Take Profit ScreenerI'm going to introduce you to the Take Profit Screener tool.
It allows you to manually scan your watchlist to determine at a glance the assets that have the best profitability potential.
It is a 2 in 1 tool that allows you to :
identify where your Take Profit ratios are located whether you are in SHAD or Cycle Strategy
identify the potential reward percentages when approaching the key Fibonacci levels
Before you start using it, you need to:
sort your watchlist according to the price (Last) in order to have price ranges more or less close to each other when jumping from a symbol to another
disable the Auto Scale and Magnet feature
select your first symbol
display the tool (the indicator more exactly)
The settings dialog box is organised in 3 sections:
Strategy : By setting this section, you will answer the question " Where do my Take Profit ratios stand in relation to my entry price, and according to Risk Management Strategy adopted (SHAD or Cycle)? "
Fibonacci : By setting this section, you will answer the question " What percentage gain can I expect as I approach one of the key Fibonacci levels? "
Layout : This is the settings for the look and feel
Strategy Section
Active : This part of the indicator won't display on your chart if unchecked
Type : Choose between SHAD or Cycle Strategy. When choosing SHAD, you can select 2, 4, 8 or 16 SHAD Levels. When choosing Cycle, you can enter whatever ratio value you wish in the Strategy Ratio (Cycle only) input.
SHAD xNN : When choosing SHAD Strategy, you should select at least one level and more if need be.
Strategy Ratio (Cycle only) : When choosing Strategy Type Cycle, you can enter whatever ratio value you wish there.
Freeze Entry Price & Value : Leave it unchecked whenever the current price of the asset is located within your desired area (i.e. Reload Zone) while attempting to estimate its potential reward. Conversely, keep it checked whenever the current price of the asset is outside your desired area, but you need to anticipate the potential reward of this asset if its price reaches a certain level, your Entry price. Enter this price there and check the box.
Show price : If checked, both Take Profit ratio and Price are displayed. If unchecked, then price is hidden.
Extend Line : If checked, then lines showing Take Profit levels extend to the left.
Label Offset : If checked, then the label that displays Take Profit ratio and price shift to the right by a value that ranges from 0 to 100 candles.
Label Style : You can choose between Right or Top. This will determine the orientation of the label.
Fibonacci Section
Active : This part of the indicator won't display on your chart if unchecked
Type : Choose between SHAD or Cycle Strategy. When choosing SHAD, you can select 2, 4, 8 or 16 SHAD Levels. When choosing Cycle, you can enter whatever ratio value you wish in the Strategy Ratio (Cycle only) input.
SHAD xNN : When choosing SHAD Strategy, you should select at least one level and more if need be.
Strategy Ratio (Cycle only) : When choosing Strategy Type Cycle, you can enter whatever ratio value you wish there.
Freeze Entry Price : Leave it unchecked whenever the current price of the asset is located within your desired area (i.e. Reload Zone) while attempting to estimate its potential reward. Conversely, keep it checked whenever the current price of the asset is outside your desired area, but you need to anticipate the potential reward of this asset if its price reaches a certain level, your Entry price. Enter this price there and check the box.
Color : You can define the color of Fibonacci line levels
Search in scripts for "Cycle"
Cyclic Smoothed RSI MTFAdaptive cyclic smoothed Relative Strength Indicator (csRSI MTF)
The cyclic smoothed RSI MTF indicator is an enhancement of the RSI , adding zero-lag smoothing, adaptive oversold/overbought bands and period color highlighting from higher timeframe to filter signals.
Providing the following advanced features:
using the current dominant cycle length as input for the indicator to ensure more accurate change in trends,
additional smoothing without introducing lag and maintaining clear sharp turns for signal generation,
adaptive upper and lower bands to avoid whipsaw trades and adapt the indicator to trending/cyclic conditions,
using higher time-frame csRSI oversold/overbought conditions to automatically highlight time windows with green/red backgrounds on the indicator panel for signal filtering and/or alert rules,
can be used to trigger alerts on your key symbols to get informed when a red/green windows are reached.
The following common problems with standard indicators are solved by this indicator:
First, normal indicators introduce a lot of false signals due to their noisy signal line. Second, to compensate for the noise, one would normally try to add some smoothing. But this only results in adding more delay to the indicator, which makes it almost useless. Third, oscillators contain static threshold levels to define oversold/overbought conditions. However, the market is not static and changes between trending and cycling periods. In trending periods, these static oversold/overbought levels are useless ore will trigger too much whipsaw trades. Finally, indicators don't take their state from other timeframes into account to filter signals.
All four problems described above are solved by the developed adaptive cyclic RSI with embedded MTF period highlighting.
Examples
S&P500 EMini Futures - csRSI 2H chart / 1D filter example signals
S&P E-Mini Futures 2h chart with daily higher time-frame filtering period for the csRSI, showing the standard RSI in the lower panel for signal comparison, signals from the csRSI are marked on the price chart
Bitcoin BTC /USD - csRSI 2H chart / 1D filter example signals
Bitcoin BTC /USD 2h chart with daily higher time-frame filtering period for the csRSI, signals marked
EUR/USD Forex - csRSI 20min chart / 2h filter example signals
EUR/USD 20min chart with 2H higher time-frame filtering period for the csRSI, signals marked
Info:
All three examples are setup with the basic standard settings and no additional parameter adjustments. The placed arrows on the price/indicator panel and the projection price areas have been added manually to visualize the signals for an discretionary trading approach. They are derived based on standard technical indicator oscillator readings (signal turn above/below bands). Due to the nature of the indicator (ultra-smooth, sharp curves, dynamic bands), these signals are easy to spot, and will help to avoid whipsaw trades in volatile conditions.
Settings & Parameter
The Inputs section allows you to select the time frame for the indicator signals. We recommend keeping the indicator time-frame according to your chart time frame ("Same as chart"). The cycle length allows to improve the signals by entering the dominant cycle length of the analyzed dataset. This parameter is optional if the current dominant cycle is not known. In that case, leave it at 20. The dominant cycle length can even improve the indicator signal generation. The examples above have not been optimized by using the dominant cycle length and just used the standard setting of 20.
The MTF CYCLE FILTER area is used to set the time-frame used as filter to plot the colored indicator background in red and green areas when the higher time-frame indicator is above (red) or below (green) the dynamic bands. These indicate the period of time with high probability to look for signals on the main indicator line.
The MTF Resolution parameter input is important for generating the highlighted red/green areas on the indicator panel. You must enter a higher time-frame than your indicator time-frame in order to get the reliable highlighting. We recommend the following combinations of trading time-frame and filter time-frame resolutions:
Chart Timeframe | MTF Indicator Highlighting Resolution
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20 min | 2 h
2 h | 1 d
You can enter the current dominant cycle length on the chosen higher time-frame resolution to even further optimize the indicator accuracy in the field "MTF CYCLE FILTER - Cycle Length".
The Style sections allows to active/de-active individual plots. The standard setting disables the higher time-frame csRSI indicator which is only used to indicate the colored areas. If required, you can also enable the MTF indicator and adaptive bands to be plotted in the same indicator panel. The values shown in the style section also indicate which values are available for individual alert generation.
