Adaptive Trend Ensemble [BackQuant]Adaptive Trend Ensemble
Overview
Adaptive Trend Ensemble is an online-learning trend filter that combines eight different moving-average methods into one continuously weighted trend estimate.
Instead of selecting one moving average permanently, the indicator treats each method as an independent forecasting expert. Every bar, each expert is evaluated according to whether its previous slope correctly anticipated the direction of the latest price move.
Experts that were directionally correct retain more influence. Experts that were wrong lose influence through a multiplicative penalty. The weights are then normalised and used to blend all eight moving-average values into one adaptive ensemble line.
The indicator therefore attempts to answer two separate questions:
Which smoothing method has recently aligned best with price direction?*
How strongly do the weighted methods currently agree on the direction of trend?
The final output includes:
A dynamically weighted ensemble trend line.
Bullish and bearish trend-state colouring.
A gradient between price and the ensemble.
A consensus-driven glow.
Trend-coloured candles.
A live label showing the leading expert and its current weight.
Alerts when the ensemble trend changes direction.
This is not a fixed moving average and it is not a simple average of several indicators. The contribution of each expert changes over time according to its recent directional performance.
Core idea
Moving averages respond differently to the same market.
A Hull Moving Average may respond quickly during a sharp transition, while an RMA may remain stable through temporary noise. A linear-regression estimate may follow a smooth directional move well, while a conventional EMA may perform better during a more ordinary trend.
No individual smoothing method is consistently superior across every environment.
Markets alternate between:
Persistent trends.
Fast breakouts.
Slow directional drift.
Volatile reversals.
Compressed ranges.
Noisy transitions.
A fixed indicator cannot change its mathematical personality when the environment changes. It continues using the same weighting structure regardless of whether that structure currently suits the market.
Adaptive Trend Ensemble addresses this by maintaining a bank of different smoothing methods and changing their influence through time.
The model does not attempt to decide in advance which method is best. It allows recent realised price action to determine which experts should currently receive more weight.
Prediction with expert advice
The indicator is based on a class of online-learning methods commonly described as:
Prediction with Expert Advice
In this framework:
Several experts produce predictions.
The actual outcome is observed.
Each expert receives a loss based on its prediction.
Expert weights are updated.
The combined model places more influence on better-performing experts.
The term “expert” does not imply that each method is intelligent by itself. An expert is simply an individual forecasting rule.
In this indicator, the eight experts are eight moving-average methods.
The model uses a multiplicative-weights process closely related to the Hedge and Weighted Majority families of online-learning algorithms.
The central principle is:
Do not commit permanently to one model.
Track several models simultaneously.
Reduce the weight of models that make mistakes.
Allow the combined forecast to adapt as relative performance changes.
Online learning
The model learns sequentially, one bar at a time.
It does not train on a separate historical dataset and then freeze its parameters.
At each new bar:
The previous slope of each moving average is treated as that expert's prediction.
The realised close-to-close direction is observed.
Each expert receives a loss.
Weights are updated multiplicatively.
Weights are normalised.
The current expert values are blended using the new weights.
This makes the process online and adaptive.
The weight state is carried forward from bar to bar, meaning the current ensemble reflects the accumulated results of earlier expert decisions.
The expert bank
The ensemble contains eight moving-average experts:
Simple Moving Average - SMA*
Exponential Moving Average - EMA
Weighted Moving Average - WMA*
Hull Moving Average - HMA
Double Exponential Moving Average - DEMA*
Running Moving Average - RMA
Arnaud Legoux Moving Average - ALMA*
Least-Squares Moving Average - LSMA
All experts use the same Base Length.
This is important because it keeps their nominal observation horizon comparable. The ensemble is comparing different mathematical treatments of approximately the same lookback rather than comparing completely unrelated time horizons.
Even with an identical length, the experts behave differently because they assign weight to historical observations in different ways.
Simple Moving Average - SMA
The SMA applies equal weight to every observation inside the selected window.
Its general form is:
SMA = Sum of observations / Number of observations
The SMA is stable and easy to interpret, but every included observation has the same importance.
This can make it slower to react when a new trend begins because older prices continue to influence the average until they leave the window.
Within the ensemble, the SMA acts as a neutral equal-weight baseline.
Exponential Moving Average - EMA
The EMA assigns progressively greater weight to recent observations.
Its recursive form is based on:
EMA = α × Current Price + (1 - α) × Previous EMA
where α is determined by the selected length.
Compared with an SMA of the same length, an EMA generally responds more quickly to recent movement.
Its recursive weighting makes it useful during ordinary directional markets, although it can still turn repeatedly when price oscillates in a range.
Weighted Moving Average - WMA
The WMA assigns linearly increasing weight to more recent observations.
For example, in a simplified four-period WMA, the newest value receives four units of weight, while the oldest receives one.
This makes the WMA more responsive than an equal-weight SMA while retaining a finite lookback window.
Within the ensemble, it provides a direct recency-weighted alternative to the exponential behaviour of the EMA.
Hull Moving Average - HMA
The Hull Moving Average was designed to reduce lag while preserving a relatively smooth output.
Its construction combines weighted moving averages over different horizons, applies a lag-compensation step, and then smooths the result over approximately the square root of the original length.
Conceptually:
Calculate a faster WMA.
Calculate a slower WMA.
Use their difference to compensate for lag.
Smooth the compensated result.
The HMA often reacts quickly to changes in trend direction.
That responsiveness can make it valuable during strong transitions, but it may also make it more sensitive to short-term oscillation.
Double Exponential Moving Average - DEMA
Despite its name, DEMA is not simply an EMA calculated twice.
Its general construction is:
DEMA = 2 × EMA - EMA of EMA
The second EMA estimates some of the lag in the first EMA. Subtracting it attempts to create a smoother with less delay.
DEMA can respond quickly to directional changes, although reduced lag may also increase sensitivity during unstable conditions.
Running Moving Average - RMA
RMA is commonly associated with Wilder-style smoothing.
It uses a slower recursive update than a typical EMA of the same nominal length.
Its general form places substantial influence on the previous RMA value, producing a persistent and stable estimate.
The RMA expert often changes direction less aggressively than the faster methods.
Within the ensemble, it acts as one of the more conservative smoothing models.
Arnaud Legoux Moving Average - ALMA
ALMA applies a Gaussian-style weighting curve across the observation window.
The weighting distribution can be shifted toward more recent observations while maintaining a smooth bell-shaped profile.
The script uses a recent-weighted offset and a fixed Gaussian width.
ALMA attempts to balance:
Smoothness.
Reduced lag.
Controlled weighting of the observation window.
It provides a different weighting structure from the linear, exponential and lag-compensated experts.
Least-Squares Moving Average - LSMA
The LSMA is based on linear regression.
Instead of averaging historical prices directly, it fits a straight line through the selected window and evaluates the regression estimate at the current bar.
The method attempts to represent the local directional path of price.
LSMA can follow smooth trends closely because it models slope explicitly. However, it may respond strongly when the local regression direction changes abruptly.
Within the indicator, the LSMA is produced using the rolling linear-regression output.
Base Length
The Base Length is shared by all eight experts.
Lower values:
Make every expert more responsive.
Increase sensitivity to short-term changes.
Produce faster weight and trend changes.
Increase the possibility of whipsaws.
Higher values:
Create smoother expert outputs.
Focus the ensemble on broader trend structure.
Reduce short-term changes.
Increase lag during sudden reversals.
Because all experts share the same length, changing this setting adjusts the entire ensemble horizon.
It does not change the number of experts or their relative starting weights.
Expert predictions
The model evaluates each expert using the direction of its slope.
For each moving average:
Rising slope is represented as +1.
Falling or non-rising slope is represented as -1.
To evaluate the latest completed move, the script uses the expert's slope from the previous bar.
For example:
If the expert was rising from two bars ago to the previous bar, it predicted a positive current move.
If the expert was falling, it predicted a negative current move.
