Dynamic Equalizer [DW]This is an experimental study inspired by techniques primarily utilized in the visual and audio processing worlds.
This study is designed to serve as a pre or post processing filter designer that allows you to shape the frequency spectrum of your data on a more "in-depth" level.
First the data is fed through my Band-Shelf Equalizer function.
The EQ in this script works by dividing the input signal into 6 bands and 2 shelves using a series of roofing filters.
The bands are then gain adjusted recursively (in %) to match source as closely as possible at unity gain.
The recursive adjustment size can be changed using the "Gain Adjustment Increment" input, which will affect how tightly the resulting filter approximates source at unity.
The frequency range of each band is adjustable via the period inputs. In default settings, these are the ranges:
-> Low Shelf : 256+ Samples Per Cycle. This shelf is the largest trend component of the signal. Unlike the other bands and shelf, this shelf is not zero mean unless source data is.
-> Band 1 : 128 - 256 Samples Per Cycle. This band is a moderate trend and low cyclic component of the signal.
-> Band 2 : 64 - 128 Samples Per Cycle. This band is a mild trend and moderate cyclic component of the signal.
-> Band 3 : 32 - 64 Samples Per Cycle. This band is a high cyclic component of the signal.
-> Band 4 : 16 - 32 Samples Per Cycle. This band is a high cyclic component of the signal.
-> Band 5 : 8 - 16 Samples Per Cycle. This band is a moderate cyclic and mild to moderate noise component of the signal.
-> Band 6 : 4 - 8 Samples Per Cycle. This band is a high noise component of the signal.
-> High Shelf : 4- Samples Per Cycle. This shelf is primarily noise.
Each band and shelf can be manually gain adjusted via their respective inputs.
After EQ processing, each band and shelf is then optionally fed through my Peak Envelope Compressor function for dynamics control.
The compressor in this script works by reducing band power by a specified percentage when it exceeds a user defined percentage of the peak envelope.
The peak envelope measures maximum power of the band over its period range multiplied by a user defined integer.
There is an option included to apply Butterworth smoothing to the envelope as well, which will alter the shape of the compressor.
If you want an envelope that quickly responds to power peaks, use little to no smoothing. If you desire something more static, use a large smoothing period.
Attack and release are included in the algorithm to shape the sensitivity of the compressor.
Attack controls how many bars it takes from being triggered for attenuation to reach its target amount.
Release controls how many bars it takes from being un-triggered for attenuation to reach back to 0.
In addition, the compressor is equipped with parallel processing.
The "Parallel Mix" inputs control the amount of compressed vs non-compressed signal presence in the final output.
And of course, the compressor has a post-processing gain input (in %) to fine-tune the presence of the band.
For easy visual tuning, you can view each independent band's magnitude or power by selecting them in the display inputs.
This display setup can also be beneficial analytically if you wish to analyze specific frequency components of the source signal.
The default preset for this script is meant to show how versatile EQ filtering and compression can be for technical analysis.
The EQ preset detrends the data, moderately smooths the data, and emphasizes dominant cyclical ranges.
The compression preset provides fast, moderately heavy shaping to dial in dynamics and reduce transient effects.
The resulting curve is a great filter for responsively analyzing cyclical momentum.
The script is also fully equipped with outputs that can be used externally by other scripts.
You can integrate these external outputs with your own script by using a source input. Simply select the desired output from the dropdown tab on your script.
Multiband filtering and compression are concepts that are not conventionally used in the world of finance.
However, the versatile capabilities of these concepts make this a wonderful tool to have in the arsenal.
By surgically adjusting separate frequency components of a signal, you're able to design a wide variety of filters with unique responses for a vast array of applications.
Play around with the settings and see what kinds of filters you can design!
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This is a premium script, and access is granted on an invite-only basis.
To gain access, get a copy of the script overview, or for additional inquiries, send me a direct message.
I look forward to hearing from you!
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General Disclaimer:
Trading stocks, futures, Forex, options, ETFs, cryptocurrencies or any other financial instrument has large potential rewards, but also large potential risk.
You must be aware of the risks and be willing to accept them in order to invest in stocks, futures, Forex, options, ETFs or cryptocurrencies.
Don’t trade with money you can’t afford to lose.
This is neither a solicitation nor an offer to Buy/Sell stocks, futures, Forex, options, ETFs, cryptocurrencies or any other financial instrument.
No representation is being made that any account will or is likely to achieve profits or losses of any kind.
The past performance of any trading system or methodology is not necessarily indicative of future results.
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NOTE: Unlike standard tools of this nature in other applications, I scaled the signals in % rather than dB, mainly since it's proven so far to be more user-friendly to keep things linear on here.
In addition, no transitions to frequency domain are done in this script. This EQ is an experimental variant that processes in the time domain and relies on a network of roofing filters.
When changing cutoff periods, make sure they are organized in descending order with low shelf as the highest period, and high shelf as the lowest period.
Using non-descending lengths may result in an undesired output.
Lastly, when changing cutoff periods, parts of the spectrum may leak slightly differently between bands, so the "Gain Match Adjustment Increment" may need to be changed as well if you want it to match as closely as possible at unity.
Despite these shortcomings, this tool functions surprisingly well, especially with the default periods, and it's quickly become one of my favorites. I hope you all enjoy it!
Cari dalam skrip untuk "Cycle"
CryptoSignalScanner - Pi Cycle - Golden Ratio MultiplierDESCRIPTION:
All credits are going to Philip Swift who has written an article on Medium about the PI Cycle Top and The Golden Ratio Multiplier .
Based on the article this indicator has been created to display and indicate the Bitcoin PI Cycle Top which has historically been effective in picking out the market cycle highs within 3 days. It also displays the Golden Ratio Multiplier which explores Bitcoin's adoption curve and market cycles.
