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 234
... develop , assuming an evenly balanced coin . If not enough data has been utilized to determine the coefficients of the polyno- mial equation , it is said that the model is underconstrained . In this case , there may be many combinations ...
... develop , assuming an evenly balanced coin . If not enough data has been utilized to determine the coefficients of the polyno- mial equation , it is said that the model is underconstrained . In this case , there may be many combinations ...
Página 291
... develop a model of the market ? Through the years , we have seen experts evolve who make some good predictions and make a lot of money . They develop a reputation , and often a newsletter providing advice . For awhile , they are quite ...
... develop a model of the market ? Through the years , we have seen experts evolve who make some good predictions and make a lot of money . They develop a reputation , and often a newsletter providing advice . For awhile , they are quite ...
Página 296
... develop the model . Considering all the possibilities , it would probably take forever to train and select the final network model , even on the most high - powered computers available . Further , the network needs to be retrained ...
... develop the model . Considering all the possibilities , it would probably take forever to train and select the final network model , even on the most high - powered computers available . Further , the network needs to be retrained ...
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