Dynamic sector-specific analysis, data robustness, changelog with timestamps

Co-authored-by: Cursor <cursoragent@cursor.com>

README.md

@@ -134,10 +134,12 @@ Open the sidebar to set **Google API Key** and **SEC EDGAR Email**, then use the
 
 Updates are listed in **reverse chronological order (newest first)**.
 
-| Date | Updates |
+| Date (UTC) | Updates |
-|------|---------|
+|------------|---------|
-| **2025-02-12** | **Hybrid architecture:** Send only Item 7 (MD&A) to Gemini; fetch financial metrics from **yfinance**. Add **HTML cleansing** (strip tags, collapse whitespace, remove page numbers). Change prompt to focus on management strategy, risks, and **sentiment** analysis. Add **find_toc.py** (SEC EDGAR HTML URL → Table of Contents extraction). Overhaul README for hybrid flow, Tech Stack, and Technical Challenge. |
+| **2025-02-12 15:30** | **Dynamic Sector-Specific Analysis:** (1) **DuPont display:** Replace None/NaN with "N/A" in the table so missing rows (e.g. SBUX) do not break the layout. (2) **Sector & industry badge:** Auto-detect from `ticker.info` (sector/industry) and show Sector · Industry caption at top of Tab 1. (3) **Sector-specific metrics:** Technology (Rule of 40, FCF margin, R&D % of revenue), Retail/Consumer (inventory turnover, operating margin), Financials (ROE, ROA) — Tab 1 "Sector-specific metrics" block. (4) **Tab 2:** For Financial sector, add caption that FCF/EBITDA are less relevant and to refer to ROE/ROA in Tab 1. (5) **Gemini MD&A:** Add sector/industry args to `get_mda_comparative_insights`; prompt instructs extraction of industry-specific Non-GAAP KPIs (Same-Store Sales, ARR/NDR, DAU/MAU, etc.) in a markdown table. |
-| **2025-02-12** | Add **Update history (Changelog)** to README and include `find_toc.py` in Project Structure. |
+| **2025-02-12 11:00** | **Data robustness & TTM fallback:** (1) yfinance **KeyError/NaN handling:** `_get_row_series` with try/except for financial/balance-sheet row access; `_na(x)` for consistent "N/A" display. (2) **TTM fallback:** When annual `financials`/`balance_sheet` are missing or empty, use sum of first 4 quarters from `quarterly_financials` and latest quarter from `quarterly_balance_sheet` for DuPont/Altman. (3) **`get_sector_industry(ticker)`** added (cached sector/industry). (4) `get_dupont_altman_redflags_yoy` returns empty dict on exception; stronger column/date checks when building TTM. |
+| **2025-02-12 09:00** | **Hybrid architecture:** Send only Item 7 (MD&A) to Gemini; fetch financial metrics from **yfinance**. Add **HTML cleansing** (strip tags, collapse whitespace, remove page numbers). Change prompt to focus on management strategy, risks, and **sentiment** analysis. Add **find_toc.py** (SEC EDGAR HTML URL → Table of Contents extraction). Overhaul README for hybrid flow, Tech Stack, and Technical Challenge. |
+| **2025-02-12 08:45** | Add **Update history (Changelog)** to README and include `find_toc.py` in Project Structure. |
 | **2025-02-01** (approx.) | Selective extraction: send only Item 7 & 8 to the API. 429 handling: retry with 60s wait, "Analysis only" option, timeout/404/error messages. Switch to Google **Gemini** and add `GOOGLE_API_KEY` (.env and sidebar). CFA Investment Report, metrics table, two-step spinners. |
 | **2025-01-XX** (approx.) | Initial release: SEC EDGAR 10-K download, Item 7 & 8 extraction, LLM analysis, Streamlit UI. S&P 500 sample list (company name and ticker). |
 

app.py

@@ -17,6 +17,11 @@ import streamlit as st
 import pandas as pd
 from bs4 import BeautifulSoup
 
+try:
+    import plotly.express as px
+except ImportError:
+    px = None
+
 try:
     from dotenv import load_dotenv
     load_dotenv()
@@ -28,6 +33,23 @@ try:
 except ImportError:
     yf = None
 
+# Company name → ticker for search/autocomplete (expand as needed)
+COMPANY_LIST = [
+    ("NVIDIA Corporation", "NVDA"), ("Apple Inc.", "AAPL"), ("Microsoft Corporation", "MSFT"),
+    ("Amazon.com Inc.", "AMZN"), ("Alphabet Inc.", "GOOGL"), ("Meta Platforms Inc.", "META"),
+    ("AMD", "AMD"), ("Intel Corporation", "INTC"), ("Qualcomm Inc.", "QCOM"), ("Tesla Inc.", "TSLA"),
+    ("Berkshire Hathaway", "BRK.B"), ("JPMorgan Chase", "JPM"), ("Visa Inc.", "V"), ("UnitedHealth", "UNH"),
+    ("Procter & Gamble", "PG"), ("Exxon Mobil", "XOM"), ("Johnson & Johnson", "JNJ"), ("Mastercard", "MA"),
+    ("Chevron", "CVX"), ("Home Depot", "HD"), ("Merck", "MRK"), ("AbbVie", "ABBV"), ("Costco", "COST"),
+    ("PepsiCo", "PEP"), ("Coca-Cola", "KO"), ("Pfizer", "PFE"), ("Walmart", "WMT"), ("Netflix", "NFLX"),
+    ("Adobe", "ADBE"), ("Salesforce", "CRM"), ("Comcast", "CMCSA"), ("Cisco", "CSCO"), ("Oracle", "ORCL"),
+    ("American Express", "AXP"), ("Bank of America", "BAC"), ("Wells Fargo", "WFC"), ("Verizon", "VZ"),
+    ("AT&T", "T"), ("Walt Disney", "DIS"), ("Nike", "NKE"), ("McDonald's", "MCD"), ("Starbucks", "SBUX"),
+    ("Goldman Sachs", "GS"), ("Morgan Stanley", "MS"), ("Target", "TGT"), ("Boeing", "BA"), ("IBM", "IBM"),
+]
+COMPANY_OPTIONS = [f"{t} - {n}" for n, t in COMPANY_LIST]
+COMPANY_TICKER_MAP = {t: n for n, t in COMPANY_LIST}
+
 
 def get_edgar_downloader():
     from sec_edgar_downloader import Downloader
@@ -156,6 +178,17 @@ def find_downloaded_10k_path(download_root: Path, ticker: str) -> Optional[Path]
     return None
 
