Predictive Analysis Using Python: Forecasting Apple’s Stock Price

Predictive analysis has become a cornerstone of modern financial decision-making, enabling investors to anticipate market trends and make informed investment choices. In this step-by-step guide, I’ll show you how to make use of Python for predictive analysis with the example of forecasting Apple’s stock price for the next three months (May 2024 — July 2024) based on historical data from the past five years (2019 — April 2024).

Step 1: Data Collection: Import necessary libraries and load historical stock price data for Apple.

import pandas as pd
# Load historical stock price data for Apple
apple_stock_data = pd.read_csv('apple_stock_data.csv')

Step 2: Data Preprocessing: Clean the data by handling missing values and format columns. Preprocess the data for analysis, such as calculating moving averages or technical indicators.

#Cleaning data
apple_stock_data.dropna(inplace=True)

# calculating 50-day moving average
apple_stock_data['MA_50'] = apple_stock_data['Close'].rolling(window=50).mean()

Step 3: Exploratory Data Analysis (EDA) Visualize the data to explore trends, seasonality, and correlations.

import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plt.plot(apple_stock_data['Date'], apple_stock_data['Close'], color='blue')
plt.title('Apple Stock Price Over Time')
plt.xlabel('Date')
plt.ylabel('Closing Price')
plt.show()

Step 4: Feature Selection: Select relevant features for predictive modeling and engineer additional features if necessary.

# Select relevant features
features = ['Close', 'Volume']

# Example of adding technical indicators (e.g., RSI)
from ta import momentum
apple_stock_data['RSI'] = momentum.rsi(apple_stock_data['Close'])

Step 5: Model Selection: Choose an appropriate predictive modeling technique (e.g., ARIMA for time series analysis).

# Import ARIMA model from statsmodels
from statsmodels.tsa.arima.model import ARIMA

Step 6: Training the Model: Split the data into training and testing sets, and train the ARIMA model.

# Split data into training and testing sets
train_size = int(len(apple_stock_data) * 0.8)
train, test = apple_stock_data[:train_size], apple_stock_data[train_size:]

# Train the ARIMA model
model = ARIMA(train['Close'], order=(5,1,0))
model_fit = model.fit()

Step 7: Model Evaluation: Evaluate the model performance using appropriate metrics and generate predictions for the testing set.

# Generate predictions for testing set
predictions = model_fit.forecast(steps=len(test))

# Calculate evaluation metrics (e.g., MSE, RMSE)
from sklearn.metrics import mean_squared_error
mse = mean_squared_error(test['Close'], predictions)

Step 8: Forecasting Future Stock Prices: Forecast future stock prices for the next three months (May 2024 — July 2024).

# Generate future dates for forecasting
future_dates = pd.date_range(start='2024-05-01', end='2024-07-31')

# Forecast future stock prices
future_predictions = model_fit.forecast(steps=len(future_dates))

Step 9: Model Refinement: Fine-tune the model parameters based on evaluation results and iterate on feature selection and engineering.

# Example of hyperparameter tuning
model = ARIMA(train['Close'], order=(5,2,1))
model_fit = model.fit()

Step 10: Interpretation and Action:Interpret forecasted stock prices in the context of investment strategy and use predictions as a tool for informed decision-making.

# Visualize forecasted stock prices
plt.figure(figsize=(10, 6))
plt.plot(future_dates, future_predictions, color='green')
plt.title('Forecasted Apple Stock Price (May 2024 - July 2024)')
plt.xlabel('Date')
plt.ylabel('Predicted Closing Price')
plt.show()

By following these steps and combining these code snippets in a Jupyter Notebook and running them sequentially, you’ll be able to predict the future stock price of Apple for the next three months based on historical data and the ARIMA model.