but remains the only possible bias. Between these two possibilities, intermediate colors are possible - just like looking for shorts if the day is bullish, if it is a high probability hour!
This color is displayed in the background, only if we are forming the high of the day for tops, and the low of the day for bottoms - detected with a stochastic.
All probabilities are studied in real time for the current asset.
We will call this signal "killstats", for "killzones statistics"
fractal statistics on the probability of closure under specific predefined levels according to 36 cycles.
the probabilities of several cycles are studied, for example:
NY session versus London and Asian sessions, London session compared to its opening, NY session compared to its opening, "algorithmic cycles" ( 1h30), Opening of NY compared to its intersection with London..
Each cycle producing a probability of closing with respect to the opening price of each period. The periods are : (Etc/UTC)
15-18h / 15-16h / 9-13h / 14-17h / 18-22h / 10-12h / 9-10h30 / 10h30-12h / 12-13h30 / 13h30-15h / 15h-16h30 / 16h30-18h
The cycles can be superimposed, which allows to support or attenuate a signal for the key periods of the day: 9am-12pm, and 3pm-6pm. The period of the day covered by the study of cycles is 9h-22h.
Result : ==> a straight line with a half bell. Colors = almost transparent for 53% probability (low), and very intense for a high probability (75%). The line displayed corresponds to the opening price, which we are supposed to close within the time limit - before the end of the period, where the line stops.
If the price goes in the opposite direction to the one predicted by the statistics, then a background connects the price to the close level to be respected.
if direction and close is respected, nothing is displayed : there is no opportunity, no divergence between statistics and actual price moves.
By unchecking the "light mode", you can see each close level displayed on the chart, with the corresponding probability and the number of times the cycle was detected. The color varies from intense for a high probability (75%), to light for a low probability (53%)
We will call this signal "cyclic anomalies"
By default, as shown in the indicator presentation image, the "intersection only" option is checked: only the intersection between 1) killstats and 2) cyclic anomalies is displayed. (filter +-30% of killstats signals)
MORE INFORMATIONS
/!\ : during a backtest, it is necessary to refresh the studied data to benefit from the real time signals, and for that you have to use the replay mode. if "Backtesting informations?"is checked, labels are displayed on the graph to warn of the % distortion of the signals. I recommend using the replay mode every 250 candles, and every 1000 candles for premium accounts, to have real signals.
- Alerts can be set for killzone, or intersections ( As in presentation picture)
- The ideal use is in m5. It can trigger several times a day, sometimes in opposite directions, and sometimes not trigger for several days.
- Premium account have 20k candles data, and not 5k => signals may vary depending on your tradingview subscription.
SMT [Advanced] by TMUThis is a proprietary technical analysis tool designed to detect SMT (Smart Money Time) Divergences with a specific focus on Time-Cycle Theory and advanced Data Visualization.
Originality & Technical Uniqueness Unlike standard open-source SMT indicators that simply compare Highs/Lows and clutter the chart with overlapping text, this script utilizes a custom-built "Label Registry & Stacking Engine". Standard indicators often fail when multiple divergences occur simultaneously on different timeframes. This script solves this problem using a proprietary deferred rendering algorithm:
Registry System: Instead of drawing signals immediately, the script calculates potential divergences across multiple assets/timeframes and pushes them into a dynamic array (registry).
Dynamic Stacking: A background sorting algorithm processes this stack every bar, groups signals by their timestamp and type, and renders them with calculated offsets. This ensures labels never overlap, providing a clean, professional workspace impossible to achieve with basic plotting functions.
Signal Rotation: It implements a "rotation manager" logic for 90-minute cycles. As price action evolves, the script automatically assesses whether to update an existing divergence line or create a new historical reference, keeping the analysis strictly relevant to the current cycle structure.
How it Works (Methodology) The script performs a relative strength analysis between two correlated assets (e.g., ES vs. YM) using request.security to fetch comparative data.
Pivot Analysis: It identifies structural Pivot Highs and Lows based on a configurable length, filtering out minor internal noise.
Divergence Logic:
Bearish SMT: Validated when the primary asset makes a Higher High while the comparison asset makes a Lower High.
Bullish SMT: Validated when the primary asset makes a Lower Low while the comparison asset makes a Higher Low.