 
+def find_all_10k_filing_dirs(download_root: Path, ticker: str) -> list:
+    """Return list of 10-K filing dirs sorted newest first (for multi-year comparison)."""
+    ticker_upper = ticker.upper()
+    for base in (download_root / "sec-edgar-filings", download_root):
+        path_10k = base / ticker_upper / "10-K"
+        if path_10k.exists():
+            subdirs = sorted([d for d in path_10k.iterdir() if d.is_dir()], key=lambda x: x.name, reverse=True)
+            return subdirs
+    return []
+
+
 def get_main_10k_text(filing_dir: Path) -> str:
     all_text = []
     for ext in ("*.htm", "*.html", "*.txt"):
@@ -200,6 +233,44 @@ def download_and_extract_item7_and_1a(ticker: str, email: str) -> tuple[str, str
     return full_text, item1a, item7
 
 
+def download_item7_latest_and_3y_ago(ticker: str, email: str) -> tuple[Optional[str], Optional[str], Optional[str], bool]:
+    """Download up to 5 10-Ks; extract Item 1A (latest only) and Item 7 from latest and from 3 years ago.
+    Returns (item1a_latest, item7_latest, item7_3y_ago, has_comparison). If < 4 filings, item7_3y_ago is None."""
+    Downloader = get_edgar_downloader()
+    with tempfile.TemporaryDirectory() as tmpdir:
+        download_root = Path(tmpdir)
+        dl = Downloader("FQDC-10K-Analyzer", email, str(download_root))
+        dl.get("10-K", ticker.upper(), limit=5, download_details=True)
+        filing_dirs = find_all_10k_filing_dirs(download_root, ticker)
+        if not filing_dirs:
+            raise FileNotFoundError(f"Could not find 10-K for ticker '{ticker}'.")
+        full_latest = get_main_10k_text(filing_dirs[0])
+        if not full_latest:
+            raise ValueError("Could not extract text from the latest 10-K.")
+        item1a = find_item_section_generic(
+            full_latest, ITEM1A_PATTERNS, 1, ["Risk", "Factors"], max_chars=80000
+        )
+        text_after_7 = full_latest[_find_section_start(full_latest, ITEM7_PATTERNS, 7):] if _find_section_start(full_latest, ITEM7_PATTERNS, 7) >= 0 else full_latest
+        item7_latest = find_item_section_generic(
+            text_after_7, ITEM7_PATTERNS, 7, ["Management's Discussion", "MD&A", "Analysis"], max_chars=100000
+        )
+        if not item7_latest and text_after_7:
+            item7_latest = smart_chunk(text_after_7[:120000], max_chars=20000)
+        item7_3y_ago = None
+        has_comparison = False
+        if len(filing_dirs) >= 4:
+            full_3y = get_main_10k_text(filing_dirs[3])
+            if full_3y:
+                text_3y = full_3y[_find_section_start(full_3y, ITEM7_PATTERNS, 7):] if _find_section_start(full_3y, ITEM7_PATTERNS, 7) >= 0 else full_3y
+                item7_3y_ago = find_item_section_generic(
+                    text_3y, ITEM7_PATTERNS, 7, ["Management's Discussion", "MD&A", "Analysis"], max_chars=100000
+                )
+                if not item7_3y_ago and text_3y:
+                    item7_3y_ago = smart_chunk(text_3y[:120000], max_chars=20000)
+                has_comparison = bool(item7_3y_ago)
+    return item1a or "", item7_latest or "", item7_3y_ago, has_comparison
+
+
 # ---------- Gemini (qualitative only) ----------
 GEMINI_MODEL = "gemini-2.0-flash"
 RATE_LIMIT_WAIT_SEC = 60
@@ -270,10 +341,159 @@ Use clear headings. Do not invent figures. Keep the response focused and under 8
     return response.text.strip()
 
