Kalman Quantum Drift [JOAT]KALMAN QUANTUM DRIFT
A trend-and-envelope engine built on the cleanest pair of state-space tools in quantitative finance: a Kalman filter for the centreline (Bayesian, adaptive, mathematically optimal under linear-Gaussian assumptions) and a GARCH(1,1) conditional-variance model for the envelope (the institutional standard for time-varying volatility). The script reads price as a noisy observation of an unobservable true state; the Kalman filter estimates that state recursively; GARCH estimates the noise's volatility; the envelope = mid ± k · σ_GARCH. A signal engine layered on top detects Collapse events (>3σ excursions) and Tunnel events (gap-throughs of the envelope) — the quantum analogues of state collapse and quantum tunnelling.
The Kalman filter, properly
A single-state recursive Bayesian filter. At each bar:
Predict : prior estimate = previous estimate. Prior variance = previous variance + Q.
Update : Kalman gain = prior variance / (prior variance + R). New estimate = prior + gain × (observation − prior). New variance = (1 − gain) × prior variance.
The two tuning knobs are:
Q (process noise) — how much the script trusts new observations. Higher Q = faster, noisier mid-line.
R (measurement noise) — how much the script trusts the model. Higher R = slower, smoother mid-line.
This is the Bayesian-optimal smoother for linear-Gaussian state-space data. Real markets are not perfectly linear-Gaussian, but the Kalman estimate is robust enough to be the cleanest mid-line you can build without going into heavy non-linear filtering.
GARCH(1,1) envelope
The envelope around the Kalman mid is not ATR or stdev — it is GARCH(1,1) :
σ²_t = ω + α · ε²_{t−1} + β · σ²_{t−1}
ω is the long-run variance baseline, α is the reaction to last shock squared (ARCH term), β is the persistence of past variance (GARCH term). For stationarity, α + β < 1 (the script's α/β defaults respect that). Optional log returns (default ON) and a warm-up window seed the variance from realised returns.
The envelope is mid ± k · σ_GARCH , rendered as a gradient (configurable number of nested fills, each at progressive transparency from edge to core).
Three-signal engine
Collapse — fires when price travels more than collapse threshold (default 3.0) σ-units from the Kalman mid. The "state collapse" event — price has decisively departed the filter's expected band. Bull or bear depending on direction.
Tunnel — fires when a bar gaps through the entire envelope from one side to the other. The "quantum tunnel" event — a discontinuous jump that bypasses the band gradient.
Buy / Sell crosses — fire when price crosses the Kalman mid from one side. Optional Collapse confirmation gate (default ON) — Buy / Sell only fires when a Collapse occurred within the configurable lookback window. This dramatically improves signal quality.
A configurable signal cooldown (default 5 bars) prevents stacking.
Visual system
Kalman mid-line — coloured by its own slope (bull / bear), configurable width.
Gradient envelope — nested fills (configurable steps, default 6) using the same base hue with progressive transparency from edge to core. Strict two-hue discipline (bull cyan / bear pink only).
Price bar colouring by mid slope (toggleable).
Background tint on extremes — subtle bgcolor when price is at envelope edge (toggleable, default 92 transparency).
Event glyphs — C (Collapse) and T (Tunnel) markers at the event bar. Configurable size.
A locked Quantum palette (cyan bull / pink bear / muted cyan mid on a deep violet-black) gives the chart a distinctive quant-physics identity.
Dashboard
Monospaced table positionable to any of nine corners. Surfaces:
Current Kalman mid value with slope direction.
σ_GARCH value and the envelope half-width.
Distance of price from mid in σ-units.
Last Collapse / Tunnel / Buy / Sell event with bar age.
Q / R settings in use.
GARCH ω / α / β confirmation.
