Grover Llorens Activator [alexgrover & Lucía Llorens] Trailing stops play a key role in technical analysis and are extremely popular trend following indicators. Their main strength lie in their ability to minimize whipsaws while conserving a decent reactivity, the most popular ones include the Supertrend, Parabolic SAR and Gann Hilo activator. However, and like many indicators, most trailing stops assume an infinitely long trend, which penalize their ability to provide early exit points, this isn't the case of the parabolic SAR who take this into account and thus converge toward the price at an increasing speed the longer a trend last.
Today a similar indicator is proposed. From an original idea of alexgrover & Lucía Llorens who wanted to revisit the classic parabolic SAR indicator, the Llorens activator aim to converge toward the price the longer a trend persist, thus allowing for potential early and accurate exit points. The code make use of the idea behind the price curve channel that you can find here :
I tried to make the code as concise as possible.
The Indicator
The indicator posses 2 user settings, length and mult , length control the rate of convergence of the indicator, with higher values of length making the indicator output converge more slowly toward the price. Mult is also related with the rate of convergence, basically once the price cross the trailing stop its value will become equal to the previous trailing stop value plus/minus mult*atr depending on the previous trailing stop value, therefore higher values of mult will require more time for the trailing stop to reach the closing price, use higher values of mult if you want to avoid potential whipsaws.
Above the indicator with slow convergence time (high length) and low mult.
Points with early exit points are highlighted.
Usage For Oscillators
The difference between the closing price and an overlay indicator can provide an oscillator with characteristics depending on the indicators used for differencing, Lucía Llorens stated that we should find indicators for differencing that highlight the cycles in the price, in other terms : Price - Signal , where we want to find Signal such that we maximize the visibility of the cycles, it can be demonstrated that in the case where the closing price is an additive model : Trend + Cycles + Noise , the zero lag estimation of the Trend component can allow for the conservation of the cycle and noise component, that is : Price - Estimate(Trend) , for example the difference between the price and moving average isn't optimal because of the moving average lag, instead the use of zero lag moving averages is more suitable, however the proposed indicator allow for a surprisingly good representation of the cycles when using differencing.
The normalization of this oscillator (via the RSI) allow to make the peak amplitude of the cycles more constant. Note however that such method can return an output with a sign inverse to the one of the original cycle component.
Conclusion
We proposed an indicator which share the logic of the SAR indicator, that is using convergence toward the price in order to provide early exit points detection. We have seen that this indicator can be used to highlight cycles when used for differencing and i don't exclude publishing more indicators based on this method.
Lucía Llorens has been a great person to work with, and provided enormous feedback and support while i was coding the indicator, this is why i include her in the indicator name as well as copyright notice. I hope we can make more indicators togethers in the future.
(altho i was against using buy/sells labels xD !)
Thanks for reading !
Komut dosyalarını "curve" için ara
Yope BTC PL channelThis is a new version of the old "Yope BTC tops channel", but modified to reflect a power-law curve fitted, similar to the model proposed by Harold Christopher Burger in his medium article "Bitcoin’s natural long-term power-law corridor of growth".
My original tops channel fitting is still there for comparison. In fact, it looks like the old tops channel was a bit too pessimistic.
Note that these channels are still pure naive curve-fitting, and do not represent an underlying model that explains it, like is the case for PlanB's "Modeling Bitcoin's Value with Scarcity" which uses Stock-to-Flow.
The motivation for this exercise is to observe how long this empirical extrapolation is valid. Will the price of bitcoin stay in either of both channels?
Note on usage: This script _only_ works with the BLX "BraveNewCoin Liquid Index for Bitcoin" in the 1D, 3D and 1W time-frames!
It may be necessary to zoom in and out a few times to overcome drawing glitches caused by the extreme time-shifting of plots in order to draw the extrapolated part.
The Vostro Indicator by KIVANÇ fr3762The VOSTRO indicator is a trend indicator that automatically provides buying and selling signals. The indicator marks in a window the potential turning points. The indicator is recommended for scalping.
The Vostro indicator determines the overbought zones (value greater than +80) and the oversold zones (less than the -80 level)
BUY signal: The Vostro curve moves below the -80 level and forms a trough – Turnaround of the upward trend
SELL signal: The Vostro curve moves above the +80 level and forms a peak – Downward trend
further info:
www.prorealcode.com
Here's the link to a complete list of all my indicators:
t.co
Yazar: KıvanÇ @fr3762 twitter
Şimdiye kadar paylaştığım indikatörlerin tam listesi için: t.co
Advanced PSAR v1 [wm]A port off Dennis Meyers Advance PSAR outlined in Stocks and Commodities V13:4
The shape, slope and speed of the SAR is controlled by three parameters: the starting acceleration factor (AF), the increment that the AF can change when a new price high or low is made, and the maximum AF. Because of the way the SAR is calculated, the shape of the SAR curve resembles a parabola - hence its name.
Most software packages only allow the user to vary the AF increment and the AF maximum, fixing the starting AF at 0.02. This restriction hampers the trend-following abilities of the parabolic.
Frequently as the SAR hugs the price curve, it is penetrated by a price bar by a minuscule amount, causing the SAR to generate an opposite signal. The price then immediately turns around and resumes going in the direction it was going before this penetration occurred, causing a costly whipsaw loss.
Many of the whipsaw losses are caused by noise or randomness in the price series. Thus, if the SAR is to represent the trend of a real price series, it must have the capability to ignore penetrations of noise level amounts. To this end, I have modified the parabolic SAR formula to include a variable that allows the SAR not to reverse unless penetrated by a defined amount. This new parameter is defined as ‘XO Increment’ for crossover increment
This version is configured for pips. If using on other assets with much larger values should be used. Also note the starting values have not been optimised. Should users of this script find good values please comment and share with the community if you could
ALMA Trend DirectionHere is a very simple tool that uses the Arnaud Legoux Moving Average(ALMA). The ALMA is based on a normal distribution and is a reliable moving average due to its ability to reduce lag while still keeping a high degree of smoothness.
Input Options:
-Offset : Value in range {0,1} that adjusts the curve of the Gaussian Distribution. A higher value will result in higher responsiveness but lower smoothness. A lower value will mean higher smoothness but less responsiveness.
-Length : The lookback window for the ALMA calculation.
-Sigma : Defines the sharpe of the curve coefficients.
I find that this indicator is best used with a longer length and a 4 Hour timeframe. Overall, its purpose is to help identify the direction of a trend and determine whether a security is in an uptrend or a downtrend. For this purpose, it is best to use a lower offset value since we are looking to identify long-term, significant price movement rather than small fluctuations.
The Chart:
The ALMA is plotted as the aqua and pink alternating line. It is aqua when bullish and pink when bearish.
The low price for each candle is then compared to the ALMA. If the low is greater than the ALMA, then there is a bullish trend and the area between the candles and ALMA is filled green. The area between the ALMA and candles is filled red when the low price is less than the ALMA.
The difference between the slow ALMA and candles can reveal a lot about the current market state. If there is a significant green gap between the two, then we know that there is a significant uptrend taking place. On the other hand, a large red gap would indicate a significant downtrend. Similarly, if the gap between the two is narrowing and the ALMA line switches from aqua to pink, then we know that a reversal could be coming shortly.
~Happy Trading~
Double ALMAIncludes fast and slow Arnaud Legoux Moving Averages (ALMA). ALMA is a moving average based on a Gaussian(normal) distribution that reduces lag while still retaining smoothness.
