Moon Phases by Shailesh DesaiTrading Strategy Based on Lunar Phases
This custom trading indicator leverages the power of lunar cycles to provide unique market insights based on the four primary moon phases: New Moon, First Quarter, Full Moon, and Third Quarter. By aligning your trades with the natural rhythm of the moon, this strategy offers a different perspective to trading and can help enhance decision-making based on the cyclical nature of the market.
Key Features:
1. Moon Phase Identification:
o The indicator automatically identifies the current moon phase based on the user's selected timeframe and marks it on the chart.
o Each phase is visualized with a specific symbol and color to help traders easily recognize the current moon phase:
New Moon/Waxing Moon: Represented by a circle (colored as per user input).
First Quarter: Represented by a cross (colored as per user input).
Full Moon/Waning Moon: Represented by a circle (colored as per user input).
Third Quarter: Represented by a cross (colored as per user input).
2. Automatic Moon Phase Transition Detection:
o The indicator tracks and highlights when a phase change occurs. This feature ensures you are always aware of when the market moves from one phase to another.
o Moon phase changes are only visualized on the first bar of each new phase to avoid cluttering the chart.
3. Background Color Indicators:
o The background color dynamically changes according to the current moon phase, helping to reinforce the phase context for the trader. This feature makes it easy to see at a glance which phase the market is in.
4. Customizable Appearance:
o Customize the color of each moon phase to suit your preferences. Adjust the colors for the New Moon, First Quarter, Full Moon, and Third Quarter to align with your visual strategy.
5. Avoids Unsupported Timeframes:
o This indicator does not support monthly timeframes, ensuring that it operates smoothly only on timeframes that are compatible with the lunar cycle.
How to Use:
• The moon phases are thought to have an influence on human behavior and the market's psychology, making this indicator useful for traders who wish to integrate lunar cycles into their strategy.
• Traders can use the phase changes as an indicator of potential market momentum or reversal points. For example:
o New Moon may indicate the beginning of a new cycle, signaling a potential upward or downward move.
o Full Moon might suggest a peak or significant shift in market direction.
o First Quarter and Third Quarter phases may represent moments of consolidation or decision points.
Ideal for:
• Traders interested in cycle-based strategies or looking to experiment with new approaches.
• Those who believe in the influence of natural forces, including moon phases, on market movements.
• Technical analysts who want to add another layer of insights to their chart analysis.
Important Notes:
• The indicator uses precise astronomical calculations to identify the correct phase, ensuring accuracy.
• It’s important to understand that moon phase-based trading is not a standalone strategy but should ideally be combined with other technical analysis tools for maximum effectiveness.
Komut dosyalarını "Cycle" için ara
Holt-Winters Forecast BandsDescription:
The Holt-Winters Adaptive Bands indicator combines seasonal trend forecasting with adaptive volatility bands. It uses the Holt-Winters triple exponential smoothing model to project future price trends, while Nadaraya-Watson smoothed bands highlight dynamic support and resistance zones.
This indicator is ideal for traders seeking to predict future price movements and visualize potential market turning points. By focusing on broader seasonal and trend data, it provides insight into both short- and long-term market directions. It’s particularly effective for swing trading and medium-to-long-term trend analysis on timeframes like daily and 4-hour charts, although it can be adjusted for other timeframes.
Key Features:
Holt-Winters Forecast Line: The core of this indicator is the Holt-Winters model, which uses three components — level, trend, and seasonality — to project future prices. This model is widely used for time-series forecasting, and in this script, it provides a dynamic forecast line that predicts where price might move based on historical patterns.
Adaptive Volatility Bands: The shaded areas around the forecast line are based on Nadaraya-Watson smoothing of historical price data. These bands provide a visual representation of potential support and resistance levels, adapting to recent volatility in the market. The bands' fill colors (red for upper and green for lower) allow traders to identify potential reversal zones without cluttering the chart.
Dynamic Confidence Levels: The indicator adapts its forecast based on market volatility, using inputs such as average true range (ATR) and price deviations. This means that in high-volatility conditions, the bands may widen to account for increased price movements, helping traders gauge the current market environment.
How to Use:
Forecasting: Use the forecast line to gain insight into potential future price direction. This line provides a directional bias, helping traders anticipate whether the price may continue along a trend or reverse.
Support and Resistance Zones: The shaded bands act as dynamic support and resistance zones. When price enters the upper (red) band, it may be in an overbought area, while the lower (green) band may indicate oversold conditions. These bands adjust with volatility, so they reflect the current market conditions rather than fixed levels.
Timeframe Recommendations:
This indicator performs best on daily and 4-hour charts due to its reliance on trend and seasonality. It can be used on lower timeframes, but accuracy may vary due to increased price noise.
For traders looking to capture swing trades, the daily and 4-hour timeframes provide a balance of trend stability and signal reliability.
Adjustable Settings:
Alpha, Beta, and Gamma: These settings control the level, trend, and seasonality components of the forecast. Alpha is generally the most sensitive setting for adjusting responsiveness to recent price movements, while Beta and Gamma help fine-tune the trend and seasonal adjustments.
Band Smoothing and Deviation: These settings control the lookback period and width of the volatility bands, allowing users to customize how closely the bands follow price action.
Parameters:
Prediction Length: Sets the length of the forecast, determining how far into the future the prediction line extends.
Season Length: Defines the seasonality cycle. A setting of 14 is typical for bi-weekly cycles, but this can be adjusted based on observed market cycles.
Alpha, Beta, Gamma: These parameters adjust the Holt-Winters model's sensitivity to recent prices, trends, and seasonal patterns.
Band Smoothing: Determines the smoothing applied to the bands, making them either more reactive or smoother.
Ideal Use Cases:
Swing Trading and Trend Following: The Holt-Winters model is particularly suited for capturing larger market trends. Use the forecast line to determine trend direction and the bands to gauge support/resistance levels for potential entries or exits.
Identifying Reversal Zones: The adaptive bands act as dynamic overbought and oversold zones, giving traders potential reversal areas when price reaches these levels.
Important Notes:
No Buy/Sell Signals: This indicator does not produce direct buy or sell signals. It’s intended for visual trend analysis and support/resistance identification, leaving trade decisions to the user.
Not for High-Frequency Trading: Due to the nature of the Holt-Winters model, this indicator is optimized for higher timeframes like the daily and 4-hour charts. It may not be suitable for high-frequency or scalping strategies on very short timeframes.
Adjust for Volatility: If using the indicator on lower timeframes or more volatile assets, consider adjusting the band smoothing and prediction length settings for better responsiveness.
Elliott Wave Theory [Alerts]This indicator may be one of the first to provide signals & alerts for the Elliott Wave Theory Pattern. Unfortunately, there are few, if any, indicators that are public which allow the Elliott Wave Theory to be plotted+with alerts.
Because this is experimental, I'm going to offer access to it free of charge. Send me a direct message requesting access.
Elliott Wave Theory is a complex chart pattern to learn, and even harder to master. It requires being able to identify the wave lengths of price history and using various tools to plot and assess the price structure in order to find the wave counts. Whilst there are many ways to compute the waves apart of the pattern, there is no universal method that everyone would agree upon. For this indicator, I am using the traditional method.
The different colors represent different cycle types. Using it on a higher timeframe is strongly encouraged for best results.
This version currently provides alerts for the final wave, wave 5. I WILL BE ADDING MORE TO THIS INDICATOR SHORTLY, SEE BELOW:
-Will be adding the other counts to display all the waves
-Will be adding 'correction wave' alerts
-Will be adding 'inverse' pattern alerts
-Will be improving the the labels to include their cycle type
This is not a buy & sell indicator.. This is a TOOL to help analyze the market using Elliott Wave Theory.
