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"""Valuation panel — key ratios, DCF model, comparable companies."""
import pandas as pd
import plotly.graph_objects as go
import streamlit as st
from services.data_service import get_company_info, get_free_cash_flow_series
from services.fmp_service import get_key_ratios, get_peers, get_ratios_for_tickers
from services.valuation_service import run_dcf
from utils.formatters import fmt_ratio, fmt_pct, fmt_large, fmt_currency
def render_valuation(ticker: str):
tab_ratios, tab_dcf, tab_comps = st.tabs(["Key Ratios", "DCF Model", "Comps"])
with tab_ratios:
_render_ratios(ticker)
with tab_dcf:
_render_dcf(ticker)
with tab_comps:
_render_comps(ticker)
# ── Key Ratios ───────────────────────────────────────────────────────────────
def _render_ratios(ticker: str):
ratios = get_key_ratios(ticker)
info = get_company_info(ticker)
if not ratios and not info:
st.info("Ratio data unavailable. Check your FMP API key.")
return
# Prefer FMP ratios, fall back to yfinance info
def r(fmp_key, yf_key=None, fmt=fmt_ratio):
val = ratios.get(fmp_key) if ratios else None
if val is None and yf_key and info:
val = info.get(yf_key)
return fmt(val) if val is not None else "—"
rows = [
("Valuation", [
("P/E (TTM)", r("peRatioTTM", "trailingPE")),
("Forward P/E", r("priceEarningsRatioTTM", "forwardPE")),
("P/S (TTM)", r("priceToSalesRatioTTM", "priceToSalesTrailing12Months")),
("P/B", r("priceToBookRatioTTM", "priceToBook")),
("EV/EBITDA", r("enterpriseValueMultipleTTM", "enterpriseToEbitda")),
("EV/Revenue", r("evToSalesTTM", "enterpriseToRevenue")),
]),
("Profitability", [
("Gross Margin", r("grossProfitMarginTTM", "grossMargins", fmt_pct)),
("Operating Margin", r("operatingProfitMarginTTM", "operatingMargins", fmt_pct)),
("Net Margin", r("netProfitMarginTTM", "profitMargins", fmt_pct)),
("ROE", r("returnOnEquityTTM", "returnOnEquity", fmt_pct)),
("ROA", r("returnOnAssetsTTM", "returnOnAssets", fmt_pct)),
("ROIC", r("returnOnInvestedCapitalTTM", fmt=fmt_pct)),
]),
("Leverage & Liquidity", [
("Debt/Equity", r("debtEquityRatioTTM", "debtToEquity")),
("Current Ratio", r("currentRatioTTM", "currentRatio")),
("Quick Ratio", r("quickRatioTTM", "quickRatio")),
("Interest Coverage", r("interestCoverageTTM")),
("Dividend Yield", r("dividendYieldTTM", "dividendYield", fmt_pct)),
("Payout Ratio", r("payoutRatioTTM", "payoutRatio", fmt_pct)),
]),
]
for section_name, metrics in rows:
st.markdown(f"**{section_name}**")
cols = st.columns(6)
for col, (label, val) in zip(cols, metrics):
col.metric(label, val)
st.write("")
# ── DCF Model ────────────────────────────────────────────────────────────────
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")
if not shares:
st.info("Shares outstanding not available — DCF cannot be computed.")
return
fcf_series = get_free_cash_flow_series(ticker)
if fcf_series.empty:
st.info("Free cash flow data unavailable.")
return
st.markdown("**Assumptions**")
col1, col2, col3 = st.columns(3)
with col1:
wacc = st.slider("WACC (%)", min_value=5.0, max_value=20.0, value=10.0, step=0.5) / 100
with col2:
terminal_growth = st.slider("Terminal Growth (%)", min_value=0.5, max_value=5.0, value=2.5, step=0.5) / 100
with col3:
projection_years = st.slider("Projection Years", min_value=3, max_value=10, value=5, step=1)
result = run_dcf(
fcf_series=fcf_series,
shares_outstanding=shares,
wacc=wacc,
terminal_growth=terminal_growth,
projection_years=projection_years,
)
if not result:
st.warning("Insufficient data to run DCF model.")
return
iv = result["intrinsic_value_per_share"]
# ── Summary metrics ──────────────────────────────────────────────────────
m1, m2, m3, m4 = st.columns(4)
m1.metric("Intrinsic 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.write("")
# ── Waterfall chart ───────────────────────────────────────────────────────
years = [f"Year {y}" for y in result["years"]]
discounted = result["discounted_fcfs"]
terminal_pv = result["terminal_value_pv"]
bar_labels = years + ["Terminal Value"]
bar_values = discounted + [terminal_pv]
bar_colors = ["#4F8EF7"] * len(years) + ["#F7A24F"]
fig = go.Figure(
go.Bar(
x=bar_labels,
y=[v / 1e9 for v in bar_values],
marker_color=bar_colors,
text=[f"${v / 1e9:.2f}B" for v in bar_values],
textposition="outside",
)
)
fig.update_layout(
title="PV of Projected FCFs + Terminal Value (Billions)",
yaxis_title="USD (Billions)",
plot_bgcolor="rgba(0,0,0,0)",
paper_bgcolor="rgba(0,0,0,0)",
margin=dict(l=0, r=0, t=40, b=0),
height=360,
)
st.plotly_chart(fig, use_container_width=True)
# ── Comps Table ──────────────────────────────────────────────────────────────
def _render_comps(ticker: str):
peers = get_peers(ticker)
if not peers:
st.info("No comparable companies found. Check your FMP API key.")
return
# Include the subject ticker
all_tickers = [ticker.upper()] + [p for p in peers[:9] if p != ticker.upper()]
with st.spinner("Loading comps..."):
ratios_list = get_ratios_for_tickers(all_tickers)
if not ratios_list:
st.info("Could not load ratios for peer companies.")
return
display_cols = {
"symbol": "Ticker",
"peRatioTTM": "P/E",
"priceToSalesRatioTTM": "P/S",
"priceToBookRatioTTM": "P/B",
"enterpriseValueMultipleTTM": "EV/EBITDA",
"netProfitMarginTTM": "Net Margin",
"returnOnEquityTTM": "ROE",
"debtEquityRatioTTM": "D/E",
}
df = pd.DataFrame(ratios_list)
available = [c for c in display_cols if c in df.columns]
df = df[available].rename(columns=display_cols)
# Format numeric columns
pct_cols = {"Net Margin", "ROE"}
for col in df.columns:
if col == "Ticker":
continue
if col in pct_cols:
df[col] = df[col].apply(lambda v: fmt_pct(v) if v is not None else "—")
else:
df[col] = df[col].apply(lambda v: fmt_ratio(v) if v is not None else "—")
# Highlight subject ticker row
def highlight_subject(row):
if row["Ticker"] == ticker.upper():
return ["background-color: rgba(79,142,247,0.15)"] * len(row)
return [""] * len(row)
st.dataframe(
df.style.apply(highlight_subject, axis=1),
use_container_width=True,
hide_index=True,
)
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