Input Options:
-Offset : Value in range {0,1} that adjusts the curve of the Gaussian Distribution. A higher value will result in higher responsiveness but lower smoothness. A lower value will mean higher smoothness but less responsiveness.
-Lengths : The lookback for each ALMA calculation.
-Sigma : Defines the sharpe of the curve coefficients.
The slow ALMA is the thickest red and green alternating line that indicates bullish or bearish movement. When slow ALMA is bullish, the graph's background changes to green. When the slow ALMA is bearish, the background is red.
The fast ALMA uses a smaller lookback and is more responsive than the slow ALMA as a result of the shorter length and higher default offset parameter.
The two dotted lines represent (slowALMA +/- 1.25 * stdev(slowALMA, slowALMA period *2)).
The indicator bases its buy and sell signals based on the trend identified by the slow ALMA and the fast ALMA's crossings of the standard deviation bands.
Comes with pre-set buy and sell alerts.
Modified Gann HiLo ActivatorIntroduction
The gann hilo activator is a trend indicator developed by Robert Krausz published into W. D. Gann Treasure Discovered: Simple Trading Plans for Stocks & Commodities . This indicator crate a trailing stop aiming to show the direction of the trend.
This indicator is fairly easy to compute and dont require lot of skills to understand. First we calculate the simple moving average of both price high and price low, when the close price is higher than the moving average of the price high the indicator return the moving average of the price low, else the indicator return the moving average of the price high if the close price is lower than the moving average of the price low.
My indicator add a different calculation method in order to avoid whipsaw trades as well as adding significance to the moving average length. A Median method has been added to provide more robustness.
The Indicator
The indicator is a simple trailing stop aiming to show the direction of the trend. The indicator use a different source instead of the price high/low for its calculation. The first method is the "SMA" method which like the classic hilo indicator use a simple moving average for the calculation of the indicator.
Sma Method with length = 25
The "Median" use a moving median instead of a simple moving average, this provide more robustness.
Median Method with length = 25
The shape is less curved and the indicator can sometimes avoid whipsaw with high's length periods.
Mult Parameter
The mult parameter is a parameter set to be lower or equal to 1 and greater or equal to 0. High values allow the indicator to be far from the price thus avoiding whipsaw trades, lower ones lower the distance from the price. A mult parameter of 0.1 approximate the original hilo indicator.
In blue the indicator with mult = 0.1 and in radical red the original hilo activator.
Conclusion
The modifications allow more control over the indicator as well as adding more robustness while the original one is destined to fail when market price is more complex.
Thanks for reading :)
For any questions/suggestions feel free to pm me
Quadratic Regression Slope [DW]This is a study geared toward identifying price trends using Quadratic regression.
Quadratic regression is the process of finding the equation of a parabola that best fits the set of data being analyzed.
In this study, first a quadratic regression curve is calculated, then the slope of the curve is calculated and plotted.
Custom bar colors are included. The color scheme is based on whether the slope is positive or negative, and whether it is increasing or decreasing.
XPloRR MA-Trailing-Stop StrategyXPloRR MA-Trailing-Stop Strategy
Long term MA-Trailing-Stop strategy with Adjustable Signal Strength to beat Buy&Hold strategy
None of the strategies that I tested can beat the long term Buy&Hold strategy. That's the reason why I wrote this strategy.
Purpose: beat Buy&Hold strategy with around 10 trades. 100% capitalize sold trade into new trade.
My buy strategy is triggered by the fast buy EMA (blue) crossing over the slow buy SMA curve (orange) and the fast buy EMA has a certain up strength.
My sell strategy is triggered by either one of these conditions:
the EMA(6) of the close value is crossing under the trailing stop value (green) or
the fast sell EMA (navy) is crossing under the slow sell SMA curve (red) and the fast sell EMA has a certain down strength.
The trailing stop value (green) is set to a multiple of the ATR(15) value.
ATR(15) is the SMA(15) value of the difference between the high and low values.
The scripts shows a lot of graphical information:
The close value is shown in light-green. When the close value is lower then the buy value, the close value is shown in light-red. This way it is possible to evaluate the virtual losses during the trade.
the trailing stop value is shown in dark-green. When the sell value is lower then the buy value, the last color of the trade will be red (best viewed when zoomed)(in the example, there are 2 trades that end in gain and 2 in loss (red line at end))
the EMA and SMA values for both buy and sell signals are shown as a line
the buy and sell(close) signals are labeled in blue
How to use this strategy?
Every stock has it's own "DNA", so first thing to do is tune the right parameters to get the best strategy values voor EMA , SMA, Strength for both buy and sell and the Trailing Stop (#ATR).
Look in the strategy tester overview to optimize the values Percent Profitable and Net Profit (using the strategy settings icon, you can increase/decrease the parameters)
Then keep using these parameters for future buy/sell signals only for that particular stock.
Do the same for other stocks.
Important : optimizing these parameters is no guarantee for future winning trades!
Here are the parameters:
Fast EMA Buy: buy trigger when Fast EMA Buy crosses over the Slow SMA Buy value (use values between 10-20)
Slow SMA Buy: buy trigger when Fast EMA Buy crosses over the Slow SMA Buy value (use values between 30-100)
Minimum Buy Strength: minimum upward trend value of the Fast SMA Buy value (directional coefficient)(use values between 0-120)
Fast EMA Sell: sell trigger when Fast EMA Sell crosses under the Slow SMA Sell value (use values between 10-20)
Slow SMA Sell: sell trigger when Fast EMA Sell crosses under the Slow SMA Sell value (use values between 30-100)
Minimum Sell Strength: minimum downward trend value of the Fast SMA Sell value (directional coefficient)(use values between 0-120)
Trailing Stop (#ATR): the trailing stop value as a multiple of the ATR(15) value (use values between 2-20)
Example parameters for different stocks (Start capital: 1000, Order=100% of equity, Period 1/1/2005 to now) compared to the Buy&Hold Strategy(=do nothing):
BEKB(Bekaert): EMA-Buy=12, SMA-Buy=44, Strength-Buy=65, EMA-Sell=12, SMA-Sell=55, Strength-Sell=120, Stop#ATR=20
NetProfit: 996%, #Trades: 6, %Profitable: 83%, Buy&HoldProfit: 78%
BAR(Barco): EMA-Buy=16, SMA-Buy=80, Strength-Buy=44, EMA-Sell=12, SMA-Sell=45, Strength-Sell=82, Stop#ATR=9
NetProfit: 385%, #Trades: 7, %Profitable: 71%, Buy&HoldProfit: 55%
AAPL(Apple): EMA-Buy=12, SMA-Buy=45, Strength-Buy=40, EMA-Sell=19, SMA-Sell=45, Strength-Sell=106, Stop#ATR=8
NetProfit: 6900%, #Trades: 7, %Profitable: 71%, Buy&HoldProfit: 2938%
TNET(Telenet): EMA-Buy=12, SMA-Buy=45, Strength-Buy=27, EMA-Sell=19, SMA-Sell=45, Strength-Sell=70, Stop#ATR=14
NetProfit: 129%, #Trade
Donchian Channel Trend Intensity [DW]This is an experimental study designed to analyze trend intensity using two Donchian Channels.
The DCTI curve is calculated by comparing the differences between Donchian highs and lows over a major an minor period, and expressing them as a positive and negative percentage.
The curve is then smoothed with an exponential moving average to provide a signal line.
Custom bar colors included with two coloring methods to choose from.
