WLD DeepCoin 단계별 구조 재편 및 설정·문서 통합

로고스/루트 레거시를 제거하고 deepcoin 패키지·scripts 01~05 CLI·docs/reference로 데이터·GT·분석·매칭·운영 단계를 정리했다. config와 .env 기반 설정, trade_anaysis.html 동기화 포함. Co-authored-by: Cursor <cursoragent@cursor.com>
2026-05-30 22:58:25 +09:00
parent e631a5701f
commit b52d61b777
76 changed files with 11552 additions and 4567 deletions
--- a/deepcoin/analysis/general_analysis_report.py
+++ b/deepcoin/analysis/general_analysis_report.py
@@ -0,0 +1,73 @@
+"""
+general_analysis HTML 요약 리포트.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pandas as pd
+
+from deepcoin.analysis.general_analysis_config import DEFAULT_OUTPUT_CSV, DEFAULT_OUTPUT_HTML
+
+
+def write_analysis_report(
+    csv_path: Path | str = DEFAULT_OUTPUT_CSV,
+    html_path: Path | str = DEFAULT_OUTPUT_HTML,
+) -> Path:
+    """
+    스냅샷 CSV를 읽어 모듈별 컬럼 수·샘플 테이블 HTML 생성.
+
+    Returns:
+        HTML 경로.
+    """
+    df = pd.read_csv(csv_path)
+    html_out = Path(html_path)
+    html_out.parent.mkdir(parents=True, exist_ok=True)
+
+    modules = {
+        "지표 (ga_)": [c for c in df.columns if "_ga_" in c or c.startswith("ga_")],
+        "패턴": [c for c in df.columns if "ga_pattern_" in c],
+        "파동·구조": [c for c in df.columns if "ga_struct_" in c or "ga_elliott" in c or "ga_wyckoff" in c or "ga_fib_" in c],
+        "차트": [c for c in df.columns if "ga_chart_" in c],
+        "MTF 합성": [c for c in df.columns if "ga_align_" in c],
+        "레거시": [c for c in df.columns if c.endswith("_RSI") or c.endswith("_bb_pos")],
+    }
+
+    summary_rows = ""
+    for name, cols in modules.items():
+        summary_rows += f"<tr><td>{name}</td><td>{len(cols)}</td></tr>"
+
+    sample = df.head(5)[
+        ["dt", "action", "price", "ga_align_timing_buy_score", "ga_align_mtf_conflict", "d1_RSI", "m3_RSI"]
+    ].to_html(index=False, classes="tbl") if "d1_RSI" in df.columns else df.head(3).to_html(index=False)
+
+    buy_mean = df[df["action"] == "buy"]["ga_align_timing_buy_score"].mean() if "ga_align_timing_buy_score" in df.columns else 0
+    sell_mean = df[df["action"] == "sell"]["ga_align_timing_sell_score"].mean() if "ga_align_timing_sell_score" in df.columns else 0
+
+    content = f"""<!DOCTYPE html>
+<html lang="ko"><head><meta charset="utf-8"/>
+<title>general_analysis 실행 리포트</title>
+<style>
+body {{ font-family: "Malgun Gothic", Arial, sans-serif; margin: 24px; background: #f5f5f5; }}
+table.tbl {{ border-collapse: collapse; width: 100%; background: #fff; font-size: 0.85rem; }}
+th, td {{ border: 1px solid #e2e8f0; padding: 6px 8px; }}
+th {{ background: #e2e8f0; }}
+</style></head><body>
+<h1>general_analysis 실행 리포트</h1>
+<p>타점 {len(df)}건 · 컬럼 {len(df.columns)}개 · CSV: {csv_path}</p>
+<h2>모듈별 컬럼 수</h2>
+<table class="tbl"><thead><tr><th>모듈</th><th>컬럼 수</th></tr></thead>
+<tbody>{summary_rows}</tbody></table>
+<h2>MTF 합성 평균</h2>
+<ul>
+<li>매수 타점 timing_buy_score 평균: {buy_mean:.3f}</li>
+<li>매도 타점 timing_sell_score 평균: {sell_mean:.3f}</li>
+</ul>
+<h2>샘플 5건</h2>
+{sample}
+<p>전체 데이터: <code>{csv_path}</code></p>
+</body></html>"""
+    html_out.write_text(content, encoding="utf-8")
+    print(f"리포트: {html_out}")
+    return html_out