6b2c962ed8
- Move all backtest/simulation scripts to tests/ - Add sys.path.insert to each script for correct import resolution - Move pkl cache files to data/ (git-ignored) - Move log files to logs/ (git-ignored) - Update main.py: trading.log path → logs/trading.log - Add ecosystem.config.js: pm2 log paths → logs/pm2*.log - Update .gitignore: ignore data/ and logs/ instead of *.pkl/*.log - core/fng.py: increase cache TTL 3600→86400s (API updates daily at KST 09:00) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
309 lines
12 KiB
Python
309 lines
12 KiB
Python
"""sim10m.py — 10분봉 + 극단적 거래량 즉시 진입 전략 시뮬.
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기존 전략 (1h봉 vol-lead) vs 신규 전략 (10분봉 + 100x 이상 거래량 즉시 진입) 비교.
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데이터: 최근 45일 Upbit API
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"""
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import pickle
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import time
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import sys
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from pathlib import Path
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from datetime import datetime, timedelta
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import pandas as pd
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import pyupbit
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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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SIM_DAYS = 45
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TOP30_FILE = Path("top30_tickers.pkl")
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CACHE_FILE = Path("sim10m_cache.pkl")
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TOP_N = 20
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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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# A: 기존 1h봉
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A_LOCAL_VOL = 5 # 봉수 (= 5h)
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A_VOL_MULT = 2.0
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A_QUIET_PCT = 2.0
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A_THRESH = 4.8
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A_SIGNAL_TO = 8 # 신호 유효 봉수 (= 8h)
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A_ATR_CANDLES = 5
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A_TIME_STOP = 8 # 타임스탑 봉수 (= 8h)
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# B: 신규 10분봉
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B_LOCAL_VOL = 30 # 봉수 (5h = 30 × 10min)
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B_VOL_MULT = 2.0
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B_EXTREME_VOL = 100 # 이 이상 → 횡보 조건 면제, 다음 봉 즉시 진입
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B_QUIET_CANDLES = 12 # 2h = 12봉
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B_QUIET_PCT = 2.0
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B_THRESH = 4.8
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B_SIGNAL_TO = 48 # 신호 유효 봉수 (8h = 48봉)
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B_ATR_CANDLES = 30 # ATR 봉수 (5h = 30봉)
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B_TIME_STOP = 48 # 타임스탑 봉수 (8h = 48봉)
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# ── 데이터 로드 ──────────────────────────────────────────
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def fetch_ohlcv(ticker, interval, days):
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"""OHLCV 과거 데이터 페이징 로드."""
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target_start = datetime.now() - timedelta(days=days)
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all_dfs, to, prev_oldest = [], None, None
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while True:
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kwargs = dict(ticker=ticker, interval=interval, count=200)
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if to:
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kwargs["to"] = to
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df = pyupbit.get_ohlcv(**kwargs)
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if df is None or df.empty:
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break
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df.index = df.index.tz_localize(None)
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oldest = df.index[0]
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# 더 이상 과거로 못 가면 중단 (상장일에 도달)
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if prev_oldest is not None and oldest >= prev_oldest:
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all_dfs.append(df)
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break
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all_dfs.append(df)
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prev_oldest = oldest
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if oldest <= target_start:
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break
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to = oldest.strftime("%Y-%m-%d %H:%M:%S")
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time.sleep(0.15)
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if not all_dfs:
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return None
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result = pd.concat(all_dfs).sort_index().drop_duplicates()
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return result[result.index >= target_start]
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def load_data(tickers):
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if CACHE_FILE.exists():
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print(f"캐시 로드: {CACHE_FILE}")
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return pickle.load(open(CACHE_FILE, "rb"))
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data = {"1h": {}, "10m": {}}
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for idx, ticker in enumerate(tickers, 1):
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print(f" [{idx}/{len(tickers)}] {ticker}...", end=" ", flush=True)
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df1h = fetch_ohlcv(ticker, "minute60", SIM_DAYS)
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df10m = fetch_ohlcv(ticker, "minute10", SIM_DAYS)
