GT MTF 프로필·캘리브레이션과 04 매칭/시뮬/실거래 파이프라인을 추가한다.
3분~일봉 GT 타점 분석(03c), leg 체결 순서 수정, 총자산 90% 검증 루프, walk-forward Go/No-Go 시뮬, monitor·live_trader 및 reference 문서를 포함한다. Co-authored-by: Cursor <cursoragent@cursor.com>
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318
deepcoin/matching/rule_eval.py
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318
deepcoin/matching/rule_eval.py
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"""
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규칙 조건 벡터 평가·MTF 스캔 프레임 병합.
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"""
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from __future__ import annotations
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from typing import Any
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import numpy as np
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import pandas as pd
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from config import GENERAL_ANALYSIS_INTERVALS, MATCH_PRIMARY_INTERVAL
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from deepcoin.analysis.general_analysis_core import interval_tf_prefix
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from deepcoin.analysis.general_analysis_pipeline import general_analysis_enrich_bars
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from config import (
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ALIGN_RSI_CONFLICT_TIMING_HIGH,
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ALIGN_RSI_CONFLICT_TIMING_LOW,
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ALIGN_RSI_CONFLICT_TREND_HIGH,
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ALIGN_RSI_CONFLICT_TREND_LOW,
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ALIGN_RSI_OVERBOUGHT,
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ALIGN_RSI_OVERSOLD,
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TIMING_INTERVALS,
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TREND_INTERVALS,
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)
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from deepcoin.analysis.general_analysis_core import ga_col
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def _add_align_columns_vectorized(frame: pd.DataFrame) -> pd.DataFrame:
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"""
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스캔 프레임에 ga_align_* 컬럼을 벡터 연산으로 추가합니다.
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Args:
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frame: TF 접두사 컬럼이 포함된 DataFrame.
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Returns:
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align 컬럼이 추가된 DataFrame.
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"""
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out = frame.copy()
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rsi_oversold = pd.Series(0, index=out.index, dtype=float)
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rsi_overbought = pd.Series(0, index=out.index, dtype=float)
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n_timing = 0
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for iv in TIMING_INTERVALS:
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p = interval_tf_prefix(iv)
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rk = f"{p}_RSI"
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if rk not in out.columns:
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continue
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n_timing += 1
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rsi = pd.to_numeric(out[rk], errors="coerce")
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rsi_oversold += (rsi < ALIGN_RSI_OVERSOLD).astype(int)
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rsi_overbought += (rsi > ALIGN_RSI_OVERBOUGHT).astype(int)
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trend_up = pd.Series(0, index=out.index, dtype=float)
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trend_down = pd.Series(0, index=out.index, dtype=float)
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n_trend = 0
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for iv in TREND_INTERVALS:
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p = interval_tf_prefix(iv)
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sk = f"{p}_{ga_col('struct_trend')}"
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if sk not in out.columns:
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continue
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n_trend += 1
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st = out[sk].astype(str)
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trend_up += (st == "up").astype(int)
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trend_down += (st == "down").astype(int)
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denom_t = max(n_timing, 1)
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denom_r = max(n_trend, 1)
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out["ga_align_rsi_oversold_tf"] = rsi_oversold
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out["ga_align_rsi_overbought_tf"] = rsi_overbought
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out["ga_align_trend_up_tf"] = trend_up
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out["ga_align_trend_down_tf"] = trend_down
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out["ga_align_timing_buy_score"] = (rsi_oversold / denom_t).round(3)
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out["ga_align_timing_sell_score"] = (rsi_overbought / denom_t).round(3)
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out["ga_align_trend_score"] = ((trend_up - trend_down) / denom_r).round(3)
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conflict = pd.Series(0, index=out.index, dtype=int)
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m3_rsi = out.get("m3_RSI")
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d1_rsi = out.get("d1_RSI")
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if m3_rsi is not None and d1_rsi is not None:
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m3v = pd.to_numeric(m3_rsi, errors="coerce")
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d1v = pd.to_numeric(d1_rsi, errors="coerce")
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conflict = (
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((m3v < ALIGN_RSI_CONFLICT_TIMING_LOW) & (d1v > ALIGN_RSI_CONFLICT_TREND_HIGH))
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| ((m3v > ALIGN_RSI_CONFLICT_TIMING_HIGH) & (d1v < ALIGN_RSI_CONFLICT_TREND_LOW))
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).astype(int)
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out["ga_align_mtf_conflict"] = conflict
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return out
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def _scalar_float(val: Any) -> float:
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"""Series/ndarray 스칼라를 float로 변환."""