• The PI Cycle Top is based on the 350DMA (Daily Moving Average) multiplied by 2 and the 111DMA (Daily Moving Average)
• The Golden Ratio Multiplier is based on the 350DMA (Daily Moving Average) the The Golden Ratio which is defines as 350DMA * 1.61803398875 and the Fibonacci Sequence which is defined as 350DMA * 2, 350DMA * 3, 350DMA * 5, 350DMA * 8, 350DMA * 13 and 350DMA * 21
HOW TO USE:
• The PI Cycle Top is picking the market cycle tops within 3 days.
When the 350DMA x2 crosses below the 111DMA Bitcoin price peaks in its market cycle. This indicates that the market is overbought and it is time to take profit.
• The Golden Ratio Multiplier pics the top on every market cycle in Bitcoin’s history and forecasts when Bitcoin will top in the coming market cycle.
In 2011 the top was at 350DMA * 21
In 2013 the top was at 350DMA * 13
In 2014 the top was at 350DMA * 8
In 2018 the top was at 350DMA * 5
If we look at the results above the forecast for next top should be at 350DMA * 3
FEATURES:
• You can change the Long Moving Average which is by default 350
• You can change the Short Moving Average which is by default 111
• You can show/hide the Pi Cycle Top labels
• You can show/hide the Pi Cycle Bottom labels
• You can show/hide the Pi Cycle Moving Averages
• You can show/hide the Golden Ratio
• You can show/hide the Fibonacci Sequence
• You can set an alert when the Pi Cycle Top is reached
REMARKS:
• This advice is NOT financial advice.
• We do not provide personal investment advice and we are not a qualified licensed investment advisor.
• All information found here, including any ideas, opinions, views, predictions, forecasts, commentaries, suggestions, or stock picks, expressed or implied herein, are for informational, entertainment or educational purposes only and should not be construed as personal investment advice.
• We will not and cannot be held liable for any actions you take as a result of anything you read here.
• We only provide this information to help you make a better decision.
• While the information provided is believed to be accurate, it may include errors or inaccuracies.
HOW TO GET ACCESS TO THE SCRIPT:
• Access to this script is free of charge
• You can drop me a message to get access to the script
Good Luck,
SEOCO
(1) Genie Cycles VS-200The Genie Cycles indicator contains two primary components. The first generates the primary turning-point Entry/Exit signals based on a hybrid algorithms that utilize multiple moving filters and oscillators, all working in concert. The second is our version of Hurst Cycles allowing the trader to view the harmonic convergence of short and long cycles.
The turning-point signals are generated by two Center of Gravity Oscillators (COG) originally developed by John Ehlers and published in Technical Analysis of Stocks and Commodities in its May 2002 issue.
COG produces a moving filter that heavily weights the most extreme and most current values in the stream of data within the window of the indicator. COG excels at determining and indicating where, within a parabolic path, tipping or turning points have occurred. Two COG indicators, each one set to a different length and different inputs are incorporated. The output of these two COG filters are them put through another Ehler’s filter, the Pass Band; July 2016 issue of TAOSAC. A pass band filter has the unique ability of removing the higher and lower frequencies from the signal, leaving behind only the core signal. Here we are taking a longer COG period of (10) days, utilizing the candles body size as it’s input and then subtracting a short period of (7) days utilizing only the close of the day. The result is an emphasis on the extreme values, i.e., the maximum apex and the minimum vertex of each parabolic swing. Finally, the Arnaud Legoux Moving Average (ALMA) is utilized as smoothing a filter to slightly shift the weighting from the COG Pass band filter, in a selective and adjustable manor to more current bars, not the most current bar. This is desirable because COG dramatically emphasizes the most current candle or bar as well as large candles and strong deviations from within the moving average.
This provides the trader with excellent responsiveness within a very smooth output signal with very few artifacts or whipsaws, producing highly reliable trading signals that indicate optimal entry and exit points with a high level of accuracy and very little lag.
The primary principals of Hurst cycles are price moves in waves that exhibit cyclic attributes based on their time scales. Genie Cycles incorporates Hurst cycles theories, but utilizes only two nested Laguerre moving filters. Laguerre moving filters have significantly less lag than traditional moving averages. These moving filters take as there inputs the highest high and the lowest lows for the two adjustable periods. The point of the indicator is to determine when a short-term swing cycle harmonizes or aligns with a long-term cycle, i.e., determining when the tops and bottoms of these cycles align.
The resulting nested channels produce natural bounding boxes. This dramatically highlights likely support and resistance levels as they often occur at prior highs or lows that this indicator is drawing. Convergence of the different cycle lengths can indicate strong trends that make excellent trading opportunities. Decoupling of the cycles indicates the end of the trend.
MVRV Ratio [Alpha Extract]The MVRV Ratio Indicator provides valuable insights into Bitcoin market cycles by tracking the relationship between market value and realized value. This powerful on-chain metric helps traders identify potential market tops and bottoms, offering clear buy and sell signals based on historical patterns of Bitcoin valuation.