Automatic Signals & Alerts
It is possible to create your own automatic signals with the csRSI MTF indicator using the TradingView alert function. Click on the three dots "More" beside the indicator name label and select "Add Alert on csRSI ..." from the context menu. For example, if you want to receive an alert when the high probability periods (red/green highlighted areas) have been reached for a symbol without manually watching the indicator panel, you can set up a custom alert. The csRSI indicator provides the raw values necessary to set up your alarm conditions. Set the "CSRSI MTF" as the value for the "Out of Channel" condition and select the "HigBand MTF" and "LowBand MTF" indicator values as the upper and lower limit parameters in the alarm's dialog box. Once you have set up this alarm, you will not need to monitor your charts manually. The TradingView alert will inform you as soon as an important time zone is reached. These are the situations when you would open the chart and watch for trigger signals on the indicator line. If you set up this alert as an email, you can even focus on other things and let the csRSI MTF highlighter condition alert you when you should pay attention to the trading chart.
Usage & Trade Signals
Classic rules apply as with every technical oscillator. In addition use this indicator to identify the following conditions:
Indicator turns above/below the adaptive upper and lower bands (expected trend reversals)
Indicator crosses below upper band / crossed above lower band (start of trend reversal)
Indicator crosses above upper band / crossed below lower band (trend continuation/confirmation)
Divergence between price / indicator indicate strong signal confidence
Hidden divergences between price/indicator indicate string signal confidence
After strong price movements, wait for the second signal confirmed by a divergence
Use the mentioned conditions in the highlighted red/green periods indicated by the MTF settings
Purpose & Disclaimer
This indicator is not designed for use as an automated trading strategy. This is an improved technical indicator using the dominant cycle to provide its advanced features. The basic applications of technical analysis for using oscillators apply. The script is intended for use in discretionary trading and can be used as a part of automated systems. Indicator signal failures will occur as you should expect with every technical indicator. If you are not sure if this indicator might help your trading style, please try and check our open source public version which will give you basic understanding upfront.
Basic open-source public version
This indicator is an advanced version of our public available open-source cyclic smoothed RSI indicator named "RSI cyclic smoothed v2". The advanced invite-only version provides fully automatic time frame highlighting by using a cyclically smoothed RSI from a higher time frame to indicate time frames with high probability signals. These high probability windows are highlighted when the indicator from the higher time frame is in dynamic overbought or oversold territory. You will find the basic open-source public version here below for your own review:
How to get access
Please check the "authors instructions" section for further details.
Paid script
Quarterly Theory IndicatorQuarterly Theory Indicator (from Daye's Theory)
Functionalities:
1) Monthly Quarterly Cycles (division with vertical lines) & the latest Monthly True Open- only visible in the weekly TF (horizontal line).
2) Weekly Quarterly Cycles (division with vertical lines) & the latest Weekly True Open (horizontal line).
3) Daily Quarterly Cycles (division with vertical lines) & the latest Daily True Open (horizontal line).
4) 90Min "Sessional" Quarterly Cycles (division with vertical lines) & the four 90Min cycle True Open lines of the latest day (horizontal lines).
FLD 3DFLD 3D - Future Lines of Demarcation Indicator
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THEORETICAL FOUNDATION
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This indicator implements Future Lines of Demarcation (FLD), a key concept from J.M. Hurst's cyclic analysis theory. FLDs are price-based lines displaced forward in time by half the wavelength of a dominant cycle, creating a predictive framework for price movement analysis.
The core principle: when price crosses an FLD line, it indicates a potential change in the current cycle phase. FLDs act as dynamic support/resistance levels that "anticipate" where price should be based on the dominant cycle's rhythm.
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CALCULATION METHODOLOGY
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The indicator calculates three FLD lines:
1. FLD HIGH: Takes the bar's high price and shifts it forward by offset bars
2. FLD LOW: Takes the bar's low price and shifts it forward by offset bars
3. FLD MEDIAN: Calculates a median price using the selected method, then shifts forward
The offset is calculated as: offset = Period / 2
This displacement represents the half-cycle concept: if a cycle has a period of 48 bars, the FLD will be displaced 24 bars into the future. This creates a "lead" indicator that shows where price should theoretically be based on the cycle's wave pattern.
PRICE METHODS AVAILABLE:
- HL2: (High + Low) / 2 - Simple midpoint
- HLC3: (High + Low + Close) / 3 - Weighted with close
- HLCC4: (High + Low + Close + Close) / 4 - Close has double weight
- OHLC4: (Open + High + Low + Close) / 4 - Full bar average
- VWAP-like: Volume-weighted high/low average
- True Range: Uses previous close for range calculation
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AUTO-PERIOD ADJUSTMENT FEATURE
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The indicator includes multi-timeframe adaptation logic. When "Auto Period" is enabled:
1. Detects current chart timeframe (minutes, hours, days, weeks)
2. Compares it to the reference timeframe setting
3. Calculates adjustment ratio: Reference TF / Current TF
4. Applies ratio to base period: Adjusted Period = Base Period × Ratio
Example: If Base Period = 48, Reference TF = 60min, Current chart = 15min
→ Ratio = 60/15 = 4
→ Adjusted Period = 48 × 4 = 192 bars
This ensures the indicator tracks the same real-time cycle length across different chart timeframes, maintaining consistency in cycle analysis.
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VISUAL COMPONENTS
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- RED LINE: FLD High (upper boundary)
- BLUE LINE: FLD Low (lower boundary)
- ORANGE LINE: FLD Median (centerline)
- GRAY AREA: Fills between High and Low FLDs
- RIGHT LABEL: Shows FLD identifier and period used (asterisk indicates auto-adjustment)
All lines extend into the future by the calculated offset, creating a "projection zone" ahead of current price.
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ACKNOWLEDGMENTS
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This work is inspired by the Italian cyclic analysis community and dedicated educators. Due to TradingView's House Rules on promotional content, I cannot mention specific names or groups, but my gratitude goes to those who know they contributed to this development through their teaching and guidance. Thank Emiliano!
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.
Trend Condition [TradersPro]
OVERVIEW
The Trend Condition Indicator measures the strength of the bullish or bearish trend by using a ribbon pattern of exponential moving averages and scoring system. Trend cycles naturally expand and contract as a normal part of the cycle. It is the rhythm of the market. Perpetual expansion and contraction of trend.
As trend cycles develop the indicator shows a compression of the averages. These compression zones are key locations as trends typically expand from there. The expansion of trend can be up or down.
As the trend advances the ribbon effect of the indicator can be seen as each average expands with the price action. Once they have “fanned” the probability of the current trend slowing is high.
This can be used to recognize a powerful trend may be concluding. Traders can tighten stops, exit positions or utilize other prudent strategies.
CONCEPTS
Each line will display green if it is higher than the prior period and red if it is lower than the prior period. If the average is green it is considered bullish and will score one point in the bullish display. Red lines are considered bearish and will score one point in the bearish display.
The indicator can then be used at a quick glance to see the number of averages that are bullish and the number that are bearish.
A trader may use these on any tradable instrument. They can be helpful in stock portfolio management when used with an index like the S&P 500 to determine the strength of the current market trend. This may affect trade decisions like possession size, stop location and other risk factors.
