Fib Swing Counter [A@J]Fib Swing Counter — Trade the Rhythm of the Market
This indicator automatically marks swing highs and lows with Fibonacci numbers (1, 1, 2, 3, 5, 8, 13, …), helping you track market structure, count price legs, and identify hidden order behind price movement.
Core Features:
Auto-detects pivots and labels them with the Fibonacci sequence.
Alternates between highs and lows — no repeats, no noise.
Custom reset time — start your count at the New York session open, a major news event, or your own strategic point.
Clean and simple visual display, adaptable to your chart style.
How Traders Use It:
Liquidity cycles: Spot when price is expanding or contracting in Fibonacci-driven waves.
Entry timing: Wait for setups to align with a key Fib count.
Confluence with other tools: Combine with ICT concepts, SMT divergence, supply/demand blocks, or Fibonacci retracements.
Session-based analysis: Restart the sequence everyMarket Open, Midnight, New York or London open to study price behavior from a fresh anchor point.
Whether you're into smart money concepts, price action, or algorithmic patterns, this tool adds a rhythmic layer to your analysis — because markets move with sequence, not randomness.
Search in scripts for "Cycle"
Bitcoin Logarithmic Regression BandsOverview
This indicator displays logarithmic regression bands for Bitcoin. Logarithmic regression is a statistical method used to model data where growth slows down over time. I initially created these bands in 2019 using a spreadsheet, and later coded them in TradingView in 2021. Over time, the bands proved effective at capturing Bitcoin's bull market peaks and bear market lows. In 2024, I decided to share this indicator because I believe these logarithmic regression bands offer the best fit for the Bitcoin chart.
How It Works
The logarithmic regression lines are fitted to the Bitcoin (BTCUSD) chart using two key factors: the 'a' factor (slope) and the 'b' factor (intercept). The two lines in the upper and lower bands share the same 'a' factor, but I adjust the 'b' factor by 0.2 to more accurately capture the bull market peaks and bear market lows. The formula for logaritmic regression is 10^((a * ln) - b).
How to Use the Logarithmic Regression Bands
1. Lower Band (Support Band):
The two lines in the lower band create a potential support area for Bitcoin’s price. Historically, Bitcoin’s price has always found its lows within this band during past market cycles. When the price is within the lower band, it suggests that Bitcoin is undervalued and could be set for a rebound.
2. Upper Band (Resistance Band):
The two lines in the upper band create a potential resistance area for Bitcoin’s price. Bitcoin has consistently reached its highs in this band during previous market cycles. If the price is within the upper band, it indicates that Bitcoin is overvalued, and a potential price correction may be imminent.
Use Cases
- Price Bottoming:
Bitcoin tends to bottom out at the lower band before entering a prolonged bull market or a period of sideways movement.
- Price Topping:
In reverse, Bitcoin tends to top out at the upper band before entering a bear market phase.
- Profitable Strategy:
Buying at the lower band and selling at the upper band can be a profitable trading strategy, as these bands often indicate key price levels for Bitcoin’s market cycles.
Altcoin Season Index - AdamThe "Altcoin Season Index" is a powerful tool for understanding market dynamics between Bitcoin and altcoins. This indicator helps traders identify whether the market is favoring Bitcoin or if it has shifted to favor altcoins. Understanding this can be crucial for making informed decisions about allocating your investments within the crypto market.
Overview of the Altcoin Season Index
The Altcoin Season Index calculates how well the top 10 altcoins are performing compared to Bitcoin over a given period. It helps traders determine if they are currently in an "Altcoin Season" or a "Bitcoin Season." The indicator gives a score from 0 to 100, representing the percentage of altcoins outperforming Bitcoin over a specific time window. When many altcoins are performing better than Bitcoin, it suggests a possible "Altcoin Season," whereas the opposite may indicate a period of Bitcoin dominance.
Key Features:
1. Top 10 Altcoin Performance Comparison: The indicator evaluates the performance of the top 10 altcoins compared to Bitcoin. It provides a clear view of how well altcoins are doing relative to the market leader, Bitcoin.
2. Customizable Performance Period: The period of analysis is adjustable, allowing users to set a specific timeframe, typically in days, to evaluate the relative performance of altcoins versus Bitcoin.
3. Dynamic Replacement of Altcoins: The indicator includes a feature to replace the last coin in the list, ensuring that the data stays relevant as market conditions change. For example, when a new altcoin enters the top 10 in terms of market cap, the indicator can replace an older coin that is falling out of the top ranks.
4. Threshold Indicators: The indicator uses predefined thresholds to determine and visualize whether it is an "Altcoin Season" or a "Bitcoin Season":
- A value above 75 indicates an Altcoin Season, suggesting that altcoins are outperforming Bitcoin.
- A value below 25 suggests Bitcoin dominance, where Bitcoin is outperforming the majority of altcoins.
How the Indicator Works:
1. Performance Calculation: The indicator calculates the percentage change in price for each of the top 10 altcoins and Bitcoin over a given number of days. The comparison is made by looking at how much each asset's price has changed over the specified period.
2. Altcoin Season Calculation: The indicator counts the number of altcoins that have outperformed Bitcoin during the given period. The result is then expressed as a percentage, known as the Altcoin Season Index. If 8 out of 10 altcoins are outperforming Bitcoin, the index will be 80%, signaling a strong altcoin season.
3. Visual Representation: The indicator is visualized on a separate panel within TradingView, showing the Altcoin Season Index over time. Additionally, thresholds are marked on the chart, and background colors are applied to provide visual cues:
- Red Background: When the Altcoin Season Index is above 75, indicating a strong altcoin season.
- Blue Background: When the Altcoin Season Index is below 25, indicating Bitcoin dominance.
Practical Use:
- Identify Market Cycles: Traders can use this indicator to identify when the market is moving into or out of an altcoin season. This can help traders decide whether to rotate capital into altcoins or Bitcoin.
- Investment Strategy Adjustment: During altcoin seasons, altcoins tend to outperform Bitcoin. Traders might allocate more of their portfolio to promising altcoins. Conversely, during Bitcoin-dominant periods, shifting investments towards Bitcoin could provide more stability.
- Support Technical Analysis: This indicator complements other forms of technical analysis by providing macro-level insights about market direction and which asset classes might be favored.
Example Usage:
Imagine that the Altcoin Season Index is currently at 80%. This means that 8 of the top 10 altcoins have performed better than Bitcoin over the selected period. This strong altcoin performance suggests that the market has entered an "Altcoin Season." A trader observing this might consider reallocating funds towards altcoins to capitalize on the positive momentum.
Alternatively, if the index is at 20%, only 2 out of the top 10 altcoins are outperforming Bitcoin, indicating that Bitcoin is currently the stronger player. In this scenario, traders may choose to prioritize Bitcoin or maintain a more conservative portfolio allocation.
Note:
This indicator includes a feature to replace the bottom-ranked altcoin (typically a coin that falls out of the top 10) with a new altcoin when market conditions change. This ensures that the analysis remains relevant by focusing on the top-performing assets by market capitalization.
Conclusion:
The Altcoin Season Index is a helpful tool for understanding broader trends in the cryptocurrency market and making strategic investment decisions. By monitoring which assets are performing better, traders can adapt their strategies and make more informed choices, particularly during shifts in market sentiment.
Please leave your feedback or contributions if there are any inaccuracies in my indicator. Thank you!
GKD-C PA Adaptive Fisher Transform [Loxx]The Giga Kaleidoscope GKD-C PA Adaptive Fisher Transform is a confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ GKD-C PA Adaptive Fisher Transform
Phase Accumulation Adaptive Fisher Transform is an adaptive Fisher Transform using a modified version of Ehlers Phase Accumulation Cycle Period. This version of Phase Accumulation Cylce Period accepts as inputs: 1) total number of cycles you wish to inject into the calculation, this works as a multiplier so the higher this number, the longer the period output; 2) filter is to change the alpha value of the final smother before returning the period output.
What is the Phase Accumulation Cycle?
The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample.
The phase accumulation method of cycle measurement always uses one full cycle’s worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio.
What is Fisher Transform?
The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution.
The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, and the Average Directional Index (ADX).
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker CC Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Advance Trend Pressure as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
? Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
Fast Fourier Transform [ScorsoneEnterprises]The SCE Fast Fourier Transform (FFT) is a tool designed to analyze periodicities and cyclical structures embedded in price. This is a Fourier analysis to transform price data from the time domain into the frequency domain, showing the rhythmic behaviors that are otherwise invisible on standard charts.
