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>
324 lines
12 KiB
Python
324 lines
12 KiB
Python
"""Shadow 재활 시뮬레이션 v3 - 실제 전략 필터 포함.
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전략 조건:
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1. 추세: 현재가 vs 2h 전 가격 >= TREND_MIN_GAIN_PCT%
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2. 모멘텀: 현재가 > MA20(일봉) AND 최근 1h 거래량 > 로컬 5h 평균 * VOL_MULT
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3. 15분 워치리스트: 첫 신호 후 15분 재확인
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"""
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import os as _os, sys as _sys
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_sys.path.insert(0, _os.path.dirname(_os.path.dirname(_os.path.abspath(__file__))))
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import os, time
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from datetime import datetime, timedelta
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from collections import defaultdict
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from dotenv import load_dotenv
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load_dotenv()
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import oracledb
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import pyupbit
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# ── 파라미터 ──────────────────────────────────────────
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STOP_LOSS_PCT = float(os.getenv("STOP_LOSS_PCT", "1.5")) / 100
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TIME_STOP_HOURS = int(os.getenv("TIME_STOP_HOURS", "8"))
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TIME_STOP_MIN_PCT = float(os.getenv("TIME_STOP_MIN_GAIN_PCT", "3")) / 100
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TREND_HOURS = 2
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TREND_MIN_PCT = float(os.getenv("TREND_MIN_GAIN_PCT", "5")) # Neutral 기준
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MA_PERIOD = 20
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LOCAL_VOL_HOURS = 5
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VOL_MULT = float(os.getenv("VOLUME_MULTIPLIER", "2.0"))
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CONFIRM_MINUTES = 15
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FEE = 0.0005
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REHABILITATE_WINS = 2
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# ── DB 연결 ───────────────────────────────────────────
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def get_conn():
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return oracledb.connect(
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user=os.getenv('ORACLE_USER'),
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password=os.getenv('ORACLE_PASSWORD'),
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dsn=os.getenv('ORACLE_DSN'),
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config_dir=os.getenv('ORACLE_WALLET')
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)
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def load_price_history(cur, ticker, from_dt, to_dt):
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"""price_history 전체 로드."""
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cur.execute("""
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SELECT price, recorded_at FROM price_history
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WHERE ticker = :t AND recorded_at BETWEEN :f AND :e
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ORDER BY recorded_at
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""", t=ticker, f=from_dt, e=to_dt)
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return cur.fetchall() # [(price, dt), ...]
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# ── pyupbit 과거 데이터 캐시 ───────────────────────────
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_daily_cache = {} # (ticker, date_str) → df
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_hourly_cache = {} # (ticker, hour_str) → df
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def get_ma20(ticker, as_of_dt):
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"""as_of_dt 기준 MA20 (일봉 종가)."""
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date_str = as_of_dt.strftime("%Y-%m-%d")
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key = (ticker, date_str)
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if key not in _daily_cache:
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try:
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df = pyupbit.get_ohlcv(ticker, interval="day", count=MA_PERIOD + 2,
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to=as_of_dt.strftime("%Y-%m-%d 09:00:00"))
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_daily_cache[key] = df
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time.sleep(0.1)
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except Exception as e:
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_daily_cache[key] = None
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df = _daily_cache[key]
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if df is None or len(df) < MA_PERIOD:
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return None
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return df["close"].iloc[-MA_PERIOD:].mean()
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def get_volume_ratio(ticker, as_of_dt):
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"""최근 1h 거래량 / 로컬 5h 평균. (직전 완성봉 기준)"""
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# 시간봉은 해당 시각의 이전 7h 데이터 필요
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hour_str = as_of_dt.strftime("%Y-%m-%d %H:00:00")
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key = (ticker, hour_str)
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if key not in _hourly_cache:
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try:
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df = pyupbit.get_ohlcv(ticker, interval="minute60", count=LOCAL_VOL_HOURS + 3,
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to=as_of_dt.strftime("%Y-%m-%d %H:%M:%S"))
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_hourly_cache[key] = df
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time.sleep(0.1)
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except Exception as e:
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_hourly_cache[key] = None
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df = _hourly_cache[key]
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if df is None or len(df) < LOCAL_VOL_HOURS + 1:
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return 0.0
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recent_vol = df["volume"].iloc[-2]
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local_avg = df["volume"].iloc[-(LOCAL_VOL_HOURS + 1):-2].mean()
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if local_avg <= 0:
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return 0.0
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return recent_vol / local_avg
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# ── 전략 조건 체크 ────────────────────────────────────
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def check_trend(prices, idx):
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"""현재 idx 기준 2h 전(12틱 전) 대비 +TREND_MIN_PCT% 이상."""
