Project 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.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- 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
Projects steps
Step 1: Introduction
Step 2: Data Wrangling and Exploration
Step 3: Data Preprocessing
Step 4: Build and Train a Basic RNN Model
Step 5: Build and Train an LSTM Model
Step 6: Add a Convolutional Layer
Step 7: Optimize the Model
Step 8: Evaluate Model Performance
Step 9: Next Steps
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