Instead of merely observing raw prices, this implementation applies the FFT on the logarithmic returns of the asset:
Log Return(𝑚) = log(close / close )
This ensures stationarity and stabilizes variance, making the analysis statistically robust and less influenced by trends or large price swings.
For a user-defined lookback window 𝑁:
Each frequency component 𝑘 is computed by summing real and imaginary projections of log-returns multiplied by complex exponential functions:
𝑒^−𝑖𝜃 = cos(𝜃)−𝑖sin(𝜃)
where:
θ = 2πkm / N
he result is the magnitude spectrum, calculated as:
Magnitude(𝑘) = sqrt(Real_Sum(𝑘)^2 + Imag_Sum(𝑘)^2)
This spectrum represents the strength of oscillations at each frequency over the lookback period, helping traders identify dominant cycles.
Visual Analysis & Interpretation
To give traders context for the FFT spectrum’s values, this script calculates:
25th Percentile (Purple Line)
Represents relatively low cyclical intensity.
Values below this threshold may signal quiet, noisy, or trendless periods.
75th Percentile (Red Line)
Represents heightened cyclical dominance.
Values above this threshold may indicate significant periodic activity and potential trend formation or rhythm in price action.
The FFT magnitude of the lowest frequency component (index 0) is plotted directly on the chart in teal. Observing how this signal fluctuates relative to its percentile bands provides a dynamic measure of cyclical market activity.
Chart examples
In this NYSE:CL chart, we see the regime of the price accurately described in the spectral analysis. We see the price above the 75th percentile continue to trend higher until it breaks back below.
In long trending markets like NYSE:PL has been, it can give a very good explanation of the strength. There was confidence to not switch regimes as we never crossed below the 75th percentile early in the move.
The script is also usable on the lower timeframes. There is no difference in the usability from the different timeframes.
Script Parameters
Lookback Value (N)
Default: 30
Defines how many bars of data to analyze. Larger N captures longer-term cycles but may smooth out shorter-term oscillations.
APA-Adaptive, Ehlers Early Onset Trend [Loxx]APA-Adaptive, Ehlers Early Onset Trend is Ehlers Early Onset Trend but with Autocorrelation Periodogram Algorithm dominant cycle period input.
What is Ehlers Early Onset Trend?
The Onset Trend Detector study is a trend analyzing technical indicator developed by John F. Ehlers , based on a non-linear quotient transform. Two of Mr. Ehlers' previous studies, the Super Smoother Filter and the Roofing Filter, were used and expanded to create this new complex technical indicator. Being a trend-following analysis technique, its main purpose is to address the problem of lag that is common among moving average type indicators.
The Onset Trend Detector first applies the EhlersRoofingFilter to the input data in order to eliminate cyclic components with periods longer than, for example, 100 bars (default value, customizable via input parameters) as those are considered spectral dilation. Filtered data is then subjected to re-filtering by the Super Smoother Filter so that the noise (cyclic components with low length) is reduced to minimum. The period of 10 bars is a default maximum value for a wave cycle to be considered noise; it can be customized via input parameters as well. Once the data is cleared of both noise and spectral dilation, the filter processes it with the automatic gain control algorithm which is widely used in digital signal processing. This algorithm registers the most recent peak value and normalizes it; the normalized value slowly decays until the next peak swing. The ratio of previously filtered value to the corresponding peak value is then quotiently transformed to provide the resulting oscillator. The quotient transform is controlled by the K coefficient: its allowed values are in the range from -1 to +1. K values close to 1 leave the ratio almost untouched, those close to -1 will translate it to around the additive inverse, and those close to zero will collapse small values of the ratio while keeping the higher values high.
Indicator values around 1 signify uptrend and those around -1, downtrend.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
Bitcoin Power Law [LuxAlgo]The Bitcoin Power Law tool is a representation of Bitcoin prices first proposed by Giovanni Santostasi, Ph.D. It plots BTCUSD daily closes on a log10-log10 scale, and fits a linear regression channel to the data.
This channel helps traders visualise when the price is historically in a zone prone to tops or located within a discounted zone subject to future growth.
🔶 USAGE
Giovanni Santostasi, Ph.D. originated the Bitcoin Power-Law Theory; this implementation places it directly on a TradingView chart. The white line shows the daily closing price, while the cyan line is the best-fit regression.
A channel is constructed from the linear fit root mean squared error (RMSE), we can observe how price has repeatedly oscillated between each channel areas through every bull-bear cycle.
Excursions into the upper channel area can be followed by price surges and finishing on a top, whereas price touching the lower channel area coincides with a cycle low.
Users can change the channel areas multipliers, helping capture moves more precisely depending on the intended usage.
This tool only works on the daily BTCUSD chart. Ticker and timeframe must match exactly for the calculations to remain valid.
🔹 Linear Scale
Users can toggle on a linear scale for the time axis, in order to obtain a higher resolution of the price, (this will affect the linear regression channel fit, making it look poorer).
🔶 DETAILS
One of the advantages of the Power Law Theory proposed by Giovanni Santostasi is its ability to explain multiple behaviors of Bitcoin. We describe some key points below.
🔹 Power-Law Overview
A power law has the form y = A·xⁿ , and Bitcoin’s key variables follow this pattern across many orders of magnitude. Empirically, price rises roughly with t⁶, hash-rate with t¹² and the number of active addresses with t³.
When we plot these on log-log axes they appear as straight lines, revealing a scale-invariant system whose behaviour repeats proportionally as it grows.
🔹 Feedback-Loop Dynamics
Growth begins with new users, whose presence pushes the price higher via a Metcalfe-style square-law. A richer price pool funds more mining hardware; the Difficulty Adjustment immediately raises the hash-rate requirement, keeping profit margins razor-thin.
A higher hash rate secures the network, which in turn attracts the next wave of users. Because risk and Difficulty act as braking forces, user adoption advances as a power of three in time rather than an unchecked S-curve. This circular causality repeats without end, producing the familiar boom-and-bust cadence around the long-term power-law channel.
🔹 Scale Invariance & Predictions
Scale invariance means that enlarging the timeline in log-log space leaves the trajectory unchanged.
The same geometric proportions that described the first dollar of value can therefore extend to a projected million-dollar bitcoin, provided no catastrophic break occurs. Institutional ETF inflows supply fresh capital but do not bend the underlying slope; only a persistent deviation from the line would falsify the current model.
🔹 Implications
The theory assigns scarcity no direct role; iterative feedback and the Difficulty Adjustment are sufficient to govern Bitcoin’s expansion. Long-term valuation should focus on position within the power-law channel, while bubbles—sharp departures above trend that later revert—are expected punctuations of an otherwise steady climb.
Beyond about 2040, disruptive technological shifts could alter the parameters, but for the next order of magnitude the present slope remains the simplest, most robust guide.
Bitcoin behaves less like a traditional asset and more like a self-organising digital organism whose value, security, and adoption co-evolve according to immutable power-law rules.
🔶 SETTINGS
🔹 General
Start Calculation: Determine the start date used by the calculation, with any prior prices being ignored. (default - 15 Jul 2010)
Use Linear Scale for X-Axis: Convert the horizontal axis from log(time) to linear calendar time
🔹 Linear Regression
Show Regression Line: Enable/disable the central power-law trend line
Regression Line Color: Choose the colour of the regression line
Mult 1: Toggle line & fill, set multiplier (default +1), pick line colour and area fill colour
Mult 2: Toggle line & fill, set multiplier (default +0.5), pick line colour and area fill colour
Mult 3: Toggle line & fill, set multiplier (default -0.5), pick line colour and area fill colour
Mult 4: Toggle line & fill, set multiplier (default -1), pick line colour and area fill colour
🔹 Style
Price Line Color: Select the colour of the BTC price plot
Auto Color: Automatically choose the best contrast colour for the price line
Price Line Width: Set the thickness of the price line (1 – 5 px)
Show Halvings: Enable/disable dotted vertical lines at each Bitcoin halving
Halvings Color: Choose the colour of the halving lines
bands ⚡ What This Script Does
This is a structured trading system specifically designed for navigating Bitcoin cycles and identifying higher-probability buy setups.
It is not a simple combination of public indicators instead, it applies a rules-based logic to adapt signals dynamically depending on the current market phase (bull/bear), while also using a triple confirmation framework (macro trend + volatility bands + buy signals).
