297 lines
11 KiB
Python
297 lines
11 KiB
Python
"""45일 복리 KRW 시뮬레이션 — 40분봉.
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sim10m_cache.pkl(10분봉)을 40분봉으로 리샘플링 후
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sim_365.py 와 동일한 복리·WF·MAX_POSITIONS 로직 적용.
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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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import pandas as pd
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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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# ── 파라미터 ─────────────────────────────────────────────
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CACHE_FILE = Path("sim10m_cache.pkl")
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TOP30_FILE = Path("top30_tickers.pkl")
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TOP_N = 20
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BUDGET = 15_000_000
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MIN_BUDGET = BUDGET * 3 // 10
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MAX_POS = 3
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FEE = 0.0005
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TIME_STOP_MIN_PCT = 3.0
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ATR_MULT = 1.5
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ATR_MIN = 0.010
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ATR_MAX = 0.020
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VOL_MULT = 2.0
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QUIET_PCT = 2.0
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THRESH = 4.8
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# 40분봉 기준 시간 파라미터 → 봉수 환산 (60/40 = 1.5봉/h)
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LOCAL_VOL_N = 7 # 5h × 1.5
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QUIET_N = 3 # 2h × 1.5
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SIGNAL_TO_N = 12 # 8h × 1.5
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ATR_N = 7 # 5h × 1.5
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TS_N = 12 # 8h × 1.5
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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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# ── 리샘플링 ─────────────────────────────────────────────
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def resample_40m(df: pd.DataFrame) -> pd.DataFrame:
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return (
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df.resample("40min")
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.agg({"open": "first", "high": "max", "low": "min",
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"close": "last", "volume": "sum"})
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.dropna(subset=["close"])
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)
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# ── ATR ──────────────────────────────────────────────────
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def calc_atr(df: pd.DataFrame, buy_idx: int) -> float:
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sub = df.iloc[max(0, buy_idx - ATR_N - 1):buy_idx]
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if len(sub) < 3:
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return ATR_MIN
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try:
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avg = ((sub["high"] - sub["low"]) / sub["low"]).iloc[-ATR_N:].mean()
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return float(max(ATR_MIN, min(ATR_MAX, avg * ATR_MULT)))
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except Exception:
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return ATR_MIN
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# ── 포지션 시뮬 ──────────────────────────────────────────
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def simulate_pos(df: pd.DataFrame, buy_idx: int,
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buy_price: float, stop_pct: float):
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peak = buy_price
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for i in range(buy_idx + 1, len(df)):
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row = df.iloc[i]
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ts = df.index[i]
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if row["high"] > peak:
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peak = row["high"]
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if row["low"] <= peak * (1 - stop_pct):
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sp = peak * (1 - stop_pct)
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pnl = (sp * (1 - FEE) - buy_price * (1 + FEE)) / (buy_price * (1 + FEE)) * 100
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return pnl > 0, ts, pnl
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pnl_now = (row["close"] - buy_price) / buy_price * 100
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if (i - buy_idx) >= TS_N and pnl_now < TIME_STOP_MIN_PCT:
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pnl = (row["close"] * (1 - FEE) - buy_price * (1 + FEE)) / (buy_price * (1 + FEE)) * 100
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return pnl > 0, ts, pnl
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last = df.iloc[-1]["close"]
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pnl = (last * (1 - FEE) - buy_price * (1 + FEE)) / (buy_price * (1 + FEE)) * 100
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return pnl > 0, df.index[-1], pnl
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# ── vol-lead 전략 ─────────────────────────────────────────
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def run_vol_lead(df: pd.DataFrame, ticker: str) -> list:
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trades = []
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sig_i = sig_p = None
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in_pos = False
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buy_idx = buy_price = stop_pct = None
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i = max(LOCAL_VOL_N + 2, QUIET_N + 1)
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while i < len(df):
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if in_pos:
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is_win, sdt, pnl = simulate_pos(df, buy_idx, buy_price, stop_pct)
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next_i = next((j for j in range(i, len(df)) if df.index[j] > sdt), len(df))
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trades.append((is_win, pnl, df.index[buy_idx], sdt, ticker))
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in_pos = False
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sig_i = sig_p = None
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i = next_i
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continue
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close = df.iloc[i]["close"]
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vol_p = df.iloc[i - 1]["volume"]
