324d69dde0
- core/strategy.py: replace trend strategy with volume-lead accumulation (vol spike + 2h quiet → signal, +4.8% rise → entry) - core/trader.py: compound budget adjusts on both profit and loss (floor 30%) - core/notify.py: add accumulation signal telegram notification - ohlcv_db.py: Oracle ADB OHLCV cache (insert, load, incremental update) - sim_365.py: 365-day compounding simulation loading from DB - krw_sim.py: KRW-based simulation with MAX_POSITIONS constraint - ticker_sim.py: ticker count expansion comparison - STRATEGY.md: full strategy documentation - .gitignore: exclude *.pkl cache files Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
230 lines
8.6 KiB
Python
230 lines
8.6 KiB
Python
"""365일 복리 KRW 시뮬레이션.
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- 상위 20개 종목 × vol-lead +4.8% 전략
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- MAX_POSITIONS=3, 복리 포지션 크기 (이득 시 증가 / 손실 시 차감)
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- 최소 예산 = 초기 예산의 30%
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- 데이터: Oracle ADB ohlcv_hourly 테이블
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"""
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import pickle
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import sys
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from pathlib import Path
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from dotenv import load_dotenv
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load_dotenv(dotenv_path=Path(__file__).parent / ".env")
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sys.path.insert(0, str(Path(__file__).parent))
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from ohlcv_db import load_from_db
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from vol_lead_sim import run_vol_lead_thresh
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# ── 파라미터 ───────────────────────────────────────────
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BUDGET = 15_000_000
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MIN_BUDGET = BUDGET * 3 // 10 # 하한 30% = 4,500,000원
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MAX_POS = 3
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THRESH = 4.8
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FROM_DATE = "2025-03-02"
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TOP30_FILE = Path("top30_tickers.pkl")
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def load_data() -> dict:
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top30 = pickle.load(open(TOP30_FILE, "rb"))
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print(f"DB 로드 중... ({len(top30)}종목)")
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data = load_from_db(top30, from_date=FROM_DATE)
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valid = {t: df for t, df in data.items() if len(df) >= 500}
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print(f"유효 종목: {len(valid)}개 로드 완료")
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return valid
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pickle.dump(data, open(CACHE_FILE, "wb"))
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print(f"\n캐시 저장: {CACHE_FILE} ({len(data)}종목)\n")
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return data
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# ── WF 필터 (종목별 적용) ──────────────────────────────
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WF_WINDOW = 2
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WF_MIN_WIN_RATE = 0.01
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WF_SHADOW_WINS = 2
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def apply_wf(trades: list) -> tuple:
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"""종목별 WF 필터: 2연패 시 차단, shadow 2연승 시 복귀."""
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history = []
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shadow_streak = 0
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blocked = False
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accepted = []
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blocked_cnt = 0
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for trade in trades:
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is_win = int(trade[0])
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if not blocked:
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accepted.append(trade)
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history.append(is_win)
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if len(history) >= WF_WINDOW:
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wr = sum(history[-WF_WINDOW:]) / WF_WINDOW
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if wr < WF_MIN_WIN_RATE:
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blocked = True
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shadow_streak = 0
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else:
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blocked_cnt += 1
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if is_win:
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shadow_streak += 1
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if shadow_streak >= WF_SHADOW_WINS:
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blocked = False
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history = []
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shadow_streak = 0
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else:
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shadow_streak = 0
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return accepted, blocked_cnt
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# ── MAX_POSITIONS 필터 ─────────────────────────────────
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def collect_trades(data: dict, tickers: list) -> list:
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all_trades = []
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wf_total_blocked = 0
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for t in tickers:
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if t not in data:
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continue
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raw = [(is_win, pnl, buy_dt, sell_dt, reason)
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for is_win, pnl, buy_dt, sell_dt, reason
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in run_vol_lead_thresh(data[t], THRESH)]
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filtered, blocked = apply_wf(raw)
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wf_total_blocked += blocked
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for is_win, pnl, buy_dt, sell_dt, reason in filtered:
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all_trades.append((buy_dt, sell_dt, t, is_win, pnl, reason))
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print(f" WF 필터 차단: {wf_total_blocked}건")
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all_trades.sort(key=lambda x: x[0])
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return all_trades
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def apply_max_positions(all_trades: list) -> tuple:
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open_exits, accepted, skipped = [], [], []
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for trade in all_trades:
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buy_dt, sell_dt = trade[0], trade[1]
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open_exits = [s for s in open_exits if s > buy_dt]
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if len(open_exits) < MAX_POS:
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open_exits.append(sell_dt)
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accepted.append(trade)
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else:
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skipped.append(trade)
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return accepted, skipped
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# ── 복리 시뮬레이션 ────────────────────────────────────
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def simulate(accepted: list) -> dict:
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portfolio = float(BUDGET)
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total_krw = 0.0
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monthly = {}
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trade_log = []