The realised outcome is determined from the current close relative to the previous close:
Close above previous close = positive realised direction.
Close below previous close = negative realised direction.
Unchanged close = zero realised direction.
The model therefore scores directional slope prediction, not the numerical distance between each moving average and price.
An expert is rewarded for getting direction right, even if its plotted value is relatively far from the market.
Likewise, an expert is penalised for getting direction wrong even if its line remains visually close to price.
Loss functions
The indicator provides two loss functions:
Directional 0/1*
Magnitude-weighted
The selected loss determines how strongly incorrect experts are penalised.
Correct experts receive zero loss under both modes.
Directional 0/1 loss
Directional mode treats every incorrect prediction equally.
The loss is:
0 when the expert predicted the realised direction correctly.
1 when the expert predicted incorrectly.
This means that an incorrect prediction on a very small move receives the same loss as an incorrect prediction on a large move.
Directional mode answers a simple question:
Was the expert right or wrong?
It does not consider how important the move was.
This mode can produce consistent learning because every directional observation is treated equally, but it may respond to small and insignificant price changes as strongly as major moves.
Magnitude-weighted loss
Magnitude-weighted mode scales the penalty according to the size of the realised move.
The move is normalised using ATR:
Move = Absolute close-to-close change / ATR
The ATR uses the shared Base Length.
The incorrect expert's loss becomes:
Loss = Normalised Move
with the magnitude capped at 3.
The cap prevents a single extreme bar from creating an unlimited penalty.
This mode gives greater importance to mistakes during large movements.
For example:
An incorrect expert during a 0.10 ATR move receives a small penalty.
An incorrect expert during a 1.00 ATR move receives a larger penalty.
An incorrect expert during a move above 3 ATR receives the capped penalty of 3.
Magnitude-weighted mode answers:
How costly was the directional mistake relative to current volatility?
This can make the ensemble adapt more strongly after significant movements while paying less attention to small fluctuations.
Flat price bars
If the current close is unchanged from the previous close, the realised direction is zero.
Because expert directions are encoded as either positive or negative, no expert can exactly match a zero realised direction.
Under Directional mode, all experts receive the same incorrect classification.
Because every weight is multiplied by the same penalty factor, their relative weight distribution remains effectively unchanged after normalisation.
Under Magnitude-weighted mode, the realised move is zero, so the resulting penalty is also zero.
In both cases, a completely flat close-to-close bar does not materially change the relative ranking of the experts.
Multiplicative weight update
Each expert begins with an equal weight:
Initial Weight = 1 / 8
After the loss is calculated, the weight is updated using:
New Unnormalised Weight = Old Weight × exp(-η × Loss)
where η is the Learning Rate.
This is the central Hedge or multiplicative-weights update.
Correct experts have zero loss:
exp(-η × 0) = 1
Their unnormalised weight is unchanged.
Incorrect experts have a positive loss, so their weight is multiplied by a value below one.
For example, in Directional mode with a Learning Rate of 2:
Incorrect Weight Multiplier = exp(-2) ≈ 0.135
An incorrect expert retains only about 13.5% of its previous unnormalised weight before the weight set is normalised again.
This does not mean its final displayed weight will necessarily fall by exactly 86.5%, because all expert weights are subsequently rescaled so they sum to one.
Why multiplicative updates are used
An additive system might subtract a fixed quantity from each incorrect expert.
That can create problems:
Weights can become negative.
The same penalty has a different effect on large and small weights.
The model may not adapt proportionally.
A multiplicative update preserves non-negative weights and penalises experts proportionally to their current influence.
It also allows the distribution to become concentrated around consistently successful methods.
Learning Rate - η
The Learning Rate controls how aggressively the ensemble shifts weight after mistakes.
Higher values:
Penalise incorrect experts more strongly.
Move influence rapidly toward recent winners.
Can produce winner-take-all behaviour.
Can make the leader change abruptly after a few important bars.
Lower values:
Produce gradual weight changes.
Keep the expert distribution more diversified.
Reduce sensitivity to short-term performance.
Make the model slower to adapt.
The Learning Rate does not change the moving averages themselves. It changes only how quickly their relative influence evolves.
High Learning Rate behaviour
At high settings, a wrong expert may lose most of its weight after one or two mistakes.
This can be beneficial when one smoothing method is clearly better suited to the current regime.
It can also create instability:
A recent winner can dominate the ensemble.
A temporary performance streak can cause excessive concentration.
The model can switch leaders quickly when conditions reverse.
Low Learning Rate behaviour
At low settings, the ensemble behaves more like a slowly adapting average of the expert bank.
No single observation dramatically changes the distribution.
This produces smoother adaptation, but a poorly suited expert may retain substantial influence for longer.
Weight normalisation
After all expert weights are updated, they are normalised:
Normalised Weight = Expert Weight / Sum of All Expert Weights
This ensures that the complete weight set sums to one.
The weights can then be interpreted as each expert's share of the ensemble.
For example:
A 25% weight means that expert contributes one quarter of the weighted output.
A 5% weight means its current influence is relatively small.
The weights are not probabilities that the experts will be correct on the next bar.
They are adaptive influence coefficients based on accumulated relative loss.
Weight Floor
The optional Weight Floor preserves a minimum allocation for every expert.
After normalisation, the adjusted weight is calculated so that:
Every expert receives at least the selected floor.
The remaining weight is distributed according to the normalised Hedge weights.
The full set continues to sum to one.
For eight experts, a floor of 0.01 reserves at least 1% for each expert.
This assigns:
A minimum combined mass of 8%.
The remaining 92% according to relative performance.
A floor of 0.05 reserves at least 5% for each of the eight experts, using 40% of the total distribution as minimum allocations.
The remaining 60% is distributed according to current performance.
Why use a floor?
Without a floor, repeatedly incorrect experts can approach a weight extremely close to zero.
Because the update only reduces weights after losses, an expert with almost no weight may require a long period of relative outperformance before it becomes influential again.
A positive floor keeps all methods alive.
This allows an expert that performed poorly in the previous regime to recover more quickly when the market environment changes.
Weight Floor set to zero
With a zero floor:
The model is free to concentrate almost entirely in one expert.
Recent winners can dominate strongly.
The ensemble can become highly specialised.
This produces the purest multiplicative-weights behaviour but increases the risk of weight collapse.
Positive Weight Floor
With a positive floor:
The expert bank remains diversified.
Cold experts retain some influence.
The model can recover more easily after regime changes.
The leading expert's maximum possible weight is reduced.
The floor therefore controls the balance between specialisation and diversity.
Ensemble output
After the weight update, the current values of the eight experts are blended:
Ensemble = Sum of Expert Weight × Expert Value
This is a weighted average in which the weights are determined by online directional performance.
If the HMA currently has the greatest weight, the ensemble will behave more like the HMA.
If the RMA and SMA dominate, the output will become smoother and more conservative.
If the weights are distributed evenly, the line represents a broad blend of all eight methods.
The output can therefore change its effective smoothing behaviour without changing the user-selected Base Length.
Line Smoothing
The weighted ensemble may be passed through an optional EMA for visual smoothing.
A setting of 1 effectively disables this additional stage.
Higher settings:
Create a smoother displayed line.
Reduce small slope changes.
Delay bullish and bearish flips.
This smoothing is cosmetic in the sense that it occurs after the online expert weighting.
It does not affect:
Expert predictions.
Expert losses.
Weight updates.
Consensus.
Leader selection.
It does affect the final plotted line and the trend state derived from that line.
Trend state
Trend direction is determined from the slope of the smoothed ensemble line.
If the line is above its previous value, trend becomes bullish.
If the line is below its previous value, trend becomes bearish.
If the line is unchanged, the previous trend persists.
This creates a persistent two-state regime.
A bullish flip occurs when the trend changes from bearish to bullish.
A bearish flip occurs when it changes from bullish to bearish.
The trend state is based on the ensemble's slope, not on price crossing the ensemble.
