refactor: MVC 구조 분리 + 미사용 파일 archive 정리
- tick_trader.py를 Controller로 축소, 로직을 3개 모듈로 분리: - core/signal.py: 시그널 감지, 지표 계산 (calc_vr, calc_atr, detect_signal) - core/order.py: Upbit 주문 실행 (매수/매도/취소/조회) - core/position_manager.py: 포지션 관리, DB sync, 복구, 청산 조건 - type hints, Google docstring, 구체적 예외 타입 적용 - 50줄 초과 함수 분리 (process_signal, restore_positions) - 미사용 파일 58개 archive/ 폴더로 이동 - README.md 추가 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
joungmin
357
archive/tests/sweep_nbar.py
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357
archive/tests/sweep_nbar.py
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"""1분봉 N봉 연속 상승 vol spike 전략 파라미터 스윕.
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N=2/3/4 연속 조건 (양봉 + 상승 + vol spike) 모두 Oracle SQL에서 처리.
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Python은 ATR 파라미터 + VOL 임계값 스윕만 담당.
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시그널 조건 (N_BARS=N):
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봉[n-(N-1)]~봉[n]: 모두 vol_ratio >= VOL_MIN, 양봉, 연속 상승
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진입: 봉[n+1] close 즉시
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추적: 1분봉 trail stop (ATR) + time stop / 월별 배치
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"""
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import sys, os, itertools
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from datetime import datetime, timedelta
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from dotenv import load_dotenv
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load_dotenv(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), '.env'))
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import pandas as pd
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import oracledb
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import time as _time
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# ── 고정 파라미터 ─────────────────────────────────────────────────────────────
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VOL_LOOKBACK = 61
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ATR_LOOKBACK = 28
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TS_N = 240
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TIME_STOP_PCT = 0.0 / 100
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FEE = 0.0005
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BUDGET = 15_000_000
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MAX_POS = 3
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PER_POS = BUDGET // MAX_POS
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# ── 스윕 파라미터 ─────────────────────────────────────────────────────────────
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VOL_SWEEP = [1.5, 2.0, 3.0, 5.0, 8.0, 10.0, 15.0, 20.0, 25.0]
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ATR_SWEEP = {
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"ATR_MULT": [1.5, 2.0, 2.5, 3.0],
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"ATR_MIN": [0.005, 0.010, 0.015],
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"ATR_MAX": [0.020, 0.025, 0.030],
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}
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N_BARS_LIST = [2, 3, 4]
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VOL_MIN = min(VOL_SWEEP) # SQL pre-filter (모든 봉 >= 1.5x) (모든 봉에 적용)
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# ── 시뮬 구간 ─────────────────────────────────────────────────────────────────
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SIM_START = datetime(2025, 8, 1)
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SIM_END = datetime(2026, 3, 4)
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WARMUP_MINS = 120
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TICKERS = [
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'KRW-XRP','KRW-BTC','KRW-ETH','KRW-SOL','KRW-DOGE',
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'KRW-ADA','KRW-SUI','KRW-NEAR','KRW-KAVA','KRW-SXP',
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'KRW-AKT','KRW-SONIC','KRW-IP','KRW-ORBS','KRW-VIRTUAL',
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'KRW-BARD','KRW-XPL','KRW-KITE','KRW-ENSO','KRW-0G',
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]
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_TK = ",".join(f"'{t}'" for t in TICKERS)
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def _months(start: datetime, end: datetime):
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m = start.replace(day=1)
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while m < end:
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nxt = (m + timedelta(days=32)).replace(day=1)
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if nxt > end:
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nxt = end
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yield m, nxt
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m = nxt
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def _get_conn():
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kwargs = dict(user=os.environ["ORACLE_USER"],
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password=os.environ["ORACLE_PASSWORD"],
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dsn=os.environ["ORACLE_DSN"])
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wallet = os.environ.get("ORACLE_WALLET")
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if wallet:
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kwargs["config_dir"] = wallet
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return oracledb.connect(**kwargs)
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# ── N별 SQL 생성 ──────────────────────────────────────────────────────────────
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# 공통 CTE: base(TR + LAG N개), indicators(vol_ratio N개 + ATR)
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# signals: N봉 연속 조건 모두 SQL에서 처리
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# vol_ratio 컬럼은 Python VOL 스윕용으로 모두 반환
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def build_sql(n: int) -> str:
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"""n봉 연속 조건을 SQL로 구현. 반환 컬럼에 vol_ratio 0~n-1 포함."""
