APA-Adaptive, Ehlers Early Onset Trend [Loxx]APA-Adaptive, Ehlers Early Onset Trend is Ehlers Early Onset Trend but with Autocorrelation Periodogram Algorithm dominant cycle period input.
What is Ehlers Early Onset Trend?
The Onset Trend Detector study is a trend analyzing technical indicator developed by John F. Ehlers , based on a non-linear quotient transform. Two of Mr. Ehlers' previous studies, the Super Smoother Filter and the Roofing Filter, were used and expanded to create this new complex technical indicator. Being a trend-following analysis technique, its main purpose is to address the problem of lag that is common among moving average type indicators.
The Onset Trend Detector first applies the EhlersRoofingFilter to the input data in order to eliminate cyclic components with periods longer than, for example, 100 bars (default value, customizable via input parameters) as those are considered spectral dilation. Filtered data is then subjected to re-filtering by the Super Smoother Filter so that the noise (cyclic components with low length) is reduced to minimum. The period of 10 bars is a default maximum value for a wave cycle to be considered noise; it can be customized via input parameters as well. Once the data is cleared of both noise and spectral dilation, the filter processes it with the automatic gain control algorithm which is widely used in digital signal processing. This algorithm registers the most recent peak value and normalizes it; the normalized value slowly decays until the next peak swing. The ratio of previously filtered value to the corresponding peak value is then quotiently transformed to provide the resulting oscillator. The quotient transform is controlled by the K coefficient: its allowed values are in the range from -1 to +1. K values close to 1 leave the ratio almost untouched, those close to -1 will translate it to around the additive inverse, and those close to zero will collapse small values of the ratio while keeping the higher values high.
Indicator values around 1 signify uptrend and those around -1, downtrend.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, 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, 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."
Search in scripts for "Cycle"
Adaptive Look-back/Volatility Phase Change Index on Jurik [Loxx]Adaptive Look-back, Adaptive Volatility Phase Change Index on Jurik is a Phase Change Index but with adaptive length and volatility inputs to reduce phase change noise and better identify trends. This is an invese indicator which means that small values on the oscillator indicate bullish sentiment and higher values on the oscillator indicate bearish sentiment
What is the Phase Change Index?
Based on the M.H. Pee's TASC article "Phase Change Index".
Prices at any time can be up, down, or unchanged. A period where market prices remain relatively unchanged is referred to as a consolidation. A period that witnesses relatively higher prices is referred to as an uptrend, while a period of relatively lower prices is called a downtrend.
The Phase Change Index (PCI) is an indicator designed specifically to detect changes in market phases.
This indicator is made as he describes it with one deviation: if we follow his formula to the letter then the "trend" is inverted to the actual market trend. Because of that an option to display inverted (and more logical) values is added.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
That's why investors, banks and institutions worldwide ask for the Jurik Research Moving Average ( JMA ). You may apply it just as you would any other popular moving average. However, JMA's improved timing and smoothness will astound you.
What is adaptive Jurik volatility
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers, 2013, 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, 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."
Included
-Your choice of length input calculation, either fixed or adaptive cycle
-Invert the signal to match the trend
-Bar coloring to paint the trend
Happy trading!
Bitcoin Power Law [LuxAlgo]The Bitcoin Power Law tool is a representation of Bitcoin prices first proposed by Giovanni Santostasi, Ph.D. It plots BTCUSD daily closes on a log10-log10 scale, and fits a linear regression channel to the data.
This channel helps traders visualise when the price is historically in a zone prone to tops or located within a discounted zone subject to future growth.
🔶 USAGE
Giovanni Santostasi, Ph.D. originated the Bitcoin Power-Law Theory; this implementation places it directly on a TradingView chart. The white line shows the daily closing price, while the cyan line is the best-fit regression.
A channel is constructed from the linear fit root mean squared error (RMSE), we can observe how price has repeatedly oscillated between each channel areas through every bull-bear cycle.
Excursions into the upper channel area can be followed by price surges and finishing on a top, whereas price touching the lower channel area coincides with a cycle low.
Users can change the channel areas multipliers, helping capture moves more precisely depending on the intended usage.
This tool only works on the daily BTCUSD chart. Ticker and timeframe must match exactly for the calculations to remain valid.
🔹 Linear Scale
Users can toggle on a linear scale for the time axis, in order to obtain a higher resolution of the price, (this will affect the linear regression channel fit, making it look poorer).
🔶 DETAILS
One of the advantages of the Power Law Theory proposed by Giovanni Santostasi is its ability to explain multiple behaviors of Bitcoin. We describe some key points below.
🔹 Power-Law Overview
A power law has the form y = A·xⁿ , and Bitcoin’s key variables follow this pattern across many orders of magnitude. Empirically, price rises roughly with t⁶, hash-rate with t¹² and the number of active addresses with t³.
When we plot these on log-log axes they appear as straight lines, revealing a scale-invariant system whose behaviour repeats proportionally as it grows.
🔹 Feedback-Loop Dynamics
Growth begins with new users, whose presence pushes the price higher via a Metcalfe-style square-law. A richer price pool funds more mining hardware; the Difficulty Adjustment immediately raises the hash-rate requirement, keeping profit margins razor-thin.
A higher hash rate secures the network, which in turn attracts the next wave of users. Because risk and Difficulty act as braking forces, user adoption advances as a power of three in time rather than an unchecked S-curve. This circular causality repeats without end, producing the familiar boom-and-bust cadence around the long-term power-law channel.
🔹 Scale Invariance & Predictions
Scale invariance means that enlarging the timeline in log-log space leaves the trajectory unchanged.
The same geometric proportions that described the first dollar of value can therefore extend to a projected million-dollar bitcoin, provided no catastrophic break occurs. Institutional ETF inflows supply fresh capital but do not bend the underlying slope; only a persistent deviation from the line would falsify the current model.
🔹 Implications
The theory assigns scarcity no direct role; iterative feedback and the Difficulty Adjustment are sufficient to govern Bitcoin’s expansion. Long-term valuation should focus on position within the power-law channel, while bubbles—sharp departures above trend that later revert—are expected punctuations of an otherwise steady climb.
Beyond about 2040, disruptive technological shifts could alter the parameters, but for the next order of magnitude the present slope remains the simplest, most robust guide.
Bitcoin behaves less like a traditional asset and more like a self-organising digital organism whose value, security, and adoption co-evolve according to immutable power-law rules.