The indicator should be used for the following:
-Aiding with EWT analysis
-Helping find potential exit points
-Assist with learning EWT by using this as a template
-Timing trades and improving risk management
-Plotting the overall market
Adaptive, Zero lag Schaff Trend Cycle [Loxx]TASC's March 2008 edition Traders' Tips includes an article by John Ehlers titled "Measuring Cycle Periods," and describes the use of bandpass filters to estimate the length, in bars, of the currently dominant price cycle.
What are Dominant Cycles and Why should we use them?
Even the most casual chart reader will be able to spot times when the market is cycling and other times when longer-term trends are in play. Cycling markets are ideal for swing trading however attempting to “trade the swing” in a trending market can be a recipe for disaster. Similarly, applying trend trading techniques during a cycling market can equally wreak havoc in your account. Cycle or trend modes can readily be identified in hindsight. But it would be useful to have an objective scientific approach to guide you as to the current market mode.
There are a number of tools already available to differentiate between cycle and trend modes. For example, measuring the trend slope over the cycle period to the amplitude of the cyclic swing is one possibility.
We begin by thinking of cycle mode in terms of frequency or its inverse, periodicity. Since the markets are fractal ; daily, weekly, and intraday charts are pretty much indistinguishable when time scales are removed. Thus it is useful to think of the cycle period in terms of its bar count. For example, a 20 bar cycle using daily data corresponds to a cycle period of approximately one month.
When viewed as a waveform, slow-varying price trends constitute the waveform's low frequency components and day-to-day fluctuations (noise) constitute the high frequency components. The objective in cycle mode is to filter out the unwanted components--both low frequency trends and the high frequency noise--and retain only the range of frequencies over the desired swing period. A filter for doing this is called a bandpass filter and the range of frequencies passed is the filter's bandwidth.
Indicator Features
-Zero lag or Regular Schaff Trend Cycle calculation
- Fixed or Band-pass Dominant Cycle for Schaff Trend Cycle MA period inputs
-10 different moving average options for Zero lag calculations
-Separate Band-pass Dominant Cycle calculations for both Schaff Trend Cycle and MA calculations
- Slow-to-Fast Band-pass Dominant Cycle input to tweak the ratio of Schaff Trend Cycle MA input periods as they relate to each other
Financial Astrology Mars LongitudeMars energy control the initial impulse, the courage to execute a risky action or to start a new entrepreneurship, to declare the war and fight. It allow us to focus our energy into impulsive action that will require a lot of our forces to produce the initial movement and momentum. Mars drives and directs our motivation into quick and impulsive actions. This planet also rules the angry, fight, conflict, wars and explosive reactions. Therefore, from trading perspective, Mars rules all the industries that imply a higher risks: sports, military, defence, startups (new entrepreneurship), high volatility industries and so forth. Aries zodiac sigh, the domicile of Mars is the archetype of the persons that are willing to move quick from the idea into the action, that are looking to explore new territories and take high risks.
With the manifestation of this impulsive and initiating energy through the zodiac signs we can predict the level of risk that the traders influenced by Mars and dominated by fire will take. This individuals, will desire higher risks when Mars is located in a zodiac sign that strengthens the fire force. Is not a surprise that BTCUSD is more bullish when Mars transits Aries, Gemini (air strength fire) and Sagittarius and bearish when transits Leo (this energy becomes more oriented to pleasures, parties, romance, passions), Virgo (challenge the impulse with the analytic thinking), Aquarius (boring of the existing holding needs to move into another stuff and is desiring a change), Pisces (period of reflexion and mediation of the results of the impulsive cycle that completes).
The most relevant Mars bullish zodiac signs positions for BTCUSD are: Aries 62% days, Gemini 66% days, Sagittarius 58%. The all history buy/sell frequency distribution is 55% (bull) 45% (sell) so BTCUSD has bias to the bullish side, even considering that, the bull frequency on this signs seems to be very relevant and can be analysed with this indicator in the BTCUSD TradingView index that provide historical price since 2010.
With this indicator there is unlimited possibilities to explore the impulsive risk prone actions across different markets to study how this plays out, no more manual chart annotations to identify the zodiac sign location of Mars. We encourage you to analyse this zodiac sign cycles in different markets and share with us your observations, leave us a comment with your research outcomes. Happy research!
Note: The Mars tropical longitude indicator is based on an ephemeris array that covers years 2010 to 2030, prior or after this years the longitude is not available, this daily ephemeris are based on UTC time so in order to align properly with the price bars times you should set UTC as your chart reference timezone.
Pi Cycle Top Indicator for BTCUSDThis indicator adds the Pi Cycle Top Indicator for BTCUSD to your chart.
Indicator Overview
The Pi Cycle Top Indicator has historically been effective in picking out the timing of market cycle highs to within 3 days. It uses the 111 day moving average (111DMA) and a newly created multiple of the 350 day moving average, the 350DMA x 2.
For the past three market cycles, when the 111DMA moves up and crosses the 350DMA x 2 we see that it coincides with the price of Bitcoin peaking. It is also interesting to note that 350 / 111 is 3.153, which is very close to Pi = 3.142. In fact, it is the closest we can get to Pi when dividing 350 by another whole number.
It once again demonstrates the cyclical nature of Bitcoin price action over long time frames. Though in this instance it does so with a high degree of accuracy over the past 7 years.
How It Can Be Used
Pi Cycle Top is useful to indicate when the market is very overheated. So overheated that the shorter term moving average, which is the 111 day moving average, has reached a x2 multiple of the 350 day moving average. Historically it has proved advantageous to sell Bitcoin at this time in Bitcoin's price cycles.
Pi-cycle top for Bitcoin bull-runUsage
Whenever the Pi-Cycle top conditions are met, the red circle appears at the bottom of the chart. Theoretically, this marks the top of the bull-run in Bitcoin within 3 days.
Credit and overview
Indicator Overview
The Pi Cycle Top Indicator has historically been effective in picking out the timing of market cycle highs to within 3 days.
It uses the 111 day moving average (111DMA) and a newly created multiple of the 350 day moving average, the 350DMA x 2.
Note: The multiple is of the price values of the 350DMA not the number of days.
For the past three market cycles, when the 111DMA moves up and crosses the 350DMA x 2 we see that it coincides with the price of Bitcoin peaking.
It is also interesting to note that 350 / 111 is 3.153, which is very close to Pi = 3.142. In fact, it is the closest we can get to Pi when dividing 350 by another whole number.
It once again demonstrates the cyclical nature of Bitcoin price action over long time frames. Though in this instance it does so with a high degree of accuracy over the past 7 years.
How It Can Be Used
Pi Cycle Top is useful to indicate when the market is very overheated. So overheated that the shorter term moving average, which is the 111 day moving average, has reached a x2 multiple of the 350 day moving average. Historically it has proved advantageous to sell Bitcoin at this time in Bitcoin’s price cycles.
Created By Philip Swift
MACD with DPO Strategy by NGExplanation of the MACD with DPO Strategy:
MACD (Moving Average Convergence Divergence):
The MACD is a trend-following indicator that shows the relationship between two moving averages of a price.
In this script:
We calculate the MACD line by subtracting the slow moving average (typically 26-period EMA) from the fast moving average (typically 12-period EMA).
The Signal line is calculated as a 9-period EMA of the MACD line.
The Histogram is the difference between the MACD line and the Signal line, indicating the momentum of the price trend.
Buy Condition: The script generates a buy signal when the MACD histogram crosses from negative to positive (indicating a bullish momentum) and DPO is also positive.
Sell Condition: The script generates a sell signal when the MACD histogram crosses from positive to negative (indicating a bearish momentum) and DPO is also negative.
