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 plotly.io as pio
+import os
+
+# Update the default color sequence for the plotly_dark template to green.
+pio.templates["plotly_dark"].layout.colorway = ["#00cc44"]
+
+# Global API key and constant limit (backend only)
+API_KEY = os.getenv("FMP_API_KEY")
+BASE_URL = "https://financialmodelingprep.com/api/v3/stock-screener"
+LIMIT = 1000
+
+# Set wide layout and page title
+st.set_page_config(layout="wide", page_title="Stock Screener")
+
+# App explanation in main area
+st.title("Stock Screener")
+
+st.write(
+    "A research tool for filtering stocks by price, volume, market cap, sector, and other fundamentals. "
+    "Useful for identifying sets of securities based on specific criteria."
+)
+
+st.info(
+    "Use the sidebar to set filters. Click **Run Analysis** to retrieve results and display charts. "
+    "All filters are optional. If no results appear, try loosening the constraints."
+)
+
+# Sidebar inputs
+st.sidebar.header("Filters")
+
+# Numeric Filters
+with st.sidebar.expander("Numeric Filters", expanded=True):
+    market_cap_min = st.number_input(
+        "Market Cap More Than",
+        value=1000000000,
+        help="Minimum market capitalization."
+    )
+    market_cap_max = st.number_input(
+        "Market Cap Lower Than",
+        value=50000000000,
+        help="Maximum market capitalization."
+    )
+    price_min = st.number_input(
+        "Price More Than",
+        value=10.0,
+        help="Minimum stock price."
+    )
+    price_max = st.number_input(
+        "Price Lower Than",
+        value=500.0,
+        help="Maximum stock price."
+    )
+    beta_min = st.number_input(
+        "Beta More Than",
+        value=0.5,
+        help="Minimum beta value."
+    )
+    beta_max = st.number_input(
+        "Beta Lower Than",
+        value=2.0,
+        help="Maximum beta value."
+    )
+    volume_min = st.number_input(
+        "Volume More Than",
+        value=10000,
+        help="Minimum trading volume."
+    )
+    volume_max = st.number_input(
+        "Volume Lower Than",
+        value=1000000,
+        help="Maximum trading volume."
+    )
+    dividend_min = st.number_input(
+        "Dividend More Than",
+        value=0.0,
+        help="Minimum dividend value."
+    )
+    dividend_max = st.number_input(
+        "Dividend Lower Than",
+        value=5.0,
+        help="Maximum dividend value."
+    )
+
+# Categorical Filters
+with st.sidebar.expander("Categorical Filters", expanded=True):
+    sector_options = [
+        "Technology", "Financial Services", "Consumer Cyclical", "Energy", "Industrials",
+        "Basic Materials", "Communication Services", "Consumer Defensive", "Healthcare",
+        "Real Estate", "Utilities", "Industrial Goods", "Financial", "Services", "Conglomerates"
+    ]
+    selected_sectors = st.multiselect(
+        "Sector",
+        options=sector_options,
+        help="Select one or more sectors."
+    )
+    industry_options = [
+        "Autos", "Banks", "Banks Diversified", "Software", "Banks Regional",
+        "Beverages Alcoholic", "Beverages Brewers", "Beverages Non-Alcoholic"
+    ]
+    selected_industries = st.multiselect(
+        "Industry",
+        options=industry_options,
+        help="Select one or more industries."
+    )
+    country_options = ["US", "UK", "MX", "BR", "RU", "HK", "CA"]
+    selected_countries = st.multiselect(
+        "Country",
+        options=country_options,
+        help="Select one or more countries."
+    )
+    exchange_options = ["nyse", "nasdaq", "amex", "euronext", "tsx", "etf", "mutual_fund"]
+    selected_exchanges = st.multiselect(
+        "Exchange",
+        options=exchange_options,
+        help="Select one or more exchanges."
+    )
+
+# Boolean Filters
+with st.sidebar.expander("Boolean Filters", expanded=True):
+    is_etf = st.checkbox(
+        "Is ETF",
+        value=False,
+        help="Check to return only ETFs."
+    )
+    is_fund = st.checkbox(
+        "Is Fund",
+        value=False,
+        help="Check to return only funds."
+    )
+    is_actively_trading = st.checkbox(
+        "Is Actively Trading",
+        value=True,
+        help="Check to return only actively traded stocks."
+    )
+
+# Run Analysis button (placed outside expanders)
+run_analysis = st.sidebar.button("Run Analysis")
+
+def get_stock_data(params):
+    # Copy parameters and add API key.
+    filters = params.copy()
+    filters["apikey"] = API_KEY
+    # Convert list values to comma-separated strings.
+    for key, value in filters.items():
+        if isinstance(value, list) and value:
+            filters[key] = ",".join(map(str, value))
+    try:
+        response = requests.get(BASE_URL, params=filters, timeout=10)
+        response.raise_for_status()
+        data = response.json()
+        if not data:
+            st.error("No results found for the provided filters.")
+            return pd.DataFrame()
+        return pd.DataFrame(data)
+    except Exception:
+        st.error("An error occurred while fetching data.")
+        return pd.DataFrame()
+
+if run_analysis:
+    # Build parameter dictionary.