🔶 CALCULATION The indicator processes MVRV ratio data through several analytical methods:
Raw MVRV Data: Collects MVRV data directly from INTOTHEBLOCK for Bitcoin
Optional Smoothing: Applies simple moving average (SMA) to reduce noise
Status Classification: Categorizes market conditions into four distinct states
Signal Generation: Produces trading signals based on MVRV thresholds
Price Estimation: Calculates estimated realized price (Current price / MVRV ratio)
Historical Context: Compares current values to historical extremes
Formula:
MVRV Ratio = Market Value / Realized Value
Smoothed MVRV = SMA(MVRV Ratio, Smoothing Length)
Estimated Realized Price = Current Price / MVRV Ratio
Distance to Top = ((3.5 / MVRV Ratio) - 1) * 100
Distance to Bottom = ((MVRV Ratio / 0.8) - 1) * 100
🔶 DETAILS Visual Features:
MVRV Plot: Color-coded line showing current MVRV value (red for overvalued, orange for moderately overvalued, blue for fair value, teal for undervalued)
Reference Levels: Horizontal lines indicating key MVRV thresholds (3.5, 2.5, 1.0, 0.8)
Zone Highlighting: Background color changes to highlight extreme market conditions (red for potentially overvalued, blue for potentially undervalued)
Information Table: Comprehensive dashboard showing current MVRV value, market status, trading signal, price information, and historical context
Interpretation:
MVRV ≥ 3.5: Potential market top, strong sell signal
MVRV ≥ 2.5: Overvalued market, consider selling
MVRV 1.5-2.5: Neutral market conditions
MVRV 1.0-1.5: Fair value, consider buying
MVRV < 1.0: Potential market bottom, strong buy signal
🔶 EXAMPLES
Market Top Identification: When MVRV ratio exceeds 3.5, the indicator signals potential market tops, highlighting periods where Bitcoin may be significantly overvalued.
Example: During bull market peaks, MVRV exceeding 3.5 has historically preceded major corrections, helping traders time their exits.
Bottom Detection: MVRV values below 1.0, especially approaching 0.8, have historically marked excellent buying opportunities.
Example: During bear market bottoms, MVRV falling below 1.0 has identified the most profitable entry points for long-term Bitcoin accumulation.
Tracking Market Cycles: The indicator provides a clear visualization of Bitcoin's market cycles from undervalued to overvalued states.
Example: Following the progression of MVRV from below 1.0 through fair value and eventually to overvalued territory helps traders position themselves appropriately throughout Bitcoin's market cycle.
Realized Price Support: The estimated realized price often acts as a significant
support/resistance level during market transitions.
Example: During corrections, price often finds support near the realized price level calculated by the indicator, providing potential entry points.
🔶 SETTINGS
Customization Options:
Smoothing: Toggle smoothing option and adjust smoothing length (1-50)
Table Display: Show/hide the information table
Table Position: Choose between top right, top left, bottom right, or bottom left positions
Visual Elements: All plots, lines, and background highlights can be customized for color and style
The MVRV Ratio Indicator provides traders with a powerful on-chain metric to identify potential market tops and bottoms in Bitcoin. By tracking the relationship between market value and realized value, this indicator helps identify periods of overvaluation and undervaluation, offering clear buy and sell signals based on historical patterns. The comprehensive information table delivers valuable context about current market conditions, helping traders make more informed decisions about market positioning throughout Bitcoin's cyclical patterns.
Fast Fourier Transform (FFT) FilterDear friends!
I'm happy to present an implementation of the Fast Fourier Transform (FFT) algorithm. The script uses the FFT procedure to decompose the input time series into its cyclical constituents, in other words, its frequency components , and convert it back to the time domain with modified frequency content, that is, to filter it.
Input Description and Usage
Source and Length :
Indicates where the data comes from and the size of the lookback window used to build the dataset.
Standardize Input Dataset :
If enabled, the dataset is preprocessed by subtracting its mean and normalizing the result by the standard deviation, which is sometimes useful when analyzing seasonalities. This procedure is not recommended when using the FFT filter for smoothing (see below), as it will not preserve the average of the dataset.
Show Frequency-Domain Power Spectrum :
When enabled, the results of Fourier analysis (for the last price bar!) are plotted as a frequency-domain power spectrum , where “power” is a measure of the significance of the component in the dataset. In the spectrum, lower frequencies (longer cycles) are on the right, higher frequencies are on the left. The graph does not display the 0th component, which contains only information about the mean value. Frequency components that are allowed to pass through the filter (see below) are highlighted in magenta .
Dominant Cycles, Rows :
If this option is activated, the periods and relative powers of several dominant cyclical components that is, those that have a higher power, are listed in the table. The number of the component in the power spectrum (N) is shown in the first column. The number of rows in the table is defined by the user.
Show Inverse Fourier Transform (Filtered) :
When enabled, the reconstructed and filtered time-domain dataset (for the last price bar!) is displayed.
Apply FFT Filter in a Moving Window :
When enabled, the FFT filter with the same parameters is applied to each bar. The last data point of the reconstructed and filtered dataset is used to build a new time series. For example, by getting rid of high-frequency noise, the FFT filter can make the data smoother. By removing slowly evolving low-frequency components (including non-periodic constituents), one can reveal and analyze shorter cycles. Since filtering is done in real-time in a moving window (similar to the moving average), the modified data can potentially be used as part of a strategy and be subjected to other technical indicators.
Lowest Allowed N :
Indicates the number of the lowest frequency component used in the reconstructed time series.
Highest Allowed N :
Indicates the number of the highest frequency component used in the reconstructed time series.
Filtering Time Range block:
Specifies the time range over which real-time FFT filtering is applied. The reason for the presence of this block is that the FFT procedure is relatively computationally intensive. Therefore, the script execution may encounter the time limit imposed by TradingView when all historical bars are processed.
As always, I look forward to your feedback!
Also, leave a comment if you'd be interested in the tutorial on how to use this tool and/or in seeing the FFT filter in a strategy.
QT/TD.Den Quarterly Theory QT//Quarterly Theory/OPTD
These Quarters represent:
A - Accumulation (required for a cycle to occur)
M - Manipulation
D - Distribution
X - Reversal/Continuation
The latter are going to always be in this specific sequence; however the cycle can be transposed to have its beginning in X, trivially followed by A, M, and finally D.
