kis_trader/scratch/audit_ob_0814.py

#!/usr/bin/env python3
"""2026-08-14 실체결 vs filter_eval 근접 통계 (인덱스 친화)."""
from __future__ import annotations

import sys
from datetime import datetime, timedelta
from pathlib import Path
from typing import Any, Dict, List, Optional

ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

from database import TradeDB
from kis_trader.utils.env import get_env_from_db, invalidate_merged_env_cache


def ts14(raw: Any) -> str:
    s = str(raw or "").strip()
    s = s.replace("-", "").replace(":", "").replace(" ", "").replace("T", "")
    if len(s) < 8:
        return ""
    return (s + "000000")[:14]


def parse14(st: str) -> Optional[datetime]:
    if len(st) < 14:
        return None
    try:
        return datetime.strptime(st[:14], "%Y%m%d%H%M%S")
    except ValueError:
        return None


def main() -> None:
    invalidate_merged_env_cache()
    db = TradeDB()
    try:
        extra_keys = [
            "WS_ORDERBOOK_TICK_MAX_AGE_SEC",
            "SLOT_MONEY_DEFAULT",
            "MOMENTUM_SLOT_MONEY",
            "BREAKOUT_SLOT_MONEY",
            "SCALP_SLOT_MONEY",
            "TAIL_SLOT_MONEY",
            "MOMENTUM_ORDERBOOK_ENTRY_ASK_MAX_MULT",
            "BREAKOUT_ORDERBOOK_ENTRY_ASK_MAX_MULT",
            "SCALP_ORDERBOOK_ENTRY_ASK_MAX_MULT",
            "TAIL_ORDERBOOK_ENTRY_ASK_MAX_MULT",
            "LIVE_OB_PROVIDER",
            "WS_TRIGGER_EVAL_SAVE_ENABLED",
            "MOMENTUM_ORDERBOOK_COLLECT_ENABLED",
            "BREAKOUT_ORDERBOOK_COLLECT_ENABLED",
            "KIWOOM_WS_ORDERBOOK_ENABLED",
            "LS_WS_UH1_ENABLED",
            "LS_CONDITION_ORDERBOOK",
        ]
        print("=== extra env ===")
        for k in extra_keys:
            print(f"  {k}={get_env_from_db(k, '')!r}")

        day = "20260814"
        like = day + "%"
        trades = [
            dict(r)
            for r in db.conn.execute(
                "SELECT id, code, name, strategy, buy_price, qty, buy_date, sell_date, sell_reason "
                "FROM trade_history WHERE buy_date LIKE %s OR buy_date LIKE %s "
                "ORDER BY buy_date",
                ("2026-08-14%", like),
            ).fetchall()
        ]
        opens = [
            dict(r)
            for r in db.conn.execute(
                "SELECT code, name, strategy, avg_buy_price AS buy_price, current_qty AS qty, buy_date "
                "FROM active_trades WHERE buy_date LIKE %s OR buy_date LIKE %s",
                ("2026-08-14%", like),
            ).fetchall()
        ]
        print(f"\nclosed buys 0814={len(trades)} open buys 0814={len(opens)}")

        fe_n = db.conn.execute(
            "SELECT COUNT(*) AS n FROM ws_orderbook WHERE source=%s AND snap_time LIKE %s",
            ("filter_eval", like),
        ).fetchone()
        print("filter_eval 0814 n=", dict(fe_n)["n"])
        rej_n = db.conn.execute(
            "SELECT COUNT(*) AS n FROM ws_orderbook WHERE source=%s AND snap_time LIKE %s "
            "AND reject_code IS NOT NULL AND reject_code <> %s",
            ("filter_eval", like, ""),
        ).fetchone()
        print("filter_eval reject 0814 n=", dict(rej_n)["n"])
        pass_n = db.conn.execute(
            "SELECT COUNT(*) AS n FROM ws_orderbook WHERE source=%s AND snap_time LIKE %s "
            "AND (reject_code IS NULL OR reject_code = %s)",
            ("filter_eval", like, ""),
        ).fetchone()
        print("filter_eval pass 0814 n=", dict(pass_n)["n"])

