app.py

import streamlit as st
import pandas as pd
import plotly.express as px
import requests
import numpy as np
import yfinance as yf
import os

# Global API key
API_KEY = os.getenv("FMP_API_KEY")

# ----------------------------
# Page configuration
# ----------------------------
st.set_page_config(page_title="Financial Ratios Dashboard", layout="wide")

# ----------------------------
# Helper function to interpret ratios
# ----------------------------
def interpret_ratios(df, metric_list, section_title):
    existing_cols = [m for m in metric_list if m in df.columns]
    if not existing_cols or df.empty:
        return f"**{section_title}**: Data is not available."

    df_valid = df[['date'] + existing_cols].dropna(subset=existing_cols, how='all')
    if df_valid.empty:
        return f"**{section_title}**: No valid data entries."

    df_valid = df_valid.sort_values("date")
    latest_row = df_valid.iloc[-1]
    latest_date = latest_row['date']

    if len(df_valid) > 1:
        prior_row = df_valid.iloc[-2]
        prior_date = prior_row['date']
    else:
        prior_row = None
        prior_date = None

    values_only = df_valid[existing_cols].astype(float)
    mean_vals = values_only.mean()
    min_vals = values_only.min()
    max_vals = values_only.max()
    std_vals = values_only.std()

    text = f"### {section_title}\n\n"
    text += "**Recent Data:**\n"
    text += f"- **Latest Record Date:** {latest_date.date()}\n"
    for col in existing_cols:
        latest_val = latest_row[col]
        if pd.isna(latest_val):
            text += f"- **{col}:** Data missing.\n"
        else:
            text += f"- **{col}:** {latest_val:.2f}\n"

    if prior_row is not None:
        text += f"\n**Comparison with previous record ({prior_date.date()}):**\n"
        for col in existing_cols:
            latest_val = latest_row[col]
            prior_val = prior_row[col]
            if pd.isna(latest_val) or pd.isna(prior_val):
                text += f"- **{col}:** Comparison not possible.\n"
            else:
                diff = latest_val - prior_val
                if diff > 0:
                    text += f"- **{col}:** Increased by {diff:.2f}.\n"
                elif diff < 0:
                    text += f"- **{col}:** Decreased by {abs(diff):.2f}.\n"
                else:
                    text += f"- **{col}:** Remained the same.\n"

    text += "\n**Historical Summary:**\n"
    for col in existing_cols:
        text += (f"- **{col}:** Mean = {mean_vals[col]:.2f}, "
                 f"Min = {min_vals[col]:.2f}, Max = {max_vals[col]:.2f}, "
                 f"Std Dev = {std_vals[col]:.2f}.\n")

    text += "\n**Final Interpretation:**\n"
    if section_title == "Liquidity Ratios":
        text += (
            "- High liquidity ratios (current, quick, cash) suggest the company can meet short-term obligations.\n"
            "- Low ratios may raise concerns about paying bills on time.\n"
            "- The cash ratio shows how much cash is on hand relative to liabilities.\n"
        )
    elif section_title == "Efficiency & Turnover Ratios":
        text += (
            "- Longer days of sales or inventory may signal slower turnover.\n"
            "- Faster turnover indicates efficient use of assets.\n"
            "- Extreme values call for a closer look at operations.\n"
        )
    elif section_title == "Profitability Ratios":
        text += (
            "- Higher margins and returns point to strong profit generation.\n"
            "- Lower margins may indicate rising costs or pricing pressure.\n"
            "- Tax rates combined with margins offer insight into net profitability.\n"
        )
    elif section_title == "Debt & Coverage Ratios":
        text += (
            "- Lower debt ratios and higher interest coverage suggest safer leverage.\n"
            "- High debt or low coverage ratios may increase financial risk.\n"
            "- These ratios help assess the firm's capacity to cover its debts.\n"
        )
    elif section_title == "Valuation Ratios":
        text += (
            "- Lower valuation ratios may hint at an undervalued stock.\n"
            "- High ratios could point to overvaluation or high growth expectations.\n"
            "- Comparing these with the stock price provides context for market sentiment.\n"
        )
    elif section_title == "Per Share & Distribution Ratios":
        text += (
            "- Higher cash flow per share numbers are positive for shareholders.\n"
            "- A high payout or dividend payout ratio means more earnings are returned as dividends.\n"
            "- These ratios help gauge share performance and distribution policies.\n"
        )
    else:
        text += "- Review these trends to understand their impact.\n"

    return text

# ----------------------------
# App header and description
# ----------------------------
st.title("Key Financial Ratios")
st.markdown("""
This dashboard shows key financial ratios for companies.
The ratios are grouped into sections for comparison.
Use the sidebar to set inputs and click **Run Analysis**.
""")