Alerts
Six alert conditions, each independently controllable:
Collapse Up / Down (>kσ excursion)
Tunnel Up / Down (envelope gap-through)
Slope Flip (Kalman mid changes direction)
Sigma Spike (σ_GARCH exceeds its own recent baseline)
How to read it
Three reads, in order of conviction:
Buy/Sell after a Collapse (the script's intended signal) — the cleanest trend-entry the engine produces. A Collapse means price decisively departed expected range; the subsequent mid-line cross confirms the new direction with the strongest possible context. This is the highest-conviction read.
Tunnel — an exceptional, rare event. When a single bar jumps the entire envelope, the market has experienced a discontinuity (news, large block, exchange dislocation). Often produces the day's largest moves; almost always followed by elevated volatility.
σ Spike alert without a directional event — a regime warning. Volatility just expanded without a directional commitment yet. The next signal that fires is statistically more likely to be meaningful than the one before the spike.
Suggested settings
Defaults (Q = 0.02, R = 1.5, GARCH ω=2e-6 / α=0.10 / β=0.85, k = 2.5, gradient steps 6) are tuned for 15m–1H on liquid markets. For lower timeframes drop k to 2.0. For HTF raise R to 3.0 (more model trust on smoother data). The GARCH α/β defaults are the institutional standard; α + β remains under 1 for stationarity.
Originality
Kalman filtering and GARCH(1,1) are textbook quantitative-finance methods — both decades-old, both well-documented. The implementation here — the single-state recursive Kalman with exposed Q/R, the GARCH(1,1) variance recursion with warm-up window, the gradient-envelope render using strict two-hue alpha-only variation, the three-signal engine (Collapse / Tunnel / Cross), the optional Collapse-confirmed Buy/Sell gating, the event glyph markers, and the background tint on extremes — is JOAT-original. No third-party code reused. The pairing of Kalman + GARCH + quantum-inspired signal naming is the original presentation.
Limitations
The Kalman filter assumes linear-Gaussian state dynamics — real markets violate this, especially around news and gaps. The Q / R tuning is exposed precisely because no single setting is universally correct; tune to your instrument's noise profile. GARCH's α/β must sum to less than 1 for stationarity — the defaults respect this; if you push them too aggressively the variance can explode. Collapse and Tunnel events are confirmed on bar close (non-repainting).
—
-made with passion by jackofalltrades
Indicator
Kalman Volume Trend [BigBeluga]🔵 OVERVIEW
Kalman Volume Trend is an advanced trend-following system that combines the predictive power of a Kalman Filter with real-time volume delta analysis. Unlike standard moving averages that suffer from significant lag, the Kalman Filter uses a recursive mathematical algorithm to estimate the "true" trend by filtering out market noise.
The indicator not only identifies directional regimes but also visualizes the intensity of buying and selling pressure directly on the trend line, providing a multi-dimensional view of market conviction.
🔵 CONCEPT
Kalman Filter Logic — A state-space model that predicts price movement and then corrects itself based on new data, resulting in a smoother yet more responsive trend line than traditional EMAs.
Adaptive ATR Bands — The trend direction is determined by price breaking through volatility-adjusted bands, reducing whipsaws in sideways markets.
Volume-Weighted Trend Lines — The indicator plots "Volume Bars" extending from the trend line, where the length and color represent the relative strength of the volume delta.
Cumulative Trend Statistics — It tracks the total buy volume, sell volume, and net delta from the exact moment a new trend begins.
🔵 HOW IT WORKS (IN-DEPTH)
1️⃣ The Kalman Filtering Process
The script utilizes two primary parameters: Process Noise (Q) and Measurement Noise (R) .
It calculates a "State Estimate" (the trend) by balancing its previous prediction against the current price.
If the price is "jittery" (high R), the filter smooths the line; if the trend is moving decisively (low Q), it tracks the price more aggressively.
2️⃣ Trend Direction & Volatility Bands
Two bands are projected around the Kalman line based on a multiplier of the Average True Range (ATR) .
A Bullish trend is triggered when price closes above the upper band.
A Bearish trend is triggered when price closes below the lower band.
Once a trend is established, the opposite band acts as the trailing "Trend Line" to provide a clear buffer for price fluctuations.