Price may be above or below the line without immediately changing its direction.
Consensus calculation
The indicator calculates a separate weighted directional vote.
Each expert's current slope direction is multiplied by its current weight:
Weighted Vote = Sum of Weight × Direction
Because each direction is either +1 or -1 and the weights sum to one, the vote lies between -1 and +1.
Examples:
+1 means all meaningful weight is assigned to rising experts.
-1 means all meaningful weight is assigned to falling experts.
0 means bullish and bearish weighted influence is evenly balanced.
The displayed consensus strength is:
Consensus Strength = Absolute Value of Weighted Vote
This converts the result to a range from zero to one.
0% means the weighted expert bank is evenly divided.
100% means the weighted influence is entirely aligned in one direction.
Weighted consensus versus expert count
Consensus is not calculated by simply counting how many of the eight experts are rising.
An expert with a 40% weight contributes more than one with a 2% weight.
For example:
Five low-weight experts may be bullish.
Three high-weight experts may be bearish.
The final weighted vote can still be bearish.
This means consensus measures the agreement of the current weighted model, not the raw number of methods on each side.
With a zero Weight Floor, consensus may become very high when one expert dominates, even if several near-zero-weight experts disagree.
With a positive floor, disagreement from the remaining experts has more influence on the consensus value.
Consensus is not confidence
The consensus percentage should not be interpreted as a probability that the trend will continue.
It measures only the current alignment of weighted expert slopes.
High consensus means:
The influential experts point in the same direction.
It does not guarantee:
Future price continuation.
A profitable entry.
Low reversal risk.
Strong agreement can occur late in a mature trend as well as early in a new one.
Leading method
The live information label identifies the expert with the highest current weight.
It displays:
The expert name.
Its current percentage weight.
The weighted consensus strength.
The current ensemble direction.
For example:
Leading: HMA (34.5%)*
Consensus: 78% ▲
This means the HMA currently has the largest share of the ensemble and the weighted expert bank is strongly aligned upward.
The leader percentage is not a win probability.
It is only the experts share of the current normalised weight distribution.
Leader changes
The leading method can change when:
The current leader makes directional mistakes.
Another expert remains correct while competitors are penalised.
A large magnitude-weighted move strongly changes relative weights.
The market transitions into a regime better suited to another smoother.
Leader changes can help reveal how the ensemble is adapting.
For example:
A shift toward HMA or DEMA may reflect stronger preference for responsive methods.
A shift toward SMA or RMA may reflect better recent performance from slower methods.
A shift toward LSMA may occur during a smooth local directional path.
These interpretations are contextual and should not be treated as fixed rules.
Gradient fill
The indicator fills the area between price and the ensemble line.
When price is above the line:
A bullish gradient is displayed.
When price is below the line:
A bearish gradient is displayed.
The gradient visually separates price from the adaptive trend estimate.
The fill reflects price location, while the line colour reflects the slope-derived ensemble trend.
These can temporarily disagree.
For example:
Price may fall below a still-rising ensemble during a pullback.
Price may rise above a still-falling ensemble during a counter-trend rally.
This disagreement can provide useful context.
Consensus glow
A glow is drawn around the ensemble line.
Its brightness changes according to weighted consensus.
When consensus is high:
The glow becomes brighter and more visible.
When the experts are divided:
The glow becomes more transparent.
The glow width is scaled using ATR based on the Base Length, helping the effect remain proportional across instruments and volatility environments.
The glow is a visual representation of model agreement. It does not modify the line or trend calculation.
Candle colouring
Candles can be coloured according to the current ensemble trend:
Bullish trend uses the selected bullish colour.
Bearish trend uses the selected bearish colour.
Candle colouring is based on the direction of the ensemble line, not the direction of each individual candle.
A bearish candle can therefore remain green during a bullish ensemble regime, and a bullish candle can remain red during a bearish regime.
How to interpret the indicator
Bullish ensemble trend
A bullish state means the final ensemble line is rising.
This indicates that the current weighted combination of experts is moving upward.
It does not require all individual experts to be bullish.
Bearish ensemble trend
A bearish state means the final ensemble line is falling.
The weighted combination is moving downward, even if one or more individual experts remain bullish.
High bullish consensus
A strongly positive vote means most influential expert weight is assigned to rising methods.
This can indicate broad directional alignment.
High bearish consensus
A strongly negative vote means the influential experts are predominantly falling.
Low consensus
A consensus near zero means weighted expert directions are divided.
This can occur during:
Trend transitions.
Sideways ranges.
Pullbacks.
Disagreement between faster and slower methods.
Low consensus does not automatically mean price will remain sideways. It means the ensemble's components are not currently aligned.
High leader weight and high consensus
This indicates that:
One method currently dominates.
The broader weighted bank is aligned with it.
The model is highly concentrated and directionally unified.
This can produce a responsive and decisive ensemble, but it also means the output depends heavily on the current leader.
Distributed weights and high consensus
This means several experts maintain meaningful weights while pointing in the same direction.
The trend is supported by a more diversified group of methods.
Leader weight high but consensus low
This can occur when the dominant expert points one way while several remaining experts point the other way.
The ensemble may still follow the leader, but internal disagreement is present.
How to use the indicator
1. Trend regime filter
Use the ensemble slope as directional context:
Prioritise long setups during bullish regimes.
Prioritise short setups during bearish regimes.
The indicator does not define entry price, stop placement or profit targets.
2. Consensus filter
A user may require stronger consensus before acting on the trend state.
For example:
A bullish flip with low consensus may represent an early or uncertain transition.
A bullish regime with high consensus indicates broader weighted alignment.
No universal consensus threshold is appropriate for every market.
3. Pullback analysis
During a bullish ensemble regime:
Price moving toward or below the line may represent a pullback.
The ensemble remaining bullish suggests its trend estimate has not yet reversed.
During a bearish regime:
Price moving toward or above the line may represent a counter-trend rally.
Price interaction with the line should be combined with structure and risk management.
4. Regime adaptation observation
The Leading Method label can be used to study how different smoothers perform through changing environments.
Rather than assuming one moving average is always best, the user can observe:
Which expert gains weight during trends.
Which expert takes over during transitions.
How concentrated the model becomes.
How quickly weights change under different Learning Rates.
5. Bullish and bearish flips
Trend flips can be used as:
Regime-change alerts.
Confirmation for another setup.
Potential exit conditions.
A directional filter for discretionary trades.
Because flips are based on line slope, responsive settings can generate repeated changes during ranges.
Suggested configurations
Balanced adaptive configuration
Moderate Base Length.
Moderate Learning Rate.
Directional loss.
Small positive Weight Floor.
Minimal Line Smoothing.
This keeps the model adaptive while preserving some expert diversity.
Fast adaptation configuration
Shorter Base Length.
Higher Learning Rate.
Magnitude-weighted loss.
Zero or very small Weight Floor.
Line Smoothing of 1 or 2.
This allows rapid concentration around recent winners but can create unstable leader changes.
Conservative diversified configuration
Longer Base Length.
Lower Learning Rate.
Directional loss.
Positive Weight Floor.
Additional Line Smoothing.
This creates slower and more diversified adaptation.
Large-move-focused configuration
Magnitude-weighted loss can be used when mistakes during large ATR-normalised moves should matter more than errors during minor fluctuations.
This may reduce the influence of small alternating bars on the weight distribution.
Pure directional configuration
Directional loss is useful when every close-to-close directional observation should be treated equally.
It creates a straightforward right-or-wrong scoring process.
How this differs from averaging moving averages
A normal moving-average ribbon or composite may calculate:
Average of SMA, EMA, HMA and other methods.
If every method receives equal weight permanently, its influence never changes.
Adaptive Trend Ensemble instead calculates:
Performance-dependent weights.
Sequential loss updates.
A dynamically changing weighted output.
Two bars with the same expert values can produce different ensemble values if the weight distributions differ.