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n1h = len(df1h) if df1h is not None else 0
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n10m = len(df10m) if df10m is not None else 0
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print(f"1h={n1h}봉 10m={n10m}봉")
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if df1h is not None and n1h >= 50: data["1h"][ticker] = df1h
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if df10m is not None and n10m >= 200: data["10m"][ticker] = df10m
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pickle.dump(data, open(CACHE_FILE, "wb"))
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print(f"캐시 저장: {CACHE_FILE}\n")
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return data
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# ── ATR 계산 (시뮬용) ─────────────────────────────────────
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def calc_atr(df, buy_idx, n):
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sub = df.iloc[max(0, buy_idx - 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[-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, buy_idx, buy_price, stop_pct, time_stop_candles):
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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, sp, ts, f"트레일링({pnl:+.1f}%)", pnl
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pnl_now = (row["close"] - buy_price) / buy_price * 100
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if (i - buy_idx) >= time_stop_candles 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, row["close"], ts, "타임스탑", 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, last, df.index[-1], "데이터종료", pnl
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# ── 전략 A: 기존 1h봉 vol-lead ──────────────────────────
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def run_a(df):
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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(A_LOCAL_VOL + 2, 3)
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while i < len(df):
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if in_pos:
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is_win, sp, sdt, reason, pnl = simulate_pos(df, buy_idx, buy_price, stop_pct, A_TIME_STOP)
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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, "일반"))
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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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chg_2h = abs(close - df.iloc[i-2]["close"]) / df.iloc[i-2]["close"] * 100
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quiet = chg_2h < A_QUIET_PCT
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vol_p = df.iloc[i-1]["volume"]
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vol_avg = df.iloc[i-A_LOCAL_VOL-1:i-1]["volume"].mean()
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spike = vol_avg > 0 and vol_p >= vol_avg * A_VOL_MULT
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if quiet and spike:
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if sig_i is None: 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: sig_i = sig_p = None
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if sig_i is not None and (i - sig_i) > A_SIGNAL_TO:
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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 >= A_THRESH:
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in_pos = True; buy_idx = i; buy_price = close
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stop_pct = calc_atr(df, i, A_ATR_CANDLES)
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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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# ── 전략 B: 10분봉 + 극단적 거래량 즉시 진입 ────────────
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def run_b(df):
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# trade tuple: (is_win, pnl, buy_dt, sell_dt, entry_type)
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# entry_type: '일반' | '극단'
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trades = []
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sig_i = sig_p = None
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extreme_pending = False
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in_pos = False
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buy_idx = buy_price = stop_pct = None
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i = max(B_LOCAL_VOL + 2, B_QUIET_CANDLES + 1)
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while i < len(df):
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if in_pos:
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is_win, sp, sdt, reason, pnl = simulate_pos(df, buy_idx, buy_price, stop_pct, B_TIME_STOP)
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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, entry_type))
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in_pos = False
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sig_i = sig_p = None
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extreme_pending = False
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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-B_LOCAL_VOL-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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# 극단적 거래량 → 다음 봉 진입 대기 설정
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if not extreme_pending and vol_r >= B_EXTREME_VOL:
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extreme_pending = True
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i += 1
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continue
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# 극단적 거래량 다음 봉 → 즉시 진입
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if extreme_pending:
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in_pos = True; buy_idx = i; buy_price = close
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stop_pct = calc_atr(df, i, B_ATR_CANDLES)
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entry_type = "극단"
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extreme_pending = False
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sig_i = sig_p = None
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i += 1
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continue
