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are available On this interval, the position error covariance due to all three sources increases with time Within 10 seconds of losing the position measurement, the INS errors (ie, Q) dominate the error growth Therefore, if better position accuracy is required for t (110, 200], the system performance can be improved considerably by investing in better inertial sensors If the series of four simulations is repeated assuming that the nominal value of the diagonal values of Q are [00, 50 10 6 , 20 10 9 ], then the error budget simulations change to those shown in Figure 65 The design values for Q were selected to make the INS and position sensor portions of the error covariance at t = 200s be approximately equal In an actual analysis, each component of Q could be analyzed separately to determine which characteristic of the original system was driving the original performance Note that the Kalman gains used in these simulations to produce Figure 65 are distinct from those used to produce Figure 64 The change in the system design results in di erent system characteristics in the rst simulation of each gure, which yields the gains for the remaining simulations
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Backtesting Ideas With the help of a trading friend, we conducted an independent backtest of the five technical indicators in 3 using Fidelity Investment s Wealth Lab Pro Why should you backtest First, backtesting allows you to test your trading ideas and indicators, playing what if games with the results, and changing time periods and rules Backtesting is a powerful and flexible tool that can also be a fund saver For example, several years ago, an investment bank backtested Bernard Madoff s Ponzi-scheme trading strategy, but it couldn t match his results This blaring red flag convinced the bank to invest its money elsewhere, saving its client s portfolio Obviously, backtesting is not 100 percent foolproof Sometimes the profits seem almost too good to be true, but when you try to duplicate the results in real life, you end up making far less, and perhaps even lose money For example, backtesting doesn t include false or misleading signals, which adversely affects results And backtesting assumes no human interference, which is probably not realistic for most retail traders Also, just because these indicators worked in the past doesn t guarantee they will work in the future conditions may change I had my trader friend choose a really wild three-year period, when the market crashed one year, but came roaring back the following year (2007 to 2010) For our simulation, we included all of the stocks in the S&P 500, assuming $5,000 to buy or sell each stock We also compared the indicators with a buy-and-hold strategy, buying every stock in the S&P 500 one time ($5,000 x 500), and holding Because of time limitations, my trader friend couldn t do the kind of precise, detailed testing that professional traders and statisticians require This was simply a first-run test to give us an idea of how indicators performed during a random three-year period Based on the results of the backtest, I learned that no one should favor one indicator over another If anything, these tests show the power and potential of backtesting.
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