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for buy_dt, sell_dt, ticker, is_win, pnl, reason in accepted:
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pos_size = max(portfolio, MIN_BUDGET) / MAX_POS
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krw_profit = pos_size * pnl / 100
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portfolio = max(portfolio + krw_profit, MIN_BUDGET)
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total_krw += krw_profit
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ym = buy_dt.strftime("%Y-%m")
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if ym not in monthly:
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monthly[ym] = {"trades": 0, "wins": 0, "pnl_krw": 0.0}
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monthly[ym]["trades"] += 1
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monthly[ym]["wins"] += int(is_win)
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monthly[ym]["pnl_krw"] += krw_profit
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trade_log.append({
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"buy_dt": buy_dt,
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"sell_dt": sell_dt,
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"ticker": ticker,
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"is_win": is_win,
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"pnl_pct": pnl,
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"pos_size": pos_size,
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"krw_profit": krw_profit,
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"portfolio": portfolio,
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"reason": reason,
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})
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wins = sum(1 for t in accepted if t[3])
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return {
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"portfolio": portfolio,
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"total_krw": total_krw,
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"roi_pct": (portfolio - BUDGET) / BUDGET * 100,
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"total": len(accepted),
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"wins": wins,
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"wr": wins / len(accepted) * 100 if accepted else 0,
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"monthly": monthly,
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"trade_log": trade_log,
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}
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# ── 메인 ──────────────────────────────────────────────
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def main() -> None:
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data = load_data()
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top30 = pickle.load(open(TOP30_FILE, "rb"))
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valid = [t for t in top30 if t in data and len(data[t]) >= 500]
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use20 = valid[:20]
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print(f"{'='*65}")
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print(f"365일 복리 시뮬레이션 | vol-lead +{THRESH}% | {len(use20)}종목")
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print(f"초기 예산: {BUDGET:,}원 | 최소 예산(하한): {MIN_BUDGET:,}원")
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print(f"기간: {FROM_DATE[:10]} ~ 2026-03-02")
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print(f"{'='*65}")
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all_trades = collect_trades(data, use20)
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accepted, skipped = apply_max_positions(all_trades)
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result = simulate(accepted)
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print(f"\n── 전체 결과 ──────────────────────────────────────────")
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print(f" 신호 발생: {len(all_trades):>4}건")
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print(f" 실제 진입: {result['total']:>4}건 ({len(skipped)}건 MAX_POS 스킵)")
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print(f" 승/패: {result['wins']}승 {result['total']-result['wins']}패 (승률 {result['wr']:.0f}%)")
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print(f" ─────────────────────────────────────────────────")
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print(f" 초기 예산: {BUDGET:>14,}원")
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print(f" 최종 자산: {result['portfolio']:>14,.0f}원")
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print(f" 순수익: {result['total_krw']:>+14,.0f}원")
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print(f" 수익률: {result['roi_pct']:>+13.2f}%")
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print(f" 연환산: {result['roi_pct']:>+13.2f}% (이미 1년)")
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# 최대 낙폭
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peak = BUDGET
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max_dd = 0.0
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for t in result["trade_log"]:
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peak = max(peak, t["portfolio"])
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dd = (peak - t["portfolio"]) / peak * 100
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max_dd = max(max_dd, dd)
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print(f" 최대 낙폭: {-max_dd:>+13.2f}% ({-max_dd/100*BUDGET:>+,.0f}원)")
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# 월별
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print(f"\n── 월별 수익 ──────────────────────────────────────────")
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print(f" {'월':^8} │ {'거래':>4} {'승률':>5} │ {'월수익(KRW)':>14} {'누적수익(KRW)':>15} {'예산':>14}")
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print(f" {'─'*70}")
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cum = 0.0
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budget_now = float(BUDGET)
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for ym, m in sorted(result["monthly"].items()):
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wr = m["wins"] / m["trades"] * 100 if m["trades"] else 0
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cum += m["pnl_krw"]
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budget_now = max(BUDGET + cum, MIN_BUDGET)
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print(f" {ym:^8} │ {m['trades']:>4}건 {wr:>4.0f}% │ "
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f"{m['pnl_krw']:>+14,.0f}원 {cum:>+14,.0f}원 {budget_now:>13,.0f}원")
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# 종목별
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print(f"\n── 종목별 기여 ({len(use20)}종목) ──────────────────────────")
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print(f" {'종목':<14} │ {'거래':>4} {'승률':>5} │ {'KRW수익':>14} {'평균/건':>10}")
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print(f" {'─'*58}")
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stats: dict = {}
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for t in result["trade_log"]:
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k = t["ticker"]
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if k not in stats:
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stats[k] = {"n": 0, "wins": 0, "krw": 0.0}
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stats[k]["n"] += 1
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stats[k]["wins"] += int(t["is_win"])
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stats[k]["krw"] += t["krw_profit"]
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for t, s in sorted(stats.items(), key=lambda x: -x[1]["krw"]):
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wr = s["wins"] / s["n"] * 100 if s["n"] else 0
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avg = s["krw"] / s["n"] if s["n"] else 0
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print(f" {t:<14} │ {s['n']:>4}건 {wr:>4.0f}% │ "
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f"{s['krw']:>+14,.0f}원 {avg:>+9,.0f}원/건")
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if __name__ == "__main__":
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main()
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