Zero-Lag GARCH Bands | NAL1. Overview
Zero-Lag GARCH Bands | NAL is an adaptive volatility band indicator built from a Zero-Lag EMA baseline and an optimized GARCH-style volatility engine.
The indicator does not use a standard fixed-width channel. Instead, it estimates market variance through a recursive GARCH framework, smooths that volatility with a Zero-Lag EMA, and uses the result to create dynamic upper and lower bands around price structure.
The purpose of the indicator is to identify when price escapes a volatility-adjusted regime boundary, while allowing the band width to adapt to the underlying variance environment.
2. Calculation
The indicator starts by estimating volatility from lagged log returns. These returns are squared to create a variance component, which becomes the foundation of the GARCH model.
GARCH_LogReturn = math.log(close / close )
GARCH_SquaredLogReturn = math.pow(GARCH_LogReturn, 2.0)
GARCH_RealizedVariance = ta.sma(GARCH_SquaredLogReturn, GARCH_Lookback)
The script then searches through possible coefficient weights to find a beta/lambda value that better fits recent realized variance behavior. A second optimization loop is used to estimate gamma, which controls the long-run variance contribution.
These optimized coefficients are combined into a GARCH-style variance model using three components: long-run variance, recent shock variance, and lagged variance.
GARCH_Variance =
GARCH_Gamma * GARCH_LongRunVariance +
GARCH_Alpha * GARCH_SquaredLogReturn +
GARCH_Beta * GARCH_LaggedVariance
After the variance estimate is created, it is smoothed using a Zero-Lag EMA. This gives the volatility engine a faster response while still reducing noise.
GARCH_ProjectedVariance = f_zlema(GARCH_Variance, GARCH_SmoothLen)
GARCH_Volatility = math.sqrt(math.max(GARCH_ProjectedVariance, 0.0))
The baseline is also built with a Zero-Lag EMA, applied after a light EMA pre-smoothing step. This creates the central reference line for the band structure.
The final bands are created by scaling the Zero-Lag GARCH volatility against the selected source and band pressure setting. Higher band pressure creates a tighter band, while lower pressure allows the band structure to expand.
upperBand = baseline + (baseline_src / band_pressure) * GARCH_VolatilityMultiplier
lowerBand = baseline - (baseline_src / band_pressure) * GARCH_VolatilityMultiplier
A bullish state triggers when price closes above the upper band. A bearish state triggers when price closes below the lower band. When price remains inside the bands, the previous regime is held.
3. Key Features
Zero-Lag EMA baseline for reduced-lag price structure.
Optimized GARCH-style volatility engine.
Adaptive variance model using shock, lagged, and long-run components.
Zero-Lag smoothing applied to projected volatility.
Dynamic upper and lower volatility bands.
Band pressure control for adjusting channel tightness.
State-based candle coloring, band coloring, glow effect, and directional fills.
4. Use
Zero-Lag GARCH Bands is designed to identify when price begins escaping its volatility-adjusted structure. A close above the upper band reflects bullish expansion, while a close below the lower band reflects bearish expansion.
The GARCH engine gives the indicator a deeper volatility layer than a standard ATR or deviation channel. Instead of only measuring recent range, it models variance behavior and projects that into the band structure.
This indicator is best used as a specialized module within a complete strategy framework. Its role is to isolate volatility-adjusted regime expansion, where price is evaluated against a dynamic variance boundary rather than a static channel. The full value comes from how this volatility regime signal is integrated into a broader process for timing, structure, and execution.
Indicator
Volatility Halo | NAL1. Overview
Volatility Halo | NAL is an adaptive volatility band indicator built from a Zero-Lag EMA baseline, ATR band structure, and a recursive GARCH-style volatility regime multiplier.
The indicator does not use fixed-width bands. Instead, it starts with ATR-based bands and then adjusts their width using a projected volatility regime model. This allows the bands to respond differently when market volatility is expanding, contracting, or stabilizing.
2. Calculation
The indicator starts by calculating a Zero-Lag EMA baseline from the selected source. This baseline acts as the central trend reference, helping reduce lag compared to a standard EMA while still keeping the structure smooth.
float baseline = f_zlema(src, baseline_len)
float ATR_Value = ta.atr(ATR_Len)
The ATR value is then multiplied by the user-defined ATR multiple. This forms the base volatility distance used for the upper and lower bands.
The more advanced part of the indicator is the recursive GARCH-style regime multiplier. It begins by calculating log returns and converting them into shock variance. A long-run variance estimate is then built from recent shock variance.
GARCH_LogReturn = close > 0.0 and close > 0.0 ? math.log(close / close ) : 0.0
GARCH_ShockVariance = math.pow(GARCH_LogReturn, 2.0)
GARCH_LongRunVariance = ta.ema(GARCH_ShockVariance, GARCH_LongRunLen)
The model recursively updates conditional variance using three components: recent shock variance, long-run variance, and previous conditional variance. When adaptive coefficients are enabled, the script searches for coefficient weights that better fit recent variance behavior.
GARCH_ConditionalVariance :=
GARCH_Gamma * GARCH_LongRunVariance +
GARCH_Alpha * GARCH_ShockVariance +
GARCH_Beta * GARCH_PreviousConditionalVariance
The conditional variance is then projected and converted into a volatility estimate. This volatility is compared against its own regime baseline to create a volatility regime multiplier. The multiplier is clamped between a minimum and maximum value, preventing the bands from becoming too narrow or too wide.
GARCH_Volatility = math.sqrt(math.max(GARCH_ProjectedVariance, 0.0))
GARCH_RegimeMultiplierRaw = GARCH_Volatility / GARCH_RegimeBase
GARCH_RegimeMultiplier = f_clamp(GARCH_RegimeMultiplierSmooth, GARCH_MinMult, GARCH_MaxMult)
The final band width is created by combining ATR with the GARCH regime multiplier. The upper and lower bands are placed around the Zero-Lag EMA baseline.
hybridBandWidth = ATR_Value * ATR_Mult * GARCH_RegimeMultiplier
upperBand = baseline + hybridBandWidth
lowerBand = baseline - hybridBandWidth
A bullish state triggers when price closes above the upper band. A bearish state triggers when price closes below the lower band. When price remains inside the bands, the previous state is held.
3. Key Features
Zero-Lag EMA baseline for reduced-lag trend structure.
