MathGeometryCurvesChaikinLibrary "MathGeometryCurvesChaikin"
Implements the chaikin algorithm to create a curved path, from assigned points.
chaikin(points_x, points_y, closed) Chaikin algorithm method, uses provided points to generate a smoothed path.
Parameters:
points_x : float array, the x value of points.
points_y : float array, the y value of points.
closed : bool, default=false, is the path closed or not.
Returns: tuple with 2 float arrays.
smooth(points_x, points_y, iterations, closed) Iterate the chaikin algorithm, to smooth a sample of points into a curve path.
Parameters:
points_x : float array, the x value of points.
points_y : float array, the y value of points.
iterations : int, number of iterations to apply the smoothing.
closed : bool, default=false, is the path closed or not.
Returns: array of lines.
draw(path_x, path_y, closed) Draw the path.
Parameters:
path_x : float array, the x value of the path.
path_y : float array, the y value of the path.
closed : bool, default=false, is the path closed or not.
Returns: array of lines.
"curve" için komut dosyalarını ara
Volume Weighted Linear Regression BandThe Volume-Weighted Linear Regression Band (VWLRBd) is a volatility channel that uses a Linear Regression line as its dynamic baseline. Its primary feature is the decomposition of total volatility into two distinct components, visualized as layered bands.
Key Features:
Volatility Decomposition: The indicator separates volatility based on the 'Estimate Bar Statistics' option.
Standard Mode (Estimate Bar Statistics = OFF): The indicator functions as a standard (Volume-Weighted) Linear Regression Channel. It plots a single set of bands based on the standard deviation of the residuals (the error between the Source price and the regression line).
Decomposition Mode (Estimate Bar Statistics = ON): The indicator uses a statistical model ('Estimator') to calculate within-bar volatility. (Assumption: In this mode, the Source input is ignored, and an estimated mean for each bar is used for the regression). This mode displays two sets of bands:
Inner Bands: Show only the contribution of the 'residual' (trend noise) volatility, calculated proportionally.
Outer Bands: Show the total volatility (the sum of residual and within-bar components).
Regression Baseline (Linear / Exponential): The central line is a (Volume-Weighted) Linear Regression curve. An optional 'Normalize' mode performs all calculations in logarithmic space, transforming the baseline into an Exponential Regression Curve and the bands into constant percentage deviations, suitable for analyzing growth assets.
Volume Weighting: An option (Volume weighted) allows for volume to be incorporated into the calculation of both the regression baseline and the volatility decomposition, giving more influence to high-participation bars.
Multi-Timeframe (MTF) Engine: The indicator includes an MTF conversion block. When a Higher Timeframe (HTF) is selected, advanced options become available: Fill Gaps handles data gaps, and Wait for timeframe to close prevents repainting by ensuring the indicator only updates when the HTF bar closes.
Integrated Alerts: Includes a full set of built-in alerts for the source price crossing over or under the central regression line and the outermost calculated volatility band.
DISCLAIM_
For Informational/Educational Use Only: This indicator is provided for informational and educational purposes only. It does not constitute financial, investment, or trading advice, nor is it a recommendation to buy or sell any asset.
Use at Your Own Risk: All trading decisions you make based on the information or signals generated by this indicator are made solely at your own risk.
No Guarantee of Performance: Past performance is not an indicator of future results. The author makes no guarantee regarding the accuracy of the signals or future profitability.
No Liability: The author shall not be held liable for any financial losses or damages incurred directly or indirectly from the use of this indicator.
Signals Are Not Recommendations: The alerts and visual signals (e.g., crossovers) generated by this tool are not direct recommendations to buy or sell. They are technical observations for your own analysis and consideration.
Power RSI Segment Runner [CHE] Power RSI Segment Runner — Tracks RSI momentum across higher timeframe segments to detect directional switches for trend confirmation.
Summary
This indicator calculates a running Relative Strength Index adapted to segments defined by changes in a higher timeframe, such as daily closes, providing a smoothed view of momentum within each period. It distinguishes between completed segments, which fix the final RSI value, and ongoing ones, which update in real time with an exponential moving average filter. Directional switches between bullish and bearish momentum trigger visual alerts, including overlay lines and emojis, while a compact table displays current trend strength as a progress bar. This segmented approach reduces noise from intra-period fluctuations, offering clearer signals for trend persistence compared to standard RSI on lower timeframes.
Motivation: Why this design?
Standard RSI often generates erratic signals in choppy markets due to constant recalculation over fixed lookback periods, leading to false reversals that mislead traders during range-bound or volatile phases. By resetting the RSI accumulation at higher timeframe boundaries, this indicator aligns momentum assessment with broader market cycles, capturing sustained directional bias more reliably. It addresses the gap between short-term noise and long-term trends, helping users filter entries without over-relying on absolute overbought or oversold thresholds.
What’s different vs. standard approaches?
- Baseline Reference: Diverges from the classic Wilder RSI, which uses a fixed-length exponential moving average of gains and losses across all bars.
- Architecture Differences:
- Segments momentum resets at higher timeframe changes, isolating calculations per period instead of continuous history.
- Employs persistent sums for ups and downs within segments, with on-the-fly RSI derivation and EMA smoothing.
- Integrates switch detection logic that clears prior visuals on reversal, preventing clutter from outdated alerts.
- Adds overlay projections like horizontal price lines and dynamic percent change trackers for immediate trade context.
- Practical Effect: Charts show discrete RSI endpoints for past segments alongside a curved running trace, making momentum evolution visually intuitive. Switches appear as clean, extendable overlays, reducing alert fatigue and highlighting only confirmed directional shifts, which aids in avoiding whipsaws during minor pullbacks.
How it works (technical)
The indicator begins by detecting changes in the specified higher timeframe, such as a new daily bar, to define segment boundaries. At each boundary, it finalizes the prior segment's RSI by summing positive and negative price changes over that period and derives the value from the ratio of those sums, then applies an exponential moving average for smoothing. Within the active segment, it accumulates ongoing ups and downs from price changes relative to the source, recalculating the running RSI similarly and smoothing it with the same EMA length.
Points for the running RSI are collected into an array starting from the segment's onset, forming a curved polyline once sufficient bars accumulate. Comparisons between the running RSI and the last completed segment's value determine the current direction as long, short, or neutral, with switches triggering deletions of old visuals and creation of new ones: a label at the RSI pane, a vertical dashed line across the RSI range, an emoji positioned via ATR offset on the price chart, a solid horizontal line at the switch price, a dashed line tracking current close, and a midpoint label for percent change from the switch.
Initialization occurs on the first bar by resetting accumulators, and visualization gates behind a minimum bar count since the segment start to avoid early instability. The trend strength table builds vertically with filled cells proportional to the rounded RSI value, colored by direction. All drawing objects update or extend on subsequent bars to reflect live progress.
Parameter Guide
EMA Length — Controls the smoothing applied to the running RSI; higher values increase lag but reduce noise. Default: 10. Trade-offs: Shorter settings heighten sensitivity for fast markets but risk more false switches; longer ones suit trending conditions for stability.
Source — Selects the price data for change calculations, typically close for standard momentum. Default: close. Trade-offs: Open or high/low may emphasize gaps, altering segment intensity.
Segment Timeframe — Defines the higher timeframe for segment resets, like daily for intraday charts. Default: D. Trade-offs: Shorter frames create more frequent but shorter segments; longer ones align with major cycles but delay resets.
Overbought Level — Sets the upper threshold for potential overbought conditions (currently unused in visuals). Default: 70. Trade-offs: Adjust for asset volatility; higher values delay bearish warnings.
Oversold Level — Sets the lower threshold for potential oversold conditions (currently unused in visuals). Default: 30. Trade-offs: Lower values permit deeper dips before signaling bullish potential.
Show Completed Label — Toggles labels at segment ends displaying final RSI. Default: true. Trade-offs: Enables historical review but can crowd charts on dense timeframes.
Plot Running Segment — Enables the curved polyline for live RSI trace. Default: true. Trade-offs: Visualizes intra-segment flow; disable for cleaner panes.
Running RSI as Label — Displays current running RSI as a forward-projected label on the last bar. Default: false. Trade-offs: Useful for quick reads; may overlap in tight scales.
Show Switch Label — Activates RSI pane labels on directional switches. Default: true. Trade-offs: Provides context; omit to minimize pane clutter.
Show Switch Line (RSI) — Draws vertical dashed lines across the RSI range at switches. Default: true. Trade-offs: Marks reversal bars clearly; extends both ways for reference.
Show Solid Overlay Line — Projects a horizontal line from switch price forward. Default: true. Trade-offs: Acts as dynamic support/resistance; wider lines enhance visibility.
Show Dashed Overlay Line — Tracks a dashed line from switch to current close. Default: true. Trade-offs: Shows price deviation; thinner for subtlety.
Show Percent Change Label — Midpoint label tracking percent move from switch. Default: true. Trade-offs: Quantifies progress; centers dynamically.
Show Trend Strength Table — Displays right-side table with direction header and RSI bar. Default: true. Trade-offs: Instant strength gauge; fixed position avoids overlap.
Activate Visualization After N Bars — Delays signals until this many bars into a segment. Default: 3. Trade-offs: Filters immature readings; higher values miss early momentum.
