Code Editor for app.py

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

# Page config
st.set_page_config(layout="wide")

# Mock data generation
def generate_mock_stock_data():
    stocks = {
        'AAPL': {'mean': 150, 'volatility': 0.02},
        'GOOGL': {'mean': 2800, 'volatility': 0.025},
        'MSFT': {'mean': 280, 'volatility': 0.018},
        'AMZN': {'mean': 3300, 'volatility': 0.03},
        'META': {'mean': 300, 'volatility': 0.035}
    }
    
    end_date = datetime.now()
    start_date = end_date - timedelta(days=365)
    dates = pd.date_range(start=start_date, end=end_date, freq='D')
    
    all_stocks_data = {}
    
    for ticker, params in stocks.items():
        np.random.seed(42)  # For reproducibility
        
        # Generate daily returns
        daily_returns = np.random.normal(
            loc=params['volatility']/np.sqrt(252),
            scale=params['volatility'],
            size=len(dates)
        )
        
        # Generate price path
        price = params['mean'] * np.exp(np.cumsum(daily_returns))
        
        # Generate other price components
        volume = np.random.lognormal(mean=np.log(1000000), sigma=0.5, size=len(dates))
        high = price * (1 + abs(np.random.normal(0, 0.015, size=len(dates))))
        low = price * (1 - abs(np.random.normal(0, 0.015, size=len(dates))))
        open_price = price * (1 + np.random.normal(0, 0.01, size=len(dates)))
        
        df = pd.DataFrame({
            'Date': dates,
            'Open': open_price,
            'High': high,
            'Low': low,
            'Close': price,
            'Volume': volume.astype(int),
            'MA20': pd.Series(price).rolling(window=20).mean(),
            'MA50': pd.Series(price).rolling(window=50).mean(),
            'MA200': pd.Series(price).rolling(window=200).mean(),
        })
        
        df.set_index('Date', inplace=True)
        all_stocks_data[ticker] = df
    
    return all_stocks_data

# Load data
@st.cache_data
def load_data():
    return generate_mock_stock_data()

stock_data = load_data()

# Sidebar
st.sidebar.title('Stock Analysis')
selected_stock = st.sidebar.selectbox('Select Stock', list(stock_data.keys()))
chart_type = st.sidebar.selectbox('Chart Type', ['Candlestick', 'Line'])

# Date range selector
col1, col2 = st.sidebar.columns(2)
with col1:
    start_date = st.date_input('Start Date', stock_data[selected_stock].index.min())
with col2:
    end_date = st.date_input('End Date', stock_data[selected_stock].index.max())

# Technical indicators
show_ma = st.sidebar.checkbox('Show Moving Averages', True)
show_volume = st.sidebar.checkbox('Show Volume', True)

# Main content
st.title(f'{selected_stock} Stock Analysis')

# Filter data based on date range
mask = (stock_data[selected_stock].index.date >= start_date) & (stock_data[selected_stock].index.date <= end_date)
df = stock_data[selected_stock][mask]

# Create the main price chart
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, 
                    vertical_spacing=0.03, 
                    row_heights=[0.7, 0.3])

if chart_type == 'Candlestick':
    fig.add_trace(
        go.Candlestick(
            x=df.index,
            open=df['Open'],
            high=df['High'],
            low=df['Low'],
            close=df['Close'],
            name='OHLC'
        ),
        row=1, col=1
    )
else:
    fig.add_trace(
        go.Scatter(
            x=df.index,
            y=df['Close'],
            name='Close Price',
            line=dict(color='blue')
        ),
        row=1, col=1
    )

# Add moving averages
if show_ma:
    fig.add_trace(go.Scatter(x=df.index, y=df['MA20'], name='MA20', line=dict(color='yellow', width=1)), row=1, col=1)
    fig.add_trace(go.Scatter(x=df.index, y=df['MA50'], name='MA50', line=dict(color='orange', width=1)), row=1, col=1)
    fig.add_trace(go.Scatter(x=df.index, y=df['MA200'], name='MA200', line=dict(color='red', width=1)), row=1, col=1)

# Add volume chart
if show_volume:
    fig.add_trace(
        go.Bar(x=df.index, y=df['Volume'], name='Volume'),
        row=2, col=1
    )

# Update layout
fig.update_layout(
    height=800,
    showlegend=True,
    xaxis_rangeslider_visible=False,
    title=f'{selected_stock} Stock Price'
)

st.plotly_chart(fig, use_container_width=True)

# Statistics
st.header('Stock Statistics')
col1, col2, col3, col4 = st.columns(4)

with col1:
    st.metric('Current Price', f"${df['Close'].iloc[-1]:.2f}")
with col2:
    st.metric('Average Price', f"${df['Close'].mean():.2f}")
with col3:
    daily_returns = df['Close'].pct_change()
    volatility = daily_returns.std() * np.sqrt(252)  # Annualized volatility
    st.metric('Volatility', f"{volatility:.2%}")
with col4:
    total_return = (df['Close'].iloc[-1] - df['Close'].iloc[0]) / df['Close'].iloc[0]
    st.metric('Total Return', f"{total_return:.2%}")

# Stock comparison
st.header('Stock Comparison')
comparison_stocks = st.multiselect('Compare with', list(stock_data.keys()), default=[list(stock_data.keys())[1]])

if comparison_stocks:
    fig_comparison = go.Figure()
    
    # Normalize prices to 100
    fig_comparison.add_trace(go.Scatter(
        x=df.index,
        y=df['Close'] / df['Close'].iloc[0] * 100,
        name=selected_stock
    ))
    
    for comp_stock in comparison_stocks:
        comp_df = stock_data[comp_stock][mask]
        fig_comparison.add_trace(go.Scatter(
            x=comp_df.index,
            y=comp_df['Close'] / comp_df['Close'].iloc[0] * 100,
            name=comp_stock
        ))
    
    fig_comparison.update_layout(
        title='Price Performance Comparison (Normalized to 100)',
        yaxis_title='Normalized Price',
        height=500
    )
    
    st.plotly_chart(fig_comparison, use_container_width=True)