+    params = {}
+    params["marketCapMoreThan"] = market_cap_min
+    params["marketCapLowerThan"] = market_cap_max
+    params["priceMoreThan"] = price_min
+    params["priceLowerThan"] = price_max
+    params["betaMoreThan"] = beta_min
+    params["betaLowerThan"] = beta_max
+    params["volumeMoreThan"] = volume_min
+    params["volumeLowerThan"] = volume_max
+    params["dividendMoreThan"] = dividend_min
+    params["dividendLowerThan"] = dividend_max
+
+    if selected_sectors:
+        params["sector"] = selected_sectors
+    if selected_industries:
+        params["industry"] = selected_industries
+    if selected_countries:
+        params["country"] = selected_countries
+    if selected_exchanges:
+        params["exchange"] = selected_exchanges
+
+    params["isEtf"] = is_etf
+    params["isFund"] = is_fund
+    params["isActivelyTrading"] = is_actively_trading
+
+    # Set limit in the backend.
+    params["limit"] = LIMIT
+
+    with st.spinner("Fetching stock data..."):
+        df = get_stock_data(params)
+
+    if not df.empty:
+        st.success("Data fetched successfully!")
+        
+        #with st.expander("Results", expanded=True):
+        with st.container(border=True):
+            # Display the data table.
+            with st.container(border=True):
+                st.dataframe(df)
+
+            # Common hover data for scatter plots.
+            hover_fields = ["symbol", "companyName", "sector", "industry", "exchangeShortName", "country"]
+
+            # Chart 1: Price vs Market Cap (Scatter)
+            with st.container(border=True):
+                try:
+                    fig1 = px.scatter(
+                        df,
+                        x="price",
+                        y="marketCap",
+                        size="volume",
+                        title="Price vs Market Cap",
+                        hover_data=hover_fields,
+                        template="plotly_dark"
+                    )
+                    st.plotly_chart(fig1, use_container_width=True)
+                except Exception:
+                    st.warning("Scatter plot could not be generated.")
+
+            # Chart 2: Sector Distribution (Bar Chart)
+            with st.container(border=True):
+                try:
+                    sector_counts = df["sector"].value_counts().reset_index()
+                    sector_counts.columns = ["Sector", "Count"]
+                    fig2 = px.bar(
+                        sector_counts,
+                        x="Sector",
+                        y="Count",
+                        title="Sector Distribution",
+                        template="plotly_dark"
+                    )
+                    st.plotly_chart(fig2, use_container_width=True)
+                except Exception:
+                    st.warning("Sector distribution chart could not be generated.")
+
+            # Chart 3: Price Distribution (Histogram)
+            with st.container(border=True):
+                try:
+                    fig3 = px.histogram(
+                        df,
+                        x="price",
+                        nbins=30,
+                        title="Price Distribution",
+                        template="plotly_dark"
+                    )
+                    st.plotly_chart(fig3, use_container_width=True)
+                except Exception:
+                    st.warning("Price distribution chart could not be generated.")
+
+            # Chart 4: Market Cap vs Volume (Scatter)
+            with st.container(border=True):
+                try:
+                    fig4 = px.scatter(
+                        df,
+                        x="volume",
+                        y="marketCap",
+                        size="price",
+                        title="Market Cap vs Volume",
+                        hover_data=hover_fields,
+                        template="plotly_dark"
+                    )
+                    st.plotly_chart(fig4, use_container_width=True)
+                except Exception:
+                    st.warning("Market Cap vs Volume chart could not be generated.")
+
+            # Chart 5: Country Breakdown (Bar Chart)
+            with st.container(border=True):
+                try:
+                    country_counts = df["country"].value_counts().reset_index()
+                    country_counts.columns = ["Country", "Count"]
+                    fig5 = px.bar(
+                        country_counts,
+                        x="Country",
+                        y="Count",
+                        title="Country Breakdown",
+                        template="plotly_dark",
+                        #color_discrete_sequence=px.colors.qualitative.Plotly  # override global green
+                    )
+                    st.plotly_chart(fig5, use_container_width=True)
+                except Exception:
+                    st.warning("Country breakdown chart could not be generated.")
+
+            # Chart 6: Dividend vs Price (Scatter)
+            with st.container(border=True):
+                try:
+                    if "lastAnnualDividend" in df.columns:
+                        fig6 = px.scatter(
+                            df,
+                            x="price",
+                            y="lastAnnualDividend",
+                            title="Dividend vs Price",
+                            hover_data=hover_fields,
+                            template="plotly_dark"
+                        )
+                        st.plotly_chart(fig6, use_container_width=True)
+                    else:
+                        st.info("Dividend data is not available.")
+                except Exception:
+                    st.warning("Dividend vs Price chart could not be generated.")
+
+            # Chart 7: Beta Distribution (Histogram)
+            with st.container(border=True):
+                try:
+                    fig7 = px.histogram(
+                        df,
+                        x="beta",
+                        nbins=30,
+                        title="Beta Distribution",
+                        template="plotly_dark"
+                    )
+                    st.plotly_chart(fig7, use_container_width=True)
+                except Exception:
+                    st.warning("Beta distribution chart could not be generated.")
+
+            # Chart 8: Exchange Breakdown (Bar Chart)
+            with st.container(border=True):
+                try:
+                    if "exchangeShortName" in df.columns:
+                        exchange_counts = df["exchangeShortName"].value_counts().reset_index()
+                        exchange_counts.columns = ["Exchange", "Count"]
+                        fig8 = px.bar(
+                            exchange_counts,
+                            x="Exchange",
+                            y="Count",
+                            title="Exchange Breakdown",
+                            template="plotly_dark"
+                        )
+                        st.plotly_chart(fig8, use_container_width=True)
+                    else:
+                        st.info("Exchange data is not available.")
+                except Exception:
+                    st.warning("Exchange breakdown chart could not be generated.")
+            
+# Hide default Streamlit style
+st.markdown(
+    """
+    <style>
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    </style>
+    """,
+    unsafe_allow_html=True
+)