🔶 SETTINGS
🔹 General
Start Calculation: Determine the start date used by the calculation, with any prior prices being ignored. (default - 15 Jul 2010)
Use Linear Scale for X-Axis: Convert the horizontal axis from log(time) to linear calendar time
🔹 Linear Regression
Show Regression Line: Enable/disable the central power-law trend line
Regression Line Color: Choose the colour of the regression line
Mult 1: Toggle line & fill, set multiplier (default +1), pick line colour and area fill colour
Mult 2: Toggle line & fill, set multiplier (default +0.5), pick line colour and area fill colour
Mult 3: Toggle line & fill, set multiplier (default -0.5), pick line colour and area fill colour
Mult 4: Toggle line & fill, set multiplier (default -1), pick line colour and area fill colour
🔹 Style
Price Line Color: Select the colour of the BTC price plot
Auto Color: Automatically choose the best contrast colour for the price line
Price Line Width: Set the thickness of the price line (1 – 5 px)
Show Halvings: Enable/disable dotted vertical lines at each Bitcoin halving
Halvings Color: Choose the colour of the halving lines
Global M2 YoY % Increase signalThe script produces a signal each time the global M2 increases more than 2.5%. This usually coincides with bitcoin prices pumps, except when it is late in the business cycle or the bitcoin price / halving cycle.
It leverages dylanleclair Global M2 YoY % change, with several modifications:
adding a 10 week lead at the YoY Change plot for better visibility, so that the bitcoin pump moreless coincides with the YoY change.
signal increases > 2.5 in Global M2 at the point at which they occur with a green triangle up.
Moon+Lunar Cycle Vertical Delineation & Projection
Automatically highlights the exact candle in which Moonphase shifts occur.
Optionally including shifts within the Microphases of the total Lunar Cycle.
This allow traders to pre-emptively identify time-based points of volatility,
focusing on mean-reversion; further simplified via the use of projections.
Projections are calculated via candle count, values displayed in "Debug";
these are useful in understanding the function & underlying mechanics.
CCI with Signals & Divergence [AIBitcoinTrend]👽 CCI with Signals & Divergence (AIBitcoinTrend)
The Hilbert Adaptive CCI with Signals & Divergence takes the traditional Commodity Channel Index (CCI) to the next level by dynamically adjusting its calculation period based on real-time market cycles using Hilbert Transform Cycle Detection. This makes it far superior to standard CCI, as it adapts to fast-moving trends and slow consolidations, filtering noise and improving signal accuracy.
Additionally, the indicator includes real-time divergence detection and an ATR-based trailing stop system, helping traders identify potential reversals and manage risk effectively.
👽 What Makes the Hilbert Adaptive CCI Unique?
Unlike the traditional CCI, which uses a fixed-length lookback period, this version automatically adjusts its lookback period using Hilbert Transform to detect the dominant cycle in the market.
✅ Hilbert Transform Adaptive Lookback – Dynamically detects cycle length to adjust CCI sensitivity.
✅ Real-Time Divergence Detection – Instantly identifies bullish and bearish divergences for early reversal signals.
✅ Implement Crossover/Crossunder signals tied to ATR-based trailing stops for risk management
👽 The Math Behind the Indicator
👾 Hilbert Transform Cycle Detection
The Hilbert Transform estimates the dominant market cycle length based on the frequency of price oscillations. It is computed using the in-phase and quadrature components of the price series:
tp = (high + low + close) / 3
smooth = (tp + 2 * tp + 2 * tp + tp ) / 6
detrender = smooth - smooth
quadrature = detrender - detrender
inPhase = detrender + quadrature
outPhase = quadrature - inPhase
instPeriod = 0.0
deltaPhase = math.abs(inPhase - inPhase ) + math.abs(outPhase - outPhase )
instPeriod := nz(3.25 / deltaPhase, instPeriod )
dominantCycle = int(math.min(math.max(instPeriod, cciMinPeriod), 500))
Where:
In-Phase & Out-Phase Components are derived from a detrended version of the price series.
Instantaneous Frequency measures the rate of cycle change, allowing the CCI period to adjust dynamically.
The result is bounded within a user-defined min/max range, ensuring stability.
👽 How Traders Can Use This Indicator
👾 Divergence Trading Strategy
Bullish Divergence Setup:
Price makes a lower low, while CCI forms a higher low.
Buy signal is confirmed when CCI shows upward momentum.
Bearish Divergence Setup:
Price makes a higher high, while CCI forms a lower high.
Sell signal is confirmed when CCI shows downward momentum.
👾 Trailing Stop & Signal-Based Trading
Bullish Setup:
✅ CCI crosses above -100 → Buy signal.
✅ A bullish trailing stop is placed at Low - (ATR × Multiplier).
✅ Exit if the price crosses below the stop.
Bearish Setup:
✅ CCI crosses below 100 → Sell signal.
✅ A bearish trailing stop is placed at High + (ATR × Multiplier).
✅ Exit if the price crosses above the stop.
👽 Why It’s Useful for Traders
Hilbert Adaptive Period Calculation – No more fixed-length periods; the indicator dynamically adapts to market conditions.
Real-Time Divergence Alerts – Helps traders anticipate market reversals before they occur.
ATR-Based Risk Management – Stops automatically adjust based on volatility.
Works Across Multiple Markets & Timeframes – Ideal for stocks, forex, crypto, and futures.
👽 Indicator Settings
Min & Max CCI Period – Defines the adaptive range for Hilbert-based lookback.
Smoothing Factor – Controls the degree of smoothing applied to CCI.
Enable Divergence Analysis – Toggles real-time divergence detection.
Lookback Period – Defines the number of bars for detecting pivot points.
Enable Crosses Signals – Turns on CCI crossover-based trade signals.
ATR Multiplier – Adjusts trailing stop sensitivity.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
AHR999X IndexAHR999X Index - A Tool to Watch BITSTAMP:BTCUSD Bitcoin Tops
The AHR999X Index is designed as an extension of the well-known AHR999 Index, specifically to help identify Bitcoin's market tops. This index combines two critical components:
200-Day Fixed Investment Cost:
The average cost if you invested a fixed amount into Bitcoin every day over the last 200 days (using a geometric mean).
Growth Estimate:
A price estimate derived from a logarithmic regression model based on Bitcoin's age.
The formula for AHR999X is:
AHR999X = (Bitcoin Price ÷ 200-Day Fixed Investment Cost) × (Bitcoin Price ÷ Growth Estimate) × 3
How to Interpret AHR999X
Above 8: Accumulation Zone – Bitcoin is historically undervalued.
