A Gentle Guide to Automated Trading Strategies using Machine Learning

Automated trading strategies have become increasingly popular. These strategies use machine learning algorithms to analyze market data and make trading decisions without human intervention. In this tutorial, we will explore how to build an automated trading strategy using machine learning techniques in Python.

Photo by Andrew Neel on Unsplash

To begin, we will need historical financial data to train our machine learning model. We can download this data directly using the yfinance library, which provides access to a wide range of financial data for real assets. Let's start by installing the yfinance library using the following command:

pip install yfinance

Once the library is installed, we can import it into our Python script along with other necessary libraries:

import yfinance as yf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Now, let’s download the historical data for a few financial assets such as JPM, GS, MS, BLK and C. We will download data until the end of November 2023 to have a sufficient period for analysis. We can use the yf.download() function to download the data:

# Define the assets
assets = ['JPM', 'GS', 'MS', 'BLK', 'C']
start_date = '2018-01-01'
end_date = '2023-11-30'…