Time-Cycle Isolation: The analysis is confined within strictly defined temporal windows (Daily, Weekly, and custom 90-minute intraday blocks). The script detects cracks in correlation specifically within these isolated sessions rather than looking at infinite history.
Features
Smart Filter: Advanced logic to filter out "Internal" structure and focus only on major external pivot breaches.
Multi-Cycle Dashboard: A real-time table monitoring the SMT status of Monthly, Weekly, Daily, and intraday cycles simultaneously.
Auto-Ticker Selection: Automatically detects the current asset class (Indices/Forex) and selects the appropriate comparison symbol (e.g., selects YM when viewing ES).
Settings
Comparisons: Manual or Auto-ticker selection.
Visuals: Custom colors, line styles, and label positioning modes.
Alerts: Customizable alerts for valid SMT formation on any monitored timeframe.
Ehlers Adaptive Relative Strength Index (RSI) [Loxx]Ehlers Adaptive Relative Strength Index (RSI) is an implementation of RSI using Ehlers Autocorrelation Periodogram Algorithm to derive the length input for RSI. Other implementations of Ehers Adaptive RSI rely on the inferior Hilbert Transformer derive the dominant cycle.
In his book "Cycle Analytics for Traders Advanced Technical Trading Concepts", John F. Ehlers describes an implementation for Adaptive Relative Strength Index in order to solve for varying length inputs into the classic RSI equation.
What is an adaptive cycle, and what is the Autocorrelation Periodogram Algorithm?
From his Ehlers' book mentioned above, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average (KAMA) and Tushar Chande’s variable index dynamic average (VIDYA) adapt to changes in volatility. By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic, relative strength index (RSI), commodity channel index (CCI), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator.This look-back period is commonly a fixed value. However, since the measured cycle period is changing, as we have seen in previous chapters, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the autocorrelation periodogram algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
What is Adaptive RSI?
From his Ehlers' book mentioned above, page 137:
"The adaptive RSI starts with the computation of the dominant cycle using the autocorrelation periodogram approach. Since the objective is to use only those frequency components passed by the roofing filter, the variable "filt" is used as a data input rather than closing prices. Rather than independently taking the averages of the numerator and denominator, I chose to perform smoothing on the ratio using the SuperSmoother filter. The coefficients for the SuperSmoother filters have previously been computed in the dominant cycle measurement part of the code."
Happy trading!
Gann Square of 144 (Master Price & Time)🔹 What this tool does
Draws a 144-unit square in price & time (0 → 144)
Plots all key horizontal & vertical levels:
0, 18, 36, 48, 54, 72, 90, 96, 108, 126, 144
Highlights the main 1/2 level (72) as thick midline
Marks 1/3 and 2/3 (48 & 96) as special harmonic levels
Draws internal diagonals (0–144, 144–0 and sub-squares)
Plots an 8-ray Gann fan from the 0-point (0 → 36 / 72 / 108 / 144 etc.)
Keeps price–time ratio consistent inside the box:
the 1×1 angle has a fixed slope = price_per_bar
The idea: once the square is calibrated to a major swing, you can study how price respects these angles and harmonic zones over time.
🔧 Inputs & how to set it up correctly
Choose your timeframe
Works best on Daily and Weekly charts.
Use one timeframe consistently when calibrating the square.
Start offset (bars back)
Start offset (bars back) shifts the whole square left/right.
Increase the value to move the square further into the past, decrease it to move it closer to the current bars.
Box width (bars)
Box width (bars) = how many bars the square spans horizontally.
Bigger value = projects the structure further into the future.
Example: 288 bars ≈ 2×144 units in time, 720 bars for longer-term projection, etc.
Bottom price
Bottom price is your 0-level in price.
Usually set this to a major swing low (cycle low, bear market low, important pivot).
The bottom-left corner of the square conceptually sits at:
(start_offset_bar, bottom_price)
Price per bar (slope 1×1) (if your version has this input)
This defines the slope of the 1×1 angle (main Gann angle).
Recommended way to set it:
Pick a major impulsive move from Swing Low → Swing High.
Measure:
Price range = High − Low
Number of bars between them.
Compute:
price_per_bar = price_range / number_of_bars
Use that as your 1×1 value in the input.
Now the main diagonal from 0 to 144 represents the true Gann 1×1 for that swing.
Important: The 1×1 angle is mathematically correct (price-per-bar), even if it does not always look like a perfect 45° line visually in TradingView due to chart scaling.
