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 283
... SELECTING DATA FOR TRAINING select training data depends on what kind of network your are planning If you have carefully ... selection and train the network . 1 0.9 0.8 0.7 + 0.6 + 0.5 0.4 0.3 Making Profits with Data Preprocessing 283.
... SELECTING DATA FOR TRAINING select training data depends on what kind of network your are planning If you have carefully ... selection and train the network . 1 0.9 0.8 0.7 + 0.6 + 0.5 0.4 0.3 Making Profits with Data Preprocessing 283.
Página 286
... selection have been developed . Most work well on clean data where relationships are strong . I have found only one that works well on noisy data of the type found in financial modeling problems : Ge- netic Variable Selection . As part ...
... selection have been developed . Most work well on clean data where relationships are strong . I have found only one that works well on noisy data of the type found in financial modeling problems : Ge- netic Variable Selection . As part ...
Página 287
... selection is the best overall approach that was tested . It was im- plemented as a binary chromosome in which each ... selection approach was able to identify these subsets . When an application starts with 50 or more candidate variables ...
... selection is the best overall approach that was tested . It was im- plemented as a binary chromosome in which each ... selection approach was able to identify these subsets . When an application starts with 50 or more candidate variables ...
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