Adaptive Volume Confluence OscillatorWhat it is
One pane that fuses seven different reads of the bar into a single 0–100 confluence score, gates that score by a trend-vs-chop regime filter, confirms it against an auto-mapped higher timeframe, and — most importantly — forward-calibrates its own Buy/Sell signals against an unconditional base rate, so you can see whether the construction actually carries an edge on your instrument.
The seven votes: momentum sign · momentum vs its signal · money flow · trend structure (MA fan) · price location vs VWAP · trend slope · higher-timeframe bias.
The displayed wave is a volume-flow ribbon; the votes drive the score, the signals and the verdict. A plain-language verdict and a subtle pane tint make it readable at a glance (Simple view); a full analytic layer is available for advanced users (Pro view).
Why these are combined (mashup rationale)
A single oscillator whipsaws and a single signal over-fires. Combining helps only when the inputs key on different quantities and their agreement is checked. Each vote reads a different thing — momentum, momentum-vs-signal, volume flow, multi-MA structure, location vs a session mean, slope, and a higher-timeframe read — so the count that agrees carries more information than any one of them alone. A Kaufman Efficiency-Ratio regime gate suppresses conviction in chop, and a forward-calibration harness ties the whole construction back to realised forward outcomes.
An honest caveat, stated up front: the votes are not statistically independent. The oscillator itself embeds money flow, and vote 2 is derived from vote 1's series. Treat the score as a weight-of-evidence read, not as seven independent confirmations. The harness exists precisely so you can check whether the construction earns its keep on your instrument rather than taking the claim on faith.
How it works
Score — how many of the seven votes are bullish, scaled 0–100.
Regime — Kaufman Efficiency Ratio. Below the chop threshold, conviction dims, signals are withheld, and the verdict reads "WAIT – choppy".
HTF — the chart timeframe auto-maps to a confirming higher timeframe (~4–6×), requested with lookahead_off and offset by one bar while the live bar forms.
Signals — Buy/Sell fire only when the oscillator crosses its signal at a statistical OB/OS extreme and the score agrees and the regime isn't choppy and the visible wave isn't already at the opposite extreme.
Climax — a volume spike at an OB/OS extreme prints a Possible Bottom/Top exhaustion mark.
Divergence (Pro) — regular + hidden, from confirmed pivots on the momentum oscillator.
Calibration — each Buy/Sell is queued and resolved a fixed horizon later, then compared with the unconditional same-horizon base rate. The dashboard shows, per side: Hit %, Edge = Hit − Base, sample size, and a Wilson-gated star.
How to use it
Read the verdict and the score. Above the gate = bullish weight of evidence; below = bearish; in between, or in chop, the tool says WAIT — and it means it.
Treat Buy/Sell marks as context, not triggers. They already require the score, the regime and the wave to agree, but they remain a description of conditions — not a recommendation.
Read the Edge row before you weight any signal. If Buy/Sell Edge isn't clearly positive with an adequate sample and a star, this construction is not carrying an edge on this instrument — weight it down or ignore it. Do not tune the parameters until the Edge turns green: that is curve-fitting, and the harness is there to catch it, not to be defeated.
Combine with your own levels, structure and risk rules.
Universal across markets
Price / high / low are inputs, so the engine runs on any symbol or timeframe. The volume votes (money flow, climax, VWAP location) need real volume — prefer a futures contract or a stock. On a symbol with no volume the tool degrades gracefully: money flow is neutralised, the score falls back to the price-only votes, and the dashboard says "NO VOLUME", so you're never misled by a blank or a phantom reading.
Non-repainting
Votes read confirmed closes. The HTF series uses lookahead_off and is offset by one bar while the live bar forms. Divergences come from ta.pivot* and confirm a few bars after the pivot; once printed they don't move. The calibration harness logs and resolves only on confirmed bars, so its statistics never inflate intrabar. The live oscillator updates each bar, like any oscillator.
