Quinn-Fernandes Fourier Transform of Filtered Price [Loxx]Down the Rabbit Hole We Go: A Deep Dive into the Mysteries of Quinn-Fernandes Fast Fourier Transform and Hodrick-Prescott Filtering
In the ever-evolving landscape of financial markets, the ability to accurately identify and exploit underlying market patterns is of paramount importance. As market participants continuously search for innovative tools to gain an edge in their trading and investment strategies, advanced mathematical techniques, such as the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter, have emerged as powerful analytical tools. This comprehensive analysis aims to delve into the rich history and theoretical foundations of these techniques, exploring their applications in financial time series analysis, particularly in the context of a sophisticated trading indicator. Furthermore, we will critically assess the limitations and challenges associated with these transformative tools, while offering practical insights and recommendations for overcoming these hurdles to maximize their potential in the financial domain.
Our investigation will begin with a comprehensive examination of the origins and development of both the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter. We will trace their roots from classical Fourier analysis and time series smoothing to their modern-day adaptive iterations. We will elucidate the key concepts and mathematical underpinnings of these techniques and demonstrate how they are synergistically used in the context of the trading indicator under study.
As we progress, we will carefully consider the potential drawbacks and challenges associated with using the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter as integral components of a trading indicator. By providing a critical evaluation of their computational complexity, sensitivity to input parameters, assumptions about data stationarity, performance in noisy environments, and their nature as lagging indicators, we aim to offer a balanced and comprehensive understanding of these powerful analytical tools.
In conclusion, this in-depth analysis of the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter aims to provide a solid foundation for financial market participants seeking to harness the potential of these advanced techniques in their trading and investment strategies. By shedding light on their history, applications, and limitations, we hope to equip traders and investors with the knowledge and insights necessary to make informed decisions and, ultimately, achieve greater success in the highly competitive world of finance.
█ Fourier Transform and Hodrick-Prescott Filter in Financial Time Series Analysis
Financial time series analysis plays a crucial role in making informed decisions about investments and trading strategies. Among the various methods used in this domain, the Fourier Transform and the Hodrick-Prescott (HP) Filter have emerged as powerful techniques for processing and analyzing financial data. This section aims to provide a comprehensive understanding of these two methodologies, their significance in financial time series analysis, and their combined application to enhance trading strategies.
█ The Quinn-Fernandes Fourier Transform: History, Applications, and Use in Financial Time Series Analysis
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique developed by John J. Quinn and Mauricio A. Fernandes in the early 1990s. It builds upon the classical Fourier Transform by introducing an adaptive approach that improves the identification of dominant frequencies in noisy signals. This section will explore the history of the Quinn-Fernandes Fourier Transform, its applications in various domains, and its specific use in financial time series analysis.
History of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform was introduced in a 1993 paper titled "The Application of Adaptive Estimation to the Interpolation of Missing Values in Noisy Signals." In this paper, Quinn and Fernandes developed an adaptive spectral estimation algorithm to address the limitations of the classical Fourier Transform when analyzing noisy signals.
The classical Fourier Transform is a powerful mathematical tool that decomposes a function or a time series into a sum of sinusoids, making it easier to identify underlying patterns and trends. However, its performance can be negatively impacted by noise and missing data points, leading to inaccurate frequency identification.
Quinn and Fernandes sought to address these issues by developing an adaptive algorithm that could more accurately identify the dominant frequencies in a noisy signal, even when data points were missing. This adaptive algorithm, now known as the Quinn-Fernandes Fourier Transform, employs an iterative approach to refine the frequency estimates, ultimately resulting in improved spectral estimation.
Applications of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform has found applications in various fields, including signal processing, telecommunications, geophysics, and biomedical engineering. Its ability to accurately identify dominant frequencies in noisy signals makes it a valuable tool for analyzing and interpreting data in these domains.
For example, in telecommunications, the Quinn-Fernandes Fourier Transform can be used to analyze the performance of communication systems and identify interference patterns. In geophysics, it can help detect and analyze seismic signals and vibrations, leading to improved understanding of geological processes. In biomedical engineering, the technique can be employed to analyze physiological signals, such as electrocardiograms, leading to more accurate diagnoses and better patient care.
Use of the Quinn-Fernandes Fourier Transform in Financial Time Series Analysis
In financial time series analysis, the Quinn-Fernandes Fourier Transform can be a powerful tool for isolating the dominant cycles and frequencies in asset price data. By more accurately identifying these critical cycles, traders can better understand the underlying dynamics of financial markets and develop more effective trading strategies.
The Quinn-Fernandes Fourier Transform is used in conjunction with the Hodrick-Prescott Filter, a technique that separates the underlying trend from the cyclical component in a time series. By first applying the Hodrick-Prescott Filter to the financial data, short-term fluctuations and noise are removed, resulting in a smoothed representation of the underlying trend. This smoothed data is then subjected to the Quinn-Fernandes Fourier Transform, allowing for more accurate identification of the dominant cycles and frequencies in the asset price data.
By employing the Quinn-Fernandes Fourier Transform in this manner, traders can gain a deeper understanding of the underlying dynamics of financial time series and develop more effective trading strategies. The enhanced knowledge of market cycles and frequencies can lead to improved risk management and ultimately, better investment performance.
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique that has proven valuable in various domains, including financial time series analysis. Its adaptive approach to frequency identification addresses the limitations of the classical Fourier Transform when analyzing noisy signals, leading to more accurate and reliable analysis. By employing the Quinn-Fernandes Fourier Transform in financial time series analysis, traders can gain a deeper understanding of the underlying financial instrument.
Drawbacks to the Quinn-Fernandes algorithm
While the Quinn-Fernandes Fourier Transform is an effective tool for identifying dominant cycles and frequencies in financial time series, it is not without its drawbacks. Some of the limitations and challenges associated with this indicator include:
1. Computational complexity: The adaptive nature of the Quinn-Fernandes Fourier Transform requires iterative calculations, which can lead to increased computational complexity. This can be particularly challenging when analyzing large datasets or when the indicator is used in real-time trading environments.
2. Sensitivity to input parameters: The performance of the Quinn-Fernandes Fourier Transform is dependent on the choice of input parameters, such as the number of harmonic periods, frequency tolerance, and Hodrick-Prescott filter settings. Choosing inappropriate parameter values can lead to inaccurate frequency identification or reduced performance. Finding the optimal parameter settings can be challenging, and may require trial and error or a more sophisticated optimization process.
3. Assumption of stationary data: The Quinn-Fernandes Fourier Transform assumes that the underlying data is stationary, meaning that its statistical properties do not change over time. However, financial time series data is often non-stationary, with changing trends and volatility. This can limit the effectiveness of the indicator and may require additional preprocessing steps, such as detrending or differencing, to ensure the data meets the assumptions of the algorithm.
4. Limitations in noisy environments: Although the Quinn-Fernandes Fourier Transform is designed to handle noisy signals, its performance may still be negatively impacted by significant noise levels. In such cases, the identification of dominant frequencies may become less reliable, leading to suboptimal trading signals or strategies.
5. Lagging indicator: As with many technical analysis tools, the Quinn-Fernandes Fourier Transform is a lagging indicator, meaning that it is based on past data. While it can provide valuable insights into historical market dynamics, its ability to predict future price movements may be limited. This can result in false signals or late entries and exits, potentially reducing the effectiveness of trading strategies based on this indicator.
Despite these drawbacks, the Quinn-Fernandes Fourier Transform remains a valuable tool for financial time series analysis when used appropriately. By being aware of its limitations and adjusting input parameters or preprocessing steps as needed, traders can still benefit from its ability to identify dominant cycles and frequencies in financial data, and use this information to inform their trading strategies.
█ Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
1. The first term represents the deviation of the data from the trend.
2. The second term represents the smoothness of the trend.
3. λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
Another significant advantage of the HP Filter is its ability to adapt to changes in the underlying trend. This feature makes it particularly well-suited for analyzing financial time series, which often exhibit non-stationary behavior. By employing the HP Filter to smooth financial data, traders can more accurately identify and analyze the long-term trends that drive asset prices, ultimately leading to better-informed investment decisions.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
█ Combined Application of Fourier Transform and Hodrick-Prescott Filter
The integration of the Fourier Transform and the Hodrick-Prescott Filter in financial time series analysis can offer several benefits. By first applying the HP Filter to the financial data, traders can remove short-term fluctuations and noise, effectively isolating the underlying trend. This smoothed data can then be subjected to the Fourier Transform, allowing for the identification of dominant cycles and frequencies with greater precision.
By combining these two powerful techniques, traders can gain a more comprehensive understanding of the underlying dynamics of financial time series. This enhanced knowledge can lead to the development of more effective trading strategies, better risk management, and ultimately, improved investment performance.
The Fourier Transform and the Hodrick-Prescott Filter are powerful tools for financial time series analysis. Each technique offers unique benefits, with the Fourier Transform being adept at identifying dominant cycles and frequencies, and the HP Filter excelling at isolating long-term trends from short-term noise. By combining these methodologies, traders can develop a deeper understanding of the underlying dynamics of financial time series, leading to more informed investment decisions and improved trading strategies. As the financial markets continue to evolve, the combined application of these techniques will undoubtedly remain an essential aspect of modern financial analysis.
█ Features
Endpointed and Non-repainting
This is an endpointed and non-repainting indicator. These are crucial factors that contribute to its usefulness and reliability in trading and investment strategies. Let us break down these concepts and discuss why they matter in the context of a financial indicator.
1. Endpoint nature: An endpoint indicator uses the most recent data points to calculate its values, ensuring that the output is timely and reflective of the current market conditions. This is in contrast to non-endpoint indicators, which may use earlier data points in their calculations, potentially leading to less timely or less relevant results. By utilizing the most recent data available, the endpoint nature of this indicator ensures that it remains up-to-date and relevant, providing traders and investors with valuable and actionable insights into the market dynamics.
2. Non-repainting characteristic: A non-repainting indicator is one that does not change its values or signals after they have been generated. This means that once a signal or a value has been plotted on the chart, it will remain there, and future data will not affect it. This is crucial for traders and investors, as it offers a sense of consistency and certainty when making decisions based on the indicator's output.
Repainting indicators, on the other hand, can change their values or signals as new data comes in, effectively "repainting" the past. This can be problematic for several reasons:
a. Misleading results: Repainting indicators can create the illusion of a highly accurate or successful trading system when backtesting, as the indicator may adapt its past signals to fit the historical price data. This can lead to overly optimistic performance results that may not hold up in real-time trading.
b. Decision-making uncertainty: When an indicator repaints, it becomes challenging for traders and investors to trust its signals, as the signal that prompted a trade may change or disappear after the fact. This can create confusion and indecision, making it difficult to execute a consistent trading strategy.
The endpoint and non-repainting characteristics of this indicator contribute to its overall reliability and effectiveness as a tool for trading and investment decision-making. By providing timely and consistent information, this indicator helps traders and investors make well-informed decisions that are less likely to be influenced by misleading or shifting data.
Inputs
Source: This input determines the source of the price data to be used for the calculations. Users can select from options like closing price, opening price, high, low, etc., based on their preferences. Changing the source of the price data (e.g., from closing price to opening price) will alter the base data used for calculations, which may lead to different patterns and cycles being identified.
Calculation Bars: This input represents the number of past bars used for the calculation. A higher value will use more historical data for the analysis, while a lower value will focus on more recent price data. Increasing the number of past bars used for calculation will incorporate more historical data into the analysis. This may lead to a more comprehensive understanding of long-term trends but could also result in a slower response to recent price changes. Decreasing this value will focus more on recent data, potentially making the indicator more responsive to short-term fluctuations.
Harmonic Period: This input represents the harmonic period, which is the number of harmonics used in the Fourier Transform. A higher value will result in more harmonics being used, potentially capturing more complex cycles in the price data. Increasing the harmonic period will include more harmonics in the Fourier Transform, potentially capturing more complex cycles in the price data. However, this may also introduce more noise and make it harder to identify clear patterns. Decreasing this value will focus on simpler cycles and may make the analysis clearer, but it might miss out on more complex patterns.
Frequency Tolerance: This input represents the frequency tolerance, which determines how close the frequencies of the harmonics must be to be considered part of the same cycle. A higher value will allow for more variation between harmonics, while a lower value will require the frequencies to be more similar. Increasing the frequency tolerance will allow for more variation between harmonics, potentially capturing a broader range of cycles. However, this may also introduce noise and make it more difficult to identify clear patterns. Decreasing this value will require the frequencies to be more similar, potentially making the analysis clearer, but it might miss out on some cycles.
Number of Bars to Render: This input determines the number of bars to render on the chart. A higher value will result in more historical data being displayed, but it may also slow down the computation due to the increased amount of data being processed. Increasing the number of bars to render on the chart will display more historical data, providing a broader context for the analysis. However, this may also slow down the computation due to the increased amount of data being processed. Decreasing this value will speed up the computation, but it will provide less historical context for the analysis.
Smoothing Mode: This input allows the user to choose between two smoothing modes for the source price data: no smoothing or Hodrick-Prescott (HP) smoothing. The choice depends on the user's preference for how the price data should be processed before the Fourier Transform is applied. Choosing between no smoothing and Hodrick-Prescott (HP) smoothing will affect the preprocessing of the price data. Using HP smoothing will remove some of the short-term fluctuations from the data, potentially making the analysis clearer and more focused on longer-term trends. Not using smoothing will retain the original price fluctuations, which may provide more detail but also introduce noise into the analysis.
Hodrick-Prescott Filter Period: This input represents the Hodrick-Prescott filter period, which is used if the user chooses to apply HP smoothing to the price data. A higher value will result in a smoother curve, while a lower value will retain more of the original price fluctuations. Increasing the Hodrick-Prescott filter period will result in a smoother curve for the price data, emphasizing longer-term trends and minimizing short-term fluctuations. Decreasing this value will retain more of the original price fluctuations, potentially providing more detail but also introducing noise into the analysis.
Alets and signals
This indicator featues alerts, signals and bar coloring. You have to option to turn these on/off in the settings menu.
Maximum Bars Restriction
This indicator requires a large amount of processing power to render on the chart. To reduce overhead, the setting "Number of Bars to Render" is set to 500 bars. You can adjust this to you liking.