This feature is not automatic and at the subjective discretion of the Analyst.
Note: this theory has been developed on Futures, hence its validity and reliability may change depending on the market Time.
This tool does provide a dynamic and auto-adapting aspect to different market types and Times, however they must be seen as experimental.
> Quarterly Cycles
The Quarterly Cycles currently supported are: Yearly, Monthly, Weekly, Daily, 90 Minute, Micro Sessions.
– Yearly Cycle:
Analogously to financial quarters, the year is divided in four sections of three months each
Q1 - January, February, March
Q2 - April, May, June (True Open, April Open)
Q3 - July, August, September
Q4 - October, November, December
VIDYA with Dynamic Length Based on ICPThis script is a Pine Script-based indicator that combines two key concepts: the Instantaneous Cycle Period (ICP) from Dr. John Ehlers and the Variable Index Dynamic Average (VIDYA). Here's an overview of how the script works:
Components:
Instantaneous Cycle Period (ICP):
This part of the indicator uses Dr. John Ehlers' approach to detect the market cycle length dynamically. It calculates the phase of price movement by computing the in-phase and quadrature components of the price detrended over a specific period.
The ICP helps adjust the smoothing length dynamically, giving a real-time estimate of the dominant cycle in price action. The script uses a phase calculation, adjusts it for cycle dynamics, and smoothes it for more reliable readings.
VIDYA (Variable Index Dynamic Average):
VIDYA is a moving average that dynamically adjusts its smoothing length based on the market conditions, in this case, using the RSI (Relative Strength Index) as a weight.
The length of VIDYA is determined by the dynamically calculated ICP, allowing it to adapt to changing market cycles.
This indicator performs several recursive layers of VIDYA smoothing (applying VIDYA multiple times) to provide a more refined result.
Key Features:
Dynamic Length: The length for the VIDYA calculation is derived from the smoothed ICP value, meaning that the smoothing adapts to the detected cycle length in real-time, making the indicator more responsive to market conditions.
Multiple VIDYA Layers: The script applies multiple layers of VIDYA smoothing (up to 5 iterations), further refining the output to smooth out market noise while maintaining responsiveness.
Plotting: The final smoothed VIDYA value and the smoothed ICP length are plotted. Additionally, overbought (70) and oversold (30) horizontal lines are provided for visual reference.
Application:
This indicator helps identify trends, smooths out price data, and adapts dynamically to market cycles. It's useful for detecting shifts in momentum and trends, and traders can use it to identify overbought or oversold conditions based on dynamically calculated thresholds.
Intellect_city - World Cycle - Ath - Timeframe 1D and 1WIndicator Overview
The Pi Cycle Top Indicator has historically been effective in picking out the timing of market cycle highs 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. However, in this instance, it does so with a high degree of accuracy over Bitcoin's adoption phase of growth.
Bitcoin Price Prediction Using This Tool
The Pi Cycle Top Indicator forecasts the cycle top of Bitcoin’s market cycles. It attempts to predict the point where Bitcoin price will peak before pulling back. It does this on major high time frames and has picked the absolute tops of Bitcoin’s major price moves throughout most of its history.
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 an x2 multiple of the 350-day moving average. Historically, it has proved advantageous to sell Bitcoin around this time in Bitcoin's price cycles.
It is also worth noting that this indicator has worked during Bitcoin's adoption growth phase, the first 15 years or so of Bitcoin's life. With the launch of Bitcoin ETF's and Bitcoin's increased integration into the global financial system, this indicator may cease to be relevant at some point in this new market structure.
Gherkinit Futures Cycle█ OVERVIEW
Presented here is code for the " NYSE:GME Futures cycle theory" originally conceived by Gherkinit (Pi-Fi) and his quantitative analysts which is still under peer review.
This theory was built upon the knowledge that many intelligent investors on Reddit accrued over the past year in regards to the Mother Of All Short Squeezes this stock has to offer.
Up until now, what happened in January 2021 was considered an anomaly brought on by FOMO and retail interest but it's starting to look like unfair market makers and similar went to cover and ran head on into retail FOMO which is why they cut off the buying at that time. In order to understand what happened and what's to come, visualizing the theory with ease is essential.
█ WHAT THE SETTINGS MEAN
- Enable Draw | Visual Clean up
(True/False) Quarterly dates : Enables or disables the quarterly dates that repeat every "cycle".
(True/False) Roll dates : Enables or disables the roll dates that repeat every "cycle".
(True/False) Expiration dates : Enables or disables the expiration dates that repeat every "cycle".
(True/False) Run dates : Enables or disables the run dates that repeat every "cycle".
- Date Colors | Making things look good
(Color) Quarterly : Color for the respective date.
(Color) Roll : Color for the respective date.
(Color) Expiration : Color for the respective date.
(Color) Run : Color for the respective date.
- Extended Cycle | Look into the future
(Integer) Extended line height multiplier : A multiplier value for the height of the lines representing the selected "future" cycle.
(Dollar Amount) Extended line height : The height value in dollars of the lines representing the selected "future" cycle.
(Integer) Extended line width : The width of the lines representing the selected "future" cycle.
(Integer) Extended cycle ID : The cycle you want to see "ahead" or in the "future". For example if you set the value to "0" you'll only see cycles from the past up until the present (already occurred). If you set the value to "1" you will see the estimated dates for the specific cycle in the future i.e. 1 cycle ahead of the last completed/visible cycle on the chart.
█ EXTRA INFO
This indicator was simply made by a bored CS student who didn't want to endlessly mark dates on a graph after learning more about the theory.
Hope this help whoever uses this. To the moon fellow apes!
