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Technical Indicator Engineering for Machine Learning: Volume I: Detecting Major Stock Market Bottoms with Moving-Average Diffusion Indicators By David Aronson,

  • Title: Technical Indicator Engineering for Machine Learning: Volume I: Detecting Major Stock Market Bottoms with Moving-Average Diffusion Indicators
  • Author: David Aronson
  • ISBN: -
  • Page: 173
  • Format: Kindle Edition
  • This work describes the NV PA PV Sequence, a method for detecting major stock market bottoms using moving average diffusion indicators It introduces a filtering and weighting method to enhance the signal to noise ratio of oscillators We show the performance of buy signals back to 1962 using the SP 500 Index We show how to create an ensemble of alarm indicators deriThis work describes the NV PA PV Sequence, a method for detecting major stock market bottoms using moving average diffusion indicators It introduces a filtering and weighting method to enhance the signal to noise ratio of oscillators We show the performance of buy signals back to 1962 using the SP 500 Index We show how to create an ensemble of alarm indicators derived from moving average diffusion indicators These indicators should be useful to both practitioners of machine learning and discretionary traders.
    Technical Indicator Engineering for Machine Learning Volume I Detecting Major Stock Market Bottoms with Moving Average Diffusion Indicators This work describes the NV PA PV Sequence a method for detecting major stock market bottoms using moving average diffusion indicators It introduces a filtering and weighting method to enhance the sig

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