Segment End Label — Color for completed RSI labels. Default: 7E57C2. Trade-offs: Purple tones for finality.
Running RSI — Color for polyline and running elements. Default: yellow. Trade-offs: Bright for live tracking.
Long — Color for bullish switch visuals. Default: green. Trade-offs: Standard for uptrends.
Short — Color for bearish switch visuals. Default: red. Trade-offs: Standard for downtrends.
Solid Line Width — Thickness of horizontal overlay line. Default: 2. Trade-offs: Bolder for emphasis on key levels.
Dashed Line Width — Thickness of tracking and vertical lines. Default: 1. Trade-offs: Finer to avoid dominance.
Reading & Interpretation
Completed segment RSIs appear as static points or labels in purple, indicating the fixed momentum at period close—values drifting toward the upper half suggest building strength, while lower half implies weakness. The yellow curved polyline traces the live smoothed RSI within the current segment, rising for accumulating gains and falling for losses. Directional labels and lines in green or red flag switches: green for running momentum exceeding the prior segment's, signaling potential uptrend continuation; red for the opposite.
The right table's header colors green for long, red for short, or gray for neutral/wait, with filled purple bars scaling from bottom (low RSI) to top (high), topped by the numeric value. Overlay elements project from switch bars: the solid green/red line as a price anchor, dashed tracker showing pullback extent, and percent label quantifying deviation—positive for alignment with direction, negative for counter-moves. Emojis (up arrow for long, down for short) float above/below price via ATR spacing for quick chart scans.
Practical Workflows & Combinations
- Trend Following: Enter long on green switch confirmation after a higher high in structure; filter with table strength above midpoint for conviction. Pair with volume surge for added weight.
- Exits/Stops: Trail stops to the solid overlay line on pullbacks; exit if percent change reverses beyond 2 percent against direction. Use wait bars to confirm without chasing.
- Multi-Asset/Multi-TF: Defaults suit forex/stocks on 1H-4H with daily segments; for crypto, shorten EMA to 5 for volatility. Scale segment TF to weekly for daily charts across indices.
- Combinations: Overlay on EMA clouds for confluence—switch aligning with cloud break strengthens signal. Add volatility filters like ATR bands to debounce in low-volume regimes.
Behavior, Constraints & Performance
Signals confirm on bar close within segments, with running polyline updating live but gated by minimum bars to prevent flicker. Higher timeframe changes may introduce minor repaints on timeframe switches, mitigated by relying on confirmed HTF closes rather than intrabar peeks. Resource limits cap at 500 labels/lines and 50 polylines, pruning old objects on switches to stay efficient; no explicit loops, but array growth ties to segment length—suitable for up to 500-bar histories without lag.
Known limits include delayed visualization in short segments and insensitivity to overbought/oversold levels, as thresholds are inputted but not actively visualized. Gaps in source data reset accumulators prematurely, potentially skewing early RSI.
Sensible Defaults & Quick Tuning
Start with EMA length 10, daily segments, and 3-bar wait for balanced responsiveness on hourly charts. For excessive switches in ranging markets, increase wait bars to 5 or EMA to 14 to dampen noise. If signals lag in trends, drop EMA to 5 and use 1H segments. For stable assets like indices, widen to weekly segments; tune colors for dark/light themes without altering logic.
What this indicator is—and isn’t
This tool serves as a momentum visualization and switch detector layered over price action, aiding trend identification and confirmation in segmented contexts. It is not a standalone trading system, predictive model, or risk calculator—always integrate with broader analysis, position sizing, and stop-loss discipline. View it as an enhancement for discretionary setups, not automated alerts without validation.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Quantum Flux Universal Strategy Summary in one paragraph
Quantum Flux Universal is a regime switching strategy for stocks, ETFs, index futures, major FX pairs, and liquid crypto on intraday and swing timeframes. It helps you act only when the normalized core signal and its guide agree on direction. It is original because the engine fuses three adaptive drivers into the smoothing gains itself. Directional intensity is measured with binary entropy, path efficiency shapes trend quality, and a volatility squash preserves contrast. Add it to a clean chart, watch the polarity lane and background, and trade from positive or negative alignment. For conservative workflows use on bar close in the alert settings when you add alerts in a later version.
Scope and intent
• Markets. Large cap equities and ETFs. Index futures. Major FX pairs. Liquid crypto
• Timeframes. One minute to daily
• Default demo used in the publication. QQQ on one hour
• Purpose. Provide a robust and portable way to detect when momentum and confirmation align, while dampening chop and preserving turns
• Limits. This is a strategy. Orders are simulated on standard candles only
Originality and usefulness
• Unique concept or fusion. The novelty sits in the gain map. Instead of gating separate indicators, the model mixes three drivers into the adaptive gains that power two one pole filters. Directional entropy measures how one sided recent movement has been. Kaufman style path efficiency scores how direct the path has been. A volatility squash stabilizes step size. The drivers are blended into the gains with visible inputs for strength, windows, and clamps.
• What failure mode it addresses. False starts in chop and whipsaw after fast spikes. Efficiency and the squash reduce over reaction in noise.
• Testability. Every component has an input. You can lengthen or shorten each window and change the normalization mode. The polarity plot and background provide a direct readout of state.
• Portable yardstick. The core is normalized with three options. Z score, percent rank mapped to a symmetric range, and MAD based Z score. Clamp bounds define the effective unit so context transfers across symbols.
Method overview in plain language
The strategy computes two smoothed tracks from the chart price source. The fast track and the slow track use gains that are not fixed. Each gain is modulated by three drivers. A driver for directional intensity, a driver for path efficiency, and a driver for volatility. The difference between the fast and the slow tracks forms the raw flux. A small phase assist reduces lag by subtracting a portion of the delayed value. The flux is then normalized. A guide line is an EMA of a small lead on the flux. When the flux and its guide are both above zero, the polarity is positive. When both are below zero, the polarity is negative. Polarity changes create the trade direction.
Base measures
• Return basis. The step is the change in the chosen price source. Its absolute value feeds the volatility estimate. Mean absolute step over the window gives a stable scale.
• Efficiency basis. The ratio of net move to the sum of absolute step over the window gives a value between zero and one. High values mean trend quality. Low values mean chop.
• Intensity basis. The fraction of up moves over the window plugs into binary entropy. Intensity is one minus entropy, which maps to zero in uncertainty and one in very one sided moves.
Components
• Directional Intensity. Measures how one sided recent bars have been. Smoothed with RMA. More intensity increases the gain and makes the fast and slow tracks react sooner.
• Path Efficiency. Measures the straightness of the price path. A gamma input shapes the curve so you can make trend quality count more or less. Higher efficiency lifts the gain in clean trends.
• Volatility Squash. Normalizes the absolute step with Z score then pushes it through an arctangent squash. This caps the effect of spikes so they do not dominate the response.
• Normalizer. Three modes. Z score for familiar units, percent rank for a robust monotone map to a symmetric range, and MAD based Z for outlier resistance.
• Guide Line. EMA of the flux with a small lead term that counteracts lag without heavy overshoot.
Fusion rule
• Weighted sum of the three drivers with fixed weights visible in the code comments. Intensity has fifty percent weight. Efficiency thirty percent. Volatility twenty percent.
• The blend power input scales the driver mix. Zero means fixed spans. One means full driver control.
• Minimum and maximum gain clamps bound the adaptive gain. This protects stability in quiet or violent regimes.
Signal rule
• Long suggestion appears when flux and guide are both above zero. That sets polarity to plus one.
• Short suggestion appears when flux and guide are both below zero. That sets polarity to minus one.
• When polarity flips from plus to minus, the strategy closes any long and enters a short.
• When flux crosses above the guide, the strategy closes any short.
What you will see on the chart
• White polarity plot around the zero line
• A dotted reference line at zero named Zen
• Green background tint for positive polarity and red background tint for negative polarity
• Strategy long and short markers placed by the TradingView engine at entry and at close conditions
• No table in this version to keep the visual clean and portable
Inputs with guidance
Setup
• Price source. Default ohlc4. Stable for noisy symbols.
• Fast span. Typical range 6 to 24. Raising it slows the fast track and can reduce churn. Lowering it makes entries more reactive.
• Slow span. Typical range 20 to 60. Raising it lengthens the baseline horizon. Lowering it brings the slow track closer to price.
Logic
• Guide span. Typical range 4 to 12. A small guide smooths without eating turns.
• Blend power. Typical range 0.25 to 0.85. Raising it lets the drivers modulate gains more. Lowering it pushes behavior toward fixed EMA style smoothing.
• Vol window. Typical range 20 to 80. Larger values calm the volatility driver. Smaller values adapt faster in intraday work.
• Efficiency window. Typical range 10 to 60. Larger values focus on smoother trends. Smaller values react faster but accept more noise.
• Efficiency gamma. Typical range 0.8 to 2.0. Above one increases contrast between clean trends and chop. Below one flattens the curve.
• Min alpha multiplier. Typical range 0.30 to 0.80. Lower values increase smoothing when the mix is weak.
• Max alpha multiplier. Typical range 1.2 to 3.0. Higher values shorten smoothing when the mix is strong.
