Analyzing Trade Entry and Exit Positions with Regression Analysis with Quandl

Analyzing Trade Entry and Exit Positions with Regression Analysis.py

# Import necessary libraries
import quandl
import numpy as np
import matplotlib.pyplot as plt

# Set your Quandl API key (you need to sign up on Quandl's website to get your API key)
quandl.ApiConfig.api_key = 'YOUR_API_KEY'

# Define the dataset codes for Google and Yahoo
google_dataset = 'WIKI/GOOGL'  # Replace with the correct dataset code for Google
yahoo_dataset = 'WIKI/YHOO'    # Replace with the correct dataset code for Yahoo

# Define the date range for the past 350 weeks
end_date = '2023-08-31'  # Replace with the desired end date

# Fetch the price data for Google and Yahoo
google_data = quandl.get(google_dataset, end_date=end_date)
yahoo_data = quandl.get(yahoo_dataset, end_date=end_date)

# Calculate the returns for Google and Yahoo
google_returns = google_data['Adj. Close'].pct_change().dropna()
yahoo_returns = yahoo_data['Adj. Close'].pct_change().dropna()

# Calculate the regression channel for Google and Yahoo
google_x = np.arange(len(google_returns))
yahoo_x = np.arange(len(yahoo_returns))

google_slope, google_intercept = np.polyfit(google_x, google_returns, 1)
yahoo_slope, yahoo_intercept = np.polyfit(yahoo_x, yahoo_returns, 1)

google_regression = google_intercept + google_slope * google_x
yahoo_regression = yahoo_intercept + yahoo_slope * yahoo_x

# Calculate the confidence intervals for the regression lines
google_residuals = google_returns - google_regression
yahoo_residuals = yahoo_returns - yahoo_regression

google_std = np.std(google_residuals)
yahoo_std = np.std(yahoo_residuals)

google_confidence_interval = 2 * google_std
yahoo_confidence_interval = 2 * yahoo_std

# Plot the regression channel for Google
plt.figure(figsize=(12, 6))
plt.plot(google_x, google_returns, label='Google Returns', alpha=0.5)
plt.plot(google_x, google_regression, label='Google Regression', color='green')
plt.fill_between(google_x, google_regression - google_confidence_interval, google_regression + google_confidence_interval, color='green', alpha=0.2)
plt.title('Regression Channel: Google')
plt.xlabel('Weeks')
plt.ylabel('Returns')
plt.legend()
plt.grid(True)

# Plot the regression channel for Yahoo
plt.figure(figsize=(12, 6))
plt.plot(yahoo_x, yahoo_returns, label='Yahoo Returns', alpha=0.5)
plt.plot(yahoo_x, yahoo_regression, label='Yahoo Regression', color='blue')
plt.fill_between(yahoo_x, yahoo_regression - yahoo_confidence_interval, yahoo_regression + yahoo_confidence_interval, color='blue', alpha=0.2)
plt.title('Regression Channel: Yahoo')
plt.xlabel('Weeks')
plt.ylabel('Returns')
plt.legend()
plt.grid(True)

# Show the plots
plt.show()