Fix valuation methodology and documentation

2 files changed, 37 insertions, 9 deletions

diff --git a/components/news.py b/components/news.py
index cea678e..522826c 100644
--- a/components/news.py
+++ b/components/news.py
@@ -7,11 +7,11 @@ from services.fmp_service import get_company_news as get_fmp_news
 
 def _sentiment_badge(sentiment: str) -> str:
     badges = {
-        "bullish": "🟢 Bullish",
-        "bearish": "🔴 Bearish",
-        "neutral": "⚪ Neutral",
+        "bullish": "🟢 Bullish (heuristic)",
+        "bearish": "🔴 Bearish (heuristic)",
+        "neutral": "⚪ Neutral (heuristic)",
     }
-    return badges.get(sentiment.lower(), "⚪ Neutral")
+    return badges.get(sentiment.lower(), "⚪ Neutral (heuristic)")
 
 
 def _classify_sentiment(article: dict) -> str:
@@ -52,8 +52,9 @@ def render_news(ticker: str):
         col1.metric("Articles (7d)", buzz.get("articlesInLastWeek", "—"))
         bull_pct = score.get("bullishPercent")
         bear_pct = score.get("bearishPercent")
-        col2.metric("Bullish %", f"{bull_pct * 100:.1f}%" if bull_pct else "—")
-        col3.metric("Bearish %", f"{bear_pct * 100:.1f}%" if bear_pct else "—")
+        col2.metric("Bullish %", f"{bull_pct * 100:.1f}%" if bull_pct is not None else "—")
+        col3.metric("Bearish %", f"{bear_pct * 100:.1f}%" if bear_pct is not None else "—")
+        st.caption("Sentiment tags below are rule-based headline heuristics, not model-scored article sentiment.")
         st.divider()
 
     # Fetch articles — Finnhub first, FMP as fallback
diff --git a/components/valuation.py b/components/valuation.py
index 62ee1e3..82e0f0d 100644
--- a/components/valuation.py
+++ b/components/valuation.py
@@ -61,7 +61,7 @@ def _render_ratios(ticker: str):
     rows = [
         ("Valuation", [
             ("P/E (TTM)", r("peRatioTTM", "trailingPE")),
-            ("Forward P/E", r("priceEarningsRatioTTM", "forwardPE")),
+            ("Forward P/E", fmt_ratio(info.get("forwardPE")) if info.get("forwardPE") is not None else "—"),
             ("P/S (TTM)", r("priceToSalesRatioTTM", "priceToSalesTrailing12Months")),
             ("P/B", r("priceToBookRatioTTM", "priceToBook")),
             ("EV/EBITDA", r("enterpriseValueMultipleTTM", "enterpriseToEbitda")),
@@ -99,6 +99,15 @@ def _render_dcf(ticker: str):
     info = get_company_info(ticker)
     shares = info.get("sharesOutstanding") or info.get("floatShares")
     current_price = info.get("currentPrice") or info.get("regularMarketPrice")
+    total_debt = info.get("totalDebt") or 0.0
+    cash_and_equivalents = (
+        info.get("totalCash")
+        or info.get("cash")
+        or info.get("cashAndCashEquivalents")
+        or 0.0
+    )
+    preferred_equity = info.get("preferredStock") or 0.0
+    minority_interest = info.get("minorityInterest") or 0.0
 
     if not shares:
         st.info("Shares outstanding not available — DCF cannot be computed.")
@@ -143,22 +152,40 @@ def _render_dcf(ticker: str):
         terminal_growth=terminal_growth,
         projection_years=projection_years,
         growth_rate_override=fcf_growth_pct / 100,
+        total_debt=total_debt,
+        cash_and_equivalents=cash_and_equivalents,
+        preferred_equity=preferred_equity,
+        minority_interest=minority_interest,
     )
 
     if not result:
         st.warning("Insufficient data to run DCF model.")
         return
+    if result.get("error"):
+        st.warning(result["error"])
+        return
 
     iv = result["intrinsic_value_per_share"]
 
     m1, m2, m3, m4 = st.columns(4)
-    m1.metric("Intrinsic Value / Share", fmt_currency(iv))
+    m1.metric("Equity Value / Share", fmt_currency(iv))
     if current_price:
         upside = (iv - current_price) / current_price
         m2.metric("Current Price", fmt_currency(current_price))
         m3.metric("Upside / Downside", f"{upside * 100:+.1f}%", delta=f"{upside * 100:+.1f}%")
     m4.metric("FCF Growth Used", f"{result['growth_rate_used'] * 100:.1f}%")
 
+    st.caption(
+        "DCF is modeled on firm-level free cash flow, so enterprise value is bridged to equity value "
+        "using cash and debt before calculating per-share value."
+    )
+
+    bridge1, bridge2, bridge3, bridge4 = st.columns(4)
+    bridge1.metric("Enterprise Value", fmt_large(result["enterprise_value"]))
+    bridge2.metric("Net Debt", fmt_large(result["net_debt"]))
+    bridge3.metric("Equity Value", fmt_large(result["equity_value"]))
+    bridge4.metric("Terminal Value PV", fmt_large(result["terminal_value_pv"]))
+
     st.write("")
 
     years = [f"Year {y}" for y in result["years"]]
@@ -173,7 +200,7 @@ def _render_dcf(ticker: str):
         textposition="outside",
     ))
     fig.update_layout(
-        title="PV of Projected FCFs + Terminal Value (Billions)",
+        title="Enterprise Value Build: PV of Forecast FCFs + Terminal Value (Billions)",
         yaxis_title="USD (Billions)",
         plot_bgcolor="rgba(0,0,0,0)",
         paper_bgcolor="rgba(0,0,0,0)",