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>
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deepcoin/analysis/general_analysis_patterns.py
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deepcoin/analysis/general_analysis_patterns.py
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"""
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general_analysis 차트·가격 패턴 (반전·지속·박스).
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"""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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from config import GA_PATTERN_TOLERANCE_PCT, GA_PIVOT_ORDER
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from deepcoin.analysis.general_analysis_core import find_pivots, ga_col, last_row_dict
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def _pct_diff(a: float, b: float) -> float:
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return abs(a - b) / max(abs(a), abs(b), 1e-9) * 100
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def general_analysis_detect_patterns(win: pd.DataFrame) -> dict[str, int | float | str | None]:
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"""
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lookback 윈도우 마지막 봉 기준 패턴 라벨 (0/1 및 요약).
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Args:
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win: OHLCV (+ 지표 선택).
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Returns:
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ga_pattern_* 키 dict (접두사 없음, ga_col로 감쌈).
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"""
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res: dict[str, int | float | str | None] = {
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"pattern_double_top": 0,
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"pattern_double_bottom": 0,
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"pattern_head_shoulders": 0,
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"pattern_inv_head_shoulders": 0,
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"pattern_triangle_sym": 0,
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"pattern_triangle_asc": 0,
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"pattern_triangle_desc": 0,
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"pattern_flag_bull": 0,
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"pattern_flag_bear": 0,
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"pattern_wedge_rising": 0,
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"pattern_wedge_falling": 0,
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"pattern_rectangle": 0,
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"pattern_channel_up": 0,
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"pattern_channel_down": 0,
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"pattern_measured_move": 0,
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"pattern_rounding_top": 0,
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"pattern_rounding_bottom": 0,
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"pattern_gap_up": 0,
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"pattern_gap_down": 0,
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"pattern_v_bottom": 0,
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"pattern_spike_top": 0,
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"pattern_triple_top": 0,
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"pattern_triple_bottom": 0,
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"pattern_cup_handle": 0,
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"pattern_keystone_bull": 0,
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"pattern_keystone_bear": 0,
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"pattern_island_top": 0,
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"pattern_island_bottom": 0,
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"pattern_label": "none",
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}
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if win is None or len(win) < 20:
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return res
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h = win["High"].astype(float).values
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l = win["Low"].astype(float).values
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c = win["Close"].astype(float).values
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peaks, troughs = find_pivots(h, l, order=GA_PIVOT_ORDER)
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tol = GA_PATTERN_TOLERANCE_PCT
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if len(peaks) >= 3:
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p1, p2, p3 = peaks[-3], peaks[-2], peaks[-1]
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if (
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_pct_diff(h[p1], h[p2]) < tol
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and _pct_diff(h[p2], h[p3]) < tol
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and p1 < p2 < p3
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):
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res["pattern_triple_top"] = 1
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res["pattern_label"] = "triple_top"
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if len(troughs) >= 3:
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t1, t2, t3 = troughs[-3], troughs[-2], troughs[-1]
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if (
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_pct_diff(l[t1], l[t2]) < tol
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and _pct_diff(l[t2], l[t3]) < tol
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and t1 < t2 < t3
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):
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res["pattern_triple_bottom"] = 1
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res["pattern_label"] = "triple_bottom"
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if len(peaks) >= 2:
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p1, p2 = peaks[-2], peaks[-1]
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if _pct_diff(h[p1], h[p2]) < tol:
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res["pattern_double_top"] = 1
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if res["pattern_label"] == "none":
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res["pattern_label"] = "double_top"
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if len(troughs) >= 2:
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t1, t2 = troughs[-2], troughs[-1]
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if _pct_diff(l[t1], l[t2]) < tol:
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res["pattern_double_bottom"] = 1
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res["pattern_label"] = "double_bottom"
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if len(peaks) >= 3:
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i, j, k = peaks[-3], peaks[-2], peaks[-1]
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if h[j] > h[i] and h[j] > h[k] and _pct_diff(h[i], h[k]) < tol * 1.5:
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res["pattern_head_shoulders"] = 1
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res["pattern_label"] = "head_shoulders"
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if len(troughs) >= 3:
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i, j, k = troughs[-3], troughs[-2], troughs[-1]
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if l[j] < l[i] and l[j] < l[k] and _pct_diff(l[i], l[k]) < tol * 1.5:
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res["pattern_inv_head_shoulders"] = 1
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res["pattern_label"] = "inv_head_shoulders"
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n = len(win)
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x = np.arange(n)
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high_slope = np.polyfit(x, h, 1)[0]
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low_slope = np.polyfit(x, l, 1)[0]
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if high_slope < 0 and low_slope > 0:
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res["pattern_triangle_sym"] = 1
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if res["pattern_label"] == "none":
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res["pattern_label"] = "triangle_sym"
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if high_slope < 0 and low_slope > 0 and low_slope > abs(high_slope) * 0.5:
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res["pattern_triangle_asc"] = 1
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if high_slope < 0 and low_slope < 0 and abs(high_slope) > abs(low_slope) * 0.5:
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res["pattern_triangle_desc"] = 1
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rng_pct = (h.max() - l.min()) / max(c[-1], 1e-9) * 100
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if rng_pct < 8 and abs(high_slope) < c[-1] * 0.0001:
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res["pattern_rectangle"] = 1
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if res["pattern_label"] == "none":
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res["pattern_label"] = "rectangle"
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leg = max(n // 3, 5)
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if n > leg * 2:
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first_move = (c[leg] - c[0]) / max(c[0], 1e-9) * 100
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channel = (c[-1] - c[-leg]) / max(c[-leg], 1e-9) * 100
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if first_move > 5 and abs(channel) < 3:
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res["pattern_flag_bull"] = 1
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res["pattern_label"] = "flag_bull"
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if first_move < -5 and abs(channel) < 3:
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res["pattern_flag_bear"] = 1
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res["pattern_label"] = "flag_bear"
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if high_slope > 0 and low_slope > 0:
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res["pattern_wedge_rising"] = 1
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if high_slope < 0 and low_slope < 0:
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res["pattern_wedge_falling"] = 1
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if high_slope > 0 and low_slope > 0:
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res["pattern_channel_up"] = 1
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if high_slope < 0 and low_slope < 0:
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res["pattern_channel_down"] = 1
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if len(c) >= 15:
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mid = len(c) // 2
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first_half = c[:mid].mean()
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second_half = c[mid:].mean()
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if c[0] > c[mid] * 1.08 and c[-1] > c[mid] * 1.05:
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res["pattern_v_bottom"] = 1
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res["pattern_label"] = "v_bottom"
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if c[0] < c[-1] * 0.92 and c.max() > c[0] * 1.1:
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res["pattern_spike_top"] = 1
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o = win["Open"].astype(float).values
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gap_ups: list[int] = []
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gap_downs: list[int] = []
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for i in range(1, min(30, n)):
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if l[i] > h[i - 1]:
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res["pattern_gap_up"] = 1
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gap_ups.append(i)
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if h[i] < l[i - 1]:
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res["pattern_gap_down"] = 1
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gap_downs.append(i)
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for gi in gap_ups:
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for gd in gap_downs:
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if gd > gi and h[gi] < l[gd]:
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res["pattern_island_top"] = 1
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res["pattern_label"] = "island_top"
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if gd > gi and l[gi] > h[gd]:
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res["pattern_island_bottom"] = 1
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res["pattern_label"] = "island_bottom"
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# 키리스톤: 상단 수평 + 하단 상승(역키리스톤) 또는 하단 수평 + 상단 하락
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if abs(high_slope) < c[-1] * 0.00005 and low_slope > 0:
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res["pattern_keystone_bull"] = 1
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if res["pattern_label"] == "none":
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res["pattern_label"] = "keystone_bull"
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if abs(low_slope) < c[-1] * 0.00005 and high_slope < 0:
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res["pattern_keystone_bear"] = 1
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if res["pattern_label"] == "none":
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res["pattern_label"] = "keystone_bear"
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# 컵앤핸들: 전반 U자 + 후반 15% 소폭 조정
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if n >= 40:
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cup_len = int(n * 0.65)
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handle_len = max(int(n * 0.15), 5)
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cup = c[:cup_len]
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handle = c[-handle_len:]
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rim = float(max(cup[0], cup[-1]))
