"""build_operational_t20_outcome_ledger_v1.py — ALPHA_FEEDBACK_LOOP_V2

매수/매도 결정 20거래일 후의 실제 수익률을 추적하여 레저(Ledger)를 구축한다.
성과 인텔리전스(Phase 4)의 핵심 데이터 소스로 활용됨.

로직:
  1. alpha_history 시트에서 과거 결정(Decision) 데이터를 읽음.
  2. 현재 가격(Close)을 T+20 가격으로 가정하여 실현 수익률 계산.
  3. outcome_ledger.json 생성.
"""
from __future__ import annotations

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

ROOT = Path(__file__).resolve().parents[1]
TEMP = ROOT / "Temp"
DEFAULT_JSON = ROOT / "GatherTradingData.json"
DEFAULT_OUT  = TEMP / "outcome_ledger_v1.json"

def _load(path: Path) -> Any:
    if not path.exists(): return {}
    try: return json.loads(path.read_text(encoding="utf-8"))
    except: return {}

def _f(v: Any, default: float = 0.0) -> float:
    try: return float(v)
    except: return default

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--json", default=str(DEFAULT_JSON))
    ap.add_argument("--out", default=str(DEFAULT_OUT))
    args = ap.parse_args()

    payload = _load(Path(args.json))
    data = payload.get("data", {})
    
    # alpha_history: 과거 예측 데이터
    alpha_history = data.get("alpha_history", [])
    # data_feed: 현재 가격 데이터 (T+20 프록시)
    df_rows = data.get("data_feed", [])
    current_prices = {str(r.get("Ticker")): _f(r.get("Close")) for r in df_rows if r.get("Ticker")}

    ledger_rows = []
    for h in alpha_history:
        ticker = str(h.get("ticker"))
        entry_price = _f(h.get("close_at_record"))
        current_price = current_prices.get(ticker, 0.0)
        
        if entry_price > 0 and current_price > 0:
            return_pct = round((current_price - entry_price) / entry_price * 100, 2)
            verdict = h.get("synthesis_verdict")
            
            # 예측 적중 여부 (간단 로직: BUY면 +, EXIT면 -)
            is_correct = False
            if "BUY" in str(verdict) and return_pct > 0: is_correct = True
            if "EXIT" in str(verdict) and return_pct < 0: is_correct = True
            
            ledger_rows.append({
                "date": h.get("date"),
                "ticker": ticker,
                "verdict": verdict,
                "entry_price": entry_price,
                "exit_price": current_price,
                "return_pct": return_pct,
                "is_correct": is_correct
            })

    win_rate = round(sum(1 for r in ledger_rows if r["is_correct"]) / len(ledger_rows) * 100, 2) if ledger_rows else 0

    result = {
        "formula_id": "ALPHA_FEEDBACK_LOOP_V2",
        "as_of_date": datetime.now().strftime("%Y-%m-%d"),
        "total_cases": len(ledger_rows),
        "win_rate_pct": win_rate,
        "ledger": ledger_rows
    }

    Path(args.out).parent.mkdir(parents=True, exist_ok=True)
    Path(args.out).write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
    print(f"Outcome Ledger Built. Total Cases: {len(ledger_rows)}, Win Rate: {win_rate}%")
    return 0

if __name__ == "__main__":
    main()