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# LAG 컬럼 정의 (base CTE)
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lag_cols = "\n".join(
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f" LAG(close_p, {i}) OVER (PARTITION BY ticker ORDER BY ts) prev_close_{i},\n"
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f" LAG(open_p, {i}) OVER (PARTITION BY ticker ORDER BY ts) prev_open_{i},\n"
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f" LAG(volume_p, {i}) OVER (PARTITION BY ticker ORDER BY ts) prev_vol_{i},"
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for i in range(1, n)
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)
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# indicators CTE: vol_ratio 0 ~ n-1
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vr_cols = []
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vr_cols.append(f""" volume_p / NULLIF(
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AVG(volume_p) OVER (PARTITION BY ticker ORDER BY ts
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ROWS BETWEEN {VOL_LOOKBACK} PRECEDING AND 2 PRECEDING), 0
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) vol_ratio_0,""")
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for i in range(1, n):
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vr_cols.append(f""" prev_vol_{i} / NULLIF(
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AVG(volume_p) OVER (PARTITION BY ticker ORDER BY ts
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ROWS BETWEEN {VOL_LOOKBACK} PRECEDING AND 2 PRECEDING), 0
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) vol_ratio_{i},""")
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vr_cols_str = "\n".join(vr_cols)
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# pass-through LAG 컬럼 from base → indicators
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lag_passthrough = "\n".join(
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f" prev_close_{i}, prev_open_{i},"
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for i in range(1, n)
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)
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# signals WHERE 조건: 현재봉 + 이전 n-1봉 all 양봉 + 연속 상승 + vol
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cond_lines = [
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" AND vol_ratio_0 >= :min_vol",
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" AND close_p > open_p", # 현재봉 양봉
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]
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for i in range(1, n):
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cond_lines.append(f" AND vol_ratio_{i} >= :min_vol")
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cond_lines.append(f" AND prev_close_{i} > prev_open_{i}") # 양봉
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if i == 1:
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cond_lines.append(f" AND close_p > prev_close_{i}") # 현재봉 > 1봉전
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else:
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cond_lines.append(f" AND prev_close_{i-1} > prev_close_{i}") # 상승 연속
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cond_str = "\n".join(cond_lines)
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# SELECT: vol_ratio 0~n-1 반환 (Python VOL 스윕용)
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vr_select = ", ".join(f"vol_ratio_{i}" for i in range(n))
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return f"""
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WITH
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base AS (
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SELECT ticker, ts, open_p, close_p, high_p, low_p, volume_p,
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{lag_cols}
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GREATEST(
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high_p - low_p,
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ABS(high_p - LAG(close_p,1) OVER (PARTITION BY ticker ORDER BY ts)),
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ABS(low_p - LAG(close_p,1) OVER (PARTITION BY ticker ORDER BY ts))
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) tr
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FROM backtest_ohlcv
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WHERE interval_cd = 'minute1'
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AND ts >= TO_TIMESTAMP(:load_since, 'YYYY-MM-DD HH24:MI:SS')
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AND ts < TO_TIMESTAMP(:sim_end, 'YYYY-MM-DD HH24:MI:SS')
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AND ticker IN ({_TK})
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),
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indicators AS (
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SELECT ticker, ts, open_p, close_p,
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{lag_passthrough}
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{vr_cols_str}
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AVG(tr) OVER (PARTITION BY ticker ORDER BY ts
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ROWS BETWEEN {ATR_LOOKBACK} PRECEDING AND 1 PRECEDING)
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/ NULLIF(prev_close_1, 0) atr_raw
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FROM base
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),
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signals AS (
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SELECT ticker, ts sig_ts, close_p sig_price,
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{vr_select}, atr_raw
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FROM indicators
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WHERE ts >= TO_TIMESTAMP(:sim_start, 'YYYY-MM-DD HH24:MI:SS')
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AND ts < TO_TIMESTAMP(:sim_end, 'YYYY-MM-DD HH24:MI:SS')
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{cond_str}
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),
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entry_cands AS (
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SELECT s.ticker, s.sig_ts, {vr_select.replace('vol_ratio_', 's.vol_ratio_')}, s.atr_raw,
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e.ts entry_ts, e.close_p entry_price,
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ROW_NUMBER() OVER (PARTITION BY s.ticker, s.sig_ts ORDER BY e.ts) rn
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FROM signals s
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JOIN backtest_ohlcv e
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ON e.ticker = s.ticker
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AND e.interval_cd = 'minute1'
