TASC 2022.11 Phasor Analysis█ OVERVIEW
TASC's November 2022 edition Traders' Tips includes an article by John Ehlers titled "Recurring Phase Of Cycle Analysis". This is the code that implements the phasor analysis indicator presented in this publication.
█ CONCEPTS
The article explores the use of phasor analysis to identify market trends.
An ordinary rotating phasor diagram is a two-dimensional vector, anchored to the origin, whose rotation rate corresponds to the cycle period in the price data stream. Similarly, Ehlers' phasor is a representation of angular phase rotation along the course of time. Its angle reflects the current phase of the cycle. Angles -180, -90, +90 and +180 degrees correspond to the beginning, valley, peak and end of the cycle, respectively.
If the observed cycle is very long, the market can be considered trending . In his article, John Ehlers defined trending behavior to occur when the derived instantaneous cycle period value is greater that 60 bars. The author also introduced guidelines for long and short entries in a trending state. Depending on the tuning of the indicator period input, a long entry position may occur when the phasor angle is around the approximate vicinity of −90 degrees, while a short entry position may occur when the phasor angle will be around the approximate vicinity of +90 degrees. Applying these definitive guidelines, the author proposed a state variable that is indicated by +1 for a trending long position, 0 for cycling, and −1 for a trending short position (or out).
The phasor angle, the cycle period, and the state variable are made available with three selectable display modes provided for this TradingView indicator.
█ CALCULATIONS
The calculations are carried out as follows.
First, the price data stream is correlated with cosine and sine of a fixed cycle period. This produces two new data streams that correspond to the projections of the frequency domain phasor diagram to the horizontal (so-called real ) and vertical (so-called imaginary ) axis respectively. The wavelength of the cycle period input should be set for the midrange vicinities of the phasor to coincide with the peaks and valleys of the charted price data.
Secondly, the phase angle of the phasor is easily computed as the arctangent of the ratio of the imaginary component to the real component. The difference between the current phasor values and its last is then employed to calculate a derived instantaneous period and market state. This computation is then repeated successively for each individual bar over the entire duration of the data set.
Cerca negli script per "Cycle"
Bitcoin Power Law Clock [LuxAlgo]The Bitcoin Power Law Clock is a unique representation of Bitcoin prices proposed by famous Bitcoin analyst and modeler Giovanni Santostasi.
It displays a clock-like figure with the Bitcoin price and average lines as spirals, as well as the 12, 3, 6, and 9 hour marks as key points in the cycle.
🔶 USAGE
Giovanni Santostasi, Ph.D., is the creator and discoverer of the Bitcoin Power Law Theory. He is passionate about Bitcoin and has 12 years of experience analyzing it and creating price models.
As we can see in the above chart, the tool is super intuitive. It displays a clock-like figure with the current Bitcoin price at 10:20 on a 12-hour scale.
This tool only works on the 1D INDEX:BTCUSD chart. The ticker and timeframe must be exact to ensure proper functionality.
According to the Bitcoin Power Law Theory, the key cycle points are marked at the extremes of the clock: 12, 3, 6, and 9 hours. According to the theory, the current Bitcoin prices are in a frenzied bull market on their way to the top of the cycle.
🔹 Enable/Disable Elements
All of the elements on the clock can be disabled. If you disable them all, only an empty space will remain.
The different charts above show various combinations. Traders can customize the tool to their needs.
🔹 Auto scale
The clock has an auto-scale feature that is enabled by default. Traders can adjust the size of the clock by disabling this feature and setting the size in the settings panel.
The image above shows different configurations of this feature.
🔶 SETTINGS
🔹 Price
Price: Enable/disable price spiral, select color, and enable/disable curved mode
Average: Enable/disable average spiral, select color, and enable/disable curved mode
🔹 Style
Auto scale: Enable/disable automatic scaling or set manual fixed scaling for the spirals
Lines width: Width of each spiral line
Text Size: Select text size for date tags and price scales
Prices: Enable/disable price scales on the x-axis
Handle: Enable/disable clock handle
Halvings: Enable/disable Halvings
Hours: Enable/disable hours and key cycle points
🔹 Time & Price Dashboard
Show Time & Price: Enable/disable time & price dashboard
Location: Dashboard location
Size: Dashboard size
Clock&Flow MM+InfoThis script is an indicator that helps you visualize various moving averages directly on the price chart and gain some additional insights.
Here's what it essentially does:
Displays Different Moving Averages: You can choose to see groups of moving averages with different periods, set to nominal cyclical durations. You can also opt to configure them for instruments traded with classic or extended trading hours (great for Futures), and they'll adapt to your chosen timeframe.
Colored Bands: It allows you to add colored bands to the background of the chart that change weekly or daily, helping you visualize time cycles. You can customize the band colors.
Information Table: A small table appears in a corner of the chart, indicating which cycle the moving averages belong to (daily, weekly, monthly, etc.), corresponding to the timeframe you are using on the chart.
Customization: You can easily enable or disable the various groups of moving averages or the colored bands through the indicator's settings.
It's a useful tool for traders who use moving averages to identify trends and support/resistance levels, and who want a quick overview of market cycles.
Questo script è un indicatore che aiuta a visualizzare diverse medie mobili direttamente sul grafico dei prezzi e a ottenere alcune informazioni aggiuntive.
