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The look-back parameter was stepped through the same range as in previous tests. All look-backs produced positive returns. The best in-sample results were with a look-back of 85, which yielded a return of 36.2% annually; the probability is less than 2% (33% when corrected for multiple tests) that this was due to chance. In-sample, long positions again yielded greater profits than short positions. Surprisingly, out-of-sample, the short side produced a small profit, while the long side resulted in a loss! With a return of -2.3%, out-of-sample performance was poor, but not as bad as for many other systems tested. In-sample, there were 43% wins and the average trade produced an $1,558 profit; out-of-sample, 41% were winners and the average trade lost $912. The equity curve in Figure 5-l may seem to contradict the negative out-ofsample returns, but the trend in the out-of-sample data was up and on par with the trend in the latter half of the in-sample period. The apparent contradiction results from a bump in the equity curve at the start of the out-of-sample period. Nevertheless, the HHLL breakout with entry on a limit (together with the standard exit) is not a system that one would want to trade after June 1988: The return was too low relative to the risk represented by the fluctuation of equity above and below the least-squares polynomial trendline (also shown in Figure 5-l). All currencies and oils had positive in-sample results. Strong out-of-sample returns were seen for the Swiss Franc, Canadian Dollar, and Deutschemark, as well as for Heating Oil and Light Crude; small losses were observed for the British Pound, Eurodollar, and Unleaded Gasoline. Coffee was profitable in both samples. java code 128 library Code 128 for Java - KeepAutomation.com
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add barcode rdlc report Barcode for Java barcode generator , users can create advanced Code 128 and many other 1D and 2D symbologies in Java class. Rich parameters of barcodes ... is decided on the entry bar by the stop itself, the highest high and lowest low are calculated for bars up to and including the current bar. The relative position of the close, with respect to the breakout thresholds, is used to avoid posting multiple intrabar orders. If the close is nearer to the upper threshold, then the buy stop is posted; if the close is closer to the lower threshold, the sell stop is posted. Both orders are never posted together. By implementing the HHLL breakout with stop orders, a faster response to breakout conditions is achieved; there is no need to wait for the next bar after a signal is given to enter the market. Entry, therefore, occurs earlier in the course of any market movement and no move will ever be missed, as might happen with a limit while waiting for a pull-back that never takes place. However, the reduced lag or response time may come at a cost: entry at a less favorable price. There is greater potential for slippage, when buying into momentum on a stop, and entry takes place at the breakout price, rather than at a better price on a retracement. The look-back parameter was optimized as usual. The best in-sample lookback was 95, with look-backs of 65 through 100 being profitable. Annual returns were 8.7%. Although the results were better than those for Test 4, they were not as good as for Test 5. Faster response bought some advantage, but not as much as java code 128 barcode generator JVM Exit Code 128 - Java Service Wrapper
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c# generate 2d barcode Java Barcode generates barcode Code - 128 images in Java applications. ... addChecksum : Apply Checksum digit at the end of some linear barcode value. 25 34 waiting for a retracement where entry can occur at a more favorable price The percentage of winning trades was 41% and the average trade yielded a $430 profit Out-of-sample, the picture was much worse, as might be expected given the low returns and poor statistics on the in-sample data This model lost an average of $798 per trade About 37% of the trades were winners The model made most of its profits before June 1988, and lost money after January 1992 All currencies, except Eurodollars, had positive returns in the optimization period In the verification period, the Japanese Yen, Canadian Dollar, and Deutschemark, had solid returns in the 30% to 50% range The model also generated moderate retmns on the oils Coffee traded well, with a 212% return in-sample and a 618% return out-of-sample Random Lumber also had positive returns in both samples. 35 44 VOLATILITY BREAKOUT ENTRIES The next three tests evaluate volatility breakout entry models, in which the trader buys when prices rise above an upper volatility band, and sells short when they fall below a lower volatility band Volatility bands are bands placed above and below current prices When volatility increases, the bands expand; when it decreases, they contract The balance point around which the bands are drawn may be the most recent closing price, a moving average, or some other measure of current value Test 7: Volatdity Breakout wdth Entry at Next Open This model buys at tomorrow s open when today s close pushes above the upper volatility band, and sells short at the next open when the close drops below the lower volatility band. 45 54 The volatility bands are determined by adding to (for the upper band) and subtracting from (for the lower band) the estimate of current value a multiple (bw) of the at&n-bar average true range (a measure of volatility) The estimate of value is a m&n-bar exponential moving average of the closing price If the moving average length (m&n) is one, this estimate degrades to the closing price on the breakout or signal bar Because the volatility breakout model has three parameters, genetic optimization was chosen for the current test Using genetic optimization, the bandwidth parameter (bw) was evaluated over a range of 15 to 45, with a grid size (increment) of 0. 55 64 java code 128 Jenkins returned status code 128 with github - Stack Overflow
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