election-stock-analysis
mport pandas as pd
import yfinance as yf
import plotly.graph_objs as go
from plotly.subplots import make_subplots
import numpy as np
def plot_stock_price_during_elections(ticker_symbol, output_csv_filename):
# Fetch the data from Yahoo Finance
stock_data = yf.download(ticker_symbol, start='1980-01-01', end='2024-12-31')
# Resample data to monthly frequency
stock_data_monthly = stock_data.resample('M').mean()
# Calculate percentage change in closing prices on a monthly basis
stock_data_monthly['Monthly % Change'] = stock_data_monthly['Close'].pct_change() * 100
# Define election years and months along with the party elected
election_periods = [
['1951-10-01', '1952-02-29', 'Congress'],
['1957-02-01', '1957-03-31', 'Congress'],
['1962-02-01', '1962-02-28', 'Congress'],
['1967-02-01', '1967-03-31', 'Congress'],
['1971-02-01', '1971-03-31', 'Congress'],
['1977-03-01', '1977-03-31', 'Janata Party'],
['1980-01-01', '1980-01-31', 'Congress'],
['1984-12-01', '1984-12-31', 'Congress'],
['1989-11-01', '1989-12-31', 'Congress'],
['1991-05-01', '1991-06-30', 'Congress'],
['1996-04-01', '1996-05-31', 'BJP'],
['1998-02-01', '1998-03-31', 'BJP'],
['1999-09-01', '1999-10-31', 'BJP'],
['2004-04-01', '2004-05-31', 'Congress'],
['2009-04-01', '2009-05-31', 'Congress'],
['2014-04-01', '2014-05-31', 'BJP'],
['2019-04-01', '2019-05-31', 'BJP'],
['2024-04-01', '2024-05-31', 'Unknown']
]
# Create a subplot
fig = make_subplots(rows=1, cols=1)
# Color the bars based on positive or negative change
bar_colors = ['red' if x < 0 else 'green' for x in stock_data_monthly['Monthly % Change']]
# Add bar trace for monthly percentage change
fig.add_trace(go.Bar(x=stock_data_monthly.index, y=stock_data_monthly['Monthly % Change'],
marker_color=bar_colors, name='Monthly % Change'))
# Add grid lines for election months and markers for parties
for start, end, party in election_periods:
fig.add_vline(x=start, line=dict(color="rgba(0, 0, 0, 0.5)", width=1, dash="dashdot"))
fig.add_vline(x=end, line=dict(color="rgba(0, 0, 0, 0.5)", width=1, dash="dashdot"))
# Add annotation for the party elected
fig.add_annotation(x=(pd.to_datetime(start) + pd.to_timedelta((pd.to_datetime(end) - pd.to_datetime(start)) / 2)),
y=stock_data_monthly['Monthly % Change'].max(),
text=party,
showarrow=False,
font=dict(color="black", size=16), # Adjust font size here
align="center",
yanchor="bottom")
# Add title and labels
fig.update_layout(title=f'{ticker_symbol} Stock Price During Election Years and Party Elected with Percentage Gain or Loss',
xaxis_title='Date',
yaxis_title='Monthly % Change (%)',
height=800) # Adjust height as needed
# Update x-axis to display month names
fig.update_xaxes(
tickmode='array',
tickvals=stock_data_monthly.index[::2], # Display every other month for better readability
ticktext=stock_data_monthly.index[::2].strftime('%b %Y'),
tickangle=90
)
# Add analysis by Soumil Shah
analysis_text = """
## Analysis by Soumil Shah
"""
fig.add_annotation(
text=analysis_text,
align='left',
showarrow=False,
xref='paper',
yref='paper',
x=0.01,
y=0.95,
bordercolor='black',
borderwidth=1,
bgcolor='white',
font=dict(size=10)
)
# Save the data to CSV file
stock_data_monthly.to_csv(output_csv_filename)
# Display the plot
fig.show()
# Example usage
plot_stock_price_during_elections('^NSEBANK', 'stock_data.csv') # You can replace 'AAPL' with any ticker symbol you want