AWESOME OSCILLATOR V2 by KIVANCfr3762AWESOME OSCILLATOR V2 by KIVANC @fr3762
CONVERTING THE OSCILLATOR to a curved line and added a 7 period SMA as a signal line,
crosses are BUY or SELL signals like in MACD
Buy: when AO line crosses above signal line
Sell: when Signal line crosses above AO line
Stefan Krecher: Jeddingen DivergenceThe main idea is to identify a divergence between momentum and price movement. E.g. if the momentum is rising but price is going down - this is what we call a divergence. The divergence will be calculated by comparing the direction of the linear regression curve of the price with the linear regression curve of momentum.
A bearish divergence can be identified by a thick red line, a bullish divergence by a green line.
When there is a divergence, it is likeley that the current trend will change it's direction.
Looking at the chart, there are three divergences that need to get interpreted:
1) bearish divergence, RSI is overbought but MACD does not clearly indicate a trend change. Right after the divergence, price and momentum are going up. No clear signal for a sell trade
2) bearish divergence, RSI still overbought, MACD histogram peaked, MACD crossed the signal line, price and momentum are going down. Very clear constellation for a sell trade.
3) two bullish diverences, RSI is oversold, MACD crossover near the end of the second divergence, price and momentum started rising. Good constellation for a buy trade. Could act as exit signal for the beforementioned sell trade.
More information on the Jeddingen Divergence is available here: www.forexpython.com
Power Law Correlation Indicator 2.0 The Power Law Correlation Indicator is an attempt to chart when a stock/currency/futures contract goes parabolic forming a upward or downward curve that accelerates according to power laws.
I've read about power laws from Sornette Diedler ( www.marketcalls.in ). And I think the theory is a good one.
The idea behind this indicator is that it will rise to 1.0 as the curve resembles a parabolic up or down swing. When it is below zero, the stock will flatten out.
There are many ways to use this indicator. One way I am testing it out is in trading Strangles or Straddle option trades. When this indicator goes below zero and starts to turn around, it means that it has flattened out. This is like a squeeze indicator. (see the TTM squeeze indicator).
Since this indicator goes below zero and the squeeze plays tend to be mean-reverting; then its a great time to put on a straddle/strangle.
Another way to use it is to think of it in terms of trend strength. Think of it as a kind of ADX, that measures the trend strength. When it is rising, the trend is strong; when it is dropping, the trend is weak.
Lastly I think this indicator needs some work. I tried to put the power (x^n) function into it but my coding skill is limited. I am hoping that Lazy Bear or Ricardo Santos can do it some justice.
Also I think that if we can figure out how to do other power law graphs, perhaps we can plot them together on one indicator.
So far I really like this one for finding Strangle spots. So check it out.
Peace
SpreadEagle71
EMA vs TMA Regime FilterEMA vs TMA Regime Filter
This indicator is built as a visual study tool to compare the behavior of the Exponential Moving Average (EMA) and the Triangular Moving Average (TMA).
The EMA applies an exponential weighting to price data, giving stronger importance to the most recent values. This makes it a faster, more responsive line that reflects short-term momentum. The TMA, by contrast, applies a double-smoothing process (or in the “True TMA” option, a split SMA sequence), which produces a much slower curve. The TMA emphasizes balance over reactivity, often used for filtering noise and observing longer-term structure.
When both are plotted on the same chart, their differences become clear. The shaded region between them highlights times when short-term price dynamics diverge from longer-term smoothing. This is where the idea of “regime” comes in — not as a trading signal, but as a descriptive way of seeing whether market action is currently dominated by speed or by stability.
Users can customize:
Line styles, widths, and colors.
Cloud transparency for visual clarity.
Whether to color bars based on relative position (optional, purely visual).
The goal is not to create a system, but to help traders experiment, observe, and learn how different smoothing techniques can emphasize different aspects of price. By switching between the legacy and true TMA, or adjusting lengths, users can study how each approach interprets the same data differently.
Adaptive Valuation [BackQuant]Adaptive Valuation
What this is
A composite, zero-centered oscillator that standardizes several classic indicators and blends them into one “valuation” line. It computes RSI, CCI, Demarker, and the Price Zone Oscillator, converts each to a rolling z-score, then forms a weighted average. Optional smoothing, dynamic overbought and oversold bands, and an on-chart table make the inputs and the final score easy to inspect.
How it works
Components
• RSI with its own lookback.
• CCI with its own lookback.
• DM (Demarker) with its own lookback.
• PZO (Price Zone Oscillator) with its own lookback.
Standardization via z-score
Each component is transformed using a rolling z-score over lookback bars:
z = (value − mean) ÷ stdev , where the mean is an EMA and the stdev is rolling.
This puts all inputs on a comparable scale measured in standard deviations.
Weighted blend
The z-scores are combined with user weights w_rsi, w_cci, w_dm, w_pzo to produce a single valuation series. If desired, it is then smoothed with a selected moving average (SMA, EMA, WMA, HMA, RMA, DEMA, TEMA, LINREG, ALMA, T3). ALMA’s sigma input shapes its curve.
Dynamic thresholds (optional)
Two ways to set overbought and oversold:
• Static : fixed levels at ob_thres and os_thres .
• Dynamic : ±k·σ bands, where σ is the rolling standard deviation of the valuation over dynLen .
Bands can be centered at zero or around the valuation’s rolling mean ( centerZero ).
Visualization and UI
• Zero line at 0 with gradient fill that darkens as the valuation moves away from 0.
• Optional plotting of band lines and background highlights when OB or OS is active.
• Optional candle and background coloring driven by the valuation.
• Summary table showing each component’s current z-score, the final score, and a compact status.
How it can be used
• Bias filter : treat crosses above 0 as bullish bias and below 0 as bearish bias.
• Mean-reversion context : look for exhaustion when the valuation enters the OB or OS region, then watch for exits from those regions or a return toward 0.
• Signal confirmation : use the final score to confirm setups from structure or price action.
• Adaptive banding : with dynamic thresholds, OB and OS adjust to prevailing variability rather than relying on fixed lines.
• Component tuning : change weights to emphasize trend (raise DM, reduce RSI/CCI) or range behavior (raise RSI/CCI, reduce DM). PZO can help in swing environments.
Why z-score blending helps
Indicators often live on different scales. Z-scoring places them on a common, unitless axis, so a one-sigma move in RSI has comparable influence to a one-sigma move in CCI. This reduces scale bias and allows transparent weighting. It also facilitates regime-aware thresholds because the dynamic bands scale with recent dispersion.
Inputs to know
• Component lookbacks : rsilb, ccilb, dmlb, pzolb control each raw signal.
• Standardization window : lookback sets the z-score memory. Longer smooths, shorter reacts.
• Weights : w_rsi, w_cci, w_dm, w_pzo determine each component’s influence.
• Smoothing : maType, smoothP, sig govern optional post-blend smoothing.
• Dynamic bands : dyn_thres, dynLen, thres_k, centerZero configure the adaptive OB/OS logic.
• UI : toggle the plot, table, candle coloring, and threshold lines.
Reading the plot
• Above 0 : composite pressure is positive.
• Below 0 : composite pressure is negative.
• OB region : valuation above the chosen OB line. Risk of mean reversion rises and momentum continuation needs evidence.
• OS region : mirror logic on the downside.
• Band exits : leaving OB or OS can serve as a normalization cue.
Strengths
• Normalizes heterogeneous signals into one interpretable series.
• Adjustable component weights to match instrument behavior.
• Dynamic thresholds adapt to changing volatility and drift.
• Transparent diagnostics from the on-chart table.
• Flexible smoothing choices, including ALMA and T3.
Limitations and cautions
• Z-scores assume a reasonably stationary window. Sharp regime shifts can make recent bands unrepresentative.
