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 292
... input , output , and the model itself . In regression , or curve fitting , the point that is emphasized is that it is important to have a ... input . After considering the input transformations and normalization , we need 292 CHAPTER 17.
... input , output , and the model itself . In regression , or curve fitting , the point that is emphasized is that it is important to have a ... input . After considering the input transformations and normalization , we need 292 CHAPTER 17.
Página 295
... input and unimportant inputs will have associated weights close to zero . What was not considered is that as the number of inputs increases so too does the complexity of the network and the required train- ing time . This approach also ...
... input and unimportant inputs will have associated weights close to zero . What was not considered is that as the number of inputs increases so too does the complexity of the network and the required train- ing time . This approach also ...
Página 302
... Input A Input B Input C Input D Double Single Double Triple Output synthesized automatically from a flat file database where each column is an input or output parameter ( i.e. , a variable ) , and each row contains an example set of the ...
... Input A Input B Input C Input D Double Single Double Triple Output synthesized automatically from a flat file database where each column is an input or output parameter ( i.e. , a variable ) , and each row contains an example set of the ...
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