ETFS Bloomberg Historical Price Analysis Overview

BCD -- USA Etf  

USD 26.16  0.20  0.77%

Use ETFS Bloomberg All CmdtyLngrDtdStrtK 1Fr Historical Price Analysis together with your etf portfolios to enhance returns of your portfolios and to determine Etf optimization strategy that fits your criteria.  
 Time Horizon     30 Days    Login   to change

Performance History


Highest PriceMay 31, 201827.27
Lowest PricJune 21, 201825.96
The price series of ETFS Bloomberg for the period between Wed, May 23, 2018 and Fri, Jun 22, 2018 has a statistical range of 1.57 with a coefficient of variation of 1.84. The prices are distributed with arithmetic mean of 26.93. The median price for the last 30 days is 27.06
OpenHighLowCloseVolume
06/22/2018 26.16  26.16  26.16  26.16  1,756 
06/21/2018 26.02  26.03  25.96  25.96  736.00 
06/20/2018 25.97  25.97  25.97  25.97  191.00 
06/19/2018 26.16  26.16  26.03  26.05  892.00 
06/18/2018 26.55  26.55  26.55  26.55  152.00 
06/15/2018 26.55  26.55  26.55  26.55  200.00 
06/13/2018 26.98  26.98  26.98  26.98  604.00 
06/12/2018 26.97  26.97  26.97  26.97  43.00 
06/11/2018 28.39  28.39  26.96  26.97  1,366 
06/08/2018 27.06  27.06  27.06  27.06  19.00 
06/07/2018 27.19  27.19  27.06  27.06  4,401 
06/06/2018 27.13  27.13  27.05  27.09  1,958 
06/05/2018 26.87  27.02  26.87  27.02  2,240 
06/04/2018 27.18  27.18  27.18  27.18  169.00 
06/01/2018 27.16  27.20  27.16  27.19  4,418 
05/31/2018 27.27  27.27  27.27  27.27  11,930 
05/30/2018 27.16  27.28  27.16  27.22  5,915 

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Net Asset

ETFS Bloomberg Net Asset Analysis
ETFS Bloomberg All CmdtyLngrDtdStrtK 1Fr is fourth largest ETF in net asset as compared to similar ETFs. Total Asset Under Management (AUM) of Commodities Broad Basket category is currently estimated at about 2.66 Billion. ETFS Bloomberg adds roughly 4.08 Million in net asset claiming only tiny portion of ETFs in Commodities Broad Basket category.
Check also Trending Equities. Please also try Watchlist Optimization module to optimize watchlists to build efficient portfolio or rebalance existing positions based on mean-variance optimization algorithm.