upbit-trader/archive/tests/fng_1y_backtest.py

"""F&G 조건별 백테스트 - 1년치 데이터 (배치 수집)

60일 극공포 편향을 제거하고 Bull/Neutral/Bear 다양한 구간 포함.
데이터: 1h 캔들 배치 수집 → 약 365일치
"""
import os as _os, sys as _sys
_sys.path.insert(0, _os.path.dirname(_os.path.dirname(_os.path.abspath(__file__))))

from __future__ import annotations

import datetime, json, time, sys, urllib.request
import pandas as pd
import pyupbit
from dataclasses import dataclass

TICKERS = [
    "KRW-BTC", "KRW-ETH", "KRW-XRP", "KRW-SOL", "KRW-DOGE",
    "KRW-ADA", "KRW-DOT", "KRW-NEAR", "KRW-AVAX", "KRW-LINK",
    "KRW-SUI", "KRW-HBAR",
    "KRW-VIRTUAL", "KRW-SXP", "KRW-CFG", "KRW-HOLO",
    "KRW-KAVA", "KRW-KNC",
]

VOL_MULT   = 2.0
QUIET_2H   = 2.0
SIG_TO_H   = 8
MOM_THR    = 3.0
SIG_CANCEL = 3.0
TRAIL_STOP = 0.015
TIME_H     = 24
TIME_MIN   = 3.0


# ── 데이터 수집 ───────────────────────────────────────────────
def fetch_1y(ticker: str, total_days: int = 365) -> pd.DataFrame | None:
    """1h 캔들을 배치로 수집해 약 1년치 DataFrame 반환."""
    all_dfs = []
    end = datetime.datetime.now()
    batch = 1440  # 60일치씩
    prev_oldest = None

    while True:
        df = pyupbit.get_ohlcv(
            ticker, interval="minute60", count=batch,
            to=end.strftime("%Y-%m-%d %H:%M:%S"),
        )
        if df is None or df.empty:
            break
        all_dfs.append(df)
        oldest = df.index[0]
        # 상장 초기 종목: oldest가 진전되지 않으면 더 오래된 데이터 없음
        if prev_oldest is not None and oldest >= prev_oldest:
            break
        prev_oldest = oldest
        cutoff = datetime.datetime.now() - datetime.timedelta(days=total_days)
        if oldest <= cutoff:
            break
        end = oldest
        time.sleep(0.12)

    if not all_dfs:
        return None
    combined = pd.concat(all_dfs).sort_index()
    combined = combined[~combined.index.duplicated(keep="last")]
    cutoff = datetime.datetime.now() - datetime.timedelta(days=total_days)
    return combined[combined.index >= cutoff]


def load_fng() -> dict[str, int]:
    url = "https://api.alternative.me/fng/?limit=400&format=json"
    with urllib.request.urlopen(url, timeout=10) as r:
        data = json.loads(r.read())
    return {
        datetime.datetime.fromtimestamp(int(d["timestamp"])).strftime("%Y-%m-%d"):
        int(d["value"])
        for d in data["data"]
    }


def fng_val(fng_map, ts):
    return fng_map.get(ts.strftime("%Y-%m-%d"), 50)


# ── 시뮬레이션 ────────────────────────────────────────────────
@dataclass
class Trade:
    pnl:  float
    h:    int
    fng:  int
    exit: str


def simulate(df, fng_map, fng_lo=None, fng_hi=None) -> list[Trade]:
    closes = df["close"].values
    vols   = df["volume"].values
    idx    = df.index
    trades: list[Trade] = []
    sig_px = sig_i = None
    pos_buy = pos_peak = pos_i = pos_fng = None

    for i in range(7, len(closes) - max(TIME_H + 4, 10)):
        if pos_buy is not None:
            cur = closes[i]
            if cur > pos_peak:
                pos_peak = cur
            if (pos_peak - cur) / pos_peak >= TRAIL_STOP:
                trades.append(Trade((cur - pos_buy) / pos_buy * 100,
                                    i - pos_i, pos_fng, "trail"))
                pos_buy = pos_peak = pos_i = pos_fng = sig_px = sig_i = None
                continue
            if i - pos_i >= TIME_H:
                pnl = (cur - pos_buy) / pos_buy * 100
                if pnl < TIME_MIN:
                    trades.append(Trade(pnl, i - pos_i, pos_fng, "time"))
                    pos_buy = pos_peak = pos_i = pos_fng = sig_px = sig_i = None
                    continue
            continue

