"""CEF universe: the SEC's official "Closed-End Fund Information" report.

Primary source: https://www.sec.gov/files/investment/data/other/
closed-end-fund-information/closed-end-investment-company-<YYYY>.csv
(all ACTIVE 1940-Act closed-end companies: CIK, name, addresses,
filing status - 973 for 2026).  This supersedes the N-2 full-index
approach (N-2/N-2-A filers are the CEFs that updated their
registration within the window - a biased subset; the report is the
whole active population).

Tickers: CIK join with the SEC company_tickers.json exchange map
(305 of 973 match - the rest are small/dark/OTC funds).  Hyphenated
tickers (GAM-PB etc.) are PREFERRED series - dropped (their price
history is the preferred's near-flat NAV, useless for screening);
the common class is usually the fund's other listed security.

CEF notes for the rest of the pipeline:
- CEFs file NPORT-P (same format as open-end - xcheck.py works)
- shareholder reports are N-CSR/N-CSRS AND/OR N-2ASR (rocdetect must
  try both)
- CEFs file N-PX proxies (open-end funds don't - a CEF signal)
- price = MARKET price (premium/discount to NAV is a free variable)
- Yahoo instrumentType is usually EQUITY - do NOT apply the open-end
  MUTUALFUND filter
- BDCs are in the report too (name-flagged)
"""
from __future__ import annotations

import csv
import io
import json
import re
import urllib.request
from datetime import date, timedelta
from pathlib import Path

HERE = Path(__file__).parent
CACHE = HERE / "universe_cache"
CEF_CSV = CACHE / "cef_sec_2026.csv"
UNIVERSE = HERE / "cef_universe.json"
DATA = Path.home() / "prog/fin/stocks"
UA = {"User-Agent": "research test@example.com"}
MIN_DAYS = 1250      # >=5y of trading history (same bar as the 497 pass)
STALE_DAYS = 35      # last quote older than this = delisted


def _get(url: str) -> bytes:
    req = urllib.request.Request(url, headers=UA)
    return urllib.request.urlopen(req, timeout=120).read()


def fetch_cef_report(year: int = 2026) -> list[dict]:
    """The SEC active-CEF report (CIK, name, ...). Cached locally."""
    if CEF_CSV.exists():
        raw = CEF_CSV.read_bytes()
    else:
        CACHE.mkdir(exist_ok=True)
        # 2024+ uses hyphenated filenames, earlier underscored
        for pat in (f"closed-end-investment-company-{year}.csv",
                    f"closed-end_investment_company_{year}.csv",
                    "closed-end_investment_company.csv"):
            url = ("https://www.sec.gov/files/investment/data/other/"
                   f"closed-end-fund-information/{pat}")
            try:
                raw = _get(url)
                break
            except Exception:
                continue
        else:
            raise RuntimeError("could not fetch the SEC CEF report")
        CEF_CSV.write_bytes(raw)
    rows = list(csv.DictReader(io.StringIO(
        raw.decode("utf-8-sig"))))
    return [{"cik": r["CIK"].lstrip("0") or "0",
             "name": r["Registrant_Name"].strip()} for r in rows]


def _company_tickers() -> dict:
    """cik(int) -> (ticker, title) from the SEC exchange-listed map."""
    d = json.loads(_get("https://www.sec.gov/files/company_tickers.json"))
    out: dict[int, tuple] = {}
    for v in d.values():
        cik = v.get("cik_str", v.get("cik"))
        t = v.get("ticker", v.get("symbol"))
        if cik is None or not t:
            continue
        out[int(cik)] = (t.upper(), v.get("title", ""))
    return out


def _history(sym: str) -> tuple[int, str]:
    """(trading days, last date) from the local price file."""
    p = DATA / f"{sym}-history.csv"
    if not p.exists():
        return 0, ""
    last = ""
    n = 0
    with p.open() as f:
        next(f, None)      # header
        for line in f:
            n += 1
            last = line.split(",")[0]
    return n, last


def run() -> dict:
    cefs = fetch_cef_report()
    print(f"{len(cefs)} active CEFs in the SEC report", flush=True)
    tick = _company_tickers()

    # NOTE: do NOT exclude tickers present in the local DB - goget
    # writes <sym>.json there, so after the first download every CEF
    # would look "known".  The SEC report already defines the set.
    known = set()
    for j in (Path(__file__).parent.parent / "funds.json",
              HERE / "search_all.json", HERE / "search_external.json",
              HERE / "xcheck_report.json"):
        if j.exists():
            d = json.loads(j.read_text())
            if isinstance(d, dict):
                known |= {k.upper() for k in d}

    stale = (date.today() - timedelta(days=STALE_DAYS)).toordinal()
    out: dict[str, dict] = {}
    n_pref = n_unmatched = 0
    for c in cefs:
        hit = tick.get(int(c["cik"]))
        if not hit:
            n_unmatched += 1
            continue
        t, _yt = hit
        if "-" in t:                 # preferred/series class
            n_pref += 1
            continue
        if t in known:
            continue
        days, last = _history(t.lower())
        live = False
        try:
            live = date.fromisoformat(last).toordinal() >= stale
        except ValueError:
            pass
        out[t] = {"cik": c["cik"], "name": c["name"],
                  "bdc": bool(re.search(r"business development",
                                        c["name"], re.I)),
                  "listed": days >= MIN_DAYS and live,
                  "days": days, "last": last, "known": False}
    n_listed = sum(1 for v in out.values() if v["listed"])
    n_bdc = sum(1 for v in out.values() if v["bdc"])
    print(f"{len(out)} CEF tickers ({n_listed} live with >=5y history, "
          f"{n_bdc} BDCs, {n_pref} preferred-series dropped, "
          f"{n_unmatched} CIKs with no listed ticker)", flush=True)
    UNIVERSE.write_text(json.dumps(out, indent=1))
    print(f"wrote {UNIVERSE}")
    return out


if __name__ == "__main__":
    run()