The BIG Picture
- A unified perspective, showing how and why Jurik's modules
work well as building blocks for reliable low-lag indicators. Includes graphics. Author: Mark Jurik
Why Use JMA ?
- Outlines the four basic benchmarks for judging the quality
of moving averages with regard to financial trading. Compares JMA to classical and modern filter designs. Includes
graphics. Author: Mark Jurik
Evolution of Moving Averages
- Summarizes the recent evolution of moving average filter
design. Compares popular versions to a set of ideal performance features. Regarding how well filters process noisy time
series data with price-gaps, report shows that the latest designs are getting very close to theoretical performance
limits. Includes graphics. Author: Mark Jurik
Relating Neural Networks to Statistical Methods
- Summarizes the relationship between neural nets and modern
statistical methods. No mathematics. Author's conclusion is that "most neural networks that can learn to
generalize effectively from noisy data are similar or identical to statistical methods." Also lists neural net
models that have no close relatives in the existing statistical literature. Appended to this document is a comparison
between verbal jargon used by neural netters and statisticians. Author: Warren Sarle
Neural Networks for Trading the Markets: Primer
- A brief introduction to the use of neural networks suitable
for futures forecasting. Author: Don W. Fitzpatrick
Neural Networks for Trading the Markets: Case Study #1
TITLE: Neural Nets for Personal Investing
- This version, submitted to us by the author, is an
adaptation of his original article submitted to HEURISTICS: The Journal of Intelligent Technologies, to be published in
their special issue: Neural Networks for Financial Systems, v9, #1. Reviews the development and results of a neural-net
based trading system. Author: William Arnold
Neural Networks for Trading the Markets: Case Study
#2
TITLE: Financial Time Series Forecasting by Neural Networks
- Compares two different neural network training algorithms
used to model the time series of companies on the Shanghai Stock Exchange. Shows that the Conjugate Gradient Descent
algorithm is better than classic Gradient Descent. Authors: CHAN Man-Chung, WONG Chi-Cheong, LAM Chi-Chung -- (Hong
Kong Polytechnic University)
Overview of BackPercolation
- A non-mathematical overview of the philosophy behind the
design of the BackPercolation method of training Perceptron-based neural nets. This is the algorithm used in Braincel,
the MS Excel add-in product that builds neural nets. Includes graphics, especially very pretty weight training
trajectories. Author: Mark Jurik
Some Programming Issues in TradeStation
EasyLanguage
- This document illustrates how TradeStation may produce
counter-intuitive results when calling Easy Language functions. Alternative code that avoids the problem is provided
and each case clearly explains why one method works and the other does not. Lastly, examples are provided showing how
to avoid these situations when using studies from Jurik Research. -- Author: Mark Jurik
Fixing Problems in TradeStation 2000i
- A collection of remedies for fixing serious problems that
occur in TradeStation 2000i. Author: Jimmy Snowden
Series/Simple Functions in Easy Language
- Explains the fundamental difference between two types of
Easy Language functions in TradeStation. Charts included. Author: Mark Jurik
Optimal Forecast Horizon
- Leading indicators require data with low noise and low lag,
because that combination yields the widest window of time in which a forecast can be accurate. This paper briefly
touches on chaos theory to present the notion of any time series having an "optimal" forecast horizon.
Author: Mark Jurik
Classification Tree of Modeling Techniques
- This one page diagram shows all the modeling methods
arranged in a hierarchical tree, where results from one method feed into other methods. Great for getting the big
picture on modeling methods and how they relate. Author: Unknown
Neural Networks: Myths and Reality (web link)
- So what is neural network technology, what should and what
shouldn't a trader expect from it if he selects to use it to achieve his trading goals?
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