📖 How to read the Square of 144
Horizontal levels
0 = anchor price (bottom)
18, 36, 48, 54, 72, 90, 96, 108, 126, 144 = key price harmonics
72 (1/2) often acts as major support/resistance
48 & 96 (1/3 and 2/3) are strong “vibration” levels
Vertical levels
Same units but in time (bars).
When important pivots in price occur near these verticals, you get time–price confluence.
Midlines (1/2)
The thick horizontal and vertical lines at 72 mark the center of the square.
Crossings around these often signal important cycle turns.
1/3 & 2/3 zones (48–54 and 90–96)
These narrow bands are powerful reversal / decision zones.
Price often reacts strongly there or accelerates if they break.
Gann fan from 0-point
These rays represent major trends:
1×1 equivalent (main diagonal)
Faster & slower angles (e.g. 2×1, 1×2, etc depending on configuration)
If price breaks one fan angle cleanly, it often “falls” or “climbs” toward the next one.
🎯 Practical use cases
Project future support/resistance zones based on a major low.
See where price is in the square: early in the cycle (0–36), mid (around 72), or late (108–144).
Watch how price respects:
midlines (72),
1/3 and 2/3 bands (48–54, 90–96),
and the fan angles from 0.
Combine with your own price action / Fibonacci / trend tools – this is not a signal generator, but a time–price map.
⚠️ Notes & limitations
This tool is for educational & analytical purposes only.
It does not generate buy/sell signals.
Visual 45° angles in TradingView can change when you zoom or rescale the chart.
→ The script keeps the internal price-per-bar logic stable, even if the drawing looks steeper/flatter when zooming.
Always confirm zones with price action, volume, and higher timeframe context.
Puell Multiple Variants [OperationHeadLessChicken]Overview
This script contains three different, but related indicators to visualise Bitcoin miner revenue.
The classical Puell Multiple : historically, it has been good at signaling Bitcoin cycle tops and bottoms, but due to the diminishing rewards miners get after each halving, it is not clear how you determine overvalued and undervalued territories on it. Here is how the other two modified versions come into play:
Halving-Corrected Puell Multiple : The idea is to multiply the miner revenue after each halving with a correction factor, so overvalued levels are made comparable by a horizontal line across cycles. After experimentation, this correction factor turned out to be around 1.63. This brings cycle tops close to each other, but we lose the ability to see undervalued territories as a horizontal region. The third variant aims to fix this:
Miner Revenue Relative Strength Index (Miner Revenue RSI) : It uses RSI to map miner revenue into the 0-100 range, making it easy to visualise over/undervalued territories. With correct parameter settings, it eliminates the diminishing nature of the original Puell Multiple, and shows both over- and undervalued revenues correctly.
Example usage
The goal is to determine cycle tops and bottoms. I recommend using it on high timeframes, like monthly or weekly . Lower than that, you will see a lot of noise, but it could still be used. Here I use monthly as the example.
The classical Puell Multiple is included for reference. It is calculated as Miner Revenue divided by the 365-day Moving Average of the Miner Revenue . As you can see in the picture below, it has been good at signaling tops at 1,3,5,7.
The problems:
- I have to switch the Puell Multiple to a logarithmic scale
- Still, I cannot use a horizontal oversold territory
- 5 didn't touch the trendline, despite being a cycle top
- 9 touched the trendline despite not being a cycle top
Halving-Corrected Puell Multiple (yellow): Multiplies the Puell Multiple by 1.63 (a number determined via experimentation) after each halving. In the picture below, you can see how the Classical (white) and Corrected (yellow) Puell Multiples compare:
Advantages:
- Now you can set a constant overvalued level (12.49 in my case)
- 1,3,7 are signaled correctly as cycle tops
- 9 is correctly not signaled as a cycle top
Caveats:
- Now you don't have bottom signals anymore
- 5 is still not signaled as cycle top
Let's see if we can further improve this:
Miner Revenue RSI (blue):
On the monthly, you can see that an RSI period of 6, an overvalued threshold of 90, and an undervalued threshold of 35 have given historically pretty good signals.
