app.py

import streamlit as st
import requests
import pandas as pd
import plotly.express as px
import os

# Global API key and default ETF symbols
API_KEY = os.getenv("FMP_API_KEY")

#ETF_ASSET_SYMBOL = "SPY"  # Used for asset composition
#ETF_SYMBOL = "QDVE.DE"     # For sector and country composition, you may adjust this as needed.

def format_number(n):
    """
    Format a number with K, M, or B suffix.
    """
    abs_n = abs(n)
    if abs_n < 1e3:
        return str(round(n))
    elif abs_n < 1e6:
        return f"{round(n/1e3)}K"
    elif abs_n < 1e9:
        return f"{round(n/1e6)}M"
    else:
        return f"{round(n/1e9)}B"

#############################
# ETF Asset Composition Functions
#############################
@st.cache_data(show_spinner=False)
def get_etf_asset_composition(etf_symbol: str) -> pd.DataFrame:
    url = f"https://financialmodelingprep.com/api/v3/etf-holder/{etf_symbol}?apikey={API_KEY}"
    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()
        if not data:
            return pd.DataFrame()
        df = pd.DataFrame(data)
        df['marketValue'] = pd.to_numeric(df['marketValue'], errors='coerce')
        df_sorted = df.sort_values(by="marketValue", ascending=False)
        return df_sorted
    except:
        # Generic failover, no mention of the source
        return pd.DataFrame()

def plot_etf_asset_composition(df: pd.DataFrame, etf_symbol: str):
    if df.empty:
        return None
    date_as_of = df.iloc[0]['updated'] if ('updated' in df.columns and not df.empty) else "N/A"
    tickers = df['asset'].tolist()
    market_values = df['marketValue'].tolist()
    labels = []
    for _, row in df.iterrows():
        mv_formatted = format_number(row['marketValue'])
        shares_formatted = format_number(row['sharesNumber'])
        weight_formatted = f"{round(row['weightPercentage'], 2)}%"
        labels.append(
            f"{row['asset']}<br>Market Value: {mv_formatted}<br>"
            f"Shares: {shares_formatted}<br>Weight: {weight_formatted}"
        )
    title = f"ETF Holdings by Market Value for {etf_symbol} (as of {date_as_of})"
    fig = px.bar(
        x=tickers,
        y=market_values,
        text=labels,
        title=title,
        labels={"x": "Asset", "y": "Market Value (USD)"}
    )
    fig.update_traces(textposition='outside')
    fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
    return fig

#############################
# Sector Composition Functions
#############################
@st.cache_data(show_spinner=False)
def get_etf_sector_composition(etf_symbol: str) -> pd.DataFrame:
    url = f"https://financialmodelingprep.com/api/v3/etf-sector-weightings/{etf_symbol}?apikey={API_KEY}"
    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()
        if not data:
            return pd.DataFrame()
        df = pd.DataFrame(data)
        df['weightPercentage'] = df['weightPercentage'].str.rstrip('%').astype(float)
        df_sorted = df.sort_values(by="weightPercentage", ascending=False)
        return df_sorted
    except:
        return pd.DataFrame()

def plot_etf_sector_composition(df: pd.DataFrame, etf_symbol: str):
    if df.empty:
        return None
    sectors = df['sector'].tolist()
    weights = df['weightPercentage'].tolist()
    labels = [f"{sector}<br>Weight: {weight:.2f}%" for sector, weight in zip(sectors, weights)]
    title = f"ETF Sector Weighting for {etf_symbol}"
    fig = px.bar(
        x=sectors,
        y=weights,
        text=labels,
        title=title,
        labels={"x": "Sector", "y": "Weight Percentage"}
    )
    fig.update_traces(textposition='outside')
    fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
    return fig