Between 0.45 and 8: Neutral Zone – Bitcoin is within a reasonable price range.
Below 0.45: Exit Zone – Historically signals market tops and high-risk areas.
A Cycle Observation
One important point to note:
The bottom value of AHR999X increases with every Bitcoin market cycle.
This reflects Bitcoin's long-term price appreciation and diminishing volatility over time.
Altcoins vs BTC Market Cap HeatmapAltcoins vs BTC Market Cap Heatmap
"Ground control to major Tom" 🌙 👨🚀 🚀
This indicator provides a visual heatmap for tracking the relationship between the market cap of altcoins (TOTAL3) and Bitcoin (BTC). The primary goal is to identify potential market cycle tops and bottoms by analyzing how the TOTAL3 market cap (all cryptocurrencies excluding Bitcoin and Ethereum) compares to Bitcoin’s market cap.
Key Features:
• Market Cap Ratio: Plots the ratio of TOTAL3 to BTC market caps to give a clear visual representation of altcoin strength versus Bitcoin.
• Heatmap: Colors the background red when altcoins are overheating (TOTAL3 market cap equals or exceeds BTC) and blue when altcoins are cooling (TOTAL3 market cap is half or less than BTC).
• Threshold Levels: Includes horizontal lines at 1 (Overheated), 0.75 (Median), and 0.5 (Cooling) for easy reference.
• Alerts: Set alert conditions for when the ratio crosses key levels (1.0, 0.75, and 0.5), enabling timely notifications for potential market shifts.
How It Works:
• Overheated (Ratio ≥ 1): Indicates that the altcoin market cap is on par or larger than Bitcoin's, which could signal a top in the cycle.
• Cooling (Ratio < 0.5): Suggests that the altcoin market cap is half or less than Bitcoin's, potentially signaling a market bottom or cooling phase.
• Median (Ratio ≈ 0.75): A midpoint that provides insight into the market's neutral zone.
Use this tool to monitor market extremes and adjust your strategy accordingly when the altcoin market enters overheated or cooling phases.
SeasonsThis code represents a seasonal indicator that has a number of unique functions to help traders better understand the market and make informed decisions. Let's take a closer look at each of them:
1. **Chart background shading for each season:** This function allows you to visually see seasonal changes in the market. You'll be able to easily track how the market changes in different seasons, thanks to the color labeling: blue for winter, green for summer, orange for autumn, and yellow for spring.
2. **Vertical markings for each month:** Additional markers on the chart help you orient yourself in time and better understand price dynamics throughout the year. This is especially useful when analyzing seasonal changes and identifying market cyclicality.
3. **Halving timers:** Connecting halving timers on the chart allows you to track important events, such as the reduction of bitcoin mining rewards. Knowing the timing of halving can be a key moment for decision-making and can affect asset prices.
These functions help traders better analyze the market, identify trends and cyclicality, and optimize their trading strategy. Use this indicator in your trading practice to unleash its full potential and reach new heights in your trading career. Don't miss the opportunity to improve your results - apply the seasonal indicator today!
The seasonal indicator is a powerful tool for traders, helping them analyze the market and make informed decisions based on seasonal and cyclical changes. Here are a few reasons why using this indicator can be advantageous:
1. **Identifying seasonal trends:** The seasonal indicator helps identify seasonal trends in the market, such as changes in activity during different seasons or months. For example, some markets may be more volatile or predictable at certain times of the year, and knowing these trends can help in making decisions about entering or exiting positions.
2. **Optimizing trading strategy:** Understanding seasonal changes in the market allows traders to optimize their trading strategy based on the time of year. For example, they may adjust their risk management approaches or choose specific types of trades according to the current season.
3. **Predicting market cyclicality:** The seasonal indicator can also help in predicting market cyclicality and identifying recurring price movement patterns. This enables traders to build their strategies based on past market behavior within specific time intervals.
How to use the seasonal indicator:
1. **Study seasonal changes:** Use the indicator to analyze how the market changes throughout the year. Pay attention to changes in volatility, trading volumes, and price directions depending on the season.
2. **Optimize trading strategy:** Use the data obtained to optimize your trading strategy. Consider entering or exiting positions within specific time intervals to account for seasonal factors.
3. **Predict cyclicality:** Analyze past market behavior using the seasonal indicator to identify cyclicality and recurring patterns. This will help you make more informed decisions based on expected price movements in the future.
Ultimately, using the seasonal indicator allows traders to better understand the market, adapt their strategies, and make more informed decisions based on seasonal and cyclical changes.
All elements on the chart of a particular color will be attributed to the corresponding season. For example, trend lines or levels marked in blue will be associated with winter.
______________________________________________________
Winter
Explanation of price movement during the winter season:
1. Number 1 and the blue line denote the maximum price of Bitcoin. Note that they always form at the peaks, which is consistent.
2. Number 2 and the blue line represent the minimum price specifically during the winter period. This is indeed the minimum price and the bottom point in the cycle.
3. Number 3 and the blue line indicate a local maximum after the breakthrough, after which the price starts to rise towards line number 1, which acts as global resistance.
4. Number 4 denotes the last winter cycle before the breakthrough of the global maximum. It should be noted that in 2017, the resistance was not broken immediately - first in spring, and then at the beginning of 2018, the maximum was set, and the asset growth occurred in winter.
Additionally, it's worth noting that numbers 1 form the maximum, numbers 2 form the minimum, and since the trend is descending, I have marked its line in blue.
______________________________________________________
Summer
Now let's consider the price behavior chart for the summer. To make the situation clearer, I've left a descending trend in blue on the graph. I reiterate that the elements shown in green on the graph pertain specifically to the summer period.
1. Number 1 on the graph denotes the first summer period! The price during this period remains within a narrow range 90% of the time; however, it's worth noting that impulsive movements can occur at the beginning, middle, or end. Thus, 90% of the time the price is in a low volatility zone, while the remaining percentage is in a high volatility zone.
2. Number 2 on the graph represents the second summer period, where a pattern is observed: the price tends to rise at the beginning of the summer period and fall towards the end. Therefore, I've marked this time with an arc, and there's a pattern to it. It's worth noting that during the period of the descending trend from 2014 to 2016, the situation after the downward trend differs from the situation in 2018 and 2023, when changes in the arrangement of this situation occur after the breakout of the descending trend based on wave analysis and the price of the asset itself.
