Predicting Stock Prices With Deep Neural Networks

So in this project, I will walk you through the end to end data science lifecycle of developing a predictive model for stock price movements with Alpha Vantage APIs, and a very powerful machine learning algorithm called LSTM.

Let’s start coding

#@title Load Python libraries

! pip install alpha_vantage -q

# pip install numpy
import numpy as np

# pip install torch
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

# pip install matplotlib
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure

# pip install alpha_vantage
from alpha_vantage.timeseries import TimeSeries 

print("All libraries loaded")

This python program focuses on importing necessary libraries and modules for our project. At the beginning of the code, it tries to install the alpha_vantage package using the pip package manager. This package is going to provide a way to access financial data via the API.

After that the subsequent lines import various scientific computing and machine leanring libraries.

1. numpy is imported for numerical operations on array, this is a foundational in processing and manipulating data.
2. pytorch library is imported with it’s core modules torch, torch.nn, torch.nn.functional and torch.optim. These will be used for building and training neural networks. Utilities for dealing…