• Normalization window. Typical range 100 to 300. Larger values reduce drift in the baseline.
• Normalization mode. Z score, percent rank, or MAD Z. Use MAD Z for outlier heavy symbols.
• Clamp level. Typical range 2.0 to 4.0. Lower clamps reduce the influence of extreme runs.
Filters
• Efficiency filter is implicit in the gain map. Raising efficiency gamma and the efficiency window increases the preference for clean trends.
• Micro versus macro relation is handled by the fast and slow spans. Increase separation for swing, reduce for scalping.
• Location filter is not included in v1.0. If you need distance gates from a reference such as VWAP or a moving mean, add them before publication of a new version.
Alerts
• This version does not include alertcondition lines to keep the core minimal. If you prefer alerts, add names Long Polarity Up, Short Polarity Down, Exit Short on Flux Cross Up in a later version and select on bar close for conservative workflows.
Strategy has been currently adapted for the QQQ asset with 30/60min timeframe.
For other assets may require new optimization
Properties visible in this publication
• Initial capital 25000
• Base currency Default
• Default order size method percent of equity with value 5
• Pyramiding 1
• Commission 0.05 percent
• Slippage 10 ticks
• Process orders on close ON
• Bar magnifier ON
• Recalculate after order is filled OFF
• Calc on every tick OFF
Honest limitations and failure modes
• Past results do not guarantee future outcomes
• Economic releases, circuit breakers, and thin books can break the assumptions behind intensity and efficiency
• Gap heavy symbols may benefit from the MAD Z normalization
• Very quiet regimes can reduce signal contrast. Use longer windows or higher guide span to stabilize context
• Session time is the exchange time of the chart
• If both stop and target can be hit in one bar, tie handling would matter. This strategy has no fixed stops or targets. It uses polarity flips for exits. If you add stops later, declare the preference
Open source reuse and credits
• None beyond public domain building blocks and Pine built ins such as EMA, SMA, standard deviation, RMA, and percent rank
• Method and fusion are original in construction and disclosure
Legal
Education and research only. Not investment advice. You are responsible for your decisions. Test on historical data and in simulation before any live use. Use realistic costs.
Strategy add on block
Strategy notice
Orders are simulated by the TradingView engine on standard candles. No request.security() calls are used.
Entries and exits
• Entry logic. Enter long when both the normalized flux and its guide line are above zero. Enter short when both are below zero
• Exit logic. When polarity flips from plus to minus, close any long and open a short. When the flux crosses above the guide line, close any short
• Risk model. No initial stop or target in v1.0. The model is a regime flipper. You can add a stop or trail in later versions if needed
• Tie handling. Not applicable in this version because there are no fixed stops or targets
Position sizing
• Percent of equity in the Properties panel. Five percent is the default for examples. Risk per trade should not exceed five to ten percent of equity. One to two percent is a common choice
Properties used on the published chart
• Initial capital 25000
• Base currency Default
• Default order size percent of equity with value 5
• Pyramiding 1
• Commission 0.05 percent
• Slippage 10 ticks
• Process orders on close ON
• Bar magnifier ON
• Recalculate after order is filled OFF
• Calc on every tick OFF
Dataset and sample size
• Test window Jan 2, 2014 to Oct 16, 2025 on QQQ one hour
• Trade count in sample 324 on the example chart
Release notes template for future updates
Version 1.1.
• Add alertcondition lines for long, short, and exit short
• Add optional table with component readouts
• Add optional stop model with a distance unit expressed as ATR or a percent of price
Notes. Backward compatibility Yes. Inputs migrated Yes.
Lorentzian Harmonic Flow - Temporal Market Dynamic Lorentzian Harmonic Flow - Temporal Market Dynamic (⚡LHF)
By: DskyzInvestments
What this is
LHF Pro is a research‑grade analytical instrument that models market time as a compressible medium , extracts directional flow in curved time using heavy‑tailed kernels, and consults a history‑based memory bank for context before synthesizing a final, bounded probabilistic score . It is not a mashup; each subsystem is mathematically coupled to a single clock (time dilation via gamma) and a single lens (Lorentzian heavy‑tailed weighting). This script is dense in logic (and therefore heavy) because it prioritizes rigor, interpretability, and visual clarity.
Intended use
Education and research. This tool expresses state recognition and regime context—not guarantees. It does not place orders. It is fully functional as published and contains no placeholders. Nothing herein is financial advice.
Why this is original and useful
Curved time: Markets do not move at a constant pace. LHF Pro computes a Lorentz‑style gamma (γ) from relative speed so its analytical windows contract when the tape accelerates and relax when it slows.
Heavy‑tailed lens: Lorentzian kernels weight information with fat tails to respect rare but consequential extremes (unlike Gaussian decay).
Memory of regimes: A K‑nearest‑neighbors engine works in a multi‑feature space using Lorentz kernels per dimension and exponential age fade , returning a memory bias (directional expectation) and assurance (confidence mass).
One ecosystem: Squeeze, TCI, flow, acceleration, and memory live on the same clock and blend into a single final_score —visualized and documented on the dashboard.
Cognitive map: A 2D heat map projects memory resonance by age and flow regime, making “where the past is speaking” visible.
Shadow portfolio metaphor: Neighbor outcomes act like tiny hypothetical positions whose weighted average forms an educational pressure gauge (no execution, purely didactic).
Mathematical framework (full transparency)
1) Returns, volatility, and speed‑of‑market
Log return: rₜ = ln(closeₜ / closeₜ₋₁)
Realized vol: rv = stdev(r, vol_len); vol‑of‑vol: burst = |rv − rv |
Speed‑of‑market (analog to c): c = c_multiplier × (EMA(rv) + 0.5 × EMA(burst) + ε)
2) Trend velocity and Lorentz gamma (time dilation)
Trend velocity: v = |close − close | / (vel_len × ATR)
Relative speed: v_rel = v / c
Gamma: γ = 1 / √(1 − v_rel²), stabilized by caps (e.g., ≤10)
Interpretation: γ > 1 compresses market time → use shorter effective windows.
3) Adaptive temporal scale
Adaptive length: L = base_len / γ^power (bounded for safety)
Harmonic horizons: Lₛ = L × short_ratio, Lₘ = L × mid_ratio, Lₗ = L × long_ratio
4) Lorentzian smoothing and Harmonic Flow
Kernel weight per lag i: wᵢ = 1 / (1 + (d/γ)²), d = i/L
Horizon baselines: lw_h = Σ wᵢ·price / Σ wᵢ
Z‑deviation: z_h = (close − lw_h)/ATR
Harmonic Flow (HFL): HFL = (w_short·zₛ + w_mid·zₘ + w_long·zₗ) / (w_short + w_mid + w_long)
5) Flow kinematics
Velocity: HFL_vel = HFL − HFL
Acceleration (curvature): HFL_acc = HFL − 2·HFL + HFL
6) Squeeze and temporal compression
Bollinger width vs Keltner width using L
Squeeze: BB_width < KC_width × squeeze_mult
Temporal Compression Index: TCI = base_len / L; TCI > 1 ⇒ compressed time
7) Entropy (regime complexity)
Shannon‑inspired proxy on |log returns| with numerical safeguards and smoothing. Higher entropy → more chaotic regime.
8) Memory bank and Lorentzian k‑NN
Feature vector (5D):
Outcomes stored: forward returns at H5, H13, H34
Per‑dimension similarity: k(Δ) = 1 / (1 + Δ²), weighted by user’s feature weights
Age fading: weight_age = mem_fade^age_bars
Neighbor score: sᵢ = similarityᵢ × weight_ageᵢ
Memory bias: mem_bias = Σ sᵢ·outcomeᵢ / Σ sᵢ
Assurance: mem_assurance = Σ sᵢ (confidence mass)
Normalization: mem_bias normalized by ATR and clamped into band
Shadow portfolio metaphor: neighbors behave like micro‑positions; their weighted net forward return becomes a continuous, adaptive expectation.
9) Blended score and breakout proxy
Blend factor: α_mem = 0.45 + 0.15 × (γ − 1)
Final score: final_score = (1−α_mem)·tanh(HFL / (flow_thr·1.5)) + α_mem·tanh(mem_bias_norm)
Breakout probability (bounded): energy = cap(TCI−1) + |HFL_acc|×k + cap(γ−1)×k + cap(mem_assurance)×k; breakout_prob = sigmoid(energy). Caps avoid runaway “100%” readings.
Inputs — every control, purpose, mechanics, and tuning
🔮 Lorentz Core
Auto‑Adapt (Vol/Entropy): On = L responds to γ and entropy (breathes with regime), Off = static testing.
Base Length: Calm‑market anchor horizon. Lower (21–28) for fast tapes; higher (55–89+) for slow.
Velocity Window (vel_len): Bars used in v. Shorter = more reactive γ; longer = steadier.
Volatility Window (vol_len): Bars used for rv/burst (c). Shorter = more sensitive c.
Speed‑of‑Market Multiplier (c_multiplier): Raises/lowers c. Lower values → easier γ spikes (more adaptation). Aim for strong trends to peak around γ ≈ 2–4.
Gamma Compression Power: Exponent of γ in L. <1 softens; >1 amplifies adaptation swings.