Concept credits
Super Smoother and Ultimate Smoother low-lag filters — John Ehlers. Chebyshev Type-I filter — classical DSP. Recursive (Kalman) smoothing — R. E. Kalman. Volume Zone Oscillator — Walid Khalil & David Steckler. Accumulation/Distribution money-flow multiplier — Marc Chaikin. Efficiency Ratio — Perry J. Kaufman. ATR — J. Welles Wilder. Wilson score interval — Edwin B. Wilson. VWAP, Hull MA and percentile rank — standard public methods.
Original implementation; not affiliated with, nor endorsed by, any third party. No third-party code is reused.
Honest limits
The score is context, not a guarantee, and the votes are correlated (see the caveat above). The Edge figures are in-sample, close-to-close, with overlapping forward windows and no costs — descriptive context, not a verified backtest. An Edge near zero, negative, or unstable across timeframes is the harness honestly telling you the signal has no reliable edge on that instrument. Nothing here predicts price.
Disclaimer
Research and educational tool only. Not financial advice and no guarantee of profitability or accuracy. Indicators describe past behaviour; they do not predict the future. Trading carries risk of loss. Test out-of-sample and make your own decisions. The author accepts no liability for any use of this script. Indicator
Generalized Fisher Transform [LB] Concept
The Generalized Fisher Transform extends John F. Ehlers' classic Fisher Transform (2002) by introducing an adjustable shape parameter that controls the sensitivity profile of the transformation. While the original Fisher Transform maps any normalized input to a near‑Gaussian output to highlight statistical extremes, this generalized version allows traders to emphasize central regions (shape < 1) or extreme tails (shape > 1) depending on their strategy.
Mathematical Foundation
The indicator first normalizes price to a bounded range using a rolling min‑max window of length N :
x = 2 × (P - L_min) / (H_max - L_min) - 1
A signed power is then applied with a shape factor p :
x_p = sign(x) × |x|^p
The generalized Fisher Transform is computed as :
F = 0.5 × ln( (1 + x_p) / (1 - x_p) )
When p = 1 , the formula reduces to the classic Fisher Transform. Values of p < 1 amplify sensitivity near zero (central price region), while p > 1 amplify sensitivity near the edges (extreme price region). The result is smoothed by an EMA for noise reduction.
What Problem Does It Solve ?
Classic oscillators such as RSI or Stochastic use fixed non‑linear mappings that cannot adapt to different market regimes or trader preferences. The classic Fisher Transform offers a single sensitivity profile. The Generalized Fisher Transform solves this by exposing the shape parameter p , giving traders direct control over where the indicator is most responsive — near the mean or near the extremes — without changing the underlying logic or introducing additional indicators.
How To Interpret
The indicator operates in two selectable modes :
Extremes Mode – the background turns red when Fisher exceeds the upper threshold (statistically overbought), and green when it drops below the lower threshold (statistically oversold). These zones suggest potential mean‑reversion.
Direction Mode – the background turns cyan when Fisher is above zero (bullish bias) and orange when below zero (bearish bias). This mode is suited for trend‑following or directional confirmation.
In both modes, the Fisher line crossing zero indicates a shift in the price distribution relative to its recent range.
Parameters
Source – price data used for the calculation (default: close).
Normalization Period – number of bars used to compute the rolling min‑max for the normalization.
Shape Factor – exponent applied to the normalized price before the Fisher transform. 1 = classic Fisher, < 1 = center‑sensitive, > 1 = tail‑sensitive.
Smoothing Period – EMA length applied to the raw Fisher output.
Coloration Mode – switches between "Extremes" (overbought/oversold highlighting) and "Direction" (bullish/bearish highlighting).
Upper Threshold – Fisher level above which the background turns red in Extremes mode.
Lower Threshold – Fisher level below which the background turns green in Extremes mode.
Reference
Ehlers J.F., "Using the Fisher Transform", Technical Analysis of Stocks & Commodities, Vol. 20, No. 11, pp. 40‑45, November 2002.