ATR-based volatility bands.
Recursive GARCH-style conditional variance model.
Adaptive volatility regime multiplier.
Bands expand or contract based on projected volatility conditions.
State-based candle coloring, band coloring, glow effect, and regime fills.
4. Use
Volatility Halo is designed to identify moments where price begins escaping its volatility-adjusted structure. A close above the upper band reflects bullish expansion, while a close below the lower band reflects bearish expansion.
The adaptive volatility engine allows the bands to shift with the underlying market environment, making the signal more responsive to changes in pressure and regime.
This indicator is best used as a specialized module within a complete strategy framework. Its real strength appears when it is combined with a broader process for reading market behavior, timing, and risk. The full edge comes from how the signal is integrated, not from the signal existing in isolation.
Indicator
Levy Area Flow Sequencer Flow Price Lead LagLévy-Area Flow Sequencer — Flow/Price Lead-Lag
What it is
Correlation says flow and price move together; it cannot say which moves first. But the sequencing is the interesting part: when aggressive flow precedes price, moves are being built by participation before they print; when price precedes flow, price is running ahead and flow is chasing — squeeze / stop-run character. Traced together, the two series form a path in the plane, and the signed (Lévy) area that path encloses measures its rotation — a scale-free, lag-free read of lead–lag, including non-linear lead–lag that fixed-lag cross-correlation misses. This is the most experimental tool of this suite, and it is framed that way.
The mathematics (signature lead–lag metric)
The metric is the antisymmetric part of the second-level path signature of the pair (flow, price): the window sum of (X·dy − Y·dx), with both increment series normalized to unit scale so the area is dimensionless. Per the literature's interpretation, the metric is positive and grows when moves in the first series are followed by same-direction moves in the second. The first series here is cumulative order-flow delta (from lower-timeframe signed volume, with bar-shape fallback) and the second is price, so AREA > 0 → FLOW LEADS and AREA < 0 → PRICE LEADS.
The honesty steps
Significance gate — a raw signed area is noisy, so the reading is ranked against its own recent history, and a lead is declared only when rotation is unusually strong for this symbol/timeframe. Otherwise the state is BALANCED: no claim.
Sequencing ≠ causation — the literature is explicit that a signed area alone cannot establish causal direction. This tool reports a temporal-ordering tendency of past bars; treat it as tape character.
Known limitation, stated — persistent inverse co-movement between flow and price can contaminate the sign. On liquid futures they co-move and the read behaves; on instruments where they reliably anti-correlate, don't trust it.
The stability & multi-timeframe layer
States are dwell-filtered (standard anti-chattering): FLOW LEADS / PRICE LEADS / BALANCED is announced only after surviving a set number of confirmed bars, so the read doesn't flip-flop. STABILITY shows how settled it is; PENDING shows a forming state with a countdown. Cost: a few bars of lag — stated and adjustable.
The lead lane — a thin strip at the pane bottom — gives the glance-read: green = flow leads (moves better backed), amber = price leads (flow chasing, be sceptical), gray = balanced. Trust/caution colors, never direction.
The HTF STACK row shows the raw lead state on three higher timeframes derived as multiples of the chart (defaults 3×, 5×, 15×). Honesty note: lower-timeframe data cannot be requested inside a higher-timeframe request, so the HTF slots use the bar-shape delta proxy — a stated approximation. ✓ = all timeframes agree on the same significant lead; ⚠ = a higher timeframe shows the opposite lead.
How to use it
Add to a liquid intraday chart. Read the dashboard: FLOW LEADS → breakouts/drives carry more weight (participation came first); PRICE LEADS → be sceptical of extensions (flow is chasing); BALANCED → the tool makes no claim.
Tags print when the lead flips while significant; alerts fire on flips.
Use it as context alongside order-flow and structure tools — never as a standalone signal.
What makes it original
Path-signature methods are frontier quantitative machinery (rough-path theory) that has reached systematic trading but, to the author's knowledge, not chart platforms. Applying the signature lead-lag metric to the flow-vs-price pair — the pair an order-flow trader actually cares about — with an honest significance gate and stated limitations, is the contribution.
Concept credits
Signed area of stochastic paths — P. Lévy. Rough-path / signature theory — T. Lyons; Levin, Lyons & Ni (2016). Signature lead-lag metric and interpretation — I. Chevyrev & A. Kormilitzin (2016). Market applications — Bennett, Cucuringu & Reinert (2022); Cartea, Cucuringu & Jin (2023). Implementation and charting design are the author's own.
Important disclaimer
Research and education only. Not financial advice, not a signal service, not a guarantee of future results. The area measures a sequencing tendency in past data; it is not causal proof and not a prediction. Validate independently and manage your own risk. Indicator
Pulse Mean AcceleratorPulse Mean Accelerator (PMA) | MisinkoMaster
Pulse Mean Accelerator (PMA) is a high-speed adaptive trend engine designed to dynamically accelerate or stabilize its behavior depending on how aggressively price moves relative to its underlying structure. Instead of acting like a traditional moving average that simply lags behind price, PMA attempts to anticipate momentum expansion by accelerating when price pulses strengthen and stabilizing when market movement slows.
The result is a responsive yet smooth trend-following tool that adapts to both trending and consolidating markets. PMA is particularly useful for traders who want earlier participation in expanding trends without sacrificing structural clarity.
By combining adaptive acceleration, volatility awareness, and layered smoothing, PMA balances speed and stability to help traders remain aligned with developing momentum.
Key Features
Adaptive acceleration that reacts when price movement intensifies
Automatically slows down during consolidation to reduce noise
Multiple moving average types supported for flexibility
Volatility-aware responsiveness adjustment
Optional confirmation logic to filter weak signals
Multiple smoothing modes for balancing speed vs stability
Dynamic candle coloring reflecting active trend state
Automatic Long and Short markers when direction changes
Works across fast intraday and slower swing environments
Designed to reduce lag while preserving structure
How It Works
Pulse Mean Accelerator begins with a moving average structure but enhances it by measuring how aggressively price moves relative to that baseline. When price starts moving faster than the average, acceleration increases, allowing the indicator to catch up quickly.
When price slows or becomes erratic, acceleration reduces, preventing excessive reaction to noise.
Volatility measurements are incorporated to scale this acceleration, ensuring that responsiveness adapts naturally to current market conditions. Strong moves result in quicker adaptation, while quiet markets lead to smoother, calmer behavior.
