02. Feature Engineering Techniques

PRDTM2-785 AI Trading C2 L3 Vid2 Feature Engineering Techniques

Guide to Feature Engineering in Financial Data

Explore essential techniques used in feature engineering for financial data, crucial for improving machine learning model performance. Here's a simplified walkthrough:

  • Regularly Update Knowledge: Techniques evolve; stay updated with the latest literature in your domain.

  • Data Pre-processing: Fundamental step involving cleaning and transforming data. Common methods include:

    • Normalization and Standardization: Adjust the data to fit a consistent scale.
    • Log Transformation: Mitigates skewness, critical for normal data input assumptions.

  • Differencing and Stationarity:

    • Differencing: Converts non-stationary data to stationary; removes seasonality by calculating period-to-period change.

  • Rate of Change (ROC):

    • Measures trends and signals trade actions, calculated using percent change.

  • Moving Average: Smooths out noise, helping identify trends over varying frequencies.

  • Z Scores and Percentiles:

    • Facilitate cross-comparison of stock performance and volatility.

  • Relative Strength Indicator (RSI):

    • Provides insights into momentum, indicating overbought or oversold conditions, enhancing predictions for future price shifts.

These methods equip learners with practical tools for enhancing their financial models.

Which feature engineering technique is primarily used to address skewness and outliers in financial data?
SOLUTION: Log Transformation

Which of the following techniques are commonly used in feature engineering for financial data? (Select all that apply)
SOLUTION:
  • Log transformation to reduce skewness
  • Rate of Change (RoC) to identify market trends
  • Binning to categorize continuous data
  • Differencing to assess data trends over time

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