The volume weighted average price (VWAP) of a stock is a key measure of execution quality for large orders used by institutional investors.

Algorithmic trading is an area of increasing importance in financial exchanges. Trading algorithms range from high-frequency algorithms that submit and cancel orders thousands of times per second, to algorithms that trade less frequently, whose objective varies from minimising trading costs to tracking the performance of a benchmark. One of the most common benchmarks for judging the execution quality of individual stocks, used by institutional investors such as pension funds and mutual funds, is the daily volume-weighted average price (VWAP),

In the United States, survey data in a report by Bank of America (2007) indicate that VWAP orders represent close to 50% of all trading by institutional investors, while Almgren, Thum, Hauptmann, and Li (2005) report that VWAP orders for individual stocks represent 16% of all trades. Domowitz and Yegerman (2011) report that when trying to place an order that ranges from 0.5% to 5% of daily market volume between 23% and 27% of all algorithmic trades follow VWAP strategies, while Australian Securities Exchange (2010) estimate that 32.3% of buy-side algorithmic trades are VWAP orders. End-of-day VWAP for individual stocks is reported by exchanges, and guaranteed VWAP orders are available through most brokerage houses. For example, Interactive Brokers offer a guarranteed VWAP order for a cost of 10 basis points, while Bia lkowski, Darolles, and Le Fol (2008) report that in Europe the cost of VWAP orders ranges between 10 and 20 basis points. Beyond its popularity with institutional investors, VWAP has been used by regulators for assessing taxes in cases of issuance of shares to existing shareholders.


  • It is based only on the price and volume data of the current session. (In contrast, typical moving averages include prices from the past session.)
  • Used to measure trading efficiency for large institutional orders. For instance, you manage to execute a buy order at an average price below the end-of-day VWAP. Then, you’ve enjoyed a better than average price.
  • More sensitive to price and volume changes at the beginning of the session. Becomes less responsive as the session progresses.

VWAP is a valuable tool that reduces market noise and illuminates price bias. This indicator incorporates both the price and volume of each session. Hence, it’s a handy benchmark for judging the market bias.

There are five steps involved in the VWAP calculation. First, compute the typical price for the intraday period. This is the average of the high, low, and close: {(H+L+C)/3)}. Second, multiply the typical price by the period’s volume. Third, create a running total of these values. This is also known as a cumulative total. Fourth, create a running total of volume (cumulative volume). Fifth, divide the running total of price-volume by the running total of volume.

Cumulative(Volume x Typical Price)/Cumulative(Volume)


  • As the market pulls away from the VWAP it crosses more distant Standard Deviation bands. A mean reversion system will look to sell when the market is more than 4 standard deviations from the VWAP, and buy when it is more than 4 standad deviations below the VWAP i.e. the distant standard deviation bands act as resistance/support.
  • If the market is trending, then a mean reversion strategy will not work (and vice versa). A strategy that follows the trend will look to buy/sell in an uptrend/downtrend when the market is close to the VWAP i.e. the VWAP acts as support/resistance.
Mean reversion technique will sell the upper bands, and buy the lower bands. Only works in ranging markets.

About the author

Trading and Investment

Traded the markets for over 15 years, including Commodities, Bonds, Currencies, Equities, and Indices. I have also worked as a Chartered Financial Planner.
CeMAP, CeFA, DipFA, AdvDipFA, Ba(Hons) Economics, Chartered ALIBF

Leave a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.