In pratica, fa queste cose:
Mostra diverse medie mobili: Puoi scegliere di vedere gruppi di medie mobili con periodi diversi impostati sulle durate cicliche nominali. Puoi scegliere se impostarle per uno strumento quotato con orario di negoziazione classico o esteso (ottimo per i Futures) e si adattano al tuo timeframe).
Bande colorate: Ti permette di aggiungere delle bande colorate sullo sfondo del grafico che cambiano ogni settimana o ogni giorno, per aiutarti a visualizzare i cicli temporali. Puoi scegliere il colore delle bande.
Tabella informativa: In un angolo del grafico, compare una piccola tabella che indica a quale ciclo appartengono le medie mobili (giornaliero, settimanale, mensile, ecc.) e corrispondono in base al timeframe che stai usando sul grafico.
Personalizzazione: Puoi facilmente attivare o disattivare i vari gruppi di medie mobili o le bande colorate tramite le impostazioni dell'indicatore.
È uno strumento utile per i trader che usano le medie mobili per identificare trend e supporti/resistenze, e che vogliono avere un colpo d'occhio sui cicli di mercato.
Economic Seasons [Daveatt]Ever wondered what season your economy is in?
Just like Mother Nature has her four seasons, the economy cycles through its own seasons! This indicator helps you visualize where we are in the economic cycle by tracking two key metrics:
📊 What We're Tracking:
1. Interest Rates (USIRYY) - The yearly change in interest rates
2. Inflation Rate (USINTR) - The rate at which prices are rising
The magic happens when we normalize these values (fancy math that makes the numbers play nice together) and compare them to their recent averages. We use a lookback period to calculate the standard deviation and determine if we're seeing higher or lower than normal readings.
🔄 The Four Economic Seasons & Investment Strategy:
1. 🌸 Goldilocks (↑Growth, ↓Inflation)
"Not too hot, not too cold" - The economy is growing steadily without overheating.
BEST TIME TO: Buy growth stocks, technology, consumer discretionary
WHY: Companies can grow earnings in this ideal environment of low rates and stable prices
2. 🌞 Reflation (↑Growth, ↑Inflation)
"Party time... but watch your wallet!" - The economy is heating up.
BEST TIME TO: Buy commodities, banking stocks, real estate
WHY: These sectors thrive when inflation rises alongside growth
3. 🌡️ Inflation (↓Growth, ↑Inflation)
"Ouch, my purchasing power!" - Growth slows while prices keep rising.
BEST TIME TO: Rotate into value stocks, consumer staples, healthcare
WHY: These defensive sectors maintain pricing power during inflationary periods
4. ❄️ Deflation (↓Growth, ↓Inflation)
"Winter is here" - Both growth and inflation are falling.
BEST TIME TO: Focus on quality bonds, cash positions, and dividend aristocrats
WHY: Capital preservation becomes key; high-quality fixed income provides safety
🎯 Strategic Trading Points:
- BUY AGGRESSIVELY: During late Deflation/early Goldilocks (the spring thaw)
- HOLD & ACCUMULATE: Throughout Goldilocks and early Reflation
- START TAKING PROFITS: During late Reflation/early Inflation
- DEFENSIVE POSITIONING: Throughout Inflation and Deflation
⚠️ Warning Signs to Watch:
- Goldilocks → Reflation: Time to reduce growth stock exposure
- Reflation → Inflation: Begin rotating into defensive sectors
- Inflation → Deflation: Quality becomes crucial
- Deflation → Goldilocks: Start building new positions
The blue dot shows you where we are right now in this cycle.
The red arrows in the middle remind us that this is a continuous cycle - one season flows into the next, just like in nature!
💡 Pro Tip: The transitions between seasons often provide the best opportunities - but also the highest risks. Use additional indicators and fundamental analysis to confirm these shifts.
Remember: Just like you wouldn't wear a winter coat in summer, you shouldn't use a Goldilocks strategy during Inflation! Time your trades with the seasons. 🎯
Happy Trading! 📈
[GYTS] Filters ToolkitFilters Toolkit indicator
🌸 Part of GoemonYae Trading System (GYTS) 🌸
🌸 --------- 1. INTRODUCTION --------- 🌸
💮 Overview
The GYTS Filters Toolkit indicator is an advanced, interactive interface built atop the high‐performance, curated functions provided by the FiltersToolkit library . It allows traders to experiment with different combinations of filtering methods -— from smoothing low-pass filters to aggressive detrenders. With this toolkit, you can build custom indicators tailored to your specific trading strategy, whether you're looking for trend following, mean reversion, or cycle identification approaches.
🌸 --------- 2. FILTER METHODS AND TYPES --------- 🌸
💮 Filter categories
The available filters fall into four main categories, each marked with a distinct symbol:
🌗 Low Pass Filters (Smoothers)
These filters attenuate high-frequency components (noise) while allowing low-frequency components (trends) to pass through. Examples include:
Ultimate Smoother
Super Smoother (2-pole and 3-pole variants)
MESA Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA)
BiQuad Low Pass Filter
ADXvma (Adaptive Directional Volatility Moving Average)
A2RMA (Adaptive Autonomous Recursive Moving Average)
Low pass filters are displayed on the price chart by default, as they follow the overall price movement. If they are combined with a high-pass or bandpass filter, they will be displayed in the subgraph.
