Add historical ratios, forward estimates, insider transactions, SEC filings

- services/fmp_service.py: add get_historical_ratios, get_historical_key_metrics,
  get_analyst_estimates, get_insider_transactions, get_sec_filings
- components/valuation.py: add Historical Ratios and Forward Estimates subtabs
- components/insiders.py: new — insider buy/sell summary, monthly chart, detail table
- components/filings.py: new — SEC filings with type filter and direct links
- app.py: wire in Insiders and Filings top-level tabs

1 files changed, 161 insertions, 0 deletions

diff --git a/components/insiders.py b/components/insiders.py
new file mode 100644
index 0000000..05561ba
--- /dev/null
+++ b/components/insiders.py
@@ -0,0 +1,161 @@
+"""Insider transactions — recent buys/sells with summary and detail table."""
+import pandas as pd
+import plotly.graph_objects as go
+import streamlit as st
+from datetime import datetime, timedelta
+
+from services.fmp_service import get_insider_transactions
+from utils.formatters import fmt_currency, fmt_large
+
+
+def _parse_date(date_str: str) -> datetime | None:
+    for fmt in ("%Y-%m-%d", "%Y-%m-%dT%H:%M:%S"):
+        try:
+            return datetime.strptime(str(date_str)[:19], fmt)
+        except ValueError:
+            continue
+    return None
+
+
+def _classify(row: dict) -> str:
+    """Return 'Buy' or 'Sell' from the transaction type field."""
+    tx = str(row.get("transactionType") or row.get("acquistionOrDisposition") or "").strip()
+    buy_keywords = ("purchase", "p -", "p-", "acquisition", "a -", "a-", "grant", "award", "exercise")
+    sell_keywords = ("sale", "s -", "s-", "disposition", "d -", "d-")
+    tx_lower = tx.lower()
+    if any(k in tx_lower for k in sell_keywords):
+        return "Sell"
+    if any(k in tx_lower for k in buy_keywords):
+        return "Buy"
+    # Fallback: FMP often uses acquistionOrDisposition = "A" or "D"
+    aod = str(row.get("acquistionOrDisposition") or "").strip().upper()
+    if aod == "D":
+        return "Sell"
+    if aod == "A":
+        return "Buy"
+    return "Other"
+
+
+def render_insiders(ticker: str):
+    with st.spinner("Loading insider transactions…"):
+        raw = get_insider_transactions(ticker)
+
+    if not raw:
+        st.info("No insider transaction data available. Requires FMP API key.")
+        return
+
+    # Build enriched rows
+    rows = []
+    for item in raw:
+        direction = _classify(item)
+        shares = item.get("securitiesTransacted") or item.get("shares") or 0
+        price = item.get("price") or 0.0
+        try:
+            value = float(shares) * float(price)
+        except (TypeError, ValueError):
+            value = 0.0
+        date_obj = _parse_date(str(item.get("transactionDate") or item.get("filingDate") or ""))
+        rows.append({
+            "date": date_obj,
+            "date_str": str(item.get("transactionDate") or "")[:10],
+            "name": item.get("reportingName") or item.get("insiderName") or "—",
+            "title": item.get("typeOfOwner") or item.get("title") or "—",
+            "direction": direction,
+            "shares": shares,
+            "price": price,
+            "value": value,
+            "filing_url": item.get("link") or item.get("secLink") or "",
+        })
+
+    # ── Summary: last 6 months ────────────────────────────────────────────
+    cutoff = datetime.now() - timedelta(days=180)
+    recent = [r for r in rows if r["date"] and r["date"] >= cutoff]
+
+    buys = [r for r in recent if r["direction"] == "Buy"]
+    sells = [r for r in recent if r["direction"] == "Sell"]
+    total_buy_val = sum(r["value"] for r in buys)
+    total_sell_val = sum(r["value"] for r in sells)
+
+    st.markdown("**Insider Activity — Last 6 Months**")
+    c1, c2, c3, c4 = st.columns(4)
+    c1.metric("Buy Transactions", len(buys))
+    c2.metric("Total Bought", fmt_large(total_buy_val) if total_buy_val else "—")
+    c3.metric("Sell Transactions", len(sells))
+    c4.metric("Total Sold", fmt_large(total_sell_val) if total_sell_val else "—")
+
+    # Net buy/sell bar chart (monthly)
+    if recent:
+        monthly: dict[str, dict] = {}
+        for r in recent:
+            if r["date"]:
+                key = r["date"].strftime("%Y-%m")
+                monthly.setdefault(key, {"Buy": 0.0, "Sell": 0.0})
+                if r["direction"] in ("Buy", "Sell"):
+                    monthly[key][r["direction"]] += r["value"]
+
+        months = sorted(monthly.keys())
+        buy_vals = [monthly[m]["Buy"] / 1e6 for m in months]
+        sell_vals = [-monthly[m]["Sell"] / 1e6 for m in months]
+
+        fig = go.Figure()
+        fig.add_trace(go.Bar(
+            x=months, y=buy_vals,
+            name="Buys",
+            marker_color="#2ecc71",
+        ))
+        fig.add_trace(go.Bar(
+            x=months, y=sell_vals,
+            name="Sells",
+            marker_color="#e74c3c",
+        ))
+        fig.update_layout(
+            title="Monthly Insider Net Activity ($M)",
+            barmode="relative",
+            yaxis_title="Value ($M)",
+            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=280,
+            legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
+        )
+        st.plotly_chart(fig, use_container_width=True)
+
+    st.divider()
+
+    # ── Transaction table ─────────────────────────────────────────────────
+    st.markdown("**All Transactions**")
+
+    filter_col, _ = st.columns([1, 3])
+    with filter_col:
+        direction_filter = st.selectbox(
+            "Filter", options=["All", "Buy", "Sell"], index=0, key="insider_filter"
+        )
+
+    filtered = rows if direction_filter == "All" else [r for r in rows if r["direction"] == direction_filter]
+
+    if not filtered:
+        st.info("No transactions match the current filter.")
+        return
+
+    df = pd.DataFrame([{
+        "Date": r["date_str"],
+        "Insider": r["name"],
+        "Title": r["title"],
+        "Type": r["direction"],
+        "Shares": f"{int(r['shares']):,}" if r["shares"] else "—",
+        "Price": fmt_currency(r["price"]) if r["price"] else "—",
+        "Value": fmt_large(r["value"]) if r["value"] else "—",
+    } for r in filtered])
+
+    def _color_type(row):
+        if row["Type"] == "Buy":
+            return [""] * 2 + ["background-color: rgba(46,204,113,0.15)"] + [""] * 4
+        if row["Type"] == "Sell":
+            return [""] * 2 + ["background-color: rgba(231,76,60,0.15)"] + [""] * 4
+        return [""] * len(row)
+
+    st.dataframe(
+        df.style.apply(_color_type, axis=1),
+        use_container_width=True,
+        hide_index=True,
+    )