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 319
... pattern is not perfectly smooth and there may be jiggles . When we tried to generate such rules to represent what we were subjectively identifying as a pattern , the rules caused a lot of wrong patterns to be marked , which we would not ...
... pattern is not perfectly smooth and there may be jiggles . When we tried to generate such rules to represent what we were subjectively identifying as a pattern , the rules caused a lot of wrong patterns to be marked , which we would not ...
Página 321
... patterns . 2. Mark those patterns on a long chart to find as many instances of each pattern as possible . 3. Train a number of neural networks , each of which will be tasked to recognize one of the patterns identified . 4. Evaluate the ...
... patterns . 2. Mark those patterns on a long chart to find as many instances of each pattern as possible . 3. Train a number of neural networks , each of which will be tasked to recognize one of the patterns identified . 4. Evaluate the ...
Página 323
... pattern , which yielded six patterns in total . A " pull - back - in - trend " is a situation in which a trend in ... pattern , there were 194 marks ; 140 instances were identified for the shorts . The " multiple bottoms and tops ...
... pattern , which yielded six patterns in total . A " pull - back - in - trend " is a situation in which a trend in ... pattern , there were 194 marks ; 140 instances were identified for the shorts . The " multiple bottoms and tops ...
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