This approach aims to reduce false signals and align trading decisions with Bitcoin’s well-known cyclical behavior.
⚡ Core Concept & Components
The system combines three complementary elements:
A macro trend filter band (red/green), shown at the bottom of the chart, representing Bitcoin’s macro trend environment.
Adaptive volatility bands using advanced smoothing techniques including HMA, KAMA, WVMA, combined with moving average (MA) and average true range (ATR) logic to capture dynamic “cheap” and “expensive” price zones. These bands adapt to Bitcoin’s volatility structure better than standard Bollinger Bands or SMA plus ATR setups.
Multi-timeframe RSI-based Buy Signals on 8h, 1D, 1W, and 1M timeframes historically calibrated for Bitcoin cycles.
These components work together through a rules-based process, dynamically adapting signal validity depending on the macro trend state.
⚡ Signal System and how to use
The red and green band at the bottom of the chart represents Bitcoin’s macro trend environment:
Light Green → Likely start of a bull market
Dark Green → Market is bullish but becoming extended; potentially nearing a local top
Red → Bear market conditions
Our trading approach uses four distinct BUY signals, depending on the market phase:
Red (8h) → weak buy signal
Yellow (1D) → medium buy signal
Green (1W) → strong buy signal
Blue (1M) → strongest buy signal
However, Day Trading and Swing Trading signals are automatically blocked during bear markets (Red Band).
Reason: low timeframe signals (1 minute to 1 day) tend to perform poorly in bear markets, as major bottoms typically form on higher timeframes (1 week or 1 month).
Therefore, during Red Band conditions, only Buy Bear Market and Buy Recession signals remain active.
to use it correctly you must go to configuration of the indicator, section input and enable 4 buy signals and check every day timefarme 8h 1d 1w and 1m.
⚡ Invalidation Conditions
To exit the bear market, the system includes an invalidation condition:
If the price closes above a specific SMA on a defined timeframe, the Red Band switches to Green → signaling a potential market recovery.
Additionally, for the Red Band to activate initially, the system requires that:
Price must break below a specific Hull Moving Average (HMA) on a defined timeframe and length.
⚡ Why These 3 Indicators Work Well Together
If the band is green (bull market conditions) and a Buy signal (any color) appears → it is generally safer to buy in a bull market than in a bear market.
(I’m trying to apply the famous phrase "the trend is your friend" in this trading indicator and trading strategy.)
If the price is also touching the lower green band, and a Buy signal appears → the buy becomes even more reliable, as you are combining big trend plus band support plus signal confirmation.
This gives you triple confirmation:
Band color plus band level touch plus Buy signal → increasing the probability that the trade is going to work.
By combining:
Blocking low timeframe signals during bear markets
Using a clear invalidation point to detect recovery
Requiring a structural break via HMA to enter a bear market phase, and requiring a break above a specific SMA (length and timeframe) to enter a bull market
Applying a triple confirmation logic when conditions are favorable
→ this framework helps you navigate Bitcoin markets more securely and profitably than using unfiltered signals alone.
⚡ Why It’s Not a Simple Mashup
The logic of the system is not just an overlay of RSI, moving averages and bands:
It applies a structured "state machine" logic:
Macro Band determines which signals are allowed.
Band-level touches condition the strength of signals.
Triple confirmation (macro trend plus band level plus signal) governs high-probability setups.
Invalidation points (SMA breakouts) dynamically switch macro state, ensuring no lagging bull signals in a bear market or vice versa.
This makes the system superior to using public domain components in isolation, as those do not provide dynamic signal filtering nor respect Bitcoin’s macro cyclicality explicitly.
⚡ Why This is Invite-Only
This script reflects deep backtesting and original integration of state logic specific to Bitcoin cycles, I also tried to choose the the correct conditions and invalidation points by using hma and smas in specific timeframes and lengths,
it has a system that also block many wrong buy signals during bearmarkets.
It encapsulates a rules-based trading process which goes beyond simply combining public indicators. The aim is to provide traders with a coherent framework that reduces false signals, adapts to bitcoin cycles, and promotes risk-aware participation in Bitcoin markets.
I also refined the line aesthetics and thicknesses to improve chart readability and help users quickly identify key levels.
⚡ Disclaimer
This is an analytical tool, not financial advice. Use with appropriate risk management and as part of a broader trading strategy.
Past positive results this indicator achieved do NOT guarantee future success !!
Per TradingView rules:
The logic is described sufficiently so that traders understand what it does and how it works.
This is not a simple mashup, but an original framework applying structured logic to Bitcoin macro trading.
This is a COMPLEMENTARY tool designed for use by my existing clients who are already familiar with my trading strategy and risk management approach. If you are not one of my clients or do not know my trading strategy, please do NOT request access or attempt to purchase it !!
⚡ Conformance
This description is written to comply with TradingView’s script publishing rules (tradingview.com/pine-script-docs/en/v5/writing/Publishing.html), as per recent moderator feedback.
If further clarification is required, I welcome additional feedback.
Global M2 YoY % Increase signalThe script produces a signal each time the global M2 increases more than 2.5%. This usually coincides with bitcoin prices pumps, except when it is late in the business cycle or the bitcoin price / halving cycle.
It leverages dylanleclair Global M2 YoY % change, with several modifications:
adding a 10 week lead at the YoY Change plot for better visibility, so that the bitcoin pump moreless coincides with the YoY change.
signal increases > 2.5 in Global M2 at the point at which they occur with a green triangle up.
Moon+Lunar Cycle Vertical Delineation & Projection
Automatically highlights the exact candle in which Moonphase shifts occur.
Optionally including shifts within the Microphases of the total Lunar Cycle.
This allow traders to pre-emptively identify time-based points of volatility,
focusing on mean-reversion; further simplified via the use of projections.
Projections are calculated via candle count, values displayed in "Debug";
these are useful in understanding the function & underlying mechanics.
CCI with Signals & Divergence [AIBitcoinTrend]👽 CCI with Signals & Divergence (AIBitcoinTrend)
The Hilbert Adaptive CCI with Signals & Divergence takes the traditional Commodity Channel Index (CCI) to the next level by dynamically adjusting its calculation period based on real-time market cycles using Hilbert Transform Cycle Detection. This makes it far superior to standard CCI, as it adapts to fast-moving trends and slow consolidations, filtering noise and improving signal accuracy.
Additionally, the indicator includes real-time divergence detection and an ATR-based trailing stop system, helping traders identify potential reversals and manage risk effectively.
👽 What Makes the Hilbert Adaptive CCI Unique?
Unlike the traditional CCI, which uses a fixed-length lookback period, this version automatically adjusts its lookback period using Hilbert Transform to detect the dominant cycle in the market.
✅ Hilbert Transform Adaptive Lookback – Dynamically detects cycle length to adjust CCI sensitivity.
✅ Real-Time Divergence Detection – Instantly identifies bullish and bearish divergences for early reversal signals.
✅ Implement Crossover/Crossunder signals tied to ATR-based trailing stops for risk management
👽 The Math Behind the Indicator
👾 Hilbert Transform Cycle Detection
The Hilbert Transform estimates the dominant market cycle length based on the frequency of price oscillations. It is computed using the in-phase and quadrature components of the price series:
tp = (high + low + close) / 3
smooth = (tp + 2 * tp + 2 * tp + tp ) / 6
detrender = smooth - smooth
quadrature = detrender - detrender
inPhase = detrender + quadrature
outPhase = quadrature - inPhase
instPeriod = 0.0
deltaPhase = math.abs(inPhase - inPhase ) + math.abs(outPhase - outPhase )
instPeriod := nz(3.25 / deltaPhase, instPeriod )
dominantCycle = int(math.min(math.max(instPeriod, cciMinPeriod), 500))
Where:
In-Phase & Out-Phase Components are derived from a detrended version of the price series.
Instantaneous Frequency measures the rate of cycle change, allowing the CCI period to adjust dynamically.
The result is bounded within a user-defined min/max range, ensuring stability.
👽 How Traders Can Use This Indicator
👾 Divergence Trading Strategy
Bullish Divergence Setup:
Price makes a lower low, while CCI forms a higher low.
Buy signal is confirmed when CCI shows upward momentum.
Bearish Divergence Setup:
Price makes a higher high, while CCI forms a lower high.
Sell signal is confirmed when CCI shows downward momentum.
👾 Trailing Stop & Signal-Based Trading
Bullish Setup:
✅ CCI crosses above -100 → Buy signal.
