01. Introduction to Exploratory Data Analysis

PRDTM2-785 AI Trading C2 L4 Vid1 Introduction

Understanding Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) is essential before developing machine learning models, especially in trading strategy development. This process helps in understanding and summarizing data attributes using visual tools.

Purpose of EDA:

  • Understand Data Structure:
    • Use histograms, bar charts, and scatter plots to reveal data patterns and trends.
  • Detect Anomalies & Patterns:
    • Identify misleading data points and significant patterns for better analysis.
  • Validate Assumptions:
    • Check if data meets model assumptions, like normal distribution.

Tools and Techniques:

  • Visualization:
    • Line plots for tracking price movements
    • Histograms for stock return distribution
  • Correlation Analysis:
    • Use correlation matrices to examine how variables like price and volume relate.
  • Technical Analysis:
    • Moving averages and scatter plots for trend and relationship insights.

EDA is instrumental in ensuring data quality and uncovering insights that inform robust trading models. It forms a solid foundation for future analysis and decision-making. Common packages used for EDA in Python include Matplotlib and Plotlib.

What is the primary purpose of exploratory data analysis (EDA) in the context of building machine-learning models for trading strategies?
SOLUTION: To summarize data attributes and visualize relationships in order to uncover insights that guide trading strategies.

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