Ehlers J.F., "Cybernetic Analysis for Stocks and Futures", Chapter 4 – The Fisher Transform, John Wiley & Sons, 2004. Indicator
Trend Sniper v2.5 [Jamallo](2025)
Intro
Trend Sniper v2.5 is built around a novel-unique core construction —
the Butterworth Stop : a ratcheting trailing stop anchored to a 2-Pole Butterworth Super Smoother and dynamically sized by Parkinson Volatility.
While each algorithm used is individually well-documented, this specific combination is rare and not found in standard indicator libraries — most Butterworth implementations are simply plotted as trend lines, not used as the structural anchor of a volatility-adaptive stop mechanism.
Break down:
2-Pole Butterworth Super Smoother (Core Trend Line) (smoothed price)
The backbone of the indicator is a 2-Pole Butterworth Super Smoother, a concept brought into trading by engineer and author John Ehlers, first published in his 2004 book Cybernetic Analysis for Stocks and Futures
In this indicator: The Butterworth filter runs on close with a user-defined period (default 20) and produces bw_trend — a clean, lag-minimized version of price. This smoothed value is not plotted directly. Instead, it acts as the anchor point for the trailing stop. The stop is placed at bw_trend ± stop_dist , meaning the stop hugs the filtered trend rather than raw price, making it far less susceptible to wick noise and erratic bars. Without this pre-filtering step, the trailing stop would oscillate erratically on volatile bars.
Parkinson Volatility (Sizes the Stop Distance)
"Developed by physicist Michael Parkinson in 1980, it estimates volatility using the natural logarithm of the high-to-low ratio each period, capturing intraday range rather than just close-to-close movement."
In this indicator: Parkinson Volatility is calculated over a rolling window (default 50 bars) and multiplied by stop_mult (default 1.2) to produce stop_dist . When markets are volatile, stop_dist expands and the stop gives price more room. When markets are quiet, it contracts and the stop tightens.
Butterworth Stop State Machine (The Main Line You Watch)
This is the primary plotted line, It's a classic ratcheting trailing stop built on top of outputs from steps 1 and 2.
In this indicator: The logic is a simple state machine with two modes — uptrend and downtrend. In an uptrend, the stop only moves up (never down), tracking at bw_trend − stop_dist . In a downtrend, it only moves down, tracking at bw_trend + stop_dist . When price closes on the wrong side of the stop, the state flips and the stop resets on the other side of the trend line. Because both the anchor bw_trend and the buffer stop_dist are noise-filtered and volatility-adjusted, the BW Stop flips far less often than a raw price-based trailing stop would, keeping you in trends longer.
Chande Momentum Oscillator + Deadband (Colors the BW Stop Line)
"The CMO measures momentum by calculating the difference between the sum of gains and the sum of losses over a specified period, dividing by their total to produce a normalized scale from -100 to +100 that accounts for both up and down days simultaneously."
In this indicator: CMO is calculated on bw_stop itself (not raw price), over 14 bars by default. A deadband of ±10 is applied — the line only turns bull-colored when CMO exceeds +10, and bear-colored when it drops below −10. It does not change color in between.
KAMA Midpoint (The Dynamic Midline and Fill Zone Anchor)
"Introduced by Perry Kaufman in 1995, KAMA dynamically adjusts its smoothing based on the relative efficiency of price movement".
In this indicator: Rather than applying KAMA to price directly, it's applied to the midpoint between the BW Stop and the Slow SuperTrend. This midpoint is a computed halfway value between the two lines. KAMA then smooths that midpoint adaptively, producing `kama_mid`. The fill colors you see on the chart are drawn between kama_mid and bw_stop — not between price and anything else.
Dual SuperTrend (Signals and Fill State Logic)
"Created by Olivier Seban, SuperTrend combines ATR-based volatility with trend detection into a single line that repositions dynamically as price confirms or breaks the current trend direction."
In this indicator: Two SuperTrend lines run simultaneously.
The Slow ST (9× ATR) is the macro trend reference.
