simple_web_demo.py

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

# Configure page
st.set_page_config(
    page_title="📊 Pandas-TA Demo",
    page_icon="📊",
    layout="wide"
)

# Title
st.title("📊 Pandas-TA Technical Analysis Demo")
st.markdown("**Simple demo of technical analysis indicators**")

# Sidebar
st.sidebar.header("🔧 Settings")
days = st.sidebar.slider("Days of Data", 30, 365, 90)
start_price = st.sidebar.number_input("Starting Price", 10.0, 1000.0, 150.0)

# Generate sample data
@st.cache_data
def generate_data(days, start_price):
    dates = pd.date_range(start=datetime.now() - timedelta(days=days), 
                         end=datetime.now(), freq='D')
    
    np.random.seed(42)
    returns = np.random.normal(0.001, 0.02, len(dates))
    
    prices = [start_price]
    for ret in returns[1:]:
        prices.append(prices[-1] * (1 + ret))
    
    data = []
    for i, (date, close) in enumerate(zip(dates, prices)):
        high = close * (1 + abs(np.random.normal(0, 0.01)))
        low = close * (1 - abs(np.random.normal(0, 0.01)))
        open_price = close + np.random.normal(0, close * 0.005)
        volume = int(np.random.uniform(1000000, 5000000))
        
        data.append({
            'Date': date,
            'Open': open_price,
            'High': max(open_price, high, close),
            'Low': min(open_price, low, close),
            'Close': close,
            'Volume': volume
        })
    
    df = pd.DataFrame(data)
    df.set_index('Date', inplace=True)
    return df

# Generate button
if st.sidebar.button("🚀 Generate Analysis"):
    with st.spinner("Generating data and indicators..."):
        # Generate data
        df = generate_data(days, start_price)
        
        # Calculate indicators
        df['SMA_20'] = ta.sma(df['Close'], length=20)
        df['RSI'] = ta.rsi(df['Close'], length=14)
        
        st.success(f"✅ Generated {len(df)} days of data with indicators!")
        
        # Store in session state
        st.session_state['df'] = df

# Display results
if 'df' in st.session_state:
    df = st.session_state['df']
    
    # Show metrics
    col1, col2, col3 = st.columns(3)
    with col1:
        st.metric("Latest Price", f"${df['Close'].iloc[-1]:.2f}")
    with col2:
        st.metric("Latest RSI", f"{df['RSI'].iloc[-1]:.1f}")
    with col3:
        st.metric("Days of Data", len(df))
    
    # Show chart
    st.subheader("📈 Price Chart")
    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))
    
    # Price and SMA
    ax1.plot(df.index, df['Close'], label='Close Price', color='blue')
    ax1.plot(df.index, df['SMA_20'], label='SMA 20', color='red')
    ax1.set_title('Stock Price with SMA')
    ax1.legend()
    ax1.grid(True)
    
    # RSI
    ax2.plot(df.index, df['RSI'], label='RSI', color='purple')
    ax2.axhline(y=70, color='red', linestyle='--', alpha=0.7)
    ax2.axhline(y=30, color='green', linestyle='--', alpha=0.7)
    ax2.set_title('RSI Indicator')
    ax2.set_ylim(0, 100)
    ax2.legend()
    ax2.grid(True)
    
    plt.tight_layout()
    st.pyplot(fig)
    
    # Show data table
    st.subheader("📋 Data Table")
    st.dataframe(df.tail(10))
    
    # Download button
    csv = df.to_csv()
    st.download_button(
        "📥 Download CSV",
        csv,
        "technical_analysis.csv",
        "text/csv"
    )

else:
    st.info("👆 Click 'Generate Analysis' in the sidebar to start!")
    
    st.markdown("""
    ### Features:
    - Generate sample stock data
    - Calculate SMA and RSI indicators
    - Interactive charts
    - Data table display
    - CSV download
    """)

# Footer
st.markdown("---")
st.markdown("**Powered by pandas-ta library** 📊")