3️⃣ Volume Delta Visualization
Small vertical candles ("Volume Bars") are plotted along the trend line.
These bars represent the Normalized Volume Delta (Close vs. Open and Volume intensity).
Large bars indicate high-conviction participation, while small bars suggest waning interest or consolidation.
4️⃣ Extreme Volume & Cumulative Dashboard
When volume exceeds 1.5x its recent average, an "X" label appears on the chart to mark an Exhaustion or Ignition point.
A bottom-right dashboard displays a vertical histogram showing the balance of power (BUY vs. SELL vs. DELTA) for the current trend only .
🔵 KEY FEATURES
Recursive Kalman Algorithm: High-accuracy trend tracking with minimal lag.
Integrated Volume Profiling: See volume delta without needing a separate sub-window.
Dynamic Trend Dashboard: Automatically resets at every trend flip to show fresh volume stats.
Volatility-Aware: Uses 200-period ATR to ensure bands adapt to changing market conditions.
Volume Extreme Alerts: Identifies high-volume spikes that often precede trend reversals.
🔵 DASHBOARD METRICS
BUY — Total volume accumulated on bullish candles since the trend started.
SELL — Total volume accumulated on bearish candles since the trend started.
DELTA — The net difference between buying and selling pressure.
TOTAL VOLUME — The total "fuel" spent during the current directional regime.
🔵 HOW TO USE
Riding the Trend: Stay in the trade as long as the Kalman line color remains consistent.
Spotting Weakness: If the Kalman line is Bullish (Blue) but the Volume Bars are consistently negative or shrinking, the trend may be losing steam.
High-Volume Breakouts: Look for the "X" labels at the start of a trend shift; this confirms institutional participation in the new direction.
Dashboard Confirmation: Use the vertical histogram to confirm if the buyers or sellers are truly in control during a pullback to the trend line.
🔵 CONCLUSION
Kalman Volume Trend offers a sophisticated approach to trend analysis by merging high-level signal processing with raw volume data. By focusing on "clean" price data and weighting it with volume delta, it helps traders filter out market noise and focus on high-conviction movements. Indicator
Kalman Filter Oscillator v4The Kalman Filter Oscillator v4 is an advanced tool designed to help traders and investors identify trends more effectively while reducing the impact of market noise. As the latest iteration in its development, this version integrates improvements that make it more adaptive and precise, catering to the challenges of today’s financial markets.
This indicator operates on the principle of the Kalman filter, a well-regarded mathematical approach used for estimating the state of a dynamic system. By filtering out random fluctuations, it smooths price data to provide clearer insights into underlying trends. Unlike traditional methods such as moving averages, which often lag and can miss rapid shifts, the Kalman Filter Oscillator is reactive in real time, making it particularly suited for dynamic markets.
Version v4 builds on earlier versions by offering a refined combination of short-term and long-term trend analysis. Through adjustable parameters, traders can balance sensitivity to immediate price changes with a broader perspective of the market direction. Additionally, the oscillator incorporates a unique feature that tracks a price’s position relative to its recent highs and lows, which enhances its ability to pinpoint potential turning points or key market conditions.
The indicator’s value lies in its adaptability and practicality. Traders can use it to confirm trends, identify overbought or oversold conditions, or smooth out erratic price movements, reducing the likelihood of false signals. By presenting information in a clear and actionable format, it allows users to make better-informed decisions with greater confidence.
As of late 2024, the Kalman Filter Oscillator v4 represents a sophisticated yet user-friendly advancement in trend analysis. While not a one-size-fits-all solution, it serves as a valuable component in a trader’s toolkit, complementing other strategies and enhancing overall market understanding. Indicator
Kalman Trend Levels [BigBeluga]Kalman Trend Levels is an advanced trend-following indicator designed to highlight key support and resistance zones based on Kalman filter crossovers. With dynamic trend analysis and actionable signals, it helps traders interpret market direction and momentum shifts effectively.
