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 295
... nodes as in the input layer . This may or may not work . We must remember several points when selecting the topology . If there are too few hidden nodes , there may not be sufficient degrees of freedom for the network to model the ...
... nodes as in the input layer . This may or may not work . We must remember several points when selecting the topology . If there are too few hidden nodes , there may not be sufficient degrees of freedom for the network to model the ...
Página 296
... nodes in a hidden layer and then prune ( or eliminate ) nodes that have little influence as ontogenic neu- ral networks . We specifically exclude these methods from this class . The reason is that the large network must be trained to ...
... nodes in a hidden layer and then prune ( or eliminate ) nodes that have little influence as ontogenic neu- ral networks . We specifically exclude these methods from this class . The reason is that the large network must be trained to ...
Página 297
... nodes are shown on the left and the output nodes are on the upper right . The intersection of the lines represents the weights . After the initial training , if the problem is linearly separa- ble , the problem is solved . If there is ...
... nodes are shown on the left and the output nodes are on the upper right . The intersection of the lines represents the weights . After the initial training , if the problem is linearly separa- ble , the problem is solved . If there is ...
Contenido
Chapter | 3 |
Quantifying a Markets Upside and Downside Potential | 12 |
Exiting a Market | 76 |
Derechos de autor | |
Otras 16 secciones no mostradas
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