- Winter ;)
P.s. Pickle 4 Life
Financial Astrology Saturn LongitudeSaturn energy strengthen the temperance, rectitude, constancy, greed, pessimism and precautionary. Under this influence the crowd will move with caution, slow and with strong and rigorous sense, analysing the environment in detail and deducting all the possible action outcomes based on the past experiences and utilising all the accesible wisdom. This cycle rules the land and real state, the state and institutions, officials, and regulations.
Due to the essential nature of this energy is expected that traders take more caution and reflexion in their investment decisions where Saturn transits through earth element (Taurus, Virgo, Capricorn) because the persons become more prudent and rigid. In water elements (Cancer, Scorpio and Pisces) traders will be reducing exposure to risky assets because the emotions are more unstable and the fear to loss results in risk aversion.
This cycle takes 29 years to complete so we don't have enough observations in the crypto-currencies sector to evaluate the potential effect of Saturn through all the zodiac signs but with the historical data available, there are some interesting patterns: the most bearish zodiac signs was Scorpio (water) and Capricorn (earth) and the most bullish was Sagittarius and Aquarius. This correlates well with other planet cycles where we have observed that air zodiac signs are usually bullish.
This indicator provides longitude since 2010 so will be limited in the zodiac signs that is possible to be analysed, however the periods of retrogradation and stationary speed phases could give interesting trading signals. We encourage you to analyse this cycles in different markets and share with us your observations, leave us a comment with your research outcomes. Happy research!
Note: The Saturn 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.
Bitcoin Logarithmic Growth Curve 2024The Bitcoin logarithmic growth curve is a concept used to analyze Bitcoin's price movements over time. The idea is based on the observation that Bitcoin's price tends to grow exponentially, particularly during bull markets. It attempts to give a long-term perspective on the Bitcoin price movements.
The curve includes an upper and lower band. These bands often represent zones where Bitcoin's price is overextended (upper band) or undervalued (lower band) relative to its historical growth trajectory. When the price touches or exceeds the upper band, it may indicate a speculative bubble, while prices near the lower band may suggest a buying opportunity.
Unlike most Bitcoin growth curve indicators, this one includes a logarithmic growth curve optimized using the latest 2024 price data, making it, in our view, superior to previous models. Additionally, it features statistical confidence intervals derived from linear regression, compatible across all timeframes, and extrapolates the data far into the future. Finally, this model allows users the flexibility to manually adjust the function parameters to suit their preferences.
The Bitcoin logarithmic growth curve has the following function:
y = 10^(a * log10(x) - b)
In the context of this formula, the y value represents the Bitcoin price, while the x value corresponds to the time, specifically indicated by the weekly bar number on the chart.
How is it made (You can skip this section if you’re not a fan of math):
To optimize the fit of this function and determine the optimal values of a and b, the previous weekly cycle peak values were analyzed. The corresponding x and y values were recorded as follows:
113, 18.55
240, 1004.42
451, 19128.27
655, 65502.47
The same process was applied to the bear market low values:
103, 2.48
267, 211.03
471, 3192.87
676, 16255.15
Next, these values were converted to their linear form by applying the base-10 logarithm. This transformation allows the function to be expressed in a linear state: y = a * x − b. This step is essential for enabling linear regression on these values.
For the cycle peak (x,y) values:
2.053, 1.268
2.380, 3.002
2.654, 4.282
2.816, 4.816
And for the bear market low (x,y) values:
2.013, 0.394
2.427, 2.324
2.673, 3.504
2.830, 4.211
Next, linear regression was performed on both these datasets. (Numerous tools are available online for linear regression calculations, making manual computations unnecessary).
Linear regression is a method used to find a straight line that best represents the relationship between two variables. It looks at how changes in one variable affect another and tries to predict values based on that relationship.
The goal is to minimize the differences between the actual data points and the points predicted by the line. Essentially, it aims to optimize for the highest R-Square value.
Below are the results:
It is important to note that both the slope (a-value) and the y-intercept (b-value) have associated standard errors. These standard errors can be used to calculate confidence intervals by multiplying them by the t-values (two degrees of freedom) from the linear regression.
These t-values can be found in a t-distribution table. For the top cycle confidence intervals, we used t10% (0.133), t25% (0.323), and t33% (0.414). For the bottom cycle confidence intervals, the t-values used were t10% (0.133), t25% (0.323), t33% (0.414), t50% (0.765), and t67% (1.063).
The final bull cycle function is:
y = 10^(4.058 ± 0.133 * log10(x) – 6.44 ± 0.324)
The final bear cycle function is:
y = 10^(4.684 ± 0.025 * log10(x) – -9.034 ± 0.063)
The main Criticisms of growth curve models:
The Bitcoin logarithmic growth curve model faces several general criticisms that we’d like to highlight briefly. The most significant, in our view, is its heavy reliance on past price data, which may not accurately forecast future trends. For instance, previous growth curve models from 2020 on TradingView were overly optimistic in predicting the last cycle’s peak.
This is why we aimed to present our process for deriving the final functions in a transparent, step-by-step scientific manner, including statistical confidence intervals. It's important to note that the bull cycle function is less reliable than the bear cycle function, as the top band is significantly wider than the bottom band.
Even so, we still believe that the Bitcoin logarithmic growth curve presented in this script is overly optimistic since it goes parly against the concept of diminishing returns which we discussed in this post:
This is why we also propose alternative parameter settings that align more closely with the theory of diminishing returns.
Our recommendations:
Drawing on the concept of diminishing returns, we propose alternative settings for this model that we believe provide a more realistic forecast aligned with this theory. The adjusted parameters apply only to the top band: a-value: 3.637 ± 0.2343 and b-parameter: -5.369 ± 0.6264. However, please note that these values are highly subjective, and you should be aware of the model's limitations.
