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"""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,
)
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