Advantages:
- Uses two simple and clear horizontal levels for undervalued and overvalued levels
- Signaling 1,3,5,7 correctly as cycle tops
- Correctly does not signal 9 as a cycle top
- Signaling 4,6,8 correctly as cycle bottoms
Caveats:
- Misses two as a cycle bottom, although it was a long time ago when the Bitcoin market was much less mature
- In the past, gave some early overvalued signals
Usage
Using the example above, you can apply these indicators to any timeframe you like and tweak their parameters to obtain signals for overvalued/undervalued BTC prices
You can show or hide any of the three indicators individually
Set overvalued/undervalued thresholds for each => the background will highlight in green (undervalued) or red (overvalued)
Set special parameters for the given indicators: correction factor for the Corrected Puell and RSI period for Revenue RSI
Show or hide halving events on the indicator panel
All parameters and colours are adjustable
Fractal Market Model [BLAZ]Version 1.0 – Published August 2025: Initial release
1. Overview & Purpose
1.1. What This Indicator Does
The Fractal Market Model is an original multi-timeframe technical analysis tool that bridges the critical gap between macro-level market structure and micro-level price execution. Designed to work across all financial markets including Forex, Stocks, Crypto, Futures, and Commodities. While traditional Smart Money Concepts indicators exist, this implementation analyses multi-timeframe liquidity zones and price action shifts, marking potential reversal points where Higher Timeframe (HTF) liquidity sweeps coincide with Low Timeframe (LTF) price action dynamics changes.
Snapshot details: NASDAQ:GOOG , 1W Timeframe, Year 2025
1.2. What Sets This Indicator Apart
The Fractal Market Model analyses multi-timeframe correlations between HTF structural events and LTF price action. This creates a dynamic framework that reveals patterns observed historically in price behaviour that are believed to reflect institutional activity across multiple time dimensions.
The indicator recognizes that markets move in fractal cycles following the AMDX pattern (Accumulation, Manipulation, Distribution, Continuation/Reversal). By tracking this pattern across timeframes, it flags zones where price action dynamics characteristics have historically shown shifts. In the LTF, the indicator monitors for price closing through the open of an opposing candle near HTF swing highs or lows, marking this as a Change in State of Delivery (CISD), a threshold event where price action historically transitions direction.
Practical Value:
Multi-Timeframe Integration: Connects HTF structural events with LTF execution patterns.
Fractal Pattern Recognition: Identifies AMDX cycles across different time dimensions.
Price Behavior Analysis: Tracks CISD patterns that may reflect historical shifts in order flow commonly associated with institutional activity.
Range-Based Context: Analyses price action within established HTF liquidity zones.
1.3. How It Works
The indicator employs a systematic 5-candle HTF tracking methodology:
Candles 0-1: Accumulation phase identification.
Candle 2: Manipulation detection (raids previous highs/lows).
Candle 3: Distribution phase recognition.
Candle 4: Continuation/reversal toward opposite liquidity.
The system monitors for CISD patterns on the LTF when HTF manipulation candles close with confirmed sweeps, highlighting zones where order flow dynamics historically shifted within the established HTF range.
Snapshot details: FOREXCOM:AUDUSD , 1H Timeframe, 17 to 28 July 2025
Note: The Candle 0-5 and AMDX labels shown in the accompanying image are for demonstration purposes only and are not part of the indicator’s actual functionality.
2. Visual Elements & Components
2.1. Complete FMM Setup Overview
A fully developed Fractal Market Model setup displays multiple analytical components that work together to provide comprehensive market structure analysis. Each visual element serves a specific purpose in identifying and tracking the AMDX cycle across timeframes.
2.2. Core Visual Components
Snapshot details: FOREXCOM:EURUSD , 5 Minutes Timeframe, 27 May 2025.
Note: The numbering labels 1 to 14 shown in the accompanying image are for demonstration purposes only and are not part of the indicator’s actual functionality.
2.2.1. HTF Structure Elements
(1) HTF Candle Visualization: Displays the 5-candle sequence being tracked (configurable quantity up to 10).
(2) HTF Candle Labels (C2-C4): Numbered identification for each candle in the AMDX cycle.
(3) HTF Resolution Label: Shows the higher timeframe being analysed.
(4) Time Remaining Indicator: Countdown to HTF candle closure.
(5) Vertical Separation Lines: Clearly delineates each HTF candle period.
2.2.2. Key Price Levels
(6) Liquidity Levels: High/low levels from HTF candles 0 and 1 representing potential target zones.