🌓 High Pass Filters (Detrenders)
These filters do the opposite of low pass filters - they remove low-frequency components (trends) while allowing high-frequency components to pass through. Examples include:
Butterworth High Pass Filter
BiQuad High Pass Filter
High pass filters are displayed as oscillators in the subgraph below the price chart, as they fluctuate around a zero line.
🌑 Band Pass Filters (Cycle Isolators)
These filters combine aspects of both low and high pass filters, isolating specific frequency ranges while attenuating both higher and lower frequencies. Examples include:
Ehlers Bandpass Filter
Cyber Cycle
Relative Vigor Index (RVI)
BiQuad Bandpass Filter
Band pass filters are also displayed as oscillators in a separate panel.
🔮 Predictive Filter
Voss Predictive Filter: A special filter that attempts to predict future values of band-limited signals (only to be used as post-filter). Keep its prediction horizon short (1–3 bars) for reasonable accuracy.
Note that the the library contains elaborate documentation and source material of each filter.
🌸 --------- 3. INDICATOR FEATURES --------- 🌸
💮 Multi-filter configuration
One of the most powerful aspects of this indicator is the ability to configure multiple filters. compare them and observe their combined effects. There are four primary filters, each with its own parameter settings.
💮 Post-filtering
Process a filter’s output through an additional filter by enabling the post-filter option. This creates a filter chain where the output of one filter becomes the input to another. Some powerful combinations include:
Ultimate Smoother → MAMA: Creates an adaptive smoothing effect that responds well to market changes, good for trend-following strategies
Butterworth → Super Smoother → Butterworth: Produces a well-behaved oscillator with minimal phase distortion, John Ehlers also calls a "roofing filter". Great for identifying overbought/oversold conditions with minimal lag.
A bandpass filter → Voss Prediction filter: Attempts to predict future movements of cyclical components, handy to find peaks and troughs of the market cycle.
💮 Aggregate filters
Arguably the coolest feature: aggregating filters allow you to combine multiple filters with different weights. Important notes about aggregation:
You can only aggregate filters that appear on the same chart (price chart or oscillator panel).
The weights are automatically normalised, so only their relative values matter
Setting a weight to 0 (zero) excludes that filter from the aggregation
Filters don't need to be visibly displayed to be included in aggregation
💮 Rich visualisation & alerts
The indicator intelligently determines whether a filter is displayed on the price chart or in the subgraph (as an oscillator) based on its characteristics.
Dynamic colour palettes, adjustable line widths, transparency, and custom fill between any of enabled filters or between oscillators and the zero-line.
A clear legend showing which filters are active and how they're configured
Alerts for direction changes and crossovers of all filters
🌸 --------- 4. ACKNOWLEDGEMENTS --------- 🌸
This toolkit builds on the work of numerous pioneers in technical analysis and digital signal processing:
John Ehlers, whose groundbreaking research forms the foundation of many filters.
Robert Bristow-Johnson for the BiQuad filter formulations.
The TradingView community, especially @The_Peaceful_Lizard, @alexgrover, and others mentioned in the code of the library.
Everyone who has provided feedback, testing and support!
WD Gann: Close Price X Bars Ago with Line or Candle PlotThis indicator is inspired by the principles of WD Gann, a legendary trader known for his groundbreaking methods in time and price analysis. It helps traders track the close price of a security from X bars ago, a technique that is often used to identify key price levels in relation to past price movements. This concept is essential for Gann’s market theories, which emphasize the relationship between time and price.
WD Gann’s analysis often revolved around specific numbers that he considered significant, many of which correspond to squared numbers (e.g., 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361, 400, 441, 484, 529, 576, 625, 676, 729, 784, 841, 900, 961, 1024, 1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849, 1936). These numbers are believed to represent natural rhythms and cycles in the market. This indicator can help you explore how past price levels align with these significant numbers, potentially revealing key price zones that could act as support, resistance, or reversal points.
Key Features:
- Historical Close Price Calculation: The indicator calculates and displays the close price of a security from X bars ago (where X is customizable). This method aligns with Gann's focus on price relationships over specific time intervals, providing traders with valuable reference points to assess market conditions.
- Customizable Plot Type: You can choose between two plot types for visualizing the historical close price:
- Line Plot: A simple line that represents the close price from X bars ago, ideal for those who prefer a clean and continuous representation.
- Candle Plot: Displays the close price as a candlestick chart, providing a more detailed view with open, high, low, and close prices from X bars ago.
- Candle Color Coding: For the candle plot type, the script color-codes the candles. Green candles appear when the close price from X bars ago is higher than the open price, indicating bullish sentiment; red candles appear when the close is lower, indicating bearish sentiment. This color coding gives a quick visual cue to market sentiment.
- Customizable Number of Bars: You can adjust the number of bars (X) to look back, providing flexibility for analyzing different timeframes. Whether you're conducting short-term or long-term analysis, this input can be fine-tuned to suit your trading strategy.
- Gann Method Application: WD Gann's methods involved analyzing price action over specific time periods to predict future movements. This indicator offers traders a way to assess how the price of a security has behaved in the past in relation to a chosen time interval, a critical concept in Gann's theories.