Additional smoothing layers can then be applied, allowing traders to choose between faster responsiveness or more stable structure depending on their trading style.
Optional confirmation logic ensures that signals are not triggered solely by temporary price spikes, helping filter weaker moves.
The outcome is a moving average framework that behaves more like a dynamic trend engine rather than a static lagging indicator.
Trend Detection Logic
Trend direction is determined by how price behaves relative to the accelerated mean structure.
Bullish phases occur when price maintains strength above the adaptive mean while momentum confirms upward pressure. Bearish phases occur when price weakens below the structure and downward momentum dominates.
Signals appear when participation shifts strongly enough to confirm directional change, helping traders detect transitions from consolidation to expansion phases.
Acceleration Behavior
A defining characteristic of PMA is its pulse acceleration mechanism.
• Strong price pulses increase responsiveness
• Weak or slow price movement reduces acceleration
• Volatility conditions influence adaptation speed
• Structure remains smooth when momentum is weak
This dynamic adjustment helps traders enter trends earlier while avoiding excessive reactions during sideways markets.
Smoothing Modes
PMA includes multiple smoothing options so users can tune responsiveness:
• Raw acceleration for fastest reaction
• Exponential stabilization for balanced behavior
• Additional smoothing layers for structural clarity
• Double smoothing for maximum noise reduction
This flexibility allows PMA to be tailored for scalping, intraday trading, or higher-timeframe trend following.
Visual Signals
The indicator provides several visual cues for ease of interpretation:
• Candle coloring reflects active trend direction
• Adaptive mean and accelerated mean are plotted together
• Long and Short markers appear when trend shifts occur
• Filled areas highlight separation between price and structure
These features help traders read market structure quickly without relying on numerical interpretation.
Inputs Overview
Users can customize behavior through adjustable components including:
• Price source selection used in calculations
• Moving average type controlling base structure
• Length settings affecting responsiveness
• Acceleration sensitivity determining reaction speed
• Volatility measurement type influencing adaptation
• Smoothing mode selection for stability control
• Optional confirmation filtering for signal validation
These controls allow the tool to be tuned for both aggressive and conservative trading approaches.
Usage Notes
Ideal for traders needing faster adaptation to momentum expansion
Helps detect early stages of trend acceleration
Useful for filtering sideways noise while remaining reactive to breakouts
Works well in volatile assets where traditional averages lag
Can be combined with support/resistance or volume tools for confirmation
Higher smoothing settings suit swing traders, lower smoothing benefits intraday traders
Confirmation mode reduces false signals in choppy markets
Parameter tuning improves performance across different assets
Best Use Scenarios
Pulse Mean Accelerator performs particularly well in:
• Momentum expansion phases
• Breakouts from consolidation ranges
• Trend continuation environments
• High-volatility market conditions
• Assets showing periodic acceleration bursts
• Markets transitioning from low to high volatility
It is especially effective where traditional moving averages react too slowly to developing moves.
Summary
Pulse Mean Accelerator transforms traditional moving average logic into an adaptive trend engine capable of accelerating when price momentum expands and stabilizing during calm conditions. By blending acceleration, volatility awareness, and flexible smoothing, it provides traders with a faster yet structured view of market direction.
PMA is best suited for traders seeking earlier trend participation while maintaining smooth, readable structure across both fast-moving and consolidating markets. Indicator
Stalonte EMA - Stable Long-Term EMA with AlertsStalonte EMA - The Adaptive & Stable EMA - Almost Eternal
Here's why you will love "Stalonte":
The Stalonte (Stable Long-Term EMA) is a highly versatile trend-following tool. Unlike standard EMAs with fixed periods, it uses a configurable smoothing constant (alpha), allowing traders to dial in the exact level of responsiveness and stability they need. Finding the "sweet spot" (e.g., alpha ~0.03) creates a uniquely effective moving average: it is smooth enough to filter out noise and identify safe, high-probability trends, yet responsive enough to provide actionable signals without extreme lag. It includes alerts for crossovers and retests.
Pros and Cons of the Stalonte EMA
Pros:
Unparalleled Adaptability: This is its greatest strength. The alpha input lets you seamlessly transform the indicator from an ultra-slow "trend-revealer" (low alpha) into a highly effective and "safe" trend-following tool (medium alpha, e.g., 0.03), all the way to a more reactive one.
Optimized for Safety & Signal Quality: As you astutely pointed out, with the proper setting (like 0.03), it finds the perfect balance. It provides a smoother path than a standard 20-50 period EMA, which reduces whipsaws and false breakouts, leading to safer, higher-confidence signals.
Superior Trend Visualization: It gives a cleaner and more intuitive representation of the market's direction than many conventional moving averages, making it easier to "see" the trend and stick with it.
Objective Dynamic Support/Resistance: The line created with a medium alpha setting acts as a powerful dynamic support in uptrends and resistance in downtrends, offering excellent areas for entries on retests with integrated alerts.
Cons:
Requires Calibration: The only "con" is that its performance is not plug-and-play; it requires the user to find their optimal alpha value for their specific trading style and the instrument they are trading. This demands a period of testing and customization, which a standard 50-period EMA does not.
Conceptual Hurdle: For traders only familiar with period-based EMAs, the concept of a "smoothing constant" can be initially confusing compared to simply setting a "length."
In summary:
The Stalonte EMA is not a laggy relic. It is a highly sophisticated and adaptable tool. Its design allows for precise tuning, enabling a trader to discover a setting that offers a superior blend of stability and responsiveness—a "sweet spot" that provides safer and often more effective signals than many traditional moving averages. Thank you for pushing for a more accurate and fair assessment.
Use Case Example:
You can combine it with classical EMAs to find the perfect entry.
Indicator
RSI-Adaptive T3 [ChartPrime]The RSI-Adaptive T3 is a precision trend-following tool built around the legendary T3 smoothing algorithm developed by Tim Tillson , designed to enhance responsiveness while reducing lag compared to traditional moving averages. Current implementation takes it a step further by dynamically adapting the smoothing length based on real-time RSI conditions — allowing the T3 to “breathe” with market volatility. This dynamic length makes the curve faster in trending moves and smoother during consolidations.
To help traders visualize volatility and directional momentum, adaptive volatility bands are plotted around the T3 line, with visual crossover markers and a dynamic info panel on the chart. It’s ideal for identifying trend shifts, spotting momentum surges, and adapting strategy execution to the pace of the market.