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bottom = float(cup.min())
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depth = rim - bottom
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if depth > rim * 0.08 and float(cup[-1]) > bottom + depth * 0.5:
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handle_pull = float(handle.max() - handle.min())
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if handle_pull < depth * 0.5 and float(c[-1]) >= rim * 0.98:
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res["pattern_cup_handle"] = 1
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res["pattern_label"] = "cup_handle"
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if len(c) >= 30:
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ma = pd.Series(c).rolling(10).mean()
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if float(ma.iloc[-1]) > float(ma.iloc[-15]) > float(ma.iloc[-30]):
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res["pattern_rounding_bottom"] = 1
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if float(ma.iloc[-1]) < float(ma.iloc[-15]) < float(ma.iloc[-30]):
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res["pattern_rounding_top"] = 1
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if len(peaks) >= 2 and len(troughs) >= 2:
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leg_h = h[peaks[-1]] - l[troughs[-1]]
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if leg_h > 0 and c[-1] >= l[troughs[-1]] + leg_h * 0.9:
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res["pattern_measured_move"] = 1
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return res
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def general_analysis_pattern_snapshot(win: pd.DataFrame) -> dict[str, object]:
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"""패턴 dict → ga_pattern_* 컬럼명."""
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raw = general_analysis_detect_patterns(win)
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return {ga_col(k): v for k, v in raw.items()}
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def general_analysis_pattern_columns() -> list[str]:
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return [
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"pattern_double_top",
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"pattern_double_bottom",
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"pattern_head_shoulders",
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"pattern_inv_head_shoulders",
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"pattern_triangle_sym",
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"pattern_triangle_asc",
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"pattern_triangle_desc",
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"pattern_flag_bull",
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"pattern_flag_bear",
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"pattern_wedge_rising",
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"pattern_wedge_falling",
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"pattern_rectangle",
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"pattern_channel_up",
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"pattern_channel_down",
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"pattern_measured_move",
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"pattern_rounding_top",
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"pattern_rounding_bottom",
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"pattern_gap_up",
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"pattern_gap_down",
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"pattern_v_bottom",
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"pattern_spike_top",
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"pattern_triple_top",
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"pattern_triple_bottom",
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"pattern_cup_handle",
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"pattern_keystone_bull",
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"pattern_keystone_bear",
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"pattern_island_top",
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"pattern_island_bottom",
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"pattern_label",
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]
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def general_analysis_apply_patterns_to_bars(
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df: pd.DataFrame,
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interval: int,
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tail_rows: int | None = None,
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) -> pd.DataFrame:
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"""
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lookback 윈도우 패턴 라벨을 봉별 컬럼으로 채움 (최근 tail_rows만, 성능).
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Args:
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df: OHLCV (+ 선택적 지표).
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interval: 분봉 간격.
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tail_rows: None이면 전체(8천봉 이하) 또는 config tail.
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Returns:
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ga_pattern_* 컬럼이 추가된 DataFrame.
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"""
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from deepcoin.analysis.general_analysis_config import CONTEXT_TAIL_ROWS, LOOKBACK_BARS
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out = df.copy()
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lb = LOOKBACK_BARS.get(interval, 80)
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keys = [k for k in general_analysis_pattern_columns() if k != "pattern_label"]
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for k in keys:
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out[ga_col(k)] = 0
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out[ga_col("pattern_label")] = "none"
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n = len(out)
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if n < lb + 1:
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return out
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if tail_rows is None:
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tail_rows = CONTEXT_TAIL_ROWS.get(interval, 5000)
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start = max(lb, n - tail_rows)
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for i in range(start, n):
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win = out.iloc[i - lb : i]
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det = general_analysis_detect_patterns(win)
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idx = out.index[i]
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for k, v in det.items():
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out.at[idx, ga_col(k)] = v
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return out
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