QuantEngineByItz/tools/backfill_eod_replay_history.py

from __future__ import annotations

import argparse
import json
from datetime import date, datetime, timedelta
from pathlib import Path
from typing import Any

from pykrx import stock


ROOT = Path(__file__).resolve().parents[1]
DEFAULT_JSON = ROOT / "GatherTradingData.json"
DEFAULT_HISTORY = ROOT / "Temp" / "proposal_evaluation_history.json"


def _load_json(path: Path) -> dict[str, Any]:
    if not path.exists():
        return {}
    try:
        obj = json.loads(path.read_text(encoding="utf-8"))
    except Exception:
        return {}
    return obj if isinstance(obj, dict) else {}


def _parse_rows(value: Any) -> list[dict[str, Any]]:
    if isinstance(value, list):
        return [r for r in value if isinstance(r, dict)]
    if isinstance(value, str):
        try:
            parsed = json.loads(value)
            if isinstance(parsed, list):
                return [r for r in parsed if isinstance(r, dict)]
        except Exception:
            return []
    return []


def _text(v: Any) -> str:
    return str(v or "").strip()


def _to_num(v: Any) -> float | None:
    try:
        if v is None or v == "":
            return None
        return float(v)
    except Exception:
        return None


def _expected_direction(action: str, order_type: str) -> str:
    raw = f"{action} {order_type}".upper()
    if "BUY" in raw or "ADD" in raw:
        return "UP"
    if "SELL" in raw or "TRIM" in raw or "EXIT" in raw or "STOP" in raw:
        return "DOWN_OR_RISK_REDUCED"
    if "WATCH" in raw:
        return "NEUTRAL_TO_UP"
    return "NEUTRAL"


def _classify(ret: float, expected: str, action: str, horizon: str) -> str:
    if horizon == "t1":
        up_pass, up_fail = 0.5, -1.0
        down_pass, down_fail = 0.5, 1.5
        nu_lo, nu_hi, nu_fail_lo, nu_fail_hi, neut = -1.5, 3.0, -2.5, 5.0, 1.5
    elif horizon == "t5":
        up_pass, up_fail = 2.0, -3.0
        down_pass, down_fail = 1.0, 4.0
        nu_lo, nu_hi, nu_fail_lo, nu_fail_hi, neut = -3.0, 7.0, -6.0, 12.0, 3.0
    else:
        up_pass, up_fail = 5.0, -8.0
        down_pass, down_fail = 2.0, 10.0
        nu_lo, nu_hi, nu_fail_lo, nu_fail_hi, neut = -5.0, 15.0, -10.0, 25.0, 6.0

    if expected == "UP":
        if ret >= up_pass:
            return "MATCHED"
        if ret <= up_fail:
            return "MISMATCHED"
        return "INCONCLUSIVE"
    if expected == "DOWN_OR_RISK_REDUCED":
        if ret <= down_pass:
            return "MATCHED"
        if ret >= down_fail:
            return "MISMATCHED"
        return "INCONCLUSIVE"
    if expected == "NEUTRAL_TO_UP":
        if nu_lo <= ret <= nu_hi:
            return "MATCHED"
        if ret <= nu_fail_lo or ret >= nu_fail_hi:
            return "MISMATCHED"
        return "INCONCLUSIVE"
    if abs(ret) <= neut:
        return "MATCHED"
    if abs(ret) >= neut * 2:
        return "MISMATCHED"
    return "INCONCLUSIVE"


def _summarize(records: list[dict[str, Any]]) -> dict[str, Any]:
    def hsum(status_key: str, outcome_key: str, ret_key: str) -> dict[str, Any]:
        ev = [r for r in records if str(r.get(status_key) or "").startswith("EVALUATED_")]
        m = [r for r in ev if r.get(outcome_key) == "MATCHED"]
        mm = [r for r in ev if r.get(outcome_key) == "MISMATCHED"]
        rets = [r.get(ret_key) for r in ev if isinstance(r.get(ret_key), (int, float))]
        return {
            "evaluated_count": len(ev),
            "matched_count": len(m),
            "mismatched_count": len(mm),
            "match_rate_pct": round((len(m) / len(ev)) * 100, 2) if ev else None,
            "avg_return_pct": round(sum(rets) / len(rets), 2) if rets else None,
        }

    t1 = [r for r in records if r.get("evaluation_status") == "EVALUATED_T1"]
    t1m = [r for r in t1 if r.get("outcome") == "MATCHED"]
    t1mm = [r for r in t1 if r.get("outcome") == "MISMATCHED"]
    return {
        "evaluated_count": len(t1),
        "matched_count": len(t1m),
        "mismatched_count": len(t1mm),
        "match_rate_pct": round((len(t1m) / len(t1)) * 100, 2) if t1 else None,
        "t5_horizon": hsum("t5_evaluation_status", "t5_outcome", "t5_return_pct"),
        "t20_horizon": hsum("t20_evaluation_status", "t20_outcome", "t20_return_pct"),
        "last_updated": datetime.now().isoformat(timespec="seconds"),
    }


