"""OHLCV 시계열 캐시 — Oracle ADB ohlcv_hourly 테이블.

기능:
  - 테이블 생성 (없으면)
  - pkl → DB 최초 적재
  - DB → DataFrame dict 로드 (시뮬용)
  - 증분 업데이트 (신규 봉만 API 페치)
"""

from __future__ import annotations

import os
import pickle
import time
from datetime import datetime
from pathlib import Path

import pandas as pd
import pyupbit
from dotenv import load_dotenv

load_dotenv(dotenv_path=Path(__file__).parent / ".env")

from core.price_db import _conn

# ── DDL ───────────────────────────────────────────────
_DDL = """
CREATE TABLE ohlcv_hourly (
    ticker      VARCHAR2(20)  NOT NULL,
    candle_time TIMESTAMP     NOT NULL,
    open_price  NUMBER(20,8)  NOT NULL,
    high_price  NUMBER(20,8)  NOT NULL,
    low_price   NUMBER(20,8)  NOT NULL,
    close_price NUMBER(20,8)  NOT NULL,
    volume      NUMBER(30,8)  NOT NULL,
    CONSTRAINT pk_ohlcv PRIMARY KEY (ticker, candle_time)
)
"""


def ensure_table() -> None:
    with _conn() as conn:
        cur = conn.cursor()
        cur.execute("SELECT COUNT(*) FROM user_tables WHERE table_name='OHLCV_HOURLY'")
        if cur.fetchone()[0] == 0:
            conn.cursor().execute(_DDL)
            print("ohlcv_hourly 테이블 생성 완료")
        else:
            print("ohlcv_hourly 테이블 이미 존재")


# ── 적재 ──────────────────────────────────────────────
def insert_df(ticker: str, df: pd.DataFrame, batch: int = 500) -> int:
    """DataFrame → ohlcv_hourly 배치 삽입.

    신규 레코드만 삽입: 기존 candle_time 조회 후 Python에서 필터링.
    """
    sql_existing = """
        SELECT candle_time FROM ohlcv_hourly
        WHERE ticker = :1
    """
    sql_insert = """
        INSERT INTO ohlcv_hourly
            (ticker, candle_time, open_price, high_price, low_price, close_price, volume)
        VALUES (:1, :2, :3, :4, :5, :6, :7)
    """

    rows = [
        (
            ticker,
            row.name.to_pydatetime().replace(tzinfo=None),
            float(row["open"]),
            float(row["high"]),
            float(row["low"]),
            float(row["close"]),
            float(row["volume"]),
        )
        for _, row in df.iterrows()
    ]

    with _conn() as conn:
        cur = conn.cursor()
        # 기존 candle_time 조회 → 중복 제거
        cur.execute(sql_existing, [ticker])
        existing = {r[0].replace(tzinfo=None) for r in cur.fetchall()}
        new_rows = [r for r in rows if r[1] not in existing]

        if not new_rows:
            return 0

        for i in range(0, len(new_rows), batch):
            cur.executemany(sql_insert, new_rows[i : i + batch])

    return len(new_rows)


def load_from_pkl(pkl_path: str | Path) -> None:
    """pkl 파일의 모든 종목을 DB에 적재."""
    pkl_path = Path(pkl_path)
    data = pickle.load(open(pkl_path, "rb"))
    ensure_table()
    total = 0
    for ticker, df in data.items():
        n = insert_df(ticker, df)
        total += n
        print(f"  {ticker}: {n}건 적재")
    print(f"\n총 {total:,}건 적재 완료")


# ── 로드 ──────────────────────────────────────────────
def load_from_db(tickers: list[str], from_date: str = "2025-03-02") -> dict:
    """DB → {ticker: DataFrame} 반환 (시뮬용)."""
    from_dt = datetime.strptime(from_date, "%Y-%m-%d")
    data = {}
    sql = """
        SELECT candle_time, open_price, high_price, low_price, close_price, volume
        FROM ohlcv_hourly
        WHERE ticker = :1 AND candle_time >= :2
        ORDER BY candle_time
    """
    with _conn() as conn:
        for ticker in tickers:
            cur = conn.cursor()
            cur.execute(sql, [ticker, from_dt])
            rows = cur.fetchall()
            if not rows:
                continue
            df = pd.DataFrame(
                rows,
                columns=["candle_time", "open", "high", "low", "close", "volume"],
            )
            df.set_index("candle_time", inplace=True)
            df.index = pd.to_datetime(df.index)
            data[ticker] = df
    return data


# ── 증분 업데이트 ──────────────────────────────────────
def update_incremental(tickers: list[str]) -> None:
    """각 종목의 최신 봉 이후 데이터를 API에서 가져와 적재."""
    sql_max = "SELECT MAX(candle_time) FROM ohlcv_hourly WHERE ticker = :1"

    for ticker in tickers:
        with _conn() as conn:
            cur = conn.cursor()
            cur.execute(sql_max, [ticker])
            row = cur.fetchone()

        latest = row[0] if row and row[0] else None

        if latest:
            to_dt = None  # 최신까지 fetch
            kwargs: dict = dict(ticker=ticker, interval="minute60", count=200)
            df = pyupbit.get_ohlcv(**kwargs)
            if df is None or df.empty:
                continue
            df.index = df.index.tz_localize(None)
            # latest 이후만 삽입
            new_df = df[df.index > latest.replace(tzinfo=None)]
            if new_df.empty:
                print(f"  {ticker}: 신규 봉 없음")
                continue
            n = insert_df(ticker, new_df)
            print(f"  {ticker}: +{n}봉 추가")
        else:
            print(f"  {ticker}: DB에 없음, 전체 로드 필요")

        time.sleep(0.2)


# ── CLI ───────────────────────────────────────────────
if __name__ == "__main__":
    import sys

    cmd = sys.argv[1] if len(sys.argv) > 1 else "status"

    if cmd == "init":
        # pkl → DB 최초 적재
        pkl = sys.argv[2] if len(sys.argv) > 2 else "vol_lead_cache_365.pkl"
        print(f"pkl 적재: {pkl}")
        load_from_pkl(pkl)

    elif cmd == "update":
        # 증분 업데이트
        import pickle as _pk
        top30 = _pk.load(open("top30_tickers.pkl", "rb"))
        print("증분 업데이트...")
        update_incremental(top30)

    elif cmd == "status":
        # 종목별 레코드 수 확인
        with _conn() as conn:
            cur = conn.cursor()
            try:
                cur.execute("""
                    SELECT ticker, COUNT(*), MIN(candle_time), MAX(candle_time)
                    FROM ohlcv_hourly
                    GROUP BY ticker
                    ORDER BY ticker
                """)
                rows = cur.fetchall()
                if rows:
                    print(f"{'종목':<16} {'봉수':>6}  {'시작':^12}  {'종료':^12}")
                    print("-" * 52)
                    for r in rows:
                        print(f"{r[0]:<16} {r[1]:>6}봉  "
                              f"{r[2].strftime('%y-%m-%d'):^12}  "
                              f"{r[3].strftime('%y-%m-%d'):^12}")
                    print(f"\n총 {sum(r[1] for r in rows):,}봉 / {len(rows)}종목")
                else:
                    print("ohlcv_hourly 테이블이 비어 있거나 없음")
            except Exception as e:
                print(f"오류: {e}")