3. Number 3 represents the third summer period! During this period, the price movement direction is upward and then downward, forming a correction in the upward trend. It should be noted that in this movement, all lows gradually rise, while highs renew all previous local highs of the asset price. This period exhibits increased volatility and impulsive movements, with the asset price mostly staying within a range of minimal volatility, with volatility not exceeding 1-2% on some stretches.
4. Under number 4, the fourth summer period is indicated, which has an overall upward direction. In this period, the movement is aggressively upward. Starting from the first month until the middle of summer, the price moves downward, forming a correction in the upward trend. Then, during the next month, the price moves aggressively upward, renewing price highs. Volatility in this period is anomalously high, resembling a hot July summer.
Additionally, based on the price movement in the summer period, we can assume that fractals are evident here, which we can use to our advantage for profit.
______________________________________________________
Shark Trading - We urge all traders to delve deeper into this indicator and incorporate it into their trading practices. It can become an invaluable aid in market analysis and help traders reach new heights in their trading endeavors.
Adaptive, Double Jurik Filter Moving Average (AJFMA) [Loxx]Adaptive, Double Jurik Filter Moving Average (AJFMA) is moving average like Jurik Moving Average but with the addition of double smoothing and adaptive length (Autocorrelation Periodogram Algorithm) and power/volatility {Juirk Volty) inputs to further reduce noise and identify trends.
What is Jurik Volty?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
That's why investors, banks and institutions worldwide ask for the Jurik Research Moving Average ( JMA ). You may apply it just as you would any other popular moving average. However, JMA's improved timing and smoothness will astound you.
What is adaptive Jurik volatility?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, 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, 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."
Included
- Double calculation of AJFMA for even smoother results
Custom Date MarkersCustom Date Markers - Pine Script Indicator
This indicator provides a powerful visual tool for technical and pattern analysis by allowing traders to mark up to 10 specific historical dates with customizable vertical lines on any chart. Each date can be assigned its own unique color, making it easy to categorize and distinguish between different types of events or market catalysts.
Primary Use Cases:
The indicator excels at identifying cyclical patterns and recurring market behavior. By marking significant dates such as earnings announcements, Federal Reserve meetings, dividend ex-dates, or seasonal events, traders can quickly visualize whether stocks consistently react in similar ways around these recurring dates. This is particularly valuable for discovering hidden patterns that might not be obvious from price action alone.
Practical Applications:
Earnings Analysis: Mark historical earnings dates to see if a stock tends to rally or sell-off before/after announcements
Macro Events: Identify how assets respond to FOMC meetings, CPI releases, or other economic data
Seasonal Patterns: Track dates that show recurring volatility or directional moves (like tax deadline periods, end-of-quarter re balancing, etc.)
Event Studies: Analyze the impact of company-specific events like product launches, FDA approvals, or leadership changes
Advanced Insights:
What makes this tool particularly interesting is its ability to reveal non-obvious correlations. For example, you might discover that a retail stock consistently experiences volume spikes 2-3 weeks before Black Friday across multiple years, or that certain tech stocks show weakness during specific conference dates. The color-coding feature allows you to layer multiple event types simultaneously—perhaps using red for bearish catalysts and green for bullish ones—creating a visual heat map of historical market reactions.
The indicator's 6-month default spacing (covering 4.5 years) is strategically designed to capture multiple business cycles while maintaining clarity on the chart. This timeframe is long enough to identify genuine patterns rather than coincidences, yet focused enough to remain relevant to current market conditions.
Pro Tip: Combine this indicator with volume analysis or other technical indicators to validate whether the patterns you observe are accompanied by meaningful market participation or if they're statistical noise.
SZN - Altcoin OscillatorSZN Altcoin Oscillator – Identify Market Phases with Precision
What is the SZN Altcoin Oscillator?
The SZN Altcoin Oscillator is a multi-layered analysis indicator specifically developed for the altcoin market.
It combines different signal groups (trend, relative strength, volume, market environment, and overheating filters) into a unified oscillator in the 0–100 range.
--> The goal is to make major market movements visible while separating short-term fluctuations from broader trends.
Why not just use RSI or MACD?
Classic single indicators like RSI or MACD often react too sensitively to short-term fluctuations.
This leads to many false signals – especially during volatile altcoin phases.
--> The SZN Altcoin Oscillator solves this problem by combining and filtering multiple signal sources.
--> This smooths out overreactions and identifies true trend movements more reliably.
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How does it work?
The oscillator consists of five signal groups:
Price & Trend Momentum
checks the relative position of fast and slow moving averages
detects whether an altcoin is in a confirmed uptrend or downtrend
Relative Strength vs. Bitcoin & Ethereum
measures performance compared to the two market leaders
shows whether capital is rotating into altcoins or staying in majors
Volume and Breakout Filter
detects whether breakouts are confirmed by increased volume
prevents short “fakeouts” from appearing as buy signals
Market Environment (Regime Filter)
includes overall market data such as BTC dominance or TOTAL3
ensures that buy signals only trigger in suitable market phases
Overheating & Oversold Filter
marks statistically extreme zones
upward cross from oversold → buy signal
downward cross from overheated → sell signal
--> All results are displayed in a 0–100 oscillator.
Buy signal: upward cross from oversold zones
Sell signal: downward cross from overheated zones
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Variants (selection in the settings menu)
The indicator offers 5 preconfigured variants, optimized for different altcoin groups:
Default
The neutral standard configuration – balanced between sensitivity and stability. Suitable for a wide range of altcoins with medium to large market capitalization.
Large Caps
More conservative parameters with stronger smoothing. Designed for established projects (Top 20), where trends develop more slowly but with higher reliability.
Mid Caps
A balanced approach for mid-sized market caps. More sensitive than the Large-Cap variant, but filters more noise than the Small-Cap settings.
Small Caps
Higher sensitivity, optimized for more volatile coins (Top 100–200). Detects dynamic moves faster, but with higher risk of false signals.
Meme Coins
Adapted for highly speculative tokens. Accounts for extreme volatility and shorter cycles to better highlight overheating phases.
--> This allows each user to select the variant best suited to the asset category being analyzed.
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Recommended Usage
Timeframe: Daily chart (highest precision). For very new projects, the 8h chart can be used.
Asset selection: Coins with sufficient history (at least 200 trading days).
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Interpretation:
Oscillator rising from oversold → potential bottom / upward trend reversal
Oscillator falling from overheated → potential top / profit-taking zone
Movements in between indicate interim rallies or correction phases
The indicator is not a day-trading tool, but optimized for cyclical moves and swing trading.