HOIW IT WORKS
At its core, this indicator fuses two ideas:
The T3 Moving Average — a 6-stage recursively smoothed exponential average created by Tim Tillson , designed to reduce lag without sacrificing smoothness. It uses a volume factor to control curvature.
A Dynamic Length Engine — powered by the RSI. When RSI is low (market oversold), the T3 becomes shorter and more reactive. When RSI is high (overbought), the T3 becomes longer and smoother. This creates a feedback loop between price momentum and trend sensitivity.
// Step 1: Adaptive length via RSI
rsi = ta.rsi(src, rsiLen)
rsi_scale = 1 - rsi / 100
len = math.round(minLen + (maxLen - minLen) * rsi_scale)
pine_ema(src, length) =>
alpha = 2 / (length + 1)
sum = 0.0
sum := na(sum ) ? src : alpha * src + (1 - alpha) * nz(sum )
sum
// Step 2: T3 with adaptive length
e1 = pine_ema(src, len)
e2 = pine_ema(e1, len)
e3 = pine_ema(e2, len)
e4 = pine_ema(e3, len)
e5 = pine_ema(e4, len)
e6 = pine_ema(e5, len)
c1 = -v * v * v
c2 = 3 * v * v + 3 * v * v * v
c3 = -6 * v * v - 3 * v - 3 * v * v * v
c4 = 1 + 3 * v + v * v * v + 3 * v * v
t3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3
The result: an evolving trend line that adapts to market tempo in real-time.
KEY FEATURES
⯁ RSI-Based Adaptive Smoothing
The length of the T3 calculation dynamically adjusts between a Min Length and Max Length , based on the current RSI.
When RSI is low → the T3 shortens, tracking reversals faster.
When RSI is high → the T3 stretches, filtering out noise during euphoria phases.
Displayed length is shown in a floating table, colored on a gradient between min/max values.
⯁ T3 Calculation (Tim Tillson Method)
The script uses a 6-stage EMA cascade with a customizable Volume Factor (v) , as designed by Tillson (1998) .
Formula:
T3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3
This technique gives smoother yet faster curves than EMAs or DEMA/Triple EMA.
⯁ Visual Trend Direction & Transitions
The T3 line changes color dynamically:
Color Up (default: blue) → bullish curvature
Color Down (default: orange) → bearish curvature
Plot fill between T3 and delayed T3 creates a gradient ribbon to show momentum expansion/contraction.
Directional shift markers (“🞛”) are plotted when T3 crosses its own delayed value — helping traders spot trend flips or pullback entries.
⯁ Adaptive Volatility Bands
Optional upper/lower bands are plotted around the T3 line using a user-defined volatility window (default: 100).
Bands widen when volatility rises, and contract during compression — similar to Bollinger logic but centered on the adaptive T3.
Shaded band zones help frame breakout setups or mean-reversion zones.
⯁ Dynamic Info Table
A live stats panel shows:
Current adaptive length
Maximum smoothing (▲ MaxLen)
Minimum smoothing (▼ MinLen)
All values update in real time and are color-coded to match trend direction.
HOW TO USE
Use T3 crossovers to detect trend transitions, especially during periods of volatility compression.
Watch for volatility contraction in the bands — breakouts from narrow band periods often precede trend bursts.
The adaptive smoothing length can also be used to assess current market tempo — tighter = faster; wider = slower.
CONCLUSION
RSI-Adaptive T3 modernizes one of the most elegant smoothing algorithms in technical analysis with intelligent RSI responsiveness and built-in volatility bands. It gives traders a cleaner read on trend health, directional shifts, and expansion dynamics — all in a visually efficient package. Perfect for scalpers, swing traders, and algorithmic modelers alike, it delivers advanced logic in a plug-and-play format. Indicator
Indicator
Lead-Lag Market Detector [CryptoSea]The Lead-Lag Market Detector is an advanced tool designed to help traders identify leading and lagging assets within a chosen market. This indicator leverages correlation analysis to rank assets based on their influence, making it ideal for traders seeking to optimise their portfolio or spot key market trends.
Key Features
Dynamic Asset Ranking: Utilises real-time correlation calculations to rank assets by their influence on the market, helping traders identify market leaders and laggers.
Customisable Parameters: Includes adjustable lookback periods and correlation thresholds to adapt the analysis to different market conditions and trading styles.
Comprehensive Asset Coverage: Supports up to 30 assets, offering broad market insights across cryptocurrencies, stocks, or other markets.
Gradient-Enhanced Table Display: Presents results in a colour-coded table, where assets are ranked dynamically with influence scores, aiding in quick visual analysis.
In the example below, the ranking highlights how assets tend to move in groups. For instance, BTCUSDT, ETHUSDT, BNBUSDT, SOLUSDT, and LTCUSDT are highly correlated and moving together as a group. Similarly, another group of correlated assets includes XRPUSDT, FILUSDT, APEUSDT, XTZUSDT, THETAUSDT, and CAKEUSDT. This grouping of assets provides valuable insights for traders to diversify or spread exposure.
If you believe one asset in a group is likely to perform well, you can spread your exposure into other correlated assets within the same group to capitalise on their collective movement. Additionally, assets like AVAXUSDT and ZECUSDT, which appear less correlated or uncorrelated with the rest, may offer opportunities to act as potential hedges in your trading strategy.
How it Works
Correlation-Based Scoring: Calculates pairwise correlations between assets over a user-defined lookback period, identifying assets with high influence scores as market leaders.
Customisable Thresholds: Allows traders to define a correlation threshold, ensuring the analysis focuses only on significant relationships between assets.
Dynamic Score Calculation: Scores are updated dynamically based on the timeframe and input settings, providing real-time insights into market behaviour.
Colour-Enhanced Results: The table display uses gradients to visually distinguish between leading and lagging assets, simplifying data interpretation.
Application
Portfolio Optimisation: Identifies influential assets to help traders allocate their portfolio effectively and reduce exposure to lagging assets.
Market Trend Identification: Highlights leading assets that may signal broader market trends, aiding in strategic decision-making.
Customised Trading Strategies: Adapts to various trading styles through extensive input settings, ensuring the analysis meets the specific needs of each trader.
