David Aronson is a pioneer in machine learning and nonlinear trading system development and signal boosting/filtering.
He is author of “Evidence Based Technical Analysis” and his most recent book "Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments" is an in-depth look at developing predictive-model-based trading systems.
He was also an adjunct professor of finance, regularly teaching MBA and financial engineering students a graduate-level course in technical analysis, data mining and predictive analytics.
In this episode David shares research into the effectiveness of indicators to identify Bull and Bear markets; he’s tested a large number of indicators and combinations with some interesting results! We also discuss issues with data mining, conditions where traditional methods of measuring data mining levels can be problematic and then finish up with the future state of Technical Analysis.
Topics discussed
- What the popular Golden and Death Cross can tell us about the probability of a Bull or Bear market
- Using the RSI indicator to determine market state
- Methods to reduce the lag the 50/200 Moving Average crossover experiences
- Using ADX and Price Variance to identify Bull and Bear markets
- Creating indicators based on the value of other indicators
- Modifying the McClelland Summation Index indicator to identify market states
- How datamining increases the chance of good luck in the results
- Why the White's Reality Check and Monte Carlo Permutation methods breakdown using certain data-mining approaches
- How the role of Technical Analysis could change over the next 10 years
- New developments in Machine Learning which may see the end of the role of technicians
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