Building an AI-based Forecasting Model for Trading

In today’s fast-paced financial markets, having the ability to predict intraday price movements accurately can provide a significant edge to traders. One way to achieve this is by building an AI-based forecasting model that incorporates market microstructure data with real-time learning. In this tutorial, we will walk through the process of creating such a model using Python, focusing on object-oriented programming concepts and leveraging the Keras library for deep learning.

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Environment Setup

To begin, we need to gather real financial data for our project. We will use the yfinance library to download historical price data for a diverse set of securities listed on Yahoo Finance. Let's start by installing the library using the following shell command:

pip install yfinance

Next, we will import the necessary libraries in Python, including numpy for numerical operations and yfinance for fetching financial data. We will also import matplotlib for plotting visualizations of the data. Let's include these imports in our Python script:

import numpy as np
import yfinance as yf
import matplotlib.pyplot as plt

Downloading Historical Price Data