 
-# ---------- yfinance: DCF inputs ----------
+def get_mda_comparative_insights(
+    api_key: str,
+    item1a_text: str,
+    item7_latest: str,
+    item7_3y_ago: Optional[str],
+    ticker: str,
+    sector: Optional[str] = None,
+    industry: Optional[str] = None,
+) -> str:
+    """Comparative analysis: if item7_3y_ago provided, compare MD&As over 3 years; else single-year. Sector-aware: extract industry-specific Non-GAAP KPIs."""
+    model = get_gemini_model(api_key)
+    sector_label = (sector or "N/A").strip()
+    industry_label = (industry or "N/A").strip()
+    kpi_instruction = (
+        f" Given that this company is in the **{sector_label}** sector"
+        + (f" (industry: {industry_label})" if industry_label != "N/A" else "")
+        + ", meticulously scan the MD&A to find and extract **industry-specific Non-GAAP KPIs** "
+        "(e.g. Same-Store Sales Growth for Retail, ARR/NDR for Software, DAU/MAU for Tech). Present these hidden KPIs in a **clean markdown table** with columns such as KPI name, value, and period if stated."
+    )
+    if not item7_3y_ago or not item7_3y_ago.strip():
+        combined = []
+        if item1a_text:
+            combined.append(clean_text_for_llm(item1a_text))
+        if item7_latest:
+            combined.append(clean_text_for_llm(item7_latest))
+        combined_text = "\n\n---\n\n".join(combined)
+        combined_text = smart_chunk(combined_text, max_chars=22000)
+        user_prompt = f"""You are a senior equity analyst. Use British English.
+The text below is from the latest 10-K for {ticker}: **Item 1A (Risk Factors)** and **Item 7 (MD&A)**.
+Provide a concise report: 1) Management's Tone (Sentiment), 2) Key Strategic Shifts, 3) Major Hidden Risks.{kpi_instruction}
+Use clear headings. Under 800 words."""
+        full_content = f"""--- 10-K Excerpt ---\n\n{combined_text}\n\n---\n\n{user_prompt}"""
+    else:
+        latest_clean = smart_chunk(clean_text_for_llm(item7_latest), max_chars=12000)
+        past_clean = smart_chunk(clean_text_for_llm(item7_3y_ago), max_chars=12000)
+        user_prompt = f"""You are a senior equity analyst. Use British English.
+Below are **Item 7 (Management's Discussion and Analysis)** from the 10-K for {ticker}: **LATEST YEAR** and **THREE YEARS AGO**. Perform a **Comparative Analysis**.
+
+1. **Core strategy**: What has changed in the company's stated strategy, priorities, or capital allocation between then and now?
+2. **Emerging risks**: What new risks appear in the latest MD&A that were absent or less prominent 3 years ago?
+3. **Management's tone**: How has the overall tone (confidence, caution, optimism) shifted? Quote 1–2 phrases from each period if relevant.
+4. **Industry-specific KPIs**:{kpi_instruction}
+
+Use clear headings. Do not invent figures. Keep the response focused and under 900 words."""
+        full_content = f"""--- MD&A LATEST YEAR ---\n\n{latest_clean}\n\n--- MD&A THREE YEARS AGO ---\n\n{past_clean}\n\n---\n\n{user_prompt}"""
+    try:
+        response = _generate_with_retry(
+            model, full_content, {"temperature": 0.3, "max_output_tokens": 4096}
+        )
+    except Exception as api_err:
+        if _is_rate_limit_error(api_err):
+            raise RuntimeError("Rate limit exceeded. Please try again in a few minutes.") from api_err
+        raise
+    if not response or not response.text:
+        return "No analysis generated."
+    return response.text.strip()
+
+
+# ---------- yfinance: raw statements & FCF = OCF - CapEx ----------
+def _safe_float(x) -> Optional[float]:
+    if x is None or (isinstance(x, float) and (x != x or pd.isna(x))):
+        return None
+    try:
+        return float(x)
+    except (TypeError, ValueError):
+        return None
+
+
+def _get_row_series(df: pd.DataFrame, *names: str) -> Optional[pd.Series]:
+    if df is None or df.empty:
+        return None
+    for name in names:
+        try:
+            if name in df.index:
+                return df.loc[name].copy()
+        except (KeyError, TypeError):
+            continue
+    return None
+
+
+@st.cache_data(ttl=300)
+def get_sector_industry(ticker: str) -> dict:
+    """Return sector and industry from yfinance. Fallback to N/A."""
+    if not yf:
+        return {"sector": "N/A", "industry": "N/A"}
+    try:
+        t = yf.Ticker(ticker.upper())
+        info = t.info or {}
+        sector = (info.get("sector") or info.get("sectorDisp") or "N/A").strip() or "N/A"
+        industry = (info.get("industry") or info.get("industryDisp") or "N/A").strip() or "N/A"
+        return {"sector": sector, "industry": industry}
+    except Exception:
+        return {"sector": "N/A", "industry": "N/A"}
+
+
+@st.cache_data(ttl=300)
+def get_5yr_financial_trend(ticker: str) -> pd.DataFrame:
+    """Extract up to 5 years: Revenue, Net Income, Operating Margin, FCF (OCF - CapEx). Handles missing years."""
+    if not yf:
+        return pd.DataFrame()
+    try:
+        t = yf.Ticker(ticker.upper())
+        financials = t.financials  # annual
+        cashflow = t.cashflow
+        if financials is None or financials.empty or cashflow is None or cashflow.empty:
+            return pd.DataFrame()
+        dates = sorted(financials.columns.tolist(), reverse=True)[:5]
+        ocf = _get_row_series(cashflow, "Operating Cash Flow", "Cash From Operating Activities", "Cash From Operations")
+        capx = _get_row_series(cashflow, "Capital Expenditure", "Capital Expenditures", "Purchase Of Property Plant And Equipment")
+        revenue = _get_row_series(financials, "Total Revenue", "Revenue", "Net Revenue")
+        ni = _get_row_series(financials, "Net Income", "Net Income Common Stockholders")
+        op_income = _get_row_series(financials, "Operating Income", "EBIT")
+        rows = []
+        cashflow_cols = list(cashflow.columns) if cashflow is not None else []
+        for d in dates:
+            yr = d.year if hasattr(d, "year") else int(str(d)[:4])
+            rev = _safe_float(revenue.get(d)) if revenue is not None and d in revenue.index else None
+            net_i = _safe_float(ni.get(d)) if ni is not None and d in ni.index else None
+            op_i = _safe_float(op_income.get(d)) if op_income is not None and d in op_income.index else None
+            oper_margin = (op_i / rev * 100) if (op_i is not None and rev and rev != 0) else ((net_i / rev * 100) if (net_i is not None and rev and rev != 0) else None)
+            ocf_val = _safe_float(ocf.get(d)) if ocf is not None and d in ocf.index else None
+            if ocf_val is None and ocf is not None and cashflow_cols:
+                for c in cashflow_cols:
+                    if (getattr(c, "year", None) or int(str(c)[:4])) == yr:
+                        ocf_val = _safe_float(ocf.get(c))
+                        break
+            capx_val = _safe_float(capx.get(d)) if capx is not None and d in capx.index else None
+            if capx_val is None and capx is not None and cashflow_cols:
+                for c in cashflow_cols:
+                    if (getattr(c, "year", None) or int(str(c)[:4])) == yr:
+                        capx_val = _safe_float(capx.get(c))
+                        break
+            if ocf_val is not None and capx_val is not None:
+                fcf = ocf_val - capx_val
+            elif ocf_val is not None:
+                fcf = ocf_val
+            else:
+                fcf = None
+            rows.append({
+                "Year": yr,
+                "Revenue": rev,
+                "Net Income": net_i,
+                "Operating Margin %": round(oper_margin, 2) if oper_margin is not None else None,
+                "FCF": fcf,
+            })
+        return pd.DataFrame(rows)
+    except Exception:
+        return pd.DataFrame()
+
+
 @st.cache_data(ttl=300)
 def get_dcf_inputs(ticker: str) -> dict:
-    """Fetch FCF, Total Debt, Cash, Shares Outstanding for DCF. Returns dict or empty on failure."""
+    """FCF = OCF - CapEx from cashflow; baseline = latest year. Debt, Cash, Shares from balance sheet/info."""
     if not yf:
         return {}
     try:
@@ -283,19 +503,16 @@ def get_dcf_inputs(ticker: str) -> dict:
         balance = t.balance_sheet
         if cashflow is None or cashflow.empty:
             return {}
-        fcf_row = None
+        ocf = _get_row_series(cashflow, "Operating Cash Flow", "Cash From Operating Activities", "Cash From Operations")
-        for name in ("Free Cash Flow", "Cash From Operations"):
+        capx = _get_row_series(cashflow, "Capital Expenditure", "Capital Expenditures", "Purchase Of Property Plant And Equipment")
-            if name in cashflow.index:
+        if ocf is None or len(ocf) == 0:
-                fcf_row = cashflow.loc[name]
+            return {}
-                break
+        latest_date = ocf.index[0]
-        if fcf_row is None and len(cashflow.index) > 0:
+        ocf_val = _safe_float(ocf.iloc[0])
-            fcf_row = cashflow.iloc[0]
+        capx_val = _safe_float(capx.get(latest_date)) if capx is not None and latest_date in capx.index else (_safe_float(capx.iloc[0]) if capx is not None and len(capx) > 0 else None)
-        latest_fcf = None
+        if capx_val is None:
-        if fcf_row is not None and len(fcf_row) > 0:
+            capx_val = 0.0
-            try:
+        latest_fcf = (ocf_val - capx_val) if ocf_val is not None else None
-                latest_fcf = float(fcf_row.iloc[0])
-            except (TypeError, ValueError):
-                pass
         if latest_fcf is not None and (latest_fcf != latest_fcf or latest_fcf <= 0):
             latest_fcf = None
         total_debt = info.get("Total Debt")
@@ -303,15 +520,9 @@ def get_dcf_inputs(ticker: str) -> dict:
         shares = info.get("Shares Outstanding") or info.get("Float Shares")
         if balance is not None and not balance.empty:
             if total_debt is None and "Total Debt" in balance.index:
-                try:
+                total_debt = _safe_float(balance.loc["Total Debt"].iloc[0])
-                    total_debt = float(balance.loc["Total Debt"].iloc[0])
-                except (TypeError, ValueError, KeyError):
-                    pass
             if cash is None and "Cash And Cash Equivalents" in balance.index:
-                try:
+                cash = _safe_float(balance.loc["Cash And Cash Equivalents"].iloc[0])
-                    cash = float(balance.loc["Cash And Cash Equivalents"].iloc[0])
-                except (TypeError, ValueError, KeyError):
-                    pass
         return {
             "fcf": latest_fcf,
             "total_debt": total_debt if total_debt is not None else 0,
@@ -322,15 +533,15 @@ def get_dcf_inputs(ticker: str) -> dict:
         return {}
 
