Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
from datetime import datetime, timedelta
import random

# Set page config
st.set_page_config(page_title="Stock Analysis Dashboard", layout="wide")

# Import data generation functions
from data_generator import generate_stock_data, calculate_technical_indicators

# Generate mock data for 5 tech stocks
@st.cache_data
def load_stock_data():
    stocks = {
        'AAPL': {'name': 'Apple Inc.', 'start_price': 150},
        'GOOGL': {'name': 'Alphabet Inc.', 'start_price': 2800},
        'MSFT': {'name': 'Microsoft Corp.', 'start_price': 330},
        'TSLA': {'name': 'Tesla Inc.', 'start_price': 800},
        'NVDA': {'name': 'NVIDIA Corp.', 'start_price': 220}
    }
    
    all_data = {}
    for symbol, info in stocks.items():
        data = generate_stock_data(symbol, info['start_price'], info['name'])
        all_data[symbol] = calculate_technical_indicators(data)
    
    return all_data

# Load data
stock_data = load_stock_data()

# App title
st.title("📈 Stock Analysis Dashboard")
st.markdown("---")

# Sidebar controls
st.sidebar.header("📊 Analysis Controls")

# Stock selector
selected_stock = st.sidebar.selectbox(
    "Select Stock",
    options=list(stock_data.keys()),
    format_func=lambda x: f"{x} - {stock_data[x]['Name'].iloc[0]}"
)

# Date range picker
df = stock_data[selected_stock]
min_date = df['Date'].min().date()
max_date = df['Date'].max().date()

date_range = st.sidebar.date_input(
    "Select Date Range",
    value=(min_date, max_date),
    min_value=min_date,
    max_value=max_date
)

# Chart type toggle
chart_type = st.sidebar.radio(
    "Chart Type",
    ["Candlestick", "Line", "Area"]
)

# Technical indicators
st.sidebar.subheader("Technical Indicators")
show_ma = st.sidebar.checkbox("Moving Averages", value=True)
show_volume = st.sidebar.checkbox("Volume", value=True)
show_rsi = st.sidebar.checkbox("RSI", value=False)

# Stock comparison
st.sidebar.subheader("Stock Comparison")
compare_stocks = st.sidebar.multiselect(
    "Compare with other stocks",
    options=[s for s in stock_data.keys() if s != selected_stock],
    default=[]
)

# Filter data by date range
if len(date_range) == 2:
    start_date, end_date = date_range
    filtered_df = df[(df['Date'].dt.date >= start_date) & (df['Date'].dt.date <= end_date)]
else:
    filtered_df = df

# Main content area
col1, col2 = st.columns([3, 1])

with col1:
    # Stock info
    current_price = filtered_df['Close'].iloc[-1]
    prev_price = filtered_df['Close'].iloc[-2] if len(filtered_df) > 1 else current_price
    price_change = current_price - prev_price
    price_change_pct = (price_change / prev_price) * 100
    
    st.subheader(f"{selected_stock} - {filtered_df['Name'].iloc[-1]}")
    
    # Price metrics
    col_a, col_b, col_c, col_d = st.columns(4)
    with col_a:
        st.metric("Current Price", f"${current_price:.2f}", f"{price_change:+.2f}")
    with col_b:
        st.metric("Change %", f"{price_change_pct:+.2f}%")
    with col_c:
        st.metric("Volume", f"{filtered_df['Volume'].iloc[-1]:,.0f}")
    with col_d:
        st.metric("Market Cap", f"${filtered_df['Volume'].iloc[-1] * current_price / 1000000:.1f}M")

with col2:
    # Basic statistics
    st.subheader("📊 Basic Stats")
    st.write(f"**Mean Price:** ${filtered_df['Close'].mean():.2f}")
    st.write(f"**Volatility:** {filtered_df['Close'].std():.2f}")
    st.write(f"**52W High:** ${filtered_df['Close'].max():.2f}")
    st.write(f"**52W Low:** ${filtered_df['Close'].min():.2f}")
    st.write(f"**Avg Volume:** {filtered_df['Volume'].mean():,.0f}")

# Main chart
st.subheader("📈 Price Chart")

# Create subplots for price and volume
if show_volume:
    fig = make_subplots(
        rows=2, cols=1,
        shared_xaxes=True,
        vertical_spacing=0.03,
        subplot_titles=('Price', 'Volume'),
        row_width=[0.2, 0.7]
    )
else:
    fig = make_subplots(rows=1, cols=1)

