Indicator Formula

Technical Details — Volume-Weighted Exponential Moving Average (VWEMA)

Overview

The Volume-Weighted EMA (VWEMA) applies EMA smoothing to volume-weighted price data. Each price is scaled by its relative volume before the exponential averaging, combining the responsiveness of EMA with the volume-informed weighting of VWAP.

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Mathematical Derivation

Step 1 — Calculate Volume-Weighted Price

Purpose: Scale each price by its relative volume contribution.

$$VWPrice[t] = Close[t] \times Volume[t]$$

Where:

• $$Close[t]$$ = Closing price
• $$Volume[t]$$ = Trading volume

What This Measures: Volume-scaled price for EMA input

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Step 2 — Apply EMA to Weighted Data

Purpose: Smooth the volume-weighted prices and volumes separately using EMA, then divide.

$$VWEMA[t] = \frac{EMA(VWPrice, n)[t]}{EMA(Volume, n)[t]}$$

Where:

• $$n$$ = EMA period

What This Measures: Exponentially smoothed volume-weighted average price

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Compact Formula Summary

$$VWEMA[t] = \frac{EMA(Close \times Volume, n)}{EMA(Volume, n)}$$

Default Parameter: Period (n) = 14

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Complete Calculation Example

VWEMA applies EMA separately to (Price × Volume) and Volume, then divides. High-volume bars pull the average toward their price more strongly than low-volume bars.

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Key Takeaways from the Example

1. Dual Weighting: Combines exponential time decay with volume importance
2. More Responsive to Volume Spikes: Large volume bars have outsized influence on the average
3. EMA Base: Inherits EMA’s responsiveness and lack of drop-off effect
4. Requires Volume Data: Only meaningful on assets with reliable volume data

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Caution

Important: VWEMA is particularly useful for crypto markets where volume spikes often precede significant moves. A divergence between VWEMA and regular EMA signals that volume is concentrating at different price levels than the simple average suggests.