05. Demo: Forward and Feature Returns

Part 2 - Forward and Feature Returns

Historical Returns Calculation

In this demo, a process to compute returns from historical stock prices is demonstrated, focusing on how this aids in analysis:

• Objective: Standardize price data into returns, making analyses independent of currency or price range.

• Function Implementation: A function is created to automate the conversion of price data into returns, which can be reused for various analyses.

• Procedure:

  1. Utilize the Pandas pct_change function to compute returns.
  2. Shift forward returns backward to align data for prediction modeling.
  3. Organize tickers in a stacked format using the unstack function for uniformity across the dataset.

• Outcome: Achieves a comprehensive returns dataset, whereby relationships between returns can be analyzed on a larger scale.

• Function Storage: Saves the computation function in a library for future applications.

Demo Discussion

Function for Forward and Feature Returns

Understanding and using a specific function is crucial for analysis in various contexts.

• Purpose: This function facilitates calculating forward and feature returns, key for analyzing data.
• Application: It will be consistently used in upcoming chapters for generating accurate predictions.
• Benefits:
  • Efficiently extracts insights from data.
  • Helps in examining historical trends.
  • Assists in forecasting future movements.
  • Evaluates trading strategies.
• Importance: The function is reliable and versatile, serving as a foundational tool that ensures robust and accurate analysis.

Leveraging this function is essential in building a strong analytical approach and making informed decisions based on data-driven insights.