# Add price chart
if chart_type == "Candlestick":
    fig.add_trace(
        go.Candlestick(
            x=filtered_df['Date'],
            open=filtered_df['Open'],
            high=filtered_df['High'],
            low=filtered_df['Low'],
            close=filtered_df['Close'],
            name=selected_stock
        ),
        row=1, col=1
    )
elif chart_type == "Line":
    fig.add_trace(
        go.Scatter(
            x=filtered_df['Date'],
            y=filtered_df['Close'],
            mode='lines',
            name=selected_stock,
            line=dict(width=2)
        ),
        row=1, col=1
    )
else:  # Area
    fig.add_trace(
        go.Scatter(
            x=filtered_df['Date'],
            y=filtered_df['Close'],
            mode='lines',
            fill='tonexty',
            name=selected_stock,
            line=dict(width=1)
        ),
        row=1, col=1
    )

# Add moving averages
if show_ma:
    fig.add_trace(
        go.Scatter(
            x=filtered_df['Date'],
            y=filtered_df['MA_20'],
            mode='lines',
            name='MA 20',
            line=dict(color='orange', width=1)
        ),
        row=1, col=1
    )
    fig.add_trace(
        go.Scatter(
            x=filtered_df['Date'],
            y=filtered_df['MA_50'],
            mode='lines',
            name='MA 50',
            line=dict(color='red', width=1)
        ),
        row=1, col=1
    )

# Add comparison stocks
for comp_stock in compare_stocks:
    comp_df = stock_data[comp_stock]
    if len(date_range) == 2:
        comp_filtered = comp_df[(comp_df['Date'].dt.date >= start_date) & (comp_df['Date'].dt.date <= end_date)]
    else:
        comp_filtered = comp_df
    
    # Normalize prices for comparison
    base_price = comp_filtered['Close'].iloc[0]
    main_base = filtered_df['Close'].iloc[0]
    normalized_prices = (comp_filtered['Close'] / base_price) * main_base
    
    fig.add_trace(
        go.Scatter(
            x=comp_filtered['Date'],
            y=normalized_prices,
            mode='lines',
            name=f"{comp_stock} (normalized)",
            line=dict(width=1, dash='dash')
        ),
        row=1, col=1
    )

# Add volume chart
if show_volume:
    fig.add_trace(
        go.Bar(
            x=filtered_df['Date'],
            y=filtered_df['Volume'],
            name='Volume',
            marker_color='lightblue',
            opacity=0.7
        ),
        row=2, col=1
    )

# Update layout
fig.update_layout(
    title=f"{selected_stock} Stock Analysis",
    xaxis_title="Date",
    yaxis_title="Price ($)",
    height=600,
    showlegend=True,
    xaxis_rangeslider_visible=False
)

if show_volume:
    fig.update_yaxes(title_text="Volume", row=2, col=1)

st.plotly_chart(fig, use_container_width=True)

# RSI Chart
if show_rsi:
    st.subheader("📊 RSI (Relative Strength Index)")
    rsi_fig = go.Figure()
    rsi_fig.add_trace(
        go.Scatter(
            x=filtered_df['Date'],
            y=filtered_df['RSI'],
            mode='lines',
            name='RSI',
            line=dict(color='purple')
        )
    )
    rsi_fig.add_hline(y=70, line_dash="dash", line_color="red", annotation_text="Overbought (70)")
    rsi_fig.add_hline(y=30, line_dash="dash", line_color="green", annotation_text="Oversold (30)")
    rsi_fig.update_layout(
        title="RSI Indicator",
        xaxis_title="Date",
        yaxis_title="RSI",
        height=300,
        yaxis=dict(range=[0, 100])
    )
    st.plotly_chart(rsi_fig, use_container_width=True)

# Volume Analysis
st.subheader("📊 Volume Analysis")
col1, col2 = st.columns(2)

with col1:
    # Volume distribution
    vol_fig = px.histogram(
        filtered_df,
        x='Volume',
        nbins=30,
        title="Volume Distribution"
    )
    vol_fig.update_layout(height=300)
    st.plotly_chart(vol_fig, use_container_width=True)

with col2:
    # Price vs Volume correlation
    corr_fig = px.scatter(
        filtered_df,
        x='Volume',
        y='Close',
        title="Price vs Volume Correlation",
        opacity=0.6
    )
    corr_fig.update_layout(height=300)
    st.plotly_chart(corr_fig, use_container_width=True)

# Data table
st.subheader("📋 Recent Data")
st.dataframe(
    filtered_df[['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'MA_20', 'MA_50']].tail(10),
    use_container_width=True
)

# Footer
st.markdown("---")
st.markdown("*This dashboard uses mock data for demonstration purposes. Real trading decisions should not be based on this data.*")