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Notes:
The SZN Altcoin Oscillator is an additional analysis tool and does not replace independent trading decisions.
All signals are probabilistic; there is no guarantee of profits.
Effectiveness depends on the specific altcoin and the current market phase.
The indicator provides insights into broader market phases, not short-term price moves.
Path of the Planets🪐 Path of the Planets
Path of the Planets is an open-source Pine Script™ v6 indicator. It is inspired by W.D. Gann’s Path of Planets chart, specifically the Chart 5-9 artistic replica by Patrick Mikula "shown below". The script visualizes planetary positions so you can explore possible correlations with price. It overlays geocentric and heliocentric longitudes and declinations using the AstroLib library and includes an optional positions table that shows, at a glance, each body’s geocentric longitude, heliocentric longitude, and declination. This is an educational tool only and not trading advice.
Key Features
Start point: Choose a date and time to begin plotting so studies can align with market events.
Adjustments: Mirror longitudes and shift by 360° multiples to re-frame cycles.
Planets: Toggle geocentric and heliocentric longitudes and declinations for Sun, Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune, and Pluto. Moon declination is available.
Positions table: Optional color-coded table (bottom-right) with three columns labeled Geo, Helio, and Dec. Values show degrees with the zodiac sign for the longitudes and degrees for declinations.
Visualization: Solid lines for geocentric longitudes, circles for heliocentric longitudes, and columns for declinations. Includes a zero-declination reference line.
How It Works
Converts bar timestamps to Julian days via AstroLib.
Fetches positions with AstroLib types: geocentric (0), heliocentric (1), and declination (3).
Normalizes longitudes to the −180° to +180° range, applies optional mirroring and 360° shifts, and converts longitudes to zodiac sign labels for the table.
Plots and the table update only on and after the selected start time.
Usage Tips
Apply on daily or higher timeframes when studying broader cycles. For degrees, use the left scale.
Limitations at the moment: default latitude, longitude, and timezone are set to 0; aspects and retrogrades are not included; the focus is on raw paths.
License and Credits
Dependency: @BarefootJoey Astrolib
Contributions and observations are welcome.
Bitcoin: The Puell MultipleBitcoin: The Puell Multiple Indicator Overview
The Puell Multiple is an indicator originally used to analyze Bitcoin's valuation based on mining revenue. However, this approximate version uses Bitcoin's current price to give us a similar perspective. It’s helpful for understanding whether Bitcoin’s price is currently high or low compared to its historical trend.
Recommended Timeframe:
For optimal insights, it’s recommended to use this indicator on the weekly timeframe. This timeframe smooths out daily fluctuations, making it easier to capture long-term valuation trends and better understand market cycles.
What Does the Indicator Show?
This indicator compares the current price of Bitcoin to its average price over the past 365 days. Here’s what it helps you see:
When Bitcoin Might Be Undervalued:
If the indicator value is below a certain low threshold (e.g., 0.51 by default), it suggests that Bitcoin might be undervalued compared to its long-term trend. Historically, periods where the indicator is low have sometimes coincided with good buying opportunities, as Bitcoin is seen as “cheap” in relation to its recent average.
When Bitcoin Might Be Overvalued:
If the indicator value is above a certain high threshold (e.g., 3.4 by default), it suggests that Bitcoin might be overvalued. In the past, these high points have sometimes signaled times to consider selling, as Bitcoin is viewed as “expensive” relative to its recent trend.
How to Read the Indicator
Indicator Line: The main line in the indicator panel shows the value of the Puell Multiple over time, fluctuating based on the comparison between current and past prices.
Threshold Lines: Two horizontal lines represent the high and low thresholds:
Bottom Threshold (Red Line): Indicates a high value, suggesting that Bitcoin might be overvalued.
Top Threshold (Green Line): Indicates a low value, suggesting that Bitcoin might be undervalued.
Color Coding:
The background may appear green when the indicator is below the low threshold (suggesting undervaluation) or red when it’s above the high threshold (suggesting overvaluation).
How You Can Use This Indicator
Long-Term Investment Insights: This indicator can help you identify favorable buying or selling conditions based on historical price trends. When the value is low, Bitcoin might be in a more attractive price range; when it’s high, the price might be inflated compared to its yearly trend.
Market Timing: This tool is best used alongside other indicators, as it’s primarily helpful for understanding broader trends rather than predicting short-term movements.
The Puell Multiple (Approximate) indicator thus offers a historical lens on Bitcoin’s valuation, helping you make decisions informed by past price trends. For best results, keep in mind the weekly timeframe recommendation to capture meaningful market cycles.
Trend Titan Neutronstar [QuantraSystems]Trend Titan NEUTRONSTAR
Credits
The Trend Titan NEUTRONSTAR is a comprehensive aggregation of nearly 100 unique indicators and custom combinations, primarily developed from unique and public domain code.
We'd like to thank our TradingView community members: @IkKeOmar for allowing us to add his well-built "Normalized KAMA Oscillator" and "Adaptive Trend Lines " indicators to the aggregation, as well as @DojiEmoji for his valuable "Drift Study (Inspired by Monte Carlo Simulations with BM)".
Introduction
The Trend Titan NEUTRONSTAR is a robust trend following algorithm meticulously crafted to meet the demands of crypto investors. Designed with a multi layered aggregation approach, NEUTRONSTAR excels in navigating the unique volatility and rapid shifts of the cryptocurrency market. By stacking and refining a variety of carefully selected indicators, it combines their individual strengths while reducing the impact of noise or false signals. This "aggregation of aggregators" approach enables NEUTRONSTAR to produce a consistently reliable trend signal across assets and timeframes, making it an exceptional tool for investors focused on medium to long term market positioning.
NEUTRONSTAR ’s powerful trend following capabilities provide investors with straightforward, data driven analysis. It signals when tokens exhibit sustained upward momentum and systematically removes allocations from assets showing signs of weakness. This structure aids investors in recognizing peak market phases. In fact, one of NEUTRONSTAR ’s most valuable applications is its potential to help investors time exits near the peak of bull markets. This aims to maximize gains while mitigating exposure to downturns.
Ultimately, NEUTRONSTAR equips investors with a high precision, adaptable framework for strategic decision making. It offers robust support to identify strong trends, manage risk, and navigate the dynamic crypto market landscape.
