Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

def generate_mock_data():
    dates = pd.date_range(start='2022-01-01', end='2023-01-01', freq='B')
    tickers = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'FB']
    data = {}
    for ticker in tickers:
        prices = np.random.normal(loc=100, scale=10, size=len(dates))
        volume = np.random.randint(1000000, 10000000, len(dates))
        data[ticker] = pd.DataFrame({
            'Date': dates,
            'Open': prices * np.random.uniform(0.95, 1.05, len(dates)),
            'Close': prices,
            'High': prices * np.random.uniform(1.01, 1.1, len(dates)),
            'Low': prices * np.random.uniform(0.9, 0.99, len(dates)),
            'Volume': volume,
        })
        data[ticker].set_index('Date', inplace=True)
    return data

mock_data = generate_mock_data()

st.title('Stock Analysis App')

selected_stock = st.selectbox('Select Stock:', list(mock_data.keys()))

st.sidebar.header('Price Trend Settings')
date_range = st.sidebar.date_input('Select Date Range', [pd.to_datetime('2022-01-01'), pd.to_datetime('2023-01-01')])
indicator = st.sidebar.checkbox('Show Moving Averages')
chart_type = st.sidebar.selectbox('Chart Type:', ['Line', 'Bar'])

selected_data = mock_data[selected_stock].loc[date_range[0]:date_range[1]]

st.subheader(f'Price Trend for {selected_stock}')
if chart_type == 'Line':
    plt.figure(figsize=(10, 5))
    plt.plot(selected_data.index, selected_data['Close'], label='Close')
    if indicator:
        plt.plot(selected_data.index, selected_data['Close'].rolling(window=5).mean(), label='5-Day MA', linestyle='--')
        plt.plot(selected_data.index, selected_data['Close'].rolling(window=20).mean(), label='20-Day MA', linestyle='--')
    plt.title('Price Trend')
    plt.xlabel('Date')
    plt.ylabel('Price')
    plt.legend()
    st.pyplot()
elif chart_type == 'Bar':
    plt.figure(figsize=(10, 5))
    plt.bar(selected_data.index, selected_data['Close'], label='Close')
    plt.title('Price Trend')
    plt.xlabel('Date')
    plt.ylabel('Price')
    plt.legend()
    st.pyplot()

st.subheader('Basic Statistics')
mean_price = selected_data['Close'].mean()
volatility = selected_data['Close'].std()
st.write(f'Mean Price: ${mean_price:.2f}')
st.write(f'Price Volatility: ${volatility:.2f}')

st.subheader('Volume Analysis')
plt.figure(figsize=(10, 5))
plt.bar(selected_data.index, selected_data['Volume'], color='orange', label='Volume')
plt.title('Volume Analysis')
plt.xlabel('Date')
plt.ylabel('Volume')
plt.legend()
st.pyplot()

st.subheader('Compare Stocks')
compare_stocks = st.multiselect('Select Stocks to Compare:', list(mock_data.keys()), default=[selected_stock])
for stock in compare_stocks:
    data_to_compare = mock_data[stock].loc[date_range[0]:date_range[1]]
    plt.plot(data_to_compare.index, data_to_compare['Close'], label=stock)
plt.title('Stock Comparison')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
st.pyplot()