"""
UI helper functions for rendering analyst consensus, sensitivity tables, etc.
"""
import pandas as pd
import streamlit as st
from utils.dcf import excel_style_dcf
def _render_analyst_consensus(ticker: str):
"""Render analyst consensus rating with visual badge and target prices."""
from data.valuation import get_analyst_consensus
consensus = get_analyst_consensus(ticker)
if not consensus:
st.caption("Analyst consensus data not available.")
return
rec = (consensus.get("recommendationKey") or "N/A").upper()
target_mean = consensus.get("targetMeanPrice")
target_high = consensus.get("targetHighPrice")
target_low = consensus.get("targetLowPrice")
num_analysts = consensus.get("numberOfAnalystOpinions", "N/A")
current = consensus.get("currentPrice")
# Rating badge colors
if rec in ("BUY", "STRONG_BUY", "STRONG BUY"):
badge_bg = "rgba(52, 211, 153, 0.15)"
badge_border = "rgba(52, 211, 153, 0.4)"
badge_color = "#34D399"
elif rec in ("SELL", "STRONG_SELL", "STRONG SELL"):
badge_bg = "rgba(248, 113, 113, 0.15)"
badge_border = "rgba(248, 113, 113, 0.4)"
badge_color = "#F87171"
else:
badge_bg = "rgba(251, 191, 36, 0.15)"
badge_border = "rgba(251, 191, 36, 0.4)"
badge_color = "#FBBF24"
rec_display = rec.replace("_", " ")
upside = ""
if target_mean and current and current > 0:
upside_pct = (target_mean - current) / current * 100
upside_color = "#34D399" if upside_pct > 0 else "#F87171"
upside = f'{upside_pct:+.1f}% implied'
if target_mean:
st.markdown(f"""
ANALYST RATING
{rec_display}
{upside}
TARGET (MEAN)
${target_mean:,.2f}
""", unsafe_allow_html=True)
else:
st.caption("Analyst targets not available.")
def _render_sensitivity_table(fcf: float, total_debt: float, cash: float, shares: float):
"""Render DCF sensitivity table: WACC vs Terminal Growth Rate."""
wacc_range = [0.065, 0.070, 0.075, 0.080, 0.085, 0.090, 0.095, 0.100]
tgr_range = [0.020, 0.025, 0.030, 0.035, 0.040]
rows = []
for tgr in tgr_range:
row = {"TGR": f"{tgr*100:.1f}%"}
for w in wacc_range:
res = excel_style_dcf(fcf, w, tgr, 0.10, total_debt, cash, shares)
val = res.get("value_per_share", 0)
row[f"{w*100:.1f}%"] = f"${val:,.0f}" if val and val > 0 else "N/A"
rows.append(row)
return pd.DataFrame(rows).set_index("TGR")