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.
"Cycle" için komut dosyalarını ara
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.
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Based on time-quarter analysis and correlation divergence concepts. Designed to help identify potential reversal zones through systematic divergence detection across multiple time cycles.
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
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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
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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
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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
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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
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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
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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.
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.
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!
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.
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.
Gann Dynamic Levels [SmartFoxy]# 🌌 Gann Dynamic Levels
Gann Dynamic Levels is a dynamic Gann-based framework that calculates proportional and exponential levels using customizable methods — including planetary ratios.
Perfect for traders focused on cycles , ratios , and harmonic structures .
Inspired by the geometric and harmonic principles of W.D. Gann , this multifunctional tool automatically plots time–price projection levels based on user-defined anchor points.
It combines multiple calculation techniques to capture both linear and exponentia l market symmetries.
The indicator adapts dynamically to price movement, helping traders identify potential reversal zones , time clusters , and harmonic expansions derived from proportional and planetary relationships.
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## ⚙️ Core Features
Five Calculation Methods — Linear, ratio-based, geometric, and exponential spacing for multi-perspective analysis.
Planetary Scaling Mode — Optional mode based on astronomical distances (Titius–Bode Law), adding an astronomical dimension to level spacing.
Adaptive Offset Control — Shifts all projected levels left or right proportionally without changing their internal spacing.
Automatic Label Management — Dynamically updates or reuses labels for better clarity and improved chart performance.
Custom Styling — Full control over colors, widths, label positions, and line styles for each method.
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## 🌐 Purpose
Designed for traders who combine Gann theory , harmonic ratios , and cyclical timing to visualize equilibrium zones and future market symmetry.
Whether used for short-term timing or long-term structural projections, Gann Dynamic Levels provides an adaptive, geometry-based framework for interpreting market behavior.
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## 📘 How to Use
When first applied, the indicator prompts you to place two points on the chart — for example, at the start and end of a significant price range.
The indicator calculates the number of bars between these two points, known as Delta .
Delta serves as the base unit for all calculations in Methods #1–#5 .
The computed results are displayed in Table 1 , which can be toggled using the parameter “📱 Show Gann Levels Table”.
You can reset or reposition the initial points in two ways:
Drag the existing points to new positions on the chart.
Hover over the indicator name, click ⦁⦁⦁ (More) → select “ Reset Points ”, then set new reference points.
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## ⚙️ Method Logic
Classic – Evenly spaced levels based on the base Delta value. Ideal for identifying key support and resistance zones.
Coefficient (Coeff) – Scales Delta by fractional or whole-number coefficients for proportional level spacing.
Rounded – Rounds each calculated level to the nearest significant price value to align with major zones.
Subtractive – Generates levels by subtracting multiples of Delta from a reference point, emphasizing retracement-type structures.
Exponential – Applies an exponential growth model (10a = 4 + 3×2ⁿ) to project dynamic, non-linear level expansion.
Planetary – Uses the average distances of planets from the Sun (in Astronomical Units, AU ) as ratio multipliers to create harmonic projections.
Planetary distances can be customized in the user settings.
Data for Method #6 (Planetary) is displayed in Table 2 , toggled via “ 🪐 Show Planetary Table. ”
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## ➡️ Additional Feature
Offset – Shifts all Gann levels horizontally (left or right) without changing their spacing.
Useful for visually aligning levels with key market structures.
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### 🧭 Summary
A multi-method Gann framework combining geometric, harmonic, and planetary ratios for dynamic level projection and cycle analysis.
r - g Oscillator | Norm + Sigma-BandsThe r–g Oscillator measures the macro-liquidity regime by tracking the gap between real interest rates (r) and nominal GDP growth (g).
It approximates real rate pressure using the 10-Year Treasury yield minus the 5-Year/5-Year forward inflation expectation, and compares that to either Real or Nominal U.S. GDP YoY growth.
Green (g > r): Expansionary backdrop — growth outpaces real yields; liquidity tailwinds.
Red (r > g): Contractionary backdrop — real rates restrictive; liquidity headwinds.
The σ-bands (standard-deviation envelopes) highlight statistically extreme expansions or contractions in the r–g spread.
The “sweet-spot” shading marks moments when r–g breaks strongly above/below zero — early-cycle thrusts or late-cycle stress.
Optional normalization rescales r–g between –1 and +1 to compare across cycles.