• Highly correlated components can overweight the same effect. Consider adjusting weights to avoid double counting.
• More smoothing adds lag. Less smoothing adds noise.
• Dynamic bands recalibrate with dynLen ; if set too short, bands may swing excessively. If too long, bands can be slow to adapt.
Practical tuning tips
• Trending symbols: increase w_dm , use a modest smoother like EMA or T3, and use centerZero dynamic bands.
• Choppy symbols: increase w_rsi and w_cci , consider ALMA with a higher sigma , and widen bands with a larger thres_k .
• Multiday swing charts: lengthen lookback and dynLen to stabilize the scale.
• Lower timeframes: shorten component lookbacks slightly and reduce smoothing to keep signals timely.
Alerts
• Enter and exit of Overbought and Oversold, based on the active band choice.
• Bullish and bearish zero crosses.
Use alerts as prompts to review context rather than as stand-alone trade commands.
Final Remarks
We created this to show people a different way of making indicators & trading.
You can process normal indicators in multiple ways to enhance or change the signal, especially with this you can utilise machine learning to optimise the weights, then trade accordingly.
All of the different components were selected to give some sort of signal, its made out of simple components yet is effective. As long as the user calibrates it to their Trading/ investing style you can find good results. Do not use anything standalone, ensure you are backtesting and creating a proper system.
BPS Multi-MA 5 — 22/30, SMA/WMA/EMA# Multi-MA 5 — 22/30 base, SMA/WMA/EMA
**What it is**
A lightweight 5-line moving-average ribbon for fast visual bias and trend/mean-reversion reads. You can switch the MA type (SMA/WMA/EMA) and choose between two ways of setting lengths: by monthly “session-based” base (22 or 30) with multipliers, or by entering exact lengths manually. An optional info table shows the effective settings in real time.
---
## How it works
* Calculates five moving averages from the selected price source.
* Lengths are either:
* **Multipliers mode:** `Base × Multiplier` (e.g., base 22 → 22/44/66/88/110), or
* **Manual mode:** any five exact lengths (e.g., 10/22/50/100/200).
* Plots five lines with fixed legend titles (MA1…MA5); the **info table** displays the actual type and lengths.
---
## Inputs
**Length Mode**
* **Multipliers** — choose a **Base** of **22** (≈ trading sessions per month) or **30** (calendar-style, smoother) and set **×1…×5** multipliers.
* **Manual** — enter **Len1…Len5** directly.
**MA Settings**
* **MA Type:** SMA / WMA / EMA
* **Source:** any series (e.g., `close`, `hlc3`, etc.)
* **Use true close (ignore Heikin Ashi):** when enabled, the MA is computed from the underlying instrument’s real `close`, not HA candles.
* **Show info table:** toggles the on-chart table with the current mode, type, base, and lengths.
---
## Quick start
1. Add the indicator to your chart.
2. Pick **MA Type** (e.g., **WMA** for faster response, **SMA** for smoother).
3. Choose **Length Mode**:
* **Multipliers:** set **Base = 22** for session-based monthly lengths (stocks/FX), or **30** for heavier smoothing.
* **Manual:** enter your exact lengths (e.g., 10/22/50/100/200).
4. (Optional) On **Heikin Ashi** charts, enable **Use true close** if you want the lines based on the instrument’s real close.
---
## Tips & notes
* **1 month ≈ 21–22 sessions.** Using 30 as “monthly” yields a smoother, more delayed curve.
* **WMA** reacts faster than **SMA** at the same length; expect earlier signals but more whipsaws in chop.
* **Len = 1** makes the MA track the chosen source (e.g., `close`) almost exactly.
* If changing lengths doesn’t move the lines, ensure you’re editing fields for the **active Length Mode** (Multipliers vs Manual).
* For clean comparisons, use the **same timeframe**. If you later wrap this in MTF logic, keep `lookahead_off` and handle gaps appropriately.
---
## Use cases
* Trend ribbon and dynamic bias zones
* Pullback entries to the mid/slow lines
* Crossovers (fast vs slow) for confirmation
* Volatility filtering by spreading lengths (e.g., 22/44/88/132/176)
---
**Credits:** Built for clarity and speed; designed around session-based “monthly” lengths (22) or smoother calendar-style (30).
Machine Learning BBPct [BackQuant]Machine Learning BBPct
What this is (in one line)
A Bollinger Band %B oscillator enhanced with a simplified K-Nearest Neighbors (KNN) pattern matcher. The model compares today’s context (volatility, momentum, volume, and position inside the bands) to similar situations in recent history and blends that historical consensus back into the raw %B to reduce noise and improve context awareness. It is informational and diagnostic—designed to describe market state, not to sell a trading system.
Background: %B in plain terms
Bollinger %B measures where price sits inside its dynamic envelope: 0 at the lower band, 1 at the upper band, ~ 0.5 near the basis (the moving average). Readings toward 1 indicate pressure near the envelope’s upper edge (often strength or stretch), while readings toward 0 indicate pressure near the lower edge (often weakness or stretch). Because bands adapt to volatility, %B is naturally comparable across regimes.
Why add (simplified) KNN?
Classic %B is reactive and can be whippy in fast regimes. The simplified KNN layer builds a “nearest-neighbor memory” of recent market states and asks: “When the market looked like this before, where did %B tend to be next bar?” It then blends that estimate with the current %B. Key ideas:
• Feature vector . Each bar is summarized by up to five normalized features:
– %B itself (normalized)
– Band width (volatility proxy)
– Price momentum (ROC)
– Volume momentum (ROC of volume)
– Price position within the bands
• Distance metric . Euclidean distance ranks the most similar recent bars.
• Prediction . Average the neighbors’ prior %B (lagged to avoid lookahead), inverse-weighted by distance.
• Blend . Linearly combine raw %B and KNN-predicted %B with a configurable weight; optional filtering then adapts to confidence.
This remains “simplified” KNN: no training/validation split, no KD-trees, no scaling beyond windowed min-max, and no probabilistic calibration.
How the script is organized (by input groups)
1) BBPct Settings
• Price Source – Which price to evaluate (%B is computed from this).
• Calculation Period – Lookback for SMA basis and standard deviation.
• Multiplier – Standard deviation width (e.g., 2.0).
• Apply Smoothing / Type / Length – Optional smoothing of the %B stream before ML (EMA, RMA, DEMA, TEMA, LINREG, HMA, etc.). Turning this off gives you the raw %B.
2) Thresholds
• Overbought/Oversold – Default 0.8 / 0.2 (inside ).
• Extreme OB/OS – Stricter zones (e.g., 0.95 / 0.05) to flag stretch conditions.
3) KNN Machine Learning
• Enable KNN – Switch between pure %B and hybrid.
• K (neighbors) – How many historical analogs to blend (default 8).
• Historical Period – Size of the search window for neighbors.
• ML Weight – Blend between raw %B and KNN estimate.
• Number of Features – Use 2–5 features; higher counts add context but raise the risk of overfitting in short windows.
4) Filtering
• Method – None, Adaptive, Kalman-style (first-order),
or Hull smoothing.
• Strength – How aggressively to smooth. “Adaptive” uses model confidence to modulate its alpha: higher confidence → stronger reliance on the ML estimate.
5) Performance Tracking
• Win-rate Period – Simple running score of past signal outcomes based on target/stop/time-out logic (informational, not a robust backtest).
• Early Entry Lookback – Horizon for forecasting a potential threshold cross.
• Profit Target / Stop Loss – Used only by the internal win-rate heuristic.
6) Self-Optimization
• Enable Self-Optimization – Lightweight, rolling comparison of a few canned settings (K = 8/14/21 via simple rules on %B extremes).