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vol_avg = df.iloc[i - LOCAL_VOL_N - 1:i - 1]["volume"].mean()
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vol_r = vol_p / vol_avg if vol_avg > 0 else 0
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close_qh = df.iloc[i - QUIET_N]["close"]
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chg_qh = abs(close - close_qh) / close_qh * 100
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quiet = chg_qh < QUIET_PCT
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spike = vol_r >= VOL_MULT
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if quiet and spike:
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if sig_i is None:
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sig_i, sig_p = i, close
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else:
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if sig_i is not None and close < sig_p:
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sig_i = sig_p = None
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if sig_i is not None and (i - sig_i) > SIGNAL_TO_N:
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sig_i = sig_p = None
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if sig_i is not None and (close - sig_p) / sig_p * 100 >= THRESH:
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in_pos = True
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buy_idx = i
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buy_price = close
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stop_pct = calc_atr(df, i)
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sig_i = sig_p = None
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i += 1
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return trades
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# ── WF 필터 ──────────────────────────────────────────────
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def apply_wf(trades: list) -> tuple:
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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 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[2], trade[3]
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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 시뮬 ────────────────────────────────────────────
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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 is_win, pnl, buy_dt, sell_dt, ticker 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, "sell_dt": sell_dt, "ticker": ticker,
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"is_win": is_win, "pnl_pct": pnl,
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"pos_size": pos_size, "krw_profit": krw_profit,
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"portfolio": portfolio,
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})
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wins = sum(1 for t in accepted if t[0])
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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():
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print("캐시 로드 중...")
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cache = pickle.load(open(CACHE_FILE, "rb"))
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top30 = pickle.load(open(TOP30_FILE, "rb"))
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tickers = [t for t in top30[:TOP_N] if t in cache["10m"]]
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print(f"유효 종목: {len(tickers)}개\n")
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# 리샘플링 + 전략 실행
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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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df40 = resample_40m(cache["10m"][t])
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if len(df40) < 50:
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continue
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raw = run_vol_lead(df40, t)
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filtered, blocked = apply_wf(raw)
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wf_total_blocked += blocked
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all_trades.extend(filtered)
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all_trades.sort(key=lambda x: x[2])
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accepted, skipped = apply_max_positions(all_trades)
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result = simulate(accepted)
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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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# 기간 추출
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if result["trade_log"]:
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start_dt = result["trade_log"][0]["buy_dt"].strftime("%Y-%m-%d")
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end_dt = result["trade_log"][-1]["sell_dt"].strftime("%Y-%m-%d")
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else:
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start_dt = end_dt = "N/A"
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print(f"{'='*60}")
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print(f"45일 복리 시뮬 | 40분봉 vol-lead +{THRESH}% | {len(tickers)}종목")
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print(f"기간: {start_dt} ~ {end_dt}")
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print(f"{'='*60}")
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print(f" 신호 발생: {len(all_trades) + wf_total_blocked:>4}건 (WF 차단: {wf_total_blocked}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"
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f" (승률 {result['wr']:.1f}%)")
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print(f" {'─'*50}")
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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" 최대 낙폭: {-max_dd:>+13.2f}%"
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f" ({-max_dd / 100 * BUDGET:>+,.0f}원)")
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monthly_krw = [m["pnl_krw"] for m in result["monthly"].values()]
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avg_monthly = sum(monthly_krw) / len(monthly_krw) if monthly_krw else 0
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print(f" 월평균 수익: {avg_monthly:>+13,.0f}원")
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print(f"\n── 월별 수익 {'─'*40}")
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print(f" {'월':^8} │ {'거래':>4} {'승률':>5} │ {'월수익(KRW)':>14} {'누적수익(KRW)':>15}")
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cum = 0.0
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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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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")
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print(f"{'='*60}")
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if __name__ == "__main__":
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main()
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