Phase-Accumulation Adaptive EMA w/ Expanded Source Types [Loxx]Phase-Accumulation Adaptive EMA w/ Expanded Source Types is a Phase Accumulation Adaptive Exponential Moving Average with Loxx's Expanded Source Types. This indicator is meant to better capture trend movements using dominant cycle inputs. Alerts are included.
What is Phase Accumulation?
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.
Included:
-Toggle on/off bar coloring
-Alerts
even_better_sinewave_mod
Description:
Even better sinewave was an indicator developed by John F. Ehlers (see Cycle Analytics for Trader, pg. 159), in which improvement to cycle measurements completely relies on strong normalization of the waveform. The indicator aims to create an artificially predictive indicator by transferring the cyclic data swings into a sine wave. In this indicator, the modified is on the weighted moving average as a smoothing function, instead of using the super smoother, aim to be more adaptive, and the default length is set to 55 bars.
Sinewave
smoothing = (7*hp + 6*hp_1 + 5*hp_2+ 4*hp_3 + 3*hp_4 + 2*hp5 + hp_6) /28
normalize = wave/sqrt(power)
Notes:
sinewave indicator crossing over -0.9 is considered to beginning of the cycle while crossing under 0.9 is considered as an end of the cycle
line color turns to green considered as a confirmation of an uptrend, while turns red as a confirmation of a downtrend
confidence of using indicator will be much in confirmation paired with another indicator such dynamic trendline e.g. moving average
as cited within Ehlers book Cycle Analytic for Traders, the indicator will be useful if the satisfied market cycle mode and the period of the dominant cycle must be estimated with reasonable accuracy
Other Example
One-Sided Hodrick-Prescott FilterTechnical & Mathematical Architecture
This indicator represents a significant departure from standard Moving Averages or traditional Hodrick-Prescott (HP) filter implementations found on Trading View. It utilizes a State-Space Model approach to decompose time-series data into trend and cyclical components, solved recursively via a Kalman Filter (Forward Pass) and a Rauch-Tung-Striebel (RTS) Smoother (Backward Pass). Furthermore, it introduces a proprietary Maximum Likelihood Estimation (MLE) loop to adapt the smoothing parameter (λ) dynamically in response to market regimes.
1.1 The State-Space Formulation
The standard HP filter minimizes a specific loss function involving the sum of squared deviations and the sum of squared second differences. While typically solved via batch matrix inversion, this script reformulates the problem as a Local Linear Trend (LLT) model, a stochastic structural model defined by:
Measurement Equation:
y = μ + ε
(Where ε is normally distributed noise)
State Transition Equations:
μ = μ + β + η
β = β + ζ
Where μ represents the stochastic level (trend) and β represents the stochastic slope (drift). The crucial link to the HP filter is the signal-to-noise ratio. By setting the variance of η to 0 (smooth trend) and defining λ as the ratio of measurement variance to slope variance, the Kalman Filter solution converges exactly to the One-Sided HP Filter.
1.2 The Forward Pass: Kalman Filter
The script executes a recursive estimation loop for real-time (causal) filtering:
Prediction Step: Projects the state mean and error covariance forward based on the transition matrix.
Innovation: Calculates the measurement residual (v = y - predicted y).
Update Step: Computes the Kalman Gain. The posterior state is updated based on how much the prediction missed the actual price.
Stability: The covariance update utilizes the Joseph Form subtraction to ensure the covariance matrix remains positive semi-definite, preventing numerical instability inherent in high-precision floating-point calculations over long durations.
1.3 Adaptive λ via Maximum Likelihood
Standard filters use a static λ (e.g., 1600 for quarterly data), which fails in crypto/FX markets exhibiting changing volatility. This script implements an Adaptive ML Loop.
The Kalman Filter assumes innovations are normally distributed with a specific theoretical variance (S). We compute a running variance ratio test:
Ratio = Actual Innovation Variance / Theoretical Variance
Ratio > 1: The model is "surprised" by volatility. The filter is under-fitting. The script dynamically decreases λ to increase responsiveness (reduce lag).
Ratio < 1: The model is over-fitting noise. The script increases λ to enforce a smoother trend.
This allows the filter to function as a low-lag trend follower during impulses and a robust noise filter during consolidation, automatically.
1.4 The Backward Pass: Rauch-Tung-Striebel (RTS) Smoother
This is the most complex feature of the script. While the Forward Pass provides the optimal estimate based on past data, the Backward Pass computes the optimal estimate based on all data.
The RTS algorithm runs purely on historical arrays stored in memory:
It iterates backward from the last bar to the past. It computes a "Smoother Gain" matrix based on future information. It updates the past estimates to correct them based on what happened afterwards. This results in a Minimum Mean Squared Error (MMSE) estimator. Note: This smoothed line is for analytical hindsight and back testing theoretical limits; it is distinct from the real-time filtered line used for live signaling.
Usage Guide:
This indicator is designed for precision trend following and mean-reversion trading. It separates the market price into a Trend Component (Signal) and a Cycle Component (Noise/Oscillation).
The Two Trend Lines:
The Filtered Trend (Real-Time): This is the filled/shaded line on your chart. It calculates the trend using only past data. It does not repaint. Use this for entering and exiting live trades.
Green Fill: Price is above the trend (Bullish bias).
Red Fill: Price is below the trend (Bearish bias).
The Smoothed Trend (Hindsight): (Optional, enabled via settings). This is the "God mode" line. It uses future data to show you exactly where the trend was.
WARNING: This line repaints. Do not trade the tip of this line. Its purpose is to show you the ideal path for training your eye or optimizing parameters.
Mean Reversion Signals:
The script calculates the "Cycle," which is the percentage deviation of price from the HP Trend.
Bands: The Upper and Lower bands represent the Cycle Threshold.
Long Signal (L): Triggered when the Cycle is Oversold (below lower band) AND begins to turn up, while the Filtered Drift (slope) is positive. This suggests a "dip buy" in an uptrend.
Short Signal (S): Triggered when the Cycle is Overbought (above upper band) AND begins to turn down, while the Filtered Drift is negative. This suggests selling a rally in a downtrend.
Adaptive Lambda Panel:
Enable the "Lambda Panel" to see the engine under the hood.
Rising Lambda (Blue): The market is noisy or consolidating. The filter is becoming "stiffer" to ignore the chop.
Falling Lambda (Orange): The market is trending impulsively. The filter is becoming "looser" to track the price closely and reduce lag.
Strategy: You can use low Lambda values as a confirmation of high-volatility breakout regimes.
Performance Table:
A dashboard in the bottom right corner displays the efficiency of the Kalman Filter:
MSE Filtered vs. Smoothed: Shows the Mean Squared Error of the real-time prediction vs. the hindsight-optimal smooth.
Improvement %: A higher percentage indicates that the RTS Smoother is extracting significantly more noise than the real-time filter (common in choppy markets).
Kalman Gains (K1, K2): These display the current weight the filter assigns to new price data for updating the Level and Slope respectively.
Summary of Settings
Base Lambda: The starting stiffness. Higher = smoother (long-term trend). Lower = responsive (short-term trend).
Adaptation Speed: Recommended between 0.01 and 0.05. Controls how fast λ reacts to volatility shocks.
Smoother Lookback: How far back (in bars) the RTS algorithm re-optimizes the historical line.
Best Practice: Use the Filtered Trend for execution. Use the Smoothed Trend to analyze past price action and determine if your Base Lambda setting is appropriate for the asset's volatility profile.
