Swati Meena

Swati Meena

Data Scientist, Sprinklr

Daily stock price prediction of NIFTY index and various campanies listed in NSE.

Date:

More information here

  • Project description:

    • Processed past 10 years financial data from Yahoo Finance to extract technical indicator features such as RSI, MASI, and EMA. This project focuses on predicting stock price trend for a company in near future.

    • The prepared dataset looks like the figure shown below .

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  • Proposed Methods:
    • Linear Regression
    • Regression using SVM
    • ANN Regression

  • Created Indictor functions:

    • Technical indicators use statistical properties of the present and previous samples, so it can be considered as more related feature as it becomes easy to uptrend and downtrend.

    • Technical indicators used:

      • Relative Strength Index (RSI)
        • Measures speed and change of price movements.
        • RSI = 100 – [100 / ( 1 + (Average of Upward Price Change / Average of Downward Price Change))]
        • Generally, oscillates between 0 and 100, we consider overbrought above 70 and oversold below 30.
      • Money Flow Index (MFI)
        • Related to RSI but incorporates volume too where RSI considers prices only.
        • Typical Price = (High + Low + Close)/3
        • Money Flow (not the Money Flow Index) is calculated by multiplying the period’s Typical Price by the volume.
        • Money Flow = Typical Price * Volume
        • If today’s Typical Price is greater than yesterday’s typical Price,it is considered Positive Money Flow.
        • If today’s price is less, it is considered Negative Money Flow.
        • Money Ratio = Positive Money Flow / Negative Money Flow.
      • Exponential Moving average (EMI)
        • SMA = avg of price data, EMA = more weight to data which is more current.
        • EMA is more sensitive to price movement and used to determine trend direction
        • EMA = (K x (C - P)) + P
          where, C = Current Price
          P = Previous periods EMA (A SMA is used for the first periods calculations)
          K = Exponential smoothing constant.
      • Stochastic Oscillator (SO)
        • Shows the location of the close relative to high-low range over a set number of periods.
        • The default setting is 14 periods, which can be days,weeks,months or an intraday timeframe.
        • K=100[(C-L5close)➗(H5-L5)]
          C=the most recent closing price
          L5=the low of the five previous trading sessions
          H5=the highest price traded during the same five-day pe
      • Jupyter Notebook for Technical indicators calculations
  • Evaluated extensive comparisons among the metrics MSE, MAE, and percentage error.
  • Implemented different models such as linear regression, support vector regression and ANN.
  • Achieved error rate as low as 0.64% using support vector regression in NIFTY index prediction.

The resources for the Project are here.