Max Kernel Span: Upper bound on smoothing loop (quality vs CPU).
🎼 Harmonic Flow
Short/Mid/Long Horizon Ratios: Partition L into fast/medium/slow views. Smaller short_ratio → faster reaction; larger long_ratio → sturdier bias.
Weights (w_short/w_mid/w_long): Governs HFL blend. Higher w_short → nimble; higher w_long → stable.
📈 Signals
Squeeze Strictness: Threshold for BB1 = compressed (coiled spring); <1 = dilated.
v/c: Relative speed; near 1 denotes extreme pacing. Diagnostic only.
Entropy: Regime complexity; high entropy suggests caution, smaller size, or waiting for order to return.
HFL: Curved‑time directional flow; sign and magnitude are the instantaneous bias.
HFL_acc: Curvature; spikes often accompany regime ignition post‑squeeze.
Mem Bias: Directional expectation from historical analogs (ATR‑normalized, bounded). Aligns or conflicts with HFL.
Assurance: Confidence mass from neighbors; higher → more reliable memory bias.
Squeeze: ON/RELEASE/OFF from BB
Sweep2Trade Pro [CHE]Sweep2Trade Pro \ — Liquidity Sweep → Trend → Confirmation
Sweep2Trade Pro \ helps you catch high-probability reversals or continuations that start with a liquidity sweep, align with the T3 trend, and finalize with a structure confirmation (BOS). It’s designed to reduce noise, time your entries, and keep you out of weak, chop-driven signals.
What’s a “sweep”?
A liquidity sweep happens when price briefly breaks a prior swing high/low (where many stops sit), triggers those stops, and then snaps back. This “stop-hunt” creates liquidity for bigger players and often precedes a sharp move in the opposite direction if the break fails, or fuels continuation if structure actually shifts.
What’s a BOS (Break of Structure)?
A BOS is a price action event where the market takes out a recent swing level in the trend’s direction, signaling continuation and confirming that structure has shifted (bullish BOS through a recent swing high, bearish BOS through a recent swing low).
How the indicator works (at a glance)
1. Regime Filter (T3 + R²)
T3 Moving Average: A smoother, faster-responding moving average that aims to reduce lag while filtering noise, so trend direction changes are clearer.
R² (Coefficient of Determination): Measures how “linear” the recent price path is (0→1). Higher values = stronger, cleaner trend; lower values = more chop. Used here to allow trades only when trend quality exceeds a user-set threshold.
2. Sweep Detection
Bullish sweep: price pokes below a prior swing low and closes back above it.
Bearish sweep: price pokes above a prior swing high and closes back below it.
Lookback length is configurable.
3. Sequence Lock (built-in FSM)
The script manages state in phases so you don’t jump the gun:
Phase 1: Sweep detected → wait for T3 to turn in the corresponding direction.
Phase 2: T3 direction confirmed → show “SWEEP OK” and wait for final confirmation.
Trade Signal: Only fires if confirmation arrives before a timeout.
4. Confirmation Layer
BOS via wick or close (you choose),
Strong close toward the signal (top/bottom quartile of the candle),
Optional “close above/below T3” condition.
These checks help avoid weak sweeps that immediately fade.
5. Alerts & Visuals
“SWEEP OK” markers show when the sweep + T3 direction align.
Final BUY/SELL arrows appear only when the confirmation layer passes.
Ready-made alert conditions for automation.
What you can do with it
Time reversals after sweeps: Enter when a stop-hunt fades and structure confirms.
Ride continuations: Use BOS with the T3 trend to pyramid or re-enter with structure on your side.
Filter chop: Let R² gate entries to periods with cleaner directional drift.
Automate: Use the included alerts with your platform or webhook setup.
Inputs (key settings)
Regime Filter
T3 Length / Volume Factor: Controls smoothness and responsiveness. Smaller length → faster, more sensitive; higher volume factor → smoother curve.
R² Lookback & Threshold: Length of the linear fit window and the minimum “trend quality” required. Higher thresholds mean fewer, cleaner signals.
Sweep / Sequence
Swing Lookback: How far back to define the “reference” high/low for sweeps.
Timeout: Maximum bars allowed between phases to keep signals fresh.
Restart timeout on Phase 2: Optional safety so entries don’t go stale.
Confirmation
BOS Lookback: Micro-pivot window for structure breaks.
Wick vs Close BOS: Conservative traders may prefer close.
Require close above/below T3: Tightens confirmation with trend alignment.
Practical guide (quick start)
1. Timeframe & markets: Works across majors, indices, and crypto. Start with 5m–1h intraday or 1h–4h swing; adjust R² threshold upward on noisier pairs.
2. Entry recipe (Long):
Bullish sweep of a prior low → T3 turns up → BOS/strong close.
Optional: enable “close above T3” for extra confirmation.
3. Entry recipe (Short): Mirror the above.
4. Stops: Common choices are just beyond the sweep wick (tighter) or past the BOS invalidation (safer).
5. Targets: Previous structural levels, measured move, or a T3 trail (exit when price closes back through T3).
6. Avoid low-quality contexts: If R² is very low, market is likely ranging erratically—skip or widen filters.
Tips & best practices
Context first: The same sweep means different things in a strong trend vs. flat regime; that’s why the T3+R² filter exists.
BOS choice: Wick-based BOS is earlier but noisier; close-based BOS is slower but cleaner. Tune per market.
Backtest -> Forward test: Validate settings per symbol/timeframe; then paper trade before going live.
Risk: Fixed fractional risk with asymmetric R\:R (e.g., 1:1.5–1:3) generally performs better than “all-in” discretionary sizing.
Behind the scenes (for the curious)
T3 is a multi-stage EMA construction that produces a smooth curve with reduced lag versus simple/standard EMAs.
R² is the square of correlation (0–1). Here it’s used as a moving gauge of how well price aligns to a linear path—our “trend quality” dial.
Stop-hunts / sweeps are a recognized microstructure phenomenon where clustered stops provide the liquidity that fuels the next move.
Disclaimer
No indicator guarantees profits. Sweep2Trade Pro \ is a decision aid; always combine with solid risk management and your own judgment. Backtest, forward test, and size responsibly.
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Enhance your trading precision and confidence 🚀
Happy trading
Chervolino
Transfer Function Filter [theUltimator5]The Transfer Function Filter is an engineering style approach to transform the price action on a chart into a frequency, then filter out unwanted signals using Butterworth-style filter approach.
This indicator allows you to analyze market structure by isolating or removing different frequency components of price movement—similar to how engineers filter signals in control systems and electrical circuits.
🔎 Features
Four Filter Types
1) Low Pass Filter – Smooths price data, highlighting long-term trends while filtering out short-term noise. This filter acts similar to an EMA, removing noisy signals, resulting in a smooth curve that follows the price of the stock relative to the filter cutoff settings.
Real world application for low pass filter - Used in power supplies to provide a clean, stable power level.
2) High Pass Filter – Removes slow-moving trends to emphasize short-term volatility and rapid fluctuations. The high pass filter removes the "DC" level of the chart, removing the average price moves and only outputting volatility.
Real world application for high pass filter - Used in audio equalizers to remove low-frequency noise (like rumble) while allowing higher frequencies to pass through, improving sound clarity.
3) Band Pass Filter – Allows signals to plot only within a band of bar ranges. This filter removes the low pass "DC" level and the high pass "high frequency noise spikes" and shows a signal that is effectively a smoothed volatility curve. This acts like a moving average for volatility.
Real world application for band pass filter - Radio stations only allow certain frequency bands so you can change your radio channel by switching which frequency band your filter is set to.
4) Band Stop Filter – Suppresses specific frequency bands (cycles between two cutoffs). This filter allows through the base price moving average, but keeps the high frequency volatility spikes. It allows you to filter out specific time interval price action.
Real world application for band stop filter - If there is prominent frequency signal in the area which can cause unnecessary noise in your system, a band stop filter can cancel out just that frequency so you get everything else
Configurable Parameters
• Cutoff Periods – Define the cycle lengths (in bars) to filter. This is a bit counter-intuitive with the numbering since the higher the bar count on the low-pass filter, the lower the frequency cutoff is. The opposite holds true for the high pass filter.
• Filter Order – Adjust steepness and responsiveness (higher order = sharper filtering, but with more delay).
• Overlay Option – Display Low Pass & Band Stop outputs directly on the price chart, or in a separate pane. This is enabled by default, plotting the filters that mimic moving averages directly onto the chart.
• Source Selection – Apply filters to close, open, high, low, or custom sources.
Histograms for Comparison
• BS–LP Histogram – Shows distance between Band Stop and Low Pass filters.
• BP–HP Histogram – Highlights differences between Band Pass and High Pass filters.
Histograms give the visualization of a pseudo-MACD style indicator
Visual & Informational Aids
• Customizable colors for each filter line.
• Optional zero-line for histogram reference.
• On-chart info table summarizing active filters, cutoff settings, histograms, and filter order.
📊 Use Cases
Trend Detection – Use the Low Pass filter to smooth noise and follow underlying market direction.
Volatility & Cycle Analysis – Apply High Pass or Band Pass to capture shorter-term patterns.