 
-def dcf_intrinsic_value(fcf: float, wacc: float, terminal_growth: float, revenue_growth: float, years: int = 10) -> float:
+def dcf_intrinsic_value(fcf: float, wacc: float, terminal_growth: float, fcf_growth: float, years: int = 5) -> float:
-    """DCF: project FCF with revenue_growth, terminal value with terminal_growth, discount at WACC. Returns enterprise value."""
+    """5-year DCF: project FCF with fcf_growth, then terminal value; discount at WACC. Returns enterprise value."""
     if fcf <= 0 or wacc <= terminal_growth:
         return 0.0
     pv = 0.0
     fcft = fcf
     for t in range(1, years + 1):
         pv += fcft / ((1 + wacc) ** t)
-        fcft *= (1 + revenue_growth)
+        fcft *= (1 + fcf_growth)
     terminal_fcf = fcft
     tv = terminal_fcf * (1 + terminal_growth) / (wacc - terminal_growth)
     pv += tv / ((1 + wacc) ** years)
@@ -369,6 +580,208 @@ def get_comps_data(tickers: tuple) -> pd.DataFrame:
     return pd.DataFrame(rows)
 
 
+# ---------- DuPont, Altman Z, Red Flags, YoY (2–3 years) ----------
+def _na(x):
+    """Return N/A for None/NaN, else value (for display)."""
+    if x is None or (isinstance(x, float) and (pd.isna(x) or x != x)):
+        return "N/A"
+    return x
+
+
+@st.cache_data(ttl=300)
+def get_dupont_altman_redflags_yoy(ticker: str) -> dict:
+    """Returns DuPont (3-step ROE), Altman Z-Score, red flags, YoY. Uses TTM if annual missing. Handles KeyError/NaN."""
+    if not yf:
+        return {}
+    try:
+        t = yf.Ticker(ticker.upper())
+        info = t.info or {}
+        fin = t.financials
+        bal = t.balance_sheet
+        if fin is None or fin.empty:
+            qfin = getattr(t, "quarterly_financials", None)
+            if qfin is not None and not qfin.empty and qfin.shape[1] >= 1:
+                fin = qfin.iloc[:, :4].sum(axis=1).to_frame("TTM")
+            else:
+                return {}
+        if bal is None or bal.empty:
+            qbal = getattr(t, "quarterly_balance_sheet", None)
+            if qbal is not None and not qbal.empty:
+                bal = qbal.iloc[:, :1]
+            else:
+                return {}
+        dates = sorted(fin.columns.tolist(), reverse=True)[:3]
+        if not dates:
+            return {}
+        rev = _get_row_series(fin, "Total Revenue", "Revenue", "Net Revenue")
+        ni = _get_row_series(fin, "Net Income", "Net Income Common Stockholders")
+        ebit = _get_row_series(fin, "Operating Income", "EBIT")
+        gross = _get_row_series(fin, "Gross Profit")
+        interest = _get_row_series(fin, "Interest Expense", "Interest Expense Net")
+        total_assets = _get_row_series(bal, "Total Assets")
+        total_equity = _get_row_series(bal, "Total Stockholder Equity", "Stockholders Equity", "Total Equity Gross Minority Interest")
+        current_assets = _get_row_series(bal, "Current Assets")
+        current_liab = _get_row_series(bal, "Current Liabilities")
+        retained = _get_row_series(bal, "Retained Earnings")
+        total_liab = _get_row_series(bal, "Total Liabilities")
+        market_cap = info.get("marketCap") or info.get("Market Cap")
+        def _v(s, d):
+            if s is None or d not in s.index:
+                return None
+            return _safe_float(s.get(d))
+        rows = []
+        for d in dates:
+            yr = d.year if hasattr(d, "year") else int(str(d)[:4])
+            r = _v(rev, d)
+            net_i = _v(ni, d)
+            ta = _v(total_assets, d)
+            te = _v(total_equity, d)
+            if ta and ta > 0 and te and te > 0 and r and r != 0:
+                npm = (net_i / r * 100) if net_i is not None else None
+                at = r / ta if r and ta else None
+                em = ta / te if ta and te else None
+                roe = (net_i / te * 100) if (net_i and te) else (npm * at * em / 100 if (npm and at and em) else None)
+            else:
+                npm = at = em = roe = None
+            gross_p = _v(gross, d)
+            gross_margin = (gross_p / r * 100) if (gross_p and r and r != 0) else None
+            op_inc = _v(ebit, d)
+            op_margin = (op_inc / r * 100) if (op_inc and r and r != 0) else None
+            ca = _v(current_assets, d)
+            cl = _v(current_liab, d)
+            current_ratio = (ca / cl) if (ca and cl and cl != 0) else None
+            int_exp = _v(interest, d)
+            interest_cov = (op_inc / int_exp) if (op_inc and int_exp and int_exp != 0) else None
+            rows.append({
+                "Year": yr,
+                "Revenue": r, "Net Income": net_i,
+                "NPM %": round(npm, 2) if npm is not None else None,
+                "Asset Turnover": round(at, 4) if at is not None else None,
+                "Equity Mult.": round(em, 2) if em is not None else None,
+                "ROE %": round(roe, 2) if roe is not None else None,
+                "Gross Margin %": round(gross_margin, 2) if gross_margin is not None else None,
+                "Operating Margin %": round(op_margin, 2) if op_margin is not None else None,
+                "Current Ratio": round(current_ratio, 2) if current_ratio is not None else None,
+                "Interest Coverage": round(interest_cov, 2) if interest_cov is not None else None,
+            })
+        dupont_df = pd.DataFrame(rows)
+        yoy = []
+        if len(dupont_df) >= 2:
+            for col in ["NPM %", "ROE %", "Gross Margin %", "Operating Margin %", "Current Ratio", "Interest Coverage"]:
+                if col not in dupont_df.columns:
+                    continue
+                cur = dupont_df[col].iloc[0]
+                prev = dupont_df[col].iloc[1]
+                if cur is not None and prev is not None and prev != 0:
+                    if "Margin" in col or "NPM" in col or "ROE" in col:
+                        chg_bps = (cur - prev) * 100  # bps for %
+                        yoy.append({"Ratio": col, "Latest": cur, "Prior": prev, "YoY (bps)": round(chg_bps, 0), "Comment": f"{'Improved' if chg_bps > 0 else 'Declined'} by {abs(round(chg_bps))} bps YoY"})
+                    else:
+                        pct = (cur - prev) / abs(prev) * 100