The Fast ST (6× ATR) is never plotted at full size — it's compressed halfway toward the BW Stop and shown as orange circles fast_shortened
Putting It All Together
The flow through the indicator is linear: Butterworth smooths price → Parkinson sizes the stop → the state machine builds the stop line → CMO colors it → KAMA anchors the midline fill → the dual SuperTrend fires signals and shades conviction.
END
Every component feeds the next, meaning the final signals and visuals are the product of five sequential filters rather than any single calculation — which is what gives the indicator its resistance to false signals in noisy market conditions. Indicator
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MACD Pseudo Super Smoother [MACDPSS]The MACD Pseudo Super Smoother (MACDPSS) is a variation of the classic Moving Average Convergence Divergence (MACD) indicator. It utilizes the Pseudo Super Smoother (PSS) filter, a Finite Impulse Response (FIR) filter, to smooth both the MACD line and the signal line, providing a potentially refined representation of momentum compared to the traditional MACD which typically uses Exponential Moving Averages (EMAs).
The PSS, inspired by the Super Smoother filter (an Infinite Impulse Response (IIR) filter), aims to reduce noise while minimizing lag. The MACDPSS leverages this FIR implementation to create a unique MACD variant. The core concept of MACD, which involves analyzing the relationship between two moving averages of different lengths to identify momentum shifts, remains intact.
Filter Types and Customization
The MACDPSS offers independent control over the smoothing applied to the MACD line and the signal line through two "Filter Style" inputs:
Oscillator MA Type: This setting determines the filter type used to calculate the fast and slow moving averages that form the basis of the MACD line.
Signal Line MA Type: This setting controls the filter type used to smooth the MACD line, generating the signal line.
Each of these settings allows a choice between two distinct PSS filter types:
Type 1: Provides a smoother output with a more gradual response, characterized by greater attenuation of high-frequency components.
Type 2: Exhibits increased reactivity, allowing for a faster response to shifts in momentum, but with a potential for overshoot.
This dual-filter approach provides flexibility in tailoring the indicator's responsiveness and smoothness to individual preferences and specific market conditions. The user can, for example, choose a smoother Type 1 filter for the MACD line and a more reactive Type 2 filter for the signal line, or vice-versa.
Calculations
The MACDPSS calculates the MACD line by subtracting the slow moving average from the fast moving average, both derived using the PSS filter with the selected "Oscillator MA Type." The signal line is then calculated by applying the PSS filter with the selected "Signal Line MA Type" to the MACD line. The histogram represents the difference between the MACD line and the signal line.
Interpretation
The interpretation of the MACDPSS is similar to the standard MACD. Crossovers between the MACD line and the signal line, the position of the MACD line relative to the zero line, and the slope and direction of the histogram are all used to gauge momentum and potential trend changes.
Disclaimer
The MACDPSS, while inspired by the Super Smoother, utilizes a distinct FIR approximation (the PSS). Therefore, its behavior will not perfectly mirror that of a MACD calculated using IIR filters. The PSS is designed to be a rough approximation. This indicator should be used in conjunction with other technical analysis tools, and users should be aware of the inherent differences between FIR and IIR filter characteristics when interpreting the indicator's signals. Like any moving average based indicator, the MACDPSS is a lagging indicator, although it tries to improve it. The novelty of this indicator comes from applying a unique FIR filter to a classic momentum oscillator in a configurable way. Indicator
Pseudo Super Smoother [PSS]The Pseudo Super Smoother (PSS) is a a Finite Impulse Response (FIR) filter. It provides a smoothed representation of the underlying data. This indicator can be considered a variation of a moving average, offering a unique approach to filtering price or other data series.
The PSS is inspired by the Super Smoother filter, known for its ability to reduce noise while maintaining a relatively low delay. However, the Super Smoother is an Infinite Impulse Response (IIR) filter. The PSS attempts to approximate some characteristics of the Super Smoother using an FIR design, which offers inherent stability.