Use:
Track shifts in the macro tide rather than short-term waves. Sustained green phases typically align with bull-market environments; red phases often coincide with tightening cycles or recessions. Combine with faster liquidity or breadth measures (e.g., WRESBAL ROC) for tactical confirmation.
SwingArm High Pressure V6.7.3SwingArm High Pressure V6.7.3 - User Guide
Overview
SwingArm High Pressure is a multi-timeframe trading indicator designed to identify high-probability entry zones and profit targets. This indicator works best when combined with the standard SwingArm indicator to display 8-hour and higher timeframes for complete market analysis.
Key Features
1. Multi-Timeframe Analysis
Chart Timeframe (CT): Your primary entry timeframe
Higher Timeframe 1 (HTF1): Secondary confirmation and targets
Higher Timeframe 2 (HTF2): Extended swing targets
2. Trading Type Selection
Choose between two preset configurations:
CT/15m/1H: For day trading and scalping
CT/2H/4H: For swing trading (recommended to pair with standard SwingArm for 8H+ timeframes)
3. Entry Zones
Optimal Entry Boxes (High-Pressure Zones)
BLUE boxes: Bullish optimal entry zones (high-pressure buying opportunity)
YELLOW boxes: Bearish optimal entry zones (high-pressure selling opportunity)
These represent the highest probability entries when price reaches these levels
Fresh SwingArm Zones
GREEN zones: Freshly created bullish swingarm areas
RED zones: Freshly created bearish swingarm areas
Deeper entries into zones (78.6%-88.6%) provide better risk/reward ratios
4. Fibonacci Levels
Each swingarm zone contains three Fibonacci retracement levels:
Fib. 61.8%: Early entry (consider waiting for deeper levels)
Fib. 78.6%: Good entry opportunity
Fib. 88.6%: Deep entry with excellent risk/reward
5. Zone Labels
The indicator automatically labels zones based on their function:
Internal Zones (shorter timeframes):
Display as "INTERNAL - Buy/Sell Zones"
May cycle multiple times before reaching targets
Best for scalping and quick trades
External Zones (higher timeframes):
Display as "EXTERNAL TARGET - Take Profit"
Primary profit-taking areas
Use for swing trade exits
Trading Strategy
Entry Setup
Wait for fresh zone creation (green/red zones appear)
Identify optimal entry boxes (blue/yellow high-pressure areas)
Enter at Fibonacci levels:
Best entries: 78.6%-88.6% (deeper is better)
Acceptable: 61.8% (but watch for deeper retracements)
Trade Management
Stop Loss: Place below swingarm low (long) or above swingarm high (short)
Targets: Use higher timeframe zones for profit objectives
Internal vs External: Internal zones may flip multiple times; external zones are swing targets
Timeframe Hierarchy
Lower timeframe zones = Entry areas
Higher timeframe zones = Target/profit areas
Example: Enter at 15M zones, target 2H/4H zones for exits
Alert System
Available Alerts
Fresh SwingArm Zone Alerts: Notifies when new green/red zones are created
Fib. 88.6% Break Alerts: Deepest entry level touched
Fib. 78.6% Break Alerts: Good entry level touched
Fib. 61.8% Break Alerts: Early entry level touched
Pressure ON / Optimal Alerts: High-pressure zones activated
Circle Alerts: Pressure signal confirmations
Probability Alerts: Set threshold for long/short probability notifications
Alert Messages Include:
Entry quality rating (DEEPEST, DEEP, GOOD, EARLY)
Current price level
Risk/reward guidance
Target zone information
Probability System
The indicator calculates buying and selling pressure across multiple timeframes:
Long Probability: Bullish pressure percentage
Short Probability: Bearish pressure percentage
Set custom thresholds (default 50%) to receive alerts only when probability exceeds your criteria
Customization Options
Visual Settings
RSI Candle Colors: Enable/disable and customize overbought (blue) and oversold (red) candle colors
Label Display: Toggle individual Fibonacci level labels (61.8%, 78.6%, 88.6%)
Label Colors: Customize colors for long and short labels
Label Size: Adjust label size (Tiny to Huge)
Swingarm Pressure Labels: Show/hide zone break labels
Table Display
Probability Status Table: Shows current pressure analysis
Swingarm Status: Displays current swingarm states across timeframes
Position & Size: Customize table location and text size
Statistics Table
Break Statistics: Track swingarm breaks over time
Performance Metrics: View historical break data per timeframe
Best Practices
Combine with Standard SwingArm: Use the regular SwingArm indicator to display 8-hour and higher timeframes for complete market structure
Respect Timeframe Hierarchy: Always enter on lower timeframes and target higher timeframes
Wait for Deep Entries: The 78.6% and 88.6% levels offer the best risk/reward ratios
Watch Internal Cycles: Shorter timeframe zones may reverse multiple times - don't expect straight-line moves to targets
Use Optimal Entry Boxes: Blue and yellow high-pressure zones provide the highest probability setups
Confirm with Multiple Timeframes: Look for alignment across all three selected timeframes for strongest signals
Notes
This indicator is optimized for 1m, 15m, 1H, 2H, and 4H timeframes
For best results, use in conjunction with proper risk management
Entry opportunities include both optimal entry boxes AND fresh swingarm zones
Deeper zone entries consistently provide better risk/reward ratios
Support
For questions or assistance, refer to the indicator settings tooltips or contact the developer through x.