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# 일반 vol-lead
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close_2h = df.iloc[i - B_QUIET_CANDLES]["close"]
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chg_2h = abs(close - close_2h) / close_2h * 100
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quiet = chg_2h < B_QUIET_PCT
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spike = vol_r >= B_VOL_MULT
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if quiet and spike:
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if sig_i is None: 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: sig_i = sig_p = None
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if sig_i is not None and (i - sig_i) > B_SIGNAL_TO:
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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 >= B_THRESH:
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in_pos = True; buy_idx = i; buy_price = close
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stop_pct = calc_atr(df, i, B_ATR_CANDLES)
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entry_type = "일반"
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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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# ── 통계 계산 ─────────────────────────────────────────────
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def calc_stats(trades):
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if not trades:
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return {"n": 0, "wr": 0.0, "cum": 0.0, "dd": 0.0}
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wins = sum(1 for t in trades if t[0])
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cum = peak = dd = 0.0
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for t in sorted(trades, key=lambda x: x[2]):
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cum += t[1]
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peak = max(peak, cum)
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dd = max(dd, peak - cum)
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return {"n": len(trades), "wr": wins / len(trades) * 100, "cum": cum, "dd": dd}
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# ── 메인 ─────────────────────────────────────────────────
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def main():
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top30 = pickle.load(open(TOP30_FILE, "rb"))
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tickers = top30[:TOP_N]
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print(f"{'='*60}")
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print(f"10분봉 전략 시뮬 | 최근 {SIM_DAYS}일 | {TOP_N}종목")
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print(f" A: 기존 1h봉 vol-lead")
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print(f" B: 10분봉 vol-lead + 극단거래량({B_EXTREME_VOL}x) 즉시 진입")
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print(f"{'='*60}\n")
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print(f"데이터 로드 중...")
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data = load_data(tickers)
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v1h = [t for t in tickers if t in data["1h"] and len(data["1h"][t]) >= 50]
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v10m = [t for t in tickers if t in data["10m"] and len(data["10m"][t]) >= 200]
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print(f"유효: 1h={len(v1h)}종목 / 10m={len(v10m)}종목\n")
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# 전략 A
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a_all = []
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for t in v1h:
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a_all.extend(run_a(data["1h"][t]))
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# 전략 B
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b_all = []
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for t in v10m:
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b_all.extend(run_b(data["10m"][t]))
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b_extreme = [t for t in b_all if t[4] == "극단"]
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b_normal = [t for t in b_all if t[4] == "일반"]
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sa = calc_stats(a_all)
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sb = calc_stats(b_all)
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sbe = calc_stats(b_extreme)
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sbn = calc_stats(b_normal)
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print(f"{'='*65}")
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print(f"{'전략':18} {'거래수':>6} {'승률':>6} {'누적PnL%':>10} {'최대낙폭%':>10}")
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print(f"{'─'*65}")
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print(f"{'A (1h vol-lead)':18} {sa['n']:>6}건 {sa['wr']:>5.1f}% {sa['cum']:>+9.2f}% {-sa['dd']:>+9.2f}%")
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print(f"{'B 전체 (10m)':18} {sb['n']:>6}건 {sb['wr']:>5.1f}% {sb['cum']:>+9.2f}% {-sb['dd']:>+9.2f}%")
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print(f"{' └ 극단거래량':18} {sbe['n']:>6}건 {sbe['wr']:>5.1f}% {sbe['cum']:>+9.2f}% {-sbe['dd']:>+9.2f}%")
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print(f"{' └ 일반vol-lead':18} {sbn['n']:>6}건 {sbn['wr']:>5.1f}% {sbn['cum']:>+9.2f}% {-sbn['dd']:>+9.2f}%")
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print(f"{'='*65}")
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# 극단 거래량 진입 상세
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if b_extreme:
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print(f"\n── 극단거래량 진입 상세 ({len(b_extreme)}건) ─────────────────────")
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print(f" {'매수시각':<18} {'PnL%':>7} {'청산'}")
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for t in sorted(b_extreme, key=lambda x: x[2])[:20]:
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mark = "✅" if t[0] else "❌"
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print(f" {str(t[2])[:16]:<18} {t[1]:>+6.1f}% {mark} {t[4]}")
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if __name__ == "__main__":
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main()
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