Noise Suppression – Deploy Band Stop to remove specific choppy frequencies.
Momentum Insight – Watch the histograms to spot divergences and relative filter strength.
Lorentzian Theory Classifier🧮 Lorentzian Theory Classifier: An Observatory for Market Spacetime
Transcend the flat plane of traditional charting. Enter the curved, dynamic reality of market spacetime. The Lorentzian Theory Classifier (LTC) is not an indicator; it is a computational observatory. It is an instrument engineered to decode the geometry of market behavior, revealing the hidden curvatures and resonant frequencies that precede significant turning points.
We discard the outdated tools of Euclidean simplicity and embrace a more profound truth: financial markets, much like the cosmos described by general relativity, are governed by a fabric that is warped by the mass of participation and the energy of volatility. The LTC is your lens to perceive this fabric, to move beyond predicting lines on a chart and begin reading the very architecture of probability.
The Resonance Manifold: Standard Euclidean models search for historical analogues within a rigid sphere, missing the crucial outliers that define market extremes. The LTC's Lorentzian Resonance engine operates in a curved, non-Euclidean space, allowing it to connect with these "fat-tail" events—the true genesis points of major reversals.
🌌 THE THEORETICAL FRAMEWORK: A new Grand Unified Theory of Market Analysis
The LTC is built upon a revolutionary synthesis of concepts from special relativity, quantum mechanics, and information theory. It reframes market analysis not as a problem of forecasting, but as a problem of state recognition in a non-Euclidean manifold.
1. The Lorentzian Kernel: The Mathematics of Reality
Financial markets are not Gaussian. Their reality is one of "fat tails"—sudden, high-impact events that standard models dismiss as anomalies. The LTC acknowledges this reality by using the mathematically pure and robust Lorentzian kernel as its core engine:
Similarity(x, y) = 1 / (1 + (||x − y||² / γ²))
||x − y||²: The squared distance between the current market state (x) and a historical state (y) in our 8-dimensional feature space.
γ (Gamma): A dynamic bandwidth parameter, our "Lorentz factor," which adapts to market entropy (chaos). In calm markets, gamma is small, demanding precise resonance. In chaotic markets, gamma expands, intelligently seeking broader patterns.
This heavy-tailed function is revolutionary. It correctly assigns profound significance to the rare, extreme events that truly define market structure, while gracefully tuning out the noise of mundane price action. It doesn't just calculate; it understands context.
2. The 8-Dimensional State Vector: The Market's Quantum Fingerprint
To achieve a holistic view, the LTC projects the market onto an 8-dimensional Hilbert space, where each dimension represents a critical "observable":
Momentum & Acceleration (f_rsi, f_roc): The market's velocity and its rate of change.
Cyclical Position (f_stoch, f_cci): The market's location within its recent oscillation cycles.
Energy & Participation (f_vol, f_cor): The force of capital flow and its harmony with price.
Chaos & Uncertainty (f_ent, f_mom): The degree of randomness and the standardized force of price changes.
These are not eight separate indicators. They are entangled properties of a single "market wavefunction." The LTC's genius lies in measuring the geometric distance between these complete quantum states.
3. The k-NN Oracle: A Council of Past Universes
The LTC employs a k-Nearest Neighbors algorithm, but in our curved Lorentzian spacetime. It poses a constant, profound question: " Which moments in history are most geometrically congruent to the present moment across all eight dimensions? "
It then summons a "council" of these historical neighbors. Each neighbor's future outcome (did price ascend or descend?) casts a vote, weighted by its resonant similarity. The result is a probabilistic forecast of stunning clarity:
Prognosis: The final weighted consensus on future direction.
Assurance: The degree of unanimity within the council—a direct measure of the prediction's confidence.
The Funnel of Conviction: The LTC's process is a rigorous distillation of information. Raw, chaotic market data is resolved into a clean 8-dimensional state vector. The Lorentzian Kernel filters these states for resonance, which are then passed to the k-NN Oracle for a vote. Noise is eliminated at each stage, resulting in a single, validated, high-conviction signal.
⚙️ THE COMMAND CONSOLE: A Guide to Calibrating Your Observatory
Mastering the LTC's inputs is to become an architect of your own analytical universe. Each parameter is a dial that tunes the observatory's focus, from galactic structures to subatomic fluctuations. The tooltips in-script—over 6,000 words of documentation—provide immediate reference; this guide provides the philosophy.
A summarized guide to the Core, Signal, Supreme, and Visual controls is included directly in the indicator's code and tooltips. We encourage all users to explore these settings to tune the LTC to their unique analytical style.
🏆 THE SUPREME DASHBOARD: Your Mission Control
The dashboard is not a data table; it is your command interface with market reality. It translates the intricate dance of probabilities and vectors into clear, actionable intelligence.
⚡ ORACLE STATUS
Prognosis: The primary directional vector. Its color, magnitude, and emoji (⚡) reveal the strength and conviction of the Oracle's forward guidance.
Assurance: A real-time gauge of prediction quality, from "LOW" (high uncertainty) to "ELITE" (overwhelming statistical consensus). Interpret this as your core risk metric: trade with conviction when Assurance is ELITE; trade with caution when it is LOW.
🔮 RESONANCE ANALYSIS
Chaos: A direct measurement of market entropy. "LOW CHAOS" signifies a predictable, orderly regime. "HIGH CHAOS" is a warning of randomness and unpredictability, where trend-following logic may fail.
Turbulence: A measure of raw volatility. When the market is "TURBULENT," expect wider price swings and increased risk. Use this metric to adjust stop-loss distances and profit targets dynamically.
🏆 PERFORMANCE & ⚔️ GUARD METRICS
These sections provide illustrative statistics on the script's recent historical behavior. Metrics like Yield Ratio and Guard Index offer a quick heuristic on the prevailing risk-reward environment. Crucially, these are for observational context only and are not a substitute for your own rigorous testing and analysis.
🎨 THE VISUAL MANIFESTATION: Charting the Unseen
The LTC's visuals are designed to transform your chart from a 2D price graph into a 4D informational battlespace.
The Dynamic Aura (Background Color): This is the ambient energy field of the market. A luminous green (Ascend) signifies a bullish resonance field; a deep red (Descend) indicates bearish pressure.
The Assurance Shroud (Blue Bands): A visualization of confidence. When the shroud is wide and expansive , the Oracle's vision is clear and its predictions are robust.
The Prognosis Arc (Curved Line): A geodesic projection of the market's most likely path, based on the current Prognosis.
The Turbulence Cloud (Orange Mist): A visual warning system for market chaos. When this entropic mist expands , it is a clear sign that you are navigating a nebula of high unpredictability.
Oracle Markers (▲▼): The final, validated signals. These are not merely pivot points. They are moments in spacetime where a structural pivot has been confirmed and then ratified by a high-conviction vote from the Lorentzian Oracle. They are the pinnacles of confluence.
The Analyst's Observatory: The LTC transforms your chart into a command center for market analysis, providing a complete, at-a-glance view of market state, risk, and probabilistic trajectory.
🔧 THE ARCHITECT'S VISION: From a Blank Slate to a New Cosmos
The LTC was not assembled; it was derived. It began not with code, but with first principles, asking: "If we were to build an instrument to measure the market today, unbound by the technical dogmas of the 20th century, what would it look like?" The answer was clear: it must be multi-dimensional, it must be adaptive, and it must be built on a mathematical framework that respects the "fat-tailed" nature of reality.
The decision to use a pure Lorentzian kernel was non-negotiable. It represented a commitment to intellectual honesty over computational ease. The development of the Supreme Dashboard was driven by the philosophy of the "glass cockpit"—a belief that a trader's greatest asset is not a black box signal, but a transparent and intuitive flow of high-quality information. This script is the result of that unwavering vision: to create not just another indicator, but a new lens through which to perceive the market.
⚠️ RISK DISCLOSURE & PHILOSOPHY OF USE
The Lorentzian Theory Classifier is an instrument of profound analytical power, intended for the serious, discerning trader. It does not generate infallible signals. It generates high-probability, data-driven hypotheses based on a rigorous and transparent methodology. All trading involves substantial risk, and the future is fundamentally unknowable. Past performance, whether real or simulated, is no guarantee of future results. Use this tool to augment your own skill, to confirm your own analysis, and to manage your own risk within a well-defined trading plan.
"The effort to understand the universe is one of the very few things that lifts human life a little above the level of farce, and gives it some of the grace of tragedy."
— Steven Weinberg, Nobel Laureate in Physics
Trade with rigor. Trade with perspective. Trade with enlightenment. Trade with insight. Trade with anticipation.
— Dskyz, for DAFE Trading Systems
Stochastic SuperTrend [BigBeluga]🔵 OVERVIEW
A hybrid momentum-trend tool that combines Stochastic RSI with SuperTrend logic to deliver clean directional signals based on momentum turns.
Stochastic SuperTrend is a straightforward yet powerful oscillator overlay designed to highlight turning points in momentum with high clarity. It overlays a SuperTrend-style envelope onto the Stochastic RSI, generating intuitive up/down signals when a momentum shift occurs across the neutral 50 level. Built for traders who appreciate simplicity without sacrificing reliability.
