iPath US Backtesting

STPP -- USA Etf  

USD 30.63  0.0034  0.0111%

With this equity back-testing module your can estimate the performance of a buy and hold strategy of iPath US Treasury Steepener ETN and determine expected loss or profit from investing in iPath US over given investment horizon. Also please take a look at iPath US Hype Analysis, iPath US Correlation, Portfolio Optimization, iPath US Volatility as well as analyze iPath US Alpha and Beta and iPath US Performance.
 Time Horizon     30 Days    Login   to change
SymbolX
Backtest

iPath US 'What if' Analysis

May 20, 2018
0.00
No Change 0.00  0.0%
In 31 days
June 19, 2018
0.00
If you would invest  0.00  in iPath US on May 20, 2018 and sell it all today you would earn a total of 0.00 from holding iPath US Treasury Steepener ETN or generate 0.0% return on investment in iPath US over 30 days. iPath US is related to or competes with UBS ETRACS, VelocityShares 3x, ClaymoreDelta Global, ARK Web, Direxion Daily, UBS ETRACS, and Direxion Technolo. The investment seeks to provide investors with exposure to the Barclays US Treasury 2Y10Y Yield Curve Index

iPath US Upside/Downside Indicators

Information Ratio0.12
Maximum Drawdown4.83
Value At Risk1.92
Potential Upside0.8414
  

iPath US Market Premium Indicators

Risk Adjusted Performance0.01

iPath US Treasury Backtested Returns

Macroaxis considers iPath US to be not too risky. iPath US Treasury shows Sharpe Ratio of -0.4177 which attests that iPath US Treasury had -0.4177% of return per unit of risk over the last 1 month. Macroaxis philosophy towards determining risk of any etf is to look at both systematic and un-systematic factors of the business, including all available market data and technical indicators. iPath US Treasury exposes twenty-one different technical indicators which can help you to evaluate volatility that cannot be diversified away. Please be advised to check out iPath US Treasury Mean Deviation of 0.69 to validate risk estimate we provide. The entity maintains market beta of 0.0 which attests that the returns on MARKET and iPath US are completely uncorrelated. Even though it is essential to pay attention to iPath US Treasury historical price patterns, it is always good to be careful when utilizing equity current price history. Macroaxis philosophy towards determining future performance of any etf is to check both, its past performance charts as well as the business as a whole, including all available technical indicators. iPath US Treasury exposes twenty-one different technical indicators which can help you to evaluate its performance.
Advice Volatility Trend Exposure Correlations
15 days auto-correlation 0.57 

Modest predictability

iPath US Treasury Steepener ETN has modest predictability. Overlapping area represents the amount of predictability between iPath US time series from May 20, 2018 to June 4, 2018 and June 4, 2018 to June 19, 2018. The more autocorrelation exist between current time interval and its lagged values, the more accurately you can make projection about the future pattern of iPath US Treasury price movement. The serial correlation of 0.57 indicates that roughly 57.0% of current iPath US price fluctuation can be explain by its past prices.
Correlation Coefficient 0.57
Spearman Rank Test 0.61
Price Variance 0.13
Lagged Price Variance 0.04

iPath US Treasury lagged returns against current returns

 Current and Lagged Values 
      Timeline 

iPath US regressed lagged prices vs. current prices

 Current vs Lagged Prices 
      Timeline 

iPath US Lagged Returns

 Regressed Prices 
      Timeline 

Current Sentiment - STPP

iPath US Treasury Investor Sentiment
Most of Macroaxis users are at this time bullish on iPath US Treasury Steepener ETN. What is your take regarding investing in iPath US Treasury Steepener ETN? Are you bullish or bearish?
Bullish
Bearish
98% Bullish
2% Bearish
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Also please take a look at iPath US Hype Analysis, iPath US Correlation, Portfolio Optimization, iPath US Volatility as well as analyze iPath US Alpha and Beta and iPath US Performance. Please also try Watchlist Optimization module to optimize watchlists to build efficient portfolio or rebalance existing positions based on mean-variance optimization algorithm.