Regime MapRegime Map — Volatility State Detector
This indicator is a PineScript friendly approximation of a more advanced Python regime-analysis engine.
The original backed identifies market regimes using structural break detection, Hidden-Markov Models, wavelet decomposition, and long-horizon volatility clustering. Since Pine Script cannot execute these statistical models directly, this version implements a lightweight, real-time proxy using realised volatility and statistical thresholds.
The purpose is to provide a clear visual map of evolving volatility conditions without requiring any heavy offline computation.
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Mathematical Basis: Python vs Pine
1. Volatility Estimation
Python (Realised Volatility):
RVₜ = √N × stdev( log(Pₜ) − log(Pₜ₋₁) )
Pine Approximation:
RVₜ = stdev( log(Pₜ) − log(Pₜ₋₁), lookback )
Rationale:
Realised volatility captures volatility clustering — a key characteristic of regime transitions.
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2. Regime Classification
Python (HMM Volatility States):
Volatility is modelled as belonging to hidden states with different means and variances:
State μ₁, σ₁
State μ₂, σ₂
State μ₃, σ₃
with state transitions determined by a probability matrix.
Pine Approximation (Z-Score Regimes):
Zₜ = ( RVₜ − mean(RV) ) / stdev(RV)
Regime assignment:
• Regime 0 (Low Vol): Zₜ < Zₗₒw
• Regime 1 (Normal): Zₗₒw ≤ Zₜ ≤ Zₕᵢgh
• Regime 2 (High Vol): Zₜ > Zₕᵢgh
Rationale:
Z-scores provide clean statistical boundaries that behave similarly to HMM state separation but are computable in real time.
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3. Structural Break Detection vs Rolling Windows
Python (Bai–Perron Structural Breaks):
Segments the volatility series into periods with distinct statistical properties by minimising squared error over multiple regimes.
Pine Approximation:
Rolling mean and rolling standard deviation of volatility over a long window.
Rationale:
When structural breaks are not available, long-window smoothing approximates slow regime changes effectively.
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4. Multi-Scale Cycles
Python (Wavelet Decomposition):
Volatility decomposed into long-cycle (A₄) and short-cycle components (D bands).
Pine Approximation:
Single-scale smoothing using long-horizon averages of RV.
Rationale:
Wavelets reveal multi-frequency behaviour; Pine captures the dominant low-frequency component.
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Indicator Output
The background colour reflects the active volatility regime:
• Low Volatility (Green): trending behaviour, cleaner directional movement
• Normal Volatility (Yellow): balanced environment
• High Volatility (Red): sharp swings, traps, mean-reversion phases
Regime labels appear on the chart, with a status panel displaying the current regime.
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Operational Logic
1. Compute log returns
2. Calculate short-horizon realised volatility
3. Compute long-horizon mean and standard deviation
4. Derive volatility Z-score
5. Assign regime classification
6. Update background colour and labels
This provides a stable, real-time map of market state transitions.
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Practical Applications
Intraday Trading
• Low-volatility regimes favour trend and breakout continuation
• High-volatility regimes favour mean reversion and wide stop placement
Swing Trading
• Compression phases often precede multi-day trending moves
• Volatility expansions accompany distribution or panic events
Risk Management
• Enables volatility-adjusted position sizing
• Helps avoid leverage during expansion regimes
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Notes
• Does not repaint
• Fully configurable thresholds and lookbacks
• Works across indices, stocks, FX, crypto
• Designed for real-time volatility regime identification
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Disclaimer
This script is intended solely for educational and research purposes.
It does not constitute financial advice or a recommendation to buy or sell any instrument.
Trading involves risk, and past volatility patterns do not guarantee future outcomes.
Users are responsible for their own trading decisions, and the author assumes no liability for financial loss.
ACE SqueezeACE Squeeze - Advanced Momentum Oscillator with Squeeze Detection
WHAT IT DOES
This is my take on a squeeze momentum indicator that I've been refining over time. At its core, it detects periods when volatility contracts (the squeeze) and measures momentum direction when the market breaks out. Think of it like a coiled spring - when price compresses into a tight range, it often leads to explosive moves once it breaks free.
The indicator plots a histogram oscillator that ranges from -100 to +100, with several visual elements to help you gauge signal strength and market conditions.
KEY FEATURES
Market Regime Detection - The indicator automatically adjusts its sensitivity based on whether the market is trending, ranging, choppy, or volatile. This helps reduce false signals in different market environments.
Hilbert Transform - Uses advanced cycle detection to identify the dominant market rhythm. You can toggle this on/off if you prefer the simpler calculation method.
Volume Analysis - Incorporates volume delta and footprint analysis to confirm momentum signals. Strong moves with volume confirmation get priority.
Statistical Filtering - Filters out low-quality signals by analyzing signal persistence and deviation from the mean. This helps focus on the high-probability setups.
Visual Enhancements - Three-tier glow system shows building momentum, heat maps at extreme levels, and cycle phase indicator to track market rhythm.
HOW TO USE IT
The Squeeze - When you see the purple background, that's a squeeze condition. The market is compressing and building energy. Wait for the squeeze to release (background disappears) and watch which direction the histogram breaks.
Histogram Direction - Green bars mean bullish momentum, red bars mean bearish momentum. The stronger the color and the larger the glow effect, the stronger the signal.
Zero Line - Think of this as the battleground. When the histogram crosses above zero with strong momentum, that's a buy signal. When it crosses below with conviction, that's a sell signal.
Extreme Levels - The +90/-90 zones are overbought/oversold areas. The heat map bands intensify as the signal reaches these extremes, warning you that a reversal or consolidation might be coming.
Signal Quality - The indicator has built-in quality filtering. The alerts are set to only fire when signal quality is high (above 70-80%), which helps avoid the junk trades.
BEST PRACTICES
Don't trade every signal. Wait for the high-quality setups where multiple factors align - squeeze release, strong momentum, volume confirmation, and good signal quality.
Use higher timeframes for confirmation. A squeeze on the 1-hour chart hitting at the same time as the daily chart is much more powerful than isolated signals.
Pay attention to the cycle phase line. When momentum aligns with the cycle direction, the move tends to have more follow-through.
The glow effects are your friend. When you see the tier 3 extreme glow, the market is really moving - consider trailing stops or scaling out.
PROS
Highly customizable - You can adjust almost everything from sensitivity to visual appearance.
Multi-faceted analysis - Combines volatility, momentum, volume, and cycle analysis in one indicator.
Smart filtering - The regime detection and statistical filtering help adapt to different market conditions.
Visual clarity - The glow effects and color gradients make it easy to see signal strength at a glance.
Good alert system - Alerts are filtered for quality, so you're not getting pinged on every minor wiggle.
CONS
Can be complex for beginners - There are a lot of settings and concepts to understand. Start with defaults and adjust gradually.
Lags on fast markets - Like any indicator, it's based on past data. In extremely fast-moving markets, you might get late entries.
Works best in volatile markets - In super tight, low-volatility ranges, you might see fewer signals. That's by design, but it means patience is required.
Computational load - With all the enhancements turned on, it's doing a lot of calculations. On lower-end devices, you might notice some lag.
Not a holy grail - No indicator is. This is a tool to help you make better decisions, not a replacement for proper risk management and trading discipline.