How this differs from selecting the current fastest average
The indicator does not select whichever moving average is currently closest to price or whichever has moved the most.
Weights are based on whether previous expert slopes correctly anticipated realised price direction.
An expert can therefore lead even if it is not the fastest or closest line.
How this differs from an optimisation
The model does not search historical data for one set of parameters with the best backtest result.
It does not change the shared length of each expert.
Instead, it performs continuous online adaptation of the expert weights.
This avoids permanently selecting one historical winner, but it also means recent performance can strongly influence the current model.
How this differs from a machine-learning forecast
The indicator uses a genuine online-learning algorithm, but it is not a neural network or a price-target forecasting model.
It does not estimate the size of the next move.
The experts make binary directional predictions derived from their slopes.
The learning system then adjusts how much influence each moving-average value receives.
It is therefore best understood as an adaptive model-selection and blending process.
Causality and real-time behaviour
The learning update uses:
The prior-bar slope of each expert.
The current close-to-close realised direction.
It does not use future bars.
On historical completed candles, the update is fully causal.
On the current live candle:
The close can continue changing.
The realised direction can change.
Expert values can change.
Weights and consensus can update intrabar.
A bullish or bearish flip may appear before the candle closes.
Users requiring confirmed signals should evaluate the indicator at bar close.
Strengths
Combines eight distinct smoothing methods.
Adapts expert influence through online learning.
Supports directional and magnitude-sensitive losses.
Uses multiplicative updates rather than fixed weighting.
Provides optional protection against permanent weight collapse.
Separates ensemble direction from expert consensus.
Displays the currently leading method.
Uses one shared horizon for a fairer expert comparison.
Requires no offline training process.
Provides transparent open-source calculations.
Summary
Adaptive Trend Ensemble combines eight moving-average experts using a multiplicative online-learning model.
Each expert uses the same Base Length but applies a different smoothing method. The previous slope of each expert acts as its directional prediction for the latest close-to-close move.
After the realised direction is observed, incorrect experts receive either a fixed directional loss or an ATR-normalised magnitude-weighted loss. Their weights are reduced using an exponential Hedge update, then normalised and optionally adjusted using a minimum Weight Floor.
The current expert values are blended according to these adaptive weights, producing one ensemble line whose effective behaviour changes as different methods gain or lose influence.
A separate weighted vote measures current directional agreement. This consensus controls the visual glow and is displayed beside the current leading expert.
The result is a transparent adaptive trend model that does not assume one moving average will remain optimal. Instead, it continuously redistributes influence toward the methods that have recently aligned better with realised price direction while retaining configurable control over responsiveness, diversity and visual smoothing.
Indicator
LSMA SD | GForgeLSMA SD | GForge
LSMA SD is a trend-following oscillator built for swing trading on higher timeframes. It generates rules-based long and exit signals by measuring where price sits within a statistically-defined volatility envelope anchored to a regression-based trend line.
Core Calculation
The basis line is a Least Squares Moving Average. Unlike a standard moving average which weights past prices, LSMA computes the mathematically optimal straight-line fit across a defined lookback window. This means the basis reflects the actual gradient of a trend — its slope tells you the rate and direction of price movement, not a smoothed echo of where price has been. A short EMA pass is applied to the raw LSMA output as a robustness measure, absorbing single-bar snap artifacts that occur when outlier candles enter or exit the regression window. This is not a smoothing aesthetic — it directly addresses a known fragility in raw LinReg endpoints.
The default source is hlc3 — the average of high, low, and close — rather than close alone. This distributes the regression input across the full bar range, reducing sensitivity to end-of-session price mechanics such as stop runs and last-minute order flow that can distort the trend line without reflecting genuine directional movement.
A Standard Deviation envelope is then constructed around the LSMA basis at a fixed multiplier. The band width is driven entirely by actual price volatility — it widens during high-volatility periods and tightens during quiet ones. There is no secondary adaptive scaling layer. This is intentional: additional dynamic scaling introduces a second noisy signal on top of the basis movement, which in practice degrades signal quality.
The Oscillator
The oscillator expresses where price currently sits within the SD bands on a 0–100 scale. A reading of 0 means price is at the lower band. A reading of 100 means price is at the upper band. A reading of 50 means price is sitting directly on the LSMA trend line itself — the neutral zone between the two signal thresholds represents price consolidating around the regression basis.
Long signals fire when the oscillator crosses above the long threshold (default 74), meaning price has broken decisively into the upper band zone — a momentum confirmation in the direction of the trend, not a mean-reversion trigger. Exit and short signals fire when the oscillator crosses below the short threshold (default 33).
This is a trend-continuation system, not a reversal indicator.
Parameters
The indicator is intentionally low-parameter. LSMA Length sets the regression window. StdDev Length sets the band width lookback and can differ from the LSMA length. StdDev Multiplier sets the fixed band scale. Endpoint Smoothing controls how aggressively window-edge artifacts are absorbed — setting it to 1 disables it entirely. Fewer parameters means less surface area for curve-fitting to historical data.
Default settings are optimised for BTC on the 1D timeframe. Optimize thresholds and lengths for different assets and timeframes before use.
Risk Warning
This indicator is provided for informational and educational purposes only. Past performance, including any results visible on historical bars, does not guarantee or imply future returns. All trading involves risk. You should not make trading decisions based solely on any single indicator. Always apply independent analysis and appropriate risk management.
Developed by GForge Indicator
Z-Score Regression Bands [BOSWaves]Z-Score Regression Bands – Adaptive Trend and Volatility Insight
Overview
The Z-Score Regression Bands is a trend and volatility analysis framework designed to give traders a clear, structured view of price behavior. It combines Least Squares Moving Average (LSMA) regression, a statistical method to detect underlying trends, with Z-Score standardization, which measures how far price deviates from its recent average.
Traditional moving average bands, like Bollinger Bands, often lag behind trends or generate false signals in noisy markets. Z-Score Regression Bands addresses these limitations by:
Tracking trends accurately using LSMA regression
Normalizing deviations with Z-Scores to identify statistically significant price extremes
Visualizing multiple bands for normal, strong, and extreme moves
Highlighting trend shifts using diamond markers based on Z-Score crossings
This multi-layered approach allows traders to understand trend strength, detect overextensions, and identify periods of low or high volatility — all from a single, clear chart overlay. It is designed for traders of all levels and can be applied across scalping, day trading, swing trading, and longer-term strategies.
Theoretical Foundation
The Z-Score Regression Bands are grounded in statistical and trend analysis principles. Here’s the idea in plain terms:
Least Squares Moving Average (LSMA) – Unlike standard moving averages, LSMA fits a straight line to recent price data using regression. This “best-fit” line shows the underlying trend more precisely and reduces lag, helping traders see trend changes earlier.
Z-Score Standardization – A Z-Score expresses how far the LSMA is from its recent mean in standard deviation units. This shows whether price is unusually high or low, which can indicate potential reversals, pullbacks, or acceleration of a trend.
Multi-Band Structure – The three bands represent: Band #1: Normal range of price fluctuations; Band #2: Significant deviation from the trend; Band #3: Extreme price levels that are statistically rare. The distance between bands dynamically adapts to market volatility, allowing traders to visualize expansions (higher volatility) and contractions (lower volatility).
Trend Signals – When Z-Score crosses zero, diamonds appear on the chart. These markers signal potential trend initiation, continuation, or reversal, offering a simple alert for shifts in market momentum.
How It Works
The indicator calculates and plots several layers of information:
LSMA Regression (Trend Detection)
Computes a line that best fits recent price points.
The LSMA line smooths out minor fluctuations while reflecting the general direction of the market.
Z-Score Calculation (Deviation Measurement)
Standardizes the LSMA relative to its recent average.
Positive Z-Score → LSMA above average, negative → LSMA below average.
Helps identify overbought or oversold conditions relative to the trend.
Multi-Band Construction (Volatility Envelope)
Upper and lower bands are placed at configurable multiples of standard deviation.