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lookback = TREND_HOURS * 6 # 10분봉 기준 2h = 12틱
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if idx < lookback:
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return False
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current = prices[idx][0]
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past = prices[idx - lookback][0]
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if past <= 0:
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return False
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gain = (current - past) / past * 100
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return gain >= TREND_MIN_PCT
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def check_momentum(ticker, current_price, as_of_dt):
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"""현재가 > MA20 AND 거래량 비율 >= VOL_MULT."""
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ma20 = get_ma20(ticker, as_of_dt)
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if ma20 is None:
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return False
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if current_price <= ma20:
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return False
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vol_ratio = get_volume_ratio(ticker, as_of_dt)
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return vol_ratio >= VOL_MULT
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# ── 단일 포지션 시뮬레이션 ────────────────────────────
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def simulate_position(prices, buy_idx, buy_price):
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"""buy_idx 이후 가격으로 포지션 시뮬레이션."""
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buy_dt = prices[buy_idx][1]
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peak = buy_price
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for price, ts in prices[buy_idx + 1:]:
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if price > peak:
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peak = price
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elapsed_h = (ts - buy_dt).total_seconds() / 3600
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pnl = (price - buy_price) / buy_price
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drop_from_peak = (peak - price) / peak
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if drop_from_peak >= STOP_LOSS_PCT:
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net = (price*(1-FEE) - buy_price*(1+FEE)) / (buy_price*(1+FEE)) * 100
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return (net > 0, price, ts, f"트레일링({pnl*100:+.1f}%)", net)
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if elapsed_h >= TIME_STOP_HOURS and pnl < TIME_STOP_MIN_PCT:
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net = (price*(1-FEE) - buy_price*(1+FEE)) / (buy_price*(1+FEE)) * 100
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return (net > 0, price, ts, f"타임스탑({elapsed_h:.1f}h)", net)
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last_price, last_ts = prices[-1]
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net = (last_price*(1-FEE) - buy_price*(1+FEE)) / (buy_price*(1+FEE)) * 100
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return (net > 0, last_price, last_ts, "데이터종료", net)
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# ── Shadow → 재활 → 실제 진입 시뮬레이션 ─────────────
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def run_full_sim(cur, ticker, wf_trigger_dt, end_dt):
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"""
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1. WF차단 시점부터 shadow 포지션 (전략 필터 적용)
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2. REHABILITATE_WINS 연승 → WF 해제
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3. 해제 이후 실제 진입 성과
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"""
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prices = load_price_history(cur, ticker, wf_trigger_dt, end_dt)
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if not prices:
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print(f" 가격 데이터 없음")
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return
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print(f" 가격 데이터: {len(prices)}개 ({prices[0][1].strftime('%m-%d %H:%M')} ~ {prices[-1][1].strftime('%m-%d %H:%M')})")
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# ── Phase 1: Shadow (WF 해제까지) ────────────────
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shadow_wins = 0
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rehab_dt = None
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watchlist_dt = None # 신호 첫 발생 시각
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in_position = False
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pos_buy_idx = None
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pos_buy_price = None
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shadow_trade_no = 0
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idx = 0
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while idx < len(prices) and rehab_dt is None:
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current_price, current_dt = prices[idx]
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if in_position:
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# 포지션 청산 체크
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is_win, sell_price, sell_dt, reason, pnl = simulate_position(prices, pos_buy_idx, pos_buy_price)
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# 청산 시각에 해당하는 idx로 점프
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sell_idx = next((i for i, (_, ts) in enumerate(prices) if ts >= sell_dt), len(prices)-1)
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shadow_trade_no += 1
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if is_win:
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shadow_wins += 1
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else:
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shadow_wins = 0
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mark = "✅" if is_win else "❌"
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print(f" [Shadow#{shadow_trade_no}] {pos_buy_price:.4f}→{sell_price:.4f}원 "
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f"| {mark} {pnl:+.2f}% | {reason} | 연속승={shadow_wins}/{REHABILITATE_WINS}"
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f" ({sell_dt.strftime('%m-%d %H:%M')})")
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if shadow_wins >= REHABILITATE_WINS:
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rehab_dt = sell_dt
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idx = sell_idx
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break
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in_position = False
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watchlist_dt = None
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idx = sell_idx
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continue
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# 전략 조건 체크
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trend_ok = check_trend(prices, idx)
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if trend_ok:
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mom_ok = check_momentum(ticker, current_price, current_dt)
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else:
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mom_ok = False
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if trend_ok and mom_ok:
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if watchlist_dt is None:
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watchlist_dt = current_dt # 첫 신호