(7) Sweep Detection Lines: Marks where previous HTF candle extremes have been breached on both HTF and LTF.
(8) HTF Candle Mid-Levels: 50% retracement levels of previous HTF candles displayed on current timeframe.
(9) Open Level Marker: Shows the opening price of the most recent HTF candle.
2.2.3. Institutional Analysis Tools
(10) CISD Line: Marks the Change in State of Delivery pattern identification point.
(11) Consequent Encroachment (CE): Mid-level of identified institutional order blocks.
(12) Potential Reversal Area (PRA): Zone extending from previous candle close to the mid-level.
(13) Fair Value Gap (FVG): Identifies imbalance areas requiring potential price revisits.
(14) HTF Time Labels: Individual time period labels for each HTF candle.
2.3. Interactive Features
All visual elements update dynamically as new price data confirms or invalidates the tracked patterns, providing real-time market structure analysis across the selected timeframe combination.
3. Input Parameters and Settings
3.1. Alert Configuration
Setup Notifications: Users can configure alerts to receive notifications when new FMM setups form based on their selected bias, timeframes, and filters. Enable this feature by:
Configure the bias, timeframes and filters and other settings as desired.
Toggle the "Alerts?" checkbox to ON in indicator settings.
On the chart, click the three dots menu beside the indicator's name or press Alt + A.
Select "Add Alert" and click “Create” to activate the alert.
3.2. Display Control Settings
3.2.1. Historical Setup Quantity
Setup Display Control: Customize how many historical setups appear on the chart, with support for up to 50 combined entries. The indicator displays both bullish and bearish FMM setups within the selected limit, including invalidated scenarios. For example, selecting "3 setups" will display the most recent combination of bullish and bearish patterns based on the model's detection logic.
Snapshot details: BINANCE:BTCUSD , 1H Timeframe, 27-Feb to 11-Mar 2025
Note: The labels “Setup 1, 2 & 3: Bullish or Bearish” shown in the accompanying image are for demonstration purposes only and are not part of the indicator’s actual functionality.
3.2.2. Directional Bias Filter
Bias Filter: Control which setups are displayed based on directional preference:
Bullish Only: Shows exclusively upward bias setups.
Bearish Only: Shows exclusively downward bias setups.
Balanced Mode: Displays both directional setups.
This flexibility helps align the indicator's output with broader market analysis or trading framework preferences. The chart below illustrates the same chart in 3.2.1. but when filtered to show only bullish setups.
Snapshot details: BINANCE:BTCUSD , 1H Timeframe, 27-Feb to 11-Mar 2025
Note: The labels “Setup 1, 2 & 3: Bullish” shown in the accompanying image are for demonstration purposes only and are not part of the indicator’s actual functionality.
3.2.3. Invalidated Setup Display
Invalidation Visibility: A setup becomes invalidated when price moves beyond the extreme high or low of the Manipulation candle (C2), indicating that the expected fractal pattern has been disrupted. Choose whether to display or hide setups that have been invalidated by subsequent price action. This feature helps maintain chart clarity while preserving analytical context:
Amber Labels: Setups invalidated at Candle 3 (C3).
Red Labels: Setups invalidated at Candle 4 (C4).
Count Preservation: Invalidated setups remain part of the total setup count regardless of visibility setting.
Below image illustrates balanced setups:
Left side: 1 bearish valid setup, with 2 invalidated setups visible.
Right side: 1 bearish valid setup, with 2 invalidated setups hidden for chart clarity.
Snapshot details: FOREXCOM:GBPJPY , 5M Timeframe, 30 July 2025
3.3. Timeframe Configuration
3.3.1. Multi-Timeframe Alignment
Custom Timeframe Selection: Configure preferred combinations of Higher Timeframe (HTF) and Lower Timeframe (LTF) for setup generation. While the indicator includes optimized default alignments (1Y –1Q, 1Q –1M, 1M –1W, 1M –1D, 1W–4H, 1D–1H, 4H-30m, 4H –15m, 1H –5m, 30m –3m, 15m –1m), users can define custom HTF-LTF configurations to suit their analysis preferences and market focus.
The image below illustrates two different HTF – LTF configuration, both on the 5 minutes chart:
Right side: Automatic multi-timeframe alignment, where the indicator autonomously sets the HTF pairing to 1H when the current chart timeframe is the 5 minutes.