Conservative bull cycle model:
y = 10^(3.637 ± 0.2343 * log10(x) - 5.369 ± 0.6264)
Ehlers Adaptive Center Of Gravity [CC]The Adaptive Center Of Gravity was created by John Ehlers and this is a regular center of gravity indicator combined to be use with the current cycle period. If you are not familiar with stock cycles then I would highly recommend his book on the subject: Cycle Analytics. Buy when the indicator turns green and sell when it turns red.
Let me know if there are any other indicators you want me to publish!
Strength Analyzer [DW]This is an experimental hybrid between relative strength and spectrum analysis methods aimed to deliver useful insights about cyclical dominance and momentum.
This study utilizes a modified RSI formula and a modified Goertzel algorithm to determine relative strength and spectral dominance for periods 8 through 50.
These periods are theorized by many analysts to be the main cyclical components of market movement.
In this study, you are given the option to apply equalization (EQ) to the dataset before estimating strength.
This enables you to transform your data and observe how strength estimates changes as well.
Whether you want to give emphasis to some frequencies, isolate specific bands, or completely alter the shape of your waveform, EQ filtration makes for an interesting experience.
The default EQ preset in this script cuts low end presence, dampens high frequency oscillations, and cleanly passes main cyclic components.
There are many ways to use EQ to transform your dataset, so play around with the settings and find the presets that work best for your analysis setup.
After EQ processing, the data is then passed through the modified RSI algorithm to generate momentum information
The modified RSI in this script is rescaled to oscillate between -1 and 1, and has the option to pass through a 2 pole Butterworth low pass filter before and after processing for a smoother output.
The strength thresholds are determined by the threshold value, which quantifies distance above and below 0.
The threshold value can also be thought of as conventional RSI distance from 50 rescaled so that an increment of 0.1 is equivalent to an increment of 5 on a conventional RSI.
A threshold value of 0.4 is equivalent to thresholds of 70 and 30 on a conventional RSI, so this is the default. The maximum threshold value is 1, which is equivalent to thresholds of 100 and 0.
This script plots colored sections for each period value using a gradient color scheme based on their respective strength estimates.
The color scheme in this script is a multicolored gradient that shows green scaled colors for bullish strength and red scaled colors for bearish strength.
Darker, less vibrant colors indicate lower strength. Brighter, more vibrant colors indicate higher strength.
Strength values near 0 will show the darkest colors, and values near the positive or negative threshold value will show the brightest.
The data is fed parallel through the modified Goertzel algorithm to obtain cyclic power information and to estimate the dominant cycle.
Gerald Goertzel's algorithm is a unique Fourier related transform that identifies tonal properties by quantifying resonance in a set of second order IIR filters with direct-form structure.
It is computationally more efficient than typical DFT or FFT algorithms, and yields decent spectral resolution.
In this variation of the algorithm, data is first passed through a 2 pole high pass filter to attenuate spectral dilation, then passed through a Hamming Window to tidy up the frequency range.
The clean windowed data is then passed through a recursive resonance loop over the frequency block to calculate filter coefficients, which are then used to identify real and imaginary magnitude components.
From there, the magnitude components are used to calculate cyclic power.
The power outputs of each period are then compared for dominant cycle estimation, which is plotted over the gradient.
The dominant cycle can also be optionally smoothed or halved based on your preferences.
Bar colors are included in this script. The color scheme is a gradient based on dominant cycle momentum.
Signals and alert conditions are included in this script as well, and can be customized to your liking.
The two main signal types in this script are:
-> Dominant Cycle - Signals based on dominant cycle or half dominant cycle changes from positive to negative strength or vice versa.
-> Confluence - Signals based on confluence emergence. Based on the majority of measured cycles or all measured cycles showing positive or negative strength.
The signals in this are also externally accessible by other scripts.
The output format is 1 for long signals, and -1 for short signals.
To integrate these signals with your own system, use a source input in your script and assign it to this script's "Direction Signals" output variable from the dropdown tab.
In addition, I included two external output variables that show dominant cycle strength and average cycle strength.
They can be integrated into your own scripts by using a source input and selecting the proper output variable, just like the signals.
The Strength Analyzer is a versatile and powerful analytical tool to have in the arsenal for generating unique insights about momentum and cycle dominance.
By analyzing strength on a spectral basis, we can look at relative price movements on a deeper level and gain insights that aren't necessarily obvious from simply looking at a price chart.
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This is a premium script, and access is provided on an invite-only basis.
To gain access, get a copy of the script overview, or for any other inquiries, send me a direct message!
I look forward to hearing from you!
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General Disclaimer:
Trading stocks, futures, Forex, options, ETFs, cryptocurrencies or any other financial instrument has large potential rewards, but also large potential risk.
You must be aware of the risks and be willing to accept them in order to invest in stocks, futures, Forex, options, ETFs or cryptocurrencies.
Don’t trade with money you can’t afford to lose.
This is neither a solicitation nor an offer to Buy/Sell stocks, futures, Forex, options, ETFs, cryptocurrencies or any other financial instrument.
No representation is being made that any account will or is likely to achieve profits or losses of any kind.
The past performance of any trading system or methodology is not necessarily indicative of future results.
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Note:
Because TV's UI can't handle displaying style options for 43 fills with 42 colors, the color scheme of the analyzer is currently not editable.
However, no other sacrifices to functionality or quality were made in this project.
As the TV team performs updates on the platform, the ability to customize this color scheme will likely come as well.
Also, it's important to note that this script uses a heavy amount of calculations to generate this output.
At times (very infrequently), TV will throw an error message saying "Calculation Takes Too Long", likely due to a momentary lull in available server space.
If you receive this error, simply hide then unhide the indicator, and everything should function as expected.