• Optimization Window & Stability Threshold – Governs how quickly preferred K changes and how sensitive the overfitting alarm is.
• Adaptive Thresholds – Adjust the OB/OS lines with volatility regime (ATR ratio), widening in calm markets and tightening in turbulent ones (bounded 0.7–0.9 and 0.1–0.3).
7) UI Settings
• Show Table / Zones / ML Prediction / Early Signals – Toggle informational overlays.
• Signal Line Width, Candle Painting, Colors – Visual preferences.
Step-by-step logic
A) Compute %B
Basis = SMA(source, len); dev = stdev(source, len) × multiplier; Upper/Lower = Basis ± dev.
%B = (price − Lower) / (Upper − Lower). Optional smoothing yields standardBB .
B) Build the feature vector
All features are min-max normalized over the KNN window so distances are in comparable units. Features include normalized %B, normalized band width, normalized price ROC, normalized volume ROC, and normalized position within bands. You can limit to the first N features (2–5).
C) Find nearest neighbors
For each bar inside the lookback window, compute the Euclidean distance between current features and that bar’s features. Sort by distance, keep the top K .
D) Predict and blend
Use inverse-distance weights (with a strong cap for near-zero distances) to average neighbors’ prior %B (lagged by one bar). This becomes the KNN estimate. Blend it with raw %B via the ML weight. A variance of neighbor %B around the prediction becomes an uncertainty proxy ; combined with a stability score (how long parameters remain unchanged), it forms mlConfidence ∈ . The Adaptive filter optionally transforms that confidence into a smoothing coefficient.
E) Adaptive thresholds
Volatility regime (ATR(14) divided by its 50-bar SMA) nudges OB/OS thresholds wider or narrower within fixed bounds. The aim: comparable extremeness across regimes.
F) Early entry heuristic
A tiny two-step slope/acceleration probe extrapolates finalBB forward a few bars. If it is on track to cross OB/OS soon (and slope/acceleration agree), it flags an EARLY_BUY/SELL candidate with an internal confidence score. This is explicitly a heuristic—use as an attention cue, not a signal by itself.
G) Informational win-rate
The script keeps a rolling array of trade outcomes derived from signal transitions + rudimentary exits (target/stop/time). The percentage shown is a rough diagnostic , not a validated backtest.
Outputs and visual language
• ML Bollinger %B (finalBB) – The main line after KNN blending and optional filtering.
• Gradient fill – Greenish tones above 0.5, reddish below, with intensity following distance from the midline.
• Adaptive zones – Overbought/oversold and extreme bands; shaded backgrounds appear at extremes.
• ML Prediction (dots) – The KNN estimate plotted as faint circles; becomes bright white when confidence > 0.7.
• Early arrows – Optional small triangles for approaching OB/OS.
• Candle painting – Light green above the midline, light red below (optional).
• Info panel – Current value, signal classification, ML confidence, optimized K, stability, volatility regime, adaptive thresholds, overfitting flag, early-entry status, and total signals processed.
Signal classification (informational)
The indicator does not fire trade commands; it labels state:
• STRONG_BUY / STRONG_SELL – finalBB beyond extreme OS/OB thresholds.
• BUY / SELL – finalBB beyond adaptive OS/OB.
• EARLY_BUY / EARLY_SELL – forecast suggests a near-term cross with decent internal confidence.
• NEUTRAL – between adaptive bands.
Alerts (what you can automate)
• Entering adaptive OB/OS and extreme OB/OS.
• Midline cross (0.5).
• Overfitting detected (frequent parameter flipping).
• Early signals when early confidence > 0.7.
These are purely descriptive triggers around the indicator’s state.
Practical interpretation
• Mean-reversion context – In range markets, adaptive OS/OB with ML smoothing can reduce whipsaws relative to raw %B.
• Trend context – In persistent trends, the KNN blend can keep finalBB nearer the mid/upper region during healthy pullbacks if history supports similar contexts.
• Regime awareness – Watch the volatility regime and adaptive thresholds. If thresholds compress (high vol), “OB/OS” comes sooner; if thresholds widen (calm), it takes more stretch to flag.
• Confidence as a weight – High mlConfidence implies neighbors agree; you may rely more on the ML curve. Low confidence argues for de-emphasizing ML and leaning on raw %B or other tools.
• Stability score – Rising stability indicates consistent parameter selection and fewer flips; dropping stability hints at a shifting backdrop.
Methodological notes
• Normalization uses rolling min-max over the KNN window. This is simple and scale-agnostic but sensitive to outliers; the distance metric will reflect that.
• Distance is unweighted Euclidean. If you raise featureCount, you increase dimensionality; consider keeping K larger and lookback ample to avoid sparse-neighbor artifacts.
• Lag handling intentionally uses neighbors’ previous %B for prediction to avoid lookahead bias.
• Self-optimization is deliberately modest: it only compares a few canned K/threshold choices using simple “did an extreme anticipate movement?” scoring, then enforces a stability regime and an overfitting guard. It is not a grid search or GA.
• Kalman option is a first-order recursive filter (fixed gain), not a full state-space estimator.
• Hull option derives a dynamic length from 1/strength; it is a convenience smoothing alternative.
Limitations and cautions
• Non-stationarity – Nearest neighbors from the recent window may not represent the future under structural breaks (policy shifts, liquidity shocks).
• Curse of dimensionality – Adding features without sufficient lookback can make genuine neighbors rare.
• Overfitting risk – The script includes a crude overfitting detector (frequent parameter flips) and will fall back to defaults when triggered, but this is only a guardrail.
• Win-rate display – The internal score is illustrative; it does not constitute a tradable backtest.
• Latency vs. smoothness – Smoothing and ML blending reduce noise but add lag; tune to your timeframe and objectives.
Tuning guide
• Short-term scalping – Lower len (10–14), slightly lower multiplier (1.8–2.0), small K (5–8), featureCount 3–4, Adaptive filter ON, moderate strength.
• Swing trading – len (20–30), multiplier ~2.0, K (8–14), featureCount 4–5, Adaptive thresholds ON, filter modest.
• Strong trends – Consider higher adaptive_upper/lower bounds (or let volatility regime do it), keep ML weight moderate so raw %B still reflects surges.
• Chop – Higher ML weight and stronger Adaptive filtering; accept lag in exchange for fewer false extremes.
How to use it responsibly
Treat this as a state descriptor and context filter. Pair it with your execution signals (structure breaks, volume footprints, higher-timeframe bias) and risk management. If mlConfidence is low or stability is falling, lean less on the ML line and more on raw %B or external confirmation.
Summary
Machine Learning BBPct augments a familiar oscillator with a transparent, simplified KNN memory of recent conditions. By blending neighbors’ behavior into %B and adapting thresholds to volatility regime—while exposing confidence, stability, and a plain early-entry heuristic—it provides an informational, probability-minded view of stretch and reversion that you can interpret alongside your own process.
GrayZone Sniper [CHE] — Breakout Validation System GrayZone Sniper — Breakout Validation System
Trade only the clean breakouts. Detect the sideways “gray zone,” wait for a confirmed breach, and act only when momentum (TFRSI) and range expansion (Mean Deviation) align. Clear long/short triggers, one-shot exit signals, and persistent levels keep your manual trading disciplined and repeatable.
Why it boosts manual trading
* No guesswork: Grey box marks consolidation; you trade the validated break.
* Fewer fakeouts: Triggers require momentum + volatility—not just a wick through a level.
* Rules > bias: Optional close-only signals stop intrabar noise.