🔵 CONCEPTS
Stochastic RSI: Measures momentum by applying stochastic calculations to the RSI curve instead of raw price.
SuperTrend Bands: Dynamic upper/lower bands are drawn around the smoothed Stoch RSI line using a user-defined multiplier.
Momentum Direction: Trend flips when the smoothed Stoch RSI crosses above/below the calculated bands.
Neutral Bias Filter: Directional arrows only appear when momentum turns above or below the central 50 level—adding confluence.
🔵 FEATURES
Trend Detection on Oscillator: Applies SuperTrend logic directly to the Stoch RSI curve.
Clean Entry Signals:
→ 🢁 arrow printed when trend flips bullish below 50 (bottom reversals).
→ 🢃 arrow printed when trend flips bearish above 50 (top reversals).
Custom Multiplier: Adjust sensitivity of SuperTrend band spacing around the oscillator.
Neutral Zone Highlight: Visual zone between 0–50 (green) and 50–100 (red) for quick momentum polarity reference.
Toggle SuperTrend Line: Option to show/hide the SuperTrend trail on the Stoch RSI.
🔵 HOW TO USE
Use 🢁 signals for potential bottom reversals when momentum flips bullish from oversold regions.
Use 🢃 signals for potential top reversals when momentum flips bearish from overbought areas.
Combine with price-based SuperTrend or support/resistance zones for confluence.
Suitable for scalping, swing trading, or momentum filtering across all timeframes.
🔵 CONCLUSION
Stochastic SuperTrend is a simple yet refined tool that captures clean momentum shifts with directional clarity. Whether you're identifying reversals, filtering entries, or spotting exhaustion in a trend, this oscillator overlay delivers just what you need— no clutter, just clean momentum structure.
Exponential Trend [AlgoAlpha]OVERVIEW
This script plots an adaptive exponential trend system that initiates from a dynamic anchor and accelerates based on time and direction. Unlike standard moving averages or trailing stops, the trend line here doesn't follow price directly—it expands exponentially from a pivot determined by a modified Supertrend logic. The result is a non-linear trend curve that starts at a specific price level and accelerates outward, allowing traders to visually assess trend strength, persistence, and early-stage reversal points through both base and volatility-adjusted extensions.
CONCEPTS
This indicator builds on the idea that trend-following tools often need dynamic, non-static expansion to reflect real market behavior. It uses a simplified Supertrend mechanism to define directional context and anchor levels, then applies an exponential growth function to simulate trend acceleration over time. The exponential growth is unidirectional and resets only when the direction flips, preserving trend memory. This method helps avoid whipsaws and adds time-weighted confirmation to trends. A volatility buffer—derived from ATR and modifiable by a width multiplier—adds a second layer to indicate zones of risk around the main trend path.
FEATURES
Exponential Trend Logic : Once a directional anchor is set, the base trend line accelerates using an exponential formula tied to elapsed bars, making the trend stronger the longer it persists.
Volatility-Adjusted Extension : A secondary band is plotted above or below the base trend line, widened by ATR to visualize volatility zones, act as soft stop regions or as a better entry point (Dynamic Support/Resistance).
Color-Coded Visualization : Clear green/red base and extension lines with shaded fills indicate trend direction and confidence levels.
Signal Markers & Alerts : Triangle markers indicate confirmed trend reversals. Built-in alerts notify users of bullish or bearish direction changes in real-time.
USAGE
Use this script to identify strong trends early, visually measure their momentum over time, and determine safe areas for entries or exits. Start by adjusting the *Exponential Rate* to control how quickly the trend expands—the higher the rate, the more aggressive the curve. The *Initial Distance* sets how far the anchor band is placed from price initially, helping filter out noise. Increase the *Width Multiplier* to widen the volatility zone for more conservative entries or exits. When the price crosses above or below the base line, a new trend is assumed and the exponential projection restarts from the new anchor. The base trend and its extension both shift over time, but only reset on a confirmed reversal. This makes the tool especially useful for momentum continuation setups or trailing stop logic in trending markets.
Earnings Expansion ProjectionThis indicator has no counterpart in the platform and is a professional-grade earnings visualization tool that plots EPS expansion directly on your charts, inspired by institutional-level technical analysis platforms.
The indicator creates a distinctive earnings expansion projection curve that can be a leading indicator of price direction moves.
Key features:
Clean, institutional-style, EPS-expansion projection line overlaid on price action
Visual earnings surprise indicators with beat/miss multipliers
Dashboard for rapid fundamental assessment including the stocks win rate on beatings / missing earnings historically and other fundamental information not readily available on Tradingview
What is it doing?
It collects all earnings results available and will interpolate the numbers so that we see earnings expansion as a curve.
The video below describes usage
Note: Valid on the weekly time-frame only.
Kernels©2024, GoemonYae; copied from @jdehorty's "KernelFunctions" on 2024-03-09 to ensure future dependency compatibility. Will also add more functions to this script.
Library "KernelFunctions"
This library provides non-repainting kernel functions for Nadaraya-Watson estimator implementations. This allows for easy substition/comparison of different kernel functions for one another in indicators. Furthermore, kernels can easily be combined with other kernels to create newer, more customized kernels.
rationalQuadratic(_src, _lookback, _relativeWeight, startAtBar)
Rational Quadratic Kernel - An infinite sum of Gaussian Kernels of different length scales.
Parameters:
_src (float) : The source series.
_lookback (simple int) : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.
_relativeWeight (simple float) : Relative weighting of time frames. Smaller values resut in a more stretched out curve and larger values will result in a more wiggly curve. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel.
startAtBar (simple int)
Returns: yhat The estimated values according to the Rational Quadratic Kernel.
gaussian(_src, _lookback, startAtBar)
Gaussian Kernel - A weighted average of the source series. The weights are determined by the Radial Basis Function (RBF).
Parameters:
_src (float) : The source series.
_lookback (simple int) : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.
startAtBar (simple int)
Returns: yhat The estimated values according to the Gaussian Kernel.
periodic(_src, _lookback, _period, startAtBar)
Periodic Kernel - The periodic kernel (derived by David Mackay) allows one to model functions which repeat themselves exactly.
Parameters:
_src (float) : The source series.
_lookback (simple int) : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.
_period (simple int) : The distance between repititions of the function.
startAtBar (simple int)
Returns: yhat The estimated values according to the Periodic Kernel.
locallyPeriodic(_src, _lookback, _period, startAtBar)
Locally Periodic Kernel - The locally periodic kernel is a periodic function that slowly varies with time. It is the product of the Periodic Kernel and the Gaussian Kernel.
Parameters:
_src (float) : The source series.
_lookback (simple int) : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.
_period (simple int) : The distance between repititions of the function.
startAtBar (simple int)
Returns: yhat The estimated values according to the Locally Periodic Kernel.
TrendLine ScythesTrendline Scythes is a script designed to automatically detect and draw special curved trendlines, resembling scythes or blades, based on pivotal points in price action. These trendlines adapt to the volatility of the market, providing a unique perspective on trend dynamics.
🔲 Methodology
Traditional trendlines connect consecutive pivot points on a price chart, providing a linear representation of trend direction. However, this script employs a distinctive methodology by automatically detecting price pivots and then calculating special curved trendlines based on the Average True Range (ATR) of the price. This introduces a curvature to the trendlines, resembling scythes, offering a unique way to interpret market trends.
🔲 Auto Breakout and Target Detection
Trendline Scythes includes features for automatic breakout detection, signaling potential trend changes. Additionally, the script assists in target detection, helping traders set realistic and data-driven profit-taking levels based on market volatility and user adjustment.
🔲 Utility
Trend Confirmation - Use Trendline Scythes to confirm existing trends by observing how price interacts with the curved trendlines.
Breakout Signals - Auto-detection of breakouts adds a proactive element to your trading strategy, helping you stay ahead of potential trend reversals.
Target Setting - Utilize the script to set profit-taking targets based on volatility, aligning with the current market conditions.
🔲 Settings
Pivot Length - Swing detection length
Scythe Length - Adjusts the length of the scythes blade
Sensitivity - Controls how restrained the target calculation is, higher values will result in tighter targets.
🔲 Alerts
Breakout
Breakdown
Target Reached
Target Invalidated
As well as the option to trigger 'any alert' call.
Trendline Scythes is a versatile tool combining the benefits of traditional trendlines with the dynamic adaptability of curved lines for a unique approach to trend analysis.
Relative Daily Change% by SUMIT
"Relative Daily Change%" Indicator (RDC)
The "Relative Daily Change%" indicator compares a stock's average daily price change percentage over the last 200 days with a chosen index.
It plots a colored curve. If the stock's change% is higher than the index, the curve is green, indicating it's doing better. Red means the stock is under-performing.
This indicator is designed to compare the performance of a stock with specific index (as selected) for last 200 candles.
I use this during a breakout to see whether the stock is performing well with comparison to it`s index. As I marked in the chart there was a range zone (red box), we got a breakout with good volume and it is also sustaining above 50 and 200 EMA, the RDC color is also in green so as per my indicator it is performing well. This is how I do fine-tuning of my analysis for a breakout strategy.
You can select Index from the list available in input
**Line Color Green = Avg Change% per day of the stock is more than the Selected Index
**Line Color White = Avg Change% per day of the stock is less than the Selected Index
If you want details of stocks for all index you can ask for it.