█ Related Indicators and Libraries
Goertzel Cycle Composite Wave
Goertzel Browser
Fourier Spectrometer of Price w/ Extrapolation Forecast
Fourier Extrapolator of 'Caterpillar' SSA of Price
Normalized, Variety, Fast Fourier Transform Explorer
Real-Fast Fourier Transform of Price Oscillator
Real-Fast Fourier Transform of Price w/ Linear Regression
Fourier Extrapolation of Variety Moving Averages
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolator of Price w/ Projection Forecast
Fourier Extrapolator of Price
STD-Stepped Fast Cosine Transform Moving Average
Variety RSI of Fast Discrete Cosine Transform
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Cari dalam skrip untuk "Cycle"
dmn's ICT AMD-Goldbach█ OVERVIEW
This script is built on ICT time & price theory and the theory of algorithmic market maker models, and visualizes the intraday divided using powers of three into accumulation, manipulation and distribution cycles.
It also includes an automatically calculated and plotted Goldbach level (a.k.a. IPDA level or Huddleston level) overlay, to help visualize where in the current market maker profile price is in relation to the AMD cycles, and where it might trade to.
█ CONCEPTS
Accumulation, Manipulation, Distribution Cycles
A 24 hour day, with the default set to start at 20:00 CET (the start of the Forex CLS Settlement operational timeline) is split in three parts - 9, 6 and 9 hours for the three cycles (roughly corresponding with Asia, London Open and New York + London Close sessions).
Since charts are fractals, there's also intra-cycle time fibs available in the script, to highlight the smaller fractal equivalents in each cycle.
These cycles are used to visualize the three phases (AMD) for easier identification of the current daily profile by analyzing during what cycle highs and lows of the day are made.
An example of a bullish day could be price rallying before making a low during the accumulation cycle, being manipulated higher and retracing to form an optimal trade entry during the manipulation cycle, expanding and creating the high of the day before selling off during the distribution cycle, with a potential reversal before it ends.
Goldbach levels
The Goldbach levels are based on the size of a price range (or price swing, if you will) expressed as a factor of power of three (3^n).
To decide what number to tell the script to use for the calculation, we look at what 3^n number best fits an average swing on the preferred timeframe we're trading.
For example; PO3 27 (3^3)might be fit for scalping, while PO3 243 (3^5) may correspond to the daily or weekly range, depending on the asset.
The script then calculates a range high and a range low using a power of three formula based on the current price and divides it into levels using Goldbach numbers.
At these levels one might expect to see price form various "blocks" as defined in concept by Michael J. Huddleston.
The blocks that correspond to the Goldbach levels are labeled with abbreviations as follows:
Ext = External range
Low = Range low
High = Range high
FVG = Fair value gap
RB = Rejection block
OB = Order block
LV = Liquidity void
BR = Breaker
MB = Mitigation block
Using these levels and said blocks we identify where in the current running market maker profile price is offered, and trade the preferred timeframe in line with the AMD cycles accordingly.
█ FEATURES
Custom AMD time cycles session times.
Custom time fib for fractal cycles.
Color and style customization.
Show only current or also historical cycles.
Equilibrium mode for Goldbach levels (show only high/low and midpoint)
Autodetection of asset type, with manual override.
█ NOTE
The default timings for the AMD cycles are set up for Forex pairs. For other asset types, such as indices, other timings are nessecary for optimal results.
Goldbach levels requires the correct symbol type setting for the calculation to work properly. Disable the script's autodetection and enable/disable the Forex option according to the type of chart if it fails.
BTI - Bitcoin (BTC) Top Indicator [Logue]Bitcoin top indicator. This indicator is a combination of multiple on-chain and seasonality BTC macro cycle top indicators, plus the Pi-Cycle top moving average. Because there is no magic single indicator to detect macro cycle tops in bitcoin, the BTI detects confluence of multiple indicators to select tops of each BTC macro cycle. The individual indicators used for the BTI are:
1) Cumulative Value Days Destroyed (CVDD) - The CVDD was created by Willy Woo and is the ratio of the cumulative value of Coin Days Destroyed in USD and the market age (in days). While this indicator is used to detect bottoms normally, an extension is used to allow detection of BTC tops. When the BTC price goes above the CVDD extension, BTC is generally considered to be overvalued. Because the "strength" of the BTC tops has decreased over the cycles, a logarithmic function for the extension was created by fitting past cycles as log extension = slope * time + intercept. This indicator is triggered when the BTC price is above the CVDD extension.
2) Net Unrealized Profit Loss (NUPL) - The NUPL measures the profit state of the bitcoin network to determine if past transfers of BTC are currently in an unrealized profit or loss state.
Values above zero indicate that the network is in overall profit, while values below zero indicate the network is in overall loss. Highly positive NUPL values indicate overvaluation of the BTC network. Based on decreasing "strength" of BTC tops, a decreasing linear function (trigger = slope * time + intercept) was fit to past cycle tops. Therefore, future trigger values can be calculated over time. This indicator is triggered when the NUPL is above the trigger value.
3) Market Value-Realized Value Z-score (MVRV-Z) - The MVRV-Z measures the value of the bitcoin network by comparing the market cap to the realized value and dividing by the standard deviation of the market cap (market cap – realized cap) / std(market cap)). When the market value is significantly higher than the realized value, the bitcoin network is "overvalued". Very high values have signaled cycle tops in the past. This indicator is triggered when the MVRVZ value is above 55.
4) Puell multiple (PUELL) - PUELL is the ratio between the daily coin issuance in USD and its 365-day moving average. This multiple helps to measure miner profitability. When the PUELL goes to extremely high values relative to historical values, it indicates the profitability of the miners is very high and a top may be near. This indicator triggers when the PUELL is above 3.33.
5) Calendar Seasonality Index (CSI) - The CSI takes advantage of the consistency of BTC cycles. Past cycles have formed macro tops every four years between October 21st and December 12th. Therefore, this indicator triggers at set times that are marked every four years between these two dates.
6) Halving Seasonality Index (HSI) - The HSI, as with the CSI, takes advantage of the consistency of BTC cycles following the major event that is the halving. Aside from the first halving cycle, cycles have formed macro tops approximately 538 days after each halving. Therefore, this indicator triggers at set times that are marked 528 to 548 days (i.e., 538 +- 10 days) after each halving.
7) Polylog Regression (PLR) - The BTC cycle tops and bottoms were separately fit using a polynomial regression for the PLR. The bottom band was fit on much more data than the top band, so is likely to be more reliable. The shape of the regression into the future was estimated, so may not be accurate into the future, but is the best fit of tops and bottoms to date. This indicator is used to estimate when tops and bottoms are near when the price goes into the top or bottom bands. This triggers when the BTC price is inside or above the upper polylog regression channel.
8) Realized Price (RP) - The RP is summation of the value of each BTC when it last moved divided by the total number of BTC in circulation. This gives an estimation of the average "purchase" price of BTC on the bitcoin network based on when it was last transacted. This indicator tells us if the average network participant is in a state of profit or loss. This indicator is normally used to detect BTC bottoms, but an extension can be used to detect when the bitcoin network is "highly" overvalued. Because the "strength" of the BTC tops has decreased over the cycles, a logarithmic function for the extension was created by fitting past cycles as log extension = slope * time + intercept. This indicator triggers when the BTC price is above the realized price extension.
9) Pi-cycle Top (PCT) - The PCT indicator is simply the cross of the 111-day moving average above a 2x multiple of the 350-day moving average of the BTC price. While there is no fundamental reasoning behind why this works, it has worked to indicate previous bitcoin tops by taking advantage of the cyclicality of the BTC price and measurement overextension of BTC price. This indicator triggers when the fast moving average (111-day) crosses above the 2x multiple of the slow moving average (350-day).
10) Transaction Fee Spike (TFS) - Transaction fees on the bitcoin network can signal a mania phase when they increase well above historical values. This mania phase may indicate we are near a top in the BTC price. The daily transaction fee total in USD is divided by the number of daily transactions to calculate the average transaction fee paid on the bitcoin network. The transaction fees increasing above $40 trigger this indicator.
The on-chain indicators (CVDD, NUPL, MVRV-Z, PUELL, RP, and TFS) work together to give a health check of the BTC price as compared to its network health. The seasonality indicators (CSI, HSI, and PLR) work together to map the macro cycles of BTC. The PCT gives a view of the overvaluation of the BTC price. Each of these indicators is weighted evenly when selected and if over 45% of the indicators are triggering on a candle (i.e., at least 5 of 10), the overall BTI indicator prints a clear signal -- a red dot with a white middle portion between the white horizontal lines at the top of the indicator. This signal is meant to indicate when the macro cycle top is likely already hit or is near. Each of the individual indicators used for the BTI are proven macro top indicators over multiple cycles.
Each of the individual indicators are shown in their own rows to visualize which indicators are triggering. You are able to deselect any indicator you do not wish to have considered and select it back again. To prepare you for indicators triggering, the BTI shows dark blue or dark green when the indicator is close to triggering (i.e., generally around 20% from the trigger value, a less intense background will appear, and 10% from the trigger value, a more intense background will appear). The color of the individual indicators turns pink when they are triggered. The background color of the BTI becomes blue when at least 30% of the indicators considered are triggering and it becomes purple/pink when the BTI fully triggers. See the BTC chart above the indicator showing the performance of the indicator in picking out macro top regions (red dots with white middle portion). Because not all daily data for BTC can be shown on one chart, ensure you also play with the indictor yourself. The BLX is most appropriate, but the indicator works on all BTC/USD charts. Because of the limits imposed by TradingView, the indicator doesn't work on time frames lower than 4 h or higher than the weekly.
You can use this indicator to help you understand when the BTC price is more likely topping based on past performance of these indicators. This indicator pairs with the BBI (Bitcoin (BTC) Bottom Indictor) and the BTB (Bitcoin Top and Bottom indicator).
Use this indicator at your own risk. I make no assertions that this indicator will work to detect any future top since we all know that past performance is no guarantee of future results.
BBI - Bitcoin (BTC) Bottom Indicator [Logue]This indicator is a combination of multiple on-chain and seasonality BTC macro cycle bottom indicators. Because there is no magic single indicator to detect macro cycle bottoms in bitcoin, the BBI detects confluence of multiple indicators to select bottoms of each BTC macro cycle. The individual indicators used for the BBI are:
1) Cumulative Value Days Destroyed (CVDD) - The CVDD was created by Willy Woo and is the ratio of the cumulative value of Coin Days Destroyed in USD and the market age (in days). When the BTC price goes below this value, BTC is generally considered to be undervalued. This indicator is triggered when the BTC price is below the CVDD.
2) Net Unrealized Profit Loss (NUPL) - The NUPL measures the profit state of the bitcoin network to determine if past transfers of BTC are currently in an unrealized profit or loss state.
Values above zero indicate that the network is in overall profit, while values below zero indicate the network is in overall loss. Highly negative NUPL values indicate an undervaluation of the BTC network. This indicator is triggered when the NUPL is below -15.
3) Market Value-Realized Value Z-score (MVRV-Z) - The MVRV-Z measures the value of the bitcoin network by comparing the market cap to the realized value and dividing by the standard deviation of the market cap (market cap – realized cap) / std(market cap)). When the market value is significantly lower than the realized value, the bitcoin network is "undervalued". Very low values have signaled cycle bottoms in the past. This indicator is triggered when the MVRVZ value is below 4.
4) Puell multiple (PUELL) - PUELL is the ratio between the daily coin issuance in USD and its 365-day moving average. This multiple helps to measure miner profitability. When the PUELL goes to extremely low values relative to historical values, it indicates the profitability of the miners is low and a bottom may be near. This indicator triggers when the PUELL is below 0.4.
5) Calendar Seasonality Index (CSI) - The CSI takes advantage of the consistency of BTC cycles. Past cycles have formed macro bottoms every four years between December and February. Therefore, this indicator triggers at set times that are marked every four years in December, January, or February.
6) Halving Seasonality Index (HSI) - The HSI, as with the CSI, takes advantage of the consistency of BTC cycles following the major event that is the halving. Past cycles have formed macro bottoms approximately 948 days after each halving. Therefore, this indicator triggers at set times that are marked 903-993 days (i.e., 948 +- 45 days) after each halving.
7) Polylog Regression (PLR) - The BTC cycle tops and bottoms were separately fit using a polynomial regression for the PLR. The bottom band was fit on much more data than the top band, so is likely to be more reliable. The shape of the regression into the future was estimated, so may not be accurate into the future, but is the best fit of tops and bottoms to date. This indicator is used to estimate when tops and bottoms are near when the price goes into the top or bottom bands. This triggers when the BTC price is inside or below the lower polylog regression channel.
8) Realized Price (RP) - The RP is summation of the value of each BTC when it last moved divided by the total number of BTC in circulation. This gives an estimation of the average "purchase" price of BTC on the bitcoin network based on when it was last transacted. This indicator tells us if the average network participant is in a state of profit or loss. This indicator triggers when the BTC price is below the realized price.
9) Hash Rate Capitulation (HRC) - The HRC indicator measures the rate of change of the hash rate. Steadily increasing hash rate is a sign of health of the bitcoin network. This indicator uses moving averages (20- and 100-day) of the hash rate to indicate when a decrease in the rate of change is has occurred (i.e., the 20-day MA goes below the 100-day MA). This indicator triggers when the 20-day moving average of the hash rate going below the 100-day moving average.
The on-chain indicators (CVDD, NUPL, MVRV-Z, PUELL, RP, and HRC) work together to give a health check of the BTC price as compared to its network health. The seasonality indicators (CSI, HSI, and PLR) work together to map the macro cycles of BTC. Each of these indicators is weighted evenly when selected and if over 40% of the indicators are triggering on a candle (i.e., at least 4 of 9), the overall BBI indicator prints a clear signal -- a green dot with a white middle portion between the white horizontal lines at the top of the indicator. This signal is meant to indicate when the macro cycle bottom is likely already hit or is near. Each of the individual indicators used for the BBI are proven macro bottom indicators over multiple cycles.