Disclaimer:
This indicator is for educational purposes. Always practice proper risk management and never risk more than you can afford to lose.
Alt Coin Season Indicator v2Trend Core Strategy with Alt Season Filter
This script is a comprehensive trend-following strategy designed to identify high-probability long entries for altcoins. It combines a core mean-reversion setup with a powerful, two-layer "Alt Season" filter to ensure trades are only considered when macro conditions are most favorable.
The primary goal is to enter a trade during a short-term dip (oversold RSI) but only when the broader market structure (Halving Cycle and BTC Dominance) confirms that capital is flowing into altcoins.
How It Works: The Logic
The strategy is built on two distinct layers that must align for a signal to be valid.
1. The Core Trading Setup
A potential LONG ENTRY signal is identified when a specific set of trend and momentum conditions are met:
Long-Term Trend: The price must be trading above the 200-period Slow Moving Average.
Mean Reversion Entry: The RSI must be in an oversold state (below 35).
Favorable Dominance: BTC.D must be trending down, and ETH.D must be trending up, indicating a "risk-on" environment.
2. The "Alt Season" Master Filter
This is the master switch that confirms the macro environment. A trade setup is only considered valid if the "Alt Season" filter is active. This filter has two sub-layers:
Bitcoin Halving Cycle: The script tracks the 4-year cycle and only allows signals during the two most bullish phases:
Post-Halving Accumulation (Yellow Background): The period immediately following a halving.
Parabolic Uptrend (Green Background): The primary bull market phase.
Signals are automatically disabled during the "Distribution" (Red) and "Bear Market" (Dark Red) phases.
BTC Dominance State: This defines the precise start and end of an alt season based on capital flows.
START (🚀): Alt Season becomes active when BTC.D crosses below 60%.
RESET (⚠️): The state is temporarily disabled if BTC.D reclaims 60%, acting as a warning signal.
END (🛑): The season is officially over when BTC.D crosses back above 40% from below.
On-Chart Visuals
The script provides a rich visual interface for at-a-glance analysis:
Background Colors: The chart background changes color to reflect the current Halving Cycle phase. A bright cyan overlay indicates when the "Alt Season" filter is fully active.
Dynamic Shapes:
🚀 (Rocket): Signals the start of a confirmed Alt Season. The size is dynamic—a larger rocket appears if the RSI is more deeply oversold, indicating a higher-conviction setup.
⚠️ (Warning Sign): Appears if BTC.D reclaims the 60% start level, indicating a temporary pause or "reset" of the alt season.
🛑 (Stop Sign): Marks the official end of the Alt Season.
On-Screen Table: A real-time dashboard in the top-right corner shows the status of every single condition, providing full transparency into the script's logic.
How to Use
Wait for the "Alt Season Active" (cyan) background to appear. This is your primary confirmation that macro conditions are favorable.
Look for LONG ENTRY labels. These appear when the core trading setup aligns with an active Alt Season.
Use the on-screen table to understand why a signal is or is not firing.
Set Alerts: The script includes three distinct alerts for "Alt Season Activated," "Alt Season Warning," and "Alt Season Officially Over" to keep you updated on the macro environment.
Disclaimer: This tool is for educational purposes only and should not be considered financial advice. All trading involves risk. Always conduct your own research and backtesting before making any trading decisions.
