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• 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 .

• 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.
  • Support Vector Machine
    • Jupyter Notebook
  • Artificial Neural Network
    • Jupyter Notebook
• Achieved error rate as low as 0.64% using support vector regression in NIFTY index prediction.

The resources for the Project are here.