# ----------------------------
# Sidebar inputs
# ----------------------------
st.sidebar.header("Inputs")
with st.sidebar.expander("Settings", expanded=True):
    symbol = st.text_input("Company Symbol", value="AAPL", help="Enter the company's stock symbol (e.g., AAPL).")
    period = st.selectbox("Period", options=["annual", "quarter"], help="Select annual or quarterly data.")

run_button = st.sidebar.button("Run Analysis")

# ----------------------------
# Main Analysis and Visualization
# ----------------------------
if run_button:
    try:
        # Fetch ratios data
        url = f"https://financialmodelingprep.com/api/v3/ratios/{symbol}?period={period}&apikey={API_KEY}"
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()

        if not data:
            st.error("No data returned. Check the symbol or period.")
        else:
            df = pd.DataFrame(data)
            if "date" in df.columns:
                df['date'] = pd.to_datetime(df['date'], errors='coerce')
                df.sort_values("date", inplace=True)
            
            # Fetch historical stock price data
            ticker = yf.Ticker(symbol)
            price_df = ticker.history(period="max")[["Close"]].reset_index()
            price_df.rename(columns={"Date": "date"}, inplace=True)
            price_df['date'] = pd.to_datetime(price_df['date'])
            price_df['date'] = price_df['date'].dt.tz_localize(None)
            price_df.sort_values("date", inplace=True)
            
            # Merge stock price data with ratios data using merge_asof
            df = pd.merge_asof(df.sort_values("date"), price_df.sort_values("date"), on="date", direction="backward")

            st.success("Data loaded successfully!")
            st.write("Each section shows a chart, an interpretation, and the data.")

            # Section 1: Liquidity Ratios
            st.subheader("1. Liquidity Ratios")
            liquidity_vars = ["currentRatio", "quickRatio", "cashRatio"]
            try:
                fig1 = px.line(df, x="date", y=liquidity_vars, title="Liquidity Ratios")
                fig1.update_layout(xaxis_title="Date", yaxis_title="Ratio", legend_title="Metric")
                st.plotly_chart(fig1, use_container_width=True)
            except Exception:
                st.error("Error generating the Liquidity Ratios chart.")
            with st.expander("Interpretation"):
                interp_text = interpret_ratios(df, liquidity_vars, "Liquidity Ratios")
                st.markdown(interp_text)
            with st.expander("DataFrame"):
                st.dataframe(df[["date"] + liquidity_vars])

            # Section 2: Efficiency & Turnover Ratios
            st.subheader("2. Efficiency & Turnover Ratios")
            efficiency_vars = [
                "daysOfSalesOutstanding",
                "daysOfInventoryOutstanding",
                "operatingCycle",
                "daysOfPayablesOutstanding",
                "cashConversionCycle",
                "receivablesTurnover",
                "payablesTurnover",
                "inventoryTurnover",
                "fixedAssetTurnover",
                "assetTurnover"
            ]
            try:
                fig2 = px.line(df, x="date", y=efficiency_vars, title="Efficiency & Turnover Ratios")
                fig2.update_layout(xaxis_title="Date", yaxis_title="Ratio / Days", legend_title="Metric")
                st.plotly_chart(fig2, use_container_width=True)
            except Exception:
                st.error("Error generating the Efficiency & Turnover Ratios chart.")
            with st.expander("Interpretation"):
                interp_text = interpret_ratios(df, efficiency_vars, "Efficiency & Turnover Ratios")
                st.markdown(interp_text)
            with st.expander("DataFrame"):
                st.dataframe(df[["date"] + efficiency_vars])

            # Section 3: Profitability Ratios
            st.subheader("3. Profitability Ratios")
            profitability_vars = [
                "grossProfitMargin",
                "operatingProfitMargin",
                "pretaxProfitMargin",
                "netProfitMargin",
                "effectiveTaxRate",
                "returnOnAssets",
                "returnOnEquity",
                "returnOnCapitalEmployed",
                "netIncomePerEBT",
                "ebtPerEbit",
                "ebitPerRevenue"
            ]
            try:
                fig3 = px.line(df, x="date", y=profitability_vars, title="Profitability Ratios")
                fig3.update_layout(xaxis_title="Date", yaxis_title="Percentage / Ratio", legend_title="Metric")
                st.plotly_chart(fig3, use_container_width=True)
            except Exception:
                st.error("Error generating the Profitability Ratios chart.")
            with st.expander("Interpretation"):
                interp_text = interpret_ratios(df, profitability_vars, "Profitability Ratios")
                st.markdown(interp_text)
            with st.expander("DataFrame"):
                st.dataframe(df[["date"] + profitability_vars])

