Time Series Forecasting Pytorch

Abstract

1. Introduction

Since the financial market is naturally comprised of historical sequences of equity prices, more and more quantitative researchers and finance professionals are using LTSM to model and predict market price movements. In this project, we will go through the end-to-end machine learning workflow of developing an LTSM model to predict stock market prices using PyTorch and Alpha Vantage APIs.

By the end of this project, you will have a fully functional LSTM model that predicts future stock prices based on historical price movements, all in a single Python file. This tutorial has been written in a way such that all the essential code snippets have been embedded inline. You should be able to develop, train, and test your machine learning model without referring to other external pages or documents.

2. Objective

Acquiring data from Alpha Vantage and predicting stock prices with PyTorch's LSTM

This project demonstrates how Jupyter Notebook, Machine Learning, Python can be applied to a real-world Data Science problem.

4. Technology Stack

• NumPy - pip install numpy
• PyTorch - pip install torch
• Matplotlib - pip install matplotlib
• alpha_vantage - pip install alpha_vantage

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/jinglescode/time-series-forecasting-pytorch.git
cd time-series-forecasting-pytorch

1. NumPy - pip install numpy
2. PyTorch - pip install torch
3. Matplotlib - pip install matplotlib
4. alpha_vantage - pip install alpha_vantage

import numpy as np

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset
from torch.utils.data import DataLoader

import matplotlib.pyplot as plt
from matplotlib.pyplot import figure

from alpha_vantage.timeseries import TimeSeries

print("All libraries loaded")

config = {
    "alpha_vantage": {
        "key": "YOUR_API_KEY", # Claim your free API key here: https://www.alphavantage.co/support/#api-key
        "symbol": "IBM",
        "outputsize": "full",
        "key_adjusted_close": "5. adjusted close",
    },
    "data": {
        "window_size": 20,
        "train_split_size": 0.80,
    },
    "plots": {
        "xticks_interval": 90, # show a date every 90 days
        "color_actual": "#001f3f",
        "color_train": "#3D9970",
        "color_val": "#0074D9",
        "color_pred_train": "#3D9970",
        "color_pred_val": "#0074D9",
        "color_pred_test": "#FF4136",
    },
    "model": {
        "input_size": 1, # since we are only using 1 feature, close price
        "num_lstm_layers": 2,
        "lstm_size": 32,
        "dropout": 0.2,
    },
    "training": {
        "device": "cpu", # "cuda" or "cpu"
        "batch_size": 64,
        "num_epoch": 100,
        "learning_rate": 0.01,
        "scheduler_step_size": 40,
    }
}

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by jinglescode and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.