Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import timedelta, date

# Generate mock stock data
def generate_mock_data(tickers, start_date, end_date):
    date_range = pd.date_range(start_date, end_date, freq='B')  # Business days
    data = {ticker: pd.DataFrame(index=date_range) for ticker in tickers}
    for ticker, df in data.items():
        np.random.seed(hash(ticker) % 123456)  # Set seed for reproducibility
        df['Open'] = np.random.uniform(100, 200, len(df))
        df['Close'] = df['Open'] + np.random.uniform(-5, 5, len(df))
        df['High'] = df[['Open', 'Close']].max(axis=1) + np.random.uniform(0, 3, len(df))
        df['Low'] = df[['Open', 'Close']].min(axis=1) - np.random.uniform(0, 3, len(df))
        df['Volume'] = np.random.randint(1000, 10000, len(df))
        df['Date'] = df.index
        data[ticker] = df
    return data

# Stock Tickers
stock_tickers = ['AAPL', 'GOOG', 'MSFT', 'AMZN', 'FB']
start = date.today() - timedelta(days=365)
end = date.today()
mock_data = generate_mock_data(stock_tickers, start, end)

# Streamlit UI
st.title('Stock Analysis App')
selected_ticker = st.selectbox('Select Stock Ticker', stock_tickers)

date_range = st.date_input('Select Date Range', (start, end))

# Filter data
stock_data = mock_data[selected_ticker].loc[date_range[0]:date_range[1]]

# Plot price trends
st.subheader('Price Trend')
price_chart = st.selectbox('Select Chart Type', ['Line', 'Bar'])

plt.figure(figsize=(10, 5))
if price_chart == 'Line':
    plt.plot(stock_data['Close'], label='Close', color='blue')
else:
    plt.bar(stock_data.index, stock_data['Close'], label='Close', color='blue')
plt.title(f'{selected_ticker} Price Trend')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid()
st.pyplot(plt)

# Calculate and display statistics
mean_price = stock_data['Close'].mean()
volatility = stock_data['Close'].std()
st.subheader('Basic Statistics')
st.write(f'Mean Price: ${mean_price:.2f}')
st.write(f'Volatility: ${volatility:.2f}')

# Moving averages
moving_avg_period = st.number_input('Moving Average Period', min_value=1, value=20)
moving_avg = stock_data['Close'].rolling(window=moving_avg_period).mean()
st.line_chart(moving_avg)
st.write('Moving Average')

# Volume analysis
st.subheader('Volume Analysis')
st.bar_chart(stock_data['Volume'])
st.write('Volume over Time')

# Stock comparison
comparison_ticker = st.selectbox('Select Comparison Stock', stock_tickers)
if comparison_ticker != selected_ticker:
    comparison_data = mock_data[comparison_ticker].loc[date_range[0]:date_range[1]]
    plt.figure(figsize=(10, 5))
    plt.plot(stock_data['Close'], label=selected_ticker, color='blue')
    plt.plot(comparison_data['Close'], label=comparison_ticker, color='orange')
    plt.title('Stock Comparison')
    plt.xlabel('Date')
    plt.ylabel('Price')
    plt.legend()
    plt.grid()
    st.pyplot(plt)