#############################
# Country Composition Functions
#############################
@st.cache_data(show_spinner=False)
def get_etf_country_composition(etf_symbol: str) -> pd.DataFrame:
    url = f"https://financialmodelingprep.com/api/v3/etf-country-weightings/{etf_symbol}?apikey={API_KEY}"
    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()
        if not data:
            return pd.DataFrame()
        df = pd.DataFrame(data)
        df['weightPercentage'] = df['weightPercentage'].str.rstrip('%').astype(float)
        df_sorted = df.sort_values(by="weightPercentage", ascending=False)
        return df_sorted
    except:
        return pd.DataFrame()

def plot_etf_country_composition(df: pd.DataFrame, etf_symbol: str):
    if df.empty:
        return None
    countries = df['country'].tolist()
    weights = df['weightPercentage'].tolist()
    labels = [f"{country}<br>Weight: {weight:.2f}%" for country, weight in zip(countries, weights)]
    title = f"ETF Country Weighting for {etf_symbol}"
    fig = px.bar(
        x=countries,
        y=weights,
        text=labels,
        title=title,
        labels={"x": "Country", "y": "Weight Percentage"}
    )
    fig.update_traces(textposition='outside')
    fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
    return fig

#############################
# MAIN APP
#############################
def main():
    st.set_page_config(page_title="ETF Asset Composition Dashboard", layout="wide")
    st.title("ETF Asset Composition Dashboard")
    st.write(
        "Analyze the composition of an ETF. "
        "Use the side menu to enter the ETF ticker symbol and then click the 'Run ETF Composition' button. "
        "The analysis is divided into three sections: Asset Composition, Sector Composition, and Country Composition. "
        "Each section includes a chart and a data table. Hover over the charts for more details."
    )
    
    # Initialize run button state
    if "run_etf" not in st.session_state:
        st.session_state.run_etf = False

    # Sidebar: Settings inside an expander
    with st.sidebar.expander("Settings", expanded=True):
        etf_ticker = st.text_input(
            "ETF Ticker",
            value="SPY",
            help="Enter the ticker symbol of the ETF (e.g., SPY, QQQ)."
        )
        if st.button("Run ETF Composition"):
            st.session_state.run_etf = True
            st.session_state.etf_ticker = etf_ticker

    # Main content
    if st.session_state.get("run_etf", False):
        # Section 1: ETF Asset Composition
        st.header("1. ETF Asset Composition")
        st.write(
            "Shows the top holdings by market value. "
            "The bar chart includes each asset's market value, shares held, and weight in the ETF."
        )
        asset_df = get_etf_asset_composition(st.session_state.etf_ticker)
        asset_fig = plot_etf_asset_composition(asset_df, st.session_state.etf_ticker)
        if asset_fig is not None:
            st.plotly_chart(asset_fig, use_container_width=True)
        st.subheader("Data")
        st.dataframe(asset_df, use_container_width=True)
        
        # Section 2: Sector Composition
        st.header("2. Sector Composition")
        st.write(
            "Displays the ETF's sector weighting. "
            "The bar chart visualizes each sector's percentage in the portfolio."
        )
        sector_df = get_etf_sector_composition(st.session_state.etf_ticker)
        sector_fig = plot_etf_sector_composition(sector_df, st.session_state.etf_ticker)
        if sector_fig is not None:
            st.plotly_chart(sector_fig, use_container_width=True)
        st.subheader("Data")
        st.dataframe(sector_df, use_container_width=True)
        
        # Section 3: Country Composition
        st.header("3. Country Composition")
        st.write(
            "Shows the geographic distribution of the ETF's holdings by country. "
            "The bar chart displays the weight percentage by country."
        )
        country_df = get_etf_country_composition(st.session_state.etf_ticker)
        country_fig = plot_etf_country_composition(country_df, st.session_state.etf_ticker)
        if country_fig is not None:
            st.plotly_chart(country_fig, use_container_width=True)
        st.subheader("Data")
        st.dataframe(country_df, use_container_width=True)
    else:
        st.info("Please enter the ETF ticker in the sidebar and click 'Run ETF Composition'.")

if __name__ == "__main__":
    main()

hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)