SETTINGS BREAKDOWN
Core Settings - Adjust the base cycle length (10 is good for most timeframes) and sensitivity (0.65 is balanced, lower for fewer signals, higher for more).
Enhancement Settings - Toggle the advanced features. If you're getting too many signals, try turning off RRED. If you want cleaner signals, keep statistical filtering on.
Visual Settings - Customize the appearance. The glow effects look cool but you can disable them if you prefer a cleaner chart.
Elite Settings - Market regime detection is powerful but you can disable it if you want consistent behavior across all market conditions.
TIPS FROM MY TESTING
The indicator shines best on the 15-minute to 4-hour timeframes. It works on lower timeframes but expect more noise.
Use it alongside support/resistance or supply/demand zones. When a squeeze fires near a key level, the probability increases significantly.
Don't ignore the small signals in trending markets. Sometimes the modest +30 to +40 readings in a strong uptrend are your best continuation entries.
The squeeze can last longer than you expect. Don't try to predict when it will fire - let the indicator tell you.
This indicator represents a lot of testing and refinement. It's not perfect, but it's been useful in my trading. I hope it helps you spot better setups and avoid some of the false signals that plague simpler momentum indicators.
Transfer Function Filter [theUltimator5]The Transfer Function Filter is an engineering style approach to transform the price action on a chart into a frequency, then filter out unwanted signals using Butterworth-style filter approach.
This indicator allows you to analyze market structure by isolating or removing different frequency components of price movement—similar to how engineers filter signals in control systems and electrical circuits.
🔎 Features
Four Filter Types
1) Low Pass Filter – Smooths price data, highlighting long-term trends while filtering out short-term noise. This filter acts similar to an EMA, removing noisy signals, resulting in a smooth curve that follows the price of the stock relative to the filter cutoff settings.
Real world application for low pass filter - Used in power supplies to provide a clean, stable power level.
2) High Pass Filter – Removes slow-moving trends to emphasize short-term volatility and rapid fluctuations. The high pass filter removes the "DC" level of the chart, removing the average price moves and only outputting volatility.
Real world application for high pass filter - Used in audio equalizers to remove low-frequency noise (like rumble) while allowing higher frequencies to pass through, improving sound clarity.
3) Band Pass Filter – Allows signals to plot only within a band of bar ranges. This filter removes the low pass "DC" level and the high pass "high frequency noise spikes" and shows a signal that is effectively a smoothed volatility curve. This acts like a moving average for volatility.
Real world application for band pass filter - Radio stations only allow certain frequency bands so you can change your radio channel by switching which frequency band your filter is set to.
4) Band Stop Filter – Suppresses specific frequency bands (cycles between two cutoffs). This filter allows through the base price moving average, but keeps the high frequency volatility spikes. It allows you to filter out specific time interval price action.
Real world application for band stop filter - If there is prominent frequency signal in the area which can cause unnecessary noise in your system, a band stop filter can cancel out just that frequency so you get everything else
Configurable Parameters
• Cutoff Periods – Define the cycle lengths (in bars) to filter. This is a bit counter-intuitive with the numbering since the higher the bar count on the low-pass filter, the lower the frequency cutoff is. The opposite holds true for the high pass filter.
• Filter Order – Adjust steepness and responsiveness (higher order = sharper filtering, but with more delay).
• Overlay Option – Display Low Pass & Band Stop outputs directly on the price chart, or in a separate pane. This is enabled by default, plotting the filters that mimic moving averages directly onto the chart.
• Source Selection – Apply filters to close, open, high, low, or custom sources.
Histograms for Comparison
• BS–LP Histogram – Shows distance between Band Stop and Low Pass filters.
• BP–HP Histogram – Highlights differences between Band Pass and High Pass filters.
Histograms give the visualization of a pseudo-MACD style indicator
Visual & Informational Aids
• Customizable colors for each filter line.
• Optional zero-line for histogram reference.
• On-chart info table summarizing active filters, cutoff settings, histograms, and filter order.
📊 Use Cases
Trend Detection – Use the Low Pass filter to smooth noise and follow underlying market direction.
Volatility & Cycle Analysis – Apply High Pass or Band Pass to capture shorter-term patterns.
Noise Suppression – Deploy Band Stop to remove specific choppy frequencies.
Momentum Insight – Watch the histograms to spot divergences and relative filter strength.
BigNuts MacroScript that overlays key events that are coming up as the US economy shifts into fiscal dominance and global liquidity may peak. The specified dates were cross referenced from many cycle theories including Benner and Kondratieff key cycle dates as well work of Michel Howell for Global liquidity cycles and Luke Gromen analysis for Marco. The script also then cross references all these dates with any key celestial events that have had previous historical significance for market timing. The celestial events are key dates to watch but can be toggled on and off.
Fourier Oscillator Suite [SeerQuant]| Fourier Oscillator Suite |
WHY THE FOURIER TRANSFORM?
The Discrete Fourier Transform (DFT) extracts dominant cyclical patterns from market price data. Fourier analysis allows for the decomposition of price movements into frequency components, distinguishing trend-driven behaviour from noise and identifying oscillatory cycles within the market. This approach is effective in detecting dominant cycles in data, filtering out random fluctuations, and providing insights into price behaviour beyond conventional indicators.
This indicator applies a Fourier transform to the selected price source, converting it into a frequency-based signal. Instead of directly working with raw price data, the transformed signal acts as a smoothed and cycle-adjusted input for multiple technical indicators, enhancing their ability to adapt to market conditions dynamically.
Once the Fourier transform is applied, the extracted signal is processed through a suite of technical indicators, which are then normalized and aggregated into a single, actionable metric.
FEATURES AND BENEFITS
✅ Multi-Factor Aggregation:
By blending volatility, momentum, and volume-based oscillators, this indicator provides a comprehensive view of market conditions.
✅ Enhanced Signal Clarity:
Fourier transformation filters noise, revealing more reliable trading signals.
✅ Adaptive Market Sensitivity:
Unlike static oscillators, the Fourier-enhanced input dynamically adjusts to price shifts.
INDICATOR COMPONENTS
The Fourier Oscillator Suite aggregates the output of the transformed signal into three primary market components:
1. Volatility-Based Metrics
Commodity Channel Index (CCI) – Measures price deviation from a moving average.
Bollinger Band %B (BB%) – Evaluates price positioning within the Bollinger Bands.
Relative Volatility Index (RVI) – Identifies periods of heightened or subdued volatility.
2. Momentum Indicators
Relative Strength Index (RSI) – Gauges trend momentum and overbought/oversold levels.
Coppock Curve – A long-term momentum oscillator, often used for detecting major trend shifts.
Momentum (MOM), TRIX, and Stochastic Momentum Index (SMI) – Further refine momentum analysis.
3. Volume-Based Oscillators
Money Flow Index (MFI) – Measures price strength relative to volume.
Volume Zone Oscillator (VZO) – Detects accumulation and distribution phases.
Elder's Force Index (EFI) & Klinger Volume Oscillator (KVO) – Assess money flow strength.
These individual metrics are first normalized within a defined period and then smoothed using the selected moving average type. The final composite signal is derived from a weighted combination of the volatility, momentum, and volume components, each of which can be customized by the user.
