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 271
... find that handful of candidate inputs with con- sistent predictive power . Networks with more than 5 or 10 inputs almost always wind up modeling spurious correlations in the noise . Another issue faced by anyone using neural networks is ...
... find that handful of candidate inputs with con- sistent predictive power . Networks with more than 5 or 10 inputs almost always wind up modeling spurious correlations in the noise . Another issue faced by anyone using neural networks is ...
Página 272
... find solution 2 ( c ) most appeal- ing . It is the only solution that preserves a sense of “ rationality . ” It is the only one of the three that meets the smoothness criterion : if a point lies between two others , its value should be ...
... find solution 2 ( c ) most appeal- ing . It is the only solution that preserves a sense of “ rationality . ” It is the only one of the three that meets the smoothness criterion : if a point lies between two others , its value should be ...
Página 292
... find hundreds upon hundreds of such indicators and can then use them to generate a model . Our goal is to find a way to predict the future so that we can use this prediction to make money . We have been discussing this approach with the ...
... find hundreds upon hundreds of such indicators and can then use them to generate a model . Our goal is to find a way to predict the future so that we can use this prediction to make money . We have been discussing this approach with the ...
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