✅ A bullish trailing stop is placed at Low - (ATR × Multiplier).
✅ Exit if the price crosses below the stop.
Bearish Setup:
✅ CCI crosses below 100 → Sell signal.
✅ A bearish trailing stop is placed at High + (ATR × Multiplier).
✅ Exit if the price crosses above the stop.
👽 Why It’s Useful for Traders
Hilbert Adaptive Period Calculation – No more fixed-length periods; the indicator dynamically adapts to market conditions.
Real-Time Divergence Alerts – Helps traders anticipate market reversals before they occur.
ATR-Based Risk Management – Stops automatically adjust based on volatility.
Works Across Multiple Markets & Timeframes – Ideal for stocks, forex, crypto, and futures.
👽 Indicator Settings
Min & Max CCI Period – Defines the adaptive range for Hilbert-based lookback.
Smoothing Factor – Controls the degree of smoothing applied to CCI.
Enable Divergence Analysis – Toggles real-time divergence detection.
Lookback Period – Defines the number of bars for detecting pivot points.
Enable Crosses Signals – Turns on CCI crossover-based trade signals.
ATR Multiplier – Adjusts trailing stop sensitivity.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
AHR999X IndexAHR999X Index - A Tool to Watch BITSTAMP:BTCUSD Bitcoin Tops
The AHR999X Index is designed as an extension of the well-known AHR999 Index, specifically to help identify Bitcoin's market tops. This index combines two critical components:
200-Day Fixed Investment Cost:
The average cost if you invested a fixed amount into Bitcoin every day over the last 200 days (using a geometric mean).
Growth Estimate:
A price estimate derived from a logarithmic regression model based on Bitcoin's age.
The formula for AHR999X is:
AHR999X = (Bitcoin Price ÷ 200-Day Fixed Investment Cost) × (Bitcoin Price ÷ Growth Estimate) × 3
How to Interpret AHR999X
Above 8: Accumulation Zone – Bitcoin is historically undervalued.
Between 0.45 and 8: Neutral Zone – Bitcoin is within a reasonable price range.
Below 0.45: Exit Zone – Historically signals market tops and high-risk areas.
A Cycle Observation
One important point to note:
The bottom value of AHR999X increases with every Bitcoin market cycle.
This reflects Bitcoin's long-term price appreciation and diminishing volatility over time.
Altcoins vs BTC Market Cap HeatmapAltcoins vs BTC Market Cap Heatmap
"Ground control to major Tom" 🌙 👨🚀 🚀
This indicator provides a visual heatmap for tracking the relationship between the market cap of altcoins (TOTAL3) and Bitcoin (BTC). The primary goal is to identify potential market cycle tops and bottoms by analyzing how the TOTAL3 market cap (all cryptocurrencies excluding Bitcoin and Ethereum) compares to Bitcoin’s market cap.
Key Features:
• Market Cap Ratio: Plots the ratio of TOTAL3 to BTC market caps to give a clear visual representation of altcoin strength versus Bitcoin.
• Heatmap: Colors the background red when altcoins are overheating (TOTAL3 market cap equals or exceeds BTC) and blue when altcoins are cooling (TOTAL3 market cap is half or less than BTC).
• Threshold Levels: Includes horizontal lines at 1 (Overheated), 0.75 (Median), and 0.5 (Cooling) for easy reference.
• Alerts: Set alert conditions for when the ratio crosses key levels (1.0, 0.75, and 0.5), enabling timely notifications for potential market shifts.
How It Works:
• Overheated (Ratio ≥ 1): Indicates that the altcoin market cap is on par or larger than Bitcoin's, which could signal a top in the cycle.
• Cooling (Ratio < 0.5): Suggests that the altcoin market cap is half or less than Bitcoin's, potentially signaling a market bottom or cooling phase.
• Median (Ratio ≈ 0.75): A midpoint that provides insight into the market's neutral zone.
Use this tool to monitor market extremes and adjust your strategy accordingly when the altcoin market enters overheated or cooling phases.
SeasonsThis code represents a seasonal indicator that has a number of unique functions to help traders better understand the market and make informed decisions. Let's take a closer look at each of them:
1. **Chart background shading for each season:** This function allows you to visually see seasonal changes in the market. You'll be able to easily track how the market changes in different seasons, thanks to the color labeling: blue for winter, green for summer, orange for autumn, and yellow for spring.
2. **Vertical markings for each month:** Additional markers on the chart help you orient yourself in time and better understand price dynamics throughout the year. This is especially useful when analyzing seasonal changes and identifying market cyclicality.
3. **Halving timers:** Connecting halving timers on the chart allows you to track important events, such as the reduction of bitcoin mining rewards. Knowing the timing of halving can be a key moment for decision-making and can affect asset prices.
These functions help traders better analyze the market, identify trends and cyclicality, and optimize their trading strategy. Use this indicator in your trading practice to unleash its full potential and reach new heights in your trading career. Don't miss the opportunity to improve your results - apply the seasonal indicator today!
The seasonal indicator is a powerful tool for traders, helping them analyze the market and make informed decisions based on seasonal and cyclical changes. Here are a few reasons why using this indicator can be advantageous:
1. **Identifying seasonal trends:** The seasonal indicator helps identify seasonal trends in the market, such as changes in activity during different seasons or months. For example, some markets may be more volatile or predictable at certain times of the year, and knowing these trends can help in making decisions about entering or exiting positions.
2. **Optimizing trading strategy:** Understanding seasonal changes in the market allows traders to optimize their trading strategy based on the time of year. For example, they may adjust their risk management approaches or choose specific types of trades according to the current season.
3. **Predicting market cyclicality:** The seasonal indicator can also help in predicting market cyclicality and identifying recurring price movement patterns. This enables traders to build their strategies based on past market behavior within specific time intervals.
How to use the seasonal indicator:
1. **Study seasonal changes:** Use the indicator to analyze how the market changes throughout the year. Pay attention to changes in volatility, trading volumes, and price directions depending on the season.
2. **Optimize trading strategy:** Use the data obtained to optimize your trading strategy. Consider entering or exiting positions within specific time intervals to account for seasonal factors.
3. **Predict cyclicality:** Analyze past market behavior using the seasonal indicator to identify cyclicality and recurring patterns. This will help you make more informed decisions based on expected price movements in the future.
Ultimately, using the seasonal indicator allows traders to better understand the market, adapt their strategies, and make more informed decisions based on seasonal and cyclical changes.
All elements on the chart of a particular color will be attributed to the corresponding season. For example, trend lines or levels marked in blue will be associated with winter.
______________________________________________________
Winter
Explanation of price movement during the winter season:
1. Number 1 and the blue line denote the maximum price of Bitcoin. Note that they always form at the peaks, which is consistent.
2. Number 2 and the blue line represent the minimum price specifically during the winter period. This is indeed the minimum price and the bottom point in the cycle.
3. Number 3 and the blue line indicate a local maximum after the breakthrough, after which the price starts to rise towards line number 1, which acts as global resistance.
4. Number 4 denotes the last winter cycle before the breakthrough of the global maximum. It should be noted that in 2017, the resistance was not broken immediately - first in spring, and then at the beginning of 2018, the maximum was set, and the asset growth occurred in winter.
Additionally, it's worth noting that numbers 1 form the maximum, numbers 2 form the minimum, and since the trend is descending, I have marked its line in blue.
______________________________________________________
Summer
Now let's consider the price behavior chart for the summer. To make the situation clearer, I've left a descending trend in blue on the graph. I reiterate that the elements shown in green on the graph pertain specifically to the summer period.
1. Number 1 on the graph denotes the first summer period! The price during this period remains within a narrow range 90% of the time; however, it's worth noting that impulsive movements can occur at the beginning, middle, or end. Thus, 90% of the time the price is in a low volatility zone, while the remaining percentage is in a high volatility zone.
2. Number 2 on the graph represents the second summer period, where a pattern is observed: the price tends to rise at the beginning of the summer period and fall towards the end. Therefore, I've marked this time with an arc, and there's a pattern to it. It's worth noting that during the period of the descending trend from 2014 to 2016, the situation after the downward trend differs from the situation in 2018 and 2023, when changes in the arrangement of this situation occur after the breakout of the descending trend based on wave analysis and the price of the asset itself.