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elif (current_dt - watchlist_dt).total_seconds() >= CONFIRM_MINUTES * 60:
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# 15분 재확인 → shadow 진입
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in_position = True
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pos_buy_idx = idx
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pos_buy_price = current_price
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watchlist_dt = None
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# 청산은 다음 루프에서
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else:
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watchlist_dt = None # 조건 깨지면 초기화
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idx += 1
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if rehab_dt is None:
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print(f"\n ⛔ 전략 필터 적용 시 데이터 범위 내 재활 실패 (shadow_wins={shadow_wins})")
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return
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print(f"\n 🎉 WF 해제: {rehab_dt.strftime('%m-%d %H:%M')} ({shadow_trade_no}번 shadow 거래)")
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# ── Phase 2: 실제 진입 (재활 이후) ───────────────
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print(f"\n ── 재활 후 실제 진입 ──")
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post_prices = [(p, dt) for p, dt in prices if dt >= rehab_dt]
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if not post_prices:
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print(" 재활 이후 데이터 없음")
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return
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real_wins = 0
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real_total = 0
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real_pnl_sum = 0.0
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watchlist_dt = None
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in_position = False
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pos_buy_idx_g = None # global idx in post_prices
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pos_buy_price = None
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idx2 = 0
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while idx2 < len(post_prices):
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current_price, current_dt = post_prices[idx2]
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if in_position:
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is_win, sell_price, sell_dt, reason, pnl = simulate_position(post_prices, pos_buy_idx_g, pos_buy_price)
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sell_idx2 = next((i for i, (_, ts) in enumerate(post_prices) if ts >= sell_dt), len(post_prices)-1)
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real_total += 1
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if is_win:
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real_wins += 1
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real_pnl_sum += pnl
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mark = "✅" if is_win else "❌"
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print(f" 실제#{real_total}: {pos_buy_price:.4f}→{sell_price:.4f}원 "
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f"| {mark} {pnl:+.2f}% | {reason} ({sell_dt.strftime('%m-%d %H:%M')})")
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in_position = False
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watchlist_dt = None
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idx2 = sell_idx2
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continue
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trend_ok = check_trend(post_prices, idx2)
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if trend_ok:
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mom_ok = check_momentum(ticker, current_price, current_dt)
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else:
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mom_ok = False
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if trend_ok and mom_ok:
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if watchlist_dt is None:
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watchlist_dt = current_dt
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elif (current_dt - watchlist_dt).total_seconds() >= CONFIRM_MINUTES * 60:
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in_position = True
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pos_buy_idx_g = idx2
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pos_buy_price = current_price
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watchlist_dt = None
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else:
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watchlist_dt = None
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idx2 += 1
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if real_total == 0:
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print(" 재활 후 전략 조건 충족 진입 없음")
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else:
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wr = real_wins / real_total * 100
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print(f"\n 📊 재활 후: {real_total}건 | 승률={wr:.0f}% | 누적={real_pnl_sum:+.2f}%")
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return {'trades': real_total, 'wins': real_wins, 'wr': wr, 'pnl': real_pnl_sum}
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def main():
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conn = get_conn()
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cur = conn.cursor()
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# price_history 최대 시각
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cur.execute("SELECT MAX(recorded_at) FROM price_history")
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end_dt = cur.fetchone()[0]
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summary = {}
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for ticker in ['KRW-DKA', 'KRW-LAYER', 'KRW-SIGN']:
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cur.execute("""
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SELECT traded_at FROM trade_results
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WHERE ticker = :t ORDER BY traded_at
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""", t=ticker)
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rows = cur.fetchall()
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wf_dt = rows[4][0]
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print(f"\n{'='*62}")
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print(f"[{ticker}] WF차단: {wf_dt.strftime('%m-%d %H:%M')}")
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print(f"{'='*62}")
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r = run_full_sim(cur, ticker, wf_dt, end_dt)
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if r:
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summary[ticker] = r
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print(f"\n{'='*62}")
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print("전체 요약 (전략 필터 적용)")
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print(f"{'='*62}")
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for ticker, r in summary.items():
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print(f"{ticker}: {r['trades']}건 | 승률={r['wr']:.0f}% | 누적={r['pnl']:+.2f}%")
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conn.close()
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if __name__ == "__main__":
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main()
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