Kalman VWAP Filter [BackQuant]

Kalman VWAP Filter [BackQuant]

A precision-engineered price estimator that fuses Kalman filtering with the Volume-Weighted Average Price (VWAP) to create a smooth, adaptive representation of fair value. This hybrid model intelligently balances responsiveness and stability, tracking trend shifts with minimal noise while maintaining a statistically grounded link to volume distribution.

If you would like to see my original Kalman Filter, please find it here:

Concept overview
The Kalman VWAP Filter is built on two core ideas from quantitative finance and control theory:

• Kalman filtering — a recursive Bayesian estimator used to infer the true underlying state of a noisy system (in this case, fair price).
  
• VWAP anchoring — a dynamic reference that weights price by traded volume, representing where the majority of transactions have occurred.
  

By merging these concepts, the filter produces a line that behaves like a "smart moving average": smooth when noise is high, fast when markets trend, and self-adjusting based on both market structure and user-defined noise parameters.

How it works

1. Measurement blend: Combines the chosen Price Source (e.g., close or hlc3) with either a Session VWAP or a Rolling VWAP baseline. The VWAP Weight input controls how much the filter trusts traded volume versus price movement.
   
2. Kalman recursion: Each bar updates an internal "state estimate" using the Kalman gain, which determines how much to trust new observations vs. the prior state.
   
3. Noise parameters:
   
4. Process Noise controls agility — higher values make the filter more responsive but also more volatile.
   
5. Measurement Noise controls smoothness — higher values make it steadier but slower to adapt.
   
6. Filter order (N): Defines how many parallel state estimates are used. Larger orders yield smoother output by layering multiple one-dimensional Kalman passes.
   
7. Final output: A refined price trajectory that captures VWAP-adjusted fair value while dynamically adjusting to real-time volatility and order flow.
   

Why this matters
Most smoothing techniques (EMA, SMA, Hull) trade off lag for smoothness. Kalman filtering, however, adaptively rebalances that tradeoff each bar using probabilistic weighting, allowing it to follow market state changes more efficiently. Anchoring it to VWAP integrates microstructure context — capturing where liquidity truly lies rather than only where price moves.

Use cases

• Trend tracking: Color-coded candle painting highlights shifts in slope direction, revealing early trend transitions.
  
• Fair value mapping: The line represents a continuously updated equilibrium price between raw price action and VWAP flow.
  
• Adaptive moving average replacement: Outperforms static MAs in variable volatility regimes by self-adjusting smoothness.
  
• Execution & reversion logic: When price diverges from the Kalman VWAP, it may indicate short-term imbalance or overextension relative to volume-adjusted fair value.
  
• Cross-signal framework: Use with standard VWAP or other filters to identify convergence or divergence between liquidity-weighted and state-estimated prices.
  

Parameter guidance

• Process Noise: 0.01–0.05 for swing traders, 0.1–0.2 for intraday scalping.
  
• Measurement Noise: 2–5 for normal use, 8+ for very smooth tracking.
  
• VWAP Weight: 0.2–0.4 balances both price and VWAP influence; 1.0 locks output directly to VWAP dynamics.
  
• Filter Order (N): 3–5 for reactive short-term filters; 8–10 for smoother institutional-style baselines.
  

Interpretation

• When price > Kalman VWAP and slope is positive → bullish pressure; buyers dominate above fair value.
  
• When price < Kalman VWAP and slope is negative → bearish pressure; sellers dominate below fair value.
  
• Convergence of price and Kalman VWAP often signals equilibrium; strong divergence suggests imbalance.
  
• Crosses between Kalman VWAP and the base VWAP can hint at shifts in short-term vs. long-term liquidity control.
  

Summary
The Kalman VWAP Filter blends statistical estimation with market microstructure awareness, offering a refined alternative to static smoothing indicators. It adapts in real time to volatility and order flow, helping traders visualize balance, transition, and momentum through a lens of probabilistic fair value rather than simple price averaging.