3. Number 3 represents the third summer period! During this period, the price movement direction is upward and then downward, forming a correction in the upward trend. It should be noted that in this movement, all lows gradually rise, while highs renew all previous local highs of the asset price. This period exhibits increased volatility and impulsive movements, with the asset price mostly staying within a range of minimal volatility, with volatility not exceeding 1-2% on some stretches.
4. Under number 4, the fourth summer period is indicated, which has an overall upward direction. In this period, the movement is aggressively upward. Starting from the first month until the middle of summer, the price moves downward, forming a correction in the upward trend. Then, during the next month, the price moves aggressively upward, renewing price highs. Volatility in this period is anomalously high, resembling a hot July summer.
Additionally, based on the price movement in the summer period, we can assume that fractals are evident here, which we can use to our advantage for profit.
______________________________________________________
Shark Trading - We urge all traders to delve deeper into this indicator and incorporate it into their trading practices. It can become an invaluable aid in market analysis and help traders reach new heights in their trading endeavors.
Adaptive, Double Jurik Filter Moving Average (AJFMA) [Loxx]Adaptive, Double Jurik Filter Moving Average (AJFMA) is moving average like Jurik Moving Average but with the addition of double smoothing and adaptive length (Autocorrelation Periodogram Algorithm) and power/volatility {Juirk Volty) inputs to further reduce noise and identify trends.
What is Jurik Volty?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
That's why investors, banks and institutions worldwide ask for the Jurik Research Moving Average ( JMA ). You may apply it just as you would any other popular moving average. However, JMA's improved timing and smoothness will astound you.
What is adaptive Jurik volatility?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
Included
- Double calculation of AJFMA for even smoother results
Adaptive Look-back/Volatility Phase Change Index on Jurik [Loxx]Adaptive Look-back, Adaptive Volatility Phase Change Index on Jurik is a Phase Change Index but with adaptive length and volatility inputs to reduce phase change noise and better identify trends. This is an invese indicator which means that small values on the oscillator indicate bullish sentiment and higher values on the oscillator indicate bearish sentiment
What is the Phase Change Index?
Based on the M.H. Pee's TASC article "Phase Change Index".
Prices at any time can be up, down, or unchanged. A period where market prices remain relatively unchanged is referred to as a consolidation. A period that witnesses relatively higher prices is referred to as an uptrend, while a period of relatively lower prices is called a downtrend.
The Phase Change Index (PCI) is an indicator designed specifically to detect changes in market phases.
This indicator is made as he describes it with one deviation: if we follow his formula to the letter then the "trend" is inverted to the actual market trend. Because of that an option to display inverted (and more logical) values is added.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
That's why investors, banks and institutions worldwide ask for the Jurik Research Moving Average ( JMA ). You may apply it just as you would any other popular moving average. However, JMA's improved timing and smoothness will astound you.
What is adaptive Jurik volatility
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers, 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average (KAMA) and Tushar Chande’s variable index dynamic average (VIDYA) adapt to changes in volatility. By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic, relative strength index (RSI), commodity channel index (CCI), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
Included
-Your choice of length input calculation, either fixed or adaptive cycle
-Invert the signal to match the trend
-Bar coloring to paint the trend
Happy trading!
Trend Titan Neutronstar [QuantraSystems]Trend Titan NEUTRONSTAR
Credits
The Trend Titan NEUTRONSTAR is a comprehensive aggregation of nearly 100 unique indicators and custom combinations, primarily developed from unique and public domain code.
We'd like to thank our TradingView community members: @IkKeOmar for allowing us to add his well-built "Normalized KAMA Oscillator" and "Adaptive Trend Lines " indicators to the aggregation, as well as @DojiEmoji for his valuable "Drift Study (Inspired by Monte Carlo Simulations with BM)".
Introduction
The Trend Titan NEUTRONSTAR is a robust trend following algorithm meticulously crafted to meet the demands of crypto investors. Designed with a multi layered aggregation approach, NEUTRONSTAR excels in navigating the unique volatility and rapid shifts of the cryptocurrency market. By stacking and refining a variety of carefully selected indicators, it combines their individual strengths while reducing the impact of noise or false signals. This "aggregation of aggregators" approach enables NEUTRONSTAR to produce a consistently reliable trend signal across assets and timeframes, making it an exceptional tool for investors focused on medium to long term market positioning.
NEUTRONSTAR ’s powerful trend following capabilities provide investors with straightforward, data driven analysis. It signals when tokens exhibit sustained upward momentum and systematically removes allocations from assets showing signs of weakness. This structure aids investors in recognizing peak market phases. In fact, one of NEUTRONSTAR ’s most valuable applications is its potential to help investors time exits near the peak of bull markets. This aims to maximize gains while mitigating exposure to downturns.
Ultimately, NEUTRONSTAR equips investors with a high precision, adaptable framework for strategic decision making. It offers robust support to identify strong trends, manage risk, and navigate the dynamic crypto market landscape.
With over a year of rigorous forward testing and live trading, NEUTRONSTAR demonstrates remarkable robustness and effectiveness, maintaining its performance without succumbing to overfitting. The system has been purposefully designed to avoid unnecessary optimization to past data, ensuring it can adapt as market conditions evolve. By focusing on aggregating valuable trend signals rather than tuning to historical performance, the NEUTRONSTAR serves as a reliable universal trend following system that aligns with the natural market cycles of growth and correction.
Core Methodology
The foundation of the NEUTRONSTAR lies in its multi aggregated structure, where five custom developed trend models are combined to capture the dominant market direction. Each of these aggregates has been carefully crafted with a specific trend signaling period in mind, allowing it to adapt seamlessly across various timeframes and asset classes. Here’s a breakdown of the key components:
FLARE - The original Quantra Signaling Matrix (QSM) model, best suited for timeframes above 12 hours. It forms the foundation of long term trend detection, providing stable signals.
FLAREV2 - A refined and more sophisticated model that performs well across both high and low timeframes, adding a layer of adaptability to the system.
NEBULA - An advanced model combining FLARE and FLAREV2. NEBULA brings the advantages of both components together, enhancing reliability and capturing smoother, more accurate trends.
SPARK - A high speed trend aggregator based on the QSM Universal model. It focuses on fast moving trends, providing early signals of potential shifts.
SUNBURST - A balanced aggregate that combines elements of SPARK and FLARE, confirming SPARK’s signals while minimizing false positives.
Each of these models contributes its own unique perspective on market movement. By layering fast, medium, and slower trend following signals, NEUTRONSTAR can confirm strong trends while filtering out shorter term noise. The result is a comprehensive tool that signals clear market direction with minimized false signals.
A Unique Approach to Trend Aggregation
One of the defining characteristics of NEUTRONSTAR is its deliberate choice to avoid perfectly time coherent indicators within its aggregation. In simpler terms, NEUTRONSTAR purposefully incorporates trend following indicators with slightly different signal periods, rather than synchronizing all components to a single signaling period. This choice brings significant benefits in terms of diversification, adaptability, and robustness of the overall trend signal.
When aggregating multiple trend following components, if all indicators were perfectly time coherent - meaning they responded to market changes in exactly the same way and over the time periods - the resulting signal would effectively be no different from a single trend following indicator. This uniformity would limit the system’s ability to capture a variety of market conditions, leaving it vulnerable to the same noise or false signals that any single indicator might encounter. Instead, NEUTRONSTAR leverages a balanced mix of indicators with varied timing: some fast, some slower, and some in the medium range. This choice allows the system to extract the unique strengths of each component, creating a combined signal that is stronger and more reliable than any single indicator.
By incorporating different signal periods, NEUTRONSTAR achieves what can be thought of as a form of edge accumulation. The fast components within NEUTRONSTAR , for example, are highly sensitive to quick shifts in market direction. These indicators excel at identifying early trend signals, enabling NEUTRONSTAR to react swiftly to emerging momentum. However, these fast indicators alone would be prone to reacting to market noise, potentially generating too many premature signals. This is where the medium term indicators come into play. These components operate with a slower reaction time, filtering out the short term fluctuations and confirming the direction of the trend established by the faster indicators. The combination of these varying signal speeds results in a balanced, adaptive response to market changes.
This approach also allows NEUTRONSTAR to adapt to different market regimes seamlessly. In fast moving, volatile markets, the faster indicators provide an early alert to potential trend shifts, while the slower components offer a stabilizing influence, preventing overreaction to temporary noise. Conversely, in steadier or trending markets, the medium and slower indicators sustain the trend signal, reducing the likelihood of premature exits. This flexible design enhances NEUTRONSTAR ’s ability to operate effectively across multiple asset classes and timeframes, from short term fluctuations to longer term market cycles.