Disclaimer : **This is for educational purpose only. It is not any kind of trade recommendation/tips.
Zero Lag Moving Average with Gaussian weightsIntroduction
The Zero Lag Moving Average (ZLMA) is a powerful technical indicator that aims to eliminate the lag inherent in traditional moving averages. This post provides a comprehensive exploration of the ZLMA with Gaussian Weights (GWMA) indicator, discussing the concepts, the calculations, and its application in trading.
Concepts
Zero Lag Moving Average (ZLMA): A ZLMA is an advanced moving average designed to reduce the lag in price movements associated with conventional moving averages. This reduction in lag enables traders to make more informed decisions based on the most recent price data.
Gaussian Weights: Gaussian weights are derived from the Gaussian function, which is a mathematical function used to calculate probabilities in a normal distribution. The Gaussian function is smooth, symmetric, and has a bell-shaped curve. In this context, Gaussian weights are used to calculate the weighted average of a series of data points.
Why Gaussian Weights are Beneficial
Gaussian Weights offer several advantages in comparison to traditional moving averages. One of the main reasons for using Gaussian Weights is to address the issue of lag, which is commonly associated with simple and exponential moving averages. By reducing lag, traders can make more informed decisions based on up-to-date information.
Another advantage of Gaussian Weights is their mathematical foundation, which is rooted in the Gaussian function. This function describes the normal distribution in probability theory and statistics. The smooth and symmetric bell-shaped curve of Gaussian Weights enables a more refined approach to handling data points, resulting in a more responsive and accurate moving average.
While exponential moving averages (EMAs) also assign more weight to recent data points, they can still exhibit some lag. Gaussian Weights, on the other hand, offer a smoother and more adaptive solution to different market conditions. By adjusting the smoothing period, traders can tailor the Gaussian Weights to their specific needs, making them a versatile tool for various trading strategies.
In summary, Gaussian Weights provide a valuable alternative to traditional moving averages due to their ability to reduce lag, their strong mathematical foundation, and their adaptability to different market conditions. These benefits make Gaussian Weights a worthwhile consideration for traders looking to enhance their trading strategies.
Calculations
The ZLMA with GWMA consists of two main calculations:
Gaussian Weight Calculation: The Gaussian weight for a given 'k' and 'smooth_per' is calculated using the standard deviation (sigma) and the exponent part of the Gaussian function.
Zero-Lag GWMA Calculation: The zero-lag GWMA is calculated using a source buffer, a Gaussian weighted moving average (gwma1), and an output array. The source buffer stores the input data, the gwma1 array stores the first Gaussian weighted moving average, and the output array stores the final zero-lag moving average.
Application in Trading
The ZLMA with GWMA indicator can be used to identify trends and potential entry/exit points in trading:
Trend Identification: When the ZLMA is above the price, it indicates a bearish trend, and when it is below the price, it indicates a bullish trend.
Entry/Exit Points: Traders can use crossovers between the ZLMA and price to identify potential entry and exit points. A long position could be taken when the price crosses above the ZLMA, and a short position could be taken when the price crosses below the ZLMA.
Conclusion
The Zero Lag Moving Average with Gaussian Weights is a powerful and versatile indicator that can be used in various trading strategies. By minimizing the lag associated with traditional moving averages, the ZLMA with GWMA provides traders with more accurate and timely information about price trends and potential trade opportunities.
Gaussian Moving Average (GA)The Gaussian moving average (GA) is a technical analysis tool that is used to smooth out price data and identify trends. It is similar to a simple moving average (SMA), but instead of using equal weights for each value in the calculation, it uses a Gaussian distribution to assign weights. This means that the values at the edges of the calculation window have lower weights and are given less importance in the moving average calculation, while the values at the center of the window have higher weights and are given more importance. This helps to reduce the impact of noisy or outlying data points on the moving average and make it more responsive to changes in the underlying trend.
To calculate the GA, the script first defines the standard deviation of the Gaussian distribution. This is a measure of how spread out the values in the distribution are and can be adjusted to change the shape of the curve. The default value in the script is set to one quarter of the length of the calculation window, which gives a bell-shaped curve with a peak at the center of the window.
Next, the script generates an array of indices from 1 to the length of the calculation window. This is used to calculate the weights for each value in the moving average calculation. The weights are calculated using the Gaussian distribution, with the indices as the input values and the standard deviation as a parameter. This produces a set of weights that are highest at the center of the window and decrease towards the edges.
Finally, the script calculates the weighted sum of the values in the calculation window using the weights. This is divided by the sum of the weights to give the moving average value. The resulting moving average is smoother and more responsive to changes in the underlying trend than a simple moving average, making it a useful tool for technical analysis.
Overall, this script is useful for analyzing financial data and identifying trends in the data. By using the Gaussian moving average, the script can smooth out fluctuations in the data and make trends more apparent, which can help traders make more informed decisions.
Coppock Unchanged
An implementation of the "Coppock Unchanged" plot concept by Tom McClellan.
Simply put, assume that for each bar, an alternative close creates a Coppock Plot that is unchanged , i.e. a close that generates a flat coppock curve.
This coppock unchanged plot can be used to:
1) identify a start of a trend on a long timescale (monthly) when the price goes above the coppock unchanged plot after a major correction
2) potentially identify an end of a trend when the prices goes below the coppock unchanged plot
See Tom McClellan's article 'Coppock Curve Still Working On a Major Bottom Signal' for a full explanation...
KernelFunctionsLibrary "KernelFunctions"
This library provides non-repainting kernel functions for Nadaraya-Watson estimator implementations. This allows for easy substitution/comparison of different kernel functions for one another in indicators. Furthermore, kernels can easily be combined with other kernels to create newer, more customized kernels. Compared to Moving Averages (which are really just simple kernels themselves), these kernel functions are more adaptive and afford the user an unprecedented degree of customization and flexibility.
rationalQuadratic(_src, _lookback, _relativeWeight, _startAtBar)
Rational Quadratic Kernel - An infinite sum of Gaussian Kernels of different length scales.
Parameters:
_src : The source series.
_lookback : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.
_relativeWeight : Relative weighting of time frames. Smaller values result in a more stretched-out curve, and larger values will result in a more wiggly curve. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel.
_startAtBar : Bar index on which to start regression. The first bars of a chart are often highly volatile, and omitting these initial bars often leads to a better overall fit.
Returns: yhat The estimated values according to the Rational Quadratic Kernel.
gaussian(_src, _lookback, _startAtBar)
Gaussian Kernel - A weighted average of the source series. The weights are determined by the Radial Basis Function (RBF).
Parameters:
_src : The source series.
_lookback : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.
_startAtBar : Bar index on which to start regression. The first bars of a chart are often highly volatile, and omitting these initial bars often leads to a better overall fit.
Returns: yhat The estimated values according to the Gaussian Kernel.
periodic(_src, _lookback, _period, _startAtBar)
Periodic Kernel - The periodic kernel (derived by David Mackay) allows one to model functions that repeat themselves exactly.
Parameters:
_src : The source series.
_lookback : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.
_period : The distance between repititions of the function.
_startAtBar : Bar index on which to start regression. The first bars of a chart are often highly volatile, and omitting these initial bars often leads to a better overall fit.
Returns: yhat The estimated values according to the Periodic Kernel.
locallyPeriodic(_src, _lookback, _period, _startAtBar)
Locally Periodic Kernel - The locally periodic kernel is a periodic function that slowly varies with time. It is the product of the Periodic Kernel and the Gaussian Kernel.
Parameters:
_src : The source series.
_lookback : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.
_period : The distance between repititions of the function.
_startAtBar : Bar index on which to start regression. The first bars of a chart are often highly volatile, and omitting these initial bars often leads to a better overall fit.
Returns: yhat The estimated values according to the Locally Periodic Kernel.
MTF MA Ribbon and Bands + BB, Gaussian F. and R. VWAP with StDev█ Multi Timeframe Moving Average Ribbon and Bands + Bollinger Bands, Gaussian Filter and Rolling Volume Weighted Average Price with Standard Deviation Bands
Up to 9 moving averages can be independently applied.
The length , type and timeframe of each moving average are configurable .
The lines, colors and background fill are customizable too.
This script can also display:
Moving Average Bands
Bollinger Bands
Gaussian Filter
Rolling VWAP and Standard Deviation Bands
Types of Moving Averages:
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Smoothed Moving Average (SMMA)
Weighted Moving Average (WMA)
Volume Weighted Moving Average (VWMA)
Least Squares Moving Average (LSMA)
Hull Moving Average (HMA)
Arnaud Legoux Moving Average (ALMA)
█ Moving Average
Moving Averages are price based, lagging (or reactive) indicators that display the average price of a security over a set period of time.
A Moving Average is a good way to gauge momentum as well as to confirm trends, and define areas of support and resistance.
█ Bollinger Bands
Bollinger Bands consist of a band of three lines which are plotted in relation to security prices.
The line in the middle is usually a Simple Moving Average (SMA) set to a period of 20 days (the type of trend line and period can be changed by the trader, a 20 day moving average is by far the most popular).