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if isinstance(val, pd.Series):
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val = val.iloc[0]
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return float(val)
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def conditions_columns(rules: list[dict[str, Any]]) -> set[str]:
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"""
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규칙 목록에서 참조하는 컬럼명 집합을 반환합니다.
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Args:
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rules: rule_candidates 항목 리스트.
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Returns:
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컬럼명 set.
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"""
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cols: set[str] = set()
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for rule in rules:
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for cond in rule.get("conditions", []):
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c = cond.get("col")
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if c:
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cols.add(c)
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return cols
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def build_mtf_scan_frame(
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frames: dict[int, pd.DataFrame],
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needed_cols: set[str],
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) -> pd.DataFrame:
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"""
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주간격(m3) 인덱스에 필요 컬럼만 merge_asof로 붙인 스캔용 DataFrame을 만듭니다.
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Args:
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frames: interval → OHLCV.
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needed_cols: 규칙 평가에 필요한 컬럼명.
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Returns:
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m3 인덱스 wide DataFrame (close 포함).
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"""
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primary = MATCH_PRIMARY_INTERVAL
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raw = frames.get(primary)
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if raw is None or raw.empty:
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raise RuntimeError(f"주간격 {primary}분 데이터 없음")
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print(f"[04b] Phase A: 8TF enrich (스캔용)...")
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enriched: dict[int, pd.DataFrame] = {}
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for iv in GENERAL_ANALYSIS_INTERVALS:
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r = frames.get(iv)
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if r is None or r.empty:
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continue
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label = interval_tf_prefix(iv)
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print(f" enrich {label} ({len(r):,}봉)...")
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enriched[iv] = general_analysis_enrich_bars(r, iv, full_context=True)
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base = enriched[primary].copy()
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if not isinstance(base.index, pd.DatetimeIndex):
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base.index = pd.to_datetime(base.index)
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base = base.sort_index()
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out = pd.DataFrame(index=base.index)
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close_col = "close" if "close" in base.columns else "Close"
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out["close"] = base[close_col].astype(float)
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def _source_col(prefixed: str, prefix: str, ef: pd.DataFrame) -> str | None:
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"""m3_RSI → RSI, m60_ga_struct_trend → ga_struct_trend."""
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if not prefixed.startswith(f"{prefix}_"):
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return None
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suffix = prefixed[len(prefix) + 1 :]
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if suffix in ef.columns:
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return suffix
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return None
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for iv in GENERAL_ANALYSIS_INTERVALS:
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ef = enriched.get(iv)
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if ef is None:
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continue
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p = interval_tf_prefix(iv)
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for col in needed_cols:
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if col in out.columns or not col.startswith(f"{p}_"):
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continue
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src = _source_col(col, p, ef)
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if src is None:
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continue
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if iv == primary:
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out[col] = ef[src].reindex(out.index)
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else:
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sub = ef[[src]].copy()
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if not isinstance(sub.index, pd.DatetimeIndex):
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sub.index = pd.to_datetime(sub.index)
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sub = sub.sort_index().rename(columns={src: col})
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merged = pd.merge_asof(
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out.reset_index(names="_ts"),
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sub.reset_index(names="_ts"),
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on="_ts",
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direction="backward",
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).set_index("_ts")
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out[col] = merged[col].values
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align_needed = [c for c in needed_cols if c.startswith("ga_align_")]
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if align_needed:
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out = _add_align_columns_vectorized(out)
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out = out.loc[:, ~out.columns.duplicated()]
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out = out.dropna(subset=["close"])
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print(f"[04b] 스캔 프레임: {len(out):,}봉 × {len(out.columns)}열")
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return out
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def _eval_one_condition(
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frame: pd.DataFrame,
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cond: dict[str, Any],
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) -> pd.Series:
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"""
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단일 조건 boolean Series.
