Create app.py

app.py

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+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
+
+# For sector and country composition, you may adjust this as needed.
+ETF_SYMBOL = "QDVE.DE"
+
+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}"
+    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
+
+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}"
+    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
+
+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}"
+    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
+
+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(
+        "This dashboard displays the composition of an ETF. "
+        "Use the side menu to enter the ETF ticker symbol and then click the 'Run ETF Composition' button. "
+        "The dashboard 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 session state variable
+    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: Only show dashboard if run button was pressed
+    if st.session_state.get("run_etf", False):
+        # Section 1: ETF Asset Composition
+        st.header("1. ETF Asset Composition")
+        st.write(
+            "This section shows the holdings of the ETF by asset. "
+            "The bar chart displays each asset's market value, shares held, and weight in the ETF. "
+            "The data table below shows the raw holdings data."
+        )
+        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(
+            "This section displays the ETF's sector weighting. "
+            "The bar chart visualizes the weight percentage of each sector represented in the ETF. "
+            "The data table below contains the raw sector weighting data."
+        )
+        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(
+            "This section shows the geographic distribution of the ETF's holdings by country. "
+            "The bar chart displays the weight percentage by country, and the table below lists the detailed country weighting data."
+        )
+        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)