Computerized Trading: Maximizing Day Trading and Overnight ProfitsComplete with explanatory charts, diagrams, and checklists, a detailed discussion of the use of computerized trading systems draws on the expertise of twenty expert traders, and gives step-by-step advice on strategies and reliable data to both beginning and experienced investors. |
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Page 287
A lesson to be learned from this is that you cannot rely on “theoretical” arguments
to predict the best way to preprocess data. The only effective method is to take
the time to compare a new approach to other proven methods on real data sets.
A lesson to be learned from this is that you cannot rely on “theoretical” arguments
to predict the best way to preprocess data. The only effective method is to take
the time to compare a new approach to other proven methods on real data sets.
Page 292
Our goal is to find a way to predict the future so that we can use this prediction to
make money. We have been discussing this approach with the idea that we will
create a model of some process. Figure 17.1 illustrates what it is that we are ...
Our goal is to find a way to predict the future so that we can use this prediction to
make money. We have been discussing this approach with the idea that we will
create a model of some process. Figure 17.1 illustrates what it is that we are ...
Page 294
MODEL OUTPUT: WHAT PARAMETERS SHOULD WE PREDICT? Another
question that arises when building a model is, “What is the output?” The answer
depends on what we are trying to accomplish with the model. Sometimes the
answer ...
MODEL OUTPUT: WHAT PARAMETERS SHOULD WE PREDICT? Another
question that arises when building a model is, “What is the output?” The answer
depends on what we are trying to accomplish with the model. Sometimes the
answer ...
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Contents
PART l | 1 |
Popular Trading Indicators | 13 |
Combining Trend Analysis with Indicator Readings | 30 |
Copyright | |
22 other sections not shown
Common terms and phrases
analysis approach backtesting bars benefit Bollinger Bands breakout buy signal chart close congestion contract data mining data vendors datafeed defined develop diversification drawdown equity curve evaluation example Exchange exit Exponential Moving Average Figure filter financial find first fit five formula frame futures futures contract fuzzy logic genetic algorithms Index input Intermarket intraday investors linear losing trades loss method momentum money management moving average neural networks nodes nontrending Omega Research optimization options oscillator out-of-sample pattern percent period portfolio position predict profit profitable ratio real-time Relative Strength Index risk sell signals significant simple moving average specific Statistical Network Steve Fossett stochastic stop T-bond Table technical analysis technical indicators techniques tick tion TradeStation trading strategy trading system trend trendline uptrend variables volatility volume winning trades zone
Bibliographic information
| Title | Computerized Trading: Maximizing Day Trading and Overnight Profits New York Institute of Finance Series |
| Editor | Mark Jurik |
| Edition | illustrated |
| Publisher | New York Institute of Finance, 1999 |
| Original from | the University of Michigan |
| Digitized | 2 Sep 2011 |
| ISBN | 0735200777, 9780735200777 |
| Length | 415 pages |
| Export Citation | BiBTeX EndNote RefMan |