"""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,
    )