How to Use:
1. Input Settings:
- Number of Bars (X): Choose the number of bars to look back (e.g., 100, 200, or any custom period).
- Plot Type: Select whether to display the data as a Line or Candles.
2. Interpretation:
- Using the Line plot, observe how the close price from X bars ago compares to the current market price.
- Using the Candles plot, analyze the full price action of the chosen bar from X bars ago, noting how the close price relates to the open, high, and low of that bar.
3. Gann Analysis: Integrate this indicator into your broader Gann-based analysis. By looking at past price levels and their relationship to significant squared numbers, traders can uncover potential key levels of support and resistance or even potential reversal points. The historical close price can act as a benchmark for predicting future market movements.
Suggestions on WD Gann's Emphasis in Trading:
WD Gann’s trading methods were rooted in several key principles that emphasized the relationship between time and price. These principles are vital to understanding how the "Close Price X Bars Ago" indicator fits into his overall analysis:
1. Time Cycles: Gann believed that markets move in cyclical patterns. By studying price levels from specific time intervals, traders can spot these cycles and predict future market behavior. This indicator allows you to see how the close price from X bars ago relates to current market conditions, helping to spot cyclical highs and lows.
2. Price and Time Squaring: A core concept in Gann’s theory is that certain price levels and time periods align, often marking significant reversal points. The squared numbers (e.g., 1, 4, 9, 16, 25, etc.) serve as potential key levels where price and time might "square" to create support or resistance. This indicator helps traders spot these historical price levels and their potential relevance to future price action.
3. Geometric Angles: Gann used angles (like the 45-degree angle) to predict market movements, with the belief that prices move at specific geometric angles over time. This indicator gives traders a reference for past price levels, which could align with key angles, helping traders predict future price movement based on Gann's geometry.
4. Numerology and Key Intervals: Gann paid particular attention to numbers that held significance, including squared numbers and numbers related to the Fibonacci sequence. This indicator allows traders to analyze price levels based on these key numbers, which can help in identifying potential turning points in the market.
5. Support and Resistance Levels: Gann’s methods often involved identifying levels of support and resistance based on past price action. By tracking the close price from X bars ago, traders can identify past support and resistance levels that may become significant again in future market conditions.
Perfect for:
Traders using WD Gann’s methods, such as Gann angles, time cycles, and price theory.
Analysts who focus on historical price levels to predict future price action.
Those who rely on numerology and geometric principles in their trading strategies.
By integrating this indicator into your trading strategy, you gain a powerful tool for analyzing market cycles and price movements in relation to key time intervals. The ability to track and compare the historical close price to significant numbers—like Gann’s squared numbers—can provide valuable insights into potential support, resistance, and reversal points.
Disclaimer:
This indicator is based on the methods and principles of WD Gann and is for educational purposes only. It is not intended as financial advice. Trading involves significant risk, and you should not trade with money that you cannot afford to lose. Past performance is not indicative of future results. The use of this indicator is at your own discretion and risk. Always do your own research and consider consulting a licensed financial advisor before making any investment decisions.
Financial Astrology Indexes ML Daily TrendDaily trend indicator based on financial astrology cycles detected with advanced machine learning techniques for some of the most important market indexes: DJI, UK100, SPX, IBC, IXIC, NI225, BANKNIFTY, NIFTY and GLD fund (not index) for Gold predictions. The daily price trend is forecasted through planets cycles (angular aspects, speed phases, declination zone), fast cycles are based on Moon, Mercury, Venus and Sun and Mid term cycles are based on Mars, Vesta and Ceres . The combination of all this cycles produce a daily price trend prediction that is encoded into a PineScript array using binary format "0 or 1" that represent sell and buy signals respectively. The indicator provides signals since 2021-01-01 to 2022-12-31, the past months signals purpose is to support backtesting of the indicator combined with other technical indicator entries like MAs, RSI or Stochastic . For future predictions besides 2022 a machine learning models re-train phase will be required.
When the signal moving average is increasing from 0 to 1 indicates an increase of buy force, when is decreasing from 1 to 0 indicates an increase in sell force, finally, when is sideways around the 0.4-0.6 area predicts a period of buy/sell forces equilibrium, traders indecision which result in a price congestion within a narrow price range.
We also have published same indicator for Crypto-Currencies research portfolio:
DISCLAIMER: This indicator is experimental and don’t provide financial or investment advice, the main purpose is to demonstrate the predictive power of financial astrology. Any allocation of funds following the documented machine learning model prediction is a high-risk endeavour and it’s the users responsibility to practice healthy risk management according to your situation.
BTC Dominance Zones (For Altseason)Overview
The "BTC Dominance Zones (For Altseason)" indicator is a visual tool designed to help traders navigate the different phases of the altcoin market cycle by tracking Bitcoin Dominance (BTC.D).
It provides clear, color-coded zones directly on the BTC.D chart, offering an intuitive roadmap for the progression of alt season.
Purpose & Problem Solved
Many traders often miss altcoin rotations or get caught at market tops due to emotional decision-making or a lack of a clear framework. This indicator aims to solve that problem by providing an objective, historically informed guide based on Bitcoin Dominance, helping users to prepare before the market makes its decisive moves. It distils complex market dynamics into easily digestible sections.