def main() -> int:
    ap = argparse.ArgumentParser()
    ap.add_argument("--json", default=str(DEFAULT_JSON))
    ap.add_argument("--history", default=str(DEFAULT_HISTORY))
    ap.add_argument("--lookback_days", type=int, default=90)
    ap.add_argument("--max_trade_days", type=int, default=45)
    args = ap.parse_args()

    jp = Path(args.json)
    hp = Path(args.history)
    if not jp.is_absolute():
        jp = ROOT / jp
    if not hp.is_absolute():
        hp = ROOT / hp

    payload = _load_json(jp)
    data = payload.get("data") if isinstance(payload.get("data"), dict) else {}
    hctx = data.get("_harness_context") if isinstance(data.get("_harness_context"), dict) else {}
    hist = _load_json(hp)
    records = hist.get("records") if isinstance(hist.get("records"), list) else []
    existing = {_text(r.get("proposal_id")) for r in records if isinstance(r, dict)}

    decisions = { _text(r.get("ticker")): r for r in _parse_rows(hctx.get("decisions_json")) if _text(r.get("ticker")) }
    blueprint = _parse_rows(hctx.get("order_blueprint_json"))
    names = {}
    templates: list[dict[str, Any]] = []
    for row in blueprint:
        ticker = _text(row.get("ticker"))
        if not ticker:
            continue
        dec = decisions.get(ticker, {})
        action = _text(dec.get("final_action") or row.get("order_type") or "WATCH")
        order_type = _text(row.get("order_type") or "WATCH")
        names[ticker] = _text(row.get("name"))
        templates.append({"ticker": ticker, "name": names[ticker], "action": action, "order_type": order_type})

    end_d = date.today()
    start_d = end_d - timedelta(days=max(35, args.lookback_days))
    start_s = start_d.strftime("%Y%m%d")
    end_s = end_d.strftime("%Y%m%d")

    replay_rows: list[dict[str, Any]] = []
    for t in templates:
        ticker = t["ticker"]
        try:
            df = stock.get_market_ohlcv(start_s, end_s, ticker)
        except Exception:
            continue
        if df is None or len(df.index) < 30:
            continue
        closes = []
        for idx, row in df.iterrows():
            c = _to_num(row.get("종가"))
            if c is None or c <= 0:
                continue
            d = idx.date().isoformat() if hasattr(idx, "date") else str(idx)[:10]
            closes.append((d, c))
        if len(closes) < 30:
            continue
        start_i = max(0, len(closes) - args.max_trade_days - 21)
        end_i = len(closes) - 21
        expected = _expected_direction(t["action"], t["order_type"])
        for i in range(start_i, end_i):
            proposal_date, p_close = closes[i]
            d1, c1 = closes[i + 1]
            d5, c5 = closes[i + 5]
            d20, c20 = closes[i + 20]
            pid = f"REPLAY:{proposal_date}:{ticker}:{t['order_type']}:{t['action']}"
            if pid in existing:
                continue
            ret1 = round((c1 / p_close - 1.0) * 100.0, 2)
            ret5 = round((c5 / p_close - 1.0) * 100.0, 2)
            ret20 = round((c20 / p_close - 1.0) * 100.0, 2)
            replay_rows.append({
                "proposal_id": pid,
                "record_type": "HISTORICAL_REPLAY_EOD",
                "data_origin": "REPLAY_FROM_KRX_EOD",
                "proposal_date": proposal_date,
                "ticker": ticker,
                "name": t["name"],
                "action": t["action"],
                "order_type": t["order_type"],
                "validation_status": "REPLAY_BACKFILL",
                "expected_direction": expected,
                "proposed_close": p_close,
                "proposed_limit_price": None,
                "proposed_quantity": None,
                "rule_basis": "REPLAY_BACKFILL_KRX_EOD",
                "evaluation_status": "EVALUATED_T1",
                "result_date": d1,
                "result_close": c1,
                "next_return_pct": ret1,
                "outcome": _classify(ret1, expected, t["action"], "t1"),
                "error_cause": "REPLAY_BACKFILL",
                "improvement_proposal": "REPLAY_ONLY_DO_NOT_AUTO_ADOPT",
                "t5_evaluation_status": "EVALUATED_T5",
                "t5_result_date": d5,
                "t5_return_pct": ret5,
                "t5_outcome": _classify(ret5, expected, t["action"], "t5"),
                "t20_evaluation_status": "EVALUATED_T20",
                "t20_result_date": d20,
                "t20_return_pct": ret20,
                "t20_outcome": _classify(ret20, expected, t["action"], "t20"),
            })

    records.extend(replay_rows)
    records = [r for r in records if isinstance(r, dict)]
    records.sort(key=lambda r: (_text(r.get("proposal_date")), _text(r.get("ticker")), _text(r.get("proposal_id"))))
    hist["schema_version"] = "2026-05-25-proposal-evaluation-v3-replay"
    hist["records"] = records
    hist["summary"] = _summarize(records)
    hp.parent.mkdir(parents=True, exist_ok=True)
    hp.write_text(json.dumps(hist, ensure_ascii=False, indent=2), encoding="utf-8")
    print(f"REPLAY_BACKFILL_OK records_added={len(replay_rows)} total_records={len(records)}")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())