Band #1 captures typical price movement, Band #2 signals stronger deviation, Band #3 highlights extreme moves.
Bands expand and contract with volatility, giving an intuitive visual guide to market conditions.
Trend Signals (Diamonds)
Appear when Z-Score crosses zero.
Indicates moments when momentum may shift, helping traders time entries or exits.
Visual Interpretation
Band width = volatility: wide bands indicate strong movement; narrow bands indicate calm periods.
LSMA shows underlying trend direction, while bands show how far price has strayed from that trend.
Interpretation
The Z-Score Regression Bands provide a multi-dimensional view of market behavior:
Trend Analysis – LSMA line slope shows general market direction.
Momentum & Volatility – Z-Score indicates whether the trend is accelerating or losing strength; band width indicates volatility levels.
Price Extremes – Price touching Band #2 or #3 may suggest overextension and potential reversals.
Trend Shifts – Diamonds signal statistically significant changes in momentum.
Cycle Awareness – Standard deviation bands help distinguish normal market fluctuations from extreme events.
By combining these insights, traders can avoid false signals and react to meaningful structural shifts in the market.
Strategy Integration
Trend Following
Enter trades when diamonds indicate momentum aligns with LSMA direction.
Use Band #1 and #2 for stop placement and partial exits.
Breakout Trading
Watch for narrow bands (low volatility) followed by price pushing outside Band #1 or #2.
Confirm with Z-Score movement in the breakout direction.
Mean Reversion/Pullback
If price reaches Band #2 or #3 without continuation, expect a pullback toward LSMA.
Exhaustion & Reversals
Flattening Z-Score near zero while price remains at extreme bands signals trend weakening.
Tighten stops or scale out before a potential reversal.
Multi-Timeframe Confirmation
High timeframe LSMA confirms the main trend.
Lower timeframe bands provide refined entry and exit points.
Technical Implementation
LSMA Regression : Best-fit line minimizes lag and captures trend slope.
Z-Score Standardization : Normalizes deviation to allow consistent interpretation across markets.
Multi-Band Envelope : Three layers for normal, strong, and extreme deviations.
Trend Signals : Automatic diamonds for Z-Score zero-crossings.
Band Fill Options : Optional shading to visualize volatility expansions and contractions.
Optimal Application
Asset Classes:
Forex : Capture breakouts, overextensions, and trend shifts.
Crypto : High-volatility adaptation with adjustable band multipliers.
Stocks/ETFs : Identify trending sectors, reversals, and pullbacks.
Indices/Futures : Track cycles and structural trends.
Timeframes:
Scalping (1–5 min) : Focus on Band #1 and trend signals for fast entries.
Intraday (15m–1h) : Use Bands #1–2 for continuation and breakout trades.
Swing (4h–Daily) : Bands #2–3 capture trend momentum and exhaustion.
Position (Daily–Weekly) : LSMA trend dominates; Bands #3 highlight regime extremes.
Performance Characteristics
Strong Performance:
Trending markets with moderate-to-high volatility
Assets with steady liquidity and identifiable cycles
Weak Performance:
Flat or highly choppy markets
Very short timeframes (<1 min) dominated by noise
Integration Tips
Combine with support/resistance, volume, or order flow analysis for confirmation.
Use bands for stops, targets, or scaling positions.
Apply multi-timeframe analysis: higher timeframe LSMA confirms main trend, lower timeframe bands refine entries.
Disclaimer
The Z-Score Regression Bands is a trading analysis tool, not a guaranteed profit system. Its effectiveness depends on market conditions, parameter selection, and disciplined risk management. Use it as part of a broader trading strategy, not in isolation. Indicator
Multiple Colored Moving AveragesMULTIPLE COLORED MOVING AVERAGES - USER GUIDE
DISCLAIMER
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Both the code and this documentation were created heavily using artificial intelligence. I'm lazy...
This indicator was inspired by repo32's "Moving Average Colored EMA/SMA" indicator. *
What is this indicator?
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This is a PulseWire indicator that displays up to 4 different moving averages on your chart simultaneously. Each moving average can be customized with different calculation methods, colors, and filtering options.
Why would I use multiple moving averages?
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- See trend direction across different timeframes at once
- Identify support and resistance levels
- Spot crossover signals between fast and slow MAs
- Reduce false signals with filtering options
- Compare how different MA types react to price action
What moving average types are available?
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11 different types:
- SMA: Simple average, equal weight to all periods
- EMA: Exponential, more weight to recent prices
- WMA: Weighted, linear weighting toward recent data
- RMA: Running average, smooth like EMA
- DEMA: Double exponential, reduced lag
- TEMA: Triple exponential, even less lag
- HMA: Hull, fast and smooth combination
- VWMA: Volume weighted, includes volume data
- LSMA: Least squares, based on linear regression
- TMA: Triangular, double-smoothed
- ZLEMA: Zero lag exponential, compensated for lag
How do I set up the indicator?
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Each MA has these settings:
- Enable/Disable: Turn each MA on or off
- Type: Choose from the 11 calculation methods
- Length: Number of periods (21, 50, 100, 200 are common)
- Smoothing: 0-10 levels of extra smoothing
- Noise Filter: 0-5% to ignore small changes
- Colors: Bullish (rising) and bearish (falling) colors
- Line Width: 1-5 pixels thickness
What does the smoothing feature do?
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Smoothing applies extra calculations to make the moving average line smoother. Higher levels reduce noise but make the MA respond slower to price changes. Use higher smoothing in choppy markets, lower smoothing in trending markets.
What is the noise filter?
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The noise filter ignores small percentage changes in the moving average. For example, a 0.3% filter will ignore any MA movement smaller than 0.3%. This helps eliminate false signals from minor price fluctuations.
When should I use this indicator?
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- Trend analysis: See if market is going up, down, or sideways
- Entry timing: Look for price bounces off MA levels
- Exit signals: Watch for MA slope changes or crossovers
- Support/resistance: MAs often act as dynamic levels
- Multi-timeframe analysis: Use different lengths for different perspectives
What are some good settings to start with?
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Conservative approach:
- MA 1: EMA 21 (short-term trend)
- MA 2: SMA 50 (medium-term trend)
- MA 3: SMA 200 (long-term trend)
- Low noise filtering (0.1-0.3%)
Active trading:
- MA 1: HMA 9 (very responsive)
- MA 2: EMA 21 (short-term)
- MA 3: EMA 50 (medium-term)
- Minimal or no smoothing
How do I interpret the colors?
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Each MA changes color based on its direction:
- Bullish color: MA is rising (upward trend)
- Bearish color: MA is falling (downward trend)
- Gray: MA is flat or unchanged
What should I look for in crossovers?
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- Golden Cross: Fast MA crosses above slow MA (bullish signal)
- Death Cross: Fast MA crosses below slow MA (bearish signal)
- Multiple crossovers in same direction can confirm trend changes
- Wait for clear separation between MAs after crossover
How do I use MAs for support and resistance?
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- In uptrends: MAs often provide support when price pulls back
- In downtrends: MAs may act as resistance on rallies
- Multiple MAs create support/resistance zones
- Stronger levels where multiple MAs cluster together
Can I use this with other indicators?
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Yes, it works well with:
- Volume indicators for confirmation
- RSI or MACD for timing entries
- Bollinger Bands for volatility context
- Price action patterns for setup confirmation
What if I get too many signals?
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- Increase smoothing levels
- Raise noise filter percentages
- Use longer MA periods
- Focus on major crossovers only
- Wait for multiple MA confirmation
What if signals are too slow?
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- Reduce smoothing to 0
- Lower noise filter values
- Switch to faster MA types (HMA, ZLEMA, DEMA)
- Use shorter periods
- Focus on the fastest MA only
Which MA types work best in different markets?