Key Features & Components
Color-Coded Horizontal Zones: The indicator draws fixed horizontal bands on the BTC.D chart, each representing a distinct phase of the altcoin market cycle.
Descriptive Labels: Each zone is clearly labeled with its strategic meaning (e.g., "Alts are dead," "Danger Zone") and the corresponding BTC.D percentage range, positioned to the right of the price action for clarity.
Consistent Aesthetics: All text within the labels is rendered in white for optimal visibility across the colored zones.
Symbol Restriction: The indicator includes an automatic check to ensure it only draws its visuals when applied specifically to the CRYPTOCAP:BTC.D chart. If applied to another chart, it displays a helpful message and remains invisible to prevent confusion.
Methodology & Interpretation
The indicator's methodology is based on the historical behavior of Bitcoin Dominance during various market cycles, particularly the 2021 bull run. Each zone provides a specific interpretation for altcoin strategy:
Grey Zone (BTC.D 60-70%+): "Alts Are Dead"
Interpretation: When Bitcoin Dominance is in this grey zone (typically above 60%), Bitcoin is king, and capital remains concentrated in BTC. This indicates that alt season is largely inactive or "dead". This phase is generally not conducive for aggressive altcoin trading.
Blue Zone (BTC.D 55-60%): "Alt Season Loading"
Interpretation: As BTC.D drops into this blue zone (below 60%), it signals that the market is "heating up" for altcoins. This is the time to start planning and executing your initial positions in high-conviction large-cap and strong narrative plays, as capital begins to look for more risk.
Green Zone (BTC.D 50-55%): "Alt Season Underway"
Interpretation: Entering this green zone (below 55%) signifies that "real momentum" is building, and alt season is genuinely "underway". Money is actively flowing from Ethereum into large and mid-cap altcoins. If you've positioned correctly, your portfolio should be showing strong gains in this phase.
Orange Zone (BTC.D 45-50%): "Alt Season Ending"
Interpretation: As BTC.D dips into this orange zone (below 50%), it suggests that altcoin dominance is reaching its peak, indicating the "ending" phase of alt season. While euphoria might be high, this is a critical warning zone to prepare for profit-taking, as it's a phase of "peak risk".
Red Zone (BTC.D Below 45%): "Danger Zone - Alts Overheated"
Interpretation: This red zone (below 45%) is the most critical "DANGER ZONE". It historically marks the point of maximum froth and risk, where altcoins are overheated. This is the decisive signal to aggressively take profits, de-risk, and exit positions to preserve your capital before a potential sharp correction. Historically, dominance has gone as low as 39-40% in this phase.
How to Use
Open TradingView and search for the BTC.D symbol to load the Bitcoin Dominance chart and view the indicator.
Double click the indicator to access settings.
Inputs/Settings
The indicator's zone boundaries are set to historically relevant levels for consistency with the Alt Season Blueprint strategy. However, the colors of each zone are fully customizable through the indicator's settings, allowing users to personalize the visual appearance to their preference. You can access these color options in the indicator's "Settings" menu once it's added to your chart.
Disclaimer
This indicator is provided for informational and educational purposes only. It is not financial advice. Trading cryptocurrencies involves substantial risk of loss and is not suitable for every investor. Past performance is not indicative of future results. Always conduct your own research and consult with a qualified financial professional before making any investment decisions.
About the Author
This indicator was developed by Nick from Lab of Crypto.
Release Notes
v1.0 (June 2025): Initial release featuring color-coded horizontal BTC.D zones with descriptive labels, based on Alt Season Blueprint strategy. Includes symbol restriction for correct chart application and consistent white text.
True Seasonal Pattern [tradeviZion]True Seasonal Pattern: Uncover Hidden Market Cycles
Markets have rhythms and patterns that repeat with surprising regularity. The True Seasonal Pattern indicator reveals these hidden cycles across different timeframes, helping you anticipate potential market movements based on historical seasonal tendencies.
What This Indicator Does
The True Seasonal Pattern analyzes years of historical price data to identify recurring seasonal trends. It then plots these patterns on your chart, showing you both the historical pattern and future projection based on past seasonal behavior.
Automatic Timeframe Detection: Works with Monthly, Weekly, and Daily charts
Historical Pattern Analysis: Analyzes up to 100 years of data (customizable)
Future Projection: Projects the seasonal pattern ahead on your chart
Smart Smoothing: Applies appropriate smoothing based on your timeframe
How to Use This Indicator
Add the indicator to a Daily, Weekly, or Monthly chart (not designed for intraday timeframes)
The indicator automatically detects your chart's timeframe
The blue line shows the historical seasonal pattern
Watch for potential turning points in the pattern that align with other technical signals
Seasonal patterns work best as a supporting factor in your analysis, not as standalone trading signals. They are particularly effective in markets with well-established seasonal influences.
Best Applications
Futures Markets: Commodities and futures often show strong seasonal tendencies due to production cycles, weather patterns, and economic factors
Stock Indices: Many stock markets demonstrate regular seasonal patterns (like the "Sell in May" phenomenon)
Individual Stocks: Companies with seasonal business cycles often show predictable price patterns
Practical Applications
Identify potential turning points based on historical seasonal patterns
Plan entries and exits around seasonal tendencies
Add seasonal context to your existing technical analysis
Understand why certain months or periods might show consistent behavior
Pro Tip: For best results, use this tool on instruments with at least 5+ years of historical data. Longer timeframes often reveal more reliable seasonal patterns.
