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HMM-Stock-Forecast

Stock forecasting based on (gaussian) Hidden Markov Model

Model

Model implementation is based on these two articles:

  • [1] Nguyen N. Hidden Markov Model for Stock Trading. International Journal of Financial Studies. 2018; https://doi.org/10.3390/ijfs6020036.
  • [2] M. R. Hassan and B. Nath, Stock market forecasting using hidden Markov model: a new approach 5th International Conference on Intelligent Systems Design and Applications (ISDA’05), 2005, pp. 192-196, doi: 10.1109/ISDA.2005.85.

Arguments

Application accept these arguments:

  • -t stock ticker (e.g. AAPL)
  • -f path to csv soubor (4 columns: Open, Low, High, Close)
  • -s data start date
  • -e data end date
  • -w window size (see [1] for more information about window)
  • -m type of hmm implementation:

Run

  • In first step program find number of optimal hidden states (n) based on:

    • AIC (Akaike information criterion)
    • BIC (Bayesian information criterion)
    • HQC (Hannan-Quinn information criterion)
    • CAIC (Bozdogan Consisten Akaike information criterion)
  • Then prediction is estimated for w (window) business days. Everytime historical data 0-x are used to predict closing price at day x+1

Run

Results

  • Ticker: SPY

  • Start date: 2019-01-01

  • End date: 2022-01-01

  • Window size: 100

  • MAPE: 0.861 %

  • r2: 82 %

Result

Dependencies

  • numpy
  • matplotlib
  • pandas
  • sklearn

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Stock forecasting based on (gaussian) Hidden Markov Model

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