Each of the individual indicators are shown in their own rows to visualize which indicators are triggering. You are able to deselect any indicator you do not wish to have considered and select it back again. To prepare you for indicators triggering, the BBI shows dark blue or dark green when the indicator is close to triggering (i.e., generally around 20% from the trigger value, a less intense background will appear, and 10% from the trigger value, a more intense background will appear). The color of the individual indicators turns pink when they are triggered. The background color of the BBI becomes blue when at least 30% of the indicators considered are triggering and it becomes green when the BBI fully triggers. See the BTC chart above the indicator showing the performance of the indicator in picking out macro bottom regions (green dots with white middle portion). Because not all daily data for BTC can be shown on one chart, ensure you also play with the indictor yourself. The BLX is most appropriate, but the indicator works on all BTC/USD charts. Because of the limitations of moving averages in TradingView, the indicator doesn't work on time frames lower than 4 h.
You can use this indicator to help you understand when the BTC price is more likely bottoming based on past performance of these indicators. This indicator pairs with the BTI (Bitcoin (BTC) top indictor) and the BTB (Bitcoin top and bottom) indicators.
Use this indicator at your own risk. I make no assertions that this indicator will work to detect any future bottom since we all know that past performance is no guarantee of future results.
BTB - Bitcoin (BTC) Top and Bottom Indicator [Logue]This indicator is a combination of multiple on-chain, seasonality, and momentum BTC macro cycle bottom and top indicators. The BTB detects confluence of multiple indicators to select bottoms and tops of each BTC macro cycle. More detail can be seen on the BTI and BBI indicators. The BTB indicators are:
1) Cumulative Value Days Destroyed (CVDD) - The CVDD is the ratio of the cumulative value of coin days destroyed in USD and the market age (in days). When the BTC price goes below this value, BTC is generally considered to be undervalued. The bottom indicator is triggered when the BTC price is below the CVDD or above the CVDD extension. Because the "strength" of the BTC tops has decreased over the cycles, a logarithmic function for the extension was created by fitting past cycles as log extension = slope * time + intercept.
2) Net Unrealized Profit Loss (NUPL) - The NUPL measures if past transfers of BTC are currently in an unrealized profit or loss state. Historically positive or negative NUPL values indicate an over/undervaluation of the BTC network. The bottom indicator is triggered when the NUPL is below -15 and the top is triggered above an adjusted value based on decreasing "strength" of BTC tops. A decreasing linear function (trigger = slope * time + intercept) was fit to past cycle tops and used to determine overvaluation.
3) Market Value-Realized Value Z-score (MVRV-Z) - The MVRV-Z measures the value of the bitcoin network by comparing the market cap to the realized value and dividing by the standard deviation of the market cap (market cap – realized cap) / std(market cap)). The bottom indicator is triggered when the MVRVZ value is below 4 and tops are above 55.
4) Puell multiple (PUELL) - PUELL is the ratio between the daily coin issuance in USD and its 365-day moving average. This multiple helps to measure miner profitability. Extremes in PUELL may indicate tops or bottoms. The bottom indicator triggers when the PUELL is below 0.4 and top is triggered above 3.33.
5) Calendar Seasonality Index (CSI) - The CSI takes advantage of the consistency of BTC cycles. Past cycles have formed macro bottoms every four years between December and February which triggers the bottom indicator. Past cycles have formed macro tops every four years between October 21st and December 12th, triggering the top indicator.
6) Halving Seasonality Index (HSI) - Past cycles have formed macro bottoms approximately 948 days after each halving, triggering this indicator at set times, 948 +- 45 days, after each halving. Aside from the first halving, cycles have formed macro tops approximately 538 days after each halving. Therefore, this indicator triggers at 538 +- 10 days after each halving.
7) Polylog Regression (PLR) - The BTC cycle tops and bottoms were separately fit using a polynomial regression. The shape of the regression into the future was estimated and a fit was used to estimate when tops and bottoms are near. This triggers when the BTC price is inside or below the lower polylog regression channel and when the BTC price is inside or above the upper polylog regression channel.
8) Realized Price (RP) - The RP is summation of the value of each BTC when it last moved divided by the total number of BTC in circulation. This gives an estimation of the average "purchase" price of BTC. This indicator triggers when the BTC price is below the realized price or above an RP extension. Because the "strength" of the BTC tops has decreased over the cycles, a logarithmic function for the extension was created by fitting past cycles as log extension = slope * time + intercept.
9) Plus Directional Movement (PDM) weekly index - The PDM is a momentum indicator that measures the strength of a trend in the positive direction. The weekly PDM is calculated by determining the difference between the week's high price and the previous week's high price smoothed by a 14-period moving average. Higher PDM values indicate higher momentum in the positive (higher price) direction. Based on decreasing "strength" of BTC tops, a decreasing linear function (trigger = slope * time + intercept) was fit to past cycle tops for this indicator. This indicator is triggered when the PDM is above the trigger value or below 14.
10) Logarithmic Moving Average Convergence Divergence (LMACD) weekly indicator - The LMACD is a momentum indicator that measures the strength of a trend using the difference of the log values of the 12-period and 26-week exponential moving averages. Larger positive numbers indicate a larger positive momentum. Based on decreasing "strength" of BTC tops, a decreasing linear function (trigger = slope * time + intercept) was fit to past cycle tops for this indicator. This indicator is triggered when the PDM is above the trigger value or below -0.06.
11) Hash Rate Capitulation (HRC) - The HRC indicator measures the rate of change of the hash rate. Steadily increasing hash rate is a sign of health of the bitcoin network. This indicator uses moving averages (20- and 100-day) of the hash rate to indicate when a decrease in the rate of change is has occurred (i.e., the 20-day MA goes below the 100-day MA). This indicator triggers when the 20-day moving average of the hash rate going below the 100-day moving average.
12) Pi-cycle Top (PCT) - The PCT indicator is simply the cross of the 111-day moving average above a 2x multiple of the 350-day moving average of the BTC price. While there is no fundamental reasoning behind why this works, it has worked to indicate previous bitcoin tops by taking advantage of the cyclicality of the BTC price and measurement overextension of BTC price. This indicator triggers when the fast moving average (111-day) crosses above the 2x multiple of the slow moving average (350-day).
13) Transaction Fee Spike (TFS) - Transaction fees on the bitcoin network can signal a mania phase when they increase well above historical values. This mania phase may indicate we are near a top in the BTC price. The daily transaction fee total in USD is divided by the number of daily transactions to calculate the average transaction fee paid on the bitcoin network. The transaction fees increasing above $40 trigger this indicator.
The on-chain indicators (CVDD, NUPL, MVRV-Z, PUELL, RP, and HRC) work together to give a health check of the BTC price as compared to its network health. The seasonality indicators (CSI, HSI, and PLR) work together to map the macro cycles of BTC. The momentum indicators (PDM and LMACD) give the strength of the BTC trend. Each of these indicators is weighted evenly when selected and if over 45% of the indicators are triggering on a candle, the overall BTB indicator prints a clear signal. This signal is meant to indicate when the macro cycle bottom or top is likely hit or is near.
You are able to deselect any indicator you do not wish to have considered and select it back again. To prepare you for indicators triggering, the BTB shows dark blue or dark green when the indicator is close to triggering. The background color of the BBI becomes blue when at least 30% of the indicators considered are triggering and it becomes green when the BBI fully triggers. The BLX is most appropriate chart, but the indicator works on all BTC/USD charts. Because of the limitations of TradingView, the indicator doesn't work on time frames lower than 4 h or over one week.
Use this indicator at your own risk. I make no assertions that this indicator will work to detect any future bottom or top since we all know that past performance is no guarantee of future results.
Sentient FLDOverview of the FLD
The Future Line of Demarcation (FLD) was first proposed by JM Hurst in the 1970s as a cycle analysis tool. It is a smoothed median price plotted on a time-based chart, and displaced into the future (to the right on the chart). The amount of displacement is determined by performing a cycle analysis, the line then plotted to extend beyond the right hand edge of the chart by half a cycle wavelength.
Interactions between price and the FLD
As price action unfolds, price interacts with the FLD line, either by crossing over the line, or by finding support or resistance at the line.
Targets
When price crosses an FLD a target for the price move is generated. The target consists of a price level and also expected time.
When price reaches that target it is an indication that the cycle influencing price to move up or down has completed that action and is about to turn around.
If price fails to reach a target by the expected time, it indicates bullish or bearish pressure from longer cycles, and a change in mood of the market.
Sequence of interactions
Price interacts with the FLD in a regular sequence of 8 interactions which are labelled using the letters A - H, in alphabetical order. This sequence of interactions occurs between price and a cycle called the Signal cycle. The full sequence plays out over a single wave of a longer cycle, called the Sequence cycle. The interactions are:
A category interaction is where price crosses above the FLD as it rises out of a trough of the Sequence cycle.
B & C category interactions often occur together as a pair, where price comes back to the FLD line and finds support at the level of the FLD as the first trough of the Signal cycle forms.
D category interaction is where price crosses below the FLD as it falls towards the second trough of the Signal cycle.
E category interaction is where price crosses above the FLD again as it rises out of the second trough of the Signal cycle.
F category interaction is where price crosses below the FLD as it falls towards the next trough of the Sequence cycle.
G & H category interactions often occur together as a pair, where price comes back to the FLD line and finds resistance at the level of the FLD before a final move down into the next Sequence cycle trough.
Trading Opportunities
This sequence of interactions provides the trader with trading opportunities:
A and E category interactions involve price crossing over the FLD line, for a long trading opportunity.
D and F category interactions involve price crossing below the FLD line, for a short trading opportunity.
B and C category interactions occur where price finds support at the FLD, another long trading opportunity.
G and H category interactions occur where price finds resistance at the FLD, another short trading opportunity.
3 FLD Lines Plotted
The Sentient FLD indicator plots three FLD lines, for three primary cycles on your time-based charts:
The Signal cycle (pink color, can be changed in the settings), which is used to generate trading signals on the basis of the sequence of interactions between price and the FLD
The Mid cycle (orange color, can be changed in the settings), which is used for confirmation of the signals from the signal cycle FLD.
The Sequence cycle (green color, can be changed in the settings) which is the cycle over which the entire A - H sequence of interactions plays out.
Cycle Analysis
In addition to plotting the three FLD lines, the Sentient FLD indicator performs a cycle phasing analysis and identifies the positions of the troughs of five cycles on your chart (The Signal, Mid & Sequence cycles and two longer cycles for determining the underlying trend).
The results of this analysis are plotted by using diamond symbols to mark the timing of past troughs of the cycles, and circles to mark the timing of the next expected troughs, with lines extending to each side to represent the range of time in which the trough is expected to form. These are called circles-and-whiskers. The diamonds are stacked vertically because the troughs are synchronized in time. The circles-and-whiskers therefore are also stacked, creating a nest-of-lows which is a high probability period for a trough to form.
Identifying the Interactions
The Sentient FLD also identifies the interactions between price and each one of the three FLDs plotted on your chart, and those interactions are labelled so that you can keep track of the unfolding A - H sequence.
Next Expected Interaction
Because the Sentient FLD is able to identify the sequence of interactions, it is also able to identify the next expected interaction between price and the FLD. This enables you to anticipate levels of support or resistance, or acceleration levels where price is expected to cross through the FLD.
Cycle Table
A cycle table is displayed on the chart (position can be changed in settings). The cycle table comprises 6 columns:
The Cycle Name (CYCLE): the name of the cycle which is its nominal wavelength in words.
The Nominal Wavelength (NM): The nominal wavelength of the cycle measured in bars.
The Current Wavelength (CR): The current recent wavelength of the cycle measured in bars.
The Variation (VAR): The variation between the nominal wavelength and current wavelength as a percentage (%).
The relevant Sequence Cycle (SEQ): The cycle over which the sequence of interactions with this FLD plays out.
The Mode (MODE): Whether the cycle is currently Bearish, Neutral or Bullish.
Benefits of using the Sentient FLD
The cycle analysis shown with diamonds and circles marking the troughs, and next expected troughs of the cycles enable you to anticipate the timing of market turns (troughs and peaks in the price), because of the fact that cycles, by definition, repeat with some regularity.
The results of the cycle analysis are also displayed on your chart in a table, and enable you to understand at a glance what the current mode of each cycle is, whether bullish, bearish or neutral.
The identification of the sequence of interactions between price and the FLD enables you to anticipate the next interaction, and thereby expect either a price cross of the FLD or dynamic levels of support and resistance at the levels of the FLD lines, only visible to the FLD trader.
When the next expected interaction between price and the FLD is an acceleration point (price is expected to cross over the FLD), that level can be used as a signal for entry into a trade.
Similarly when the next expected interaction between price and the FLD is either support or resistance, that level can be used as a signal for entry into a trade when price reacts as expected, finding support or resistance.
The targets that are generated as a result of price crossing the FLD represent cycle exhaustion levels and times, and can be used as take profit exits, or as levels after which stops should be tightened.
The indicator optionally also calculates targets for longer timeframes, and displays them on your chart providing useful context for the influence of longer cycles without needing to change timeframe.
Example
In this image you can see an example of the different aspects of the indicator working on a 5 minute chart (details below):
This is what the indicator shows:
The 3 FLD lines are for the 100 minute (pink), 3 hour (orange) and 6 hour (green) cycles (refer to the cycle table for the cycle names).
Previous targets can be seen, shown as pointed labels, with the same colors.
The cycle table at the bottom left of the chart is colour coded, and indicates that the cycles are all currently running a bit long, by about 14%.
Note also the grey-colored 6 hour target generated by the 15 x minute timeframe at 12:20. When targets are close together their accuracy is enhanced.
At the foot of the chart we can see a collection of circles-and-whiskers in a nest-of-lows, indicating that a 12 hour cycle trough has been due to form in the past hour.
The past interactions between price and the signal cycle are labelled and we can see the sequence of E (with some +E post-interaction taps), F and then G-H.
The next interaction between price and the signal is the A category interaction - a long trading opportunity as price bounces out of the 12 hour cycle trough.
Notice the green upward pointing triangles on the FLD lines, indicating that they are expected to provide acceleration points, where price will cross over the FLD and move towards a target above the FLD.
