Initial commit — Prism financial analysis dashboard

Streamlit app with market bar, price chart, financial statements,
DCF valuation engine, comparable companies, and news feed.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

1 files changed, 208 insertions, 0 deletions

diff --git a/components/valuation.py b/components/valuation.py
new file mode 100644
index 0000000..6549d07
--- /dev/null
+++ b/components/valuation.py
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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,
+    )