        if sig_px is not None:
            if i - sig_i > SIG_TO_H:
                sig_px = sig_i = None
            elif (closes[i] - sig_px) / sig_px * 100 < -SIG_CANCEL:
                sig_px = sig_i = None

        if sig_px is None:
            vol_avg = vols[i - 6:i - 1].mean()
            if vol_avg <= 0:
                continue
            if vols[i - 1] / vol_avg >= VOL_MULT:
                if abs(closes[i] - closes[i - 2]) / closes[i - 2] * 100 < QUIET_2H:
                    sig_px = closes[i]
                    sig_i  = i
            continue

        fv = fng_val(fng_map, idx[i])
        if fng_lo is not None and fv < fng_lo:
            continue
        if fng_hi is not None and fv > fng_hi:
            continue

        if (closes[i] - sig_px) / sig_px * 100 >= MOM_THR:
            pos_buy = pos_peak = closes[i]
            pos_i   = i
            pos_fng = fv
            sig_px  = sig_i = None

    return trades


def stats(trades):
    if not trades:
        return dict(n=0, wr=0, avg_pnl=0, total_pnl=0, rr=0,
                    avg_win=0, avg_loss=0, max_dd=0)
    wins   = [t for t in trades if t.pnl > 0]
    losses = [t for t in trades if t.pnl <= 0]
    aw = sum(t.pnl for t in wins) / len(wins) if wins else 0
    al = sum(t.pnl for t in losses) / len(losses) if losses else 0
    cum = pk = max_dd = 0.0
    for t in trades:
        cum += t.pnl
        if cum > pk: pk = cum
        if pk - cum > max_dd: max_dd = pk - cum
    return dict(
        n=len(trades), wr=len(wins) / len(trades) * 100,
        avg_pnl=sum(t.pnl for t in trades) / len(trades),
        total_pnl=sum(t.pnl for t in trades),
        rr=abs(aw / al) if al else 0,
        avg_win=aw, avg_loss=al, max_dd=max_dd,
    )


def main():
    print("F&G 데이터 로드...")
    fng_map = load_fng()

    # F&G 연간 분포 출력
    from collections import Counter
    zone_cnt = Counter()
    for v in fng_map.values():
        if   v <= 25: zone_cnt["극공포(0~25)"]  += 1
        elif v <= 45: zone_cnt["공포(26~45)"]   += 1
        elif v <= 55: zone_cnt["중립(46~55)"]   += 1
        elif v <= 75: zone_cnt["탐욕(56~75)"]   += 1
        else:         zone_cnt["극탐욕(76~100)"] += 1
    total_days = sum(zone_cnt.values())
    print(f"  1년 F&G 분포 ({total_days}일):")
    for k, v in sorted(zone_cnt.items()):
        bar = "█" * (v // 5)
        print(f"    {k:<14} {v:>3}일 ({v/total_days*100:>4.1f}%)  {bar}")

    print(f"\n종목 1년치 데이터 수집 중 ({len(TICKERS)}개)...")
    datasets = {}
    for i, tk in enumerate(TICKERS):
        try:
            df = fetch_1y(tk, total_days=365)
            if df is not None and len(df) > 100:
                datasets[tk] = df
                sys.stderr.write(f"\r  {i+1}/{len(TICKERS)} {tk} ({len(df)}h)   ")
        except Exception as e:
            sys.stderr.write(f"\r  {tk} 실패: {e}   ")
    sys.stderr.write("\n")
    print(f"  완료: {len(datasets)}개 종목\n")

    # ── 전체 기간 F&G 구간별 성과 ────────────────────────────
    CONFIGS = [
        (None, None, "필터 없음 (전체)"),
        (None,  25,  "극공포만  (0~25)"),
        (26,    45,  "공포만    (26~45)"),
        (46,    55,  "중립만    (46~55)"),
        (56,   100,  "탐욕+     (56~100)"),
        (46,   100,  "중립 이상 (46~100)"),
        (26,   100,  "공포 이상 (26~100)"),
    ]

    print("=" * 78)
    print(" F&G 조건별 성과 - 1년치 (1h 캔들 / 모멘텀 / 스탑1.5%)")
    print("=" * 78)
    print(f"  {'조건':<26} {'거래':>5}  {'승률':>6}  {'평균PnL':>8}  "
          f"{'손익비':>6}  {'총PnL':>9}  {'MaxDD':>7}")
    print("  " + "-" * 72)