SETTINGS
The indicator includes an extensive set of options for users to tailor its performance:
📌 Fourier Transform Parameters
Source Selection – Choose which price input (e.g., HLC3) is used for Fourier analysis.
Fourier Period – Defines the number of cycles analyzed for signal extraction.
📌 Aggregation Settings
Normalization Period – Controls how indicator values are scaled.
Smoothing Length – Adjusts the sensitivity of moving averages applied to oscillators.
Weight Adjustments – Fine-tune the impact of volatility, momentum, and volume-based inputs on the final signal.
📌 White Noise Control
White Noise Amplitude & Period – Filters out excessive market noise to improve signal clarity.
Enable/Disable White Noise Overlay – Provides optional visualization of filtered noise levels.
📌 Custom Styling & Visual Enhancements
Selectable Color Schemes – Choose from Default, Modern, Cool, or Monochrome.
Bull & Bear Color Customization – Define custom colors for positive/negative momentum shifts.
Adaptive Gradient Fills – Highlights market conditions dynamically based on oscillator movements.
The Fourier Oscillator Suite is designed for advanced traders seeking a noise-reduced, multi-dimensional view of market dynamics. By incorporating Fourier-transformed signals into a broad range of oscillators, this tool offers a highly adaptive, filter-enhanced, and customizable approach to momentum and trend analysis. Whether you are a trend follower, mean reversion trader, or volume analyst, this suite provides actionable insights with enhanced clarity.
Next Moon Phases 2025Next Moon Phases 2025
This custom indicator marks both past and future moon phases with vertical lines on your chart, providing a unique way to incorporate lunar cycles into your trading strategy.
This indicator is best used on the Daily timeframe. The lunar cycle is most effective when viewed in daily bars, providing the clearest correlation between moon phases and market trends.
Key Features:
Past Moon Phases (2016–2024): Marks the key lunar phases—New Moon, First Quarter, Full Moon, and Last Quarter—with vertical lines on the chart. Perfect for backtesting and analyzing the historical relationship between moon phases and market movements.
Future Moon Phases (2025): Unlike most indicators, this tool also projects upcoming moon phases for 2025, allowing you to plan ahead and anticipate potential market reactions based on future lunar events.
Adjustable Visibility: Customize which moon phases are displayed by toggling the visibility of each phase (New Moon, First Quarter, Full Moon, Last Quarter) with a simple control.
Why Moon Phases Matter in Trading:
Many traders believe that the lunar cycle can influence market sentiment and behavior. For example:
New Moon is often associated with new beginnings and potential market reversals.
Full Moon is thought to bring increased volatility and market climaxes.
First Quarter and Last Quarter may indicate periods of consolidation or momentum shifts.
By including both past and future moon phases, this indicator allows you to examine historical data while also planning for upcoming lunar events, giving you a strategic edge for both short-term and long-term trading decisions.
E9 PLRRThe E9 PLRR (Power Law Residual Ratio) is a custom-built indicator designed to evaluate the overvaluation or undervaluation of an asset, specifically by utilizing logarithmic price data and a power law-based model. It leverages a dynamic regression technique to assess the deviation of the current price from its expected value, giving insights into how much the price deviates from its long-term trend.
This indicator is primarily used to detect market extremes and cycles, often used in the analysis of long-term price movements in assets like Bitcoin, where cyclical behavior and significant price deviations are common.
This chart is back from 2019 and shows (From left to right) 2018 Bear market bottom at $3.5k (Dark Blue) , following a peak at 12k (dark red) before the Covid crash back down to EUROTLX:4K (Dark blue)
Key Components
Logarithmic Price Data:
The indicator works with logarithmic price data (ohlc4), which represents the average of open, high, low, and close prices. The logarithmic transformation is crucial in financial modeling, especially when analyzing long-term price data, as it normalizes exponential price growth patterns.
Dynamic Exponent 𝑘:
The model calculates a dynamic exponent k using regression, which defines the power law relationship between time and price. This exponent is essential in determining the expected power law price return and how far the current price deviates from that expected trend.
Power Law Price Return:
The power law price return is computed using the dynamic exponent
k over a defined period, such as 365 days (1 year). It represents the theoretical price return based on a power law relationship, which is used to compare against the actual logarithmic price data.
Risk-Free Rate:
The indicator incorporates an adjustable risk-free rate, allowing users to model the opportunity cost of holding an asset compared to risk-free alternatives. By default, the risk-free rate is set to 0%, but this can be modified depending on the user's requirements.
Volatility Adjustment:
A key feature of the PLRR is its ability to adjust for price volatility. The indicator smooths out short-term price fluctuations using a moving average, helping to detect longer-term cycles and trends.
PLRR Calculation:
The core of the indicator is the calculation of the Power Law Residual Ratio (PLRR). This is derived by subtracting the expected power law price return and risk-free rate from the logarithmic price return, then multiplying the result by a user-defined multiplier.
Color Gradient:
The PLRR values are represented visually using a color gradient. This gradient helps the user quickly identify whether the asset is in an undervalued, fair value, or overvalued state:
Dark Blue to Light Blue: Indicates undervaluation, with increasing blue tones representing a higher degree of undervaluation.
Green to Yellow: Represents fair value, where the price is aligned with the expected power law return.
Orange to Dark Red: Indicates overvaluation, with increasing red tones representing a higher degree of overvaluation.
Zero Line:
A zero line is plotted on the indicator chart, serving as a reference point. Values above the zero line suggest potential overvaluation, while values below indicate potential undervaluation.
Dots Visualization:
The PLRR is plotted using dots, with each dot color-coded based on the PLRR value. This dot-based visualization makes it easier to spot significant changes or reversals in market sentiment without overwhelming the user with continuous lines.
Bar Coloring:
The chart’s bars are colored in accordance with the PLRR value at each point in time, making it visually clear when an asset is potentially overvalued or undervalued.
Indicator Functionality
Cycle Identification : The E9 PLRR is especially useful for identifying cyclical market behavior. In assets like Bitcoin, which are known for their boom-bust cycles, the PLRR can help pinpoint when the market is likely entering a peak (overvaluation) or a trough (undervaluation).
Overvaluation and Undervaluation Detection: By comparing the current price to its expected power law return, the PLRR helps traders assess whether an asset is trading above or below its fair value. This is critical for long-term investors seeking to enter the market at undervalued levels and exit during periods of overvaluation.
Trend Following: The indicator helps users identify the broader trend by smoothing out short-term volatility. This makes it useful for both momentum traders looking to ride trends and contrarian traders seeking to capitalize on market extremes.
Customization
The E9 PLRR allows users to fine-tune several parameters based on their preferences or specific market conditions:
Lookback Period:
The user can adjust the lookback period (default: 100) to modify how the moving average and regression are calculated.
Risk-Free Rate:
Adjusting the risk-free rate allows for more realistic modeling of the opportunity cost of holding the asset.
Multiplier:
The multiplier (default: 5.688) amplifies the sensitivity of the PLRR, allowing users to adjust how aggressively the indicator responds to price movements.
This indicator was inspired by the works of Ashwin & PlanG and their work around powerLaw. Thank you. I hall be working on the calculation of this indicator moving forward to make improvements and optomisations.