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Trending markets: EMA, DEMA, TEMA (responsive to trends)
Choppy markets: SMA, TMA, HMA with smoothing (less whipsaws)
High volatility: Use higher smoothing and noise filtering
Low volatility: Use minimal filtering for better responsiveness
Do I need all the advanced features?
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No. Start with basic settings:
- Choose MA type and length
- Set colors you prefer
- Leave smoothing at 0
- Leave noise filter at 0
Add complexity only if needed to improve signal quality.
How do I know if my settings are working?
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- Backtest on historical data
- Paper trade the signals first
- Adjust based on market conditions
- Keep a trading journal to track performance
- Be willing to modify settings as markets change
Can I save different configurations?
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Yes, save different indicator templates in PulseWire for:
- Different trading styles (scalping, swing trading)
- Different market conditions (trending, ranging)
- Different instruments (stocks, forex, crypto) Indicator
Interpolated Median Volatility LSMA | OttoThis indicator combines trend-following and volatility analysis by enhancing traditional LSMA with percentile-based linear interpolation applied to both the Least Squares Moving Average (LSMA) and standard deviation. Rather than relying on raw values, it uses the interpolated median (50th percentile) to smooth out noise while preserving sensitivity to significant price shifts. This approach produces a cleaner trend signal that remains responsive to real market changes, adapts to evolving volatility conditions, and improves the accuracy of breakout detection.
Core Concept
The indicator builds on these core components:
LSMA (Least Squares Moving Average): A linear regression-based moving average that fits line using user selected source over user defined period. It offers a smoother and more reactive trend signal compared to standard moving averages.
Standard Deviation shows how much price varies from the mean. In this indicator, it’s used to measure market volatility.
Volatility Bands: Instead of traditional Bollinger-style bands, this script calculates custom upper and lower bands using percentile-based linear interpolation on both the LSMA and standard deviation. This method produces smoother bands that filter out noise while remaining adaptive to meaningful price movements, making them more aligned with real market behavior and helping reduce false signals.
Percentile interpolation estimates a specific percentile (like the median — the 50th percentile) from a set of values — even when that percentile doesn't fall exactly on one data point. Instead of selecting a single nearest value, it calculates a smoothed value between nearby points. In this script, it’s used to find the median of past LSMA and standard deviation values, reducing the impact of outliers and smoothing the trend and volatility signals for more robust results.
Signal Logic: A long signal is identified when close price goes above the upper band, and a short signal when close price goes below the lower band.
⚙️ Inputs
Source: The price source used in calculations
LSMA Length: Period for calculating LSMA
Standard Deviation Length: Period for calculating volatility
Percentile Length: Period used for interpolating percentile values of LSMA and standard deviation
Multiplier: Controls the width of the bands by scaling the interpolated standard deviation
📈 Visual Output
Colored LSMA Line: Changes color based on signal (green for bullish, purple for bearish)
Upper & Lower Bands: Volatility bands calculated using interpolated values (green for bullish, purple for bearish)
Bar Coloring: Price bars are colored to reflect signal state (green for bullish, purple for bearish)
Optional Candlestick Overlay: Enhances visual context by coloring candles to match the signal state (green for bullish, purple for bearish)
How to Use
Add the indicator to your chart and look for signals when close price goes above or below the bands.
Long Signal: close Price goes above the upper band
Short Signal: close Price goes below the lower band
🔔 Alerts:
This script supports alert conditions for long and short signals. You can set alerts based on band crossovers to be notified of potential entries/exits.
⚠️ Disclaimer:
This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate strategies before applying them in live markets. Use at your own risk.
Indicator
Normalized Linear Regression (LSMA) OscillatorNormalized Linear Regression (LSMA) Oscillator
By Nathan Farmer
The Normalized LSMA Oscillator is a trend-following indicator that enhances the classic Linear Regression (LSMA) by applying a range of normalization techniques. This indicator allows traders to smooth out and normalize LSMA signals for better trend detection and dynamic market adaptation.
Key Features:
Configurable Normalization Methods:
This indicator offers several normalization techniques, such as Z-Score, Min-Max, Mean Normalization, Robust Scaler, Logistic Function, and Quantile Transformation. Each method helps in refining LSMA outputs to improve clarity in both trending and ranging market conditions.
Smoothing Options:
Smoothing can be applied after normalization, helping to reduce noise in the signals, thus making trend-following strategies that use this indicator more effective.
Recommended Settings:
Logistic Function Normalization: Recommended length of around 12, based on my preferred signal frequency.
Z-Score Normalization: Medium period (close to the default of 50), based on my preferred signal frequency.
Min-Max Normalization: Medium period, based on my preferred signal frequency.
Mean Normalization: Medium period, based on my preferred signal frequency.
Robust Scaler: Medium period, based on my preferred signal frequency.
Quantile Transformation: Medium period, based on my preferred signal frequency.
Usage:
Designed primarily for trend-following strategies, this indicator adapts well to varying market conditions. Traders can experiment with the various normalization and smoothing settings to match the indicator to their specific needs and market preferences.
Recommendation before usage:
Always backtest the indicator for yourself with respect to how you intend to use it. Modify the parameters to suit your needs, over your preferred time frame, on your preferred asset. My preferences are for the assets I happened to be looking at when I made this indicator. Odds are, you're looking at something else, over a different time frame, in a different market environment than what my settings are tailored for.
Indicator
Adaptive Volatility-Controlled LSMA [QuantAlgo]Adaptive Volatility-Controlled LSMA by QuantAlgo 📈💫
Introducing the Adaptive Volatility-Controlled LSMA (Least Squares Moving Average) , a powerful trend-following indicator that combines trend detection with dynamic volatility adjustments. This indicator is designed to help traders and investors identify market trends while accounting for price volatility, making it suitable for a wide range of assets and timeframes. By integrating LSMA for trend analysis and Average True Range (ATR) for volatility control, this tool provides clearer signals during both trending and volatile market conditions.
💡 Core Concept and Innovation
The Adaptive Volatility-Controlled LSMA leverages the precision of the LSMA to track market trends and combines it with the sensitivity of the ATR to account for market volatility. LSMA fits a linear regression line to price data, providing a smoothed trend line that is less reactive to short-term noise. The ATR, on the other hand, dynamically adjusts the volatility bands around the LSMA, allowing the indicator to filter out false signals and respond to significant price moves. This combination provides traders with a reliable tool to identify trend shifts while managing risk in volatile markets.
📊 Technical Breakdown and Calculations
The indicator consists of the following components:
1. Least Squares Moving Average (LSMA): The LSMA calculates a linear regression line over a defined period to smooth out price fluctuations and reveal the underlying trend. It is more reactive to recent data than traditional moving averages, allowing for quicker trend detection.
2. ATR-Based Volatility Bands: The Average True Range (ATR) measures market volatility and creates upper and lower bands around the LSMA. These bands expand and contract based on market conditions, helping traders identify when price movements are significant enough to indicate a new trend.
3. Volatility Extensions: To further account for rapid market changes, the bands are extended using additional volatility measures. This ensures that trend signals are generated when price movements exceed both the standard volatility range and the extended volatility range.
⚙️ Step-by-Step Calculation:
1. LSMA Calculation: The LSMA is computed using a least squares regression method over a user-defined length. This provides a trend line that adapts to recent price movements while smoothing out noise.
2. ATR and Volatility Bands: ATR is calculated over a user-defined length and is multiplied by a factor to create upper and lower bands around the LSMA. These bands help detect when price movements are substantial enough to signal a new trend.
3. Trend Detection: The price’s relationship to the LSMA and the volatility bands is used to determine trend direction. If the price crosses above the upper volatility band, a bullish trend is detected. Conversely, a cross below the lower band indicates a bearish trend.
✅ Customizable Inputs and Features:
The Adaptive Volatility-Controlled LSMA offers a variety of customizable options to suit different trading or investing styles:
📈 Trend Settings:
1. LSMA Length: Adjust the length of the LSMA to control its sensitivity to price changes. A shorter length reacts quickly to new data, while a longer length smooths the trend line.
