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>
120 lines
4.4 KiB
Python
120 lines
4.4 KiB
Python
"""종목 수 확장 시뮬레이션 - 거래량 상위 N개 종목별 vol-lead 전략 비교."""
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import os
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import pickle
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import sys
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from pathlib import Path
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from dotenv import load_dotenv
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load_dotenv()
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import pandas as pd
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import pyupbit
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# vol_lead_sim.py의 공통 파라미터/함수 재사용
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sys.path.insert(0, str(Path(__file__).parent))
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from vol_lead_sim import (
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STOP_LOSS_PCT, TIME_STOP_HOURS, TIME_STOP_MIN_PCT,
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FEE, LOCAL_VOL_HOURS, VOL_MULT, PRICE_QUIET_PCT, SIGNAL_TIMEOUT_H,
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FROM_DATE, simulate_pos, run_trend, run_vol_lead_thresh,
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)
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CACHE_FILE = Path("vol_lead_cache_30.pkl")
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TOP30_FILE = Path("top30_tickers.pkl")
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DAYS = 46.0
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def load_data() -> dict:
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return pickle.load(open(CACHE_FILE, "rb"))
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def run_subset(data: dict, tickers: list, thresh: float) -> dict:
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agg = {"total": 0, "wins": 0, "pnl": 0.0, "per_ticker": []}
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for t in tickers:
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if t not in data:
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continue
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trades = run_vol_lead_thresh(data[t], thresh)
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n = len(trades)
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w = sum(1 for x in trades if x[0])
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p = sum(x[1] for x in trades)
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agg["total"] += n
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agg["wins"] += w
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agg["pnl"] += p
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agg["per_ticker"].append((t, n, w, p))
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agg["wr"] = agg["wins"] / agg["total"] * 100 if agg["total"] else 0
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return agg
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def main() -> None:
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data = load_data()
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top30 = pickle.load(open(TOP30_FILE, "rb"))
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# 데이터 충분한 종목만 (400봉 이상 = 16일 이상)
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valid = [t for t in top30 if t in data and len(data[t]) >= 400]
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n_max = len(valid)
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print(f"유효 종목: {n_max}개")
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print(f"기간: 46일 (2026-01-15 ~ 2026-03-02)\n")
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# ── A 현행 기준선 (9종목) ─────────────────────────
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orig9 = ["KRW-DKA","KRW-LAYER","KRW-SIGN","KRW-SOL","KRW-ETH",
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"KRW-XRP","KRW-HOLO","KRW-OM","KRW-ORBS"]
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orig9_valid = [t for t in orig9 if t in data]
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a_agg = {"total": 0, "wins": 0, "pnl": 0.0}
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for t in orig9_valid:
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trades = run_trend(data[t])
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a_agg["total"] += len(trades)
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a_agg["wins"] += sum(1 for x in trades if x[0])
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a_agg["pnl"] += sum(x[1] for x in trades)
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a_wr = a_agg["wins"] / a_agg["total"] * 100 if a_agg["total"] else 0
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print(f"[기준: A 현행 9종목] {a_agg['total']}건 | 승률={a_wr:.0f}% | 누적={a_agg['pnl']:+.2f}%\n")
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# ── 종목수별 비교 (임계값 4.8% 고정) ────────────────
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THRESH = 4.8
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subset_ns = [9, 15, 20, n_max]
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print(f"임계값 +{THRESH}% | 종목 수 확장 효과")
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print(f"{'종목수':>6} │ {'총거래':>6} {'일평균':>7} {'월환산':>7} │ {'승률':>5} {'누적PnL':>10}")
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print("─" * 56)
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for n in subset_ns:
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s = run_subset(data, valid[:n], THRESH)
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pdm = s["total"] / DAYS
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pmm = pdm * 30
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marker = " ← 현재설정" if n == 9 else ""
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print(f"{n:>5}종목 │ {s['total']:>6}건 {pdm:>6.2f}회/일 {pmm:>6.1f}회/월 │ "
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f"{s['wr']:>4.0f}% {s['pnl']:>+9.2f}%{marker}")
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# ── 임계값 × 종목수 매트릭스 ─────────────────────
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thresholds = [3.6, 4.0, 4.4, 4.8]
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col_ns = [9, 15, 20, n_max]
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print(f"\n임계값 × 종목수 매트릭스 (건수 / 승률 / 누적PnL)")
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col_w = 20
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header = f"{'임계값':>6} │"
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for n in col_ns:
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header += f" {f'{n}종목':^{col_w}}"
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print(header)
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print("─" * (10 + col_w * len(col_ns)))
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for thresh in thresholds:
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row = f"+{thresh:.1f}% │"
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for n in col_ns:
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s = run_subset(data, valid[:n], thresh)
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wr = s["wins"] / s["total"] * 100 if s["total"] else 0
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cell = f"{s['total']}건 {wr:.0f}% {s['pnl']:+.1f}%"
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row += f" {cell:<{col_w}}"
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print(row)
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# ── 전체 종목별 기여도 (4.8%) ────────────────────
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print(f"\n종목별 기여도 ({n_max}종목, +4.8%)")
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print(f"{'종목':<16} {'거래':>5} {'승률':>6} {'누적PnL':>10} {'평균PnL/거래':>12}")
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print("─" * 55)
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s = run_subset(data, valid, THRESH)
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s["per_ticker"].sort(key=lambda x: x[3], reverse=True)
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for t, n, w, p in s["per_ticker"]:
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wr = w / n * 100 if n else 0
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avg = p / n if n else 0
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print(f"{t:<16} {n:>5}건 {wr:>5.0f}% {p:>+9.2f}% {avg:>+10.2f}%/건")
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if __name__ == "__main__":
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main()
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