* Built-in exits: One-shot LS/SS (Long/Short Stop) when conditions degrade.
* Actionable visuals: Gray-zone boxes, persistent highs/lows, and a smooth T3 trendline.
What it does (short + precise)
1. Maps consolidation as a gray box (running high/low while state is neutral).
2. Validates breakouts only when:
* Mean Deviation filter says current range expands vs. its own baseline, and
* TFRSI momentum is above 50 + deadzone (long) or below 50 − deadzone (short), and
* Price closes beyond the last gray high/low (optional close-only).
→ You get L (long) or S (short).
3. Manages exits with a smooth T3 trendline plus MD trend: when MD weakens and T3 turns against the prior side, you get a single LS/SS stop signal.
4. Extends structure: Last gray-zone H/L can persist as right-extended levels for retests/targets.
5. Ready for alerts: Prebuilt alert conditions for L, S, LS, SS.
Signals at a glance
* L – Long Trigger (validated breakout up)
* S – Short Trigger (validated breakout down)
* LS – Long Stop (exit hint for open long)
* SS – Short Stop (exit hint for open short)
Why TFRSI + Mean Deviation is a killer combo
They measure different, complementary things—and that reduces correlated errors.
* Mean Deviation (MD) = range expansion filter. It checks whether current absolute deviation of Typical Price from its SMA (|TP − SMA(TP)|) is greater than its own historical mean deviation baseline. In plain English: *is the market actually moving beyond its usual wiggle?* If not, most breakouts are noise.
* TFRSI = directional momentum around a 50 baseline, normalized and smoothed to react fast while avoiding raw RSI twitchiness.
* Synergy:
* MD confirms there’s energy (volatility regime has expanded).
* TFRSI confirms where that energy points (bull or bear).
* Requiring both gives you high-quality, directional expansion—the exact condition that tends to produce follow-through, while filtering the classic “thin break, immediate snap-back.”
Result: Fewer trades, better quality. You skip most range breaks without momentum or momentum pops without real expansion.
Inputs & Functions (clean overview)
Core: TFRSI & MD
* TFRSI Length (`tfrsiLen`, default 6): Longer = smoother, slower.
* TFRSI Smoothing (`tfrsiSignalLen`, default 2): SMA on TFRSI for cleaner signals.
* Mean Deviation Period (`mdLen`, default 20): Baseline for expansion filter.
* Use classical MD (`useTaDev`, default off):
* Off: MD vs current SMA (warning-free internal baseline).
* On: Classical `ta.dev` implementation.
* TFRSI Deadzone ± around 50 (`tfrsiDeadzone`, default 1.0): Wider deadzone = stricter momentum confirmation (less chop).
Triggers & Logic
* Trigger only on bar close (`fireOnCloseOnly`, default on): Confirmed signals only; no intrabar flicker.
* Reset gray bounds after trigger (`resetGrayBoundsAfterTrigger`, default on): Clears last gray H/L once a trade triggers.
* Auto-deactivate on neutral (`autoDeactivateOnNeutral`, default off): Strict disarm when state flips back to neutral.
Gray-Zone Boxes
* Show boxes (`showGrayBoxes`, default on): Draws the neutral consolidation box.
* Max boxes (`maxGrayBoxes`, default 10): How many historic boxes to keep.
* Transparency (`boxFillTransp`/`boxBorderTransp`, defaults 85/30): Visual tuning.
Trendline (T3)
* T3 Length (`t3Length`, default 3): Smoothing depth (higher = smoother).
* T3 Volume Factor (`t3VolumeFactor`, default 0.7): Controls responsiveness of the T3 curve.
Persistent Levels
* Persist gray H/L (`saveGrayLevels`, default on): Extend last gray high/low to the right.
* Max saved level pairs (`maxSavedGrayLvls`, default 1): How many H/L pairs to keep.
* Reset levels on trigger (`resetLevelsOnTrig`, default off): Clean slate after new trigger.
Debug & Visuals
* Show debug markers (`showDebugMarkers`, default on): Display L/S/LS/SS in the pane.
* Show legend (`showLegend`, default on): Compact legend (top-right).
How to trade it (practical)
1. Keep close-only on. Let the market finish the candle.
2. Wait for a clean gray box. Let the range define itself.
3. Take only L/S triggers where MD filter passes and TFRSI confirms.
4. Use persistent levels for retests/partials/targets.
5. Respect LS/SS. When expansion fades and T3 turns, exit without debate.
Tuning tips:
* More chop? Increase `tfrsiDeadzone` or `mdLen`.
* Want faster entries? Slightly reduce `t3Length` or deadzone, but expect more noise.
* Works across assets/timeframes (crypto/FX/indices/equities).
Bottom line
GrayZone Sniper enforces a simple, robust rule: Don’t touch the market until it breaks a defined range with real expansion and aligned momentum. That’s why TFRSI + Mean Deviation is hard to beat—and why your manual breakout trades get cleaner, calmer, and more consistent.
Disclaimer:
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Enhance your trading precision and confidence with Triple Power Stop (CHE)! 🚀
Happy trading
Chervolino
Combined Futures Open Interest [Sam SDF-Solutions]The Combined Futures Open Interest indicator is designed to provide comprehensive analysis of market positioning by aggregating open interest data from the two nearest futures contracts. This dual-contract approach captures the complete picture of market participation, including rollover dynamics between front and back month contracts, offering traders crucial insights into institutional positioning and market sentiment.
Key Features:
Dual-Contract Aggregation: Automatically identifies and combines open interest from the first and second nearest futures contracts (e.g., ES1! + ES2!), providing a complete view of market positioning that single-contract analysis might miss.
Multi-Period Analysis: Tracks open interest changes across multiple timeframes:
1 Day: Immediate market sentiment shifts
1 Week: Short-term positioning trends
1 Month: Medium-term institutional flows
3 Months: Quarterly positioning aligned with contract expiration cycles
Smart Data Handling: Utilizes last known values when data is temporarily unavailable, preventing false signals from data gaps while clearly indicating when stale data is being used.
EMA Smoothing: Incorporates a customizable Exponential Moving Average (default 65 periods) to identify the underlying trend in open interest, filtering out daily noise and highlighting significant deviations.
Dynamic Visualization:
Color-coded main line showing directional changes (green for increases, red for decreases)
Optional fill areas between OI and EMA to visualize momentum
Separate contract lines for detailed rollover analysis
Customizable labels for significant percentage changes
Comprehensive Information Table: Displays real-time statistics including:
Current total open interest across both contracts
Period-over-period changes in absolute and percentage terms
EMA deviation metrics
Visual status indicators for quick assessment
Contract symbols and data quality warnings
Alert System: Configurable alerts for:
Significant daily changes (customizable threshold)
EMA crossovers indicating trend changes
Large percentage movements suggesting institutional activity
How It Works:
Contract Detection: The indicator automatically identifies the base futures symbol and constructs the appropriate contract codes for the two nearest expirations, or accepts manual symbol input for non-standard contracts.
Data Aggregation: Open interest data from both contracts is retrieved and summed, providing a complete picture that accounts for positions rolling between contracts.
Historical Comparison: The indicator calculates changes from multiple lookback periods (1/5/22/66 days) to show how positioning has evolved across different time horizons.
Trend Analysis: The EMA overlay helps identify whether current open interest is above or below its smoothed average, indicating momentum in position building or reduction.
Visual Feedback: The main line changes color based on daily changes, while the optional table provides detailed numerical analysis for traders requiring precise data.
