refactor: reorganize project structure into tests/, data/, logs/

- 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>
2026-03-03 16:08:50 +09:00
parent bfe0b4d40c
commit 6b2c962ed8
30 changed files with 1039 additions and 5 deletions
--- a/tests/ticker_sim.py
+++ b/tests/ticker_sim.py
@@ -0,0 +1,119 @@
+"""종목 수 확장 시뮬레이션 - 거래량 상위 N개 종목별 vol-lead 전략 비교."""
+
+import os
+import pickle
+import sys
+from pathlib import Path
+
+from dotenv import load_dotenv
+load_dotenv()
+
+import pandas as pd
+import pyupbit
+
+# vol_lead_sim.py의 공통 파라미터/함수 재사용
+sys.path.insert(0, str(Path(__file__).parent))
+from vol_lead_sim import (
+    STOP_LOSS_PCT, TIME_STOP_HOURS, TIME_STOP_MIN_PCT,
+    FEE, LOCAL_VOL_HOURS, VOL_MULT, PRICE_QUIET_PCT, SIGNAL_TIMEOUT_H,
+    FROM_DATE, simulate_pos, run_trend, run_vol_lead_thresh,
+)
+
+CACHE_FILE = Path("vol_lead_cache_30.pkl")
+TOP30_FILE = Path("top30_tickers.pkl")
+DAYS = 46.0
+
+
+def load_data() -> dict:
+    return pickle.load(open(CACHE_FILE, "rb"))
+
+
+def run_subset(data: dict, tickers: list, thresh: float) -> dict:
+    agg = {"total": 0, "wins": 0, "pnl": 0.0, "per_ticker": []}
+    for t in tickers:
+        if t not in data:
+            continue
+        trades = run_vol_lead_thresh(data[t], thresh)
+        n = len(trades)
+        w = sum(1 for x in trades if x[0])
+        p = sum(x[1] for x in trades)
+        agg["total"] += n
+        agg["wins"]  += w
+        agg["pnl"]   += p
+        agg["per_ticker"].append((t, n, w, p))
+    agg["wr"] = agg["wins"] / agg["total"] * 100 if agg["total"] else 0
+    return agg
+
+
+def main() -> None:
+    data  = load_data()
+    top30 = pickle.load(open(TOP30_FILE, "rb"))
+
+    # 데이터 충분한 종목만 (400봉 이상 = 16일 이상)
+    valid = [t for t in top30 if t in data and len(data[t]) >= 400]
+    n_max = len(valid)
+    print(f"유효 종목: {n_max}개")
+    print(f"기간: 46일 (2026-01-15 ~ 2026-03-02)\n")
+
+    # ── A 현행 기준선 (9종목) ─────────────────────────
+    orig9 = ["KRW-DKA","KRW-LAYER","KRW-SIGN","KRW-SOL","KRW-ETH",
+             "KRW-XRP","KRW-HOLO","KRW-OM","KRW-ORBS"]
+    orig9_valid = [t for t in orig9 if t in data]
+    a_agg = {"total": 0, "wins": 0, "pnl": 0.0}
+    for t in orig9_valid:
+        trades = run_trend(data[t])
+        a_agg["total"] += len(trades)
+        a_agg["wins"]  += sum(1 for x in trades if x[0])
+        a_agg["pnl"]   += sum(x[1] for x in trades)
+    a_wr = a_agg["wins"] / a_agg["total"] * 100 if a_agg["total"] else 0
+    print(f"[기준: A 현행 9종목] {a_agg['total']}건 | 승률={a_wr:.0f}% | 누적={a_agg['pnl']:+.2f}%\n")
+
+    # ── 종목수별 비교 (임계값 4.8% 고정) ────────────────
+    THRESH = 4.8
+    subset_ns = [9, 15, 20, n_max]
+
+    print(f"임계값 +{THRESH}% | 종목 수 확장 효과")
+    print(f"{'종목수':>6} │ {'총거래':>6} {'일평균':>7} {'월환산':>7} │ {'승률':>5} {'누적PnL':>10}")
+    print("─" * 56)
+    for n in subset_ns:
+        s   = run_subset(data, valid[:n], THRESH)
+        pdm = s["total"] / DAYS
+        pmm = pdm * 30
+        marker = " ← 현재설정" if n == 9 else ""
+        print(f"{n:>5}종목 │ {s['total']:>6}건 {pdm:>6.2f}회/일 {pmm:>6.1f}회/월 │ "
+              f"{s['wr']:>4.0f}% {s['pnl']:>+9.2f}%{marker}")
+
+    # ── 임계값 × 종목수 매트릭스 ─────────────────────
+    thresholds = [3.6, 4.0, 4.4, 4.8]
+    col_ns     = [9, 15, 20, n_max]
+
+    print(f"\n임계값 × 종목수 매트릭스  (건수 / 승률 / 누적PnL)")
+    col_w = 20
+    header = f"{'임계값':>6}  │"
+    for n in col_ns:
+        header += f" {f'{n}종목':^{col_w}}"
+    print(header)
+    print("─" * (10 + col_w * len(col_ns)))
+    for thresh in thresholds:
+        row = f"+{thresh:.1f}%   │"
+        for n in col_ns:
+            s  = run_subset(data, valid[:n], thresh)
+            wr = s["wins"] / s["total"] * 100 if s["total"] else 0
+            cell = f"{s['total']}건 {wr:.0f}% {s['pnl']:+.1f}%"
+            row += f" {cell:<{col_w}}"
+        print(row)
+
+    # ── 전체 종목별 기여도 (4.8%) ────────────────────
+    print(f"\n종목별 기여도 ({n_max}종목, +4.8%)")
+    print(f"{'종목':<16} {'거래':>5} {'승률':>6} {'누적PnL':>10}  {'평균PnL/거래':>12}")
+    print("─" * 55)
+    s = run_subset(data, valid, THRESH)
+    s["per_ticker"].sort(key=lambda x: x[3], reverse=True)
+    for t, n, w, p in s["per_ticker"]:
+        wr  = w / n * 100 if n else 0
+        avg = p / n if n else 0
+        print(f"{t:<16} {n:>5}건 {wr:>5.0f}% {p:>+9.2f}%  {avg:>+10.2f}%/건")
+
+
+if __name__ == "__main__":
+    main()