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 46
... Table 4.1 . These are loaded onto a worksheet for viewing . Next , the T matrix is computed following methods and for- mulas previously outlined , and the columns are summed and normalized to give the CPDF and PDF . Table 4.2 gives the ...
... Table 4.1 . These are loaded onto a worksheet for viewing . Next , the T matrix is computed following methods and for- mulas previously outlined , and the columns are summed and normalized to give the CPDF and PDF . Table 4.2 gives the ...
Página 148
... Table 9.5 measure the volatility of the average trade . The greater the volatility the less stable the average ... Table 9.6 ) . These low val- ues reflect a system that is extremely stable in relation to its average . This high degree ...
... Table 9.5 measure the volatility of the average trade . The greater the volatility the less stable the average ... Table 9.6 ) . These low val- ues reflect a system that is extremely stable in relation to its average . This high degree ...
Página 159
Maximizing Day Trading and Overnight Profits Mark Jurik. TABLE 9.12 PERCENTAGE OF TIME IN THE MARKET Net profit Percent - in - the - market System A $ 100,000 System B $ 100,000 60 % 100 % Table 9.12 shows a simple side - by - side ...
Maximizing Day Trading and Overnight Profits Mark Jurik. TABLE 9.12 PERCENTAGE OF TIME IN THE MARKET Net profit Percent - in - the - market System A $ 100,000 System B $ 100,000 60 % 100 % Table 9.12 shows a simple side - by - side ...
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