DPO (Detrended Price Oscillator):
The DPO removes long-term trends from prices, making it easier to identify shorter-term cycles or oscillations.
In this script:
We calculate the DPO by subtracting a shifted simple moving average (SMA) from the close price. The shifting period depends on half the specified period.
We also calculate the DPO SMA as a 30-period EMA of the DPO values.
DPO Color: The DPO line is colored green when the DPO is above zero (indicating upward momentum) and red when it is below zero (indicating downward momentum). The histogram is also colored based on whether the DPO is positive or negative.
Plotting and Alerts:
The script plots the MACD, Signal, and Histogram on the chart.
Additionally, it plots the DPO and its SMA with different colors depending on whether the DPO is above or below zero.
Buy Signal: A green arrow labeled "BUY" is plotted below the bar when both MACD and DPO indicate a bullish condition.
Sell Signal: A red arrow labeled "SELL" is plotted above the bar when both MACD and DPO indicate a bearish condition.
Background colors are used to highlight the chart whenever a buy or sell condition occurs.
The script also includes alerts for both buy and sell signals, allowing users to set notifications when conditions are met.
How to Use:
Identify Buy and Sell Signals:
The script generates a Buy signal when:
The MACD histogram crosses from negative to positive (bullish momentum), and
The DPO is above zero (indicating upward momentum).
The script generates a Sell signal when:
The MACD histogram crosses from positive to negative (bearish momentum), and
The DPO is below zero (indicating downward momentum).
Chart Visualization:
The MACD histogram and Signal line help visualize the momentum and potential trend reversal.
The DPO and DPO SMA help visualize the shorter-term price cycles.
The signals (Buy and Sell) will be plotted on the chart with arrows indicating entry points.
Customization:
You can adjust the MACD and DPO parameters (such as fast_length, slow_length, period_) to fit your trading style or market conditions.
The script can be used in any timeframe depending on your strategy (e.g., intraday trading or longer-term trading).
Example Scenario:
If you're looking for potential buy opportunities, wait for the script to generate a buy signal (green arrow) where the MACD histogram has shifted to positive, and DPO is also in the green (above zero). This signals that both momentum and cycle direction are aligned for a potential upward movement.
Conversely, for sell opportunities, wait for the red arrow where MACD momentum is turning negative and DPO is also negative (below zero), indicating a bearish condition.
This combination of MACD and DPO allows traders to identify stronger and more reliable entry/exit points by confirming the trend with the MACD and detecting shorter-term price cycles with the DPO.
[Excalibur] Ehlers AutoCorrelation Periodogram ModifiedKeep your coins folks, I don't need them, don't want them. If you wish be generous, I do hope that charitable peoples worldwide with surplus food stocks may consider stocking local food banks before stuffing monetary bank vaults, for the crusade of remedying the needs of less than fortunate children, parents, elderly, homeless veterans, and everyone else who deserves nutritional sustenance for the soul.
DEDICATION:
This script is dedicated to the memory of Nikolai Dmitriyevich Kondratiev (Никола́й Дми́триевич Кондра́тьев) as tribute for being a pioneering economist and statistician, paving the way for modern econometrics by advocation of rigorous and empirical methodologies. One of his most substantial contributions to the study of business cycle theory include a revolutionary hypothesis recognizing the existence of dynamic cycle-like phenomenon inherent to economies that are characterized by distinct phases of expansion, stagnation, recession and recovery, what we now know as "Kondratiev Waves" (K-waves). Kondratiev was one of the first economists to recognize the vital significance of applying quantitative analysis on empirical data to evaluate economic dynamics by means of statistical methods. His understanding was that conceptual models alone were insufficient to adequately interpret real-world economic conditions, and that sophisticated analysis was necessary to better comprehend the nature of trending/cycling economic behaviors. Additionally, he recognized prosperous economic cycles were predominantly driven by a combination of technological innovations and infrastructure investments that resulted in profound implications for economic growth and development.
I will mention this... nation's economies MUST be supported and defended to continuously evolve incrementally in order to flourish in perpetuity OR suffer through eras with lasting ramifications of societal stagnation and implosion.
Analogous to the realm of economics, aperiodic cycles/frequencies, both enduring and ephemeral, do exist in all facets of life, every second of every day. To name a few that any blind man can naturally see are: heartbeat (cardiac cycles), respiration rates, circadian rhythms of sleep, powerful magnetic solar cycles, seasonal cycles, lunar cycles, weather patterns, vegetative growth cycles, and ocean waves. Do not pretend for one second that these basic aforementioned examples do not affect business cycle fluctuations in minuscule and monumental ways hour to hour, day to day, season to season, year to year, and decade to decade in every nation on the planet. Kondratiev's original seminal theories in macroeconomics from nearly a century ago have proven remarkably prescient with many of his antiquated elementary observations/notions/hypotheses in macroeconomics being scholastically studied and topically researched further. Therefore, I am compelled to honor and recognize his statistical insight and foresight.
If only.. Kondratiev could hold a pocket sized computer in the cup of both hands bearing the TradingView logo and platform services, I truly believe he would be amazed in marvelous delight with a GARGANTUAN smile on his face.
INTRODUCTION:
Firstly, this is NOT technically speaking an indicator like most others. I would describe it as an advanced cycle period detector to obtain market data spectral estimates with low latency and moderate frequency resolution. Developers can take advantage of this detector by creating scripts that utilize a "Dominant Cycle Source" input to adaptively govern algorithms. Be forewarned, I would only recommend this for advanced developers, not novice code dabbling. Although, there is some Pine wizardry introduced here for novice Pine enthusiasts to witness and learn from. AI did describe the code into one super-crunched sentence as, "a rare feat of exceptionally formatted code masterfully balancing visual clarity, precision, and complexity to provide immense educational value for both programming newcomers and expert Pine coders alike."
Understand all of the above aforementioned? Buckle up and proceed for a lengthy read of verbose complexity...
This is my enhanced and heavily modified version of autocorrelation periodogram (ACP) for Pine Script v5.0. It was originally devised by the mathemagician John Ehlers for detecting dominant cycles (frequencies) in an asset's price action. I have been sitting on code similar to this for a long time, but I decided to unleash the advanced code with my fashion. Originally Ehlers released this with multiple versions, one in a 2016 TASC article and the other in his last published 2013 book "Cycle Analytics for Traders", chapter 8. He wasn't joking about "concepts of advanced technical trading" and ACP is nowhere near to his most intimidating and ingenious calculations in code. I will say the book goes into many finer details about the original periodogram, so if you wish to delve into even more elaborate info regarding Ehlers' original ACP form AND how you may adapt algorithms, you'll have to obtain one. Note to reader, comparing Ehlers' original code to my chimeric code embracing the "Power of Pine", you will notice they have little resemblance.
What you see is a new species of autocorrelation periodogram combining Ehlers' innovation with my fascinations of what ACP could be in a Pine package. One other intention of this script's code is to pay homage to Ehlers' lifelong works. Like Kondratiev, Ehlers is also a hardcore cycle enthusiast. I intend to carry on the fire Ehlers envisioned and I believe that is literally displayed here as a pleasant "fiery" example endowed with Pine. With that said, I tried to make the code as computationally efficient as possible, without going into dozens of more crazy lines of code to speed things up even more. There's also a few creative modifications I made by making alterations to the originating formulas that I felt were improvements, one of them being lag reduction. By recently questioning every single thing I thought I knew about ACP, combined with the accumulation of my current knowledge base, this is the innovative revision I came up with. I could have improved it more but decided not to mind thrash too many TV members, maybe later...