2. Price Source: Choose the type of price (e.g., close, high, low) that the LSMA uses to calculate trends, allowing for different interpretations of price data.
🌊 Volatility Controls:
ATR Length and Multiplier: Adjust the length and sensitivity of the ATR to control how volatility is measured. A higher ATR multiplier widens the bands, making the trend detection less sensitive, while a lower multiplier tightens the bands, increasing sensitivity.
🎨 Visualization and Alerts:
1. Bar Coloring: Customize bar colors to visually distinguish between uptrends and downtrends.
2. Volatility Bands: Enable or disable the display of volatility bands on the chart. The bands provide visual cues about trend strength and volatility thresholds.
3. Alerts: Set alerts for when the price crosses the upper or lower volatility bands, signaling potential trend changes.
📈 Practical Applications
The Adaptive Volatility-Controlled LSMA is ideal for traders and investors looking to follow trends while accounting for market volatility. Its key use cases include:
Identifying Trend Reversals: The indicator detects when price movements break through volatility bands, signaling potential trend reversals.
Filtering Market Noise: By applying ATR-based volatility filtering, the indicator helps reduce false signals caused by short-term price fluctuations.
Managing Risk: The volatility bands adjust dynamically to account for market conditions, helping traders manage risk and improve the accuracy of their trend-following strategies.
⭐️ Summary
The Adaptive Volatility-Controlled LSMA by QuantAlgo offers a robust and flexible approach to trend detection and volatility management. Its combination of LSMA and ATR creates clearer, more reliable signals, making it a valuable tool for navigating trending and volatile markets. Whether you're detecting trend shifts or filtering market noise, this indicator provides the tools you need to enhance your trading and investing strategy.
Note: The Adaptive Volatility-Controlled LSMA is a tool to enhance market analysis. It should be used in conjunction with other analytical tools and should not be relied upon as the sole basis for trading or investment decisions. No signals or indicators constitute financial advice, and past performance is not indicative of future results. Indicator
Versatile Moving Average StrategyVersatile Moving Average Strategy (VMAS)
Overview:
The Versatile Moving Average Strategy (VMAS) is designed to provide traders with a flexible approach to trend-following, utilizing multiple types of moving averages. This strategy allows for customization in choosing the moving average type and length, catering to various market conditions and trading styles.
Key Features:
- Multiple Moving Average Types: Choose from SMA, EMA, SMMA (RMA), WMA, VWMA, HULL, LSMA, and ALMA to best suit your trading needs.
- Customizable Inputs: Adjust the moving average length, source of price data, and stop-loss source to fine-tune the strategy.
- Target Percent: Set the percentage difference between successive profit targets to manage your risk and rewards effectively.
- Position Management: Enable or disable long and short positions, allowing for versatility in different market conditions.
- Commission and Slippage: The strategy includes realistic commission settings to ensure accurate backtesting results.
Strategy Logic:
1. Moving Average Calculation: The selected moving average is calculated based on user-defined parameters.
2. Entry Conditions:
- A long position is entered when the entry source crosses over the moving average, if long positions are enabled.
- A short position is entered when the entry source crosses under the moving average, if short positions are enabled.
3. Stop-Loss: Positions are closed if the stop-loss source crosses the moving average in the opposite direction.
4. Profit Targets: Multiple profit targets are defined, with each target set at an incremental percentage above (for long positions) or below (for short positions) the entry price.
Default Properties:
- Account Size: $10000
- Commission: 0.01% per trade
- Risk Management: Positions are sized to risk 80% of the equity per trade, because we get very tight stoploss when position is open.
- Sample Size: Backtesting has been conducted to ensure a sufficient sample size of trades, ideally more than 100 trades.
How to Use:
1. Configure Inputs: Set your preferred moving average type, length, and other input parameters.
2. Enable Positions: Choose whether to enable long, short, or both types of positions.
3. Backtest and Analyze: Run backtests with realistic settings and analyze the results to ensure the strategy aligns with your trading goals.
4. Deploy and Monitor: Once satisfied with the backtesting results, deploy the strategy in a live environment and monitor its performance.
This strategy is suitable for traders looking to leverage moving averages in a versatile and customizable manner. Adjust the parameters to match your trading style and market conditions for optimal results.
Note: Ensure the strategy settings used for publication are the same as those described here. Always conduct thorough backtesting before deploying any strategy in a live trading environment. Strategy
LSMA Z-Score [BackQuant]LSMA Z-Score
Main Features and Use in the Trading Strategy
- The indicator normalizes the LSMA into a detrended Z-Score, creating an oscillator with standard deviation levels to indicate trend strength.
- Adaptive coloring highlights the rate of change and potential reversals, with different colors for positive and negative changes above and below the midline.
- Extreme levels with adaptive coloring indicate the probability of a reversion, providing strategic entry or exit points.
- Alert conditions for crossing the midline or significant shifts in trend direction enhance its utility within a trading strategy.
1. What is an LSMA?
The Least Squares Moving Average (LSMA) is a technical indicator that smoothens price data to help identify trends. It uses the least squares regression method to fit a straight line through the selected price points over a specified period. This approach minimizes the sum of the squares of the distances between the line and the price points, providing a more statistically grounded moving average that can adapt more smoothly to price changes.
2. What is a Z-Score?
A Z-Score is a statistical measurement that describes a value's relationship to the mean of a group of values, measured in terms of standard deviations from the mean. If a Z-Score is 0, it indicates that the data point's score is identical to the mean score. A Z-Score helps in understanding if a data point is typical for a given data set or if it is atypical. In finance, a Z-Score is often used to measure how far a piece of data is from the average of a set, which can be helpful in identifying outliers or unusual data points.
3. Why Turning LSMA into a Z-Score is Innovative and Its Benefits
Converting LSMA into a Z-Score is innovative because it combines the trend identification capabilities of the LSMA with the statistical significance testing of Z-Scores. This transformation normalizes the LSMA, creating a detrended oscillator that oscillates around a mean (zero line), with standard deviation levels to show trend strength. This method offers several benefits:
Enhanced Trend Detection:
- By normalizing the LSMA, traders can more easily identify when the price is deviating significantly from its trend, which can signal potential trading opportunities.
Standardization:
- The Z-Score transformation allows for comparisons across different assets or time frames, as the score is standardized.
Objective Measurement of Trend Strength:
- The use of standard deviation levels provides an objective measure of trend strength and volatility.
4. How It Can Be Used in the Context of a Trading System
This indicator can serve as a versatile tool within a trading system for a range of things:
Trend Confirmation:
- A positive Z-Score can confirm an uptrend, while a negative Z-Score can confirm a downtrend, providing traders with signals to enter or exit trades.
Oversold/Overbought Conditions:
- Extreme Z-Score levels can indicate overbought or oversold conditions, suggesting potential reversals or pullbacks.
Volatility Assessment:
- The standard deviation levels can help traders assess market volatility, with wider bands indicating higher volatility.
5. How It Can Be Used for Trend Following
For trend following strategies, this indicator can be particularly useful:
Trend Strength Indicator:
- By monitoring the Z-Score's distance from zero, traders can gauge the strength of the current trend, with larger absolute values indicating stronger trends.
Directional Bias:
- Positive Z-Scores can be used to establish a bullish bias, while negative Z-Scores can establish a bearish bias, guiding trend following entries and exits.
Color-Coding for Trend Changes :
- The adaptive coloring of the indicator based on the rate of change and extreme levels provides visual cues for potential trend reversals or continuations.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
This is using the Midline Crossover:
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Indicator
Indicator
Indicator
TOMMAR#TOMMAR #MultiMovingAverages #MMAR
Dear fellow traders, this is Tommy, and today I'd like to introduce you to the Multi-Moving Averages Ribbon (MMAR) indicator, which I believe to be one of the best MMAR indicators available on PulseWire. Moving Averages is a popular technical analysis tool used to smooth out price data by creating an average of past price data points over a specified time period. They can be used to identify trends and provide a clearer view of price action, as well as generate buy and sell signals by observing crossovers between different moving average lines.