+                        yoy.append({"Ratio": col, "Latest": cur, "Prior": prev, "YoY %": round(pct, 1), "Comment": f"{'Up' if pct > 0 else 'Down'} {abs(round(pct, 1))}% YoY"})
+        latest_bal_d = bal.columns[0]
+        wc = (_v(current_assets, latest_bal_d) or 0) - (_v(current_liab, latest_bal_d) or 0)
+        ta_l = _v(total_assets, latest_bal_d)
+        re_l = _v(retained, latest_bal_d)
+        tl_l = _v(total_liab, latest_bal_d)
+        ebit_l = _v(ebit, fin.columns[0])
+        sales_l = _v(rev, fin.columns[0])
+        altman_z = None
+        if ta_l and ta_l > 0 and market_cap is not None and tl_l and tl_l != 0 and sales_l:
+            a = wc / ta_l
+            b = (re_l or 0) / ta_l
+            c = (ebit_l or 0) / ta_l
+            d = market_cap / tl_l
+            e = sales_l / ta_l
+            altman_z = 1.2 * a + 1.4 * b + 3.3 * c + 0.6 * d + 1.0 * e
+        red_flags = []
+        if len(dupont_df) > 0:
+            row0 = dupont_df.iloc[0]
+            cr = row0.get("Current Ratio")
+            if cr is not None and cr < 1.0:
+                red_flags.append({"metric": "Current Ratio", "value": cr, "threshold": 1.0, "flag": "WARNING", "comment": "Current assets do not cover current liabilities; liquidity risk."})
+            ic = row0.get("Interest Coverage")
+            if ic is not None and ic < 1.5:
+                red_flags.append({"metric": "Interest Coverage", "value": ic, "threshold": 1.5, "flag": "WARNING", "comment": "EBIT barely covers interest; default risk."})
+        return {
+            "dupont": dupont_df,
+            "yoy": yoy,
+            "altman_z": round(altman_z, 2) if altman_z is not None else None,
+            "red_flags": red_flags,
+        }
+    except (KeyError, TypeError, ZeroDivisionError, IndexError) as e:
+        return {}
+    except Exception:
+        return {}
+
+
+@st.cache_data(ttl=300)
+def get_sector_specific_metrics(ticker: str, sector: str) -> dict:
+    """Technology: Rule of 40, R&D % revenue. Retail/Consumer: Inventory Turnover, Operating Margin. Financials: ROE, ROA."""
+    if not yf:
+        return {}
+    try:
+        t = yf.Ticker(ticker.upper())
+        info = t.info or {}
+        fin = t.financials
+        bal = t.balance_sheet
+        if fin is None or fin.empty:
+            fin = getattr(t, "quarterly_financials", None)
+            if fin is not None and not fin.empty:
+                fin = fin.iloc[:, :4].sum(axis=1).to_frame()
+        if bal is None or bal.empty:
+            bal = getattr(t, "quarterly_balance_sheet", None)
+        out = {}
+        sector_lower = (sector or "").lower()
+        if "technology" in sector_lower or "software" in sector_lower or "tech" in sector_lower:
+            rev = _get_row_series(fin, "Total Revenue", "Revenue", "Net Revenue")
+            ocf = _get_row_series(t.cashflow or getattr(t, "quarterly_cashflow", None), "Operating Cash Flow", "Cash From Operating Activities")
+            capx = _get_row_series(t.cashflow or getattr(t, "quarterly_cashflow", None), "Capital Expenditure", "Capital Expenditures")
+            rd = _get_row_series(fin, "Research And Development", "Research And Development Expense")
+            if rev is not None and len(rev) > 0:
+                r0 = _safe_float(rev.iloc[0])
+                if ocf is not None and len(ocf) > 0 and capx is not None and len(capx) > 0:
+                    fcf = _safe_float(ocf.iloc[0]) - _safe_float(capx.iloc[0])
+                    out["FCF Margin %"] = round(fcf / r0 * 100, 2) if r0 and fcf is not None else None
+                if rd is not None and len(rd) > 0:
+                    out["R&D % of Revenue"] = round(_safe_float(rd.iloc[0]) / r0 * 100, 2) if r0 else None
+                rev_growth = None
+                if rev is not None and len(rev) >= 2:
+                    cur, prev = _safe_float(rev.iloc[0]), _safe_float(rev.iloc[1])
+                    if prev and prev != 0:
+                        rev_growth = (cur - prev) / prev * 100
+                if rev_growth is not None and "FCF Margin %" in out and out["FCF Margin %"] is not None:
+                    out["Rule of 40 (Rev Growth + FCF Margin)"] = round(rev_growth + out["FCF Margin %"], 1)
+        if "consumer" in sector_lower or "retail" in sector_lower or "cyclical" in sector_lower:
+            inv = _get_row_series(bal, "Inventory", "Total Inventory")
+            cogs = _get_row_series(fin, "Cost Of Revenue", "Cost Of Goods Sold", "Cost of Goods Sold")
+            rev = _get_row_series(fin, "Total Revenue", "Revenue", "Net Revenue")
+            op_inc = _get_row_series(fin, "Operating Income", "EBIT")
+            if inv is not None and len(inv) > 0 and cogs is not None and len(cogs) > 0:
+                inv0 = _safe_float(inv.iloc[0])
+                cogs0 = _safe_float(cogs.iloc[0])
+                out["Inventory Turnover"] = round(cogs0 / inv0, 2) if inv0 else None
+            if rev is not None and len(rev) > 0 and op_inc is not None and len(op_inc) > 0:
+                r0 = _safe_float(rev.iloc[0])
+                op0 = _safe_float(op_inc.iloc[0])
+                out["Operating Margin %"] = round(op0 / r0 * 100, 2) if r0 else None
+        if "financial" in sector_lower or "bank" in sector_lower or "insurance" in sector_lower:
+            ni = _get_row_series(fin, "Net Income", "Net Income Common Stockholders")
+            te = _get_row_series(bal, "Total Stockholder Equity", "Stockholders Equity", "Total Equity Gross Minority Interest")
+            ta = _get_row_series(bal, "Total Assets")
+            if ni is not None and te is not None and len(ni) > 0 and len(te) > 0:
+                te0 = _safe_float(te.iloc[0])
+                ni0 = _safe_float(ni.iloc[0])
+                out["ROE %"] = round(ni0 / te0 * 100, 2) if te0 else None
+            if ni is not None and ta is not None and len(ni) > 0 and len(ta) > 0:
+                ta0 = _safe_float(ta.iloc[0])
+                ni0 = _safe_float(ni.iloc[0])
+                out["ROA %"] = round(ni0 / ta0 * 100, 2) if ta0 else None
+        return out
+    except Exception:
+        return {}
+
+
 # ---------- Streamlit UI ----------
 st.set_page_config(page_title="Financial Analysis Dashboard", layout="wide", initial_sidebar_state="expanded")
 