🔵 Key Features:
Trend Levels with Crossover Boxes: Identifies trend shifts by tracking crossovers between fast and slow Kalman filters. When the fast line crosses above the slow line, a green box level appears, indicating a potential support zone. When it crosses below, a red box level forms, acting as a resistance zone.
Retest Signals for Support and Resistance Levels: Enable retest signals to capture price rejections at the established levels, providing possible re-entry points where the price confirms a support or resistance area.
Adaptive Candle Coloring by Trend Momentum: Candle colors adjust based on the trend's strength:
> During a downtrend, if the fast Kalman line shows upward movement, indicating reduced bearish momentum, candles turn gray to signal the weakening trend.
> In an uptrend, when the fast Kalman line declines, showing lower bullish momentum, candles become gray, signaling a potential slowdown in upward movement.
Crossover Signals with Price Labels: Displays arrows with price values at crossover points for quick reference, marking where the fast line overtakes or dips below the slow line. These labels provide a precise price snapshot of significant trend changes.
🔵 When to Use:
The Kalman Trend Levels indicator is ideal for traders looking to identify and act upon trend changes and significant price zones. By visualizing key levels and momentum shifts, this tool allows you to:
Define support and resistance zones that align with trend direction.
Identify and react to trend weakening or strengthening via candle color changes.
Use retest signals for potential re-entries at critical levels.
See crossover points and price values to gain a clearer view of trend changes in real time.
With its focus on trend direction, support/resistance, and momentum clarity, Kalman Trend Levels is an essential tool for navigating trending markets, providing actionable insights with every crossover and trend shift. Indicator
Adaptive Kalman Trend Filter (Zeiierman)█ Overview
The Adaptive Kalman Trend Filter indicator is an advanced trend-following tool designed to help traders accurately identify market trends. Utilizing the Kalman Filter—a statistical algorithm rooted in control theory and signal processing—this indicator adapts to changing market conditions, smoothing price data to filter out noise. By focusing on state vector-based calculations, it dynamically adjusts trend and range measurements, making it an excellent tool for both trend-following and range-based trading strategies. The indicator's adaptive nature is enhanced by options for volatility adjustment and three unique Kalman filter models, each tailored for different market conditions.
█ How It Works
The Kalman Filter works by maintaining a model of the market state through matrices that represent state variables, error covariances, and measurement uncertainties. Here’s how each component plays a role in calculating the indicator’s trend:
⚪ State Vector (X): The state vector is a two-dimensional array where each element represents a market property. The first element is an estimate of the true price, while the second element represents the rate of change or trend in that price. This vector is updated iteratively with each new price, maintaining an ongoing estimate of both price and trend direction.
⚪ Covariance Matrix (P): The covariance matrix represents the uncertainty in the state vector’s estimates. It continuously adapts to changing conditions, representing how much error we expect in our trend and price estimates. Lower covariance values suggest higher confidence in the estimates, while higher values indicate less certainty, often due to market volatility.
⚪ Process Noise (Q): The process noise matrix (Q) is used to account for uncertainties in price movements that aren’t explained by historical trends. By allowing some degree of randomness, it enables the Kalman Filter to remain responsive to new data without overreacting to minor fluctuations. This noise is particularly useful in smoothing out price movements in highly volatile markets.
⚪ Measurement Noise (R): Measurement noise is an external input representing the reliability of each new price observation. In this indicator, it is represented by the setting Measurement Noise and determines how much weight is given to each new price point. Higher measurement noise makes the indicator less reactive to recent prices, smoothing the trend further.
⚪ Update Equations:
Prediction: The state vector and covariance matrix are first projected forward using a state transition matrix (F), which includes market estimates based on past data. This gives a “predicted” state before the next actual price is known.
Kalman Gain Calculation: The Kalman gain is calculated by comparing the predicted state with the actual price, balancing between the covariance matrix and measurement noise. This gain determines how much of the observed price should influence the state vector.
