main.py

import streamlit as st

import plotly.express as px
import plotly.graph_objs as go

import pandas as pd

st.title('Bollinger Bands')

df = pd.read_csv('copx.csv')

df['SMA'] = df['Adj Close'].rolling(window=20).mean()

# Calculate the 20-period Standard Deviation (SD)
df['SD'] = df['Adj Close'].rolling(window=20).std()

# Calculate the Upper Bollinger Band (UB) and Lower Bollinger Band (LB)
df['UB'] = df['SMA'] + 2 * df['SD']
df['LB'] = df['SMA'] - 2 * df['SD']

fig = go.Figure()

# Add the price chart
fig.add_trace(go.Scatter(x=df['Date'], y=df['Adj Close'], mode='lines', name='Price', line={"color": "black"}))

# Add the Upper Bollinger Band (UB) and shade the area
fig.add_trace(go.Scatter(x=df['Date'], y=df['UB'], mode='lines'
                         , name='Upper Bollinger Band'
                         , line={"color": "red"}))
fig.add_trace(go.Scatter(x=df['Date'], y=df['LB'], fill='tonexty', mode='lines'
                         , name='Lower Bollinger Band'
                         , line={"color": "rgb(0, 0, 255, 0.01)"}
                         ))

# Add the Middle Bollinger Band (MA)
fig.add_trace(go.Scatter(x=df['Date'], y=df['SMA'], mode='lines', name='Middle Bollinger Band', line={"color": "blue"}))

# Customize the chart layout
fig.update_layout(title='COPX Stock Price with Bollinger Bands',
                  xaxis_title='Date',
                  yaxis_title='Price',
                  showlegend=True)

st.write(fig)