I am now confident Pine should have adequate overhead left over to attach various indicators to the dominant cycle via input.source(). TV, I apologize in advance if in the future a server cluster combusts into a raging inferno... Coders, be fully prepared to build entire algorithms from pure raw code, because not all of the built-in Pine functions fully support dynamic periods (e.g. length=ANYTHING). Many of them do, as this was requested and granted a while ago, but some functions are just inherently finicky due to implementation combinations and MUST be emulated via raw code. I would imagine some comprehensive library or numerous authored scripts have portions of raw code for Pine built-ins some where on TV if you look diligently enough.
Notice: Unfortunately, I will not provide any integration support into member's projects at all. I have my own projects that require way too much of my day already. While I was refactoring my life (forgoing many other "important" endeavors) in the early half of 2023, I primarily focused on this code over and over in my surplus time. During that same time I was working on other innovations that are far above and beyond what this code is. I hope you understand.
The best way programmatically may be to incorporate this code into your private Pine project directly, after brutal testing of course, but that may be too challenging for many in early development. Being able to see the periodogram is also beneficial, so input sourcing may be the "better" avenue to tether portions of the dominant cycle to algorithms. Unique indication being able to utilize the dominantCycle may be advantageous when tethering this script to those algorithms. The easiest way is to manually set your indicators to what ACP recognizes as the dominant cycle, but that's actually not considered dynamic real time adaption of an indicator. Different indicators may need a proportion of the dominantCycle, say half it's value, while others may need the full value of it. That's up to you to figure that out in practice. Sourcing one or more custom indicators dynamically to one detector's dominantCycle may require code like this: `int sourceDC = int(math.max(6, math.min(49, input.source(close, "Dominant Cycle Source"))))`. Keep in mind, some algos can use a float, while algos with a for loop require an integer.
I have witnessed a few attempts by talented TV members for a Pine based autocorrelation periodogram, but not in this caliber. Trust me, coding ACP is no ordinary task to accomplish in Pine and modifying it blessed with applicable improvements is even more challenging. For over 4 years, I have been slowly improving this code here and there randomly. It is beautiful just like a real flame, but... this one can still burn you! My mind was fried to charcoal black a few times wrestling with it in the distant past. My very first attempt at translating ACP was a month long endeavor because PSv3 simply didn't have arrays back then. Anyways, this is ACP with a newer engine, I hope you enjoy it. Any TV subscriber can utilize this code as they please. If you are capable of sufficiently using it properly, please use it wisely with intended good will. That is all I beg of you.
Lastly, you now see how I have rasterized my Pine with Ehlers' swami-like tech. Yep, this whole time I have been using hline() since PSv3, not plot(). Evidently, plot() still has a deficiency limited to only 32 plots when it comes to creating intense eye candy indicators, the last I checked. The use of hline() is the optimal choice for rasterizing Ehlers styled heatmaps. This does only contain two color schemes of the many I have formerly created, but that's all that is essentially needed for this gizmo. Anything else is generally for a spectacle or seeing how brutal Pine can be color treated. The real hurdle is being able to manipulate colors dynamically with Merlin like capabilities from multiple algo results. That's the true challenging part of these heatmap contraptions to obtain multi-colored "predator vision" level indication. You now have basic hline() food for thought empowerment to wield as you can imaginatively dream in Pine projects.
PERIODOGRAM UTILITY IN REAL WORLD SCENARIOS:
This code is a testament to the abilities that have yet to be fully realized with indication advancements. Periodograms, spectrograms, and heatmaps are a powerful tool with real-world applications in various fields such as financial markets, electrical engineering, astronomy, seismology, and neuro/medical applications. For instance, among these diverse fields, it may help traders and investors identify market cycles/periodicities in financial markets, support engineers in optimizing electrical or acoustic systems, aid astronomers in understanding celestial object attributes, assist seismologists with predicting earthquake risks, help medical researchers with neurological disorder identification, and detection of asymptomatic cardiovascular clotting in the vaxxed via full body thermography. In either field of study, technologies in likeness to periodograms may very well provide us with a better sliver of analysis beyond what was ever formerly invented. Periodograms can identify dominant cycles and frequency components in data, which may provide valuable insights and possibly provide better-informed decisions. By utilizing periodograms within aspects of market analytics, individuals and organizations can potentially refrain from making blinded decisions and leverage data-driven insights instead.
PERIODOGRAM INTERPRETATION:
The periodogram renders the power spectrum of a signal, with the y-axis representing the periodicity (frequencies/wavelengths) and the x-axis representing time. The y-axis is divided into periods, with each elevation representing a period. In this periodogram, the y-axis ranges from 6 at the very bottom to 49 at the top, with intermediate values in between, all indicating the power of the corresponding frequency component by color. The higher the position occurs on the y-axis, the longer the period or lower the frequency. The x-axis of the periodogram represents time and is divided into equal intervals, with each vertical column on the axis corresponding to the time interval when the signal was measured. The most recent values/colors are on the right side.
The intensity of the colors on the periodogram indicate the power level of the corresponding frequency or period. The fire color scheme is distinctly like the heat intensity from any casual flame witnessed in a small fire from a lighter, match, or camp fire. The most intense power would be indicated by the brightest of yellow, while the lowest power would be indicated by the darkest shade of red or just black. By analyzing the pattern of colors across different periods, one may gain insights into the dominant frequency components of the signal and visually identify recurring cycles/patterns of periodicity.
SETTINGS CONFIGURATIONS BRIEFLY EXPLAINED:
Source Options: These settings allow you to choose the data source for the analysis. Using the `Source` selection, you may tether to additional data streams (e.g. close, hlcc4, hl2), which also may include samples from any other indicator. For example, this could be my "Chirped Sine Wave Generator" script found in my member profile. By using the `SineWave` selection, you may analyze a theoretical sinusoidal wave with a user-defined period, something already incorporated into the code. The `SineWave` will be displayed over top of the periodogram.
Roofing Filter Options: These inputs control the range of the passband for ACP to analyze. Ehlers had two versions of his highpass filters for his releases, so I included an option for you to see the obvious difference when performing a comparison of both. You may choose between 1st and 2nd order high-pass filters.
Spectral Controls: These settings control the core functionality of the spectral analysis results. You can adjust the autocorrelation lag, adjust the level of smoothing for Fourier coefficients, and control the contrast/behavior of the heatmap displaying the power spectra. I provided two color schemes by checking or unchecking a checkbox.
Dominant Cycle Options: These settings allow you to customize the various types of dominant cycle values. You can choose between floating-point and integer values, and select the rounding method used to derive the final dominantCycle values. Also, you may control the level of smoothing applied to the dominant cycle values.
DOMINANT CYCLE VALUE SELECTIONS:
External to the acs() function, the code takes a dominant cycle value returned from acs() and changes its numeric form based on a specified type and form chosen within the indicator settings. The dominant cycle value can be represented as an integer or a decimal number, depending on the attached algorithm's requirements. For example, FIR filters will require an integer while many IIR filters can use a float. The float forms can be either rounded, smoothed, or floored. If the resulting value is desired to be an integer, it can be rounded up/down or just be in an integer form, depending on how your algorithm may utilize it.
AUTOCORRELATION SPECTRUM FUNCTION BASICALLY EXPLAINED:
In the beginning of the acs() code, the population of caches for precalculated angular frequency factors and smoothing coefficients occur. By precalculating these factors/coefs only once and then storing them in an array, the indicator can save time and computational resources when performing subsequent calculations that require them later.
In the following code block, the "Calculate AutoCorrelations" is calculated for each period within the passband width. The calculation involves numerous summations of values extracted from the roofing filter. Finally, a correlation values array is populated with the resulting values, which are normalized correlation coefficients.