The cycle table shows that the cycles of 6 hours and longer are all expected to be bullish (with the 12 hour cycle neutral to bullish).
On the basis that we are expecting a 12 hour trough to form, and the 6 hour cycle targets have been reached, and the next interaction with the signal cycle is an A category acceleration point, we can plan to enter into a long trade.
Two hours later
This screenshot shows the situation almost 2 hours later:
Notes:
The expected 12 hour cycle trough has been confirmed in the cycle analysis, and now displayed as a stack of diamonds at 12:25
Price did cross over the signal cycle FLD (the 100 minute cycle, pink FLD line) as expected. That price cross is labelled as an A category interaction at 13:00.
A 100 minute target was generated. That target was almost, but not quite reached in terms of price, indicating that the move out of the 12 hour cycle trough is not quite as bullish as would be expected (remember the 12 hour cycle is expected to be neutral-bullish). The time element of the target proved accurate however with a peak forming at the expected time. Stops could have been tightened at that time.
Notice that price then came back to the signal FLD (100 minute) line at the time that the next 100 minute cycle trough was expected (see the pink circle-and-whiskers between 13:40 and 14:25, with the circle at 14:05.
Price found support (as was expected) when it touched the signal FLD at 13:55 and 14:00, and that interaction has been labelled as a B-C category interaction pair.
We also have a 3 hour target above us at about 6,005. That could be a good target for the move.
Another 2 hours later
This screenshot shows the situation another 2 hours later:
Notes:
We can see that the 100 minute cycle trough has been confirmed at 13:45
The nest-of-lows marking the time the 3 hour cycle trough was expected is between 15:00 and 15:45, with a probable trough in price at 15:00
The sequence of interactions is labelled: A at 13:00; B-C at 14:00; another B-C (double B-C interactions are common) at 14:30; E at 15:10; +E (a post E tap) at 16:20
Price has just reached a cluster of targets at 6005 - 6006. The 3 hour target we noted before, as well as a 6 hour target and a 12 hour target from the 15 x minute timeframe.
Notice how after those targets were achieved, price has exhausted its upward move, and has turned down.
The next expected interaction with the signal cycle FLD is an F category interaction. The downward pointing red triangles on the line indicate that the interaction is expected to be a price cross down, as price moves down into the next 6 hour cycle trough.
Other Details
The Sentient FLD indicator works on all time-based charts from 10 seconds up to monthly.
The indicator works on all actively traded instruments, including forex, stocks, indices, commodities, metals and crypto.
TASC 2025.01 Linear Predictive Filters█ OVERVIEW
This script implements a suite of tools for identifying and utilizing dominant cycles in time series data, as introduced by John Ehlers in the "Linear Predictive Filters And Instantaneous Frequency" article featured in the January 2025 edition of TASC's Traders' Tips . Dominant cycle information can help traders adapt their indicators and strategies to changing market conditions.
█ CONCEPTS
Conventional technical indicators and strategies often rely on static, unchanging parameters, which may fail to account for the dynamic nature of market data. In his article, John Ehlers applies digital signal processing principles to address this issue, introducing linear predictive filters to identify cyclic information for adapting indicators and strategies to evolving market conditions.
This approach treats market data as a complex series in the time domain. Analyzing the series in the frequency domain reveals information about its cyclic components. To reduce the impact of frequencies outside a range of interest and focus on a specific range of cycles, Ehlers applies second-order highpass and lowpass filters to the price data, which attenuate or remove wavelengths outside the desired range. This band-limited analysis isolates specific parts of the frequency spectrum for various trading styles, e.g., longer wavelengths for position trading or shorter wavelengths for swing trading.
After filtering the series to produce band-limited data, Ehlers applies a linear predictive filter to predict future values a few bars ahead. The filter, calculated based on the techniques proposed by Lloyd Griffiths, adaptively minimizes the error between the latest data point and prediction, successively adjusting its coefficients to align with the band-limited series. The filter's coefficients can then be applied to generate an adaptive estimate of the band-limited data's structure in the frequency domain and identify the dominant cycle.
█ USAGE
This script implements the following tools presented in the article:
Griffiths Predictor
This tool calculates a linear predictive filter to forecast future data points in band-limited price data. The crosses between the prediction and signal lines can provide potential trade signals.
Griffiths Spectrum
This tool calculates a partial frequency spectrum of the band-limited price data derived from the linear predictive filter's coefficients, displaying a color-coded representation of the frequency information in the pane. This mode's display represents the data as a periodogram . The bottom of each plotted bar corresponds to a specific analyzed period (inverse of frequency), and the bar's color represents the presence of that periodic cycle in the time series relative to the one with the highest presence (i.e., the dominant cycle). Warmer, brighter colors indicate a higher presence of the cycle in the series, whereas darker colors indicate a lower presence.
Griffiths Dominant Cycle
This tool compares the cyclic components within the partial spectrum and identifies the frequency with the highest power, i.e., the dominant cycle . Traders can use this dominant cycle information to tune other indicators and strategies, which may help promote better alignment with dynamic market conditions.
Notes on parameters
Bandpass boundaries:
In the article, Ehlers recommends an upper bound of 125 bars or higher to capture longer-term cycles for position trading. He recommends an upper bound of 40 bars and a lower bound of 18 bars for swing trading. If traders use smaller lower bounds, Ehlers advises a minimum of eight bars to minimize the potential effects of aliasing.
Data length:
The Griffiths predictor can use a relatively small data length, as autocorrelation diminishes rapidly with lag. However, for optimal spectrum and dominant cycle calculations, the length must match or exceed the upper bound of the bandpass filter. Ehlers recommends avoiding excessively long lengths to maintain responsiveness to shorter-term cycles.
Cycle Trend SROverview:
This indicator draws resistance and support lines calculated by market cycles.
By default, blue dots are resistance and red dots are support.
How to calculate market cycle?:
It use sine wave indicator by John Ehlers to calculate the market cycle.
How to determine support and resistance levels?:
There are two conditions for the depiction of a resistance and support lines.
The sine wave indicator has two lines(sine and lead sine).
The first condition is the crossing of these two lines.
The second condition is a new high or low.
- In the case of a resistance, it is a move below the low of the previous candle.
- In the case of a support, it is a move above the high of the previous candle.
When the two conditions are fulfilled, the highest or lowest price of the past few candles is used.
The default setting is three, but this can be changed in parameter settings.
Cycle changes and market reversals do not always occur at the same time.
This is because price movements are not created by cycles, but by the results of trades.
This allows us to understand the true support or resistance line, not the theoretical one.
How to use?:
This indicator assumes that price movements are formed by cycles and trends.
The first step is to determine whether the cycle or the trend is stronger.
- When the price is above support or below resistance, the Cycle dominates the market.
- When the price is below support or above resistance, the Trend dominates the market.
In a cycle-dominated situation, enter the market at the time when support or resistance lines is depicted.
It is better to target only those cycles that match the upper time frame.
In a trend-dominated situation, think about riding the trend.
The timing to go outside of support and resistance is a trigger.
PB(pullback) will be drawn only in case of a strong trend.
A strong uptrend is when the price goes above a resistance line and the next support line depicted is above the resistance line.
There is a threshold for this, which is twice as high as the price of ATR for period 14.
A strong downtrend is the opposite of this.
At the end of the cycle after the PB, the END is described.
This can be used as a sign of a market reversal.
If PB and END are not needed, hide them in the settings.
Bitcoin Value Capture HeatmapBTC Value Capture Heatmap answers a question originally posed by Willy Woo:
"How much pressure on Bitcoin's market cap does one dollar of purchasing power exert?"
The higher the print, the more market cap grows per dollar invested -- adjusted for global M2 growth.
Bitcoin Value Capture Heatmap = ( market cap / global M2 ) / realized cap
A NOVEL INGREDIENT REVEALS A UNIQUE USE CASE
Adjusting bitcoin's market cap for global M2 growth sharpens a legacy metric with a normalizing factor that 'stabilizes' its view across cycles.
The metric peaked at identical levels (4.2), three bitcoin bull markets in a row. On the same day bitcoin price volatility peaked for the cycle, every time.
One might naturally expect this to coincide with cycle tops. But it doesn't.
It precede's cycle's tops: in a consistent, very specific way, that predisposing a unique use case.
BITCOIN'S VOLATILTY TOP
The metric's true use case only comes into clear focus when paired with an unrelated insight:
Whether in distribution (in Spring 2021) or a parabolic blow off top (2017 & 2013), each of the last 3 bitcoin cycle tops shows tight consistent adherence to the Wykoff Distribution Schematic.
"But Wykoff schematics apply to distribution tops, not to blow off tops."
A closer look at the last 15-20 years of parabolic blow off tops, across all asset classes , viewed through a Wykoff lens, reveals recurring tight adherence to Wykoff's Distribution Schematic.
Including (and especially) BTC's parabolic top in Dec 2017; BTC's parabolic top in 2013; and ETH's blow off top in Jan 2018.
In our age of automation, this makes sense. Wykoff's schematics mirror the timeless archetypal goal of his 'Composite Operator': max pain for all other market participants.
A process that lends itself to automation, optimized a bit more each passing year.
Peak cycle volatility maps directly to the Wykoff Distribution Schematic's 'Buying Climax'.
An event that preceded parabolic cycle tops, by about 2 weeks.
Future BTC parabolas (should they recur) would come at exponentially higher market caps, so they may take longer to unfold -- I don't take the 2 week pattern too seriously.
But Parabolic Distribution as an emergent archetypal market structure is likely encoded.
PUTTING IT ALL TOGETHER
Bitcoin Value Capture Heatmap signals peak cycle volatility, on a daily close of 4.2 on the metric's Y axis. It has never reached that level twice in the same cycle.
Awareness that:
(a) peak volatility for the cycle has likely been reached, and
(b) peak volatility has a history of tightly preceding bitcoin cycle tops, can
(c) empowers traders with a data-driven 'guide post' to their likely exactly location in an increasingly archetypal topping process.
SPECIFIC USES IN AN EXIT STRATEGY
When the Heatmap's signal level is reached, one might (for instance):
* Hedge, since bitcoin is likely closing in on its cycle top, OR
* Start to DCA out, over a pre-planned time period OR
* Rotate up the risk curve, since BTC probably doesn't have much upside left, OR
* Wait for acceptance one leg higher, which (consistent with Wykoff logic) is the likeliest place to expect an actual cycle top.
Though the ratio (in the past) touched 4.2 each cycle, a closer look shows subtly lower peaks per cycle, like most other on-chain cycle oscillators.
Extrapolating out, one might expect bitcoin's next top on volatility to print on any touch of 4.0 or higher.
Or one might give it more room to run, consistent with record institutiional flows this cycle.
Alerts are enabled for both options.
The metric works on any timeframe, but should only be used on the 1D chart.
BAERMThe Bitcoin Auto-correlation Exchange Rate Model: A Novel Two Step Approach
THIS IS NOT FINANCIAL ADVICE. THIS ARTICLE IS FOR EDUCATIONAL AND ENTERTAINMENT PURPOSES ONLY.
If you enjoy this software and information, please consider contributing to my lightning address
Prelude
It has been previously established that the Bitcoin daily USD exchange rate series is extremely auto-correlated
In this article, we will utilise this fact to build a model for Bitcoin/USD exchange rate. But not a model for predicting the exchange rate, but rather a model to understand the fundamental reasons for the Bitcoin to have this exchange rate to begin with.
This is a model of sound money, scarcity and subjective value.
Introduction
Bitcoin, a decentralised peer to peer digital value exchange network, has experienced significant exchange rate fluctuations since its inception in 2009. In this article, we explore a two-step model that reasonably accurately captures both the fundamental drivers of Bitcoin’s value and the cyclical patterns of bull and bear markets. This model, whilst it can produce forecasts, is meant more of a way of understanding past exchange rate changes and understanding the fundamental values driving the ever increasing exchange rate. The forecasts from the model are to be considered inconclusive and speculative only.
Data preparation
To develop the BAERM, we used historical Bitcoin data from Coin Metrics, a leading provider of Bitcoin market data. The dataset includes daily USD exchange rates, block counts, and other relevant information. We pre-processed the data by performing the following steps:
Fixing date formats and setting the dataset’s time index
Generating cumulative sums for blocks and halving periods
Calculating daily rewards and total supply
Computing the log-transformed price
Step 1: Building the Base Model
To build the base model, we analysed data from the first two epochs (time periods between Bitcoin mining reward halvings) and regressed the logarithm of Bitcoin’s exchange rate on the mining reward and epoch. This base model captures the fundamental relationship between Bitcoin’s exchange rate, mining reward, and halving epoch.
where Yt represents the exchange rate at day t, Epochk is the kth epoch (for that t), and epsilont is the error term. The coefficients beta0, beta1, and beta2 are estimated using ordinary least squares regression.
Base Model Regression
We use ordinary least squares regression to estimate the coefficients for the betas in figure 2. In order to reduce the possibility of over-fitting and ensure there is sufficient out of sample for testing accuracy, the base model is only trained on the first two epochs. You will notice in the code we calculate the beta2 variable prior and call it “phaseplus”.
The code below shows the regression for the base model coefficients:
\# Run the regression
mask = df\ < 2 # we only want to use Epoch's 0 and 1 to estimate the coefficients for the base model
reg\_X = df.loc\ [mask, \ \].shift(1).iloc\
reg\_y = df.loc\ .iloc\
reg\_X = sm.add\_constant(reg\_X)
ols = sm.OLS(reg\_y, reg\_X).fit()
coefs = ols.params.values
print(coefs)
The result of this regression gives us the coefficients for the betas of the base model:
\
or in more human readable form: 0.029, 0.996869586, -0.00043. NB that for the auto-correlation/momentum beta, we did NOT round the significant figures at all. Since the momentum is so important in this model, we must use all available significant figures.
Fundamental Insights from the Base Model
Momentum effect: The term 0.997 Y suggests that the exchange rate of Bitcoin on a given day (Yi) is heavily influenced by the exchange rate on the previous day. This indicates a momentum effect, where the price of Bitcoin tends to follow its recent trend.
Momentum effect is a phenomenon observed in various financial markets, including stocks and other commodities. It implies that an asset’s price is more likely to continue moving in its current direction, either upwards or downwards, over the short term.