With over a year of rigorous forward testing and live trading, NEUTRONSTAR demonstrates remarkable robustness and effectiveness, maintaining its performance without succumbing to overfitting. The system has been purposefully designed to avoid unnecessary optimization to past data, ensuring it can adapt as market conditions evolve. By focusing on aggregating valuable trend signals rather than tuning to historical performance, the NEUTRONSTAR serves as a reliable universal trend following system that aligns with the natural market cycles of growth and correction.
Core Methodology
The foundation of the NEUTRONSTAR lies in its multi aggregated structure, where five custom developed trend models are combined to capture the dominant market direction. Each of these aggregates has been carefully crafted with a specific trend signaling period in mind, allowing it to adapt seamlessly across various timeframes and asset classes. Here’s a breakdown of the key components:
FLARE - The original Quantra Signaling Matrix (QSM) model, best suited for timeframes above 12 hours. It forms the foundation of long term trend detection, providing stable signals.
FLAREV2 - A refined and more sophisticated model that performs well across both high and low timeframes, adding a layer of adaptability to the system.
NEBULA - An advanced model combining FLARE and FLAREV2. NEBULA brings the advantages of both components together, enhancing reliability and capturing smoother, more accurate trends.
SPARK - A high speed trend aggregator based on the QSM Universal model. It focuses on fast moving trends, providing early signals of potential shifts.
SUNBURST - A balanced aggregate that combines elements of SPARK and FLARE, confirming SPARK’s signals while minimizing false positives.
Each of these models contributes its own unique perspective on market movement. By layering fast, medium, and slower trend following signals, NEUTRONSTAR can confirm strong trends while filtering out shorter term noise. The result is a comprehensive tool that signals clear market direction with minimized false signals.
A Unique Approach to Trend Aggregation
One of the defining characteristics of NEUTRONSTAR is its deliberate choice to avoid perfectly time coherent indicators within its aggregation. In simpler terms, NEUTRONSTAR purposefully incorporates trend following indicators with slightly different signal periods, rather than synchronizing all components to a single signaling period. This choice brings significant benefits in terms of diversification, adaptability, and robustness of the overall trend signal.
When aggregating multiple trend following components, if all indicators were perfectly time coherent - meaning they responded to market changes in exactly the same way and over the time periods - the resulting signal would effectively be no different from a single trend following indicator. This uniformity would limit the system’s ability to capture a variety of market conditions, leaving it vulnerable to the same noise or false signals that any single indicator might encounter. Instead, NEUTRONSTAR leverages a balanced mix of indicators with varied timing: some fast, some slower, and some in the medium range. This choice allows the system to extract the unique strengths of each component, creating a combined signal that is stronger and more reliable than any single indicator.
By incorporating different signal periods, NEUTRONSTAR achieves what can be thought of as a form of edge accumulation. The fast components within NEUTRONSTAR , for example, are highly sensitive to quick shifts in market direction. These indicators excel at identifying early trend signals, enabling NEUTRONSTAR to react swiftly to emerging momentum. However, these fast indicators alone would be prone to reacting to market noise, potentially generating too many premature signals. This is where the medium term indicators come into play. These components operate with a slower reaction time, filtering out the short term fluctuations and confirming the direction of the trend established by the faster indicators. The combination of these varying signal speeds results in a balanced, adaptive response to market changes.
This approach also allows NEUTRONSTAR to adapt to different market regimes seamlessly. In fast moving, volatile markets, the faster indicators provide an early alert to potential trend shifts, while the slower components offer a stabilizing influence, preventing overreaction to temporary noise. Conversely, in steadier or trending markets, the medium and slower indicators sustain the trend signal, reducing the likelihood of premature exits. This flexible design enhances NEUTRONSTAR ’s ability to operate effectively across multiple asset classes and timeframes, from short term fluctuations to longer term market cycles.
The result is a powerful, multi-layered trend following tool that remains adaptive, capturing the benefits of both fast and medium paced reactions without becoming overly sensitive to short term noise. This unique aggregation methodology also supports NEUTRONSTAR ’s robustness, reducing the risk of overfitting to historical data and ensuring that the system can perform reliably in forward testing and live trading environments. The slightly staggered signal periods provide a greater degree of resilience, making NEUTRONSTAR a dependable choice for traders looking to capitalize on sustained trends while minimizing exposure during periods of market uncertainty.
In summary, the lack of perfect time coherence among NEUTRONSTAR ’s sub components is not a flaw - but a deliberate, robust design choice.
Risk Management through Market Mode Analysis
An essential part of NEUTRONSTAR is its ability to assess the market's underlying behavior and adapt accordingly. It employs a Market Mode Analysis mechanism that identifies when the market is either in a “Trending State” or a “Mean Reverting State.” When enough confidence is established that the market is trending, the system confirms and signals a “Trending State,” which is optimal for maintaining positions in the direction of the trend. Conversely, if there’s insufficient confidence, it labels the market as “Mean Reverting,” alerting traders to potentially avoid trend trades during likely sideways movement.
This distinction is particularly valuable in crypto, where asset prices often oscillate between aggressive trends and consolidation periods. The Market Mode Analysis keeps traders aligned with the broader market conditions, minimizing exposure during periods of potential whipsaws and maximizing gains during sustained trends.
Zero Overfitting: Design and Testing for Real World Resilience
Unlike many trend following indicators that rely heavily on backtesting and optimization, NEUTRONSTAR was built to perform well in forward testing and live trading without post design adjustments. Over a year of live market exposure has all but proven its robustness, with the system’s methodology focused on universal applicability and simplicity rather than curve fitting to past data. This approach ensures the aggregator remains effective across different market cycles and maintains relevance as new data unfolds.
By avoiding overfitting, NEUTRONSTAR is inherently more resistant to the common issue of strategy degradation over time, making it a valuable tool for traders seeking reliable market analysis you can trust for the long term.
Settings and Customization Options
To accommodate a range of trading styles and market conditions, NEUTRONSTAR includes adjustable settings that allow for fine tuning sensitivity and signal generation:
Calculation Method - Users can choose between calculating the NEUTRONSTAR score based on aggregated scores or by using the state of individual aggregates (long, neutral, short). The score method provides faster signals with slightly more noise, while the state based approach offers a smoother signal.
Sensitivity Threshold - This setting adjusts the system’s sensitivity, defining the width of the neutral zone. Higher thresholds reduce sensitivity, allowing for a broader range of volatility before triggering a trend reversal.
Market Regime Sensitivity - A sensitivity adjustment, ranging from 0 to 100, that affects the sensitivity of the sub components in market regime calculation.