___________________
This indicator is essential for futures traders, particularly those focused on index futures, commodities, or currency futures where understanding the aggregate positioning across nearby contracts is crucial. It's especially valuable during rollover periods when positions shift between contracts, and for identifying institutional accumulation or distribution patterns that single-contract analysis might miss. By combining multiple timeframe analysis with intelligent data handling and clear visualization, it simplifies the complex task of monitoring open interest dynamics across the futures curve.
SwingTrade ADX Strategy v6This is a swing trading strategy that combines VWAP (Volume Weighted Average Price), ADX (Average Directional Index) for trend strength, and volume ratios to generate long/short entry and exit signals. It's designed for daily charts but can be adapted.
#### Key Features:
- **Entries**: Based on VWAP crossovers, rising/falling delta (price deviation from VWAP), ADX trend confirmation, and volume ratios.
- **Exits**: Dynamic exits when VWAP delta reverses after a peak.
- **Filters**: Optional toggles for VWAP signals, ADX, and volume. Backtest date range for custom periods.
- **Visuals**: VWAP line, signal shapes/labels, and an info panel showing key metrics (VWAP Delta %, ADX, Volume Ratio).
- **Alerts**: Built-in alerts for buy/sell entries and exits.
#### How to Use:
1. Apply to your chart (e.g., stocks, forex, crypto).
2. Adjust parameters in the settings (e.g., ADX threshold, volume period).
3. Enable/disable indicators as needed.
4. Backtest using the date filters and review equity curve.
**Disclaimer**: This is for educational purposes only. Past performance is not indicative of future results. Not financial advice—trade at your own risk. Backtest thoroughly and use with proper risk management.
Feedback welcome! If you find it useful, give it a like.
Bitcoin Power Law [LuxAlgo]The Bitcoin Power Law tool is a representation of Bitcoin prices first proposed by Giovanni Santostasi, Ph.D. It plots BTCUSD daily closes on a log10-log10 scale, and fits a linear regression channel to the data.
This channel helps traders visualise when the price is historically in a zone prone to tops or located within a discounted zone subject to future growth.
🔶 USAGE
Giovanni Santostasi, Ph.D. originated the Bitcoin Power-Law Theory; this implementation places it directly on a TradingView chart. The white line shows the daily closing price, while the cyan line is the best-fit regression.
A channel is constructed from the linear fit root mean squared error (RMSE), we can observe how price has repeatedly oscillated between each channel areas through every bull-bear cycle.
Excursions into the upper channel area can be followed by price surges and finishing on a top, whereas price touching the lower channel area coincides with a cycle low.
Users can change the channel areas multipliers, helping capture moves more precisely depending on the intended usage.
This tool only works on the daily BTCUSD chart. Ticker and timeframe must match exactly for the calculations to remain valid.
🔹 Linear Scale
Users can toggle on a linear scale for the time axis, in order to obtain a higher resolution of the price, (this will affect the linear regression channel fit, making it look poorer).
🔶 DETAILS
One of the advantages of the Power Law Theory proposed by Giovanni Santostasi is its ability to explain multiple behaviors of Bitcoin. We describe some key points below.
🔹 Power-Law Overview
A power law has the form y = A·xⁿ , and Bitcoin’s key variables follow this pattern across many orders of magnitude. Empirically, price rises roughly with t⁶, hash-rate with t¹² and the number of active addresses with t³.
When we plot these on log-log axes they appear as straight lines, revealing a scale-invariant system whose behaviour repeats proportionally as it grows.
🔹 Feedback-Loop Dynamics
Growth begins with new users, whose presence pushes the price higher via a Metcalfe-style square-law. A richer price pool funds more mining hardware; the Difficulty Adjustment immediately raises the hash-rate requirement, keeping profit margins razor-thin.
A higher hash rate secures the network, which in turn attracts the next wave of users. Because risk and Difficulty act as braking forces, user adoption advances as a power of three in time rather than an unchecked S-curve. This circular causality repeats without end, producing the familiar boom-and-bust cadence around the long-term power-law channel.
🔹 Scale Invariance & Predictions
Scale invariance means that enlarging the timeline in log-log space leaves the trajectory unchanged.
The same geometric proportions that described the first dollar of value can therefore extend to a projected million-dollar bitcoin, provided no catastrophic break occurs. Institutional ETF inflows supply fresh capital but do not bend the underlying slope; only a persistent deviation from the line would falsify the current model.
🔹 Implications
The theory assigns scarcity no direct role; iterative feedback and the Difficulty Adjustment are sufficient to govern Bitcoin’s expansion. Long-term valuation should focus on position within the power-law channel, while bubbles—sharp departures above trend that later revert—are expected punctuations of an otherwise steady climb.
Beyond about 2040, disruptive technological shifts could alter the parameters, but for the next order of magnitude the present slope remains the simplest, most robust guide.
Bitcoin behaves less like a traditional asset and more like a self-organising digital organism whose value, security, and adoption co-evolve according to immutable power-law rules.
🔶 SETTINGS
🔹 General
Start Calculation: Determine the start date used by the calculation, with any prior prices being ignored. (default - 15 Jul 2010)
Use Linear Scale for X-Axis: Convert the horizontal axis from log(time) to linear calendar time
🔹 Linear Regression
Show Regression Line: Enable/disable the central power-law trend line
Regression Line Color: Choose the colour of the regression line
Mult 1: Toggle line & fill, set multiplier (default +1), pick line colour and area fill colour
Mult 2: Toggle line & fill, set multiplier (default +0.5), pick line colour and area fill colour
Mult 3: Toggle line & fill, set multiplier (default -0.5), pick line colour and area fill colour
Mult 4: Toggle line & fill, set multiplier (default -1), pick line colour and area fill colour
🔹 Style
Price Line Color: Select the colour of the BTC price plot
Auto Color: Automatically choose the best contrast colour for the price line
Price Line Width: Set the thickness of the price line (1 – 5 px)
Show Halvings: Enable/disable dotted vertical lines at each Bitcoin halving
Halvings Color: Choose the colour of the halving lines
Quadruple EMA (QEMA)The Quadruple Exponential Moving Average (QEMA) is an advanced technical indicator that extends the concept of lag reduction beyond TEMA (Triple Exponential Moving Average) to a fourth order. By applying a sophisticated four-stage EMA cascade with optimized coefficient distribution, QEMA provides the ultimate evolution in EMA-based lag reduction techniques.
Unlike traditional compund moving averages like DEMA and TEMA, QEMA implements a progressive smoothing system that strategically distributes alphas across four EMA stages and combines them with balanced coefficients (4, -6, 4, -1). This approach creates an indicator that responds extremely quickly to price changes while still maintaining sufficient smoothness to be useful for trading decisions. QEMA is particularly valuable for traders who need the absolute minimum lag possible in trend identification.
▶️ **Core Concepts**
Fourth-order processing: Extends the EMA cascade to four stages for maximum possible lag reduction while maintaining a useful signal
Progressive alpha system: Uses mathematically derived ratio-based alpha progression to balance responsiveness across all four EMA stages
Optimized coefficients: Employs calculated weights (4, -6, 4, -1) to effectively eliminate lag while preserving compound signal stability
Numerical stability control: Implements initialization and alpha distribution to ensure consistent results from the first calculation bar
QEMA achieves its exceptional lag reduction by combining four progressive EMAs with mathematically optimized coefficients. The formula is designed to maximize responsiveness while minimizing the overshoot problems that typically occur with aggressive lag reduction techniques. The implementation uses a ratio-based alpha progression that ensures each EMA stage contributes appropriately to the final result.
