print barcode label using vb.net PART II The Study of Enwics in Software

Printer UPC Code in Software PART II The Study of Enwics

PART II The Study of Enwics
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Finally, models that appear to work, producing positive returns in both samples! With entry at open, trades were profitable across samples. In-sample, the
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average trade made $1,393; 45% of the trades were winners; and there was only an 8.7% (uncorrected; 99.9% corrected) probability that the results were due to chance; both longs and shorts were profitable. Despite poor statistical significance in-sample, there was profitability out-of-sample: The model pulled $140 per trade (after commissions and slippage), with 38% winning trades; only shorts were profitable. The limit performed slightly worse in-sample, but much better out-of-sample. Figure 7-2 depicts the equity curve for entry on a limit. In-sample, the average profit per trade was $1,250 with 47% winning trades (the highest so far); longs and shorts were profitable; and the probability was 13.1% (uncorrected; 99.9% corrected) that the results were due to chance. Out-of-sample, the model made $985 per trade; won 44% of the time: was profitable in long and short positions; and was only 27.7% likely due to chance. It-sample, the stop had the greatest dollars-per-trade return, but the smallest number of trades; only the shorts were profitable. Out-of-sample, the system lost $589 per trade; only short positions were profitable. Regardless of the order used, this model had relatively few trades. The market-by-market analysis (see Tables 7-1 and 7-2) confirms the potential of these models to make money. The largest number of markets were profitable in-sample. Across samples, all three orders yielded profits for Light Crude and Coffee; many other markets had profitability that held up for two of the orders, but not for the third, e.g., Heating Oil, Live Cattle, Soybeans, Soybean Meal, and Lumber. SUMMARY ANALYSES
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Table 7-3 provides the results broken down by the model, order, and sample. The last two colunms on the right, and the last two rows on the bottom, are averages. The numbers at the right are averaged over order type. The numbers at the bottom have been averaged over all models. The best results across samples were for the MACD divergence model. The limit produced the best combined results in both samples: a 12.5% return (annualized) and $1,250 per trade in-sample, and a 19.5% return (annualized) and $985 per trade out-of-sample. This model is dramatically different from all others. When considered across all order types and averaged, the overbought/oversold models using the RSI were worst (especially in terms of dollars-per-trade). Also among the worst were the Stochastic divergence, Stochastic overboughtioversold, and RSI divergence models. When all models were averaged and broken down by order type, the limit order was best and the entry at open worst.
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F I G U R E
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7 - 2
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Equity for MACD Divergence Model with Entry on a Limit
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CONCLUSION With breakouts and moving averages, a limit usually was the best overall performer, perhaps because it minimizes transaction costs; a stop sometimes improved performance, depending on its interaction with the entry model. For some oscillator entries, including the profitable MACD divergence, a limit was again best. Other oscillator models preferred the stop, perhaps due to its trend filtering quality. There was an interaction between specific oscillators and models. The divergence model, for example, worked well with the MACD, but terribly with the RSI. Such results demonstrate that, when studying a model with an indicator component that may be varied without altering the model s essence, it is important to test all model-indicator combinations as certain ones may perform dramatically better. WHAT HAVE WE LEARNED
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In general, for best results, use a limit order. However, test a stop as it sometimes works better.
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Performance of and Sample
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Oscillator
Models Broken Down by Model, Order,
. When testing a model that can be used with multiple indicators, try several to see whether any yield distinct improvements. n Attempt to algorithmically implement concepts normally employed in a highly variable and subjective manner. Sometimes this can be exceptionally difficult, requiring fuzzy logic or neural network technology, along with other specialized methods.
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