Moving on to the next block of code, labeled "Decompose Fourier Components", Fourier decomposition is performed on the autocorrelation coefficients. It iterates this time through the applicable period range of 6 to 49, calculating the real and imaginary parts of the Fourier components. Frequencies 6 to 49 are the primary focus of interest for this periodogram. Using the precalculated angular frequency factors, the resulting real and imaginary parts are then utilized to calculate the spectral Fourier components, which are stored in an array for later use.
The next section of code smooths the noise ridden Fourier components between the periods of 6 and 49 with a selected filter. This species also employs numerous SuperSmoothers to condition noisy Fourier components. One of the big differences is Ehlers' versions used basic EMAs in this section of code. I decided to add SuperSmoothers.
The final sections of the acs() code determines the peak power component for normalization and then computes the dominant cycle period from the smoothed Fourier components. It first identifies a single spectral component with the highest power value and then assigns it as the peak power. Next, it normalizes the spectral components using the peak power value as a denominator. It then calculates the average dominant cycle period from the normalized spectral components using Ehlers' "Center of Gravity" calculation. Finally, the function returns the dominant cycle period along with the normalized spectral components for later external use to plot the periodogram.
POST SCRIPT:
Concluding, I have to acknowledge a newly found analyst for assistance that I couldn't receive from anywhere else. For one, Claude doesn't know much about Pine, is unfortunately color blind, and can't even see the Pine reference, but it was able to intuitively shred my code with laser precise realizations. Not only that, formulating and reformulating my description needed crucial finesse applied to it, and I couldn't have provided what you have read here without that artificial insight. Finding the right order of words to convey the complexity of ACP and the elaborate accompanying content was a daunting task. No code in my life has ever absorbed so much time and hard fricking work, than what you witness here, an ACP gem cut pristinely. I'm unveiling my version of ACP for an empowering cause, in the hopes a future global army of code wielders will tether it to highly functional computational contraptions they might possess. Here is ACP fully blessed poetically with the "Power of Pine" in sublime code. ENJOY!
Pi Cycle bitcoin bottomFull credits go to the owner, but for reasons i cannot diclose.
Introduction
With the adoption of cryptographic assets reaching new heights, it is undeniably important to continuously expand and improve current indicators just like how these assets update with new lines of code over time.
Philip Swift’s Pi-Cycle Top Indicator has effectively signaled market and local tops to within 3 days, with the most recent occurrence being on May 12th 2021.
If it were possible to find the cycle/local top of each cycle, a similar analogy could be used to pinpoint the bottom of Bitcoin’s price.
These Pi-Cycle indicators are merely just two moving averages which, when divided by each other, are equal to the value of π.
π = Long MA / Short MA
350/111 = 3.153; as per the existing Bitcoin Pi-Cycle Top indicator.
Pi-Cycle Bottom for Bitcoin
At first, the existing “Pi moving average” pair (350/111) was realigned to see whether they cross at the bottom of the Bitcoin price.
They did not, only to be a lagging indicator in both 2015 and 2018 cycle bottoms.
A possible pair was discovered when the short MA was set to 150:
π = Long MA / 150
Long MA = π * 150
Long MA = 471 (rounded to the nearest whole number)
This resulted in a Pi MA pair of 471/150.
Using the multiple x0.745 of the 471-day SMA and the 150-day EMA (exponential average to take into account of short term volatility ), the price of Bitcoin bottoms at where they two moving averages cross:
When the 150-day EMA crossed below the 471 SMA *0.475, Bitcoin’s price had bottomed for the market cycle.
Over the last two market cycles, this indicator has been accurate to within 3 days also.
Pi Cycle Top IndicatorIndicator Overview
The Pi Cycle Top Indicator has historically been effective in picking out the timing of market cycle highs to within 3 days.
It uses the 111 day moving average (111DMA) and a newly created multiple of the 350 day moving average, the 350DMA x 2.
Note: The multiple is of the price values of the 350DMA not the number of days.
For the past three market cycles, when the 111DMA moves up and crosses the 350DMA x 2 we see that it coincides with the price of Bitcoin peaking.
It is also interesting to note that 350 / 111 is 3.153, which is very close to Pi = 3.142. In fact, it is the closest we can get to Pi when dividing 350 by another whole number.
It once again demonstrates the cyclical nature of Bitcoin price action over long time frames. Though in this instance it does so with a high degree of accuracy over the past 7 years.
How It Can Be Used
Pi Cycle Top is useful to indicate when the market is very overheated. So overheated that the shorter term moving average, which is the 111 day moving average, has reached a x2 multiple of the 350 day moving average. Historically it has proved advantageous to sell Bitcoin at this time in Bitcoin's price cycles.
Created By
Philip Swift
intellect_city - World Cycle - Ath & Atl - Logarithmic - Signal.Indicator Overview
INTELLECT_city - World Cycle - ATH & ATL - Timeframe 1D and 1W - Logarithmic - Signal - The Pi Cycle Top and Bottom Oscillator is an adaptation of the original Pi Cycle Top chart. It compares the 111-Day Moving Average circle and the 2 * 350-Day Moving Average circle of Bitcoin’s Price. These two moving averages were selected as 350 / 111 = 3.153; An approximation of the important mathematical number Pi.
When the 111-Day Moving Average circle reaches the 2 * 350-Day Moving Average circle, it indicates that the market is becoming overheated. That is because the mid time frame momentum reference of the 111-Day Moving Average has caught up with the long timeframe momentum reference of the 2 * 350-Day Moving Average.
Historically this has occurred within 3 days of the very top of each market cycle.
When the 111 Day Moving Average circle falls back beneath the 2 * 350 Day Moving Average circle, it indicates that the market momentum of that cycle is significantly cooling down. The oscillator drops down into the lower green band shown where the 111 Day Moving Average is moving at a 75% discount relative to the 2 * 350 Day Moving Average.
Historically, this has highlighted broad areas of bear market lows.
IMPORTANT: You need to set a LOGARITHMIC graph. (The function is located at the bottom right of the screen)
IMPORTANT: The INTELLECT_city indicator is made for signal purchases of sales, there is also a strategic one from INTELLECT_city
IMPORTANT: The Chart shows all cycles, both buying and selling.
IMPORTANT: Suitable timeframes are 1 daily (recommended) and 1 weekly
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Описание на русском:
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Обзор индикатора
INTELLECT_city - World Cycle - ATH & ATL - Timeframe 1D and 1W - Logarithmic - Signal - Логарифмический - Сигнал - Осциллятор вершины и основания цикла Пи представляет собой адаптацию оригинального графика вершины цикла Пи. Он сравнивает круг 111-дневной скользящей средней и круг 2 * 350-дневной скользящей средней цены Биткойна. Эти две скользящие средние были выбраны как 350/111 = 3,153; Приближение важного математического числа Пи.
Когда круг 111-дневной скользящей средней достигает круга 2 * 350-дневной скользящей средней, это указывает на то, что рынок перегревается. Это происходит потому, что опорный моментум среднего временного интервала 111-дневной скользящей средней догнал опорный момент импульса длинного таймфрейма 2 * 350-дневной скользящей средней.
Исторически это происходило в течение трех дней после вершины каждого рыночного цикла.
Когда круг 111-дневной скользящей средней опускается ниже круга 2 * 350-дневной скользящей средней, это указывает на то, что рыночный импульс этого цикла значительно снижается. Осциллятор опускается в нижнюю зеленую полосу, показанную там, где 111-дневная скользящая средняя движется со скидкой 75% относительно 2 * 350-дневной скользящей средней.
Исторически это высветило широкие области минимумов медвежьего рынка.
ВАЖНО: Выставлять нужно ЛОГАРИФМИЧЕСКИЙ график. (Находиться функция с правой нижней части экрана)
ВАЖНО: Индикатор INTELLECT_city сделан для сигнальных покупок продаж, есть также и стратегический от INTELLECT_сity
ВАЖНО: На Графике видны все циклы, как на покупку так и на продажу.