Important Notes
This indicator works best on Daily, Weekly, and Monthly timeframes - not intraday charts
Seasonal patterns are tendencies, not guarantees
Always combine seasonal analysis with other technical tools
Past patterns may not repeat exactly in the future
// Sample of the seasonal calculation approach
float yearHigh = array.max(currentYearHighs)
float yearLow = array.min(currentYearLows)
// Calculate seasonality for each period
for i = 0 to array.size(currentYearCloses) - 1
float periodClose = array.get(currentYearCloses, i)
if not na(periodClose) and yearHigh != yearLow
float seasonality = (periodClose - yearLow) / (yearHigh - yearLow) * 100
I developed this indicator to help traders incorporate seasonal analysis into their trading approach without the complexity of traditional seasonal tools. Whether you're analyzing agricultural commodities, energy futures, or stock indices, understanding the seasonal context can provide valuable insights for your trading decisions.
Remember: Markets don't always follow seasonal patterns, but when they do, being aware of these tendencies can give you a meaningful edge in your analysis.
ZigCycleBarCount [MsF]Japanese below / 日本語説明は英文の後にあります。
Based on "ZigZag++" indicator by DevLucem. Thanks for the great indicator.
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This indicator that displays the candle count (bar count) at the peaks of Zigzag .
It also displays the price of the peaks.
You can easily count candles (bars) from peak to peak. Helpful for candles (bars) in cycle theory.
This logic of the indicator is based from the mt4 zigzag indicator .
Parameter:
Depth = depth (price range)
Backstep = Period
Deviation ＝ Percentage of how much the price has wrapped around the previous line.
Example:
Depth = 12
Backstep = 3
Deviation = 5
In this case, the price range is updated by 12 pips or more (Depth), and after 3 or more candlesticks line up (Backstep), if the price deviates from the previous line by 5% or more (Deviation), a peak is added.
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Zigzagの頂点にローソクカウント（バーカウント）を表示するインジケータです。
頂点の価格も表示します。
頂点から頂点までのローソク（バー）を容易にカウントすることができます。
サイクル理論のローソク（バー）に役立ちます。
Zigzagロジック自体はMT4のzigzagインジケータを流用しています。
＜パラメータ＞
Depth＝深さ（値幅）
Backstep＝期間
Deviation＝価格がどれだけ直前のラインの折り返したかの割合
例：
Depth＝12
Backstep＝3
Deviation＝5
この場合、値幅を12pips以上更新し（Depth）、ローソク足が3本以上並んだ後（Backstep）、価格が直前のラインの5％以上折り返せば（Deviation）、頂点を付けます。
＜表示オプション＞
Label_Style = "TEXT"…テキスト表示、"BALLOON"…吹き出し表示
EMD Oscillator (Zeiierman)█ Overview
The Empirical Mode Decomposition (EMD) Oscillator is an advanced indicator designed to analyze market trends and cycles with high precision. It breaks down complex price data into simpler parts called Intrinsic Mode Functions (IMFs), allowing traders to see underlying patterns and trends that aren’t visible with traditional indicators. The result is a dynamic oscillator that provides insights into overbought and oversold conditions, as well as trend direction and strength. This indicator is suitable for all types of traders, from beginners to advanced, looking to gain deeper insights into market behavior.
█ How It Works
The core of this indicator is the Empirical Mode Decomposition (EMD) process, a method typically used in signal processing and advanced scientific fields. It works by breaking down price data into various “layers,” each representing different frequencies in the market’s movement. Imagine peeling layers off an onion: each layer (or IMF) reveals a different aspect of the price action.
⚪ Data Decomposition (Sifting): The indicator “sifts” through historical price data to detect natural oscillations within it. Each oscillation (or IMF) highlights a unique rhythm in price behavior, from rapid fluctuations to broader, slower trends.
⚪ Adaptive Signal Reconstruction: The EMD Oscillator allows traders to select specific IMFs for a custom signal reconstruction. This reconstructed signal provides a composite view of market behavior, showing both short-term cycles and long-term trends based on which IMFs are included.
⚪ Normalization: To make the oscillator easy to interpret, the reconstructed signal is scaled between -1 and 1. This normalization lets traders quickly spot overbought and oversold conditions, as well as trend direction, without worrying about the raw magnitude of price changes.
The indicator adapts to changing market conditions, making it effective for identifying real-time market cycles and potential turning points.
█ Key Calculations: The Math Behind the EMD Oscillator
The EMD Oscillator’s advanced nature lies in its high-level mathematical operations:
⚪ Intrinsic Mode Functions (IMFs)
IMFs are extracted from the data and act as the building blocks of this indicator. Each IMF is a unique oscillation within the price data, similar to how a band might be divided into treble, mid, and bass frequencies. In the EMD Oscillator:
Higher-Frequency IMFs: Represent short-term market “noise” and quick fluctuations.
Lower-Frequency IMFs: Capture broader market trends, showing more stable and long-term patterns.