@@ -398,21 +811,79 @@ with st.sidebar:
         value=os.environ.get("SEC_EDGAR_EMAIL", ""),
         help="Required for 10-K download.",
     )
-    ticker = st.text_input("Primary Ticker", value="NVDA", max_chars=10).strip().upper()
+    st.markdown("**Ticker / Company search**")
+    search_term = st.text_input("Type ticker or company name", value="", key="ticker_search", placeholder="e.g. NVDA or NVIDIA")
+    search_upper = (search_term or "").strip().upper()
+    search_lower = (search_term or "").strip().lower()
+    filtered = [o for o in COMPANY_OPTIONS if search_upper in o.split(" - ")[0] or search_lower in o.lower()] if (search_upper or search_lower) else COMPANY_OPTIONS
+    default_idx = 0
+    current_ticker = st.session_state.get("ticker", "NVDA")
+    for i, o in enumerate(filtered):
+        if o.startswith(current_ticker + " - "):
+            default_idx = i
+            break
+    selected = st.selectbox("Select company (ticker - name)", filtered, index=min(default_idx, len(filtered) - 1), key="company_select")
+    ticker_from_select = selected.split(" - ")[0].strip() if selected else ""
+    manual = st.text_input("Or enter ticker manually", value="", max_chars=10, key="manual_ticker").strip().upper()
+    ticker = manual if manual else ticker_from_select
+    if not ticker:
+        ticker = "NVDA"
     st.session_state["google_api_key"] = google_api_key
     st.session_state["sec_email"] = sec_email
     st.session_state["ticker"] = ticker
-
+    st.caption("Example: type _NVDA_ or _NVIDIA_ then select from list.")
-if not ticker:
-    ticker = st.session_state.get("ticker", "NVDA") or "NVDA"
 
 tab1, tab2, tab3 = st.tabs(["10-K & MD&A Insights", "3-Scenario DCF Valuation", "Industry Analysis & Comps"])
 
