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>

deepcoin/analysis/general_analysis_align.py

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+"""
+general_analysis MTF 합성·정렬 점수.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import pandas as pd
+
+from config import (
+    ALIGN_BB_POS_HIGH,
+    ALIGN_BB_POS_LOW,
+    ALIGN_RSI_CONFLICT_TIMING_HIGH,
+    ALIGN_RSI_CONFLICT_TIMING_LOW,
+    ALIGN_RSI_CONFLICT_TREND_HIGH,
+    ALIGN_RSI_CONFLICT_TREND_LOW,
+    ALIGN_RSI_OVERBOUGHT,
+    ALIGN_RSI_OVERSOLD,
+)
+from deepcoin.analysis.general_analysis_config import TIMING_INTERVALS, TREND_INTERVALS
+from deepcoin.analysis.general_analysis_core import ga_col, interval_tf_prefix
+
+
+def general_analysis_mtf_scores(
+    prefixed_row: dict[str, Any],
+) -> dict[str, float | int | str]:
+    """
+    간격 접두사가 붙은 스냅샷 행에서 MTF 합성 점수 계산.
+
+    Args:
+        prefixed_row: m3_ga_rsi 형태 flat dict.
+
+    Returns:
+        ga_align_* 점수.
+    """
+    rsi_oversold = 0
+    rsi_overbought = 0
+    trend_up = 0
+    trend_down = 0
+    n_timing = 0
+    n_trend = 0
+    conflict = 0
+
+    for interval in TIMING_INTERVALS:
+        p = interval_tf_prefix(interval)
+        rk = f"{p}_RSI"
+        if rk in prefixed_row and prefixed_row[rk] is not None:
+            n_timing += 1
+            rsi = float(prefixed_row[rk])
+            if rsi < ALIGN_RSI_OVERSOLD:
+                rsi_oversold += 1
+            if rsi > ALIGN_RSI_OVERBOUGHT:
+                rsi_overbought += 1
+
+    for interval in TREND_INTERVALS:
+        p = interval_tf_prefix(interval)
+        sk = f"{p}_{ga_col('struct_trend')}"
+        if sk in prefixed_row:
+            n_trend += 1
+            t = prefixed_row[sk]
+            if t == "up":
+                trend_up += 1
+            elif t == "down":
+                trend_down += 1
+
+    m3_rsi = prefixed_row.get("m3_RSI")
+    d1_rsi = prefixed_row.get("d1_RSI")
+    if m3_rsi is not None and d1_rsi is not None:
+        if (
+            float(m3_rsi) < ALIGN_RSI_CONFLICT_TIMING_LOW
+            and float(d1_rsi) > ALIGN_RSI_CONFLICT_TREND_HIGH
+        ):
+            conflict = 1
+        if (
+            float(m3_rsi) > ALIGN_RSI_CONFLICT_TIMING_HIGH
+            and float(d1_rsi) < ALIGN_RSI_CONFLICT_TREND_LOW
+        ):
+            conflict = 1
+
+    timing_buy_align = rsi_oversold / max(len(TIMING_INTERVALS), 1)
+    timing_sell_align = rsi_overbought / max(len(TIMING_INTERVALS), 1)
+
+    return {
+        "ga_align_rsi_oversold_tf": rsi_oversold,
+        "ga_align_rsi_overbought_tf": rsi_overbought,
+        "ga_align_trend_up_tf": trend_up,
+        "ga_align_trend_down_tf": trend_down,
+        "ga_align_timing_buy_score": round(timing_buy_align, 3),
+        "ga_align_timing_sell_score": round(timing_sell_align, 3),
+        "ga_align_trend_score": round(
+            (trend_up - trend_down) / max(n_trend, 1), 3
+        ),
+        "ga_align_mtf_conflict": conflict,
+    }
+
+
+def general_analysis_mtf_vote_latest(
+    frames_enriched: dict[int, pd.DataFrame],
+) -> dict[str, float | int | str]:
+    """
+    각 TF 최신 완성봉 지표로 TF 가중 투표·필터 점수 산출.
+
+    Args:
+        frames_enriched: interval → enrich된 DataFrame.
+
+    Returns:
+        ga_vote_* 점수 (접두사 없음, ga_col로 감쌀 것).
+    """
+    votes_buy = 0
+    votes_sell = 0
+    trend_ok = 0
+    n = 0
+
+    for interval in TIMING_INTERVALS:
+        df = frames_enriched.get(interval)
+        if df is None or df.empty:
+            continue
+        row = df.iloc[-1]
+        n += 1
+        rsi = row.get("RSI")
+        if rsi is not None and not pd.isna(rsi):
+            if float(rsi) < ALIGN_RSI_OVERSOLD:
+                votes_buy += 1
+            if float(rsi) > ALIGN_RSI_OVERBOUGHT:
+                votes_sell += 1
+        bb_pos = row.get("bb_pos")
+        if bb_pos is not None and float(bb_pos) < ALIGN_BB_POS_LOW:
+            votes_buy += 1
+        if bb_pos is not None and float(bb_pos) > ALIGN_BB_POS_HIGH:
+            votes_sell += 1
+
+    for interval in TREND_INTERVALS:
+        df = frames_enriched.get(interval)
+        if df is None or df.empty:
+            continue
+        row = df.iloc[-1]
+        st = row.get(ga_col("struct_trend"), "range")
+        if st == "up":
+            trend_ok += 1
+        elif st == "down":
+            trend_ok -= 1
+
+    return {
+        "vote_timing_buy": votes_buy,
+        "vote_timing_sell": votes_sell,
+        "vote_trend_score": trend_ok,
+        "vote_tf_used": n,
+    }
+
+
+def general_analysis_vote_columns() -> list[str]:
+    return ["vote_timing_buy", "vote_timing_sell", "vote_trend_score", "vote_tf_used"]