Market GloryV1 -Introducing the new Market Glory indicator! In this indicator you will find:
- Dynamic Trends: a beta feature that takes into account both the maximum high and lowest low values anywhere between 5 to 200 bars back to determine the respective resistance and support levels at all times, with a trailing guidance middle bar that can serve as a meter for direction and takes into account only the close values of the defined 5-200 lookback bars! ( ***Strictly based on the 1 minute timeframe. )
- Engulfing bars: a beta feature that allows you to seek out potential reversal bars, based on the dema tema clouds and the respective bar's open and close!!
- Cycle bars: a Market Sniper classic feature, enabling you to catch momentum, consolidation, and continuation via hollow candles! This is achieved by detecting whether the open and close values stem from within the dema tema cloud's boundaries!
- Levels: also a Market Sniper classic, which lets you see support and resistance levels based on previous daily, weekly, and monthly opening and closing values! Also takes into account current closing price action, which will update the levels after being broken!! Furthermore, takes into account fibonacci steps (0.236, 0.382, and 0.5) per timeframe to determine where the nearest level will draw out. **The Calibration feature enables you to look ahead for potential upcoming resistances, with maximum precision.
- EMA crossings: A legacy feature in almost any popular indicator, as a means to correspond with the dema tema cycles for better entries and exits!!
- Multi-timeframe popup labels: By hovering (or long pressing in mobile) over the support and resistance level labels, you can see each dedicated timeframe's current cycle and crossing, to assess whether the stock is following a particular direction! (based solely on real-time close value)
- Lastly...
--- Fully customizable options in coloring and values, including ready-to-go defaults with tooltips to guide you to the Glory you deserve!!!
[blackcat] L2 Ehlers Dominant Cycle Tuned Bandpass FilterLevel: 2
Background
John F. Ehlers introuced his Dominant Cycle Tuned Bandpass Filter Strategy in Mar, 2008.
Function
In "Measuring Cycle Periods", author John Ehlers presents a very interesting technique of measuring dominant market cycle periods by means of multiple bandpass filtering. By utilizing an approach similar to audio equalizers, the signal (here, the price series) is fed into a set of simple second-order infinite impulse response bandpass filters. Filters are tuned to 8,9,10,...,50 periods. The filter with the highest output represents the dominant cycle. A full-featured formula that implements a high-pass filter and a six-tap low-pass Fir filter on input, then 42 parallel Iir band-pass filters.
I've coded John Ehlers' filter bank to measure the dominant cycle (DC) and the sine and cosine filter components in pine v4 for TradingView, based on John Ehlers' article in this issue, "Measuring Cycle Periods." The CycleFilterDC function plots and returns the DC series and its components, so it's a trivial matter to make use of them in a trading strategy.
Based on John Ehlers' article, "Measuring Cycle Periods," he chose to implement the dominant cycle-tuned bandpass filter response to test Ehlers' suggestion to use the sine and cosine crossovers as buy and sell signals. If the sine closely follows the price pattern as suggested, and the cosine is an effective leading function of the sine, then it seems to make sense that a crossover implementation would work well (Personally, what I observed this is not so accurated as his claims).
What he discovered in his tests was that crossovers happened at frequent intervals, even when price has not moved significantly. This leads to a higher percentage of losing trades, particularly when spread, slippage, and commissions are accounted for. Nevertheless, the cosine crossover was quite effective at identifying reversals very early in many cases, so this indicator could prove quite effective when used alongside other indicators. In particular, the use of an indicator to confirm a certain level of recent volatility, as well as an indicator to confirm significant rate of change, could prove quite helpful.
Key Signal
CosineLine--> Ehlers Dominant Cycle Tuned Bandpass Filter Strategy fast line
SineLine--> Ehlers Dominant Cycle Tuned Bandpass Filter Strategy slow line
Pros and Cons
100% John F. Ehlers definition translation, even variable names are the same. This help readers who would like to use pine to read his book.
Remarks
The 72th script for Blackcat1402 John F. Ehlers Week publication.
NOTE: Although Dr. Ehlers think high of Cosine and Sine wave indicator and trading strategy, my study and trading experience indicated it did not work that well as many other oscillator indicators. However, I would like to keep the original code of Dr. Ehlers for anyone who want to make a deep dive into this kind of indicator or strategy with Cosine and Sine wave.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Inter Cycle Valuation | QuantumResearchIntroducing Inter Cycle Valuation by QuantumResearch
A Multi-Factor Adaptive Z-Score System for Market Valuation & Reversal Potential
🧠 Overview
The Inter Cycle Valuation System is a sophisticated multi-factor indicator designed to evaluate the market’s cyclical valuation zones using a blend of momentum, volatility, mean-reversion, and risk-based metrics. It delivers a unified Z-Score—ranging from extremely oversold to overheated conditions—empowering traders to identify high-probability market turning points.
Rather than relying on a single indicator, Inter Cycle blends over 15 diverse Z-score factors, including RSI, ROC, VWAP deviation, Repulse, PGO, and statistical ratios like Sharpe, Sortino, and Omega. This multi-dimensional view allows traders to assess market extremes with greater confidence.
🧩 1. Key Features
📌 Multi-Factor Z-Score System
Inter Cycle integrates 16+ unique indicators into a single composite score. Each input is normalized via a Z-score to ensure balance and reduce bias, helping prevent outlier distortion.