Ehlers Stochastic Center Of Gravity [CC]The Stochastic Center Of Gravity Indicator was created by John Ehlers (Cybernetic Analysis For Stocks And Futures pgs 79-80), and this is one of the many cycle scripts that I have created but not published yet because, to be honest, I don't use cycle indicators in my everyday trading. Many of you probably do, so I will start publishing my big backlog of cycle-based indicators. These indicators work best with a trend confirmation or some other confirmation indicator to pair with it. The current cycle is the length of the trend, and since most stocks generally change their underlying trend quite often, especially during the day, it makes sense to adjust the length of this indicator to match the stock you are using it on. As you can see, the indicator gives constant buy and sell signals during a trend which is why I recommend using a confirmation indicator.
I have color-coded it to use lighter colors for normal signals and darker colors for strong signals. Buy when the line turns green and sell when it turns red.
Let me know if there are any other scripts you would like to see me publish!
SMC Pro: Real-Time (English)Title: SMC Pro: Real-Time Sessions & Daily Cycle
Description:
SMC Pro: Real-Time Sessions & Daily Cycle is a comprehensive tool designed for Smart Money Concepts (SMC) and ICT traders. This indicator automatically plots key trading sessions and identifies market structure manipulations in real-time.
Unlike standard session indicators that wait for the session to close, this tool draws boxes and lines dynamically from the very first candle, allowing you to see the range developing live.
🚀 Key Features
1. Real-Time Session Drawing
Asia, London, and New York sessions are drawn candle-by-candle.
Boxes expand automatically as price creates new highs or lows during the session.
50% Midline for the Asian range to help identify premium/discount pricing.
2. The Daily Cycle & "Type 3" Detection
Based on the "Daily Cycle" logic, the indicator monitors the Asian Range after it closes.
Type 3 Whipsaw Alert: Automatically detects and labels a "Type 3" scenario where price sweeps BOTH the Asian High and Asian Low (manipulation).
Lines extend automatically to help you trade the breakout or reversal (Sweep).
3. PDH / PDL (Previous Day High/Low)
Displays the Previous Day High and Low levels.
Logic is strictly locked to the last completed day to keep your chart clean (no clutter from historical days).
4. Entry Helper (SCOB)
Color-coded candles: Highlights potential entry candles based on engulfing patterns after a liquidity sweep.
Fully customizable colors for Buy and Sell setups.
⚙️ Settings
Customizable Times: Adjust session hours to fit your broker's time zone or your specific strategy.
Visual Styles: Choose between Solid, Dashed, or Dotted lines for the Asian range.
Clean Chart: Toggle any feature (text, fills, lines) on or off to suit your visual preference.
💡 How to Use
Wait for the Asian Range to complete.
Watch for a "Sweep" of the Asian High or Low during the London/NY session.
If price sweeps BOTH sides, the indicator will tag it as "Type 3: Whipsaw", signaling a potential reversal or high-volatility expansion.
Use the PDH/PDL levels as major liquidity targets.
Global M2 Money Supply Growth (GDP-Weighted)📊 Global M2 Money Supply Growth (GDP-Weighted)
This indicator tracks the weighted aggregate M2 money supply growth across the world's four largest economies: United States, China, Eurozone, and Japan. These economies represent approximately 69.3 trillion USD in combined GDP and account for the majority of global liquidity, making this a comprehensive macro indicator for analyzing worldwide monetary conditions.
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🔧 KEY FEATURES:
📈 GDP-Weighted Aggregation
Each economy is weighted proportionally by its nominal GDP using 2025 IMF World Economic Outlook data:
• United States: 44.2% (30.62 trillion USD)
• China: 28.0% (19.40 trillion USD)
• Eurozone: 21.6% (15.0 trillion USD)
• Japan: 6.2% (4.28 trillion USD)
The weights are fully adjustable through the indicator settings, allowing you to update them annually as new IMF forecasts are released (typically April and October).
⏱️ Multiple Time Period Options
Choose between three calculation methods to analyze different timeframes:
• YoY (Year-over-Year): 12-month growth rate for identifying long-term liquidity trends and cycles
• MoM (Month-over-Month): 1-month growth rate for detecting short-term monetary policy shifts
• QoQ (Quarter-over-Quarter): 3-month growth rate for medium-term trend analysis
🔄 Advanced Offset Function
Shift the entire indicator forward by 0-365 days to test lead/lag relationships between global liquidity and asset prices. Research suggests a 56-70 day lag between M2 changes and Bitcoin price movements, but you can experiment with different offsets for various assets (equities, gold, commodities, etc.).
🌍 Individual Country Breakdown
Real-time display of each economy's M2 growth rate with:
• Current percentage change (YoY/MoM/QoQ)
• GDP weight contribution
• Color-coded values (green = monetary expansion, red = contraction)
📊 Smart Overlay Capability
Displays directly on your main price chart with an independent left-side scale, allowing you to visually correlate global liquidity trends with any asset's price action without cluttering the chart.
🔧 Customizable GDP Weights
All GDP values can be adjusted through the indicator settings without editing code, making annual updates simple and accessible for all users.
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📡 DATA SOURCES:
All M2 money supply data is sourced from ECONOMICS (Trading Economics) for consistency and reliability:
• ECONOMICS:USM2 (United States)
• ECONOMICS:CNM2 (China)
• ECONOMICS:EUM2 (Eurozone)
• ECONOMICS:JPM2 (Japan)
All values are normalized to USD using current daily exchange rates (USDCNY, EURUSD, USDJPY) before GDP-weighted aggregation, ensuring accurate cross-country comparisons.
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💡 USE CASES & APPLICATIONS:
🔹 Liquidity Cycle Analysis
Track global monetary expansion/contraction cycles to identify when central banks are coordinating loose or tight monetary policies.
🔹 Market Timing & Risk Assessment
High M2 growth (>10%) historically correlates with risk-on environments and rising asset prices across crypto, equities, and commodities. Negative M2 growth signals monetary tightening and potential market corrections.
🔹 Bitcoin & Crypto Correlation
Compare with Bitcoin price using the offset feature to identify the optimal lag period. Many traders use 60-70 day offsets to predict crypto market movements based on liquidity changes.
🔹 Macro Portfolio Allocation
Use as a regime filter to adjust portfolio exposure: increase risk assets during liquidity expansion, reduce during contraction.
🔹 Central Bank Policy Divergence
Monitor individual country metrics to identify when major central banks are pursuing divergent policies (e.g., Fed tightening while China eases).
🔹 Inflation & Economic Forecasting
Rapid M2 growth often leads inflation by 12-18 months, making this a leading indicator for future inflation trends.
🔹 Recession Early Warning
Negative M2 growth is extremely rare and has preceded major recessions, making this a valuable risk management tool.
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📊 INTERPRETATION GUIDE:
🟢 +10% or Higher
Aggressive monetary expansion, typically during crises (2001, 2008, 2020). The COVID-19 period saw M2 growth reach 20-27%, which preceded significant inflation and asset price surges. Strong bullish signal for risk assets.
🟢 +6% to +10%
Above-average liquidity growth. Central banks are providing stimulus beyond normal levels. Generally favorable for equities, crypto, and commodities.
🟡 +3% to +6%
Normal/healthy growth rate, roughly in line with GDP growth plus 2% inflation targets. Neutral environment with moderate support for risk assets.
