54ce327c50
Scripts used to analyze and validate strategy changes: - wf_cmp.py: WF window size comparison on 42 real trades - wf_cmp2.py: WF comparison extended with price_history simulation - shadow_sim.py: shadow rehabilitation sim without strategy filters - shadow_sim2.py: post-rehabilitation performance simulation - shadow_sim3.py: shadow rehabilitation sim with full strategy filters - momentum_cmp.py: momentum filter A/B comparison - trend_check.py: 2h price gain distribution analysis per ticker Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
import os
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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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conn = oracledb.connect(user=os.getenv('ORACLE_USER'), password=os.getenv('ORACLE_PASSWORD'),
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dsn=os.getenv('ORACLE_DSN'), config_dir=os.getenv('ORACLE_WALLET'))
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cur = conn.cursor()
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for ticker in ['KRW-DKA', 'KRW-LAYER', 'KRW-SIGN']:
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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 >= TIMESTAMP '2026-02-28 20:00:00'
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ORDER BY recorded_at
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""", t=ticker)
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rows = cur.fetchall()
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lookback = 12 # 10분봉 * 12 = 2h
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gains = []
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for i in range(lookback, len(rows)):
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curr = rows[i][0]
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past = rows[i - lookback][0]
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if past > 0:
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gains.append((curr - past) / past * 100)
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if not gains:
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continue
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above_5 = sum(1 for g in gains if g >= 5.0)
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above_3 = sum(1 for g in gains if g >= 3.0)
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above_0 = sum(1 for g in gains if g >= 0.0)
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negative = sum(1 for g in gains if g < 0.0)
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print(f"[{ticker}] 2h 등락률 분포 ({len(gains)}개 틱)")
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print(f" 평균={sum(gains)/len(gains):+.2f}% 최고={max(gains):+.2f}% 최저={min(gains):+.2f}%")
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print(f" +5% 이상(신호): {above_5}건 ({above_5/len(gains)*100:.0f}%)")
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print(f" +3%~+5%: {above_3-above_5}건 ({(above_3-above_5)/len(gains)*100:.0f}%)")
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print(f" 0%~+3%: {above_0-above_3}건 ({(above_0-above_3)/len(gains)*100:.0f}%)")
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print(f" 음전(하락): {negative}건 ({negative/len(gains)*100:.0f}%)")
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print()
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conn.close()
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