The SMA then serves as a base for the Upper and Lower Bands which are used as a way to measure volatility by observing the relationship between the Bands and price.
█ Gaussian Filter
Gaussian filter can be used for smoothing.
It rejects high frequencies (fast movements) better than an EMA and has lower lag.
A Gaussian filter is one whose transfer response is described by the familiar Gaussian bell-shaped curve.
In the case of low-pass filters, only the upper half of the curve describes the filter.
The use of gaussian filters is a move toward achieving the dual goal of reducing lag and reducing the lag of high-frequency components relative to the lag of lower-frequency components.
█ Rolling VWAP
The typical VWAP is designed to be used on intraday charts, as it resets at the beginning of the day.
Such VWAPs cannot be used on daily, weekly or monthly charts. Instead, this rolling VWAP uses a time period that automatically adjusts to the chart's timeframe.
You can thus use the rolling VWAP on any chart that includes volume information in its data feed.
Because the rolling VWAP uses a moving window, it does not exhibit the jumpiness of VWAP plots that reset.
Made with the help from scripts of: adam24x, VishvaP, loxx and pmk07.
Gaussian Average Convergence DivergenceWhat exactly is the Ehlers Gaussian filter?
This filter is useful for smoothing. It rejects higher frequencies (fast movements) more effectively than an EMA and has less lag. John F. Ehlers published it in "Rocket Science For Traders." Dr. René Koch was the first to implement it in Wealth-Lab.
The transfer response of a Gaussian filter is described by the well-known Gaussian bell-shaped curve. Only the upper half of the curve describes the filter in the case of low-pass filters. The use of gaussian filters is a step toward achieving the dual goals of lowering lag and lowering the lag of high-frequency components relative to lower-frequency components.
From Ehlers Book: "The first objective of using smoothers is to eliminate or reduce the undesired high-frequency components in the price data. Therefore these smoothers are called low-pass filters, and they all work by some form of averaging. Butterworth low-pass filters can do this job, but nothing comes for free. A higher degree of filtering is necessarily accompanied by a larger amount of lag. We have come to see that is a fact of life."
References John F. Ehlers: "Rocket Science For Traders, Digital Signal Processing Applications", Chapter 15: "Infinite Impulse Response Filters"
Possible RSI [Loxx]Possible RSI is a normalized, variety second-pass normalized, Variety RSI with Dynamic Zones and optionl High-Pass IIR digital filtering of source price input. This indicator includes 7 types of RSI.
High-Pass Fitler (optional)
The Ehlers Highpass Filter is a technical analysis tool developed by John F. Ehlers. Based on aerospace analog filters, this filter aims at reducing noise from price data. Ehlers Highpass Filter eliminates wave components with periods longer than a certain value. This reduces lag and makes the oscialltor zero mean. This turns the RSI output into something more similar to Stochasitc RSI where it repsonds to price very quickly.
First Normalization Pass
RSI (Relative Strength Index) is already normalized. Hence, making a normalized RSI seems like a nonsense... if it was not for the "flattening" property of RSI. RSI tends to be flatter and flatter as we increase the calculating period--to the extent that it becomes unusable for levels trading if we increase calculating periods anywhere over the broadly recommended period 8 for RSI. In order to make that (calculating period) have less impact to significant levels usage of RSI trading style in this version a sort of a "raw stochastic" (min/max) normalization is applied.
Second-Pass Variety Normalization Pass
There are three options to choose from:
1. Gaussian (Fisher Transform), this is the default: The Fisher Transform is a function created by John F. Ehlers that converts prices into a Gaussian normal distribution. The normaliztion helps highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend.
2. Softmax: The softmax function, also known as softargmax: or normalized exponential function, converts a vector of K real numbers into a probability distribution of K possible outcomes. It is a generalization of the logistic function to multiple dimensions, and used in multinomial logistic regression. The softmax function is often used as the last activation function of a neural network to normalize the output of a network to a probability distribution over predicted output classes, based on Luce's choice axiom.
3. Regular Normalization (devaitions about the mean): Converts a vector of K real numbers into a probability distribution of K possible outcomes without using log sigmoidal transformation as is done with Softmax. This is basically Softmax without the last step.
Dynamic Zones
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
7 Types of RSI
See here to understand which RSI types are included:
Included:
Bar coloring
4 signal types
Alerts
Loxx's Expanded Source Types
Loxx's Variety RSI
Loxx's Dynamic Zones
STD-Filtered, N-Pole Gaussian Filter [Loxx]This is a Gaussian Filter with Standard Deviation Filtering that works for orders (poles) higher than the usual 4 poles that was originally available in Ehlers Gaussian Filter formulas. Because of that, it is a sort of generalized Gaussian filter that can calculate arbitrary (order) pole Gaussian Filter and which makes it a sort of a unique indicator. For this implementation, the practical mathematical maximum is 15 poles after which the precision of calculation is useless--the coefficients for levels above 15 poles are so high that the precision loss actually means very little. Despite this maximal precision utility, I've left the upper bound of poles open-ended so you can try poles of order 15 and above yourself. The default is set to 5 poles which is 1 pole greater than the normal maximum of 4 poles.
The purpose of the standard deviation filter is to filter out noise by and by default it will filter 1 standard deviation. Adjust this number and the filter selections (price, both, GMA, none) to reduce the signal noise.
What is Ehlers Gaussian filter?
This filter can be used for smoothing. It rejects high frequencies (fast movements) better than an EMA and has lower lag. published by John F. Ehlers in "Rocket Science For Traders".
A Gaussian filter is one whose transfer response is described by the familiar Gaussian bell-shaped curve. In the case of low-pass filters, only the upper half of the curve describes the filter. The use of gaussian filters is a move toward achieving the dual goal of reducing lag and reducing the lag of high-frequency components relative to the lag of lower-frequency components.
A gaussian filter with...
One Pole: f = alpha*g + (1-alpha)f
Two Poles: f = alpha*2g + 2(1-alpha)f - (1-alpha)2f
Three Poles: f = alpha*3g + 3(1-alpha)f - 3(1-alpha)2f + (1-alpha)3f
Four Poles: f = alpha*4g + 4(1-alpha)f - 6(1-alpha)2f + 4(1-alpha)3f - (1-alpha)4f
and so on...
For an equivalent number of poles the lag of a Gaussian is about half the lag of a Butterworth filters: Lag = N*P / pi^2, where,
N is the number of poles, and
P is the critical period
Special initialization of filter stages ensures proper working in scans with as few bars as possible.
From Ehlers Book: "The first objective of using smoothers is to eliminate or reduce the undesired high-frequency components in the eprice data. Therefore these smoothers are called low-pass filters, and they all work by some form of averaging. Butterworth low-pass filters can do this job, but nothing comes for free. A higher degree of filtering is necessarily accompanied by a larger amount of lag. We have come to see that is a fact of life."
References John F. Ehlers: "Rocket Science For Traders, Digital Signal Processing Applications", Chapter 15: "Infinite Impulse Response Filters"
Included
Loxx's Expanded Source Types
Signals
Alerts
Bar coloring
Related indicators
STD-Filtered, Gaussian Moving Average (GMA)
STD-Filtered, Gaussian-Kernel-Weighted Moving Average
One-Sided Gaussian Filter w/ Channels
Fisher Transform w/ Dynamic Zones
R-sqrd Adapt. Fisher Transform w/ D. Zones & Divs .
Gaussian Filter MACD [Loxx]Gaussian Filter MACD is a MACD that uses an 1-4 Pole Ehlers Gaussian Filter for its calculations. Compare this with Ehlers Fisher Transform.
What is Ehlers Gaussian filter?
This filter can be used for smoothing. It rejects high frequencies (fast movements) better than an EMA and has lower lag. published by John F. Ehlers in "Rocket Science For Traders". First implemented in Wealth-Lab by Dr René Koch.
A Gaussian filter is one whose transfer response is described by the familiar Gaussian bell-shaped curve. In the case of low-pass filters, only the upper half of the curve describes the filter. The use of gaussian filters is a move toward achieving the dual goal of reducing lag and reducing the lag of high-frequency components relative to the lag of lower-frequency components.
A gaussian filter with...
one pole is equivalent to an EMA filter.
two poles is equivalent to EMA ( EMA ())
three poles is equivalent to EMA ( EMA ( EMA ()))
and so on...
For an equivalent number of poles the lag of a Gaussian is about half the lag of a Butterworth filters: Lag = N * P / (2 * ¶2), where,
N is the number of poles, and
P is the critical period
Special initialization of filter stages ensures proper working in scans with as few bars as possible.
From Ehlers Book: "The first objective of using smoothers is to eliminate or reduce the undesired high-frequency components in the eprice data. Therefore these smoothers are called low-pass filters, and they all work by some form of averaging. Butterworth low-pass filtters can do this job, but nothing comes for free. A higher degree of filtering is necessarily accompanied by a larger amount of lag. We have come to see that is a fact of life."
References John F. Ehlers: "Rocket Science For Traders, Digital Signal Processing Applications", Chapter 15: "Infinite Impulse Response Filters"
Included
Loxx's Expanded Source Types
Signals, zero or signal crossing, signal crossing is very noisy
Alerts
Bar coloring