The Lead-Lag Market Detector by is an essential tool for traders aiming to uncover market leaders and laggers, navigate complex market dynamics, and optimise their trading strategies with precision and insight.
Indicator
Zero-Lag MA Trend Levels [ChartPrime] The Zero-Lag MA Trend Levels indicator combines a Zero-Lag Moving Average (ZLMA) with a standard Exponential Moving Average (EMA) to provide a dynamic view of the market trend. This indicator uses a color-changing cloud to represent shifts in trend momentum and plots key levels when trend reversals are detected. The addition of trend level boxes helps identify significant price zones where market shifts occur, with retest signals aiding in spotting potential continuation or reversal points.
⯁ KEY FEATURES & HOW TO USE
⯌ Zero-Lag Moving Average (ZLMA) with EMA Cloud :
The indicator employs a Zero-Lag Moving Average (ZLMA) alongside a standard EMA.
series float emaValue = ta.ema(close, length) // EMA of the closing price
series float correction = close + (close - emaValue) // Correction factor for zero-lag calculation
series float zlma = ta.ema(correction, length) // Zero-Lag Moving Average (ZLMA)
The cloud between these averages changes color depending on the trend direction. During a downtrend, if the ZLMA begins to increase, the cloud partially turns green, signaling potential strength. Conversely, during an uptrend, if the ZLMA decreases, the cloud partially turns to the downtrend color (blue by default), indicating potential weakness.
Use : Traders can monitor the cloud's color shifts for early signs of changing momentum. A fully colored cloud aligning with the current trend indicates a strong directional move, while mixed colors suggest a potential trend change.
⯌ Trend Shift and Level Boxes :
Each time a crossover between the EMA and the ZLMA occurs, indicating a trend shift, the indicator plots a box around the price level where the shift occurred. This box remains on the chart to mark the price zone of the trend change.
Use : The boxes provide clear visual markers of where market sentiment shifted. These levels can act as support and resistance zones. Traders can use these boxes to identify potential entry or exit points when the market retests these key levels.
⯌ Retest Detection with Labels :
If the price action crosses a previously plotted trend level box, the indicator marks this event with triangle labels. An upward triangle (▲) appears when the price retests the top of a box during a bullish crossover, and a downward triangle (▼) appears when the price retests the bottom of a box during a bearish crossunder.
Use : These labels help traders identify potential continuation or reversal points at critical price levels, offering additional confirmation for trading decisions.
⯌ Dynamic Color-Coding :
The color of the ZLMA and the EMA is adjusted according to their current trend direction, with the ZLMA adopting green for upward trends and blue for downward trends. This visual representation makes it easier to quickly gauge the market's momentum at a glance.
Use : Traders can use the color-coding to quickly assess the strength and direction of the current trend, allowing for more informed decision-making.
⯁ USER INPUTS
Length : Sets the period for both the ZLMA and EMA calculations.
Trend Levels : Toggle to display the trend level boxes on the chart.
Colors (+ / -) : Define the colors for bullish and bearish trends.
⯁ CONCLUSION
The Zero-Lag MA Trend Levels - ChartPrime indicator offers a nuanced approach to trend detection by combining the ZLMA with a traditional EMA. Its dynamic cloud color changes, trend level boxes, and retest labels make it a versatile tool for traders seeking to identify trend shifts and key price zones effectively. By incorporating elements of support and resistance along with trend momentum, this indicator provides a comprehensive view of market dynamics for both trend-following and counter-trend trading strategies. Indicator
TASC 2024.04 The Ultimate Smoother█ OVERVIEW
This script presents an implementation of the digital smoothing filter introduced by John Ehlers in his article "The Ultimate Smoother" from the April 2024 edition of TASC's Traders' Tips .
█ CONCEPTS
The UltimateSmoother preserves low-frequency swings in the input time series while attenuating high-frequency variations and noise. The defining input parameter of the UltimateSmoother is the critical period , which represents the minimum wavelength (highest frequency) in the filter's pass band. In other words, the filter attenuates or removes the amplitudes of oscillations at shorter periods than the critical period.
According to Ehlers, one primary advantage of the UltimateSmoother is that it maintains zero lag in its pass band and minimal lag in its transition band, distinguishing it from other conventional digital filters (e.g., moving averages ). One can apply this smoother to various input data series, including other indicators.
█ CALCULATIONS
Ehlers derived the UltimateSmoother using inspiration from the design principles he learned from his experience with analog filters , as described in the original publication. On a technical level, the UltimateSmoother's unique response involves subtracting a high-pass response from an all-pass response . At very low frequencies (lengthy periods), where the high-pass filter response has virtually no amplitude, the subtraction yields a frequency and phase response practically equivalent to the input data. At other frequencies, the subtraction achieves filtration through cancellation due to the close similarities in response between the high-pass filter and the input data.
Indicator
Indicator
Indicator
Adjustable MA & Alternating Extremities [LuxAlgo]Returns a moving average allowing the user to control the amount of lag as well as the amplitude of its overshoots thanks to a parametric kernel. The indicator displays alternating extremities and aims to provide potential points where price might reverse.
Due to user requests, we added the option to display the moving average as candles instead of a solid line.
Settings
Length: MA period, refers to the number of most recent data points to use for its calculation.
Mult: Multiplicative factor for each extremity.
As Smoothed Candles: Allows the user to show the MA as a series of candles instead of a solid line.
Show Alternating Extremities : Determines whether to display the alternating extremities or not.
Lag: Controls the amount of lag of the MA, with higher values returning a MA with more lag.
Overshoot: Controls the amplitude of the overshoots returned by the MA, with higher values increasing the amplitude of the overshoots.
Usage
Moving averages using parametric kernels allows users to have more control over characteristics such as lag or smoothness; this can greatly benefit the analyst. A moving average with reduced lag can be used as a leading moving average in a MA crossover system, while lag will benefit moving averages used as slow MA in a crossover system.
Increasing 'Lag' will increase smoothness while increasing 'overshoot' will reduce lag.
The following indicator puts more emphasis on its alternating extremities, an upper extremity will be shown once the high price crosses the upper extremity, while a low extremity will be shown once the low price crosses the lower extremity. These can be interpreted like extremities of a band indicator.
The MA using a length value of 200 with a multiplicative factor of 1.
In general, extremities will effectively return points where price might potentially bounce in ranging markets while closing prices under trending markets will often be found above an upper extremity and under a lower extremity.