ВАЖНО: Подходящие таймфреймы 1 дневной (рекомендовано) и 1 недельный
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Beschreibung - Deutsch
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Indikatorübersicht
INTELLECT_city – Weltzyklus – ATH & ATL – Zeitrahmen 1T und 1W – Logarithmisch – Signal – Der Pi-Zyklus-Top- und Bottom-Oszillator ist eine Anpassung des ursprünglichen Pi-Zyklus-Top-Diagramms. Er vergleicht den 111-Tage-Gleitenden-Durchschnittskreis und den 2 * 350-Tage-Gleitenden-Durchschnittskreis des Bitcoin-Preises. Diese beiden gleitenden Durchschnitte wurden als 350 / 111 = 3,153 ausgewählt; eine Annäherung an die wichtige mathematische Zahl Pi.
Wenn der 111-Tage-Gleitenden-Durchschnittskreis den 2 * 350-Tage-Gleitenden-Durchschnittskreis erreicht, deutet dies darauf hin, dass der Markt überhitzt. Das liegt daran, dass der Momentum-Referenzwert des 111-Tage-Gleitenden-Durchschnitts im mittleren Zeitrahmen den Momentum-Referenzwert des 2 * 350-Tage-Gleitenden-Durchschnitts im langen Zeitrahmen eingeholt hat.
Historisch gesehen geschah dies innerhalb von 3 Tagen nach dem Höhepunkt jedes Marktzyklus.
Wenn der Kreis des 111-Tage-Durchschnitts wieder unter den Kreis des 2 x 350-Tage-Durchschnitts fällt, deutet dies darauf hin, dass die Marktdynamik dieses Zyklus deutlich nachlässt. Der Oszillator fällt in das untere grüne Band, in dem der 111-Tage-Durchschnitt mit einem Abschlag von 75 % gegenüber dem 2 x 350-Tage-Durchschnitt verläuft.
Historisch hat dies breite Bereiche mit Tiefstständen in der Baisse hervorgehoben.
WICHTIG: Sie müssen ein logarithmisches Diagramm festlegen. (Die Funktion befindet sich unten rechts auf dem Bildschirm)
WICHTIG: Der INTELLECT_city-Indikator dient zur Signalisierung von Käufen oder Verkäufen, es gibt auch einen strategischen Indikator von INTELLECT_city
WICHTIG: Das Diagramm zeigt alle Zyklen, sowohl Kauf- als auch Verkaufszyklen.
WICHTIG: Geeignete Zeitrahmen sind 1 täglich (empfohlen) und 1 wöchentlich
Adaptive Bandpass Filter [Ehlers]This is my latest bandpass filter - used to determine if a security is in a trend or cycle.
Now with an adaptive period setting! I use Ehlers in-phase & quadrature dominant cycle measurement (IQ IFM) method to set the period dynamically.
This method favors longer periods which tend to produce smoother, albeit laggier bandpass oscillator plots. From my quick tests, I tend to have lag between 4 and 8 bars, depending on the Timeframe.
The lower timeframes tend to have more noise and thus produce more interfering frequencies that may cause lag.
>Settings
Source: Select the data source to perform calc's on (close, open, etc...)
Period: Select the period to tune. Periods outside of this value will be attenuated (reduced)
Adaptive: Enable to have the I-Q IFM set the period for you (disables Period setting)
Bandpass Tolerance: Allow periods that are plus/minus the chosen period to pass.
Cycle Tolerance: Sensitivity of cycle mode. Lower values consider trends more frequent, higher values consider cycles more frequent.
Bandpass tolerance example: for instance, if this setting is 0.1 (10%) and Period is set to 20, then waves with a period of 18 - 22 will pass.
>How to read
Red line is the bandpass output, showing a lagged version of the dominant cycle representing the
Black lines are the upper and lower bounds for a cycle
Green Background indicates an uptrend
Red background indicates a downtrend
The Ultimate Buy and Sell Indicator: Unholy Grail Edition"You see, Watson, the market is not random—it simply whispers in a code too complex for the average trader. Lucky for you, I am not average."
They searched for the Holy Grail of trading for decades—promises, false prophets, and overpriced PDFs.
But they were all looking in the wrong place.
This isn’t a relic buried in the desert.
This is the Unholy Grail — a machine-forged fusion of logic, engineering, and tactical overkill .
Built by Sherlock Macgyver , this is not a mystical object. It’s a surveillance system for trend detection, signal validation, and precision entries .
⚠️ Important: This script draws its own candles.
To see it properly, disable regular candles by turning off "Body", "Wick" and "Border" colors.
🔧 What You’re Looking At
This overlay plots confirmed Buy/Sell signals , momentum-based “watch” zones , adaptive candle coloring , SuperTrend bias detection , dual Bollinger Bands , and a moving average ribbon .
It’s not “minimalist” —it’s comprehensive .
📍 Configuring the Tool: Follow the Breadcrumbs
Every setting includes a tooltip — read them . They're not filler. They explain exactly how each feature functions so you can dial this thing in like you're tuning a surveillance rig in a Cold War bunker .
If you skip them, you're walking blind in a minefield .
🕰️ Timeframes: The Signal Sweet Spot
Each asset has a tempo . You need to find the one where signals align with clarity —not chaos .
Start with 4H or 1H —work up or down from there.
Too many fakeouts? → Higher timeframe
Too slow? → Drop to 15m or 5m —but expect more noise and adjust settings accordingly.
The signals scale with time, but you must find the rhythm that best fits your asset—and your trading lifestyle .
♻️ RSI Cycle = Signal Sensitivity
This is the heart of the system . It controls how reactive the RSI engine is.
Adjust based on noise level and how often you can actually monitor your charts.
Short cycle (14–24): More signals, more speed, more noise
Longer cycle (36–64): Smoother entries, better for swing traders
Tip: If your signals feel too jittery, increase the cycle. If they lag too much, reduce it.
📉 SuperTrend: Your Trend Bias Compass
This isn’t your average SuperTrend. It adapts with RSI overlay logic and detects market “silence” via EMA compression— turning white right before the chaos . That said, you still control its aggression.
ATR Length = how many bars to average
ATR Factor = how tight or loose it hugs price
Lower = more sensitive (more trades, more noise)
Higher = confirmation only (fewer, but stronger signals)
Tweak until it feels like a sniper rifle.
No, you won’t get it perfect on the first try.
Yes, it’s worth it.
🛠️ Modular Signals: Why Things Fire (or Don’t)
Buy/Sell entries require conditions to align. The logic is modular, and that’s on purpose.
RSI signals only fire if RSI crosses its smoothed MA outside the dead zone and a “Watch” condition is active.
SuperTrend signals can be enabled to act on crossovers, optionally ignoring the Watch filter .
Watch conditions (colored squares) act as early recon and hint at possible upcoming trades.
Background color changes are “pre-signal warnings” and will repaint . Use them as leading signals, not gospel.
Want more trades? Loosen your filters .
Want sniper entries? Lock them down .
🌈 Candles and MAs: Visual Market Structure
Candles adapt in real-time to MA structure:
Green = bullish (above both fast/slow MAs)
Yellow = indecision (between)
Red = bearish (below both)
Buy/Sell signals override candles with bright orange and fuchsia —because subtlety doesn’t win wars .
You can also enable up to 8 customizable moving averages —great for confluence , trend confirmation , or just looking like a wizard .
🧠 Pro Usage Tips (TL;DR for Smart People):
Use tooltips in the settings menu —every toggle and slider is explained
Test timeframes until signal frequency and reliability match your goals
Adjust RSI cycle to reduce noise or speed up signals based on how frequently you trade
Tweak SuperTrend factor and ATR to fit volatility on your asset
Start with visual confirmation :
• Are watch signals lining up with trend zones?