The result is a powerful, multi-layered trend following tool that remains adaptive, capturing the benefits of both fast and medium paced reactions without becoming overly sensitive to short term noise. This unique aggregation methodology also supports NEUTRONSTAR ’s robustness, reducing the risk of overfitting to historical data and ensuring that the system can perform reliably in forward testing and live trading environments. The slightly staggered signal periods provide a greater degree of resilience, making NEUTRONSTAR a dependable choice for traders looking to capitalize on sustained trends while minimizing exposure during periods of market uncertainty.
In summary, the lack of perfect time coherence among NEUTRONSTAR ’s sub components is not a flaw - but a deliberate, robust design choice.
Risk Management through Market Mode Analysis
An essential part of NEUTRONSTAR is its ability to assess the market's underlying behavior and adapt accordingly. It employs a Market Mode Analysis mechanism that identifies when the market is either in a “Trending State” or a “Mean Reverting State.” When enough confidence is established that the market is trending, the system confirms and signals a “Trending State,” which is optimal for maintaining positions in the direction of the trend. Conversely, if there’s insufficient confidence, it labels the market as “Mean Reverting,” alerting traders to potentially avoid trend trades during likely sideways movement.
This distinction is particularly valuable in crypto, where asset prices often oscillate between aggressive trends and consolidation periods. The Market Mode Analysis keeps traders aligned with the broader market conditions, minimizing exposure during periods of potential whipsaws and maximizing gains during sustained trends.
Zero Overfitting: Design and Testing for Real World Resilience
Unlike many trend following indicators that rely heavily on backtesting and optimization, NEUTRONSTAR was built to perform well in forward testing and live trading without post design adjustments. Over a year of live market exposure has all but proven its robustness, with the system’s methodology focused on universal applicability and simplicity rather than curve fitting to past data. This approach ensures the aggregator remains effective across different market cycles and maintains relevance as new data unfolds.
By avoiding overfitting, NEUTRONSTAR is inherently more resistant to the common issue of strategy degradation over time, making it a valuable tool for traders seeking reliable market analysis you can trust for the long term.
Settings and Customization Options
To accommodate a range of trading styles and market conditions, NEUTRONSTAR includes adjustable settings that allow for fine tuning sensitivity and signal generation:
Calculation Method - Users can choose between calculating the NEUTRONSTAR score based on aggregated scores or by using the state of individual aggregates (long, neutral, short). The score method provides faster signals with slightly more noise, while the state based approach offers a smoother signal.
Sensitivity Threshold - This setting adjusts the system’s sensitivity, defining the width of the neutral zone. Higher thresholds reduce sensitivity, allowing for a broader range of volatility before triggering a trend reversal.
Market Regime Sensitivity - A sensitivity adjustment, ranging from 0 to 100, that affects the sensitivity of the sub components in market regime calculation.
These settings offer flexibility for users to tailor NEUTRONSTAR to their specific needs, whether for medium term investment strategies or shorter term trading setups.
Visualization and Legend
For intuitive usability, NEUTRONSTAR uses color coded bar overlays to indicate trend direction:
Green - indicates an uptrend.
Gray - signals a neutral or transition phase.
Purple - denotes a downtrend.
An optional background color can be enabled for market mode visualization, indicating the overall market state as either trending or mean reverting. This feature allows traders to assess trend direction and strength at a glance, simplifying decision making.
Additional Metrics Table
To support strategic decision making, NEUTRONSTAR includes an additional metrics table for in depth analysis:
Performance Ratios - Sharpe, Sortino, and Omega ratios assess the asset’s risk adjusted returns.
Volatility Insights - Provides an average volatility measure, valuable for understanding market stability.
Beta Measurement - Calculates asset beta against BTC, offering insight into asset volatility in the context of the broader market.
These metrics provide deeper insights into individual asset behavior, supporting more informed trend based allocations. The table is fully customizable, allowing traders to adjust the position and size for a seamless integration into their workspace.
Final Summary
The Trend Titan NEUTRONSTAR indicator is a powerful and resilient trend following system for crypto markets, built with a unique aggregation of high performance models to deliver dependable, noise reduced trend signals. Its robust design, free from overfitting, ensures adaptability across various assets and timeframes. With customizable sensitivity settings, intuitive color coded visualization, and an advanced risk metrics table, NEUTRONSTAR provides traders with a comprehensive tool for identifying and riding profitable trends, while safeguarding capital during unfavorable market phases.
HSI - Halving Seasonality Index for Bitcoin (BTC) [Logue]Halving Seasonality Index (HSI) for Bitcoin (BTC) - The HSI takes advantage of the consistency of BTC cycles. Past cycles have formed macro tops around 538 days after each halving. Past cycles have formed macro bottoms every 948 days after each halving. Therefore, a linear "risk" curve can be created between the bottom and top dates to measure how close BTC might be to a bottom or a top. The default triggers are set at 98% risk for tops and 5% risk for bottoms. Extensions are also added as defaults to allow easy identification of the dates of the next top or bottom according to the HSI.
CSI - Calendar Seasonality Index for Bitcoin (BTC) [Logue]Calendar Seasonality Index (CSI) for Bitcoin (BTC) - The CSI takes advantage of the consistency of BTC cycles. Past cycles have formed macro tops every four years near November 21st, starting from in 2013. Past cycles have formed macro bottoms every four years near January 15th, starting from 2011. Therefore, a linear "risk" curve can be created between the bottom and top dates to measure how close BTC might be to a bottom or a top. The default triggers are at 98% risk for tops and 5% risk for bottoms. Extensions are also added as defaults to allow easy identification of the dates of the next top or bottom according to the CSI.
Triple Ehlers Market StateClear trend identification is an important aspect of finding the right side to trade, another is getting the best buying/selling price on a pullback, retracement or reversal. Triple Ehlers Market State can do both.
Three is always better
Ehlers’ original formulation produces bullish, bearish and trendless signals. The indicator presented here gate stages three correlation cycles of adjustable lengths and degree thresholds, displaying a more refined view of bullish, bearish and trendless markets, in a compact and novel way.
Stick with the default settings, or experiment with the cycle period and threshold angle of each cycle, then choose whether ‘Recent trend weighting’ is included in candle colouring.
John Ehlers is a highly respected trading maths head who may need no introduction here. His idea for Market State was published in TASC June 2020 Traders Tips. The awesome interpretation of Ehlers’ work on which Triple Ehlers Market State’s correlation cycle calculations are based can be found at:
DISCLAIMER: None of this is financial advice.
Innotrade LSOB Fractal Zones for Sniper EntryWelcome to Innotrade LIQUIDITY SWEEP ORDERBLOCK (LSOB) in Fractal Zones for Sniper Entry, an all-in-one institutional trading toolkit designed to identify high-probability multi-timeframe reversal zones with precision. This indicator is not just a collection of tools; it's a synergistic system where each component works together to build a complete picture of the market, from macro structure down to the entry candle.
This all-in-one indicator suite is a comprehensive toolkit designed for discretionary traders who leverage multiple confluence factors in their analysis. It integrates nine distinct, powerful, and complementary trading systems into a single, cohesive interface. The primary purpose of this script is not just to build an indicator, but to create a synergistic framework where signals from one system can be validated by others, providing a higher-probability trading environment.
The core philosophy is built around identifying Liquidity Sweep Order Blocks (LSOB) that form after sweeping significant Fractal price points. This combination pinpoints where institutional players have likely shown their hand, leaving behind clues for the retail trader to follow. The additional modules for trend, volume, structure, and multi-timeframe momentum provide the essential context needed to filter trades and boost confidence.
How Each System Works and Complements the Others
This suite is built on the principle of confluence. A signal from one system gains significance when confirmed by one or more of the other systems. Below is a breakdown of each component and its role within the suite:
1. Innotrade LSOB Fractal Zones
What it does: This core component automatically identifies and plots high-probability order blocks. Unlike standard order block indicators, the LSOB is specifically formed after a liquidity sweep of a recent, significant Fractal pivot high or low. These "sniper entry" zones represent areas where institutional orders are likely resting after engineering liquidity.
Key Enhancements & New Features:
Extensive Multi-Timeframe (MTF) Analysis: Plot LSOB zones from up to 5 Higher Timeframes (HTF) and 5 Lower Timeframes (LTF) directly on your current chart. This provides a complete, top-down view of supply and demand, showing where major and minor reversal zones are stacking up.