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AND e.ts > s.sig_ts
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AND e.ts <= s.sig_ts + INTERVAL '3' MINUTE
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),
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entries AS (
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SELECT ticker, sig_ts, {vr_select}, atr_raw, entry_ts, entry_price
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FROM entry_cands WHERE rn = 1
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),
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post_entry AS (
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SELECT
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e.ticker, e.sig_ts, e.entry_ts, e.entry_price,
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{vr_select.replace('vol_ratio_', 'e.vol_ratio_')}, e.atr_raw,
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b.close_p bar_price,
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ROW_NUMBER() OVER (PARTITION BY e.ticker, e.entry_ts ORDER BY b.ts) bar_n,
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MAX(b.close_p) OVER (PARTITION BY e.ticker, e.entry_ts
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ORDER BY b.ts ROWS UNBOUNDED PRECEDING) running_peak
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FROM entries e
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JOIN backtest_ohlcv b
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ON b.ticker = e.ticker
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AND b.interval_cd = 'minute1'
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AND b.ts >= e.entry_ts
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AND b.ts <= e.entry_ts + INTERVAL '{TS_N}' MINUTE
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)
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SELECT ticker, sig_ts, entry_ts, entry_price,
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{vr_select}, atr_raw,
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bar_n, bar_price, running_peak
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FROM post_entry
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WHERE bar_n <= :ts_n + 1
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ORDER BY ticker, entry_ts, bar_n
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"""
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# ── 월별 데이터 로드 ──────────────────────────────────────────────────────────
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print(f"N봉 연속 vol spike 전략 (VOL>={VOL_MIN}x, N={N_BARS_LIST})\n", flush=True)
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conn = _get_conn()
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cur = conn.cursor()
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cur.arraysize = 100_000
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# N별로 별도 딕셔너리
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ALL_ENTRIES: dict[int, dict] = {n: {} for n in N_BARS_LIST}
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t_load = _time.time()
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for n in N_BARS_LIST:
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sql = build_sql(n)
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print(f"── {n}봉 로드 중... ──────────────────────────", flush=True)
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n_total = 0
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for m_start, m_end in _months(SIM_START, SIM_END):
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load_since = (m_start - timedelta(minutes=WARMUP_MINS)).strftime('%Y-%m-%d %H:%M:%S')
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sim_start = m_start.strftime('%Y-%m-%d %H:%M:%S')
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sim_end = m_end.strftime('%Y-%m-%d %H:%M:%S')
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t0 = _time.time()
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cur.execute(sql, {
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"load_since": load_since,
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"sim_start": sim_start,
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"sim_end": sim_end,
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"min_vol": VOL_MIN,
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"ts_n": TS_N,
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})
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rows = cur.fetchall()
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t1 = _time.time()
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n_new = 0
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for row in rows:
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# ticker, sig_ts, entry_ts, entry_price, vr0..vr(n-1), atr_raw, bar_n, bar_price, peak
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ticker = row[0]
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entry_ts = row[2]
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entry_price= float(row[3])
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vr_vals = [float(row[4 + i]) if row[4 + i] is not None else 0.0 for i in range(n)]
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atr_raw = row[4 + n]
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bar_price = float(row[4 + n + 2]) # bar_n is at 4+n+1
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running_peak = float(row[4 + n + 3])
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key = (ticker, entry_ts)
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if key not in ALL_ENTRIES[n]:
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ALL_ENTRIES[n][key] = {
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'entry_price': entry_price,
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'vr': vr_vals,
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'atr_raw': float(atr_raw) if atr_raw is not None else float('nan'),
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'bars': [],
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}
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n_new += 1
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ALL_ENTRIES[n][key]['bars'].append((bar_price, running_peak))
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n_total += n_new
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print(f" {sim_start[:7]}: {len(rows):>8,}행 ({t1-t0:.1f}s) | 진입 {n_new:>5}건", flush=True)
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print(f" → {n}봉 합계: {n_total}건\n", flush=True)
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conn.close()
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print(f"전체 로드 완료 ({_time.time()-t_load:.1f}s)\n", flush=True)
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# ── 출구 탐색 ─────────────────────────────────────────────────────────────────
