Time-Series Forecasting on the S&P 500

  • Guided Project
  • 2 hours
  • Intermediate
  • Python

Practice building an LSTM model with a convolutional layer to forecast real-world S&P 500 index prices using Python.

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Overview

In this project, you'll assume the role of a trader on the S&P 500 futures desk aiming to forecast the index's movement to enable profitable trading. Using a real-world S&P 500 dataset from Yahoo Finance, you'll build an LSTM model with a convolutional layer in Python. You'll learn to prepare time series data, create time windowing features, and reshape data for modeling. The project covers constructing, training, and optimizing a forecasting model, as well as evaluating its performance using metrics like R-squared. You'll also visualize your predictions against the true index values. Objective: Build an LSTM forecasting model for S&P 500 index prices to inform profitable trading decisions.

What You'll Learn

  • ✓ Work with a real-world dataset for the S&P 500 index
  • ✓ Build an LSTM model with a convolutional layer
  • ✓ Train and evaluate the model for stock price prediction

Before You Start

  • ✓ Building and training basic RNN models for regression
  • ✓ Understanding advanced RNN architectures like LSTM
  • ✓ Combining convolutional layers with RNNs
  • ✓ Forecasting time series data using RNNs

Project Steps

9 steps

  1. 1 Introduction
  2. 2 Data Wrangling and Exploration
  3. 3 Data Preprocessing
  4. 4 Build and Train a Basic RNN Model
  5. 5 Build and Train an LSTM Model
  6. 6 Add a Convolutional Layer
  7. 7 Optimize the Model
  8. 8 Evaluate Model Performance
  9. 9 Next Steps
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