Reducing the lag of the moving average allows the user to obtain a more timely estimate of the underlying trend in the price, with a better fit overall. This allows the user to obtain potentially pertinent extremities where price might reverse upon a break, even under trending markets.
In the above chart, the price initially breaks the upper extremity, however, we can observe that the upper extremity eventually reaches back the price, goes above it, provides a resistance, and effectively indicates a reversal.
Users can plot candles from the moving average, these are fairly similar to heikin-ashi candles in the sense that CandleOpen(t) ≠ CandleClose(t-1) , each point of the candle is calculated as follows for our indicator:
Open = Average between MA(t-1) and MA(t-2)
High = MA using the high price as input
Low = MA using the low price as input
Close = MA using the closing price as input
Details
Lag is defined as the effect of moving averages to reflect past price variations instead of new ones, lag can be observed by the user and is the main cause of false signals. Lag is proportional to the degree of filtering returned by the moving average.
Overshooting is a common effect encountered in non-lagging moving averages, and is defined as the tendency of a moving average to exceed a maximum level (or minimum level, which can be defined as undershooting )
MA and rolling maximum/minimum, both using a length of 50 bars. While we can think of lag as a cost of smoothness, we can think of overshooting as a cost for reduced lag on some occasions.
Explaining the kernel design behind our moving average requires understanding of the logic behind lag reduction in moving averages. This can prove to be complex for non informed users, but let's just focus on the simpler part; moving averages can be defined as a weighted sum between past prices and a set of coefficients (kernel).
MA(t) = b(0)C(t) + b(1)C(t-1) + b(2)C(t-2) + ... + b(n-1)C(t-n-1)
Where n is the period of the moving average. Lag is (non optimally) reduced by "underweighting" past prices - that is multiplying them by negative numbers.
The kernel used in our moving average is based on a modified sinewave. A weighted sum making use of a sinewave as a kernel would return an oscillator centered at 0. We can divide this sinewave by an increasing linear function in order to obtain a kernel allowing us to obtain a low lag moving average instead of a centered oscillator. This is the main idea in the design of the kernel used by our moving average.
The kernel equation of our moving average is:
sin(2πx^α)(1 - x^β)
With 1>x>0 , and where α controls the lag, while β controls the overshoot amplitude.
Using this equation we can obtain the following kernels:
Here only α is changed, while β is equal to 1. Values to the left would represent the coefficients for the most recent prices. Notice how the most significant coefficients are given to the oldest prices in the case where α increases.
Higher overshoot would require more negative values, this is controlled by β
Here only β is changed, while α is equal to 1. Notice how higher values return lower negative coefficients. This effectively increases the overshoots amplitude in our moving average. We can decrease α in order for these negative coefficients to underweight more recent values.
Using α = 0 allows us to simplify the kernel equation to:
1 - x^β
Using this kernel we can obtain more classical moving averages, this can be seen from the following results:
Using β = 1 allows us to obtain a linearly decreasing kernel (the one of a WMA), while increasing allows the kernel to converge toward a rectangular kernel (the one of SMA). Indicator
Indicator
[blackcat] L2 Ehlers Zero-lag SmootherLevel: 2
Background
John F. Ehlers introuced Zero-Lag Data Smoothers in Jul, 2002.
Function
John Ehlers introduced "Zero-Lag Data Smoothers", the infinite impulse response (IIR) filter and finite impulse response (FIR) filter.
In his article this issue on zero-lag smoothing, John Ehlers notes that his favorite filter is the symmetrically weighted six-bar finite impulse response (FIR) filter. This is also known as a triangular moving average, and can be conveniently implemented as a double-smoothed simple moving average. Per Ehlers, since this filter has six elements, its lag is 2.5 bars. Via further processing, this lag can be reduced to zero, but this produces too much overshoot. As a compromise, Ehlers suggests reducing the lag to one bar. To enable a user to adjust the lag easily, I provide the pine v4 code for an Adjustable Lag Filter indicator below. The first input, Price, should typically be set to OHLC, hl2, hl3, ohlc4 etc. The second input, LagReduction, should be set to a value in the zero-to-2.5 range. Setting it to zero will result in no adjustment, and the output will match that of the raw triangular average. Setting it to 2.5 will reduce the lag to zero. Setting it to 1.5 will reduce the lag to one bar.
Key Signal
Filter--> Zero-Lag Data Smoother fast line
Trigger--> Zero-Lag Data Smoother slow line
Pros and Cons
100% John F. Ehlers definition translation, even variable names are the same. This help readers who would like to use pine to read his book.
Remarks
The 67th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy. Indicator
A Useful MA Weighting Function For Controlling Lag & SmoothnessSo far the most widely used moving average with an adjustable weighting function is the Arnaud Legoux moving average (ALMA), who uses a Gaussian function as weighting function. Adjustable weighting functions are useful since they allow us to control characteristics of the moving average such as lag and smoothness.
The following moving average has a simple adjustable weighting function that allows the user to have control over the lag and smoothness of the moving average, we will see that it can also be used to get both an SMA and WMA.
A high-resolution gradient is also used to color the moving average, makes it fun to watch, the plot transition between 200 colors, would be tedious to make but everything was made possible using a custom R script, I only needed to copy and paste the R console output in the Pine editor.
Settings
length : Period of the moving average
-Lag : Setting decreasing the lag of the moving average
+Lag : Setting increasing the lag of the moving average
Estimating Existing Moving Averages
The weighting function of this moving average is derived from the calculation of the beta distribution, advantages of such distribution is that unlike a lot of PDF, the beta distribution is defined within a specific range of values (0,1). Parameters alpha and beta controls the shape of the distribution, with alpha introducing negative skewness and beta introducing positive skewness, while higher values of alpha and beta increase kurtosis.
Here -Lag is directly associated to beta while +Lag is associated with alpha . When alpha = beta = 1 the distribution is uniform, and as such can be used to compute a simple moving average.
Moving average with -Lag = +Lag = 1 , its impulse response is shown below.
It is also possible to get a WMA by increasing -Lag , thus having -Lag = 2 and +Lag = 1 .
Using values of -Lag and +Lag equal to each other allows us to get a symmetrical impulse response, increasing these two values controls the heaviness of the tails of the impulse response.
Here -Lag = +Lag = 3 , note that when the impulse response of a moving average is symmetrical its lag is equal to (length-1)/2 .