The indicator offers two distinct filter types, selectable via the "Filter Style" input: Type 1 and Type 2 . Type 1 provides a smoother output with a more gradual response to changes in the input data. It is characterized by a greater attenuation of high-frequency components. Type 2 exhibits increased reactivity compared to Type 1 , allowing for a faster response to shifts in the underlying data trend, albeit with a potential overshoot. The choice between these two types will depend on the specific application and the preference for responsiveness versus smoothness.
The PSS calculates the FIR filter coefficients based on a decaying exponential function, adjusted according to the selected filter type and the user-defined period. The filter then applies these coefficients to a window of past data, effectively creating a weighted average that emphasizes more recent data points to varying degrees. The PSS uses a specific initialization technique that uses the first non-null data point to pre-fill the input window, which helps it start right away.
The PSS is an approximation of the Super Smoother filter using an FIR design. While it try's to emulate some of the Super Smoother's smoothing characteristics, users should be aware that the frequency response and overall behavior will differ due to it being a rough approximation. The PSS should be considered an experimental indicator and used in conjunction with other analysis techniques. This is, effectively, just another moving average, but its novelty lies in its attempt to bridge the gap between FIR and IIR filter designs for a specific smoothing goal.
Indicator
Ultimate Bands [BigBeluga]Ultimate Bands
The Ultimate Bands indicator is an advanced technical analysis tool that combines elements of volatility bands, oscillators, and trend analysis. It provides traders with a comprehensive view of market conditions, including trend direction, momentum, and potential reversal points.
🔵 KEY FEATURES
● Ultimate Bands
Consists of an upper band, lower band, and a smooth middle line
Based on John Ehler's SuperSmoother algorithm for reduced lag
Bands are calculated using Root Mean Square Deviation (RMSD) for adaptive volatility measurement
Helps identify potential support and resistance levels
● Ultimate Oscillator
Derived from the price position relative to the Ultimate Bands
Oscillates between overbought and oversold levels
Provides insights into potential reversals and trend strength
● Trend Signal Line
Based on a Hull Moving Average (HMA) of the Ultimate Oscillator
Helps identify the overall trend direction
Color-coded for easy trend interpretation
● Heatmap Visualization
Displays the current state of the oscillator and trend signal
Provides an intuitive visual representation of market conditions
Shows overbought/oversold status and trend direction at a glance
● Breakout Signals
Optional feature to detect and display breakouts beyond the Ultimate Bands
Helps identify potential trend reversals or continuations
Visualized with arrows on the chart and color-coded candles
🔵 HOW TO USE
● Trend Identification
Use the color and position of the Trend Signal Line to determine the overall market trend
Refer to the heatmap for a quick visual confirmation of trend direction
● Entry Signals
Look for price touches or breaks of the Ultimate Bands for potential entry points
Use oscillator extremes in conjunction with band touches for stronger signals
Consider breakout signals (if enabled) for trend-following entries
● Exit Signals
Use opposite band touches or breakouts as potential exit points
Monitor the oscillator for divergences or extreme readings as exit signals
● Overbought/Oversold Analysis
Use the Ultimate Oscillator and heatmap to identify overbought/oversold conditions
Look for potential reversals when the oscillator reaches extreme levels
● Confirmation
Combine Ultimate Bands, Oscillator, and Trend Signal for stronger trade confirmation
Use the heatmap for quick visual confirmation of market conditions
🔵 CUSTOMIZATION
The Ultimate Bands indicator offers several customization options:
Adjust the main calculation length for bands and oscillator
Modify the number of standard deviations for band calculation
Change the signal line length for trend analysis
Toggle the display of breakout signals and candle coloring
By fine-tuning these settings, traders can adapt the Ultimate Bands indicator to various market conditions and personal trading strategies.
The Ultimate Bands indicator provides a multi-faceted approach to market analysis, combining volatility-based bands, oscillator analysis, and trend identification in one comprehensive tool. Its adaptive nature and visual cues make it suitable for both novice and experienced traders across various timeframes and markets. The integration of multiple analytical elements offers traders a rich set of data points to inform their trading decisions. Indicator
Leading T3Hello Fellas,
Here, I applied a special technique of John F. Ehlers to make lagging indicators leading. The T3 itself is usually not realling the classic lagging indicator, so it is not really needed, but I still publish this indicator to demonstrate this technique of Ehlers applied on a simple indicator.