Wall Street Cheat Sheet IndicatorThe Wall Street Cheat Sheet Indicator is a unique tool designed to help traders identify the psychological stages of the market cycle based on the well-known Wall Street Cheat Sheet. This indicator integrates moving averages and RSI to dynamically label market stages, providing clear visual cues on the chart.
Key Features:
Dynamic Stage Identification: The indicator automatically detects and labels market stages such as Disbelief, Hope, Optimism, Belief, Thrill, Euphoria, Complacency, Anxiety, Denial, Panic, Capitulation, Anger, and Depression. These stages are derived from the emotional phases of market participants, helping traders anticipate market movements.
Technical Indicators: The script uses two key technical indicators:
200-day Simple Moving Average (SMA): Helps identify long-term market trends.
50-day Simple Moving Average (SMA): Aids in recognizing medium-term trends.
Relative Strength Index (RSI): Assesses the momentum and potential reversal points based on overbought and oversold conditions.
Clear Visual Labels: The current market stage is displayed directly on the chart, making it easy to spot trends and potential turning points.
Usefulness:
This indicator is not just a simple mashup of existing tools. It uniquely combines the concept of market psychology with practical technical analysis tools (moving averages and RSI). By labeling the psychological stages of the market cycle, it provides traders with a deeper understanding of market sentiment and potential future movements.
How It Works:
Disbelief: Detected when the price is below the 200-day SMA and RSI is in the oversold territory, indicating a potential bottom.
Hope: Triggered when the price crosses above the 50-day SMA, with RSI starting to rise but still below 50, suggesting an early uptrend.
Optimism: Occurs when the price is above the 50-day SMA and RSI is between 50 and 70, indicating a strengthening trend.
Belief: When the price is well above the 50-day SMA and RSI is between 70 and 80, showing strong bullish momentum.
Thrill and Euphoria: Identified when RSI exceeds 80, indicating overbought conditions and potential for a peak.
Complacency to Depression: These stages are identified based on price corrections and drops relative to moving averages and declining RSI values.
Best Practices:
High-Time Frame Focus: This indicator works best on high-time frame charts, specifically the 1-week Bitcoin (BTCUSDT) chart. The longer time frame provides a clearer picture of the overall market cycle and reduces noise.
Trend Confirmation: Use in conjunction with other technical analysis tools such as trendlines, Fibonacci retracement levels, and support/resistance zones for more robust trading strategies.
How to Use:
Add the Indicator: Apply the Wall Street Cheat Sheet Indicator to your TradingView chart.
Analyze Market Stages: Observe the dynamic labels indicating the current stage of the market cycle.
Make Informed Decisions: Use the insights from the indicator to time your entries and exits, aligning your trades with the market sentiment.
This indicator is a valuable tool for traders looking to understand market psychology and make informed trading decisions based on the stages of the market cycle.
Detrended Rhythm Oscillator (DRO)How to detect the current "market beat" or market cycle?
A common way to capture the current dominant cycle length is to detrend the price and look for common rhythms in the detrended series. A common approach is to use a Detrended Price Oscillator (DPO). This is done in order to identify and isolate short-term cycles.
A basic DPO description can be found here:
www.tradingview.com
Improvements to the standard DPO
The main purpose of the standard DPO is to analyze historical data in order to observe cycle's in a market's movement. DPO can give the technical analyst a better sense of a cycle's typical high/low range as well as its duration. However, you need to manually try to "see" tops and bottoms on the detrended price and measure manually the distance from low-low or high-high in order to derive a possible cycle length.
Therefore, I added the following improvements:
1) Using a DPO to detrend the price
2) Indicate the turns of the detrended price with a ZigZag lines to better see the tops/bottoms
3) Detrend the ZigZag to remove price amplitude between turns to even better see the cyclic turns ("rhythm")
4) Measure the distance from last detrended zigzag pivot (high-high / low-low) and plot the distance in bars above/below the turn
Now, you can clearly see the rhythm of the dataset indicated by the Detrended Rhythm Oscillator including the exact length between the turns. This makes the procedure to "spot" turns and "measure" distance more simple for the trader.
How to use this information
The purpose is to check if there is a common rhythm or beat in the underlying dataset. To check that, look for recurring pattern in the numbers. E.g. if you often see the same measured distance, you can conclude that there is a major dominant cycle in this market. Also watch for harmonic relations between the numbers. So in the example above you see the highlighted cluster of detected length of around 40,80 and 120. There three numbers all have a harmonic relation to 40.
