e218a8ea32
9개 간격(1~1440분) BB·일목 위치 특징을 3분 타임라인에 맞춰 분석하고, discover로 매수·매도 규칙을 찾은 뒤 HTML 차트에 해당 체결만 표시한다. simulation_1h.py를 simulation.py로 변경했으며, 파라미터 없이 실행하면 analyze→discover→차트가 한 번에 수행된다. Co-authored-by: Cursor <cursoragent@cursor.com>
223 lines
7.1 KiB
Python
223 lines
7.1 KiB
Python
"""
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모든 봉의 BB·일목 위치를 조합해 매수/매도 후보 규칙을 분석합니다.
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python simulation.py analyze
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"""
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from __future__ import annotations
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import json
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from dataclasses import asdict, dataclass, field
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from pathlib import Path
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import numpy as np
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import pandas as pd
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from candle_features import (
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FEATURE_BOOL_COLS,
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INTERVAL_LABELS,
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build_master_feature_matrix,
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describe_latest_position,
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interval_prefix,
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)
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from config import ALL_INTERVALS, ENTRY_INTERVAL, SYMBOL
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REPORT_FILE = Path(__file__).parent / "combination_report.json"
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@dataclass
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class CombinationReport:
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"""봉 조합 분석 결과."""
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generated_at: str
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intervals_loaded: list[int]
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latest_positions: list[dict]
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buy_recommendations: list[str] = field(default_factory=list)
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sell_recommendations: list[str] = field(default_factory=list)
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buy_avoid: list[str] = field(default_factory=list)
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top_buy_pairs: list[dict] = field(default_factory=list)
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top_sell_pairs: list[dict] = field(default_factory=list)
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suggested_rules: dict = field(default_factory=dict)
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def _forward_return(close: pd.Series, bars: int = 20) -> pd.Series:
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"""N봉 후 수익률 (%)."""
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future = close.shift(-bars)
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return (future - close) / close.replace(0, np.nan) * 100
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def _predicate_keys(intervals: list[int]) -> list[str]:
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keys: list[str] = []
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for iv in intervals:
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pfx = interval_prefix(iv)
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for feat in FEATURE_BOOL_COLS:
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keys.append(f"{pfx}:{feat}")
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return keys
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def analyze_forward_edge(
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matrix: pd.DataFrame,
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forward_bars: int = 20,
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min_samples: int = 40,
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) -> tuple[list[dict], list[dict], list[dict]]:
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"""
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단일 조건·2봉 조합별 미래 수익 통계 (학습용 힌트).
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Returns:
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(매수 유리 top, 매도/회피 top)
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"""
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close = matrix["Close"].astype(float)
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fwd = _forward_return(close, forward_bars)
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valid = fwd.notna()
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base_mean = float(fwd[valid].mean()) if valid.any() else 0.0
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singles: list[dict] = []
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cols = [c for c in matrix.columns if any(c.startswith(f"{interval_prefix(iv)}_") for iv in ALL_INTERVALS)]
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for col in cols:
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mask = matrix[col].fillna(0).astype(bool) & valid
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n = int(mask.sum())
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if n < min_samples:
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continue
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avg = float(fwd[mask].mean())
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singles.append(
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{
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"key": _col_to_key(col),
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"column": col,
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"count": n,
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"avg_forward_pct": round(avg, 4),
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"edge_vs_base": round(avg - base_mean, 4),
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}
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)
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singles.sort(key=lambda x: x["edge_vs_base"], reverse=True)
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buy_top = [s for s in singles if s["edge_vs_base"] > 0][:25]
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sell_top = sorted(singles, key=lambda x: x["edge_vs_base"])[:15]
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pairs: list[dict] = []
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buy_cols = [s["column"] for s in buy_top[:12]]
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for i, c1 in enumerate(buy_cols):
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for c2 in buy_cols[i + 1 :]:
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if c1.split("_")[0] == c2.split("_")[0]:
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continue
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mask = (
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matrix[c1].fillna(0).astype(bool)
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& matrix[c2].fillna(0).astype(bool)
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& valid
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)
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n = int(mask.sum())
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if n < min_samples // 2:
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continue
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avg = float(fwd[mask].mean())
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pairs.append(
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{
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"keys": [_col_to_key(c1), _col_to_key(c2)],
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"count": n,
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"avg_forward_pct": round(avg, 4),
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"edge_vs_base": round(avg - base_mean, 4),
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}
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)
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pairs.sort(key=lambda x: x["edge_vs_base"], reverse=True)
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return buy_top, pairs[:20], sell_top
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def _col_to_key(col: str) -> str:
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"""m3_cross_up_lower -> m3:cross_up_lower."""