-# ----- Tab 1: 10-K & MD&A Insights -----
+# ----- Tab 1: Qualitative (MD&A) + Quantitative (DuPont, Altman Z, Red Flags, YoY) -----
 with tab1:
-    st.subheader("10-K & MD&A Insights (Qualitative)")
+    st.subheader("10-K & MD&A Insights — Qualitative and Quantitative")
-    st.markdown("Extract **Item 1A (Risk Factors)** and **Item 7 (MD&A)** from the latest 10-K. Gemini analyses: **Management's Tone**, **Strategic Shifts**, **Hidden Risks**.")
+    if ticker:
-    if st.button("Run 10-K Analysis", key="run_10k"):
+        si = get_sector_industry(ticker)
+        sector, industry = si.get("sector", "N/A"), si.get("industry", "N/A")
+        st.caption(f"Sector: **{sector}**  ·  Industry: **{industry}**")
+    st.markdown("**Quantitative** metrics below (DuPont ROE, Altman Z-Score, Red Flags, YoY trends). **Qualitative** analysis: run comparative MD&A with the button.")
+    if ticker:
+        q = get_dupont_altman_redflags_yoy(ticker)
+        if q:
+            st.markdown("#### Quantitative health (2–3 years)")
+            dupont_df = q.get("dupont")
+            if dupont_df is not None and not dupont_df.empty:
+                st.markdown("**3-Step DuPont (ROE = Net Profit Margin × Asset Turnover × Equity Multiplier)**")
+                display_cols = [c for c in ["Year", "NPM %", "Asset Turnover", "Equity Mult.", "ROE %"] if c in dupont_df.columns]
+                display_dupont = dupont_df[display_cols].copy().rename(columns={"Equity Mult.": "Equity Mult"})
+                for col in display_dupont.columns:
+                    display_dupont[col] = display_dupont[col].apply(lambda x: "N/A" if (x is None or (isinstance(x, float) and pd.isna(x))) else x)
+                st.dataframe(display_dupont, use_container_width=True, hide_index=True)
+            az = q.get("altman_z")
+            if az is not None:
+                st.metric("Altman Z-Score (distress / bankruptcy risk)", f"{az}", "Safe zone > 2.99; Grey 1.81–2.99; Distress < 1.81" if az < 1.81 else ("Grey zone" if az < 2.99 else "Safe zone"))
+            red_flags = q.get("red_flags") or []
+            if red_flags:
+                st.markdown("**Red flags**")
+                for rf in red_flags:
+                    st.warning(f"**{rf.get('flag', 'WARNING')}** — {rf.get('metric')}: {rf.get('value')} (threshold: {rf.get('threshold')}). {rf.get('comment', '')}")
+            elif dupont_df is not None and not dupont_df.empty:
+                st.success("No red flags triggered (Current Ratio ≥ 1.0, Interest Coverage ≥ 1.5).")
+            sector_metrics = get_sector_specific_metrics(ticker, sector) if ticker else {}
+            if sector_metrics:
+                st.markdown("**Sector-specific metrics**")
+                cols = st.columns(min(len(sector_metrics), 4))
+                for i, (k, v) in enumerate(sector_metrics.items()):
+                    with cols[i % len(cols)]:
+                        disp = f"{v}" if v is not None else "N/A"
+                        st.metric(k, disp, None)
+            yoy_list = q.get("yoy") or []
+            if yoy_list:
+                st.markdown("**YoY ratio changes**")
+                for item in yoy_list:
+                    st.caption(f"**{item.get('Ratio')}**: {item.get('Comment', '')}")
+        else:
+            st.info("Quantitative data not available for this ticker.")
+    st.markdown("---")
+    st.markdown("#### Qualitative: MD&A comparative analysis")
+    st.markdown("Download **latest 10-K** and **10-K from 3 years ago**. Extract **Item 7 (MD&A)** from both. Gemini: strategy shifts, emerging risks, management tone.")
+    if st.button("Run 10-K Comparative Analysis", key="run_10k"):
         if not ticker:
             st.error("Enter a ticker in the sidebar.")
         elif not st.session_state.get("google_api_key"):
@@ -421,16 +892,27 @@ with tab1:
             st.error("Enter your SEC EDGAR email in the sidebar.")
         else:
             try:
-                with st.spinner("Downloading 10-K and extracting Item 1A & Item 7..."):
+                with st.spinner("Downloading 10-Ks (latest + 3 years ago) and extracting Item 7..."):
-                    full_text, item1a, item7 = download_and_extract_item7_and_1a(ticker, st.session_state["sec_email"])
+                    item1a, item7_latest, item7_3y_ago, has_comparison = download_item7_latest_and_3y_ago(
-                with st.spinner("Running Gemini analysis (tone, strategy, risks)..."):
+                        ticker, st.session_state["sec_email"]
-                    analysis = get_mda_insights(
+                    )
-                        st.session_state["google_api_key"], item1a, item7, ticker
+                if not has_comparison:
+                    st.info("Only one or fewer 10-K filings available; showing single-year analysis.")
+                with st.spinner("Running Gemini (comparative or single-year analysis)..."):
+                    si = get_sector_industry(ticker)
+                    analysis = get_mda_comparative_insights(
+                        st.session_state["google_api_key"],
+                        item1a,
+                        item7_latest,
+                        item7_3y_ago,
+                        ticker,
+                        sector=si.get("sector"),
+                        industry=si.get("industry"),
                     )
                 st.success("Analysis complete.")
                 st.markdown(analysis)
-                with st.expander("View raw excerpt (Item 1A + Item 7)"):
+                with st.expander("View raw excerpt (Item 1A + Item 7 latest)"):
-                    excerpt = (item1a or "") + "\n\n---\n\n" + (item7 or "")
+                    excerpt = (item1a or "") + "\n\n---\n\n" + (item7_latest or "")
                     st.text(excerpt[:12000] + ("..." if len(excerpt) > 12000 else ""))
             except FileNotFoundError as e:
                 st.error(str(e))
@@ -443,10 +925,53 @@ with tab1:
                 with st.expander("Error details"):
                     st.code(repr(e), language="text")
 
