Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from datetime import datetime, timedelta

def generate_mock_data(start_date, num_days):
    dates = [start_date + timedelta(days=i) for i in range(num_days)]
    data = {
        'Date': dates,
        'Open': np.random.rand(num_days) * 100,
        'Close': np.random.rand(num_days) * 100,
        'High': np.random.rand(num_days) * 100,
        'Low': np.random.rand(num_days) * 100,
        'Volume': np.random.randint(1000, 10000, size=num_days)
    }
    return pd.DataFrame(data)

# Generate mock data for 5 tech stocks
np.random.seed(42)
num_days = 365
start_date = datetime.now() - timedelta(days=num_days)
stocks = {'AAPL': generate_mock_data(start_date, num_days),
          'GOOGL': generate_mock_data(start_date, num_days),
          'MSFT': generate_mock_data(start_date, num_days),
          'AMZN': generate_mock_data(start_date, num_days),
          'TSLA': generate_mock_data(start_date, num_days)}

st.title('Stock Analysis Dashboard')

# Stock selector
stock_choice = st.selectbox('Select a stock:', list(stocks.keys()))

# Date range picker
start_date = st.date_input('Start date', value=datetime.now() - timedelta(days=30))
end_date = st.date_input('End date', value=datetime.now())

# Filter data based on date range
filtered_data = stocks[stock_choice][(stocks[stock_choice]['Date'] >= pd.Timestamp(start_date)) & (stocks[stock_choice]['Date'] <= pd.Timestamp(end_date))]

# Plotting price trend
fig = go.Figure()
fig.add_trace(go.Scatter(x=filtered_data['Date'], y=filtered_data['Close'], mode='lines+markers', name='Close'))
fig.update_layout(title=f'{stock_choice} Price Trend', xaxis_title='Date', yaxis_title='Price ($)', template='plotly', width=800)
st.plotly_chart(fig)

# Stats
mean_price = filtered_data['Close'].mean()
volatility = filtered_data['Close'].std()

st.subheader('Basic Statistics')
st.write(f'Mean Price: ${mean_price:.2f}')
st.write(f'Volatility: ${volatility:.2f}')

# Moving averages
ma_period = st.slider('Select Moving Average Period:', 1, 30, 5)
filtered_data[f'MA_{ma_period}'] = filtered_data['Close'].rolling(window=ma_period).mean()

# Plotting moving averages
fig_ma = go.Figure()
fig_ma.add_trace(go.Scatter(x=filtered_data['Date'], y=filtered_data['Close'], mode='lines', name='Close'))
fig_ma.add_trace(go.Scatter(x=filtered_data['Date'], y=filtered_data[f'MA_{ma_period}'], mode='lines', name=f'MA {ma_period}'))
fig_ma.update_layout(title=f'{stock_choice} Moving Averages', xaxis_title='Date', yaxis_title='Price ($)', template='plotly', width=800)
st.plotly_chart(fig_ma)

# Volume analysis
fig_vol = go.Figure()
fig_vol.add_trace(go.Bar(x=filtered_data['Date'], y=filtered_data['Volume'], name='Volume'))
fig_vol.update_layout(title=f'{stock_choice} Volume Analysis', xaxis_title='Date', yaxis_title='Volume', template='plotly', width=800)
st.plotly_chart(fig_vol)

# Stock Comparison
st.subheader('Stock Comparison')
selected_stocks = st.multiselect('Choose stocks to compare:', list(stocks.keys()))
if len(selected_stocks) > 0:
    for s in selected_stocks:
        comparison_data = stocks[s][(stocks[s]['Date'] >= pd.Timestamp(start_date)) & (stocks[s]['Date'] <= pd.Timestamp(end_date))]
        st.line_chart(comparison_data.set_index('Date')['Close'], use_container_width=True, name=s)