Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide

<p>Predictive modeling plays a crucial role in the world of finance, especially when it comes to making informed decisions in the stock market. In this article, I will walk you through the process of building a model for predicting stock prices using Python. We will cover data manipulation, fetching stock data, machine learning, and later backtesting a trading strategy. Let&rsquo;s dive into the world of quantitative finance!</p> <h2>1. Importing Necessary Libraries and Modules</h2> <p>Before we start, let&rsquo;s import the essential libraries and modules required for our predictive modeling journey. We&rsquo;ll use libraries like pandas for data manipulation, yfinance for fetching stock data, scikit-learn for machine learning, and datetime for date calculations.</p> <pre> import pandas as pd import yfinance as yf from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error from datetime import datetime, timedelta</pre> <h2>2. Fetching Historical Stock Price Data</h2> <p>To build our predictive model, we need historical stock price data. In this example, we fetch data for the &lsquo;HDFCBANK.NS&rsquo; ticker symbol using the Yahoo Finance API. We&rsquo;ll fetch data from one year ago until today&rsquo;s date.</p> <pre> # Load historical stock price data ticker_symbol = &#39;HDFCBANK.NS&#39; today = datetime.today() one_year_ago = today - timedelta(days=365) start_date = &quot;2013-08-23&quot; end_date = &quot;2023-08-22&quot; data = yf.download(ticker_symbol, start=start_date, end=end_date)</pre> <h2>3. Feature Engineering</h2> <p>In this step, we add two important features to our dataset: the 50-day moving average (&lsquo;50d_MA&rsquo;) and the 200-day moving average (&lsquo;200d_MA&rsquo;) calculated from the &lsquo;Close&rsquo; price.</p> <p><a href="https://python.plainenglish.io/predictive-modeling-for-future-stock-prices-in-python-a-step-by-step-guide-c6f03730b8ba">Click Here</a></p>