The momentum effect can be driven by several factors:
Behavioural biases: Investors may exhibit herding behaviour or be subject to cognitive biases such as confirmation bias, which could lead them to buy or sell assets based on recent trends, reinforcing the momentum.
Positive feedback loops: As more investors notice a trend and act on it, the trend may gain even more traction, leading to a self-reinforcing positive feedback loop. This can cause prices to continue moving in the same direction, further amplifying the momentum effect.
Technical analysis: Many traders use technical analysis to make investment decisions, which often involves studying historical exchange rate trends and chart patterns to predict future exchange rate movements. When a large number of traders follow similar strategies, their collective actions can create and reinforce exchange rate momentum.
Impact of halving events: In the Bitcoin network, new bitcoins are created as a reward to miners for validating transactions and adding new blocks to the blockchain. This reward is called the block reward, and it is halved approximately every four years, or every 210,000 blocks. This event is known as a halving.
The primary purpose of halving events is to control the supply of new bitcoins entering the market, ultimately leading to a capped supply of 21 million bitcoins. As the block reward decreases, the rate at which new bitcoins are created slows down, and this can have significant implications for the price of Bitcoin.
The term -0.0004*(50/(2^epochk) — (epochk+1)²) accounts for the impact of the halving events on the Bitcoin exchange rate. The model seems to suggest that the exchange rate of Bitcoin is influenced by a function of the number of halving events that have occurred.
Exponential decay and the decreasing impact of the halvings: The first part of this term, 50/(2^epochk), indicates that the impact of each subsequent halving event decays exponentially, implying that the influence of halving events on the Bitcoin exchange rate diminishes over time. This might be due to the decreasing marginal effect of each halving event on the overall Bitcoin supply as the block reward gets smaller and smaller.
This is antithetical to the wrong and popular stock to flow model, which suggests the opposite. Given the accuracy of the BAERM, this is yet another reason to question the S2F model, from a fundamental perspective.
The second part of the term, (epochk+1)², introduces a non-linear relationship between the halving events and the exchange rate. This non-linear aspect could reflect that the impact of halving events is not constant over time and may be influenced by various factors such as market dynamics, speculation, and changing market conditions.
The combination of these two terms is expressed by the graph of the model line (see figure 3), where it can be seen the step from each halving is decaying, and the step up from each halving event is given by a parabolic curve.
NB - The base model has been trained on the first two halving epochs and then seeded (i.e. the first lag point) with the oldest data available.
Constant term: The constant term 0.03 in the equation represents an inherent baseline level of growth in the Bitcoin exchange rate.
In any linear or linear-like model, the constant term, also known as the intercept or bias, represents the value of the dependent variable (in this case, the log-scaled Bitcoin USD exchange rate) when all the independent variables are set to zero.
The constant term indicates that even without considering the effects of the previous day’s exchange rate or halving events, there is a baseline growth in the exchange rate of Bitcoin. This baseline growth could be due to factors such as the network’s overall growth or increasing adoption, or changes in the market structure (more exchanges, changes to the regulatory environment, improved liquidity, more fiat on-ramps etc).
Base Model Regression Diagnostics
Below is a summary of the model generated by the OLS function
OLS Regression Results
\==============================================================================
Dep. Variable: logprice R-squared: 0.999
Model: OLS Adj. R-squared: 0.999
Method: Least Squares F-statistic: 2.041e+06
Date: Fri, 28 Apr 2023 Prob (F-statistic): 0.00
Time: 11:06:58 Log-Likelihood: 3001.6
No. Observations: 2182 AIC: -5997.
Df Residuals: 2179 BIC: -5980.
Df Model: 2
Covariance Type: nonrobust
\==============================================================================
coef std err t P>|t| \
\------------------------------------------------------------------------------
const 0.0292 0.009 3.081 0.002 0.011 0.048
logprice 0.9969 0.001 1012.724 0.000 0.995 0.999
phaseplus -0.0004 0.000 -2.239 0.025 -0.001 -5.3e-05
\==============================================================================
Omnibus: 674.771 Durbin-Watson: 1.901
Prob(Omnibus): 0.000 Jarque-Bera (JB): 24937.353
Skew: -0.765 Prob(JB): 0.00
Kurtosis: 19.491 Cond. No. 255.
\==============================================================================
Below we see some regression diagnostics along with the regression itself.
Diagnostics: We can see that the residuals are looking a little skewed and there is some heteroskedasticity within the residuals. The coefficient of determination, or r2 is very high, but that is to be expected given the momentum term. A better r2 is manually calculated by the sum square of the difference of the model to the untrained data. This can be achieved by the following code:
\# Calculate the out-of-sample R-squared
oos\_mask = df\ >= 2
oos\_actual = df.loc\
oos\_predicted = df.loc\
residuals\_oos = oos\_actual - oos\_predicted
SSR = np.sum(residuals\_oos \*\* 2)
SST = np.sum((oos\_actual - oos\_actual.mean()) \*\* 2)
R2\_oos = 1 - SSR/SST
print("Out-of-sample R-squared:", R2\_oos)
The result is: 0.84, which indicates a very close fit to the out of sample data for the base model, which goes some way to proving our fundamental assumption around subjective value and sound money to be accurate.
Step 2: Adding the Damping Function
Next, we incorporated a damping function to capture the cyclical nature of bull and bear markets. The optimal parameters for the damping function were determined by regressing on the residuals from the base model. The damping function enhances the model’s ability to identify and predict bull and bear cycles in the Bitcoin market. The addition of the damping function to the base model is expressed as the full model equation.
This brings me to the question — why? Why add the damping function to the base model, which is arguably already performing extremely well out of sample and providing valuable insights into the exchange rate movements of Bitcoin.
Fundamental reasoning behind the addition of a damping function:
Subjective Theory of Value: The cyclical component of the damping function, represented by the cosine function, can be thought of as capturing the periodic fluctuations in market sentiment. These fluctuations may arise from various factors, such as changes in investor risk appetite, macroeconomic conditions, or technological advancements. Mathematically, the cyclical component represents the frequency of these fluctuations, while the phase shift (α and β) allows for adjustments in the alignment of these cycles with historical data. This flexibility enables the damping function to account for the heterogeneity in market participants’ preferences and expectations, which is a key aspect of the subjective theory of value.
Time Preference and Market Cycles: The exponential decay component of the damping function, represented by the term e^(-0.0004t), can be linked to the concept of time preference and its impact on market dynamics. In financial markets, the discounting of future cash flows is a common practice, reflecting the time value of money and the inherent uncertainty of future events. The exponential decay in the damping function serves a similar purpose, diminishing the influence of past market cycles as time progresses. This decay term introduces a time-dependent weight to the cyclical component, capturing the dynamic nature of the Bitcoin market and the changing relevance of past events.
Interactions between Cyclical and Exponential Decay Components: The interplay between the cyclical and exponential decay components in the damping function captures the complex dynamics of the Bitcoin market. The damping function effectively models the attenuation of past cycles while also accounting for their periodic nature. This allows the model to adapt to changing market conditions and to provide accurate predictions even in the face of significant volatility or structural shifts.
Now we have the fundamental reasoning for the addition of the function, we can explore the actual implementation and look to other analogies for guidance —
Financial and physical analogies to the damping function:
Mathematical Aspects: The exponential decay component, e^(-0.0004t), attenuates the amplitude of the cyclical component over time. This attenuation factor is crucial in modelling the diminishing influence of past market cycles. The cyclical component, represented by the cosine function, accounts for the periodic nature of market cycles, with α determining the frequency of these cycles and β representing the phase shift. The constant term (+3) ensures that the function remains positive, which is important for practical applications, as the damping function is added to the rest of the model to obtain the final predictions.
Analogies to Existing Damping Functions: The damping function in the BAERM is similar to damped harmonic oscillators found in physics. In a damped harmonic oscillator, an object in motion experiences a restoring force proportional to its displacement from equilibrium and a damping force proportional to its velocity. The equation of motion for a damped harmonic oscillator is:
x’’(t) + 2γx’(t) + ω₀²x(t) = 0
where x(t) is the displacement, ω₀ is the natural frequency, and γ is the damping coefficient. The damping function in the BAERM shares similarities with the solution to this equation, which is typically a product of an exponential decay term and a sinusoidal term. The exponential decay term in the BAERM captures the attenuation of past market cycles, while the cosine term represents the periodic nature of these cycles.
Comparisons with Financial Models: In finance, damped oscillatory models have been applied to model interest rates, stock prices, and exchange rates. The famous Black-Scholes option pricing model, for instance, assumes that stock prices follow a geometric Brownian motion, which can exhibit oscillatory behavior under certain conditions. In fixed income markets, the Cox-Ingersoll-Ross (CIR) model for interest rates also incorporates mean reversion and stochastic volatility, leading to damped oscillatory dynamics.
By drawing on these analogies, we can better understand the technical aspects of the damping function in the BAERM and appreciate its effectiveness in modelling the complex dynamics of the Bitcoin market. The damping function captures both the periodic nature of market cycles and the attenuation of past events’ influence.
Conclusion
In this article, we explored the Bitcoin Auto-correlation Exchange Rate Model (BAERM), a novel 2-step linear regression model for understanding the Bitcoin USD exchange rate. We discussed the model’s components, their interpretations, and the fundamental insights they provide about Bitcoin exchange rate dynamics.
The BAERM’s ability to capture the fundamental properties of Bitcoin is particularly interesting. The framework underlying the model emphasises the importance of individuals’ subjective valuations and preferences in determining prices. The momentum term, which accounts for auto-correlation, is a testament to this idea, as it shows that historical price trends influence market participants’ expectations and valuations. This observation is consistent with the notion that the price of Bitcoin is determined by individuals’ preferences based on past information.
Furthermore, the BAERM incorporates the impact of Bitcoin’s supply dynamics on its price through the halving epoch terms. By acknowledging the significance of supply-side factors, the model reflects the principles of sound money. A limited supply of money, such as that of Bitcoin, maintains its value and purchasing power over time. The halving events, which reduce the block reward, play a crucial role in making Bitcoin increasingly scarce, thus reinforcing its attractiveness as a store of value and a medium of exchange.
The constant term in the model serves as the baseline for the model’s predictions and can be interpreted as an inherent value attributed to Bitcoin. This value emphasizes the significance of the underlying technology, network effects, and Bitcoin’s role as a medium of exchange, store of value, and unit of account. These aspects are all essential for a sound form of money, and the model’s ability to account for them further showcases its strength in capturing the fundamental properties of Bitcoin.
The BAERM offers a potential robust and well-founded methodology for understanding the Bitcoin USD exchange rate, taking into account the key factors that drive it from both supply and demand perspectives.
In conclusion, the Bitcoin Auto-correlation Exchange Rate Model provides a comprehensive fundamentally grounded and hopefully useful framework for understanding the Bitcoin USD exchange rate.
Square of Nine Planetary Time ProgressionThis indicator enables users to plot future planetary projections in alignment with Square of Nine progression.
Description:
The Square of Nine Planetary Time Progression Indicator is a time-based market forecasting tool that integrates W.D. Gann’s Square of Nine principles with planetary time intervals. By mapping planetary cycles onto a geometric framework, this indicator may help traders identify potential market turning points with precision, aligning planetary time with Square of Nine progression.
It provides the ability to use geo or helio planetary positions, including the Moon, making it applicable for both long-term and intraday market timing.
Unlike traditional planetary cycle tools that rely on zodiacal aspects, this indicator focuses on planetary time intervals and their geometric relationships within the Square of Nine to forecast potential future key points in the market.
How to Use:
1. Select a Significant High or Low on any time frame
This reference point serves as the starting measurement for planetary time calculations, from which planetary time degrees will accumulate . Each subsequent projection reflects the cumulative solar or planetary degrees from this initial pivot, establishing a structured sequence of time-based market intervals.
2. Align the Degree Interval with the Next Market Swing:
Set the degree interval to align with the next major market swing from the intial point set in step 1.
The planetary time between these two points establishes the base time measurement for further calculations.
3. The script generates Square of Nine rotational increments, where the base measurement serves as the cornerstone for all future progressions/planetary time projections. As the script continues rotating around the Square of Nine, it produces the following sequence:
360° (Full Cycle) – The next full rotation from the base measurement.
180° (Half Cycle) – The midpoint between full cycles.
90° Increment from the Base Measurement – The first step in the Square of Nine progression.
90° Increment from 180° – The next step in the sequence, leading toward the full cycle.
Each of these increments builds upon the initial planetary time measurement, accumulating degrees through a structured Square of Nine progression, ensuring that future time projections align geometrically with the starting swing measurement.
Example Calculation:
Selecting the Base Measurement:
When the indicator is loaded, lets say the user selects a significant low as the starting point for the calculation.
Next, the user sets the input of the degree interval to align with the next major swing from that significant low.
Suppose the measured swing from the starting point occurs at 145 solar degrees—this becomes the base planetary time measurement.
Applying the Square of Nine Rotational Increments:
The script takes 145° and maps it onto the Square of Nine.
From 145° , the script projects future planetary degrees intervals based on the progression of 90-degree increments around the Square of Nine structure.
Generating Key Rotational Points:
Starting from 145° on the Square of Nine, we apply successive 90° increments:
151° (First 90° increment)
157°
163°
169° (180° opposition point)
176°
183°
190°
197° (Full 360° cycle rotation on the Square of Nine) and the rotation continues onto the next rung on the square of nine.
Plotting Future Projection Intervals:
The indicator plots these calculated degree intervals as future timing markers on the chart.
The opposition (180°) and full 360° cycle rotations are visually labelled using the conjunction (☌) and opposition (☍) symbols.
These symbols do not represent planetary aspects (as in traditional astrology) but instead mark geometric aspects derived from the Square of Nine.
These projected dates may help traders forecasting key market points in time. No repaint.
This indicator is inspired by the works of W.D. Gann and Patrick Mikula and is powered by AstroLib, developed by @BarefootJoey
Intraday example:
You can see that we're on the 4-hour chart using the Moon, with our initial measurement spanning 37 lunar degrees from high to low. The Square of Nine time projections are calculated in planetary degrees and plotted on the chart, forecasting future timing intervals.