• Are backgrounds firing before price moves?
• Are candle colors agreeing with signal direction?
📣 Alerts & Integration
Alerts are available for:
Buy/Sell entries (confirmed or advanced background)
Watch signals
Full band agreement (both Bollinger bands bullish or bearish)
Use these with webhook systems , bots , or your own trade journals .
Created by Sherlock Macgyver
Because sometimes the best trade…
is knowing exactly when not to take one.
Edufx AMD~Accumulation, Manipulation, DistributionEdufx AMD Indicator
This indicator visualizes the market cycles using distinct phases: Accumulation, Manipulation, Distribution, and Reversal. It is designed to assist traders in identifying potential entry points and understanding price behavior during these phases.
Key Features:
1. Phases and Logic:
-Accumulation Phase: Highlights the price range where market accumulation occurs.
-Manipulation Phase:
- If the price sweeps below the accumulation low, it signals a potential "Buy Zone."
- If the price sweeps above the accumulation high, it signals a potential "Sell Zone."
-Distribution Phase: Highlights where price is expected to expand and establish trends.
-Reversal Phase: Marks areas where the price may either continue or reverse.
2. Weekly and Daily Cycles:
- Toggle the visibility of Weekly Cycles and Daily Cycles independently through the settings.
- These cycles are predefined with precise timings for each phase, based on your selected on UTC-5 timezone.
3. Customizable Appearance:
- Adjust the colors for each phase directly in the settings to suit your preferences.
- The indicator uses semi-transparent boxes to represent the phases, allowing easy visualization without obstructing the chart.
4. Static Boxes:
- Boxes representing the phases are drawn only once for the visible chart range and do not dynamically delete, ensuring important consistent reference points.
Daye @joshuuuThis indicator is based on Dayes studies about 90minute cycles and true opens.
Similar to how ICT teaches the true day open at 0.00, Daye came up with his true year, true month, true week and true session opens.
True Year - April 1st
True Month - 2nd Monday
True Week - Monday, 6pm
True Day - 12am (Midnight)
True Session - 1:30am (London), 7:30am (New York), 1:30pm (Afternoon)
Ideally, for a bearish scenario, we would like to see price trade above the opening price to then reverse and trade lower.
Ideally, for a bullish scenario, we would like to see price trade below the opening price to then reverse and trade higher.
The moves into the opposite direction are used my smart money to accumulate their positions and trap traders into wrong positions.
This indicator also shows 90 minutes cycles.
90min Cycle Cheat Sheet:
Q1. (A)ccumulation - Consolidation
Q2. (M)anipulation - Judas Swing (Trade this)
Q3. (D)istribution - LRLR (Trade this)
Q4. (X) - Continuation/Reversal of previous q.
Or
Q1. (X) - Continuation/Reversal of previous q.
Q2. (A)ccumulation - Consolidation
Q3. (M)anipulation - Judas Swing (Trade this)
Q4. (D)istribution - LRLR (Trade this)
This shows that if q1 consolidates and q2 takes out one side and reverses we anticipate q3 to have a strong move.
however, if q2 consolidates, we anticipate q3 to take out one side, reverse and then have a strong move in q4.
Statistical AMDOverview
The Statistical AMD ("Accumulation, Manipulation, Distribution") is a real-time statistical analyzer and visual segmentation tool for price action.
It identifies and tracks the structure of major movements within higher timeframe candles — breaking them into three key phases:
Manipulation (M): Early-stage liquidity sweeps.
Distribution (D): Mid-phase trending moves.
Accumulation (A): Late-stage compression zones.
The tool records and visualizes where highs and lows form relative to the open of a larger candle (e.g., 1-hour) and aggregates statistical behavior across sessions.
This is not a predictive indicator — it is a segmentation and statistical probability builder for real-time and historical analysis.
What It Does
Tracks High/Low Timing:
Identifies when the high and low occur during each higher timeframe candle (like hourly).
Plots Box Structures:
Color-coded boxes for each phase:
Red = Manipulation
Green = Distribution
White = Accumulation
Displays Statistical Table:
Average timing of highs and lows
Current vs historical bar position tracking
Average ranges for each phase
Historical Aggregation:
Aggregates hundreds of candles' data to build probabilistic expectations.
Live Updates:
Boxes dynamically expand as price evolves within each phase.
Key Settings
HTF Reference:
Select the higher timeframe to analyze (Default: 1 Hour).
Manually Input Legs:
Customize leg sizes for manipulation, distribution, and no-trade zones.
Defaults:
Manipulation Leg = 3 bars
Distribution Leg = 6 bars
No Trade Zone = 6 bars
Ideal For
Liquidity and Manipulation Traders:
Those analyzing sweep behaviors, fakeouts, and structural rotations.
Time-based Statistical Analysts:
Users who build mean-reversion or breakout models based on timing patterns.
ICT, Smart Money Concept (SMC) Traders:
Traders who track sweep → displacement → compression cycles.
Scalpers and Intraday Traders:
Anyone needing microstructural framing inside large candles.
Important Notes
Higher timeframe anchoring is critical.
Make sure you align the "HTF Reference" with your intended analysis frame (e.g., if you scalp on 1-min, set HTF to 1H or 4H).
The tool doesn’t predict future moves directly — it helps build a contextual, statistically-backed map of where you are inside the cycle.
Manual input flexibility allows tailoring for different asset volatility.
Final Thought
If you're trading without understanding the internal phases of a candle — you're navigating blind.
Statistical AMD arms you with objective, historical data about how and when price tends to expand, manipulate, and compress — so you can act with probability on your side.
Financial Astrology Neptune LongitudeNeptune energy influence the charity, confusion, imagination, waste, crime, intuition, occult, scandal, illusion and dreams. It rules the industries related to chemicals, gas and oil, drugs and alcoholic beverages, scams, non profit organisations, spirituality. The last decade Neptune have been traveling through Piscis sign which caused humanity to have an illusion that economical growth don't have limits, as consequence we saw US indexes growth toward new all time highs. However, Neptune is close to leave Piscis, in 7 more degrees as per July 2021 and new cycle is going to start. It will be interesting to see what happens as Neptune moves into Aries sign.
This longitude indicator show a zodiac signs horizontal line boundaries that identify the start of the sign marked in the corresponding horizontal line label in the Y axis, this simplify the analysis of a planet effect within specific zodiac sign.
Note: The Neptune longitude indicator is based on an ephemeris array that covers years 2010 to 2030, prior or after this years the data is not available, this daily ephemeris are based on UTC time so in order to align properly with the price bars times you should set UTC as your chart timezone.
Bitcoin Logarithmic Regression
This indicator displays logarithmic regression channels for Bitcoin. A logarithmic regression is a function that increases or decreases rapidly at first, but then steadily slows as time moves. The original version of this indicator/model was created as an open source script by a user called Owain but is not available on TradingView anymore. So I decided to update the code to the latest version of pinescript and fine tune some of the parameters.
How to read and use the logarithmic regression:
There are 3 different regression lines or channels visible:
Green Channel: These lines represent different levels of support derived from the logarithmic regression model.
Purpose: The green channel is used to identify potential support levels where the price might find a bottom or bounce back upwards.
Interpretation:
If the price is approaching or touching the lower green lines, it might indicate a buying opportunity or an area where the price is considered undervalued.
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Red Channel: These lines represent different levels of resistance derived from the logarithmic regression model.
Purpose: The red channel is used to identify potential resistance levels where the price might encounter selling pressure or face difficulty moving higher.
Interpretation:
If the price is approaching or touching the upper red lines, it might indicate a selling opportunity or an area where the price is considered overvalued.
