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feat: add Atlas Terminal — Next.js 14 + FastAPI full-stack migration
Complete migration from Streamlit to Next.js 14 App Router + FastAPI backend. Frontend (Next.js 14): - 10 pages: Overview, Research, Valuation, Technical, Markets, Earnings, News, Portfolio, Filings, Settings - Terminal Noir dark theme with custom Tailwind config - TradingView Lightweight Charts for candlestick/volume - Valuation: DCF, Sensitivity Matrix, Monte Carlo, Tornado, Reverse DCF - Financial Statements table with YoY growth badges and margin rows - SEC EDGAR inline filing viewer with section tabs - News split-view with iframe article embedding - Technical Analysis with RSI, MACD, Bollinger, Fibonacci, Moving Averages - Earnings beat/miss visualization - AI Copilot chat panel with Gemini integration Backend (FastAPI): - 13 routers: market_data, financials, valuation, technical, earnings, insider, edgar, news, portfolio, analysis, chat, estimates, fx - Services: DCF engine, Monte Carlo simulation, sensitivity analysis, risk metrics, SEC parser, technical indicators - yfinance + yahooquery data sources with fallback pattern - SQLite caching layer Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
shawnkim1997
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
56a9561f71
commit
b2acda81ee
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"""
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Tab 10 — Company Valuation
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PER, PBR, PSR, P/OCF cards with 5Y history, industry comparison, historical chart.
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"""
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import streamlit as st
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from config.constants import MARKET_OPTIONS
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from utils.ticker import get_global_ticker
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from data.valuation_metrics import (
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get_valuation_multiples, get_historical_multiples,
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get_industry_avg_multiples, get_pe_history_chart_data,
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)
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from data.fundamentals import get_sector_industry
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try:
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import plotly.graph_objects as go
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except ImportError:
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go = None
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def _metric_card(label, value, sub_items: dict, col):
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"""Render a single valuation metric card."""
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with col:
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val_str = f"{value:.2f}" if isinstance(value, (int, float)) and value == value else "N/A"
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st.markdown(
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f'<div style="background:rgba(255,255,255,0.03);border:1px solid rgba(255,255,255,0.08);'
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f'border-radius:10px;padding:16px 20px;min-height:160px;">'
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f'<div style="color:#9CA3AF;font-size:0.8rem;font-weight:600;">{label}</div>'
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f'<div style="color:#F3F4F6;font-size:2rem;font-weight:700;margin:4px 0;">{val_str}</div>',
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unsafe_allow_html=True,
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)
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for k, v in sub_items.items():
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v_str = f"{v:.2f}" if isinstance(v, (int, float)) and v == v else "N/A"
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color = "#9CA3AF"
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st.markdown(
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f'<div style="color:{color};font-size:0.75rem;padding:1px 0;">'
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f'{k}: <b>{v_str}</b></div>', unsafe_allow_html=True,
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)
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st.markdown('</div>', unsafe_allow_html=True)
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def _render_pe_chart(ticker: str, quant_ticker: str):
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"""Render 5Y price chart with PE overlay."""
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if not go:
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return
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df = get_pe_history_chart_data(quant_ticker)
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if df.empty:
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return
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st.markdown("#### Historical Price (5Y)")
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fig = go.Figure()
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fig.add_trace(go.Scatter(
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x=df["Date"], y=df["Close"], mode="lines",
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name="Price", line=dict(color="#34D399", width=2),
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))
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fig.update_layout(
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paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
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font_color="#E5E7EB", height=300,
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margin=dict(t=20, b=30, l=50, r=20),
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xaxis=dict(gridcolor="rgba(255,255,255,0.05)"),
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yaxis=dict(gridcolor="rgba(255,255,255,0.05)", title="Price ($)"),
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showlegend=False,
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)
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st.plotly_chart(fig, use_container_width=True)
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def _render_comparison_bar(label, current, avg_5y, industry_avg):
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"""Render a horizontal comparison bar for a metric."""
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vals = {"Current": current, "5Y Avg": avg_5y, "Industry": industry_avg}
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items = []
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for k, v in vals.items():
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if v and isinstance(v, (int, float)) and v == v:
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items.append(f'<span style="color:#9CA3AF;font-size:0.8rem;">{k}: </span>'
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f'<span style="color:#F3F4F6;font-weight:600;">{v:.2f}</span>')
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if items:
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st.markdown(
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f'<div style="padding:8px 0;border-bottom:1px solid rgba(255,255,255,0.06);">'
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f'<span style="color:#60A5FA;font-weight:600;min-width:80px;display:inline-block;">{label}</span>'
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f'{" · ".join(items)}</div>', unsafe_allow_html=True,
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)
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def render_tab10(ticker):
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"""Render the Company Valuation tab."""