RSI3M3+ v.1.8RSI3M3+ v.1.8 Indicator
This script is an advanced trading indicator based on Walter J. Bressert's cycle analysis methodology, combined with an RSI (Relative Strength Index) variation. Let me break it down and explain how it works.
Core Concepts
The RSI3M3+ indicator combines:
A short-term RSI (3-period)
A 3-period moving average to smooth the RSI
Bressert's cycle analysis principles to identify optimal trading points
RSI3M3+ Indicator VisualizationImage Walter J. Bressert's Cycle Analysis Concepts
Walter Bressert was a pioneer in cycle analysis trading who believed markets move in cyclical patterns that can be measured and predicted. His key principles integrated into this indicator include:
Trading Cycles: Markets move in cycles with measurable time spans from low to low
Timing Bands: Projected periods when the next cyclical low or high is anticipated
Oscillator Use: Using oscillators like RSI to confirm cycle position
Entry/Exit Rules: Specific rules for trade entry and exit based on cycle position
Key Parameters in the Script
Basic RSI Parameters
Required bars: Minimum number of bars needed (default: 20)
Overbought region: RSI level considered overbought (default: 70)
Oversold region: RSI level considered oversold (default: 30)
Bressert-Specific Parameters
Cycle Detection Length: Lookback period for cycle identification (default: 30)
Minimum/Maximum Cycle Length: Expected cycle duration in days (default: 15-30)
Buy Line: Lower threshold for buy signals (default: 40)
Sell Line: Upper threshold for sell signals (default: 60)
How the Indicator Works
RSI3M3 Calculation:
Calculates a 3-period RSI (sRSI)
Smooths it with a 3-period moving average (sMA)
Cycle Detection:
Identifies bottoms: When the RSI is below the buy line (40) and starting to turn up
Identifies tops: When the RSI is above the sell line (60) and starting to turn down
Records these points to calculate cycle lengths
Timing Bands:
Projects when the next cycle bottom or top should occur
Creates visual bands on the chart showing these expected time windows
Signal Generation:
Buy signals occur when the RSI turns up from below the oversold level (30)
Sell signals occur when the RSI turns down from above the overbought level (70)
Enhanced by Bressert's specific timing rules
Bressert's Five Trading Rules (Implemented in the Script)
Cycle Timing: The low must be 15-30 market days from the previous Trading Cycle bottom
Prior Top Validation: A Trading Cycle high must have occurred with the oscillator above 60
Oscillator Behavior: The oscillator must drop below 40 and turn up
Entry Trigger: Entry is triggered by a rise above the price high of the upturn day
Protective Stop: Place stop slightly below the Trading Cycle low (implemented as 99% of bottom price)
How to Use the Indicator
Reading the Chart
Main Plot Area:
Green line: 3-period RSI
Red line: 3-period moving average of the RSI
Horizontal bands: Oversold (30) and Overbought (70) regions
Dotted lines: Buy line (40) and Sell line (60)
Yellow vertical bands: Projected timing windows for next cycle bottom
Signals:
Green up arrows: Buy signals
Red down arrows: Sell signals
Trading Strategy
For Buy Signals:
Wait for the RSI to drop below the buy line (40)
Look for an upturn in the RSI from below this level
Enter the trade when price rises above the high of the upturn day
Place a protective stop at 99% of the Trading Cycle low
For Sell Signals:
Wait for the RSI to rise above the sell line (60)
Look for a downturn in the RSI from above this level
Consider exiting or taking profits when a sell signal appears
Alternative exit: When price moves below the low of the downturn day
Cycle Timing Enhancement:
Pay attention to the yellow timing bands
Signals occurring within these bands have higher probability of success
Signals outside these bands may be less reliable
Practical Tips for Using RSI3M3+
Timeframe Selection:
The indicator works best on daily charts for intermediate-term trading
Can be used on weekly charts for longer-term position trading
On intraday charts, adjust cycle lengths accordingly
Market Applicability:
Works well in trending markets with clear cyclical behavior
Less effective in choppy, non-trending markets
Consider additional indicators for trend confirmation
Parameter Adjustment:
Different markets may have different natural cycle lengths
You may need to adjust the min/max cycle length parameters
Higher volatility markets may need wider overbought/oversold levels
Trade Management:
Enter trades when all Bressert's conditions are met
Use the protective stop as defined (99% of cycle low)
Consider taking partial profits at the projected cycle high timing
Advanced Techniques
Multiple Timeframe Analysis:
Confirm signals with the same indicator on higher timeframes
Enter in the direction of the larger cycle when smaller and larger cycles align
Divergence Detection:
Look for price making new lows while RSI makes higher lows (bullish)
Look for price making new highs while RSI makes lower highs (bearish)
Confluence with Price Action:
Combine with support/resistance levels
Use with candlestick patterns for confirmation
Consider volume confirmation of cycle turns
This RSI3M3+ indicator combines the responsiveness of a short-term RSI with the predictive power of Bressert's cycle analysis, offering traders a sophisticated tool for identifying high-probability trading opportunities based on market cycles and momentum shifts.
THANK YOU FOR PREVIOUS CODER THAT EFFORT TO CREATE THE EARLIER VERSION THAT MAKE WALTER J BRESSERT CONCEPT IN TRADINGVIEW @ADutchTourist
BTC Mercenary ModelBitcoin Market Cycle Evaluation Using Subjective Z-Scores
Introduction:
I've crafted a unique indicator for Bitcoin that synthesizes multiple market indicators into a single, actionable Z-score, aiming to offer insights into the current market cycle phase. Here's the methodology:
Methodology:
Alpha Validation: Each component indicator has been tested for its predictive power (alpha) against Bitcoin's market cycle peaks and troughs from at least the last two cycles. This ensures each indicator contributes meaningfully to our model.
Z-Score Synthesis: By converting each indicator's value into a Z-score, we normalize their contributions. The average of these Z-scores provides a refined signal, indicating whether Bitcoin is in an overbought or oversold state relative to historical norms.
Features:
Individual Indicator Customization: Users can tweak inputs to optimize each indicator's alpha, enhancing the model's predictive accuracy.
Historical Averages: The script provides visibility into how both technical and fundamental indicators have scored in the past, offering a benchmark for current conditions.
ROC Flexibility: Adjust the Rate of Change (ROC) period to suit your analysis timeframe, allowing for more personalized market cycle interpretation.
Indicators Integrated:
Fundamental:
MVRV (Market Value to Realized Value) - Measures market sentiment vs. actual value.
Bitcoin Thermocap - Relates Bitcoin's market cap to its transaction volume.
NUPL (Net Unrealized Profit/Loss) - Indicates holder's profit or loss status.
CVDD (Coin Days Destroyed) - Shows the movement of long-held coins.
SOPR (Spent Output Profit Ratio) - Highlights whether coins are being spent at a profit or loss.
Technical:
RSI (Relative Strength Index) - Identifies overbought/oversold conditions.
CCI (Commodity Channel Index) - Detects cyclical turns in Bitcoin's price.
Multiple Moving Averages - For trend analysis over various time frames.
Sharpe Ratio - Evaluates risk-adjusted return.
Pi Cycle Indicator - Predicts cycle tops based on moving average crossovers.
Hodrick-Prescott Filter - Separates trend from cycle in price data.
VWAP (Volume Weighted Average Price) - Provides a trading benchmark.
How It Works Together:
This model uses a weighted average of Z-scores from these indicators to give a comprehensive view of Bitcoin's market cycle. The Z-scores are not just summed but considered in context; for example, when fundamental indicators like MVRV suggest an overvaluation while technical ones like RSI indicate a near-term correction, the model's output reflects this nuanced interaction.
Future Developments:
The next step is to include sentiment analysis, potentially from social media or news sentiment, to further refine our cycle predictions.
Chart Example:
Symbol/Timeframe: BTCUSD on a daily chart.
Script Name: Bitcoin Cycle Z-Score Evaluator
Feedback Encouraged:
I'm eager to receive feedback on how this model could be further tailored or expanded for better market insights.
-CM
Quarterly Theory ICT 04 [TradingFinder] SSMT 4Quarter Divergence🔵 Introduction
Sequential SMT Divergence is an advanced price-action-based analytical technique rooted in the ICT (Inner Circle Trader) methodology. Its primary objective is to identify early-stage divergences between correlated assets within precise time structures. This tool not only breaks down market structure but also enables traders to detect engineered liquidity traps before the market reacts.
In simple terms, SMT (Smart Money Technique) occurs when two correlated assets—such as indices (ES and NQ), currency pairs (EURUSD and GBPUSD), or commodities (Gold and Silver)—exhibit different reactions at key price levels (swing highs or lows). This lack of alignment is often a sign of smart money manipulation and signals a lack of confirmation in the ongoing trend—hinting at an imminent reversal or at least a pause in momentum.
In its Sequential form, SMT divergences are examined through a more granular temporal lens—between intraday quarters (Q1 through Q4). When SMT appears at the transition from one quarter to another (e.g., Q1 to Q2 or Q3 to Q4), the signal becomes significantly more powerful, often aligning with a critical phase in the Quarterly Theory—a framework that segments market behavior into four distinct phases: Accumulation, Manipulation, Distribution, and Reversal/Continuation.
For instance, a Bullish SMT forms when one asset prints a new low while its correlated counterpart fails to break the corresponding low from the previous quarter. This usually indicates absorption of selling pressure and the beginning of accumulation by smart money. Conversely, a Bearish SMT arises when one asset makes a higher high, but the second asset fails to confirm, signaling distribution or a fake-out before a decline.
However, SMT alone is not enough. To confirm a true Market Structure Break (MSB), the appearance of a Precision Swing Point (PSP) is essential—a specific candlestick formation on a lower timeframe (typically 5 to 15 minutes) that reveals the entry of institutional participants. The combination of SMT and PSP provides a more accurate entry point and better understanding of premium and discount zones.
The Sequential SMT Indicator, introduced in this article, dynamically scans charts for such divergence patterns across multiple sessions. It is applicable to various markets including Forex, crypto, commodities, and indices, and shows particularly strong performance during mid-week sessions (Wednesdays and Thursdays)—when most weekly highs and lows tend to form.
Bullish Sequential SMT :
Bearish Sequential SMT :
🔵 How to Use
The Sequential SMT (SSMT) indicator is designed to detect time and structure-based divergences between two correlated assets. This divergence occurs when both assets print a similar swing (high or low) in the previous quarter (e.g., Q3), but in the current quarter (e.g., Q4), only one asset manages to break that swing level—while the other fails to reach it.
This temporal mismatch is precisely identified by the SSMT indicator and often signals smart money activity, a market phase transition, or even the presence of an engineered liquidity trap. The signal becomes especially powerful when paired with a Precision Swing Point (PSP)—a confirming candle on lower timeframes (5m–15m) that typically indicates a market structure break (MSB) and the entry of smart liquidity.
🟣 Bullish Sequential SMT
In the previous quarter, both assets form a similar swing low.
In the current quarter, one asset (e.g., EURUSD) breaks that low and trades below it.
The other asset (e.g., GBPUSD) fails to reach the same low, preserving the structure.
This time-based divergence reflects declining selling pressure, potential absorption, and often marks the end of a manipulation phase and the start of accumulation. If confirmed by a bullish PSP candle, it offers a strong long opportunity, with stop-losses defined just below the swing low.
🟣 Bearish Sequential SMT
In the previous quarter, both assets form a similar swing high.
In the current quarter, one asset (e.g., NQ) breaks above that high.
The other asset (e.g., ES) fails to reach that high, remaining below it.
This type of divergence signals weakening bullish momentum and the likelihood of distribution or a fake-out before a price drop. When followed by a bearish PSP candle, it sets up a strong shorting opportunity with targets in the discount zone and protective stops placed above the swing high.
🔵 Settings
⚙️ Logical Settings
Quarterly Cycles Type : Select the time segmentation method for SMT analysis.
Available modes include: Yearly, Monthly, Weekly, Daily, 90 Minute, and Micro.
These define how the indicator divides market time into Q1–Q4 cycles.
Symbol : Choose the secondary asset to compare with the main chart asset (e.g., XAUUSD, US100, GBPUSD).
Pivot Period : Sets the sensitivity of the pivot detection algorithm. A smaller value increases responsiveness to price swings.
Activate Max Pivot Back : When enabled, limits the maximum number of past pivots to be considered for divergence detection.
Max Pivot Back Length : Defines how many past pivots can be used (if the above toggle is active).
Pivot Sync Threshold : The maximum allowed difference (in bars) between pivots of the two assets for them to be compared.
Validity Pivot Length : Defines the time window (in bars) during which a divergence remains valid before it's considered outdated.
🎨 Display Settings
Show Cycle :Toggles the visual display of the current Quarter (Q1 to Q4) based on the selected time segmentation
Show Cycle Label : Shows the name (e.g., "Q2") of each detected Quarter on the chart.
Show Bullish SMT Line : Draws a line connecting the bullish divergence points.
Show Bullish SMT Label : Displays a label on the chart when a bullish divergence is detected.
Bullish Color : Sets the color for bullish SMT markers (label, shape, and line).
Show Bearish SMT Line : Draws a line for bearish divergence.
Show Bearish SMT Label : Displays a label when a bearish SMT divergence is found.
Bearish Color : Sets the color for bearish SMT visual elements.
🔔 Alert Settings
Alert Name : Custom name for the alert messages (used in TradingView’s alert system).
Message Frequency :
All: Every signal triggers an alert.
Once Per Bar: Alerts once per bar regardless of how many signals occur.
Per Bar Close: Only triggers when the bar closes and the signal still exists.
Time Zone Display : Choose the time zone in which alert timestamps are displayed (e.g., UTC).
Bullish SMT Divergence Alert : Enable/disable alerts specifically for bullish signals.
Bearish SMT Divergence Alert : Enable/disable alerts specifically for bearish signals
🔵 Conclusion
The Sequential SMT (SSMT) indicator is a powerful and precise tool for identifying structural divergences between correlated assets within a time-based framework. Unlike traditional divergence models that rely solely on sequential pivot comparisons, SSMT leverages Quarterly Theory, in combination with concepts like liquidity sweeps, market structure breaks (MSB) and precision swing points (PSP), to provide a deeper and more actionable view of market dynamics.