Left side: Custom Timeframe enabled, where HTF is manually set to 4H, and LTF is manually set to 15 minutes, while being on the 5 minutes chart.
Snapshot details: FOREXCOM:GBPJPY , 5 minutes timeframe, 30 July 2025
3.3.2. Session-Based Filtering
Visibility Filters: Control when FMM setups appear using multiple filtering options:
Time-Based Controls:
Show Below: Limit setup visibility to timeframes below the selected threshold.
Use Session Filter: Enable session-based time window restrictions.
Session 1, 2, 3: Configure up to three custom time sessions with start and end times.
These filtering capabilities help concentrate analysis on specific market periods or timeframe contexts.
The image below illustrates the application of session filters:
Left side: The session filter is disabled, resulting in four setups being displayed throughout the day—two during the London session and two during the New York session.
Right side: The session filter is enabled to display setups exclusively within the New York session (8:00 AM – 12:00 PM). Setups outside this time window are hidden. Since the total number of setups is limited to four, the indicator backfills by identifying and displaying two qualifying setups from earlier price action that occurred within the specified New York session window.
Snapshot details: COMEX:GC1! , 5 minutes Timeframe, 29 July 2025
3.4. Annotation Systems
3.4.1. Higher Timeframe (HTF) Annotations
HTF Display Control: Enable HTF visualization using the "HTF candles" checkbox with quantity selector (default: 5 candles, expandable to 10). This displays all HTF elements detailed in the Visual Components section 2.2. above.
Customisation Categories:
Dimensions: Adjust candle offset, gap spacing, and width for optimal chart fit.
Colours: Customize body, border, and wick colours for bullish/bearish candle differentiation.
Style Options: Control line styles for HTF opens, sweep lines, and equilibrium levels.
Feature Toggles: Enable/disable Fair Value Gaps, countdown labels, and individual candle labelling.
All HTF annotation elements support individual styling controls to maintain visual clarity while preserving analytical depth. The image below shows two examples: the left side has customized styling applied, while the right side shows the default appearance.
Snapshot details: CME_MINI:NQ1! , 5 minutes Timeframe, 29 July 2025
3.4.2. Lower Timeframe (LTF) Annotations
LTF Display Control: Comprehensive annotation system for detailed execution analysis, displaying all LTF elements outlined in the Visual Components section 2.2. above.
Customization Categories:
Core Elements: Control HTF separation lines, sweep markers, CISD levels, and candle phase toggles (C2, C3, C4) to selectively show or hide the LTF annotations for each of these specific HTF candle phases.
Reference Levels: Adjust previous equilibrium lines, CISD consequent encroachment, and HTF liquidity levels.
Analysis Tools: Enable potential holding area (PHA) markers.
Styling Options: Individual visibility toggles, colour schemes, line styles, and thickness controls for each element.
All LTF components support full customization to maintain chart clarity while providing precise execution context. The image below shows two examples: the left side has customized styling applied, while the right side shows the default appearance.
Snapshot details: TVC:DXY , 5 minutes Timeframe, 28 July 2025
3.5. Performance Considerations
Higher setup counts and extended HTF displays may impact chart loading times. Adjust settings based on device performance and analysis requirements.
4. Closed-Source Protection Justification
4.1. Why This Indicator Requires Protected Source Code
The Fractal Market Model is the result of original research, development, and practical application of advanced price action frameworks. The indicator leverages proprietary algorithmic systems designed to interpret complex market behavior across multiple timeframes. To preserve the integrity of these innovations and prevent unauthorized replication, the source code is protected.
4.1.1. Key Proprietary Innovations
Real-Time Multi-Timeframe Correlation Engine: A dynamic logic system that synchronizes higher timeframe structural behaviour with lower timeframe execution shifts using custom correlation algorithms, adaptive thresholds, and time-sensitive conditions, supporting seamless fractal analysis across nested timeframes.
CISD Detection Framework: A dedicated mechanism for identifying Change in State of Delivery (CISD), where price closes through the open of an opposing candle at or near HTF swing highs or lows after liquidity has been swept. This is used to highlight potential zones of directional change based on historical order flow dynamics.
Fractal AMDX Cycle Recognition: An engineered structure that detects and classifies phases of Accumulation, Manipulation, Distribution, and Continuation/Reversal (AMDX) across configurable candle sequences, allowing traders to visualize market intent within a repeatable cycle model.