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market = st.session_state.get("market") or MARKET_OPTIONS[0]
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quant_ticker = get_global_ticker(ticker, market) if ticker else ""
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st.subheader("Company Valuation")
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if not ticker:
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st.info("Select a company from the sidebar to view valuation metrics.")
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return
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si = get_sector_industry(quant_ticker)
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sector = si.get("sector", "N/A")
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industry = si.get("industry", "N/A")
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st.caption(f"**{ticker}** · {sector} · {industry}")
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with st.spinner("Fetching valuation data..."):
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multiples = get_valuation_multiples(quant_ticker)
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hist = get_historical_multiples(quant_ticker)
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ind_avg = get_industry_avg_multiples(quant_ticker)
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if not multiples:
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st.warning("Could not fetch valuation data for this ticker.")
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return
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# ── Metric Cards: PER, PBR, PSR, P/OCF ──
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c1, c2, c3, c4 = st.columns(4)
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_metric_card("PER (Trailing)", multiples.get("PER"), {
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"Forward": multiples.get("Forward PER"),
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"5Y Avg": hist.get("5Y Avg PER"),
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"Industry": ind_avg.get("Industry Avg PER"),
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}, c1)
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_metric_card("PBR", multiples.get("PBR"), {
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"5Y Avg": hist.get("5Y Avg PBR"),
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"Industry": ind_avg.get("Industry Avg PBR"),
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}, c2)
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_metric_card("PSR", multiples.get("PSR"), {
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"Industry": ind_avg.get("Industry Avg PSR"),
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"EV/Revenue": multiples.get("EV/Revenue"),
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}, c3)
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_metric_card("P/OCF", multiples.get("P/OCF"), {
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"EV/EBITDA": multiples.get("EV/EBITDA"),
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"PEG Ratio": multiples.get("PEG"),
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}, c4)
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st.markdown("---")
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# ── Comparison Table ──
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left, right = st.columns([3, 2])
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with left:
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st.markdown("#### Valuation Comparison")
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_render_comparison_bar("PER", multiples.get("PER"),
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hist.get("5Y Avg PER"), ind_avg.get("Industry Avg PER"))
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_render_comparison_bar("PBR", multiples.get("PBR"),
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hist.get("5Y Avg PBR"), ind_avg.get("Industry Avg PBR"))
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_render_comparison_bar("PSR", multiples.get("PSR"), None,
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ind_avg.get("Industry Avg PSR"))
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_render_comparison_bar("EV/EBITDA", multiples.get("EV/EBITDA"), None, None)
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# Premium/Discount indicator
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pe_cur = multiples.get("PER")
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pe_5y = hist.get("5Y Avg PER")
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if pe_cur and pe_5y and pe_5y > 0:
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prem = (pe_cur - pe_5y) / pe_5y * 100
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color = "#F87171" if prem > 0 else "#34D399"
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word = "premium" if prem > 0 else "discount"
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st.markdown(
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f'<div style="margin-top:12px;padding:10px;background:rgba(255,255,255,0.03);'
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f'border-radius:8px;"><span style="color:{color};font-weight:700;">'
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f'{abs(prem):.1f}% {word}</span>'
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f' <span style="color:#9CA3AF;">vs 5Y average PER</span></div>',
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unsafe_allow_html=True,
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)
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with right:
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_render_pe_chart(ticker, quant_ticker)
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# ── Additional Metrics Table ──
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st.markdown("---")
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st.markdown("#### Additional Metrics")
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m1, m2, m3, m4 = st.columns(4)
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with m1:
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beta = multiples.get("Beta")
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st.metric("Beta", f"{beta:.2f}" if beta else "N/A")
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with m2:
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dy = multiples.get("Dividend Yield")
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st.metric("Div Yield", f"{dy*100:.2f}%" if dy else "N/A")
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with m3:
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h52 = multiples.get("52W High")
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st.metric("52W High", f"${h52:,.2f}" if h52 else "N/A")
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with m4:
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l52 = multiples.get("52W Low")
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st.metric("52W Low", f"${l52:,.2f}" if l52 else "N/A")
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# ── KPI Analysis ──
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from views.tab10_kpi import render_kpi_section
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render_kpi_section(ticker, sector, industry)
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