What is vwap

What VWAP Measures

Volume Weighted Average Price (VWAP) is a technical analysis tool that calculates the average price at which a security has traded during a specific period, with each price weighted according to its trading volume. Unlike simple moving averages that treat all prices equally, VWAP gives greater importance to prices at which substantial trading volume occurred, providing a more accurate representation of true market value.

How VWAP is Calculated

VWAP uses the formula: VWAP = Cumulative(Typical Price × Volume) / Cumulative(Volume), where typical price is calculated as (High + Low + Close) ÷ 3 for each period. The indicator begins fresh at market open each trading day, accumulating volume-weighted prices as trading occurs throughout the session. By dividing cumulative price-volume by total volume, it establishes the weighted average price at which most trading activity occurred.

Trading Applications

Traders use VWAP to determine whether a security is trading above or below its fair value based on volume-weighted pricing. When current price exceeds VWAP, it suggests the stock is trading at a premium; conversely, when price falls below VWAP, it indicates a discount. This information helps traders identify optimal entry and exit points for intraday trading strategies.

Institutional Use

Large institutional investors frequently use VWAP as a reference benchmark when executing block trades throughout the trading day. By executing orders with the goal of achieving an average fill price near VWAP, institutions minimize market impact and execution costs. Many algorithmic trading systems incorporate VWAP as a target metric for optimized order execution and performance measurement.

Advantages and Limitations

VWAP's primary strength is its incorporation of volume data, making it more representative of true market value than price-only metrics. It is especially valuable for large traders. However, VWAP is backward-looking, resets daily, and becomes unreliable during low-volume trading periods. It is also less useful for long-term trend analysis compared to longer-term moving averages.