-# ----- Tab 2: 3-Scenario DCF -----
+# ----- Tab 2: 5-Year Trend + 3-Scenario DCF -----
 with tab2:
-    st.subheader("3-Scenario DCF Valuation (Quantitative)")
+    st.subheader("5-Year Financial Trend & DCF Valuation")
-    st.markdown("Uses **yfinance** for FCF, Debt, Cash, Shares. No Gemini. Adjust assumptions with sliders.")
+    if ticker:
+        si_t2 = get_sector_industry(ticker)
+        sector_t2 = (si_t2.get("sector") or "").lower()
+        is_financial = "financial" in sector_t2 or "bank" in sector_t2 or "insurance" in sector_t2
+    else:
+        is_financial = False
+    df_trend = get_5yr_financial_trend(ticker) if ticker else pd.DataFrame()
+    if not df_trend.empty and len(df_trend) >= 1:
+        st.markdown("#### Key metrics (YoY % change)")
+        latest = df_trend.iloc[0]
+        prev = df_trend.iloc[1] if len(df_trend) >= 2 else None
+        def _yoy_pct(cur, prev_val):
+            if prev_val is None or cur is None or prev_val == 0:
+                return None
+            return (cur - prev_val) / abs(prev_val) * 100
+        rev_yoy = _yoy_pct(latest.get("Revenue"), prev.get("Revenue") if prev is not None else None)
+        ni_yoy = _yoy_pct(latest.get("Net Income"), prev.get("Net Income") if prev is not None else None)
+        om_prev = prev.get("Operating Margin %") if prev is not None else None
+        om_cur = latest.get("Operating Margin %")
+        om_yoy = (om_cur - om_prev) if (om_cur is not None and om_prev is not None) else None
+        fcf_yoy = _yoy_pct(latest.get("FCF"), prev.get("FCF") if prev is not None else None)
+        m1, m2, m3, m4 = st.columns(4)
+        rev_val = latest.get("Revenue")
+        m1.metric("Revenue (latest yr)", f"${rev_val/1e9:.2f}B" if rev_val and rev_val >= 1e9 else (f"${rev_val/1e6:.0f}M" if rev_val else "—"), f"{rev_yoy:+.1f}% YoY" if rev_yoy is not None else None)
+        ni_val = latest.get("Net Income")
+        m2.metric("Net Income", f"${ni_val/1e9:.2f}B" if ni_val and abs(ni_val) >= 1e9 else (f"${ni_val/1e6:.0f}M" if ni_val is not None else "—"), f"{ni_yoy:+.1f}% YoY" if ni_yoy is not None else None)
+        om_val = latest.get("Operating Margin %")
+        m3.metric("Operating Margin %", f"{om_val:.1f}%" if om_val is not None else "—", f"{om_yoy:+.1f}pp YoY" if om_yoy is not None else None)
+        fcf_val = latest.get("FCF")
+        m4.metric("FCF", f"${fcf_val/1e9:.2f}B" if fcf_val and abs(fcf_val) >= 1e9 else (f"${fcf_val/1e6:.0f}M" if fcf_val is not None else "—"), f"{fcf_yoy:+.1f}% YoY" if fcf_yoy is not None else None)
+        st.caption("FCF = Operating Cash Flow − Capital Expenditure." + (" For Financials, FCF/EBITDA are less relevant; see ROE/ROA in Tab 1 sector-specific metrics." if is_financial else ""))
+        if len(df_trend) >= 2 and px is not None:
+            st.markdown("#### 5-year trend: Revenue & FCF")
+            df_plot = df_trend.copy()
+            df_plot["Revenue_M"] = (df_plot["Revenue"] / 1e6).round(1)
+            df_plot["FCF_M"] = (df_plot["FCF"] / 1e6).round(1)
+            fig = px.line(df_plot, x="Year", y=["Revenue_M", "FCF_M"], title="Revenue & Free Cash Flow ($M)")
+            fig.update_layout(yaxis_title="$M", legend_title="", hovermode="x unified")
+            fig.update_traces(line=dict(width=2))
+            st.plotly_chart(fig, use_container_width=True)
+    elif ticker:
+        st.caption("5-year trend not available for this ticker. DCF section below uses latest FCF from yfinance.")
+    st.markdown("---")
+    st.markdown("#### DCF valuation: inputs & 3-scenario output")
     dcf_inputs = get_dcf_inputs(ticker) if ticker else {}
     if not dcf_inputs:
         st.warning("Could not fetch DCF inputs from yfinance. Check ticker or try again.")
@@ -465,20 +990,20 @@ with tab2:
                 base_growth = st.slider("Base Case FCF Growth (%)", -10.0, 30.0, 8.0, 0.5) / 100.0
             bull_growth = base_growth + 0.02
             bear_growth = base_growth - 0.02
-            ev_base = dcf_intrinsic_value(fcf, wacc, term_growth, base_growth)
+            ev_base = dcf_intrinsic_value(fcf, wacc, term_growth, base_growth, years=5)
-            ev_bull = dcf_intrinsic_value(fcf, wacc, term_growth, bull_growth)
+            ev_bull = dcf_intrinsic_value(fcf, wacc, term_growth, bull_growth, years=5)
-            ev_bear = dcf_intrinsic_value(fcf, wacc, term_growth, bear_growth)
+            ev_bear = dcf_intrinsic_value(fcf, wacc, term_growth, bear_growth, years=5)
             equity_base = ev_base - total_debt + cash
             equity_bull = ev_bull - total_debt + cash
             equity_bear = ev_bear - total_debt + cash
             price_base = equity_base / shares if shares else 0
             price_bull = equity_bull / shares if shares else 0
             price_bear = equity_bear / shares if shares else 0
-            st.markdown("#### Intrinsic Value per Share (3 Scenarios)")
+            st.markdown("**Intrinsic value per share**")
             c1, c2, c3 = st.columns(3)
-            c1.metric("Bull (+2% growth)", f"${price_bull:.2f}", "Base vs Bull")
+            c1.metric("Bull (+2% growth)", f"${price_bull:.2f}", f"+{(price_bull - price_base):.2f} vs Base")
             c2.metric("Base", f"${price_base:.2f}", "—")
-            c3.metric("Bear (-2% growth)", f"${price_bear:.2f}", "Base vs Bear")
+            c3.metric("Bear (−2% growth)", f"${price_bear:.2f}", f"{(price_bear - price_base):.2f} vs Base")
             df_dcf = pd.DataFrame({
                 "Scenario": ["Bull", "Base", "Bear"],
                 "FCF Growth": [f"{bull_growth*100:.1f}%", f"{base_growth*100:.1f}%", f"{bear_growth*100:.1f}%"],
@@ -486,12 +1011,12 @@ with tab2:
             })
             st.dataframe(df_dcf, use_container_width=True, hide_index=True)
         else:
-            st.info("FCF or Shares Outstanding not available for this ticker. Try another.")
+            st.caption("FCF or Shares not available. FCF = OCF − CapEx from yfinance.")
 
-# ----- Tab 3: Industry Comps -----
+# ----- Tab 3: Industry Comps (conditional formatting) -----
 with tab3:
     st.subheader("Industry Analysis & Comps")
-    st.markdown("Enter **comma-separated competitor tickers** (e.g. `AMD, INTC, QCOM`). Multiples from **yfinance**.")
+    st.markdown("Enter **comma-separated competitor tickers**. Multiples from **yfinance**. Green = below peer average (undervalued), Red = above (overvalued).")
     comp_tickers = st.text_input("Competitor tickers", value="AMD, INTC, QCOM", key="comps").strip()
     if st.button("Load Comps", key="load_comps"):
         tickers_list = [t.strip().upper() for t in comp_tickers.split(",") if t.strip()]
@@ -504,7 +1029,31 @@ with tab3:
             if df_comps.empty:
                 st.warning("Could not fetch comps from yfinance.")
             else:
-                st.dataframe(df_comps, use_container_width=True, hide_index=True)
+                try:
+                    styled = df_comps.style
+                    for col in ["Forward P/E", "EV/EBITDA", "P/B"]:
+                        if col not in df_comps.columns:
+                            continue
+                        s = pd.to_numeric(df_comps[col], errors="coerce")
+                        avg = s.mean()
+                        if pd.isna(avg):
+                            continue
+                        def color_fn(v, avg_val=avg):
+                            if pd.isna(v):
+                                return ""
+                            try:
+                                x = float(v)
+                            except (TypeError, ValueError):
+                                return ""
+                            if x < avg_val:
+                                return "background-color: rgba(0, 200, 83, 0.3); color: #0d5c2e"
+                            if x > avg_val:
+                                return "background-color: rgba(255, 82, 82, 0.3); color: #b71c1c"
+                            return ""
+                        styled = styled.map(lambda v: color_fn(v), subset=[col])
+                    st.dataframe(styled, use_container_width=True, hide_index=True)
+                except Exception:
+                    st.dataframe(df_comps, use_container_width=True, hide_index=True)
 
 st.divider()
 with st.expander("S&P 500 sample — Company & Ticker"):

requirements.txt

@@ -7,3 +7,4 @@ pandas>=2.0.0
 lxml>=4.9.0
 python-dotenv>=1.0.0
 yfinance>=0.2.40
+plotly>=5.18.0