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def find_exit(entry_price: float, atr_stop: float, bars: list) -> float:
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for i, (bp, pk) in enumerate(bars):
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drop = (pk - bp) / pk if pk > 0 else 0.0
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pnl = (bp - entry_price) / entry_price
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if drop >= atr_stop:
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return pnl * 100
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if i + 1 >= TS_N and pnl < TIME_STOP_PCT:
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return pnl * 100
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return (bars[-1][0] - entry_price) / entry_price * 100 if bars else 0.0
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# ── 스윕 ──────────────────────────────────────────────────────────────────────
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atr_keys = list(ATR_SWEEP.keys())
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atr_combos = list(itertools.product(*ATR_SWEEP.values()))
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total_combos = len(N_BARS_LIST) * len(VOL_SWEEP) * len(atr_combos)
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print(f"총 {total_combos}가지 조합 스윕...\n", flush=True)
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t_sweep = _time.time()
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results = []
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for n in N_BARS_LIST:
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entry_list = list(ALL_ENTRIES[n].values())
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for vol in VOL_SWEEP:
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for atr_combo in atr_combos:
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atr_params = dict(zip(atr_keys, atr_combo))
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if atr_params['ATR_MIN'] >= atr_params['ATR_MAX']:
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continue
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atr_mult = atr_params['ATR_MULT']
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atr_min = atr_params['ATR_MIN']
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atr_max = atr_params['ATR_MAX']
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trades = []
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for e in entry_list:
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# Python: vol 임계값 최종 필터 (모든 봉 vol >= vol)
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if min(e['vr']) < vol:
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continue
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ar = e['atr_raw']
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atr_s = (atr_min if (ar != ar)
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else max(atr_min, min(atr_max, ar * atr_mult)))
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pnl_pct = find_exit(e['entry_price'], atr_s, e['bars'])
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krw = PER_POS * (pnl_pct / 100) - PER_POS * FEE * 2
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trades.append((pnl_pct, krw))
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if not trades:
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results.append({'N_BARS': n, 'VOL': vol, **atr_params,
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'trades': 0, 'wins': 0,
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'win_rate': 0.0, 'avg_pnl': 0.0, 'total_krw': 0.0})
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continue
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wins = sum(1 for p, _ in trades if p > 0)
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results.append({
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'N_BARS': n,
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'VOL': vol,
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**atr_params,
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'trades': len(trades),
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'wins': wins,
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'win_rate': wins / len(trades) * 100,
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'avg_pnl': sum(p for p, _ in trades) / len(trades),
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'total_krw': sum(k for _, k in trades),
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})
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print(f"스윕 완료 ({_time.time()-t_sweep:.1f}s)\n")
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# ── 결과 출력 ─────────────────────────────────────────────────────────────────
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df_r = pd.DataFrame(results)
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df_r = df_r[df_r['trades'] > 0].sort_values('total_krw', ascending=False)
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print("=" * 105)
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print(f"{'순위':>4} {'N봉':>4} {'VOL':>5} {'ATR_M':>6} {'ATR_N':>6} {'ATR_X':>6} "
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f"{'건수':>5} {'승률':>6} {'평균PNL':>8} {'총손익':>14}")
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print("=" * 105)
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for rank, (_, row) in enumerate(df_r.head(30).iterrows(), 1):
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print(f"{rank:>4} {int(row['N_BARS']):>2}봉 {row['VOL']:>4.0f}x {row['ATR_MULT']:>6.1f} "
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f"{row['ATR_MIN']*100:>5.1f}% {row['ATR_MAX']*100:>5.1f}% "
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f"{int(row['trades']):>5}건 {row['win_rate']:>5.0f}% "
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f"{row['avg_pnl']:>+7.2f}% {row['total_krw']:>+14,.0f}원")
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# N봉 × VOL별 최상위 요약
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print("\n" + "─" * 85)
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print(f" {'N봉':>3} {'VOL':>5} {'건수':>5} {'승률':>5} {'평균PNL':>8} {'총손익':>14} (최적 ATR)")
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print("─" * 85)
|
||||
for n in N_BARS_LIST:
|
||||
for vol in VOL_SWEEP:
|
||||
sub = df_r[(df_r['N_BARS'] == n) & (df_r['VOL'] == vol)]
|
||||
if sub.empty:
|
||||
continue
|
||||
best = sub.iloc[0]
|
||||
if best['trades'] == 0:
|
||||
continue
|
||||
print(f" {int(n):>2}봉 {vol:>4.0f}x {int(best['trades']):>5}건 {best['win_rate']:>4.0f}% "
|
||||
f"{best['avg_pnl']:>+7.2f}% {best['total_krw']:>+14,.0f}원 "
|
||||
f"(M={best['ATR_MULT']:.1f} N={best['ATR_MIN']*100:.1f}% X={best['ATR_MAX']*100:.1f}%)")
|
||||
print()
|
||||
Reference in New Issue
Block a user