This repository contains code and resources for using deep learning techniques to analyze financial data. Specifically, we use autoencoders to predict and perform analysis on stock returns of S&P 500.
We use the S&P 500 dataset from Yahoo Finance. The dataset contains the historical prices of S&P 500 from 1950 to present. We use the adjusted closing prices to calculate daily returns.
We use an autoencoder to predict the stock returns of S&P 500. The autoencoder is a type of neural network that is used for unsupervised learning. It consists of an encoder and a decoder. The encoder maps the input data to a lower dimensional representation, while the decoder maps the lower dimensional representation back to the original input data. By training the autoencoder to minimize the reconstruction error, we can learn a compressed representation of the input data that captures the most important features.
The notebook in this repository demonstrates how to use autoencoders to predict and analyze stock returns of S&P 500. The notebook includes the following sections:
- Loading and preprocessing the data
- Training the autoencoder
- Evaluating the performance of the autoencoder
- Using the autoencoder for prediction
- Analyzing the predicted returns
- The notebook also includes visualizations of the input data, the compressed representation learned by the autoencoder, and the predicted returns.
You can open this notebook on google colab or use it in your local environment.
The code in this repository requires the following packages:
- NumPy
- Pandas
- TensorFlow
- Matplotlib
- Seaborn
- Sklearn
These packages can be installed using pip:
pip install numpy pandas tensorflow matplotlib seaborn sklearn
This repository provides a starting point for using deep learning techniques to analyze financial data. The autoencoder is just one of many possible models that can be used for this purpose. We hope that this code and resources will be helpful to others who are interested in this field.