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’s dive into the world of quantitative finance!</p>
<h2>1. Importing Necessary Libraries and Modules</h2>
<p>Before we start, let’s import the essential libraries and modules required for our predictive modeling journey. We’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 ‘HDFCBANK.NS’ ticker symbol using the Yahoo Finance API. We’ll fetch data from one year ago until today’s date.</p>
<pre>
# Load historical stock price data
ticker_symbol = 'HDFCBANK.NS'
today = datetime.today()
one_year_ago = today - timedelta(days=365)
start_date = "2013-08-23"
end_date = "2023-08-22"
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 (‘50d_MA’) and the 200-day moving average (‘200d_MA’) calculated from the ‘Close’ price.</p>
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