These settings offer flexibility for users to tailor NEUTRONSTAR to their specific needs, whether for medium term investment strategies or shorter term trading setups.
Visualization and Legend
For intuitive usability, NEUTRONSTAR uses color coded bar overlays to indicate trend direction:
Green - indicates an uptrend.
Gray - signals a neutral or transition phase.
Purple - denotes a downtrend.
An optional background color can be enabled for market mode visualization, indicating the overall market state as either trending or mean reverting. This feature allows traders to assess trend direction and strength at a glance, simplifying decision making.
Additional Metrics Table
To support strategic decision making, NEUTRONSTAR includes an additional metrics table for in depth analysis:
Performance Ratios - Sharpe, Sortino, and Omega ratios assess the asset’s risk adjusted returns.
Volatility Insights - Provides an average volatility measure, valuable for understanding market stability.
Beta Measurement - Calculates asset beta against BTC, offering insight into asset volatility in the context of the broader market.
These metrics provide deeper insights into individual asset behavior, supporting more informed trend based allocations. The table is fully customizable, allowing traders to adjust the position and size for a seamless integration into their workspace.
Final Summary
The Trend Titan NEUTRONSTAR indicator is a powerful and resilient trend following system for crypto markets, built with a unique aggregation of high performance models to deliver dependable, noise reduced trend signals. Its robust design, free from overfitting, ensures adaptability across various assets and timeframes. With customizable sensitivity settings, intuitive color coded visualization, and an advanced risk metrics table, NEUTRONSTAR provides traders with a comprehensive tool for identifying and riding profitable trends, while safeguarding capital during unfavorable market phases.
HSI - Halving Seasonality Index for Bitcoin (BTC) [Logue]Halving Seasonality Index (HSI) for Bitcoin (BTC) - The HSI takes advantage of the consistency of BTC cycles. Past cycles have formed macro tops around 538 days after each halving. Past cycles have formed macro bottoms every 948 days after each halving. Therefore, a linear "risk" curve can be created between the bottom and top dates to measure how close BTC might be to a bottom or a top. The default triggers are set at 98% risk for tops and 5% risk for bottoms. Extensions are also added as defaults to allow easy identification of the dates of the next top or bottom according to the HSI.
CSI - Calendar Seasonality Index for Bitcoin (BTC) [Logue]Calendar Seasonality Index (CSI) for Bitcoin (BTC) - The CSI takes advantage of the consistency of BTC cycles. Past cycles have formed macro tops every four years near November 21st, starting from in 2013. Past cycles have formed macro bottoms every four years near January 15th, starting from 2011. Therefore, a linear "risk" curve can be created between the bottom and top dates to measure how close BTC might be to a bottom or a top. The default triggers are at 98% risk for tops and 5% risk for bottoms. Extensions are also added as defaults to allow easy identification of the dates of the next top or bottom according to the CSI.
Triple Ehlers Market StateClear trend identification is an important aspect of finding the right side to trade, another is getting the best buying/selling price on a pullback, retracement or reversal. Triple Ehlers Market State can do both.
Three is always better
Ehlers’ original formulation produces bullish, bearish and trendless signals. The indicator presented here gate stages three correlation cycles of adjustable lengths and degree thresholds, displaying a more refined view of bullish, bearish and trendless markets, in a compact and novel way.
Stick with the default settings, or experiment with the cycle period and threshold angle of each cycle, then choose whether ‘Recent trend weighting’ is included in candle colouring.
John Ehlers is a highly respected trading maths head who may need no introduction here. His idea for Market State was published in TASC June 2020 Traders Tips. The awesome interpretation of Ehlers’ work on which Triple Ehlers Market State’s correlation cycle calculations are based can be found at:
DISCLAIMER: None of this is financial advice.
π Cycle Market Tops & Bottoms Performante IndicatorWhy is it called the Pi Cycle Tops & Bottoms Indicator?
When the 111-Day moving average crosses over the (350-Day moving average X 2), we've seen the price come to a key top or bottom within the Bitcoin market for the past 3 cycles.
350 divided by 111 is very close to π - hence the name the Pie cycle!
Yes, we are selecting arbitrary numbers initially, but through the use of proper back-testing, we are able to find key cycle shifts using mathematical numbers (fibs, Pi, etc)
We use this topping & bottoming signal when things look overbought over oversold within the market.
The "topping" label turns on as soon as we see the 111-Day moving average cross above the 350-Day moving average.
The "bottoming" signal turns on as soon as we see the 111-day moving average cross below the 350-Day moving average.
This indicator should only be used on the daily timeframe!
Historically speaking, we've seen this indicator become impressively accurate.
GeoWave v1.0See what other traders can't. GeoWave is the most sophisticated geometric indicator ever built for TradingView - a real-time pattern recognition engine that transforms raw price action into precise harmonic measurements and time cycle projections.
Advanced XABCD Pattern DNA
Automatically detects and measures the last 4 swings, calculating 6 critical harmonic ratios that reveal the hidden mathematical relationships governing market movements:
BcD Ratio (CD/BC) - Classic retracement relationships that predict reversal zones
AB/CD Ratio (CD/AB) - Primary harmonic structure defining complete market cycles
X1 Ratio (AD/BC) - Diagonal extensions that extend beyond traditional boundaries
X2 Ratio (AD/XC) - Complex cross relationships uncovering multi-dimensional patterns
XcD Ratio (CD/XC) - Extended retracements that capture prolonged market phases
XaD Ratio (AD/XA) - Time-space intersections where temporal and spatial forces converge
Precision Targeting Engine
Projects multiple target zones simultaneously with unprecedented accuracy:
Retracement Targets: BcD-based levels (0.382, 0.618, 1.618, 2.618) that pinpoint exact reversal points
Extension Targets: AbCd projections beyond pattern completion that anticipate future movements
Cross Targets: AdBc & AdXc harmonic intersections that identify high-probability convergence zones
Time Cycle Mastery
Don't just predict price. Predict time. GeoWave projects historical swing durations forward, identifying when turning points are statistically likely to occur. Project time cycles at 50%, 100%, and 200% (and more) of historical durations to anticipate market rhythm.
Multi-Level Geometric Analysis
Scans across 18 swing levels simultaneously, revealing nested harmonic structures that single-level indicators completely miss. Each level uses advanced adaptive filtering for precision detection of market's fractal nature.
Square the Range Integration
Implements W.D. Gann's "Square the Range" theory, creating geometric boxes where price and time vectors intersect at critical confluence zones that define major market turning points.