As for the gradient, the color is determined by the value of an RSI using the moving average as input.
I don't promise anything but I will try to respond to your comments Indicator
Indicator
Indicator
Filter Information Box - PineCoders FAQWhen designing filters it can be interesting to have information about their characteristics, which can be obtained from the set of filter coefficients (weights). The following script analyzes the impulse response of a filter in order to return the following information:
Lag
Smoothness via the Herfindahl index
Percentage Overshoot
Percentage Of Positive Weights
The script also attempts to determine the type of the analyzed filter, and will issue warnings when the filter shows signs of unwanted behavior.
DISPLAYED INFORMATION AND METHODS
The script displays one box on the chart containing two sections. The filter metrics section displays the following information:
- Lag : Measured in bars and calculated from the convolution between the filter's impulse response and a linearly increasing sequence of value 0,1,2,3... . This sequence resets when the impulse response crosses under/over 0.
- Herfindahl index : A measure of the filter's smoothness described by Valeriy Zakamulin. The Herfindahl index measures the concentration of the filter weights by summing the squared filter weights, with lower values suggesting a smoother filter. With normalized weights the minimum value of the Herfindahl index for low-pass filters is 1/N where N is the filter length.
- Percentage Overshoot : Defined as the maximum value of the filter step response, minus 1 multiplied by 100. Larger values suggest higher overshoots.
- Percentage Positive Weights : Percentage of filter weights greater than 0.
Each of these calculations is based on the filter's impulse response, with the impulse position controlled by the Impulse Position setting (its default is 1000). Make sure the number of inputs the filter uses is smaller than Impulse Position and that the number of bars on the chart is also greater than Impulse Position . In order for these metrics to be as accurate as possible, make sure the filter weights add up to 1 for low-pass and band-stop filters, and 0 for high-pass and band-pass filters.
The comments section displays information related to the type of filter analyzed. The detection algorithm is based on the metrics described above. The script can detect the following type of filters:
All-Pass
Low-Pass
High-Pass
Band-Pass
Band-Stop
It is assumed that the user is analyzing one of these types of filters. The comments box also displays various warnings. For example, a warning will be displayed when a low-pass/band-stop filter has a non-unity pass-band, and another is displayed if the filter overshoot is considered too important.
HOW TO SET THE SCRIPT UP
In order to use this script, the user must first enter the filter settings in the section provided for this purpose in the top section of the script. The filter to be analyzed must then be entered into the:
f(input)
function, where `input` is the filter's input source. By default, this function is a simple moving average of period length . Be sure to remove it.
If, for example, we wanted to analyze a Blackman filter, we would enter the following:
f(input)=>
pi = 3.14159,sum = 0.,sumw = 0.
for i = 0 to length-1
k = i/length
w = 0.42 - 0.5 * cos(2 * pi * k) + 0.08 * cos(4 * pi * k)
sumw := sumw + w
sum := sum + w*input
sum/sumw
EXAMPLES
In this section we will look at the information given by the script using various filters. The first filter we will showcase is the linearly weighted moving average (WMA) of period 9.
As we can see, its lag is 2.6667, which is indeed correct as the closed form of the lag of the WMA is equal to (period-1)/3 , which for period 9 gives (9-1)/3 which is approximately equal to 2.6667. The WMA does not have overshoots, this is shown by the the percentage overshoot value being equal to 0%. Finally, the percentage of positive weights is 100%, as the WMA does not possess negative weights.
Lets now analyze the Hull moving average of period 9. This moving average aims to provide a low-lag response.
Here we can see how the lag is way lower than that of the WMA. We can also see that the Herfindahl index is higher which indicates the WMA is smoother than the HMA. In order to reduce lag the HMA use negative weights, here 55% (as there are 45% of positive ones). The use of negative weights creates overshoots, we can see with the percentage overshoot being 26.6667%.
The WMA and HMA are both low-pass filters. In both cases the script correctly detected this information. Let's now analyze a simple high-pass filter, calculated as follows:
input - sma(input,length)
Most weights of a high-pass filters are negative, which is why the lag value is negative. This would suggest the indicator is able to predict future input values, which of course is not possible. In the case of high-pass filters, the Herfindahl index is greater than 0.5 and converges toward 1, with higher values of length . The comment box correctly detected the type of filter we were using.
Let's now test the script using the simple center of gravity bandpass filter calculated as follows:
wma(input,length) - sma(input,length)
The script correctly detected the type of filter we are using. Another type of filter that the script can detect is band-stop filters. A simple band-stop filter can be made as follows:
input - (wma(input,length) - sma(input,length))
The script correctly detect the type of filter. Like high-pass filters the Herfindahl index is greater than 0.5 and converges toward 1, with greater values of length . Finally the script can detect all-pass filters, which are filters that do not change the frequency content of the input.
WARNING COMMENTS
The script can give warning when certain filter characteristics are detected. One of them is non-unity pass-band for low-pass filters. This warning comment is displayed when the weights of the filter do not add up to 1. As an example, let's use the following function as a filter:
sum(input,length)
Here the filter pass-band has non unity, and the sum of the weights is equal to length . Therefore the script would display the following comments:
We can also see how the metrics go wild (note that no filter type is detected, as the detected filter could be of the wrong type). The comment mentioning the detection of high overshoot appears when the percentage overshoot is greater than 50%. For example if we use the following filter:
5*wma(input,length) - 4*sma(input,length)
The script would display the following comment:
We can indeed see high overshoots from the filter:
@alexgrover for PineCoders
Look first. Then leap.
Indicator
Grand Trend Forecasting - A Simple And Original Approach Today we'll link time series forecasting with signal processing in order to provide an original and funny trend forecasting method, the post share lot of information, if you just want to see how to use the indicator then go to the section "Using The Indicator".
Time series forecasting is an area dealing with the prediction of future values of a series by using a specific model, the model is the main tool that is used for forecasting, and is often an expression based on a set of predictor terms and parameters, for example the linear regression (model) is a 1st order polynomial (expression) using 2 parameters and a predictor variable ax + b . Today we won't be using the linear regression nor the LSMA.
In time series analysis we can describe the time series with a model, in the case of the closing price a simple model could be as follows :
Price = Trend + Cycles + Noise
The variables of the model are the components, such model is additive since we add the component with each others, we should be familiar with each components of the model, the trend represent a simple long term variation of high amplitude, the cycles are periodic fluctuations centered around 0 of varying period and amplitude, the noise component represent shorter term irregular variations with mean 0.