    all_results = {}
    for lo, hi, label in CONFIGS:
        all_trades = []
        for df in datasets.values():
            all_trades.extend(simulate(df, fng_map, lo, hi))
        s = stats(all_trades)
        all_results[label] = (s, all_trades)
        if s["n"] == 0:
            print(f"  {label:<26}  거래 없음 (해당 구간 진입 기회 없음)")
            continue
        sign = "+" if s["total_pnl"] > 0 else ""
        print(
            f"  {label:<26} {s['n']:>5}건  {s['wr']:>5.1f}%  "
            f"{s['avg_pnl']:>+7.3f}%  {s['rr']:>5.2f}  "
            f"{sign}{s['total_pnl']:>8.1f}%  -{s['max_dd']:>5.1f}%"
        )

    # ── 분기별 성과 (계절성) ──────────────────────────────────
    print()
    print("  분기별 성과 (전체 필터 없음 기준):")
    base_trades = all_results["필터 없음 (전체)"][1]
    for df in datasets.values():
        pass  # already computed

    # 전체 종목 합산 후 날짜로 분기 분리
    all_base = []
    for df in datasets.values():
        t_list = simulate(df, fng_map)
        # trade에 날짜 정보 추가
        # simulate에서 idx를 참조하지 않으므로 재계산
        all_base.extend(t_list)

    # F&G 수치별 세분화
    print()
    print("  F&G 10단위 구간별 세부 성과:")
    print(f"  {'구간':<16} {'건수':>5}  {'승률':>6}  {'평균PnL':>9}  {'손익비':>6}  {'의미'}")
    print("  " + "-" * 65)
    fng_zones_detail = [
        (0,   10, "극단 공포(0~10)"),
        (11,  20, "극단 공포(11~20)"),
        (21,  30, "극공포(21~30)"),
        (31,  40, "공포(31~40)"),
        (41,  50, "약공포(41~50)"),
        (51,  60, "약탐욕(51~60)"),
        (61,  75, "탐욕(61~75)"),
        (76, 100, "극탐욕(76~100)"),
    ]
    base_all = all_results["필터 없음 (전체)"][1]
    for lo, hi, name in fng_zones_detail:
        sub = [t for t in base_all if lo <= t.fng <= hi]
        if not sub:
            continue
        s = stats(sub)
        breakeven_wr = 1 / (1 + s["rr"]) * 100 if s["rr"] > 0 else 50
        profitable = "✅ 수익" if s["avg_pnl"] > 0 else ("⚠️  BEP 근접" if s["avg_pnl"] > -0.2 else "❌ 손실")
        print(
            f"  {name:<16} {s['n']:>5}건  {s['wr']:>5.1f}%  "
            f"{s['avg_pnl']:>+8.3f}%  {s['rr']:>5.2f}  {profitable}"
        )

    # ── 최적 F&G 구간 요약 ───────────────────────────────────
    print()
    best = max(
        [(label, s) for label, (s, _) in all_results.items() if s["n"] >= 50],
        key=lambda x: x[1]["avg_pnl"],
    )
    print(f"  ★ 최적 구간: {best[0]}  "
          f"(거래 {best[1]['n']}건 | 승률 {best[1]['wr']:.1f}% | "
          f"평균PnL {best[1]['avg_pnl']:+.3f}%)")

    # ── DB 저장 ──────────────────────────────────────────────
    try:
        from backtest_db import ensure_tables, insert_run, insert_result, insert_trades_bulk
        ensure_tables()
        params = {
            "tickers": len(datasets), "days": 365, "candle": "1h",
            "trail_stop": 0.015, "mom_thr": 3.0, "vol_mult": 2.0,
        }
        run_id = insert_run(
            run_name="fng_1y_backtest",
            description="F&G 구간별 성과 1년치 백테스트 (1h 캔들 / 모멘텀 / 스탑1.5%)",
            params=params,
        )
        for lo, hi, label in CONFIGS:
            if label in all_results:
                s, trades = all_results[label]
                if s["n"] > 0:
                    insert_result(run_id, label, s, lo, hi)
                    # 전체 거래는 per-ticker 분리 없이 일괄 저장 (run_id+label로 구분)
                    insert_trades_bulk(run_id, label, "_all_", trades)
        print(f"\n  [DB 저장 완료] run_id: {run_id}")
    except Exception as e:
        print(f"\n  [DB 저장 실패] {e}")


if __name__ == "__main__":
    main()