Once you have this cyclic information, you can use this number to optimize or tune technical indicators based on the current dominant cycle length. E.g. set the length parameter of a technical indicator to the detected harmonic length with the DRO indicator.
Example Use-Case
You can use this information to set the input for the following free public open-source script:
Disclaimer
This is not meant to be a technical indicator on its own and the derived cyclic length should not be used to forecast the next turn per se. The indicator should give you an indication of the current market beat or dominant beats which can be use to further optimize other oscillator or trading related settings.
Options & settings
The indicator allows to plot different versions. It allows to plot the original DPO, the DRO with ZigZag lines, the DRO with detrended ZigZag lines and length labels on/off. You can turn on or off these version in the indicator settings. So you can tweak it visually to your own needs.
Gabriel's Global Market CapGabriel's Global Market Cap is a comprehensive financial indicator designed to track and analyze the total market capitalization across multiple asset classes. It incorporates various financial markets, including stocks, bonds, real estate, cryptocurrencies, commodities, derivatives, private equity, insurance, OTC markets, and natural resources, to provide a holistic view of global market dynamics.
This indicator integrates Ehlers' Adaptive Dominant Cycle Detection and a custom VIX formula to adjust market values based on volatility and volume fluctuations, allowing for a more refined understanding of market conditions.
Key Features
✅ Multi-Market Analysis – Tracks 10+ global financial sectors, each represented by a key ETF or index.
✅ Normalization & Readability – Converts market cap values into an easy-to-read format (Millions, Billions, Trillions, Quadrillions).
✅ Volatility & Volume Adjustments – Optional VIX-based smoothing and relative volume adjustment for more dynamic readings.
✅ Ehlers’ Cycle Detection – Utilizes dominant cycle length detection to uncover market rhythms and cyclic behavior.
✅ Risk Thresholds & Background Coloring – Identifies overbought and oversold conditions with cyclic bands and background shading.
✅ Customizable Inputs – Users can toggle different market categories on/off for focused analysis.
✅ Interactive Data Table – Displays real-time values for each asset class in a structured table format.
Market Categories & Data Sources
📈 Global Stock Market – iShares MSCI ACWI ETF (ACWI)
💰 Global Bond Market – Vanguard Total World Bond ETF (BNDW)
🏡 Real Estate Market – iShares Global REIT ETF (REET)
₿ Cryptocurrency Market – Total Crypto Market Cap (CRYPTOCAP:TOTAL)
🌾 Commodities Market – Invesco DB Commodity Index Fund (DBC)
📊 Derivatives Market – CME Group (CME)
🏦 Private Equity & VC – ProShares Global Listed Private Equity ETF (PEX)
🛡️ Insurance Market – SPDR S&P Insurance ETF (KIE)
💹 OTC Markets – OTC Markets Group (OTCM)
⛽ Natural Resources – iShares Global Energy ETF (IXC)
Technical Enhancements
1️⃣ Custom Volatility Index (VIX) Calculation (Work In Progress)
Adjusts asset values based on volatility conditions using Ehlers' Cycle Detection.
Higher VIX reduces market cap, while lower VIX stabilizes it.
2️⃣ Adaptive Market Normalization
Converts absolute market values into a relative strength scale (0-100) for better visual analysis.
Uses historical min/max values to adjust dynamically.
3️⃣ Cyclic Analysis & Overbought/Oversold Levels
Detects hidden market rhythms & time cycles.
Calculates upper and lower risk bands based on dominant cycle length.
Applies background shading for visualizing low or high risk periods.
Customization Options
🔧 Enable/Disable Market Categories – Select which asset classes to track.
📊 Toggle VIX & Volume Smoothing – Adjust how market cap reacts to volatility & volume.
🎨 Cyclic Risk Bands – Highlight overbought/oversold conditions with dynamic background colors.
Visual Elements
📉 Market Cap Trends – Each category is plotted with a unique color.
🌎 Total Global Value (TGV) – A combined index representing all selected markets.
🎨 Background Coloring – Indicates high/low risk periods.
📋 Real-Time Data Table – Displays normalized & raw market cap values in an easy-to-read format.
Practical Applications
📊 Macroeconomic Analysis – Track global liquidity and investment shifts across asset classes.