The indicator does not repaint.
In the following picture you can see a comparison of normal T3 (purple) compared to a 2-bar "leading" T3 (gradient):
The range of the gradient is:
Bottom Value: the lowest slope of the last 100 bars -> green
Top Value: the highest slope of the last 100 bars -> purple
Ehlers Special Technique
John Ehlers did develop methods to make lagging indicators leading or predictive. One of these methods is the Predictive Moving Average, which he introduced in his book “Rocket Science for Traders”. The concept is to take a difference of a lagging line from the original function to produce a leading function.
The idea is to extend this concept to moving averages. If you take a 7-bar Weighted Moving Average (WMA) of prices, that average lags the prices by 2 bars. If you take a 7-bar WMA of the first average, this second average is delayed another 2 bars. If you take the difference between the two averages and add that difference to the first average, the result should be a smoothed line of the original price function with no lag.
T3
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
Thanks for checking this out and give a boost, if you enjoyed the content.
Best regards,
simwai
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Credits to @loxx Indicator
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AIR Vortex ADXThis project started as an effort to improve the user interface of the hybrid indicator ADX of Vortex, which is, as per the name, a blend of ADX and Vortex Indicator. Plotting both indicators on the same polarity and normalising the vortex, a better interpretation of the interaction between the two is possible, and trend becomes apparent.
Basically, the Vortex provides the bright punch and ADX the continuation of the trend and momentum.
A range mixer has been added to the vortex, comprising both true and interpercentile ranges (see my previous script for a desrciption of interpercentile range). Users can activate and add amounts of each as they see fit.
Finally, there is an RSI filter, the idea of which is to filter out ranging (flat) markets, where no distinct direction is yet emerging. Indicator
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Adaptive, Zero lag Schaff Trend Cycle [Loxx]TASC's March 2008 edition Traders' Tips includes an article by John Ehlers titled "Measuring Cycle Periods," and describes the use of bandpass filters to estimate the length, in bars, of the currently dominant price cycle.
What are Dominant Cycles and Why should we use them?
Even the most casual chart reader will be able to spot times when the market is cycling and other times when longer-term trends are in play. Cycling markets are ideal for swing trading however attempting to “trade the swing” in a trending market can be a recipe for disaster. Similarly, applying trend trading techniques during a cycling market can equally wreak havoc in your account. Cycle or trend modes can readily be identified in hindsight. But it would be useful to have an objective scientific approach to guide you as to the current market mode.
There are a number of tools already available to differentiate between cycle and trend modes. For example, measuring the trend slope over the cycle period to the amplitude of the cyclic swing is one possibility.
We begin by thinking of cycle mode in terms of frequency or its inverse, periodicity. Since the markets are fractal ; daily, weekly, and intraday charts are pretty much indistinguishable when time scales are removed. Thus it is useful to think of the cycle period in terms of its bar count. For example, a 20 bar cycle using daily data corresponds to a cycle period of approximately one month.
When viewed as a waveform, slow-varying price trends constitute the waveform's low frequency components and day-to-day fluctuations (noise) constitute the high frequency components. The objective in cycle mode is to filter out the unwanted components--both low frequency trends and the high frequency noise--and retain only the range of frequencies over the desired swing period. A filter for doing this is called a bandpass filter and the range of frequencies passed is the filter's bandwidth.
Indicator Features
-Zero lag or Regular Schaff Trend Cycle calculation
- Fixed or Band-pass Dominant Cycle for Schaff Trend Cycle MA period inputs
-10 different moving average options for Zero lag calculations
-Separate Band-pass Dominant Cycle calculations for both Schaff Trend Cycle and MA calculations
- Slow-to-Fast Band-pass Dominant Cycle input to tweak the ratio of Schaff Trend Cycle MA input periods as they relate to each other Indicator
