Practical Market Indicators for Algorithmic Stock Market Trading: Machine Learning Techniques and Grid Strategy

  • Ajithkumar Sreekumar
  • Prabhasa KalkurEmail author
  • Mohammed Moiz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 906)


In this paper, market indicators from three different approaches for algorithmic trading are analysed (moving average convergence divergence (MACD) crossovers, machine learning (ML) label-based indicators, and grid investing strategy). Market indicators are used by traders in the stock market, to define entry and exit points of a trade. These indicators are also useful to compare different trading strategies. We take a practical stand for the approaches mentioned above, where the same data feed from the exchange is preprocessed to remove redundant or anomalous content. Furthermore, use of correlation data between different stocks is analysed. (i) MACD crossovers are dealt in two dimensions of variability, the dimensions being frequency of trades and length of trading intervals. (ii) The outputs of different algorithms are passed through a voting classifier to get the best possible accuracy in the ML label-based approach. Precision/Recall analysis is done to qualify the algorithms for skewed data. (iii) Finally, a grid-based trading strategy is analysed. We conclude with a trading strategy, proposed using results of indicators based on the three approaches.


MACD Machine learning Grid trading Precision Recall Market indicators 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ajithkumar Sreekumar
    • 1
  • Prabhasa Kalkur
    • 2
    Email author
  • Mohammed Moiz
    • 1
  1. 1.Department of ECERVCEBengaluruIndia
  2. 2.IIScBengaluruIndia

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