🟠 0% to +3%
Slowing liquidity, potential tightening phase beginning. Central banks may be raising rates or reducing balance sheets. Caution warranted for high-beta assets.
🔴 Negative Growth
Monetary contraction - extremely rare. Only occurred during aggressive Fed tightening in 2022-2023. Strong warning signal for risk assets, often precedes recessions or major market corrections.
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🎯 OPTIMAL USAGE:
📅 Recommended Timeframes:
• Daily or Weekly charts for macro analysis
• Monthly charts for very long-term trends
💹 Compatible Asset Classes:
• Cryptocurrencies (especially Bitcoin, Ethereum)
• Equity indices (S&P 500, NASDAQ, global markets)
• Commodities (Gold, Silver, Oil)
• Forex majors (DXY correlation analysis)
⚙️ Suggested Settings:
• Default: YoY calculation with 0 offset for current liquidity conditions
• Bitcoin traders: YoY with 60-70 day offset for predictive analysis
• Short-term traders: MoM with 0 offset for recent policy changes
• Quarterly rebalancers: QoQ with 0 offset for medium-term trends
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📋 VISUAL DISPLAY:
The indicator plots a blue line showing the selected growth metric (YoY/MoM/QoQ), with a dashed reference line at 0% to clearly identify expansion vs. contraction regimes.
A comprehensive table in the top-right corner displays:
• Current global M2 growth rate (large, prominent display)
• Individual country breakdowns with their GDP weights
• Color-coded growth rates (green for positive, red for negative)
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🔄 MAINTENANCE & UPDATES:
GDP weights should be updated annually (ideally in April or October) when the IMF releases new World Economic Outlook forecasts. Simply adjust the four GDP input parameters in the indicator settings - no code editing required.
The relative GDP proportions between the Big 4 economies change very gradually (typically <1-2% per year), so even if you update weights once every 1-2 years, the impact on the indicator's accuracy is minimal.
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💭 TRADING PHILOSOPHY:
This indicator embodies the principle that "liquidity drives markets." By tracking the combined M2 money supply of the world's largest economies, weighted by their economic size, you gain insight into the fundamental liquidity conditions that underpin all asset prices.
Unlike single-country M2 indicators, this GDP-weighted approach captures the true global picture, accounting for the fact that US monetary policy has 2x the impact of Japanese policy due to economic size differences.
Perfect for macro-focused traders, long-term investors, and anyone seeking to understand the "tide that lifts all boats" in financial markets.
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Created for traders and investors who incorporate global liquidity trends into their decision-making process. Best used alongside other technical and fundamental analysis tools for comprehensive market assessment.
⚠️ Disclaimer: M2 money supply is a lagging macroeconomic indicator. Past correlations do not guarantee future results. Always use proper risk management and combine with other analysis methods.
Gann Master System - CompleteGann Master Trading System - Multi-Factor Confluence Indicator
Advanced implementation of W.D. Gann methodology combining Square of 9 calculations, Octave Theory projections, Time Cycle analysis, and Planetary Aspect windows into a systematic confluence-based trading system.
Key Features:
Square of 9 geometric price levels (180°, 270°, 360° rotations)
Octave Theory targets with harmonic divisions (0.5x, 1x, 2x, 4x)
Time cycle tracking with sub-cycle analysis
10 configurable planetary aspect windows (manual input from ephemeris)
Automatic swing pivot detection
Multi-factor confluence scoring (0-20+ points)
Visual signals: Blue (score 3-6), Red (7-10), Purple (11+)
Real-time info panel with factor status
Built-in alerts for high-probability setups
How It Works:
System calculates multiple Gann factors simultaneously and awards points when price aligns with key levels. Higher confluence scores indicate stronger probability of reversal. Combines objective mathematics with astronomical timing for systematic edge.
Best For: Daily/4H charts on Gold, Forex majors, Indices
Signal Frequency: 2-4 high-quality setups per month (score 11+)
Recommended Min Score: 7 for trading, 11+ for highest probability
Setup Required: Configure Square of 9 pivot, Octave base range, Time cycle start date, and planetary aspect dates. See full documentation for detailed guide.
BTC Power-Law Decay Channel Oscillator (0–100)🟠 BTC Power-Law Decay Channel Oscillator (0–100)
This indicator calculates Bitcoin’s position inside its long-term power-law decay channel and normalizes it into an easy-to-read 0–100 oscillator.
🔎 Concept
Bitcoin’s long-term price trajectory can be modeled by a log-log power-law channel.
A baseline is fitted, then an upper band (excess/euphoria) and a lower band (capitulation/fear).
The oscillator shows where the current price sits between those bands:
0 = near the lower band (historical bottoms)
100 = near the upper band (historical tops)
📊 How to Read
Oscillator > 80 → euphoric excess, often cycle tops
Oscillator < 20 → capitulation, often cycle bottoms
Works best on weekly or bi-weekly timeframes.
⚙️ Adjustable Parameters
Anchor date: starting point for the power-law fit (default: 2011).
Smoothing days: moving average applied to log-price (default: 365 days).
Upper / Lower multipliers: scale the bands to align with historical highs and lows.
✅ Best Use
Combine with other cycle signals (dominance ratios, macro indicators, sentiment).
Designed for long-term cycle analysis, not intraday trading.
Fractal Wave MarkerFractal Wave Marker is an indicator that processes relative extremes of fluctuating prices within 2 periodical aspects. The special labeling system detects and visually marks multi-scale turning points, letting you visualize fractal echoes within unfolding cycles dynamically.
What This Indicator Does
Identifies major and minor swing highs/lows based on adjustable period.
Uses Phi in power exponent to compute a higher-degree swing filter.
Labels of higher degree appear only after confirmed base swings — no phantom levels, no hindsight bias. What you see is what the market has validated.
Swing points unfold in a structured, alternating rhythm . No two consecutive pivots share the same hierarchical degree!
Inspired by the Fractal Market Hypothesis, this script visualizes the principle that market behavior repeats across time scales, revealing structured narrative of "random walk". This inherent sequencing ensures fractal consistency across timeframes. "Fractal echoes" demonstrate how smaller price swings can proportionally mirror larger ones in both structure and timing, allowing traders to anticipate movements by recursive patterns. Cycle Transitions highlight critical inflection points where minor pivots flip polarity such as a series of lower highs progress into higher highs—signaling the birth of a new macro trend. A dense dense clusters of swing points can indicate Liquidity Zones, acting as footprints of institutional accumulation or distribution where price action validates supply and demand imbalances.
Visualization of nested cycles within macro trend anchors - a main feature specifically designed for the chartists who prioritize working with complex wave oscillations their analysis.
111D SMA / (350D SMA * 2)Indicator: Pi Cycle Ratio
This custom technical indicator calculates a ratio between two moving averages that are used for the PI Cycle Top indicator. The PI Cycle Top indicator triggers when the 111-day simple moving average (111D SMA) crosses up with the 350-day simple moving average (350D SMA *2).
The line value is ratio is calculated as:
Line Value = 111DSMA / (350D SMA × 2)
When the 111D SMA crosses with the 350D SMA triggering the PI Cycle Top, the value of the ratio between the two lines is 1.
This visualizes the ratio between the two moving averages into a single line. This indicator can be used for technical analysis for historical and future moves.
