Code Editor for app.py

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

st.title("ETF Flow Dashboard")

# Sidebar for user inputs
st.sidebar.header("Settings")

# Define some popular ETFs
etf_dict = {
    "SPY": "SPDR S&P 500 ETF",
    "QQQ": "Invesco QQQ Trust",
    "IWM": "iShares Russell 2000 ETF",
    "EEM": "iShares MSCI Emerging Markets ETF",
    "VTI": "Vanguard Total Stock Market ETF",
    "GLD": "SPDR Gold Shares",
    "TLT": "iShares 20+ Year Treasury Bond ETF"
}

# Let user select ETFs
selected_etfs = st.sidebar.multiselect(
    "Select ETFs to analyze",
    options=list(etf_dict.keys()),
    default=["SPY", "QQQ"],
    format_func=lambda x: f"{x} - {etf_dict[x]}"
)

# Date range selector
end_date = datetime.now()
start_date = end_date - timedelta(days=365)
date_range = st.sidebar.date_input(
    "Select Date Range",
    value=(start_date, end_date),
    max_value=end_date
)

if len(date_range) == 2:
    start_date, end_date = date_range
else:
    st.error("Please select both start and end date")
    st.stop()

# Download and process data
@st.cache_data(ttl=3600)  # Cache for 1 hour
def get_etf_data(ticker, start, end):
    etf = yf.Ticker(ticker)
    hist = etf.history(start=start, end=end)
    hist['Volume_Price'] = hist['Volume'] * hist['Close']
    return hist

if selected_etfs:
    # Create tabs for different views
    tab1, tab2, tab3 = st.tabs(["Price Performance", "Volume Analysis", "Flow Comparison"])
    
    # Get data for all selected ETFs
    etf_data = {}
    for etf in selected_etfs:
        etf_data[etf] = get_etf_data(etf, start_date, end_date)

    with tab1:
        st.subheader("Price Performance")
        fig = go.Figure()
        
        for etf in selected_etfs:
            normalized_price = etf_data[etf]['Close'] / etf_data[etf]['Close'].iloc[0] * 100
            fig.add_trace(go.Scatter(
                x=normalized_price.index,
                y=normalized_price,
                name=f"{etf} ({etf_dict[etf]})",
                mode='lines'
            ))
            
        fig.update_layout(
            title="Normalized Price Performance (Base=100)",
            xaxis_title="Date",
            yaxis_title="Price (Normalized)",
            height=600
        )
        st.plotly_chart(fig, use_container_width=True)

    with tab2:
        st.subheader("Volume Analysis")
        fig = go.Figure()
        
        for etf in selected_etfs:
            volume_ma = etf_data[etf]['Volume'].rolling(window=20).mean()
            fig.add_trace(go.Scatter(
                x=volume_ma.index,
                y=volume_ma,
                name=f"{etf} - 20D MA Volume",
                mode='lines'
            ))
            
        fig.update_layout(
            title="20-Day Moving Average Volume",
            xaxis_title="Date",
            yaxis_title="Volume",
            height=600
        )
        st.plotly_chart(fig, use_container_width=True)

    with tab3:
        st.subheader("Flow Comparison")
        fig = go.Figure()
        
        for etf in selected_etfs:
            volume_price = etf_data[etf]['Volume_Price'].rolling(window=5).mean()
            fig.add_trace(go.Scatter(
                x=volume_price.index,
                y=volume_price,
                name=f"{etf} - Dollar Volume Flow",
                mode='lines'
            ))
            
        fig.update_layout(
            title="5-Day Moving Average Dollar Volume Flow",
            xaxis_title="Date",
            yaxis_title="Dollar Volume (Price × Volume)",
            height=600
        )
        st.plotly_chart(fig, use_container_width=True)

    # Summary metrics
    st.subheader("Summary Metrics")
    
    metrics_df = pd.DataFrame()
    for etf in selected_etfs:
        data = etf_data[etf]
        total_return = (data['Close'].iloc[-1] / data['Close'].iloc[0] - 1) * 100
        avg_daily_volume = data['Volume'].mean()
        avg_dollar_volume = data['Volume_Price'].mean()
        
        metrics_df.loc[etf, 'Total Return (%)'] = f"{total_return:.2f}%"
        metrics_df.loc[etf, 'Avg Daily Volume'] = f"{avg_daily_volume:,.0f}"
        metrics_df.loc[etf, 'Avg Daily Dollar Volume ($)'] = f"${avg_dollar_volume:,.2f}"
    
    st.dataframe(metrics_df)

else:
    st.warning("Please select at least one ETF from the sidebar.")