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Args:
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frame: 평가 대상.
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cond: {col, op, ...}.
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Returns:
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boolean Series.
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"""
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col = cond.get("col")
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if not col or col not in frame.columns:
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return pd.Series(False, index=frame.index)
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s = frame[col]
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op = cond.get("op", "between")
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if op == "between":
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lo, hi = float(cond["lo"]), float(cond["hi"])
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ok = pd.to_numeric(s, errors="coerce")
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part = (ok >= lo) & (ok <= hi)
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elif op == "gte":
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part = pd.to_numeric(s, errors="coerce") >= float(cond["value"])
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elif op == "lte":
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part = pd.to_numeric(s, errors="coerce") <= float(cond["value"])
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elif op == "eq":
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val = cond["value"]
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if isinstance(val, (int, float)) and pd.api.types.is_numeric_dtype(s):
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part = pd.to_numeric(s, errors="coerce") == float(val)
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else:
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part = s.astype(str) == str(val)
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elif op == "eq_int":
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part = (
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pd.to_numeric(s, errors="coerce").fillna(-999).astype(int)
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== int(cond["value"])
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)
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else:
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part = pd.Series(False, index=frame.index)
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return part.fillna(False)
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def eval_conditions(frame: pd.DataFrame, conditions: list[dict[str, Any]]) -> pd.Series:
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"""
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단일 규칙의 조건을 모두 AND로 평가합니다.
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Args:
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frame: 스캔용 DataFrame.
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conditions: {col, op, ...} 리스트.
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Returns:
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boolean Series (인덱스=frame.index).
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"""
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mask = pd.Series(True, index=frame.index)
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for cond in conditions:
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mask &= _eval_one_condition(frame, cond)
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return mask
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def eval_rule_mask(frame: pd.DataFrame, rule: dict[str, Any]) -> pd.Series:
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"""
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규칙 dict 평가 (logic=and|or).
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Args:
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frame: 스캔/스냅샷 DataFrame.
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rule: conditions, logic 키 포함.
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Returns:
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boolean Series.
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"""
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conditions = rule.get("conditions") or []
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if not conditions:
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return pd.Series(False, index=frame.index)
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logic = str(rule.get("logic", "and")).lower()
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if logic == "or":
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mask = pd.Series(False, index=frame.index)
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for cond in conditions:
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mask |= _eval_one_condition(frame, cond)
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return mask
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return eval_conditions(frame, conditions)
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def scan_rule_fires(
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frame: pd.DataFrame,
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rules: list[dict[str, Any]],
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) -> pd.DataFrame:
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"""
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모든 규칙 후보에 대해 발화 시각을 수집합니다.
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Args:
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frame: build_mtf_scan_frame 결과.
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rules: rule_candidates.
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Returns:
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fire_id, rule_id, side, dt, close 컬럼 DataFrame.
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"""
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rows: list[dict[str, Any]] = []
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fid = 0
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for rule in rules:
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rid = rule["rule_id"]
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side = rule["side"]
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mask = eval_rule_mask(frame, rule)
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hits = frame.index[mask]
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close_s = frame["close"]
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if isinstance(close_s, pd.DataFrame):
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close_s = close_s.iloc[:, 0]
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for ts in hits:
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rows.append(
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{
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"fire_id": fid,
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"rule_id": rid,
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"side": side,
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"dt": ts.strftime("%Y-%m-%d %H:%M:%S"),
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"close": _scalar_float(close_s.loc[ts]),
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}
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)
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fid += 1
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print(f" 규칙 {rid}: 발화 {len(hits):,}건")
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if not rows:
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return pd.DataFrame(columns=["fire_id", "rule_id", "side", "dt", "close"])
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return pd.DataFrame(rows)
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