Correction: The observed price is then compared to the predicted price, and the state vector is updated using this Kalman gain. The updated covariance matrix reflects any adjustment in uncertainty based on the latest data.
█ Three Kalman Filter Models
Standard Model: Assumes that market fluctuations follow a linear progression without external adjustments. It is best suited for stable markets.
Volume Adjusted Model: Adjusts the filter sensitivity based on trading volume. High-volume periods result in stronger trends, making this model suitable for volume-driven assets.
Parkinson Adjusted Model: Uses the Parkinson estimator, accounting for volatility through high-low price ranges, making it effective in markets with high intraday fluctuations.
These models enable traders to choose a filter that aligns with current market conditions, enhancing trend accuracy and responsiveness.
█ Trend Strength
The Trend Strength provides a visual representation of the current trend's strength as a percentage based on oscillator calculations from the Kalman filter. This table divides trend strength into color-coded segments, helping traders quickly assess whether the market is strongly trending or nearing a reversal point. A high trend strength percentage indicates a robust trend, while a low percentage suggests weakening momentum or consolidation.
█ Trend Range
The Trend Range section evaluates the market's directional movement over a specified lookback period, highlighting areas where price oscillations indicate a trend. This calculation assesses how prices vary within the range, offering an indication of trend stability or the likelihood of reversals. By adjusting the trend range setting, traders can fine-tune the indicator’s sensitivity to longer or shorter trends.
█ Sigma Bands
The Sigma Bands in the indicator are based on statistical standard deviations (sigma levels), which act as dynamic support and resistance zones. These bands are calculated using the Kalman Filter's trend estimates and adjusted for volatility (if enabled). The bands expand and contract according to market volatility, providing a unique visualization of price boundaries. In high-volatility periods, the bands widen, offering better protection against false breakouts. During low volatility, the bands narrow, closely tracking price movements. Traders can use these sigma bands to spot potential entry and exit points, aiming for reversion trades or trend continuation setups.
Trend Based
Volatility Based
█ How to Use
Trend Following:
When the Kalman Filter is green, it signals a bullish trend, and when it’s red, it indicates a bearish trend. The Sigma Cloud provides additional insights into trend strength. In a strong bullish trend, the cloud remains below the Kalman Filter line, while in a strong bearish trend, the cloud stays above it. Expansion and contraction of the Sigma Cloud indicate market momentum changes. Rapid expansion suggests an impulsive move, which could either signal the continuation of the trend or be an early sign of a possible trend reversal.
Mean Reversion: Watch for prices touching the upper or lower sigma bands, which often act as dynamic support and resistance.
Volatility Breakouts: Enable volatility-adjusted sigma bands. During high volatility, watch for price movements that extend beyond the bands as potential breakout signals.
Trend Continuation: When the Kalman Filter line aligns with a high trend strength, it signals a continuation in that direction.
█ Settings
Measurement Noise: Adjusts how sensitive the indicator is to price changes. Higher values smooth out fluctuations but delay reaction, while lower values increase sensitivity to short-term changes.
Kalman Filter Model: Choose between the standard, volume-adjusted, and Parkinson-adjusted models based on market conditions.
Band Sigma: Sets the standard deviation used for calculating the sigma bands, directly affecting the width of the dynamic support and resistance.
Volatility Adjusted Bands: Enables bands to dynamically adapt to volatility, increasing their effectiveness in fluctuating markets.
Trend Strength: Defines the lookback period for trend strength calculation. Shorter periods result in more responsive trend strength readings, while longer periods smooth out the calculation.
Trend Range: Specifies the lookback period for the trend range, affecting the assessment of trend stability over time.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Indicator
Adaptive Kalman filter - Trend Strength Oscillator (Zeiierman)█ Overview
The Adaptive Kalman Filter - Trend Strength Oscillator by Zeiierman is a sophisticated trend-following indicator that uses advanced mathematical techniques, including vector and matrix operations, to decompose price movements into trend and oscillatory components. Unlike standard indicators, this model assumes that price is driven by two latent (unobservable) factors: a long-term trend and localized oscillations around that trend. Through a dynamic "predict and update" process, the Kalman Filter leverages vectors to adaptively separate these components, extracting a clearer view of market direction and strength.