By using SSMT, traders gain not only the ability to identify where divergence occurs, but also when it matters most within the market cycle. This empowers them to anticipate major moves or traps before they fully materialize, and position themselves accordingly in high-probability trade zones.
Whether you're trading Forex, crypto, indices, or commodities, the true strength of this indicator is revealed when used in sync with the Accumulation, Manipulation, Distribution, and Reversal phases of the market. Integrated with other confluence tools and market models, SSMT can serve as a core component in a professional, rule-based, and highly personalized trading strategy.
Jinny Gann ArJinny Gann AR is a comprehensive technical analysis indicator designed to empower traders with the tools to analyze market movements using Gann square of 9 theory. Developed by Magic_xD, this indicator integrates various features inspired by the legendary trader W.D. Gann's methods.
The trading techniques by WD Gann are widely seen as innovative and are still studied and used by traders today. He used angles and various geometric constructions. Gann angles divide time and price into proportionate parts and are often used to predict areas of support and resistance, key tops and bottoms and future price moves. The method is based on the notion that markets rotate from angle to angle and when an angle is broken, price moves towards the next one. Several angles together make up a Gann Fan.
- Jinny Gann AR Might accurately Shows you when and what price might be the end of the Cycle,
-Gives The important pivot points
- This Allows you to Detect Next Level of Resistance/Support And when a Possible Reversal might occur ahead so you can Catch a reversal in time.
- Its Multi Language User interface English - Arabic.
Ability to customize Every thing visually.
Some Features Explained on USOIL Chart :
Gann Square of 9 Levels for USOIL:
Charts Shows and Up Cycle Started 4 May 2023 From bottom of 63.61
Indicating Important Levels and Expected End of 1 Cycle at 99.5 on 25 Sep 2024
Gann Star With Levels And Time Lines :
Vertical Dashed Lines are The time lines
Jinny Gann Grid Based on Shape Type not Static 45 Angle:
Jinny Gann Grid + Levels :
Jinny Gann Fan For Up Cycle:
Jinny Gann Fan Reverse Same Cycle:
Ability To Show Both Up/Reversal Fans on The chart:
The Number of Fann Levels you need on the chart can be customized by changing Shape Type... But Price Will Respect it Pretty Well.
Key Features:
Direction Selection: Choose between "Up" or "Down" to specify the market direction you want to analyze.
Automatic Settings Adjustment: Enable this option to allow the indicator to automatically adjust settings for optimal analysis.
Original Gann Levels: Display original Gann theory levels Based on Gann Square of 9 Equations.
Auto Detect Tops/Bottoms: Determine the number of previous candles used to automatically detect Top or Bottom in the market.
Spacing Configuration: Adjust the spacing or offset between Gann levels to fine-tune your analysis.
Manual Starting Point: Manually set the starting point for your analysis.
Geometric Shape Selection: Choose from various geometric shapes including straight lines, triangles, quadrilaterals, and more...
Custom Angle Selection: Define custom angles for geometric shapes .
Time Interval Selection: Select time intervals such as 360 or 720 Etc...
Cycle Analysis: Determine the number of cycles to analyze market movements effectively.
Decimal Precision: Customize the number of decimal places displayed for accurate analysis.
Automatic Spacing (Under Development): Future feature to automatically select spacing for enhanced user experience.
Time Levels Display: Visualize time levels to gain insights into market timing.
Gann Star Display: Show Gann stars to identify critical market points.
Star Modification: Modify the appearance of Gann stars for better visualization.
Gann Grid Display: Display Gann grids to identify key support and resistance levels.
Grid Extension: Extend Gann grid lines for extended analysis.
Gann Fan Display: Show Gann fans to analyze trend lines and potential reversals.
Reverse Fan Display: Visualize Gann fans in reverse to explore alternative analysis perspectives.
Additional Fan Options: Explore more options for Gann fan analysis.
Time Line Adjustment: Move time lines to the right or left for flexible analysis.
Star Line Extension: Extend Gann star lines for deeper insights.
Fan Line Extension: Extend Gann fan lines for comprehensive trend analysis.
Customizable Colors: Customize colors for various indicators to suit your preference.
Width Adjustment: Adjust the width of trend lines for better visualization.
Label Customization: Customize colors and positions of level and price labels for clarity.
Stock price mastery📘 Cycle-Based Support and Resistance – Price Levels
🔍 What this tool does:
This indicator helps you identify key price zones where the market is likely to start or end a trend, pause, or reverse. These zones are derived from vibrational price cycles and work like natural support and resistance levels.
It automatically calculates and plots:
Entry Zones
Take Profits (TP1, TP2, TP3)
Stop Loss Areas
Cycle Start/End points
🧠 How to Use It:
1. Start from Larger Vibrations
Always begin your analysis from higher vibration levels and move down to smaller ones.
Larger vibrations provide major turning points, while smaller ones help refine entries and exits.
2. Understand Each Level:
Cycle Start/End:
Usage: Turning points where trends may begin or end.
Style: Solid line, with shaded area.
Trade Entry Zone:
Usage: Ideal price zone to enter a trade.
Style: Dashed lines with shade.
TP1 / TP2 / TP3 (Take Profit Levels):
Usage: Take profit levels.
Style: Dotted line zone, dashed, or solid line.
Stop Loss:
Usage: Place Stop Loss (SL) beyond these zones.
Style: Dotted line.
✅ These levels flip meaning when the market direction flips. For example, a bullish cycle start becomes a bearish end if price is reversing.
⚙️ Settings Explained:
Vibration Level:
Choose the strength (depth) of the cycles.
Color:
Each vibration has a different color.
Toggle Levels:
Show or hide different vibration levels individually.
Fill Zones:
Optionally fill the zones for better visibility.
📈 Trading Tips:
Use higher vibration zones for context and bias.
Wait for price to react at a zone before entering — don’t preempt.
Combine with your existing strategy (volume, structure, indicators).
When price hits a Cycle Start, prepare for trend to begin.
When it nears a Cycle End, protect profits or exit if trend weakens.
🌀 Multi-Timeframe Example :
*same chart zoomed in for lower vibration
🧠 How to Use It
1. Start with Higher Vibration (Main Cycle)
Use higher vibration zones to define the primary trend.
These levels guide your overall bias (bullish/bearish).
Great for swing trading or trend confirmation.
2. Zoom into Lower Vibrations (Micro Cycles)
Within a big trend, smaller cycles provide entry/exit zones.
Perfect for intraday trading or refining entries on lower timeframes.
These mini-cycles follow the same logic: entry > TP > SL zones, just on a smaller scale.
🔄 All vibrations work together, like zoom levels on a map. Large cycles show direction. Small cycles show detailed movement.
Example use
Timeframe, Use Case, and What to Look For:
1D / 4H (High Vibration):
Use Case: Define major trend, strong reversal zones.
What to Look For: Trend direction, swing bias.
1H / 15m (Low Vibration):
Use Case: Refine entries, targets, stop loss (SL).
What to Look For: Exact entry/exit within a bigger move.
Back testing:
In settings check the "Rspt" for backtesting, the levels are dynamically calculated from closing price, on 1 min timeframe, so while backtesting check the box to calculate price based on the daily closing price
Market Availability
The Cycle-Based Support and Resistance indicators are versatile and designed to work across multiple asset classes:
Stocks:
Specially optimized for major Indian indices like NIFTY and BANKNIFTY, along with their constituent stocks. It helps traders identify potential pivot points based on time and price cycles, enhancing decision-making in both intraday and positional trading.
By following the hierarchy of vibrations from larger to smaller scales, users can apply the indicators effectively across different instruments and trading styles.
PA-Adaptive Hull Parabolic [Loxx]The PA-Adaptive Hull Parabolic is not your typical trading indicator. It synthesizes the computational brilliance of two famed technicians: John Ehlers and John Hull. Let's demystify its sophistication.
█ Ehlers' Phase Accumulation
John Ehlers is well-known in the trading community for his digital signal processing approach to market data. One of his standout techniques is phase accumulation. This method identifies the dominant cycle in the market by accumulating the phases of individual cycles. By doing so, it "adapts" to real-time market conditions.
Here's the brilliance of phase accumulation in this code
The indicator doesn't merely use a static look-back period. Instead, it dynamically determines the dominant market cycle through phase accumulation.
The calcComp function, rooted in Ehlers' methodology, provides a complex computation using a digital signal processing approach to filter out market noise and pinpoint the current cycle's frequency.
By measuring and adapting to the instantaneous period of the market, it ensures that the indicator remains relevant, especially in non-stationary market conditions.
Hull's Moving Average
John Hull introduced the Hull Moving Average (HMA) aiming to reduce lag and improve smoothing. The HMA's essence lies in its weighted average computation, prioritizing more recent prices.
This code takes an adaptive twist on the HMA
Instead of a fixed period, the HMA uses the dominant cycle length derived from Ehlers' phase accumulation. This makes the HMA not just fast and smooth, but also adaptive to the dominant market rhythm.
The intricate iLwmp function in the script provides this adaptive HMA computation. It's a weighted moving average, but its length isn't static; it's based on the previously determined dominant market cycle.
█ Trading Insights
The indicator paints the bars to represent the immediate trend: green for bullish and red for bearish.
Entry points, both long ("L") and short ("S"), are presented visually. These are derived from crossovers of the adaptive HMA, a clear indication of a potential shift in the trend.
Additionally, alert conditions are set, ready to notify a trader when these crossovers occur, ensuring real-time actionable insights.
█ Conclusion
The PA-Adaptive Hull Parabolic is a masterclass in advanced technical indicator design. By marrying John Ehlers' adaptive phase accumulation with John Hull's HMA, it creates a dynamic, responsive, and precise tool for traders. It's not just about capturing the trend; it's about understanding the very rhythm of the market.
[GYTS-CE] Market Regime Detector🧊 Market Regime Detector (Community Edition)
🌸 Part of GoemonYae Trading System (GYTS) 🌸
🌸 --------- INTRODUCTION --------- 🌸
💮 What is the Market Regime Detector?
The Market Regime Detector is an advanced, consensus-based indicator that identifies the current market state to increase the probability of profitable trades. By distinguishing between trending (bullish or bearish) and cyclic (range-bound) market conditions, this detector helps you select appropriate tactics for different environments. Instead of forcing a single strategy across all market conditions, our detector allows you to adapt your approach based on real-time market behaviour.
💮 The Importance of Market Regimes
Markets constantly shift between different behavioural states or "regimes":
• Bullish trending markets - characterised by sustained upward price movement
• Bearish trending markets - characterised by sustained downward price movement
• Cyclic markets - characterised by range-bound, oscillating behaviour
Each regime requires fundamentally different trading approaches. Trend-following strategies excel in trending markets but fail in cyclic ones, while mean-reversion strategies shine in cyclic markets but underperform in trending conditions. Detecting these regimes is essential for successful trading, which is why we've developed the Market Regime Detector to accurately identify market states using complementary detection methods.
🌸 --------- KEY FEATURES --------- 🌸
💮 Consensus-Based Detection
Rather than relying on a single method, our detector employs two complementary detection methodologies that analyse different aspects of market behaviour:
• Dominant Cycle Average (DCA) - analyzes price movement relative to its lookback period, a proxy for the dominant cycle
• Volatility Channel - examines price behaviour within adaptive volatility bands
These diverse perspectives are synthesised into a robust consensus that minimises false signals while maintaining responsiveness to genuine regime changes.
💮 Dominant Cycle Framework
The Market Regime Detector uses the concept of dominant cycles to establish a reference framework. You can input the dominant cycle period that best represents the natural rhythm of your market, providing a stable foundation for regime detection across different timeframes.
💮 Intuitive Parameter System
We've distilled complex technical parameters into intuitive controls that traders can easily understand:
• Adaptability - how quickly the detector responds to changing market conditions
• Sensitivity - how readily the detector identifies transitions between regimes
• Consensus requirement - how much agreement is needed among detection methods
This approach makes the detector accessible to traders of all experience levels while preserving the power of the underlying algorithms.
💮 Visual Market Feedback
The detector provides clear visual feedback about the current market regime through:
• Colour-coded chart backgrounds (purple shades for bullish, pink for bearish, yellow for cyclic)
• Colour-coded price bars
• Strength indicators showing the degree of consensus
• Customizable colour schemes to match your preferences or trading system
💮 Integration in the GYTS suite
The Market Regime Detector is compatible with the GYTS Suite , i.e. it passes the regime into the 🎼 Order Orchestrator where you can set how to trade the trending and cyclic regime.
🌸 --------- CONFIGURATION SETTINGS --------- 🌸
💮 Adaptability
Controls how quickly the Market Regime detector adapts to changing market conditions. You can see it as a low-frequency, long-term change parameter:
Very Low: Very slow adaptation, most stable but may miss regime changes
Low: Slower adaptation, more stability but less responsiveness
Normal: Balanced between stability and responsiveness
High: Faster adaptation, more responsive but less stable
Very High: Very fast adaptation, highly responsive but may generate false signals
This setting affects lookback periods and filter parameters across all detection methods.
💮 Sensitivity
Controls how sensitive the detector is to market regime transitions. This acts as a high-frequency, short-term change parameter:
Very Low: Requires substantial evidence to identify a regime change
Low: Less sensitive, reduces false signals but may miss some transitions
Normal: Balanced sensitivity suitable for most markets
High: More sensitive, detects subtle regime changes but may have more noise
Very High: Very sensitive, detects minor fluctuations but may produce frequent changes
This setting affects thresholds for regime detection across all methods.
💮 Dominant Cycle Period
This parameter allows you to specify the market's natural rhythm in bars. This represents a complete market cycle (up and down movement). Finding the right value for your specific market and timeframe might require some experimentation, but it's a crucial parameter that helps the detector accurately identify regime changes. Most of the times the cycle is between 20 and 40 bars.
💮 Consensus Mode
Determines how the signals from both detection methods are combined to produce the final market regime:
• Any Method (OR) : Signals bullish/bearish if either method detects that regime. If methods conflict (one bullish, one bearish), the stronger signal wins. More sensitive, catches more regime changes but may produce more false signals.