ATR Impulse Invalidation: An intelligent filter that automatically removes an LSOB zone if a strong counter-move (measured by the Average True Range) closes decisively against it. This keeps your chart clean by removing invalidated zones.
Advanced Alert System: Go beyond simple entry alerts with a sophisticated alert suite:
Zone Entry Alert: Standard alert when price first touches an LSOB zone.
Confirmation (Rejection) Alert: Triggers when price enters a zone and then closes back outside of it, providing powerful confirmation of a rejection and potential reversal.
Break-and-Retest (S/R Flip) Alert: A powerful feature for trend-continuation traders. When an LSOB zone is broken, the indicator remembers it. If price later returns to retest this broken zone from the opposite side (e.g., retesting a broken resistance as new support), an alert is triggered.
How to use it: The LSOB zones are your primary areas of interest for entries. A bullish LSOB forming on a key Octo MA level, confirmed by the MTF Dashboard, is a high-probability long setup.
2. Innotrade Octo MA (8 Moving Averages)
What it does: The Octo MA is the backbone of this suite, providing a clear, multi-layered view of the market trend. It plots eight fully customizable moving averages (SMA, EMA, SMMA) and their corresponding standard deviation clouds.
New Features:
Flow/Slope Coloring: An optional mode colors the MAs and clouds based on their angle (up or down), offering an immediate visual cue of momentum strength.
MA Value Labels: Display clean labels for each MA value on the last bar, with a connector line to keep the chart organized.
Synergy: The Octo MA provides the essential trend context. An LSOB signal is stronger if it forms at a key MA level. A Momentum Crossover signal is more reliable when the longer-term MAs from the Octo suite are aligned.
3. Momentum Crossover & RSI System
What it does: This system provides clear entry signals based on MA crossovers, filtered by the RSI to avoid chasing exhausted moves.
How it works: Select any two of the eight Octo MAs as your fast and slow lines. A long signal is generated on a pullback to the fast MA while it's above the slow MA, and when the RSI is not overbought. This encourages buying dips in an uptrend.
Synergy: This system gives you specific, actionable entry triggers within the broader context provided by the LSOB zones and the Octo MA trend. An entry signal here that occurs inside an LSOB zone is a very high-confidence setup.
4. PVSRA Volume Coloring
What it does: PVSRA (Price, Volume, Spread, Range Analysis) colors candles based on volume analysis to reveal the strength or weakness behind a move. It identifies high-volume (Vectors) and ultra-high-volume exhaustion candles (Climax Vectors).
Synergy: A climactic volume signal (Blue/Violet) appearing at an LSOB zone can signal an impending reversal, providing powerful confirmation for entry timing.
5. Fibonacci Time Cycles
What it does: This tool projects potential future turning points based on Fibonacci time sequences (21, 34, 55, etc.), starting from a significant user-defined pivot.
Synergy: When a price-based signal (like a touch of an LSOB zone) coincides with a Fibonacci time cycle line, it suggests that both price and time are aligned for a potential market turn.
6. Psy-Levels & Daily Open
What it does: This component plots key psychological price levels for the week (Psy-High and Psy-Low) and the daily opening price, which act as crucial intraday pivots.
Synergy: These levels act as natural magnets or areas of S/R. An LSOB block that forms right at a weekly Psy-Low provides a powerful confluence for a potential reversal.
7. Classic Fractals
What it does: Identifies simple 3-bar or 5-bar fractal patterns, which are basic short-term swing highs and lows.
Synergy: These fractals provide a quick visual confirmation of the swing points that the LSOB system uses for its liquidity sweep detection, helping to validate the identified zones.
8. Zig-Zag
What it does: The Zig-Zag overlay filters out market noise to visualize the most significant price swings and overall market structure.
Synergy: The Zig-Zag provides a "big picture" view of market structure, helping you confirm that your trade setup (e.g., from an LSOB zone) is aligned with the major swings of the market.
9. ATR & RSI Multi-Timeframe Dashboard (NEW)
What it does: This is a powerful, at-a-glance dashboard that provides a comprehensive overview of market conditions across six user-defined timeframes. It tracks two key metrics:
Volatility (ATR vs. Range): It displays the current ATR, the current bar's range, and the range as a percentage of the ATR. Crucially, it highlights when the current range exceeds the ATR, alerting you to unusual, expansionary volatility.
Momentum (RSI Status): It analyzes the RSI on each timeframe and provides a simple, color-coded status: Bull Trend, Bear Trend, Bullish Cross, or Bearish Cross. This goes beyond a simple RSI value, giving you immediate, actionable context on momentum.
How it works: The dashboard synthesizes complex data into an easy-to-read table. It also comes with its own dedicated alerts for both volatility expansion (Range > ATR) and changes in RSI status, keeping you informed of shifts in market dynamics across all key timeframes.
Synergy: The dashboard is the ultimate filter. Before taking an LSOB trade on the 15-minute chart, a quick glance can tell you if the 1H and 4H timeframes are in a strong "Bear Trend," allowing you to avoid a counter-trend trade with a low probability of success.
How to Use This Suite for a Trading Setup
Here is a sample workflow for identifying a high-probability long trade:
Context (Dashboard & MAs): Check the MTF Dashboard. Are the higher timeframes showing a "Bull Trend" or "Bullish Cross"? Check the Octo MA. Are the longer-term MAs (e.g., 50, 100, 200) trending upwards?
Area of Interest (LSOB & Levels): Look for a bullish LSOB zone to form, ideally from your current timeframe or a higher timeframe. Is it forming at a key support level like an Octo MA, a weekly Psy-Level, or a broken-and-retested resistance?
Confirmation (Volume): As price enters the LSOB zone, look for PVSRA signals. Is there a bullish vector (Green) or a sign of selling exhaustion (Blue climax candle at the low)?
Timing (Fib Cycles): Is this potential reversal aligning with a Fibonacci Time Cycle line? This adds another layer of confluence.
Entry Trigger (Momentum System): Wait for a Momentum Crossover system signal—a bullish cross or a pullback entry signal—to trigger your trade. The Zig-Zag should confirm you are buying at a higher low in the larger structure.
Stop Loss: Your stop loss can be placed just below the low of the LSOB zone.
By requiring multiple systems to align, you can filter out low-quality setups and focus on trades with a higher probability of success.
Vendor Requirements / How to Get Access (For Invite-Only Scripts)
To gain access to the Innotrade LSOB Zones indicator, please send me a private message on TradingView or follow the instructions in my signature.
Disclaimer
This indicator is a tool to aid in analysis and decision-making, not financial advice or a signal-calling system. Trading involves significant risk, and past performance is not indicative of future results. Always use proper risk management and never risk more than you are willing to lose.
Thank you for your interest, and I look forward to your feedback and comments
Gabriel's Squeeze Momentum PRO“Gabriel’s Squeeze Momentum PRO” is a next-generation evolution of the classic SQZMOM concept. It layers multiple John Ehlers filters, Jurik smoothing, adaptive cycle-detection, and a Cauchy-weighted price filter on top of the familiar Bollinger-Band-inside-Keltner-Channel squeeze logic. The goal is to pinpoint volatility contractions and immediately gauge whether forthcoming expansion is likely to break bullish or bearish—while screening out noise, lag, and regime shifts across any symbol or timeframe.
1 · What the script plots
Plot What it represents Why it matters
Momentum line (teal/red) Price-de-trended linear-regression of a Cauchy-filtered source, optionally normalized. Measures directional thrust during / after a squeeze.
Signal line (white JMA) Jurik moving average of the momentum line. Smooth trigger for crossovers / reversals.
Squeeze dots (blue, black, red, yellow, purple, green) Real-time volatility state: No squeeze → Wide → Normal → Narrow → Very Narrow → Fired. Helps anticipate explosive moves as BB exits KC.
Cyclic RSI bands (cyan / fuchsia) Dynamic overbought / oversold bands derived by MESA dominant-cycle analysis. Contextualizes momentum extremes—no fixed 70/30.
Rate-of-Change (optional) (orange / blue shading) ROC of the momentum-signal spread, scaled. Highlights acceleration / deceleration.
Reversal guide lines (optional colored rays) Drawn when momentum crosses its JMA and reversal-mode is on. Visual confirmation of early trend change.
