gistfile1.txt

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pandas_datareader import data as pdr
from datetime import datetime
import quandl

# Define the time range for data collection
start = datetime(2010, 1, 1)
end = datetime(2024, 1, 1)

ndq = quandl.get("NASDAQOMX/COMP-NASDAQ", 
              trim_start=start.strftime("%Y-%m-%d"), 
              trim_end=end.strftime("%Y-%m-%d"))

nasdaq_monthly = ndq['Index Value'].resample('M').median().round().ffill()
nasdaq_monthly.index.names = ['DATE']

# Fetch M2 Money Supply data
m2_money_supply = pdr.get_data_fred('M2SL', start=start, end=end)

# Resample to monthly data and calculate the percent change for M2 (inflation measure)
m2sl = m2_money_supply['M2SL'].resample('M').ffill()

# Combine the data into a single DataFrame
data = pd.DataFrame({'M2SL': m2sl, 'NASDAQ': nasdaq_monthly}).dropna()

# Calculate correlation
correlation = data.corr().iloc[0, 1]

# Plot the data
fig, ax1 = plt.subplots(figsize=(14, 8))

ax1.plot(data.index, data['M2SL'], color='orange', label='M2SL')
ax1.set_xlabel('Date')
ax1.set_ylabel('M2SL', color='orange')
ax1.tick_params(axis='y', labelcolor='orange')

ax2 = ax1.twinx()
ax2.plot(data.index, data['NASDAQ'], color='blue', label='NASDAQ')
ax2.set_ylabel('NASDAQ Index', color='blue')
ax2.tick_params(axis='y', labelcolor='blue')

plt.title(f'Correlation between M2SL and NASDAQ: {correlation:.2f}')
plt.show()