Dynamic Invalidation Logic: An automated monitoring system that continually evaluates the validity of active setups. Setups are invalidated in real time when price breaches the extreme of the manipulation phase (C2), ensuring analytical consistency and contextual alignment.
4.1.2. Community Value
The closed-source nature of this tool protects the author’s original intellectual property while still delivering value to the TradingView community. The indicator offers a complete, real-time visual framework, educational annotations, and intuitive controls for analysing price action structure and historically observed patterns commonly attributed to institutional behaviour across timeframes.
5. Disclaimer & Terms of Use
This indicator, titled Fractal Market Model , has been independently developed by the author based on their own study, interpretation, and practical application of the smart money concepts. The code and structure of this indicator are original and were written entirely from scratch to reflect the author's unique understanding and experience. This indicator is an invite-only script. It is closed-source to protect proprietary algorithms and research methodologies.
This tool is provided solely for educational and informational purposes. It is not intended—and must not be interpreted—as financial advice, investment guidance, or a recommendation to buy or sell any financial instrument. The indicator is designed to assist with technical analysis based on market structure theory but does not guarantee accuracy, profitability, or specific results.
Trading financial markets involves significant risk, including the possibility of loss of capital. By using this indicator, you acknowledge and accept that you are solely responsible for any decisions you make while using the tool, including all trading or investment outcomes. No part of this script or its features should be considered a signal or assurance of success in the market.
By subscribing to or using the indicator, you agree to the following:
You fully assume all responsibility and liability for the use of this product.
You release the author from any and all liability, including losses or damages arising from its use.
You acknowledge that past performance—real or hypothetical—does not guarantee future outcomes.
You understand that this indicator does not offer personalised advice, and no content associated with it constitutes a solicitation of financial action.
You agree that all purchases are final. Once access is granted, no refunds, reimbursements, or chargebacks will be issued under any circumstance.
You agree to not redistribute, resell, or reverse engineer the script or any part of its logic.
Users are expected to abide by all platform guidelines while using or interacting with this tool. For access instructions, please refer to the Author's Instructions section or access the tool through the verified vendor platform.
Quarterly Theory ICT 05 [TradingFinder] Doubling Theory Signals🔵 Introduction
Doubling Theory is an advanced approach to price action and market structure analysis that uniquely combines time-based analysis with key Smart Money concepts such as SMT (Smart Money Technique), SSMT (Sequential SMT), Liquidity Sweep, and the Quarterly Theory ICT.
By leveraging fractal time structures and precisely identifying liquidity zones, this method aims to reveal institutional activity specifically smart money entry and exit points hidden within price movements.
At its core, the market is divided into two structural phases: Doubling 1 and Doubling 2. Each phase contains four quarters (Q1 through Q4), which follow the logic of the Quarterly Theory: Accumulation, Manipulation (Judas Swing), Distribution, and Continuation/Reversal.
These segments are anchored by the True Open, allowing for precise alignment with cyclical market behavior and providing a deeper structural interpretation of price action.
During Doubling 1, a Sequential SMT (SSMT) Divergence typically forms between two correlated assets. This time-structured divergence occurs between two swing points positioned in separate quarters (e.g., Q1 and Q2), where one asset breaks a significant low or high, while the second asset fails to confirm it. This lack of confirmation—especially when aligned with the Manipulation and Accumulation phases—often signals early smart money involvement.
Following this, the highest and lowest price points from Doubling 1 are designated as liquidity zones. As the market transitions into Doubling 2, it commonly returns to these zones in a calculated move known as a Liquidity Sweep—a sharp, engineered spike intended to trigger stop orders and pending positions. This sweep, often orchestrated by institutional players, facilitates entry into large positions with minimal slippage.
Bullish :
Bearish :
🔵 How to Use
Applying Doubling Theory requires a simultaneous understanding of temporal structure and inter-asset behavioral divergence. The method unfolds over two main phases—Doubling 1 and Doubling 2—each divided into four quarters (Q1 to Q4).
The first phase focuses on identifying a Sequential SMT (SSMT) divergence, which forms when two correlated assets (e.g., EURUSD and GBPUSD, or NQ and ES) react differently to key price levels across distinct quarters. For example, one asset may break a previous low while the other maintains structure. This misalignment—especially in Q2, the Manipulation phase—often indicates early smart money accumulation or distribution.