2 · Key engine components
Cauchy PDF-weighted moving average
Creates a heavy-tailed weighting curve; center bars dominate while still capturing fat-tail outliers—excellent for choppy instruments or volume-weighting (Volume weighted?).
Butterworth High-Pass & Super-Smoother Low-Pass
Strip out drift, then smooth what’s left. This isolates true cyclic motion before momentum is computed.
Fast RMS normalizer
Converts the band-pass output into a unit-scale “power” reading—vital for adaptive thresholds.
Goertzel + MESA dominant-cycle
Auto-detects fast & slow cycles, then blends them to size overbought / oversold bands and to set the adaptiveLength (if Use Adaptive Length? is enabled).
Jurik RSX & JMA
Provide ultra-low-lag smoothing for momentum and for reversal detection.
3 · Input groups and how to tune them
Group Why change it Tips
Normalization (Unbounded / Min-Max / Standard Deviations) Puts momentum & signal on the scale that best suits the asset. Crypto / small-caps: StdDev (handles volatility).
FX / indices: Min-Max or leave unbounded for raw juice.
Cauchy Distribution Tailors the Cauchy filter. Gamma ↓ (0.1-0.4) ⇒ faster / riskier. Use Adaptive Length pairs it with MESA cycle length for auto speed control.
Rate of Change Visual momentum acceleration. Leave off (Show Rate of Change = false) if you want a cleaner pane.
Momentum Colors / Directional Momentum? Switch between classic SQZMOM coloring and trend-biased histogram. Turn on when you prefer “green-gets-greener / red-gets-redder” style signals.
Squeeze Colors & Thresholds Fine-tune what “wide / normal / narrow” mean. Larger assets (SPX, BTC-Perp): raise the thresholds a touch. Thin or low-ATR symbols: lower them.
Multi-Time-frame blocks (1 h, 4 h, D, W, M) Pre-sets for BB/KC length, squeeze thresholds, and reversal MA length per TF. The script auto-detects the chart timeframe and loads the matching row—just adjust each block once.
Reversal Signals Whether to draw vertical rays on momentum crossovers. Use on swing-trading timeframes (≥1 h) to catch early momentum flips.
4 · How to read & trade it
Scan for purple / yellow / red dots
These indicate Very-Narrow, Narrow, and Normal squeezes—markets are coiling.
Wait for a fired squeeze (green dot)
BB has pushed outside KC; volatility is expanding. Momentum direction often dictates breakout bias.
Check momentum relative to zero & signal
Bullish setup: Momentum > 0 and crossing above signal. Bearish setup: Momentum < 0 and crossing below signal. Alerts “Bullish / Bearish Trend Reversal” are raised here if enabled.
Validate with cyclic bands
If momentum launches from near the lower cyan band, bullish moves are higher-probability (symmetrical for upper fuchsia band).
Confirm trend strength
Directional-momentum histogram keeps turning brighter in trend direction; ROC is above zero and rising.
Manage the trade
First target = prior squeeze mid-range or recent swing high/low.
Consider scaling out when momentum weakens (histogram fades) or reverses through signal line.
Optional: draw the reversal rays to highlight exit zones automatically.
5 · Practical workflows
Scalpers (1-5 min)
Uncheck Use Adaptive Length, set main Length to 10-12, Gamma to 0.3.
Use ROC for ultra-fast divergences.
Treat Normal squeezes (red) as tradable; ignore Wide. Healthy Volume is ideal.
Swing traders (1 h – 4 h)
Keep default adaptive length; enable 1-H/4-H reversal blocks.
Trade only after Very-Narrow/Narrow squeezes; ride until weekly/daily reversal ray prints.
Position / Trend followers (Daily+)
Raise Wide/Normal thresholds a bit (e.g., 2.2 / 1.7).
Momentum normalization = Standard Deviations to filter regime shifts.
Combine with higher-timeframe MTF panel or moving-average ribbons.
6 · Built-in alert catalog
Alert name Fires when Typical action
🟢 Fired Squeeze Green dot appears (vol expansion already under way) Stay in trend or add on pullbacks.
🟠 Low / 🔴 Normal / 🟡 Tight / 🟣 Very Tight Respective squeeze engages Get your watch-list ready; plan trades.
🐂 Bullish / 🐻 Bearish Trend Reversal Momentum crosses signal in requested direction Entry / exit / scale adds.
Set alerts on “Once Per Bar Close” for reliable signals.
7 · Best practices & caveats
Context is king – Use higher-timeframe structure (support/resistance, VWAP, market profile) to avoid false breakouts.
Data quality – On illiquid symbols, consider turning volume weighting off (pre-market gaps distort results).
Normalization choice – Mixing different normalizations across charts can confuse muscle memory; pick one style per asset class.
Lag vs. noise – If entries feel late, lower Gamma or disable adaptive length. If too jumpy, increase Length or choose Standard-Deviation normalization.
Not a stand-alone holy grail – Combine with risk management (ATR-based stops, Kelly-fraction sizing) and confirm with price action.
Harness the script’s adaptive filtering, multi-TF presets, and rich alert suite to spot compression, time breakouts, and stay on the right side of momentum—whether you’re scalping ES futures or swing-trading alt-coins.
Bitcoin: The Puell MultipleBitcoin: The Puell Multiple Indicator Overview
The Puell Multiple is an indicator originally used to analyze Bitcoin's valuation based on mining revenue. However, this approximate version uses Bitcoin's current price to give us a similar perspective. It’s helpful for understanding whether Bitcoin’s price is currently high or low compared to its historical trend.
Recommended Timeframe:
For optimal insights, it’s recommended to use this indicator on the weekly timeframe. This timeframe smooths out daily fluctuations, making it easier to capture long-term valuation trends and better understand market cycles.
What Does the Indicator Show?
This indicator compares the current price of Bitcoin to its average price over the past 365 days. Here’s what it helps you see:
When Bitcoin Might Be Undervalued:
If the indicator value is below a certain low threshold (e.g., 0.51 by default), it suggests that Bitcoin might be undervalued compared to its long-term trend. Historically, periods where the indicator is low have sometimes coincided with good buying opportunities, as Bitcoin is seen as “cheap” in relation to its recent average.
When Bitcoin Might Be Overvalued:
If the indicator value is above a certain high threshold (e.g., 3.4 by default), it suggests that Bitcoin might be overvalued. In the past, these high points have sometimes signaled times to consider selling, as Bitcoin is viewed as “expensive” relative to its recent trend.
How to Read the Indicator
Indicator Line: The main line in the indicator panel shows the value of the Puell Multiple over time, fluctuating based on the comparison between current and past prices.
Threshold Lines: Two horizontal lines represent the high and low thresholds:
Bottom Threshold (Red Line): Indicates a high value, suggesting that Bitcoin might be overvalued.
Top Threshold (Green Line): Indicates a low value, suggesting that Bitcoin might be undervalued.
Color Coding:
The background may appear green when the indicator is below the low threshold (suggesting undervaluation) or red when it’s above the high threshold (suggesting overvaluation).
How You Can Use This Indicator
Long-Term Investment Insights: This indicator can help you identify favorable buying or selling conditions based on historical price trends. When the value is low, Bitcoin might be in a more attractive price range; when it’s high, the price might be inflated compared to its yearly trend.
Market Timing: This tool is best used alongside other indicators, as it’s primarily helpful for understanding broader trends rather than predicting short-term movements.
The Puell Multiple (Approximate) indicator thus offers a historical lens on Bitcoin’s valuation, helping you make decisions informed by past price trends. For best results, keep in mind the weekly timeframe recommendation to capture meaningful market cycles.
π Cycle Market Tops & Bottoms Performante IndicatorWhy is it called the Pi Cycle Tops & Bottoms Indicator?
When the 111-Day moving average crosses over the (350-Day moving average X 2), we've seen the price come to a key top or bottom within the Bitcoin market for the past 3 cycles.
350 divided by 111 is very close to π - hence the name the Pie cycle!
Yes, we are selecting arbitrary numbers initially, but through the use of proper back-testing, we are able to find key cycle shifts using mathematical numbers (fibs, Pi, etc)
We use this topping & bottoming signal when things look overbought over oversold within the market.
The "topping" label turns on as soon as we see the 111-Day moving average cross above the 350-Day moving average.
The "bottoming" signal turns on as soon as we see the 111-day moving average cross below the 350-Day moving average.
This indicator should only be used on the daily timeframe!
Historically speaking, we've seen this indicator become impressively accurate.
