dsyoon
b52d61b777
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>
93 lines
2.5 KiB
Python
93 lines
2.5 KiB
Python
"""
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general_analysis Volume Profile (POC, VAH, VAL).
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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_VP_BINS, GA_VP_VALUE_AREA_PCT
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from deepcoin.analysis.general_analysis_core import ga_col
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def general_analysis_volume_profile(
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win: pd.DataFrame,
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bins: int | None = None,
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value_area_pct: float | None = None,
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) -> dict[str, float | int]:
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"""
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lookback 구간 가격-거래량 분포에서 POC·VAH·VAL 계산.
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Args:
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win: OHLCV.
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bins: 가격 구간 수.
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value_area_pct: value area 누적 비율 (기본 70%).
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Returns:
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ga_vp_* 키 dict (접두사 없음).
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"""
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res: dict[str, float | int] = {
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"vp_poc": 0.0,
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"vp_vah": 0.0,
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"vp_val": 0.0,
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"vp_close_vs_poc_pct": 0.0,
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"vp_in_value_area": 0,
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}
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if bins is None:
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bins = GA_VP_BINS
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if value_area_pct is None:
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value_area_pct = GA_VP_VALUE_AREA_PCT
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if win is None or len(win) < 10 or "Volume" not in win.columns:
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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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v = win["Volume"].astype(float).values
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tp = (h + l + c) / 3.0
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lo, hi = float(l.min()), float(h.max())
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if hi <= lo:
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return res
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edges = np.linspace(lo, hi, bins + 1)
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hist = np.zeros(bins, dtype=float)
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for i in range(len(tp)):
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idx = int(np.clip(np.digitize(tp[i], edges) - 1, 0, bins - 1))
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hist[idx] += v[i]
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if hist.sum() <= 0:
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return res
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poc_idx = int(np.argmax(hist))
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poc = float((edges[poc_idx] + edges[poc_idx + 1]) / 2)
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res["vp_poc"] = poc
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order = np.argsort(hist)[::-1]
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cum = 0.0
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selected: list[int] = []
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total = hist.sum()
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for idx in order:
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selected.append(int(idx))
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cum += hist[idx]
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if cum >= total * value_area_pct:
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break
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sel_min, sel_max = min(selected), max(selected)
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res["vp_val"] = float(edges[sel_min])
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res["vp_vah"] = float(edges[sel_max + 1])
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res["vp_close_vs_poc_pct"] = float((c[-1] / poc - 1) * 100) if poc else 0.0
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res["vp_in_value_area"] = int(res["vp_val"] <= c[-1] <= res["vp_vah"])
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return res
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def general_analysis_volume_columns() -> list[str]:
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return ["vp_poc", "vp_vah", "vp_val", "vp_close_vs_poc_pct", "vp_in_value_area"]
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def general_analysis_volume_snapshot(win: pd.DataFrame) -> dict[str, object]:
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"""Volume profile → ga_vp_*."""
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return {ga_col(k): v for k, v in general_analysis_volume_profile(win).items()}
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