Once this divergence is observed, the extreme highs and lows of Doubling 1 are marked as liquidity zones. In Doubling 2, the market gravitates back toward these zones, executing a Liquidity Sweep.
This move is deliberate—designed to activate clustered stop-loss and pending orders and to exploit pockets of resting liquidity. These sweeps are typically driven by institutional forces looking to absorb liquidity and position themselves ahead of the next major price move.
The key to execution lies in the fact that, during the sweep in Doubling 2, a classic SMT divergence should also appear between the two assets. This indicates a weakening of the previous trend and adds an extra layer of confirmation.
🟣 Bullish Doubling Theory
In the bullish scenario, Doubling 1 begins with a bullish SSMT divergence, where one asset forms a lower low while the other maintains its structure. This divergence signals weakening bearish momentum and possible smart money accumulation. In Doubling 2, the market returns to the previous low and sweeps the liquidity zone—breaking below it on one asset, while the second fails to confirm, forming a bullish SMT divergence.
f this move is followed by a bullish PSP and a clear market structure break (MSB), a long entry is triggered. The stop-loss is placed just below the swept liquidity zone, while the target is set in the premium zone, anticipating a move driven by institutional buyers.
🟣 Bearish Doubling Theory
The bearish scenario follows the same structure in reverse. In Doubling 1, a bearish SSMT divergence occurs when one asset prints a higher high while the other fails to do so. This suggests distribution and weakening buying pressure. Then, in Doubling 2, the market returns to the previous high and executes a liquidity sweep, targeting trapped buyers.
A bearish SMT divergence appears, confirming the move, followed by a bearish PSP on the lower timeframe. A short position is initiated after a confirmed MSB, with the stop-loss placed
🔵 Settings
⚙️ Logical Settings
Quarterly Cycles Type : Select the time segmentation method for SMT analysis.
Available modes include : Yearly, Monthly, Weekly, Daily, 90 Minute, and Micro.
These define how the indicator divides market time into Q1–Q4 cycles.
Symbol : Choose the secondary asset to compare with the main chart asset (e.g., XAUUSD, US100, GBPUSD).
Pivot Period : Sets the sensitivity of the pivot detection algorithm. A smaller value increases responsiveness to price swings.
Pivot Sync Threshold : The maximum allowed difference (in bars) between pivots of the two assets for them to be compared.
Validity Pivot Length : Defines the time window (in bars) during which a divergence remains valid before it's considered outdated.
🎨 Display Settings
Show Cycle :Toggles the visual display of the current Quarter (Q1 to Q4) based on the selected time segmentation
Show Cycle Label : Shows the name (e.g., "Q2") of each detected Quarter on the chart.
Show Labels : Displays dynamic labels (e.g., “Q2”, “Bullish SMT”, “Sweep”) at relevant points.
Show Lines : Draws connection lines between key pivot or divergence points.
Color Settings : Allows customization of colors for bullish and bearish elements (lines, labels, and shapes)
🔔 Alert Settings
Alert Name : Custom name for the alert messages (used in TradingView’s alert system).
Message Frequenc y:
All : Every signal triggers an alert.
Once Per Bar : Alerts once per bar regardless of how many signals occur.
Per Bar Close : Only triggers when the bar closes and the signal still exists.
Time Zone Display : Choose the time zone in which alert timestamps are displayed (e.g., UTC).
Bullish SMT Divergence Alert : Enable/disable alerts specifically for bullish signals.
Bearish SMT Divergence Alert : Enable/disable alerts specifically for bearish signals
🔵 Conclusion
Doubling Theory is a powerful and structured framework within the realm of Smart Money Concepts and ICT methodology, enabling traders to detect high-probability reversal points with precision. By integrating SSMT, SMT, Liquidity Sweeps, and the Quarterly Theory into a unified system, this approach shifts the focus from reactive trading to anticipatory analysis—anchored in time, structure, and liquidity.
What makes Doubling Theory stand out is its logical synergy of time cycles, behavioral divergence, liquidity targeting, and institutional confirmation. In both bullish and bearish scenarios, it provides clearly defined entry and exit strategies, allowing traders to engage the market with confidence, controlled risk, and deeper insight into the mechanics of price manipulation and smart money footprints.