In the MMAR indicator, we have incorporated 12 different types of Moving Averages, including Simple Moving Averages (SMA), Exponential Moving Averages (EMA), Weighted Moving Averages (WMA), Hull Moving Averages (HMA), and Smoothed Moving Averages (SMMA), among others. This allows traders to choose the optimal type for their preferred trading commodities.
One common technique in technical analysis is using multiple Moving Averages with varying lengths, which provides a more comprehensive view of price action. By analyzing multiple Moving Averages with different timeframes, traders can better understand both short- and long-term trends and make more informed trading decisions. Some of the well-known combinations of multiple moving averages used by traders are (5, 9, 14, 21, 45), (6, 11, 16, 22, 51), [8, 13, 21, 55), (50, 100, 200), and (60, 120, 240).
Another way to gauge the strength of the market trend is to look for the arrangement of the Moving Averages. If they are in a sequential order, with the shortest on top and the longest on the bottom, it is most likely a bullish trend. On the other hand, if they are arranged in reverse order, with the shortest on the bottom and the longest on top, it is most likely a bearish trend. The 'Trend Light' in the indicator settings will automatically signal when the Moving Averages are in either an orderly or reverse arrangement.
Lastly, I have added a useful feature to the indicator: the 'MA Projection'. This feature projects and forecasts the Moving Averages in the future, allowing traders to easily identify confluence zones in future candlesticks. Please note that the projection levels may change in the case of extreme price action that significantly affects the Moving Averages.
This is free so any Tradingview users can use this indicator. Just search TOMMAR in the indicator section located on top of the chart.
#TOMMAR #MultiMovingAverages #MMAR
안녕하세요 트레이더 여러분, 토미입니다. 오늘 여러분들에게 소개드릴 지표는 다양한 길이의 이동평균선 조합을 사용할 수 있는 MMAR (Multiple Moving Averages Ribbon)입니다. 아마 제가 만든 MMAR 지표가 트레이딩뷰에서 가장 쓸만할 겁니다. 이동평균선, 줄여서 이평선은 말 그대로 특정 기간 범위 내의 주가들을 평균한 값들로 이루어진 선입니다. 제가 이평선 관련된 강의 자료는 예전에 올려드린 바 있으니 더 자세한 내용이 궁금하신 분들은 아래 링크/이미지 클릭하시길 바랍니다.
본 지표는 Simple Moving Averages (SMA), Exponential Moving Averages (EMA), Weighted Moving Averages (WMA), Hull Moving Averages (HMA), 그리고 Smoothed Moving Averages (SMMA) 등을 포함해 총 12개 종류의 이평선 지표를 사용할 수 있습니다. 또한 각 이평선의 길이들도 하나하나 일일이 설정하실 수 있습니다. 예를 들어 요즘에 자주 보이는 이평선들의 조합이 , , , , 그리고 등등이 존재하는데 여러분의 취향에 맞게 설정하여 사용하시면 됩니다.
몇 가지 주요 기능에 대해서 설명 드리겠습니다. 설정에서 ‘Trend Light’를 키면 이평선들의 정배열 혹은 역배열 여부를 쉽게 볼 수 있습니다. 이평선이 정배열일때는 맨 아래의 이평선에 초록불이, 역배열일때는 맨 위의 이평선에 빨간불이 켜지며 둘 다 아닐 땐 아무 불도 켜지지 않습니다. 또한 ‘MA Projection’을 키면 이평선들의 미래 예측 값들을 확장해줍니다. 당연히 가격 변동이 갑자기 크게 나오면 이평선 예측 확장 레벨들이 확 바뀌겠죠.
지표창에 TOMMAR 검색하시거나 아래 즐겨찾기 인디케이터에 넣기 클릭하시면 누구나 사용하실 수 있습니다~ 여러분의 구독, 좋아요, 댓글은 저에게 큰 힘이 됩니다.
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Ratings AlgoThe ratings algo is my discount version of the many paid-for algorithms put out by numerous different companies. A technical "rating" (by default between -10 and 10) is produced for each candle, telling the user when to buy, sell, or hold. I took 11 of my personal favorite indicators to develop a rating system. They are:
50/200 SMA crossover
10/20 SMA crossover
10/20 LSMA crossover
10/20 EMA crossover
"Arnold" a rate-of-change analysis of a smoothed LSMA
PVT and OBV momentum
MACD
RSI
DMI
Fisher Transform
The ratings system is very basic (a more complex, detailed version will be coming in the future!) where each indicator returns -1, 0, or 1, and the MAs and Oscillators are stratified with a user-defined weighting. The total calculation is based on the function:
maweight * (average of MA ratings) + oscillator weight * (average of osc ratings)
If the total value > user-defined threshold, the bar is teal, and if > 2.5 * threshold, is green, and vice versa for orange/red respectively. Purple is given if the total value is close to zero.
"Strong" signals are printed if the bar changes to either green or red and exits are printed if the bars change from green/red to any other color.
A table is also produced showing what each indicator is indicating, either "Buy" "Sell" or "Hold.
Reversal Bands are printed, intended to be used as areas where a trade might be exited if the market is sideways. If a Strong Buy signal is produced, it may be a good idea to enter the trade, and hold until the price enters the reversal bands, then hold until a candle closes outside the band for the first time.
This indicator truly shines in trending markets (like most indicators), but with very fast-acting exit signals and reversal zones, will facilitate minimal losses and possibly even profits in sideways markets.
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Find Best Performing MA For Golden CrossHello!
This script calculates the performance of any asset following a golden cross of two moving averages of any length!
The calculated moving averages are: SMA, EMA, HMA, VWMA, WMA, LSMA, and ALMA
The best performing moving average for the selected data series is listed first, followed by a descending order.
The indicator works on any timeframe, any asset, and can even be used on indicators such as RSI, %b, %k, etc.
The Moving Average Length and Source Are Customizable!
The Moving Averages Can Be Plotted on Most Data Series, Such As:
Close, Open, Low, hlc3, RSI, %B, %K, Etc.
The Script Will Recalculate for the Timeframe (1m, 5m, D, etc.)!
The (XX Candles) Indicates the Average Number of
Sessions the Shorter Ma Remains Above the Longer Ma Following an Upside Cross!
The Percentages (XX.XX%) Indicate the Average
Percentage Price Gain/Loss Following a Golden Cross,
Until the Shorter Ma Crosses Back Under the Longer Ma!
In This Example I Am Using a 63 Session Length for the
Shorter Ma for All Listed Ma Types for Closing Prices, and a 196 Candle Length for the Longer Ma!
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BUY/SELL SIGNALS from LSMA/ALMA/HMAThis indicator uses the Least Squares Moving Average (LSMA) in tandem with the Arnaud Legoux Moving Average (ALMA) and Hull Moving Average (HMA) to generate buy-sell signals, represented by the light blue and orange crosses respectively.
The yellow lines produced by the indicator show periods of market uncertainty and possible reversal, and a modified, user-defined VWAP is given along with a 200 EMA. The point of this indicator was to create a smoother, more visually appealing moving-average, price action-based indicator when compared to the trend-step and simple moving average indicators available. This indicator uses a fast (25 period) LSMA coupled with a slower (50 period) HMA and ALMA in order to make signals both smooth and fast.
This indicator will work on all markets, except the modified VWAP will naturally not function if the volume is unpublished for that market. Use of this indicator will be very strong in trending markets, as the yellow line will spot possible reversals quite early, meaning the trader can be ready early for the buy/sell signal to appear. Use of this indicator in sideways market conditions will be limited, as it is for all moving average-based indicators, but the damage will be minimal as bad trades will be quickly realized by the indicator and the color will switch to yellow, this is possible because of the settings differences between the period lengths of the LSMA vs the ALMA + HMA. Indicator
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