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for pfx in INTERVAL_LABELS.values():
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if col.startswith(f"{pfx}_"):
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return f"{pfx}:{col[len(pfx) + 1:]}"
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return col
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def build_recommendations(
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buy_top: list[dict],
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pair_top: list[dict],
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sell_top: list[dict],
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) -> tuple[list[str], list[str], list[str], dict]:
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"""사람이 읽을 수 있는 권장·규칙 초안."""
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buy_rec: list[str] = []
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sell_rec: list[str] = []
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avoid: list[str] = []
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for s in buy_top[:8]:
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buy_rec.append(
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f"{s['key']} — {s['count']}회, {s['avg_forward_pct']:+.2f}% ({s['edge_vs_base']:+.2f}%p)"
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)
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for p in pair_top[:5]:
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buy_rec.append(
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f"조합 {' + '.join(p['keys'])} — {p['count']}회, {p['edge_vs_base']:+.2f}%p"
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)
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for s in sell_top[:6]:
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if s["edge_vs_base"] < -0.05:
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avoid.append(f"매수 회피: {s['key']} ({s['edge_vs_base']:.2f}%p)")
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for s in sell_top[:5]:
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if "cross_up_upper" in s["key"] or "above_upper" in s["key"] or "ichi_above" in s["key"]:
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sell_rec.append(f"매도 후보: {s['key']}")
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suggested: dict = {"buy_all": [], "buy_any": [], "sell_all": [], "sell_stop": []}
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if pair_top:
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suggested["buy_all"] = pair_top[0]["keys"]
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elif buy_top:
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suggested["buy_all"] = [buy_top[0]["key"]]
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if sell_top:
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for s in sell_top:
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if "cross_up_upper" in s.get("key", ""):
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suggested["sell_all"] = [s["key"]]
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break
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return buy_rec, sell_rec, avoid, suggested
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def analyze_combinations(frames: dict[int, pd.DataFrame]) -> CombinationReport:
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"""전체 봉 BB·일목 위치 분석 + 조합 매매 힌트."""
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from datetime import datetime
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loaded = sorted(frames.keys())
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latest = [describe_latest_position(frames[iv], iv) for iv in ALL_INTERVALS if iv in frames]
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print("\n=== 봉별 최신 BB·일목 위치 ===")
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for p in latest:
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print(
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f" {p['label']:>6} | BB {p['bb_zone']:>6} ({p['bb_pos']:.2f}) {p['bb_state']:>16} | "
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f"일목 {p['ichi_position']:>12} TK={p['ichi_tk']} 구름={p['ichi_cloud']}"
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)
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matrix = build_master_feature_matrix(frames).iloc[52:].copy()
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print(f"\n특징 행렬: {len(matrix)}행 × {len(matrix.columns)}열")
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buy_top, pair_top, sell_top = analyze_forward_edge(matrix)
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buy_rec, sell_rec, avoid, suggested = build_recommendations(buy_top, pair_top, sell_top)
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print("\n=== 매수 유리 조건 (단일·상위) ===")
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for line in buy_rec[:10]:
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print(f" {line}")
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print("\n=== 매수 회피 / 매도 참고 ===")
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for line in avoid[:6]:
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print(f" {line}")
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for line in sell_rec[:5]:
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print(f" {line}")
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return CombinationReport(
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generated_at=datetime.now().isoformat(timespec="seconds"),
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intervals_loaded=loaded,
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latest_positions=latest,
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buy_recommendations=buy_rec,
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sell_recommendations=sell_rec,
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buy_avoid=avoid,
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top_buy_pairs=pair_top,
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suggested_rules=suggested,
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)
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def save_report(report: CombinationReport, path: Path = REPORT_FILE) -> None:
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path.write_text(json.dumps(asdict(report), ensure_ascii=False, indent=2), encoding="utf-8")
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print(f"\n저장: {path}")
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def load_frames(monitor) -> dict[int, pd.DataFrame]:
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from mtf_bb import load_frames_from_db
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return load_frames_from_db(monitor, SYMBOL)
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