⚪ Sifting Process: The Heart of EMD
The sifting process isolates each IMF by repeatedly separating and refining the data. Think of this as filtering water through finer and finer mesh sieves until only the clearest parts remain. Mathematically, it involves:
Extrema Detection: Finding all peaks and troughs (local maxima and minima) in the data.
Envelope Calculation: Smoothing these peaks and troughs into upper and lower envelopes using cubic spline interpolation (a method for creating smooth curves between data points).
Mean Removal: Calculating the average between these envelopes and subtracting it from the data to isolate one IMF. This process repeats until the IMF criteria are met, resulting in a clean oscillation without trend influences.
⚪ Spline Interpolation
The cubic spline interpolation is an advanced mathematical technique that allows smooth curves between points, which is essential for creating the upper and lower envelopes around each IMF. This interpolation solves a tridiagonal matrix (a specialized mathematical problem) to ensure that the envelopes align smoothly with the data’s natural oscillations.
To give a relatable example: imagine drawing a smooth line that passes through each peak and trough of a mountain range on a map. Spline interpolation ensures that line is as smooth and close to reality as possible. Achieving this in Pine Script is technically demanding and demonstrates a high level of mathematical coding.
⚪ Amplitude Normalization
To make the oscillator more readable, the final signal is scaled by its maximum amplitude. This amplitude normalization brings the oscillator into a range of -1 to 1, creating consistent signals regardless of price level or volatility.
█ Comparison with Other Signal Processing Methods
Unlike standard technical indicators that often rely on fixed parameters or pre-defined mathematical functions, the EMD adapts to the data itself, capturing natural cycles and irregularities in real-time. For example, if the market becomes more volatile, EMD adjusts automatically to reflect this without requiring parameter changes from the trader. In this way, it behaves more like a “smart” indicator, intuitively adapting to the market, unlike most traditional methods. EMD’s adaptive approach is akin to AI’s ability to learn from data, making it both resilient and robust in non-linear markets. This makes it a great alternative to methods that struggle in volatile environments, such as fixed-parameter oscillators or moving averages.
█ How to Use
Identify Market Cycles and Trends: Use the EMD Oscillator to spot market cycles that represent phases of buying or selling pressure. The smoothed version of the oscillator can help highlight broader trends, while the main oscillator reveals immediate cycles.
Spot Overbought and Oversold Levels: When the oscillator approaches +1 or -1, it may indicate that the market is overbought or oversold, signaling potential entry or exit points.
Confirm Divergences: If the price movement diverges from the oscillator's direction, it may indicate a potential reversal. For example, if prices make higher highs while the oscillator makes lower highs, it could be a sign of weakening trend strength.
█ Settings
Window Length (N): Defines the number of historical bars used for EMD analysis. A larger window captures more data but may slow down performance.
Number of IMFs (M): Sets how many IMFs to extract. Higher values allow for a more detailed decomposition, isolating smaller cycles within the data.
Amplitude Window (L): Controls the length of the window used for amplitude calculation, affecting the smoothness of the normalized oscillator.
Extraction Range (IMF Start and End): Allows you to select which IMFs to include in the reconstructed signal. Starting with lower IMFs captures faster cycles, while ending with higher IMFs includes slower, trend-based components.
Sifting Stopping Criterion (S-number): Sets how precisely each IMF should be refined. Higher values yield more accurate IMFs but take longer to compute.
Max Sifting Iterations (num_siftings): Limits the number of sifting iterations for each IMF extraction, balancing between performance and accuracy.
Source: The price data used for the analysis, such as close or open prices. This determines which price movements are decomposed by the indicator.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Hurst Diamond Notation PivotsThis is a fairly simple indicator for diamond notation of past hi/lo pivot points, a common method in Hurst analysis. The diamonds mark the troughs/peaks of each cycle. They are offset by their lookback and thus will not 'paint' until after they happen so anticipate accordingly. Practically, traders can use the average length of past pivot periods to forecast future pivot periods in time🔮. For example, if the average/dominant number of bars in an 80-bar pivot point period/cycle is 76, then a trader might forecast that the next pivot could occur 76-ish bars after the last confirmed pivot. The numbers/labels on the y-axis display the cycle length used for pivot detection. This indicator doesn't repaint, but it has a lot of lag; Please use it for forecasting instead of entry signals. This indicator scans for new pivots in the form of a rainbow line and circle; once the hi/lo has happened and the lookback has passed then the pivot will be plotted. The rainbow color per wavelength theme seems to be authentic to Hurst (or modern Hurst software) and has been included as a default.
DECODE Global Liquidity IndexDECODE Global Liquidity Index 🌊
The DECODE Global Liquidity Index is a powerful tool designed to track and aggregate global liquidity by combining data from the world's 13 largest economies. It offers a comprehensive view of financial liquidity, providing crucial insights into the underlying currents that can influence asset prices and market trends.
The economies covered are: United States, China, European Union, Japan, India, United Kingdom, Brazil, Canada, Russia, South Korea, Australia, Mexico, and Indonesia. The European Union accounts for major individual economies within the EU like Germany, France, Italy, Spain, Netherlands, Poland, etc.