• All Methods (AND) : Signals only when both methods agree on the regime. More conservative, reduces false signals but might miss some legitimate regime changes.
• Weighted Decision : Balances both methods with equal weighting. Provides a middle ground between sensitivity and stability.
Each mode also calculates a continuous regime strength value that's used for colour intensity in the 'unconstrained' display mode.
💮 Display Mode
Choose how to display the market regime colours:
• Unconstrained regime: Shows the regime strength as a continuous gradient. This provides more nuanced visualisation where the intensity of the colour indicates the strength of the trend.
• Consensus only: Shows only the final consensus regime with fixed colours based on the detected regime type.
The background and bar colours will change to indicate the current market regime:
• Purple shades: Bullish trending market (darker purple indicates stronger bullish trend)
• Pink shades: Bearish trending market (darker pink indicates stronger bearish trend)
• Yellow: Cyclic (range-bound) market
💮 Custom Colour Options
The Market Regime Detector allows you to customize the colour scheme to match your personal preferences or to coordinate with other indicators:
• Use custom colours: Toggle to enable your own colour choices instead of the default scheme
• Transparency: Adjust the transparency level of all regime colours
• Bullish colours: Define custom colours for strong, medium, weak, and very weak bullish trends
• Bearish colours: Define custom colours for strong, medium, weak, and very weak bearish trends
• Cyclic colour: Define a custom colour for cyclic (range-bound) market conditions
🌸 --------- DETECTION METHODS --------- 🌸
💮 Dominant Cycle Average (DCA)
The Dominant Cycle Average method forms a key part of our detection system:
1. Theoretical Foundation :
The DCA method builds on cycle analysis and the observation that in trending markets, price consistently remains on one side of a moving average calculated using the dominant cycle period. In contrast, during cyclic markets, price oscillates around this average.
2. Calculation Process :
• We calculate a Simple Moving Average (SMA) using the specified lookback period - a proxy for the dominant cycle period
• We then analyse the proportion of time that price spends above or below this SMA over a lookback window. The theory is that the price should cross the SMA each half cycle, assuming that the dominant cycle period is correct and price follows a sinusoid.
• This lookback window is adaptive, scaling with the dominant cycle period (controlled by the Adaptability setting)
• The different values are standardised and normalised to possess more resolving power and to be more robust to noise.
3. Regime Classification :
• When the normalised proportion exceeds a positive threshold (determined by Sensitivity setting), the market is classified as bullish trending
• When it falls below a negative threshold, the market is classified as bearish trending
• When the proportion remains between these thresholds, the market is classified as cyclic
💮 Volatility Channel
The Volatility Channel method complements the DCA method by focusing on price movement relative to adaptive volatility bands:
1. Theoretical Foundation :
This method is based on the observation that trending markets tend to sustain movement outside of normal volatility ranges, while cyclic markets tend to remain contained within these ranges. By creating adaptive bands that adjust to current market volatility, we can detect when price behaviour indicates a trending or cyclic regime.
2. Calculation Process :
• We first calculate a smooth base channel center using a low pass filter, creating a noise-reduced centreline for price
• True Range (TR) is used to measure market volatility, which is then smoothed and scaled by the deviation factor (controlled by Sensitivity)
• Upper and lower bands are created by adding and subtracting this scaled volatility from the centreline
• Price is smoothed using an adaptive A2RMA filter, which has a very flat and stable behaviour, to reduce noise while preserving trend characteristics
• The position of this smoothed price relative to the bands is continuously monitored
3. Regime Classification :
• When smoothed price moves above the upper band, the market is classified as bullish trending
• When smoothed price moves below the lower band, the market is classified as bearish trending
• When price remains between the bands, the market is classified as cyclic
• The magnitude of price's excursion beyond the bands is used to determine trend strength
4. Adaptive Behaviour :
• The smoothing periods and deviation calculations automatically adjust based on the Adaptability setting
• The measured volatility is calculated over a period proportional to the dominant cycle, ensuring the detector works across different timeframes
• Both the center line and the bands adapt dynamically to changing market conditions, making the detector responsive yet stable
This method provides a unique perspective that complements the DCA approach, with the consensus mechanism synthesising insights from both methods.
🌸 --------- USAGE GUIDE --------- 🌸
💮 Starting with Default Settings
The default settings (Normal for Adaptability and Sensitivity, Weighted Decision for Consensus Mode) provide a balanced starting point suitable for most markets and timeframes. Begin by observing how these settings identify regimes in your preferred instruments.
💮 Finding the Optimal Dominant Cycle
The dominant cycle period is a critical parameter. Here are some approaches to finding an appropriate value:
• Start with typical values, usually something around 25 works well
• Visually identify the average distance between significant peaks and troughs
• Experiment with different values and observe which provides the most stable regime identification
• Consider using cycle-finding indicators to help identify the natural rhythm of your market
💮 Adjusting Parameters
• If you notice too many regime changes → Decrease Sensitivity or increase Consensus requirement
• If regime changes seem delayed → Increase Adaptability
• If a trending regime is not detected, the market is automatically assigned to be in a cyclic state
• If you want to see more nuanced regime transitions → Try the "unconstrained" display mode (note that this will not affect the output to other indicators)
💮 Trading Applications
Regime-Specific Strategies:
• Bullish Trending Regime - Use trend-following strategies, trail stops wider, focus on breakouts, consider holding positions longer, and emphasize buying dips
• Bearish Trending Regime - Consider shorts, tighter stops, focus on breakdown points, sell rallies, implement downside protection, and reduce position sizes
• Cyclic Regime - Apply mean-reversion strategies, trade range boundaries, apply oscillators, target definable support/resistance levels, and use profit-taking at extremes
Strategy Switching:
Create a set of rules for each market regime and switch between them based on the detector's signal. This approach can significantly improve performance compared to applying a single strategy across all market conditions.
GYTS Suite Integration:
• In the GYTS 🎼 Order Orchestrator, select the '🔗 STREAM-int 🧊 Market Regime' as the market regime source
• Note that the consensus output (i.e. not the "unconstrained" display) will be used in this stream
• Create different strategies for trending (bullish/bearish) and cyclic regimes. The GYTS 🎼 Order Orchestrator is specifically made for this.
• The output stream is actually very simple, and can possibly be used in indicators and strategies as well. It outputs 1 for bullish, -1 for bearish and 0 for cyclic regime.
🌸 --------- FINAL NOTES --------- 🌸
💮 Development Philosophy
The Market Regime Detector has been developed with several key principles in mind:
1. Robustness - The detection methods have been rigorously tested across diverse markets and timeframes to ensure reliable performance.
2. Adaptability - The detector automatically adjusts to changing market conditions, requiring minimal manual intervention.
3. Complementarity - Each detection method provides a unique perspective, with the collective consensus being more reliable than any individual method.
4. Intuitiveness - Complex technical parameters have been abstracted into easily understood controls.
💮 Ongoing Refinement
The Market Regime Detector is under continuous development. We regularly:
• Fine-tune parameters based on expanded market data
• Research and integrate new detection methodologies
• Optimise computational efficiency for real-time analysis
Your feedback and suggestions are very important in this ongoing refinement process!
Adaptive Qualitative Quantitative Estimation (QQE) [Loxx]Adaptive QQE is a fixed and cycle adaptive version of the popular Qualitative Quantitative Estimation (QQE) used by forex traders. This indicator includes varoius types of RSI caculations and adaptive cycle measurements to find tune your signal.
Qualitative Quantitative Estimation (QQE):
The Qualitative Quantitative Estimation (QQE) indicator works like a smoother version of the popular Relative Strength Index (RSI) indicator. QQE expands on RSI by adding two volatility based trailing stop lines. These trailing stop lines are composed of a fast and a slow moving Average True Range (ATR).
There are many indicators for many purposes. Some of them are complex and some are comparatively easy to handle. The QQE indicator is a really useful analytical tool and one of the most accurate indicators. It offers numerous strategies for using the buy and sell signals. Essentially, it can help detect trend reversal and enter the trade at the most optimal positions.
Wilders' RSI:
The Relative Strength Index ( RSI ) is a well versed momentum based oscillator which is used to measure the speed (velocity) as well as the change (magnitude) of directional price movements. Essentially RSI , when graphed, provides a visual mean to monitor both the current, as well as historical, strength and weakness of a particular market. The strength or weakness is based on closing prices over the duration of a specified trading period creating a reliable metric of price and momentum changes. Given the popularity of cash settled instruments (stock indexes) and leveraged financial products (the entire field of derivatives); RSI has proven to be a viable indicator of price movements.
RSX RSI:
RSI is a very popular technical indicator, because it takes into consideration market speed, direction and trend uniformity. However, the its widely criticized drawback is its noisy (jittery) appearance. The Jurk RSX retains all the useful features of RSI , but with one important exception: the noise is gone with no added lag.
Rapid RSI:
Rapid RSI Indicator, from Ian Copsey's article in the October 2006 issue of Stocks & Commodities magazine.
RapidRSI resembles Wilder's RSI , but uses a SMA instead of a WilderMA for internal smoothing of price change accumulators.
VHF Adaptive Cycle:
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
Band-pass Adaptive Cycle:
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.
Included:
-Toggle on/off bar coloring
-Customize RSI signal using fixed, VHF Adaptive, and Band-pass Adaptive calculations
-Choose from three different RSI types
Visuals:
-Red/Green line is the moving average of RSI
-Thin white line is the fast trend
-Dotted yellow line is the slow trend
Happy trading!
Bitcoin Polynomial Regression ModelThis is the main version of the script. Click here for the Oscillator part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines. The Oscillator version can be found here.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Bitcoin Polynomial Regression OscillatorThis is the oscillator version of the script. Click here for the other part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Aeon FluxAeon Flux visualizes rolling cumulative realized volatility, as a signal-generating leading indicator.
'Realized volatility' is shorthand for the metric's true output: entropy . The uniformity (or lack of uniformity) of price and volume distributions over a rolling cumulative period, normalized across the asset's full history.
Entropy = x⋅log2(x)−(1−x)⋅log2(1−x)
AEON FLUX VISUALIZES TIME CYCLES
Aeon Flux distills any asset's cyclical pendulum-like behavior, from bull to bear and vice versa, in a visualization that surfaces and isolates the pendulum shift.
As such, Aeon Flux may be the first metric to automate visualization of time cycles.
Time cycles are a soft science and esoteric concept in markets: an opinion, hard to prove or disprove.
They're ultimately just cycles of accumulation & distribution, that tend to recur at rough consistent intervals.
(Aeon Flux does not measure accumulation & distribution directly, those forces are merely implied.)
ENTROPY AS A LEADING INDICATOR
The transitions between state (from bullish to bearish & vice versa) are often good swing entries & exits, across a wide range of high cap risk markets.
ENTROPY AS A DISTRIBUTION MONITOR
Aeon Flux has a track record of detecting higher timeframe macro distribution on the BTC Index.
The signal: two cycles in a row of lower highs, where the cycle high (the highest oscillator print achieved that cycle) is lower than the previous cycle's high.
Invalidation: if the second cycle in a row of lower highs touches the green AND red target areas on its way up, that demonstrates robust volatility, and the distribution signal is invalidated.
ALERTS & NOTIFICATIONS
Alerts are enabled for swing long & short signals. Automating alerts to monitor distribution are a potential enhancement for future iterations of the script.
Ehlers Autocorrelation Periodogram [Loxx]Ehlers Autocorrelation Periodogram contains two versions of Ehlers Autocorrelation Periodogram Algorithm. This indicator is meant to supplement adaptive cycle indicators that myself and others have published on Trading View, will continue to publish on Trading View. These are fast-loading, low-overhead, streamlined, exact replicas of Ehlers' work without any other adjustments or inputs.
Versions:
- 2013, Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers
- 2016, TASC September, "Measuring Market Cycles"
Description
The Ehlers Autocorrelation study is a technical indicator used in the calculation of John F. Ehlers’s Autocorrelation Periodogram. Its main purpose is to eliminate noise from the price data, reduce effects of the “spectral dilation” phenomenon, and reveal dominant cycle periods. The spectral dilation has been discussed in several studies by John F. Ehlers; for more information on this, refer to sources in the "Further Reading" section.
As the first step, Autocorrelation uses Mr. Ehlers’s previous installment, Ehlers Roofing Filter, in order to enhance the signal-to-noise ratio and neutralize the spectral dilation. This filter is based on aerospace analog filters and when applied to market data, it attempts to only pass spectral components whose periods are between 10 and 48 bars.
Autocorrelation is then applied to the filtered data: as its name implies, this function correlates the data with itself a certain period back. As with other correlation techniques, the value of +1 would signify the perfect correlation and -1, the perfect anti-correlation.
Using values of Autocorrelation in Thermo Mode may help you reveal the cycle periods within which the data is best correlated (or anti-correlated) with itself. Those periods are displayed in the extreme colors (orange) while areas of intermediate colors mark periods of less useful cycles.
What is an adaptive cycle, and what is the Autocorrelation Periodogram Algorithm?
From his Ehlers' book mentioned above, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator.This look-back period is commonly a fixed value. However, since the measured cycle period is changing, as we have seen in previous chapters, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
How to use this indicator
The point of the Ehlers Autocorrelation Periodogram Algorithm is to dynamically set a period between a minimum and a maximum period length. While I leave the exact explanation of the mechanic to Dr. Ehlers’s book, for all practical intents and purposes, in my opinion, the punchline of this method is to attempt to remove a massive source of overfitting from trading system creation–namely specifying a look-back period. SMA of 50 days? 100 days? 200 days? Well, theoretically, this algorithm takes that possibility of overfitting out of your hands. Simply, specify an upper and lower bound for your look-back, and it does the rest. In addition, this indicator tells you when its best to use adaptive cycle inputs for your other indicators.
Usage Example 1
Let's say you're using "Adaptive Qualitative Quantitative Estimation (QQE) ". This indicator has the option of adaptive cycle inputs. When the "Ehlers Autocorrelation Periodogram " shows a period of high correlation that adaptive cycle inputs work best during that period.
Usage Example 2
Check where the dominant cycle line lines, grab that output number and inject it into your other standard indicators for the length input.