        by_st = db.conn.execute(
            "SELECT strategy, "
            "SUM(CASE WHEN reject_code IS NOT NULL AND reject_code <> %s THEN 1 ELSE 0 END) AS rej, "
            "COUNT(*) AS n FROM ws_orderbook WHERE source=%s AND snap_time LIKE %s "
            "GROUP BY strategy",
            ("", "filter_eval", like),
        ).fetchall()
        print("filter_eval by strategy:")
        for r in by_st:
            d = dict(r)
            print(f"  {d.get('strategy')}: n={d.get('n')} rej={d.get('rej')}")

        buckets = {
            "fe_pm3": 0,
            "fe_pm30": 0,
            "fe_pm180": 0,
            "fe_none180": 0,
            "fe_rej_then_buy": 0,
            "fe_pass_then_buy": 0,
            "body_pm5": 0,
        }
        examples: List[str] = []
        all_buys = list(trades) + list(opens)
        for t in all_buys:
            code = str(t.get("code") or "").strip()
            b14 = ts14(t.get("buy_date"))
            dt = parse14(b14)
            if not dt:
                continue
            t0 = (dt - timedelta(seconds=180)).strftime("%Y%m%d%H%M%S")
            t1 = (dt + timedelta(seconds=180)).strftime("%Y%m%d%H%M%S")
            rows = [
                dict(r)
                for r in db.conn.execute(
                    "SELECT snap_time, reject_code, reject_msg, bid_qty_l3, ask_qty_l3, source, strategy "
                    "FROM ws_orderbook WHERE code=%s AND snap_time>=%s AND snap_time<=%s "
                    "AND source=%s ORDER BY snap_time",
                    (code, t0, t1, "filter_eval"),
                ).fetchall()
            ]
            t0b = (dt - timedelta(seconds=5)).strftime("%Y%m%d%H%M%S")
            t1b = (dt + timedelta(seconds=5)).strftime("%Y%m%d%H%M%S")
            body5 = db.conn.execute(
                "SELECT COUNT(*) AS n FROM ws_orderbook WHERE code=%s AND snap_time>=%s AND snap_time<=%s "
                "AND source=%s",
                (code, t0b, t1b, "kiwoom_0d"),
            ).fetchone()
            if dict(body5)["n"] > 0:
                buckets["body_pm5"] += 1

            if not rows:
                buckets["fe_none180"] += 1
                if len(examples) < 12:
                    examples.append(
                        f"NO_EVAL {t.get('strategy')} {code} {t.get('name')} buy={b14} "
                        f"body±5s={dict(body5)['n']}"
                    )
                continue
            best = None
            best_abs = 1e9
            for row in rows:
                dtr = parse14(str(row.get("snap_time") or ""))
                if not dtr:
                    continue
                ad = abs((dtr - dt).total_seconds())
                if ad < best_abs:
                    best_abs = ad
                    best = row
            if best is None:
                buckets["fe_none180"] += 1
                continue
            if best_abs <= 3:
                buckets["fe_pm3"] += 1
            if best_abs <= 30:
                buckets["fe_pm30"] += 1
            buckets["fe_pm180"] += 1
            rej = str(best.get("reject_code") or "").strip()
            if rej:
                buckets["fe_rej_then_buy"] += 1
                examples.append(
                    f"REJ_BUY Δ={best_abs:.0f}s {t.get('strategy')} {code} {t.get('name')} "
                    f"buy={b14} eval={best.get('snap_time')} {rej} {best.get('reject_msg')} "
                    f"L3 {best.get('bid_qty_l3')}/{best.get('ask_qty_l3')}"
                )
            else:
                buckets["fe_pass_then_buy"] += 1

        print("\n=== 0814 매수 vs filter_eval ===")
        print("buys total", len(all_buys))
        for k, v in buckets.items():
            print(f"  {k}={v}")
        print("\n=== 탈락기록 후 매수 / 평가없음 샘플 ===")
        for e in examples:
            print(" ", e)

        print("\n=== 0814 매도 사유 ===")
        reasons = db.conn.execute(
            "SELECT sell_reason, COUNT(*) AS n FROM trade_history "
            "WHERE sell_date LIKE %s OR sell_date LIKE %s GROUP BY sell_reason ORDER BY n DESC",
            ("2026-08-14%", like),
        ).fetchall()
        for r in reasons:
            d = dict(r)
            print(f"  {d.get('sell_reason')!r}: {d.get('n')}")
    finally:
        db.close()


if __name__ == "__main__":
    main()