As a trader we are mostly interested by the cycles and the trend, altho the cycles are relatively more technical to trade and can constitute parasitic fluctuations (think about retracements in a trend affecting your trend indicator, causing potential false signals).
If you are curious, in signal processing combining components has a specific name, "synthesis" , here we are dealing with additive synthesis, other type of synthesis are more specific to audio processing and are relatively more complex, but could be used in technical analysis.
So what to do with our components ? If we want to trade the trend, we should estimate right ? Estimating the trend component involve removing the cycle and noise component from the price, if you have read stuff about filters you should know where i'am going, yep, we should use filters, in the case of keeping the trend we can use a simple moving average of relatively high period, and here we go.
However the lag problem, which is recurrent, come back again, we end up with information easier to interpret (here the trend, which is a simple fluctuation such as a line or other smooth curve) at the cost of decision timing, that is unfortunate but as i said the information, here the moving average output, is relatively simple, and could be easily forecasted right ? If you plot a moving average of high period it would be easier for you to forecast its future values. And thats what we aim to do today, provide an estimate of the trend that should be easy to forecast, and should fit to the price relatively well in order to produce forecast that could determine the position of future closing prices observations.
Estimating And Forecasting The Trend
The parameter of the indicator dealing with the estimation of the trend is length , with higher values of length attenuating the cycle and noise component in the price, note however that high values of length can return a really long term trend unlike a simple moving average, so a small value of length, 14 for example can still produce relatively correct estimate of trend.
here length = 14.
The rough estimate of the trend is t in the code, and is an IIR filter, that is, it is based on recursion. Now i'll pass on the filter design explanation but in short, weights are constants, with higher weights allocated to the previous length values of the filter, you can see on the code that the first part of t is similar to an exponential moving average with :
t(n) = 0.9t(n-length) + 0.1*Price
However while the EMA only use the precedent value for the recursion, here we use the precedent length value, this would just output a noisy and really slow output, therefore in order to create a better fit we add : 0.9*(t(n-length) - t(n-2length)) , and this create the rough trend estimate that you can see in blue. On the parameters, 0.9 is used since it gives the best estimate in my opinion, higher values would create more periodic output and lower values would just create a rougher output.
The blue line still contain a residual of the cycle/noise component, this is why it is smoothed with a simple moving average of period length. If you are curious, a filter estimating the trend but still containing noisy fluctuations is called "Notch" filter, such filter would depending on the cutoff remove/attenuate mid term cyclic fluctuations while preserving the trend and the noise, its the opposite of a bandpass filter.
In order to forecast values, we simply sum our trend estimate with the trend estimate change with period equal to the forecasting horizon period, this is a really really simple forecasting method, but it can produce decent results, it can also allows the forecast to start from the last point of the trend estimate.
Using The Indicator
We explained the length parameter in the precedent section, src is the input series which the trend is estimated, forecast determine the forecasting horizon, recommend values for forecast should be equal to length, length/2 or length*2, altho i strongly recommend length.
here length and forecast are both equal to 14 .
The corrective parameter affect the trend estimate, it reduce the overshoot and can led to a curve that might fit better to the price.
The indicator with the non corrective version above, and the corrective one below.
The source parameter determine the source of the forecast, when "Noisy" is selected the source is the blue line, and produce a noisy forecast, when "Smooth" is selected the source is the moving average of t , this create a smoother forecast.
The width interval control...the width of the intervals, they can be seen above and under the forecast plot, they are constructed by adding/subtracting the forecast with the forecast moving average absolute error with respect to the price. Prediction intervals are often associated with a probability (determining the probability of future values being between the interval) here we can't determine such probability with accuracy, this require (i think) an analysis of the forecasting distribution as well as assumptions on the distribution of the forecasting error.
Finally it is possible to see historical forecasts, that is, forecasts previously generated by checking the "Show Historical Forecasts" option.
Examples
Good forecasts mostly occur when the price is close to the trend estimate, this include the following highlighted periods on AMD 15TF with default settings :
We can see the same thing at the end of EURUSD :
However we can't always obtain suitable fits, here it is isn't sufficient on BTCUSD :
We can see wide intervals, we could change length or use the corrective option to get better results, another option is to use a log scale.
We will end the examples with the log SPX, who posses a linear trend, so for example a linear model such as a linear regression would be really adapted, lets see how the indicator perform :
Not a great fit, we could try to use an higher length value and use "Smooth" :
Most recent fits are quite decent.
Conclusions
A forecasting indicator has been presented in this post. The indicator use an original approach toward estimating the trend component in the closing price. Of course i should have given statistics related to the forecasting error, however such analysis is worth doing with better methods and in more advanced environment allowing for optimization.
But we have learned some stuff related to signal processing as well as time series analysis, seeing a time series as the sum of various components is really helpful when it comes to make sense of chaotic and noisy series and is a basic topic in time series analysis.
You can see that in this new year i work harder on the visual of my indicators without trying to fall in the label addict trap, something that i wasn't really doing before, let me know what do you think of it.
Thanks for reading !
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Autonomous Recursive Moving AverageIntroduction
People often ask me what is my best indicators, i can't really respond to this question with a straight answer but i would say you to check this indicator. The Autonomous Recursive Moving Average (ARMA) is an adaptive moving average that try to minimize the sum of squares thanks to a ternary operator, this choice can seem surprising since most of the adaptive moving averages adapt to a smoothing variable thanks to exponential averaging, but there are lot of downsides to this method, i really wanted to have a flat filter during flat markets and this is what i achieved.
The Indicator
length control the amount of smoothing during trending periods, gamma is the trend sensitivity threshold, higher values of gamma will make an overall flat filter, adjust gamma to skip ranging markets.
gamma = 2, we can adjust to 3 while preserving smoothing reactivity with trading periods.
gamma = 3
low length and higher gamma create more boxy result, the filter add overshoots directly in the output, its unfortunate.
The Zero-Lag option can reduce the lag as well as getting additional flat results without changing gamma.
Conclusion
The indicator need work, but i can't leave without publishing it, the overshoots are a big problems, changing sma for another stable filter can help. I hope you find an use to it, i really like this indicator.
Thanks for reading Indicator