Intelligent Signal Scoring
Proprietary scoring algorithm weighs Fibonacci proximity, ratio type importance, and multi-level confluence. Color-coded signals highlight high-probability setups with detailed breakdown tooltips revealing the mathematical strength of each opportunity.
Adaptive Noise Cancellation
Proprietary filtering technology eliminates insignificant market noise, focusing only on structurally meaningful swing points that actually drive price direction and determine market fate.
Stop guessing. Start measuring. GeoWave doesn't draw pretty lines - it quantifies market geometry with mathematical precision, giving you the edge that institutional traders pay millions to develop.
Join the elite traders who've discovered the hidden geometric order behind every market move.
350DMA bands + Z-score (V2)This script extends the classic 350-day moving average (350DMA) by building dynamic valuation bands and a Z-Score framework to evaluate how far price deviates from its long-term mean.
Features
350DMA Anchor: Uses the 350-day simple moving average as the baseline reference.
Fixed Multipliers: Key bands plotted at ×0.625, ×1.0, ×1.6, ×2.0, and ×2.5 of the 350DMA — historically significant levels for cycle analysis.
Z-Score Mapping: Price is converted into a Z-Score on a scale from +2 (deep undervaluation) to –2 (extreme overvaluation), using log-space interpolation for accuracy.
Custom Display: HUD panel and on-chart label show the current Z-Score in real time.
Clamp Option: Users can toggle between raw Z values or capped values (±2).
How to Use
Valuation Context: The 350DMA is often considered a “fair value” anchor; large deviations identify cycles of under- or over-valuation.
Z-Score Insight:
Positive Z values suggest favorable accumulation zones where price is below long-term average.
Negative Z values highlight zones of stretched valuation, often associated with distribution or profit-taking.
Strategic Application: This is not a standalone trading system — it works best in confluence with other indicators, cycle models, or macro analysis.
Originality
Unlike a simple DMA overlay, this script:
Provides multiple cycle-based bands derived from the 350DMA.
Applies a logarithmic Z-Score mapping for more precise long-term scaling.
Adds an integrated HUD and labeling system for quick interpretation.
Moon Phase & Celestial Events TrackerMoon Phase & Celestial Events Tracker
Overview
A comprehensive astronomical and celestial event indicator that tracks and projects major cosmic events from 2011 to 2040. This indicator overlays important astronomical phenomena directly on your charts, allowing traders and researchers to analyze potential correlations between celestial events and market movements.
Key Features
Eclipse Tracking 🌑
Blood Moons (Total Lunar Eclipses) including 2014-2015 tetrad
Partial Lunar Eclipses with distinctive yellow markers
Solar Eclipses: Total, Annular, Partial, and Hybrid types with unique symbols
Optional eclipse season background highlighting
Moon Cycles 🌕
Supermoons at perigee (closest Earth approach)
Regular moon phases: New, First Quarter, Full, Last Quarter
Adjustable phase marking with day-offset capability
Mercury Retrograde ☿
Start and end dates clearly marked
Optional period highlighting for entire retrograde duration
Complete cycle tracking through 2040
Seasonal Transitions ✨
Spring Equinox, Summer Solstice, Autumn Equinox, Winter Solstice
Precise astronomical season changes
Future Projections 📊
Event forecasting up to 5 years ahead
Customizable projection range (30-1825 days)
Selective projection by event type
Adjustable visual styles and transparency
Interpretation Guide
Blood Moons
Total lunar eclipses where Earth's atmosphere creates the red appearance. In financial astrology, these are often watched as potential reversal or volatility periods, though correlations vary significantly.
Eclipse Seasons
Twice-yearly windows when Sun-Earth-Moon alignment allows eclipses. Some market practitioners note increased volatility during these periods, though empirical evidence remains debated.
Mercury Retrograde
The apparent backward motion of Mercury occurs 3-4 times yearly. In trading folklore, it's associated with communication issues, technical problems, and false signals. Many practitioners suggest extra caution with new positions during these periods.
Supermoons
Full or new moons at closest Earth approach. Some traders track these for potential short-term highs/lows, particularly in commodities and currencies, though effects are subtle if present.
Seasonal Markers
Astronomical season changes have been incorporated into various market timing systems, with some analysts noting clustering of trend changes around these dates.
Use Cases
Historical pattern analysis
Event-based research
Educational astronomy tracking
Market cycle studies
Long-term planning and observation
Technical Details ⚙️
Data Coverage: 2011-2040 (30 years of precise astronomical events)
Compatibility: All timeframes with smart filtering (Weekly/Monthly show only major events)
Performance: Lightweight with efficient calculations and minimal chart impact
Data Source: Based on NASA ephemeris data for precise event timing
Customization Options 🎨
Individual colors for each event type
Transparency controls for projections
Event visibility toggles
Optional date labels on events
Alert Options 🔔
Set custom alerts for any tracked event including all eclipse types, moon phases, Mercury retrograde start/end, and seasonal transitions.
⚠️ Important Note
This indicator displays astronomical events for research and educational purposes. Any perceived correlations with market movements should be thoroughly backtested. Financial astrology interpretations are included for historical context only and should not be considered trading advice. Always use proper risk management and multiple forms of analysis in trading decisions.
Best Suited For
Market researchers and analysts
Students of market cycles
Those interested in astronomical timing
Educational and observational purposes
Long-term pattern analysis
HHT Signal Analyzer (Refined)HHT Signal Analyzer
The HHT Signal Analyzer provides a real-time, smoothed approximation of the Hilbert-Huang Transform (HHT), designed to reveal adaptive cycles and phase changes in price action. It emulates Intrinsic Mode Functions (IMFs) using a double exponential moving average (EMA) filter to extract short-term oscillatory signals from price.
This indicator is helpful for identifying subtle shifts in market behavior, such as when a trend is transitioning or weakening, and is especially effective when paired with trend-based tools like GRJMOM.
How it works:
Applies a double EMA to the price (EMA of EMA)
Calculates the difference between the fast and slow EMA to emulate IMF behavior
Amplifies the signal for clear visual feedback
Highlights cycle slope changes with background coloring (green = rising, red = falling)
Use Cases:
Use slope direction to detect early phase shifts in the market
Combine with trend indicators to confirm or fade moves
Helps visualize when the market is entering a cycle crest or trough
Best for:
Traders looking to capture short-term reversals, cycle timing, or divergence with smooth and adaptive signals
Can be used on any timeframe
