Computerized Trading: Maximizing Day Trading and Overnight ProfitsNew York Institute of Finance, 1999 - 415 páginas Discover the answers to all your computerized trading questions, from basic to advanced, in this ground-breaking new guide to successful day trading. Twenty top experts reveal their techniques and strategies for successful computerized trading in this practical guide. |
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Página 180
... optimization over one period followed by several tests of the indicator over dif- ferent periods . Whatever the approach , real - time results are likely to be less impressive than the results for the optimization period . The reality ...
... optimization over one period followed by several tests of the indicator over dif- ferent periods . Whatever the approach , real - time results are likely to be less impressive than the results for the optimization period . The reality ...
Página 238
Maximizing Day Trading and Overnight Profits Mark Jurik. FIGURE 14.12 OPTIMIZATION : INTERMARKET TWO . Optimization : Intermarket Two Optimize Len1 = 3 to 10. Subtract Commissions = $ 25 , Slippage = $ 50 9.00 Len1 Net Profit 3.00 ...
Maximizing Day Trading and Overnight Profits Mark Jurik. FIGURE 14.12 OPTIMIZATION : INTERMARKET TWO . Optimization : Intermarket Two Optimize Len1 = 3 to 10. Subtract Commissions = $ 25 , Slippage = $ 50 9.00 Len1 Net Profit 3.00 ...
Página 311
... optimizing for CPM . Here , data subset E was used for training , subset G was used to evaluate each network during optimization , and subset H was used for independent testing . In each case , optimization only slightly increases ...
... optimizing for CPM . Here , data subset E was used for training , subset G was used to evaluate each network during optimization , and subset H was used for independent testing . In each case , optimization only slightly increases ...
Contenido
Chapter | 3 |
Quantifying a Markets Upside and Downside Potential | 12 |
Exiting a Market | 76 |
Derechos de autor | |
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Términos y frases comunes
apply approach backtesting bars Bollinger Bands breakout buy signal calculated chart coefficient Coefficient of variation congestion contract data mining data vendors datafeed develop DJIA drawdown equity curve evaluation example Exchange exit Exponential Moving Average Figure formula future fuzzy logic genetic algorithms Index input intraday investors linear losing trades loss Louisiana Pacific method momentum money management moving average neural networks nodes nonlinear pricing nontrending number of trades Omega Research optimization options outlier output pattern percent period portfolio position predict problem programs ratio Relative Strength Index risk run-up sell signals simple moving average Statistical Network Steve Fossett stochastic stop T-bond Table Technical Analysis technical indicators techniques tick tion TradeStation trading performance trading strategy trading system trend trendline uptrend variables volatility volume winning trades zone