⚙️ Indicator Diversity
Momentum: RSI, ROC, Chande Momentum, Repulse
Mean Reversion: VWAP deviation, Median rank, PGO
Volatility: BB% positioning, Intraday Momentum Index
Risk Ratios: Sharpe, Sortino, Omega
Fractal Geometry: Crosby Ratio
📈 Visually Intuitive Output
Gradient-based area plot for valuation intensity
Optional background heatmap for oversold/overbought zones
Table displaying real-time Z-score values for each component
Dynamic market suggestions: Accumulate, Do Nothing, or Distribute
📊 On-Chart Dashboard
The valuation dashboard displays key stats like Z-price, Z-rsi, Z-mfi, Z-roc, Z-crosby, and more—allowing for real-time interpretation without leaving the chart.
🔍 2. How It Works
1️⃣ Z-Score Normalization
Each indicator is transformed into a Z-score to standardize the values. This ensures that one factor does not dominate due to its scale or volatility.
2️⃣ Multi-Factor Aggregation
All Z-scores are averaged into a single score—called the "Inter Cycle Score"—giving you a reliable snapshot of market positioning.
3️⃣ Actionable Thresholds
🟩 Below -1.6 → “Accumulate Aggressively”
🟨 Between -1.5 and -0.65 → “Accumulate”
⚪ Between -0.5 and +0.5 → “Do Nothing”
🟧 Above +1.2 → “Distribute”
🟥 Above +1.55 → “Distribute Aggressively”
The system prints these labels automatically in real time.
📌 3. Valuation Zones
📉 Strongly Undervalued (< -1.6) → Prime accumulation
📉 Moderately Undervalued (-1.5 to -0.65) → Cautious entries
⚖️ Neutral/Fair (-0.5 to +0.5) → Wait-and-see
📈 Moderately Overheated (+1.2 to +1.55) → Begin reducing risk
🔥 Strongly Overheated (> +1.55) → Take profits, reduce exposure
This structure helps traders and investors clearly interpret current market cycles and position accordingly.
🔁 4. Use Cases & Applications
🔁 Cycle-Based Market Rotation
Great for timing market rotations by spotting macro tops and bottoms. Use the valuation dashboard to rotate capital across assets at optimal phases.
📈 Mean Reversion Entry Triggers
Z-Score combinations such as VWAP deviation + RSI + ROC help pinpoint high-probability mean reversion setups.
📉 Risk-Based Trend Exhaustion
With integrated Sharpe, Sortino, and Omega ratios, you can identify unsustainable moves fueled by low-quality momentum.
💼 Swing Trading & Portfolio Rebalancing
The Inter Cycle score can be used as a filter for swing setups or to rebalance holdings when conditions become extreme.
✅ Conclusion
Inter Cycle Valuation by QuantumResearch is a precision tool for any trader or investor seeking structured insights into market cycles. With its blend of valuation, risk, momentum, and reversion components—standardized via Z-scores—it offers a high-level framework to identify when markets are overheated or undervalued.
Who Should Use It?
✅ Swing Traders & Medium-Term Investors
✅ Portfolio Managers looking for capital rotation signals
✅ Quant Traders and Stat Arb enthusiasts
✅ Macro Traders monitoring cyclical inflection zones
⚠️ Disclaimer
The content provided by this indicator is for educational and informational purposes only. Nothing herein constitutes financial or investment advice. Trading and investing involve risk, including the potential loss of capital. Always backtest and apply risk management suited to your strategy.
OmniPulse (Fixed Version)OmniPulse (Fixed Version) – Description
OmniPulse is a multi-indicator framework designed to combine three core oscillators—RSI, Stochastic, and Momentum—at various lookback lengths, then refine their signals using placeholder features such as machine learning forecasting, adaptive cycle detection, and neural network filtering. While some of these advanced features are not natively supported in Pine Script, they are represented here in simplified forms to illustrate how a more sophisticated system could be structured.
Key Components:
Multi-Length Oscillator Arrays
RSI (calcrsi() function)
Stochastic (placeholder via ta.sma() on a typical price average)
Momentum (ta.roc())
These are calculated for multiple lengths defined by the rsiLengths, stochLengths, and momentumLengths arrays.
Dual-Threshold Convergence
Compares each oscillator’s value to user-defined upper/lower thresholds (threshold1, threshold2) to identify bullish or bearish conditions.
Summarizes results in a convergence score.
Placeholder Machine Learning Forecast
Demonstrates a simple averaging of oscillator values as a “forecast” when toggled on.
Adaptive Cycle Detection (Placeholder)
Introduces a static cycle period (e.g., 20.0) as a placeholder for more advanced transforms.
Neural Network Filter (Placeholder)
Averages convergence, forecast, and cyclePeriod into a single filteredSignal.
Signal Plotting
Plots the filtered signal on the chart.
Highlights potential bullish or bearish extremes with shape markers based on percentile thresholds.
Practical Use & Extension:
Real Multi-Timeframe Analysis: Replace placeholders with request.security() for each timeframe.
Advanced Forecasting: Incorporate custom or external machine learning models.
Genuine Cycle Detection: Implement more sophisticated logic or user-defined cycle detection tools.
Neural Network Heuristics: Expand the placeholder step into a deeper filtering or weighting system.
Overall, OmniPulse serves as an adaptable blueprint for traders and developers, showcasing how multiple indicators and advanced concepts might be combined into a cohesive, signal-generating framework.
Bitcoin CycleThis script displays 4 different Moving Averages:
2 Year Moving Average (White)
1 Year Moving Average (Doubled in value, Red)
116 Day Moving Average (Transparent, Red)
232 Day Moving Average (Transparent, White)
For the last cycles: once the 2 year MA crossed the 232 Day MA, it marked the cycle bottom within a few days and once the 1 year MA (x2) crossed the 116 Day MA, it marked the cycle top within a few days.
It is interesting to note that both 365/116 and 730/232 equal 3.1465, which is very close to Pi (3.142). It is actually the closest we can get to Pi when dividing 365 by another whole number.
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.
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.