            # Section 4: Debt & Coverage Ratios
            st.subheader("4. Debt & Coverage Ratios")
            debt_vars = [
                "debtRatio",
                "debtEquityRatio",
                "longTermDebtToCapitalization",
                "totalDebtToCapitalization",
                "interestCoverage",
                "cashFlowToDebtRatio",
                "companyEquityMultiplier",
                "shortTermCoverageRatios",
                "cashFlowCoverageRatios",
                "capitalExpenditureCoverageRatio",
                "dividendPaidAndCapexCoverageRatio"
            ]
            try:
                fig4 = px.line(df, x="date", y=debt_vars, title="Debt & Coverage Ratios")
                fig4.update_layout(xaxis_title="Date", yaxis_title="Ratio", legend_title="Metric")
                st.plotly_chart(fig4, use_container_width=True)
            except Exception:
                st.error("Error generating the Debt & Coverage Ratios chart.")
            with st.expander("Interpretation"):
                interp_text = interpret_ratios(df, debt_vars, "Debt & Coverage Ratios")
                st.markdown(interp_text)
            with st.expander("DataFrame"):
                st.dataframe(df[["date"] + debt_vars])

            # Section 5: Valuation Ratios (with Stock Price)
            st.subheader("5. Valuation Ratios")
            valuation_vars = [
                "priceBookValueRatio",
                "priceToBookRatio",
                "priceToSalesRatio",
                "priceEarningsRatio",
                "priceToFreeCashFlowsRatio",
                "priceToOperatingCashFlowsRatio",
                "priceCashFlowRatio",
                "priceEarningsToGrowthRatio",
                "priceSalesRatio",
                "dividendYield",
                "enterpriseValueMultiple",
                "priceFairValue"
            ]
            try:
                from plotly.subplots import make_subplots
                import plotly.graph_objects as go
                fig5 = make_subplots(specs=[[{"secondary_y": True}]])
                for col in valuation_vars:
                    fig5.add_trace(
                        go.Scatter(x=df['date'], y=df[col], mode='lines', name=col),
                        secondary_y=False
                    )
                # Add stock price on secondary y-axis
                fig5.add_trace(
                    go.Scatter(x=df['date'], y=df['Close'], mode='lines', name="Stock Price"),
                    secondary_y=True
                )
                fig5.update_layout(title_text="Valuation Ratios & Stock Price")
                fig5.update_xaxes(title_text="Date")
                fig5.update_yaxes(title_text="Valuation Ratios", secondary_y=False)
                fig5.update_yaxes(title_text="Stock Price", secondary_y=True)
                st.plotly_chart(fig5, use_container_width=True)
            except Exception:
                st.error("Error generating the Valuation Ratios chart.")
            with st.expander("Interpretation"):
                interp_text = interpret_ratios(df, valuation_vars, "Valuation Ratios")
                st.markdown(interp_text)
            with st.expander("DataFrame"):
                st.dataframe(df[["date"] + valuation_vars + ["Close"]])

            # Section 6: Per Share & Distribution Ratios
            st.subheader("6. Per Share & Distribution Ratios")
            share_vars = [
                "operatingCashFlowPerShare",
                "freeCashFlowPerShare",
                "cashPerShare",
                "payoutRatio",
                "operatingCashFlowSalesRatio",
                "freeCashFlowOperatingCashFlowRatio",
                "dividendPayoutRatio"
            ]
            try:
                fig6 = px.line(df, x="date", y=share_vars, title="Per Share & Distribution Ratios")
                fig6.update_layout(xaxis_title="Date", yaxis_title="Ratio", legend_title="Metric")
                st.plotly_chart(fig6, use_container_width=True)
            except Exception:
                st.error("Error generating the Per Share & Distribution Ratios chart.")
            with st.expander("Interpretation"):
                interp_text = interpret_ratios(df, share_vars, "Per Share & Distribution Ratios")
                st.markdown(interp_text)
            with st.expander("DataFrame"):
                st.dataframe(df[["date"] + share_vars])

    except Exception:
        st.error("Error fetching data. Check your connection and inputs.")


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