Key Features:
1. Customizable Liquidity Sources
Include Global M2: You can opt to include the M2 money supply from the 13 listed economies. M2 is a broad measure of money supply that includes cash, checking deposits, savings deposits, money market securities, mutual funds, and other time deposits. (Note: Australia uses M3 as its primary measure, which is included when M2 is selected for Australia).
Include Central Bank Balance Sheets (CBBS): Alternatively, or in addition, you can include the total assets held by the central banks of these economies. Central bank balance sheets expand or contract based on monetary policy operations like quantitative easing (QE) or tightening (QT).
Combined View: If you select both M2 and CBBS, and data is available for both, the indicator will display an average of the two aggregated values. If only one source type is selected, or if data for one type is unavailable despite both being selected, the indicator will display the single available and selected component. This provides flexibility in how you define and analyze global liquidity.
2. Lead/Lag Analysis (Forward Projection):
Lead Offset (Days): This feature allows you to project the liquidity index forward by a specified number of days.
Why it's useful: Global liquidity changes can often be a leading indicator for various asset classes, particularly those sensitive to risk appetite, like Bitcoin or growth stocks. These assets might lag shifts in liquidity. By applying a lead (e.g., 90 days), you can shift the liquidity data forward on your chart to more easily visualize potential correlations and identify if current asset price movements might be responding to past changes in liquidity.
3. Rate of Change (RoC) Oscillator:
Year-over-Year % View: Instead of viewing aggregate liquidity, you can switch to a Year-over-Year (YoY%) Rate of Change (ROC) oscillator.
Why it's useful:
Momentum Identification: The ROC highlights the speed and direction of liquidity changes. Positive values indicate liquidity is increasing compared to a year ago, while negative values show it's decreasing.
Turning Points: Oscillators make it easier to spot potential accelerations, decelerations, or reversals in liquidity trends. A cross above the zero line can signal strengthening liquidity momentum, while a cross below can signal weakening momentum.
Cycle Analysis: It helps in assessing the cyclical nature of liquidity provision and its potential impact on market cycles.
This indicator aims to provide a clear, customizable, and insightful measure of global liquidity to aid traders and investors in their market analysis.
Moon Phases + Daily, Weekly, Monthly, Quarterly & Yearly Breaks█ Moon Phases
From LuxAlgo description.
Trading moon phases has become quite popular among traders, believing that there exists a relationship between moon phases and market movements.
This strategy is based on an estimate of moon phases with the possibility to use different methods to determine long/short positions based on moon phases.
Note that we assume moon phases are perfectly periodic with a cycle of 29.530588853 days (which is not realistically the case), as such there exists a difference between the detected moon phases by the strategy and the ones you would see. This difference becomes less important when using higher timeframes.
█ Daily, Weekly, Monthly, Quarterly & Yearly Breaks
This indicator marks the start of the selected periods with a vertical line that help with identifying cycles.
It allows to enable or disable independently the daily, weekly, monthly, quarterly and yearly session breaks.
This script is based on LuxAlgo and kaushi / icostan scripts.
Moon Phases Strategy
Year/Quarter/Month/Week/Day breaks
Month/week breaks
UCS_CycleThis indicator was designed to remove trend from price and make it easier to identify cycles.
Although this indicator has similarities to MACD. It is better used to identify the cycle of High and Lows based on the Statistical Data (Default is set to 25).
**** DO NOT USE THIS AS A MOMENTUM INDICATOR ****
Bitcoin Logarithmic Regression BandsOverview
This indicator displays logarithmic regression bands for Bitcoin. Logarithmic regression is a statistical method used to model data where growth slows down over time. I initially created these bands in 2019 using a spreadsheet, and later coded them in TradingView in 2021. Over time, the bands proved effective at capturing Bitcoin's bull market peaks and bear market lows. In 2024, I decided to share this indicator because I believe these logarithmic regression bands offer the best fit for the Bitcoin chart.
How It Works
The logarithmic regression lines are fitted to the Bitcoin (BTCUSD) chart using two key factors: the 'a' factor (slope) and the 'b' factor (intercept). The two lines in the upper and lower bands share the same 'a' factor, but I adjust the 'b' factor by 0.2 to more accurately capture the bull market peaks and bear market lows. The formula for logaritmic regression is 10^((a * ln) - b).
How to Use the Logarithmic Regression Bands
1. Lower Band (Support Band):
The two lines in the lower band create a potential support area for Bitcoin’s price. Historically, Bitcoin’s price has always found its lows within this band during past market cycles. When the price is within the lower band, it suggests that Bitcoin is undervalued and could be set for a rebound.
2. Upper Band (Resistance Band):
The two lines in the upper band create a potential resistance area for Bitcoin’s price. Bitcoin has consistently reached its highs in this band during previous market cycles. If the price is within the upper band, it indicates that Bitcoin is overvalued, and a potential price correction may be imminent.
Use Cases
- Price Bottoming:
Bitcoin tends to bottom out at the lower band before entering a prolonged bull market or a period of sideways movement.
- Price Topping:
In reverse, Bitcoin tends to top out at the upper band before entering a bear market phase.
- Profitable Strategy:
Buying at the lower band and selling at the upper band can be a profitable trading strategy, as these bands often indicate key price levels for Bitcoin’s market cycles.
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."
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.
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.
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