💹 Volatility & Risk Assessment – Identify high-risk market conditions based on cyclic behavior.
📈 Cross-Market Comparisons – See which sectors are leading or lagging in value growth.
🔍 Crypto & Stock Market Trends – Analyze how traditional and digital assets correlate.
Regime Classifier Oscillator (AiBitcoinTrend)The Regime Classifier Oscillator (AiBitcoinTrend) is an advanced tool for understanding market structure and detecting dynamic price regimes. By combining filtered price trends, clustering algorithms, and an adaptive oscillator, it provides traders with detailed insights into market phases, including accumulation, distribution, advancement, and decline.
This innovative tool simplifies market regime classification, enabling traders to align their strategies with evolving market conditions effectively.
👽 What is a Regime Classifier, and Why is it Useful?
A Regime Classifier is a concept in financial analysis that identifies distinct market conditions or "regimes" based on price behavior and volatility. These regimes often correspond to specific phases of the market, such as trends, consolidations, or periods of high or low volatility. By classifying these regimes, traders and analysts can better understand the underlying market dynamics, allowing them to adapt their strategies to suit prevailing conditions.
👽 Common Uses in Finance
Risk Management: Identifying high-volatility regimes helps traders adjust position sizes or hedge risks.
Strategy Optimization: Traders tailor their approaches—trend-following strategies in trending regimes, mean-reversion strategies in consolidations.
Forecasting: Understanding the current regime aids in predicting potential transitions, such as a shift from accumulation to an upward breakout.
Portfolio Allocation: Investors allocate assets differently based on market regimes, such as increasing cash positions in high-volatility environments.
👽 Why It’s Important
Markets behave differently under varying conditions. A regime classifier provides a structured way to analyze these changes, offering a systematic approach to decision-making. This improves both accuracy and confidence in navigating diverse market scenarios.
👽 How We Implemented the Regime Classifier in This Indicator
The Regime Classifier Oscillator takes the foundational concept of market regime classification and enhances it with advanced computational techniques, making it highly adaptive.
👾 Median Filtering: We smooth price data using a custom median filter to identify significant trends while eliminating noise. This establishes a baseline for price movement analysis.
👾 Clustering Model: Using clustering techniques, the indicator classifies volatility and price trends into distinct regimes:
Advance: Strong upward trends with low volatility.
Decline: Downward trends marked by high volatility.
Accumulation: Consolidation phases with subdued volatility.
Distribution: Topping or bottoming patterns with elevated volatility.
This classification leverages historical price data to refine cluster boundaries dynamically, ensuring adaptive and accurate detection of market states.
Volatility Classification: Price volatility is analyzed through rolling windows, separating data into high and low volatility clusters using distance-based assignments.
Price Trends: The interaction of price levels with the filtered trendline and volatility clusters determines whether the market is advancing, declining, accumulating, or distributing.
👽 Dynamic Cycle Oscillator (DCO):
Captures cyclic behavior and overlays it with smoothed oscillations, providing real-time feedback on price momentum and potential reversals.
Regime Visualization:
Regimes are displayed with intuitive labels and background colors, offering clear, actionable insights directly on the chart.
👽 Why This Implementation Stands Out
Dynamic and Adaptive: The clustering and refit mechanisms adapt to changing market conditions, ensuring relevance across different asset classes and timeframes.
Comprehensive Insights: By combining price trends, volatility, and cyclic behaviors, the indicator provides a holistic view of the market.
This implementation bridges the gap between theoretical regime classification and practical trading needs, making it a powerful tool for both novice and experienced traders.
👽 Applications
👾 Regime-Based Trading Strategies
Traders can use the regime classifications to adapt their strategies effectively:
Advance & Accumulation: Favorable for entering or holding long positions.
Decline & Distribution: Opportunities for short positions or risk management.
👾 Oscillator Insights for Trend Analysis
Overbought/oversold conditions: Early warning of potential reversals.
Dynamic trends: Highlights the strength of price momentum.
👽 Indicator Settings
👾 Filter and Classification Settings
Filter Window Size: Controls trend detection sensitivity.
ATR Lookback: Adjusts the threshold for regime classification.
Clustering Window & Refit Interval: Fine-tunes regime accuracy.
👾 Oscillator Settings
Dynamic Cycle Oscillator Lookback: Defines the sensitivity of cycle detection.
Smoothing Factor: Balances responsiveness and stability.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