▶️ **Common Settings and Parameters**
Period: Default: 15| Base smoothing period | When to Adjust: Decrease for extremely fast signals, increase for more stable output
Alpha: Default: auto | Direct control of base smoothing factor | When to Adjust: Manual setting allows precise tuning beyond standard period settings
Source: Default: Close | Data point used for calculation | When to Adjust: Change to HL2 or HLC3 for more balanced price representation
Pro Tip: Professional traders often use QEMA with longer periods than other moving averages (e.g., QEMA(20) instead of EMA(10)) since its extreme lag reduction provides earlier signals even with longer periods.
▶️ **Calculation and Mathematical Foundation**
Simplified explanation:
QEMA works by calculating four EMAs in sequence, with each EMA taking the previous one as input. It then combines these EMAs using balancing weights (4, -6, 4, -1) to create a moving average with extremely minimal lag and high level of smoothness. The alpha factors for each EMA are progressively adjusted using a mathematical ratio to ensure balanced responsiveness across all stages.
Technical formula:
QEMA = 4 × EMA₁ - 6 × EMA₂ + 4 × EMA₃ - EMA₄
Where:
EMA₁ = EMA(source, α₁)
EMA₂ = EMA(EMA₁, α₂)
EMA₃ = EMA(EMA₂, α₃)
EMA₄ = EMA(EMA₃, α₄)
α₁ = 2/(period + 1) is the base smoothing factor
r = (1/α₁)^(1/3) is the derived ratio
α₂ = α₁ × r, α₃ = α₂ × r, α₄ = α₃ × r are the progressive alphas
Mathematical Rationale for the Alpha Cascade:
The QEMA indicator employs a specific geometric progression for its smoothing factors (alphas) across the four EMA stages. This design is intentional and aims to optimize the filter's performance. The ratio between alphas is **r = (1/α₁)^(1/3)** - derived from the cube root of the reciprocal of the base alpha.
For typical smoothing (α₁ < 1), this results in a sequence of increasing alpha values (α₁ < α₂ < α₃ < α₄), meaning that subsequent EMAs in the cascade are progressively faster (less smoothed). This specific progression, when combined with the QEMA coefficients (4, -6, 4, -1), is chosen for the following reasons:
1. Optimized Frequency Response:
Using the same alpha for all EMA stages (as in a naive multi-EMA approach) can lead to an uneven frequency response, potentially causing over-shooting of certain frequencies or creating undesirable resonance. The geometric progression of alphas in QEMA helps to create a more balanced and controlled filter response across a wider range of movement frequencies. Each stage's contribution to the overall filtering characteristic is more harmonized.
2. Minimized Phase Lag:
A key goal of QEMA is extreme lag reduction. The specific alpha cascade, particularly the relationship defined by **r**, is designed to minimize the cumulative phase lag introduced by the four smoothing stages, while still providing effective noise reduction. Faster subsequent EMAs contribute to this reduced lag.
🔍 Technical Note: The ratio-based alpha progression is crucial for balanced response. The ratio r is calculated as the cube root of 1/α₁, ensuring that the combined effect of all four EMAs creates a mathematically optimal response curve. All EMAs are initialized with the first source value rather than using progressive initialization, eliminating warm-up artifacts and providing consistent results from the first bar.
▶️ **Interpretation Details**
QEMA provides several key insights for traders:
When price crosses above QEMA, it signals the beginning of an uptrend with minimal delay
When price crosses below QEMA, it signals the beginning of a downtrend with minimal delay
The slope of QEMA provides immediate insight into trend direction and momentum
QEMA responds to price reversals significantly faster than other moving averages
Multiple QEMA lines with different periods can identify immediate support/resistance levels
QEMA is particularly valuable in fast-moving markets and for short-term trading strategies where speed of signal generation is critical. It excels at capturing the very beginning of trends and identifying reversals earlier than any other EMA-derived indicator. This makes it especially useful for breakout trading and scalping strategies where getting in early is essential.
▶️ **Limitations and Considerations**
Market conditions: Can generate excessive signals in choppy, sideways markets due to its extreme responsiveness
Overshooting: The aggressive lag reduction can create some overshooting during sharp reversals
Calculation complexity: Requires four separate EMA calculations plus coefficient application, making it computationally more intensive
Parameter sensitivity: Small changes in the base alpha or period can significantly alter behavior
Complementary tools: Should be used with momentum indicators or volatility filters to confirm signals and reduce false positives
▶️ **References**
Mulloy, P. (1994). "Smoothing Data with Less Lag," Technical Analysis of Stocks & Commodities .
Ehlers, J. (2001). Rocket Science for Traders . John Wiley & Sons.
Hull Moving Average Adaptive RSI (Ehlers)Hull Moving Average Adaptive RSI (Ehlers)
The Hull Moving Average Adaptive RSI (Ehlers) is an enhanced trend-following indicator designed to provide a smooth and responsive view of price movement while incorporating an additional momentum-based analysis using the Adaptive RSI.
Principle and Advantages of the Hull Moving Average:
- The Hull Moving Average (HMA) is known for its ability to track price action with minimal lag while maintaining a smooth curve.
- Unlike traditional moving averages, the HMA significantly reduces noise and responds faster to market trends, making it highly effective for detecting trend direction and changes.
- It achieves this by applying a weighted moving average calculation that emphasizes recent price movements while smoothing out fluctuations.
Why the Adaptive RSI Was Added:
- The core HMA line remains the foundation of the indicator, but an additional analysis using the Adaptive RSI has been integrated to provide more meaningful insights into momentum shifts.
- The Adaptive RSI is a modified version of the traditional Relative Strength Index that dynamically adjusts its sensitivity based on market volatility.
- By incorporating the Adaptive RSI, the HMA visually represents whether momentum is strengthening or weakening, offering a complementary layer of analysis.
How the Adaptive RSI Influences the Indicator:
- High Adaptive RSI (above 65): The market may be overbought, or bullish momentum could be fading. The HMA turns shades of red, signaling a possible exhaustion phase or potential reversals.
- Neutral Adaptive RSI (around 50): The market is in a balanced state, meaning neither buyers nor sellers are in clear control. The HMA takes on grayish tones to indicate this consolidation.
- Low Adaptive RSI (below 35): The market may be oversold, or bearish momentum could be weakening. The HMA shifts to shades of blue, highlighting potential recovery zones or trend slowdowns.
Why This Combination is Powerful:
- While the HMA excels in tracking trends and reducing lag, it does not provide information about momentum strength on its own.
- The Adaptive RSI bridges this gap by adding a clear visual layer that helps traders assess whether a trend is likely to continue, consolidate, or reverse.
- This makes the indicator particularly useful for spotting trend exhaustion and confirming momentum shifts in real-time.
Best Use Cases:
- Works effectively on timeframes from 1 hour (1H) to 1 day (1D), making it suitable for swing trading and position trading.
- Particularly useful for trading indices (SPY), stocks, forex, and cryptocurrencies, where momentum shifts are frequent.
- Helps identify not just trend direction but also whether that trend is gaining or losing strength.
Recommended Complementary Indicators:
- Adaptive Trend Finder: Helps identify the dominant long-term trend.
- Williams Fractals Ultimate: Provides key reversal points to validate trend shifts.
- RVOL (Relative Volume): Confirms significant moves based on volume strength.
This enhanced HMA with Adaptive RSI provides a powerful, intuitive visual tool that makes trend analysis and momentum interpretation more effective and efficient.
This indicator is for educational and informational purposes only. It should not be considered financial advice or a guarantee of performance. Always conduct your own research and use proper risk management when trading. Past performance does not guarantee future results.
