"""
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"| {name} | {len(cols)} |
"
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"""
general_analysis 실행 리포트
general_analysis 실행 리포트
타점 {len(df)}건 · 컬럼 {len(df.columns)}개 · CSV: {csv_path}
모듈별 컬럼 수
MTF 합성 평균
- 매수 타점 timing_buy_score 평균: {buy_mean:.3f}
- 매도 타점 timing_sell_score 평균: {sell_mean:.3f}
샘플 5건
{sample}
전체 데이터: {csv_path}
"""
html_out.write_text(content, encoding="utf-8")
print(f"리포트: {html_out}")
return html_out