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Purple Line This line represents to so-called "fair price" of Bitcoin according to the regression model.
Purpose: The purple line can be used to identify if the current price of Bitcoin is under- or overvalued.
Interpretation: A simple interpretation here would be that over time the price will have the tendency to always return to its "fair price", so starting to DCA more when price is under the line and less when it is over the line could be a suitable investment strategy.
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Practical Application:
You can use this regression channel to build your own, long term, trading strategies. Notice how Bitcoin seems to always act in kind of the same 4 year cycle:
- Price likes to trade around the purple line at the time of the halvings
- After the halvings we see an extended sideways range for up to 300 days
- After the sideways range Bitcoin goes into a bull market frenzy (the area between the green and red channel)
- The price tops out at the upper red channel and then enters a prolonged bear market.
Buying around the purple line or lower line of the green channel and selling once the price reaches the red channel can be a suitable and very profitable strategy.
Bitcoin Market Cap wave model weeklyThis Bitcoin Market Cap wave model indicator is rooted in the foundation of my previously developed tool, the : Bitcoin wave model
To derive the Total Market Cap from the Bitcoin wave price model, I employed a straightforward estimation for the Total Market Supply (TMS). This estimation relies on the formula:
TMS <= (1 - 2^(-h)) for any h.This equation holds true for any value of h, which will be elaborated upon shortly. It is important to note that this inequality becomes the equality at the dates of halvings, diverging only slightly during other periods.
Bitcoin wave model is based on the logarithmic regression model and the sinusoidal waves, induced by the halving events.
This chart presents the outcome of an in-depth analysis of the complete set of Bitcoin price data available from October 2009 to August 2023.
The central concept is that the logarithm of the Bitcoin price closely adheres to the logarithmic regression model. If we plot the logarithm of the price against the logarithm of time, it forms a nearly straight line.
The parameters of this model are provided in the script as follows: log(BTCUSD) = 1.48 + 5.44log(h).
The secondary concept involves employing the inherent time unit of Bitcoin instead of days:
'h' denotes a slightly adjusted time measurement intrinsic to the Bitcoin blockchain. It can be approximated as (days since the genesis block) * 0.0007. Precisely, 'h' is defined as follows: h = 0 at the genesis block, h = 1 at the first halving block, and so forth. In general, h = block height / 210,000.
Adjustments are made to account for variations in block creation time.
The third concept revolves around investigating halving waves triggered by supply shock events resulting from the halvings. These halvings occur at regular intervals in Bitcoin's native time 'h'. All halvings transpire when 'h' is an integer. These events induce waves with intervals denoted as h = 1.
Consequently, we can model these waves using a sin(2pih - a) function. The parameter determining the time shift is assessed as 'a = 0.4', aligning with earlier expectations for halving events and their subsequent outcomes.
The fourth concept introduces the notion that the waves gradually diminish in amplitude over the progression of "time h," diminishing at a rate of 0.7^h.
Lastly, we can create bands around the modeled sinusoidal waves. The upper band is derived by multiplying the sine wave by a factor of 3.1*(1-0.16)^h, while the lower band is obtained by dividing the sine wave by the same factor, 3.1*(1-0.16)^h.
The current bandwidth is 2.5x. That means that the upper band is 2.5 times the lower band. These bands are forming an exceptionally narrow predictive channel for Bitcoin. Consequently, a highly accurate estimation of the peak of the next cycle can be derived.
The prediction indicates that the zenith past the fourth halving, expected around the summer of 2025, could result in Total Bitcoin Market Cap ranging between 4B and 5B USD.
The projections to the future works well only for weekly timeframe.
Enjoy the mathematical insights!
Bitcoin wave modelBitcoin wave model is based on the logarithmic regression model and the sinusoidal waves, induced by the halving events.
This chart presents the outcome of an in-depth analysis of the complete set of Bitcoin price data available from October 2009 to August 2023.
The central concept is that the logarithm of the Bitcoin price closely adheres to the logarithmic regression model. If we plot the logarithm of the price against the logarithm of time, it forms a nearly straight line.
The parameters of this model are provided in the script as follows: log (BTCUSD) = 1.48 + 5.44log(h).
The secondary concept involves employing the inherent time unit of Bitcoin instead of days:
'h' denotes a slightly adjusted time measurement intrinsic to the Bitcoin blockchain. It can be approximated as (days since the genesis block) * 0.0007. Precisely, 'h' is defined as follows: h = 0 at the genesis block, h = 1 at the first halving block, and so forth. In general, h = block height / 210,000.
Adjustments are made to account for variations in block creation time.
The third concept revolves around investigating halving waves triggered by supply shock events resulting from the halvings. These halvings occur at regular intervals in Bitcoin's native time 'h'. All halvings transpire when 'h' is an integer. These events induce waves with intervals denoted as h = 1.
Consequently, we can model these waves using a sin(2pih - a) function. The parameter determining the time shift is assessed as 'a = 0.4', aligning with earlier expectations for halving events and their subsequent outcomes.
The fourth concept introduces the notion that the waves gradually diminish in amplitude over the progression of "time h," diminishing at a rate of 0.7^h.
Lastly, we can create bands around the modeled sinusoidal waves. The upper band is derived by multiplying the sine wave by a factor of 3.1*(1-0.16)^h, while the lower band is obtained by dividing the sine wave by the same factor, 3.1*(1-0.16)^h.
The current bandwidth is 2.5x. That means that the upper band is 2.5 times the lower band. These bands are forming an exceptionally narrow predictive channel for Bitcoin. Consequently, a highly accurate estimation of the peak of the next cycle can be derived.
The prediction indicates that the zenith past the fourth halving, expected around the summer of 2025, could result in prices ranging between 200,000 and 240,000 USD.
Enjoy the mathematical insights!
Financial Astrology Vesta LongitudeVesta is one of the largest objects in the asteroid belt between Mars and Jupiter, the orbit duration is 3.63 years and seems to be very relevant celestial object in financial astrology. The experienced financial astrologer "Bill Meridian" indicates that this asteroid rules the security business, and paper securities such as bonds and stocks. We have confirmed through statistical research that adding this asteroid to astrology machine learning models provides an increase in daily trend predictions accuracy for crypto-currencies sector.
Our statistical analysis of Vesta zodiac sign location concluded that when is transiting the signs of Aries, Gemini, Cancer, Leo and Libra the daily trend is 59% or more of the days bullish. When Vesta is located at Capricorn is very bearish with 60% of the daily trend going in downward direction. In the other zodiac signs the daily trend was neutral showing most of the time a sideways pattern.
Is very interesting to note that the exact date July 21, 2021, when Vesta entered in Libra BTCUSD started the last bullish wave that finally broke the congestion zone of the 30K-35K and started a new bullish optimism. Pay attention on what happened in the previous cycle when Vesta was located in Libra and do your conclusions.
Note: Vesta longitude indicator is based on an ephemeris array that covers years 2010 to 2030, prior or after this years the data is not available, this daily ephemeris are based on UTC time so in order to align properly with the price bars times you should set UTC as your chart timezone.
Financial Astrology North Node (Rahu) DeclinationThe North Node (Rahu) declination is a long term cycle so don't seem to provide useful pattern for short/mid term trading, however is interesting to note that when the declination was within -6 to +6 degrees the price was congested within narrow price zone. As observed in all planets declinations indicators the boundary of moving from North to South or viceversa is critical to determine trend change but in the case of the Moon Nodes it seems to show that the planets energy becomes in equilibrium which causes that price are more stable.
Note: The North Node (Rahu) declination indicator is based on an ephemeris array that covers years 2010 to 2030, prior or after this years the data is not available, this daily ephemeris are based on UTC time so in order to align properly with the price bars times you should set UTC as your chart timezone.
