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 42
... fit a lognormal distribution , a standard distribution function form . Its volatility is derived , assuming this particular form , for subsequent evaluation . The analysis here is different . The PDF is a distribution over price , not ...
... fit a lognormal distribution , a standard distribution function form . Its volatility is derived , assuming this particular form , for subsequent evaluation . The analysis here is different . The PDF is a distribution over price , not ...
Página 271
... fit the data as well as possible , yet fail to effectively capture es- sential relationships . This is the motivation for transformations that enhance the in- formation in the data and can potentially reduce the number of inputs to a ...
... fit the data as well as possible , yet fail to effectively capture es- sential relationships . This is the motivation for transformations that enhance the in- formation in the data and can potentially reduce the number of inputs to a ...
Página 298
... fit . Once this linear fit is accomplished , the hidden nodes must model the resid- ual error . Since this is no longer a pure sine wave , it becomes a difficult task . In experiments using Cascade Correlation to model the Mackey ...
... fit . Once this linear fit is accomplished , the hidden nodes must model the resid- ual error . Since this is no longer a pure sine wave , it becomes a difficult task . In experiments using Cascade Correlation to model the Mackey ...
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