█ How It Works
This indicator operates on a trend + local change Kalman Filter model. It assumes that price movements consist of two underlying components: a core trend and an oscillatory term, representing smaller price fluctuations around that trend. The Kalman Filter adaptively separates these components by observing the price series over time and performing real-time updates as new data arrives.
Predict and Update Procedure: The Kalman Filter uses an adaptive predict-update cycle to estimate both components. This cycle allows the filter to adjust dynamically as the market evolves, providing a smooth yet responsive signal. The trend component extracted from this process is plotted directly, giving a clear view of the prevailing direction. The oscillatory component indicates the tendency or strength of the trend, reflected in the green/red coloration of the oscillator line.
Trend Strength Calculation: Trend strength is calculated by comparing the current oscillatory value against a configurable number of past values.
█ Three Kalman filter Models
This indicator offers three distinct Kalman filter models, each designed to handle different market conditions:
Standard Model: This is a conventional Kalman Filter, balancing responsiveness and smoothness. It works well across general market conditions.
Volume-Adjusted Model: In this model, the filter’s measurement noise automatically adjusts based on trading volume. Higher volumes indicate more informative price movements, which the filter treats with higher confidence. Conversely, low-volume movements are treated as less informative, adding robustness during low-activity periods.
Parkinson-Adjusted Model: This model adjusts measurement noise based on price volatility. It uses the price range (high-low) to determine the filter’s sensitivity, making it ideal for handling markets with frequent gaps or spikes. The model responds with higher confidence in low-volatility periods and adapts to high-volatility scenarios by treating them with more caution.
█ How to Use
Trend Detection: The oscillator oscillates around zero, with positive values indicating a bullish trend and negative values indicating a bearish trend. The further the oscillator moves from zero, the stronger the trend. The Kalman filter trend line on the chart can be used in conjunction with the oscillator to determine the market's trend direction.
Trend Reversals: The blue areas in the oscillator suggest potential trend reversals, helping traders identify emerging market shifts. These areas can also indicate a potential pullback within the prevailing trend.
Overbought/Oversold: The thresholds, such as 70 and -70, help identify extreme conditions. When the oscillator reaches these levels, it suggests that the trend may be overextended, possibly signaling an upcoming reversal.
█ Settings
Process Noise 1: Controls the primary level of uncertainty in the Kalman filter model. Higher values make the filter more responsive to recent price changes, but may also increase susceptibility to random noise.
Process Noise 2: This secondary noise setting works with Process Noise 1 to adjust the model's adaptability. Together, these settings manage the uncertainty in the filter's internal model, allowing for finely-tuned adjustments to smoothness versus responsiveness.
Measurement Noise: Sets the uncertainty in the observed price data. Increasing this value makes the filter rely more on historical data, resulting in smoother but less reactive filtering. Lower values make the filter more responsive but potentially more prone to noise.
O sc Smoothness: Controls the level of smoothing applied to the trend strength oscillator. Higher values result in a smoother oscillator, which may cause slight delays in response. Lower values make the oscillator more reactive to trend changes, useful for capturing quick reversals or volatility within the trend.
Kalman Filter Model: Choose between Standard, Volume-Adjusted, and Parkinson-Adjusted models. Each model adapts the Kalman filter for specific conditions, whether balancing general market data, adjusting based on volume, or refining based on volatility.
Trend Lookback: Defines how far back to look when calculating the trend strength, which impacts the indicator's sensitivity to changes in trend strength. Shorter values make the oscillator more reactive to recent trends, while longer values provide a smoother reading.
Strength Smoothness: Adjusts the level of smoothing applied to the trend strength oscillator. Higher values create a more gradual response, while lower values make the oscillator more sensitive to recent changes.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Indicator
