d7848df6f7
Phase C/dry-run·미사용 모듈·재생성 HTML을 제거하고, 운영 체결을 sim_causal_hybrid와 동일한 hybrid 로직으로 통합한다. Co-authored-by: Cursor <cursoragent@cursor.com>
131 lines
4.3 KiB
Python
131 lines
4.3 KiB
Python
"""
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general_analysis ground truth 타점 MTF 스냅샷 생성.
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"""
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from __future__ import annotations
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import json
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import sys
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import time
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from pathlib import Path
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from typing import Any
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import pandas as pd
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from deepcoin.analysis.general_analysis_align import general_analysis_mtf_scores
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from deepcoin.analysis.general_analysis_config import (
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DEFAULT_OUTPUT_CSV,
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DEFAULT_TRADES_FILE,
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GENERAL_ANALYSIS_INTERVALS,
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)
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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, general_analysis_snapshot_at_bar
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from deepcoin.ground_truth.ground_truth import load_ground_truth
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def _prefixed_snap(snap: dict[str, Any], interval: int) -> dict[str, Any]:
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p = interval_tf_prefix(interval)
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return {f"{p}_{k}": v for k, v in snap.items()}
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def build_trade_mtf_snapshots(
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frames: dict[int, pd.DataFrame],
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trades: list[dict[str, Any]],
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) -> pd.DataFrame:
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"""
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모든 타점에 대해 8개 간격 general_analysis 스냅샷.
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Args:
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frames: interval → OHLCV.
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trades: ground_truth trades.
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Returns:
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wide DataFrame (1 row per trade).
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"""
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n_trades = len(trades)
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enriched: dict[int, pd.DataFrame] = {}
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t0 = time.time()
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print(
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f"[03b] MTF enrich (주·월봉 포함) — {len(GENERAL_ANALYSIS_INTERVALS)}개 간격"
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)
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sys.stdout.flush()
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for step, iv in enumerate(GENERAL_ANALYSIS_INTERVALS, start=1):
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raw = frames.get(iv)
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if raw is None or raw.empty:
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print(f" [{step}/8] {interval_tf_prefix(iv)} SKIP (데이터 없음)")
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sys.stdout.flush()
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continue
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label = interval_tf_prefix(iv)
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print(f" [{step}/8] {label} enrich 시작 ({len(raw):,}봉)...")
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sys.stdout.flush()
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t_iv = time.time()
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enriched[iv] = general_analysis_enrich_bars(raw, iv, full_context=True)
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print(f" [{step}/8] {label} 완료 — {len(enriched[iv].columns)}열, {time.time() - t_iv:.0f}초")
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sys.stdout.flush()
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print(f"[03b] Phase A 완료 (누적 {time.time() - t0:.0f}초)")
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sys.stdout.flush()
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print(f"[03b] Phase B: GT 타점 스냅샷 {n_trades}건")
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sys.stdout.flush()
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rows: list[dict[str, Any]] = []
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t_b = time.time()
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for i, t in enumerate(sorted(trades, key=lambda x: x["dt"])):
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ts = pd.Timestamp(t["dt"])
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row: dict[str, Any] = {
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"trade_idx": i,
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"dt": t["dt"],
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"action": t["action"],
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"price": t["price"],
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"weight": t.get("weight", 1.0),
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"leg_id": t.get("leg_id", 0),
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"memo": t.get("memo", ""),
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}
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flat: dict[str, Any] = {}
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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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snap = general_analysis_snapshot_at_bar(ef, ts, iv)
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flat.update(_prefixed_snap(snap, iv))
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row.update(flat)
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row.update(general_analysis_mtf_scores(flat))
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rows.append(row)
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done = i + 1
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if done == 1 or done % 25 == 0 or done == n_trades:
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elapsed = time.time() - t_b
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rate = done / elapsed if elapsed > 0 else 0
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eta = (n_trades - done) / rate if rate > 0 else 0
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print(
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f" 타점 {done}/{n_trades} "
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f"({elapsed:.0f}초 경과, ETA 약 {eta:.0f}초)"
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)
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sys.stdout.flush()
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print(f"[03b] Phase B 완료 ({time.time() - t_b:.0f}초)")
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sys.stdout.flush()
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return pd.DataFrame(rows)
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def export_trade_snapshots(
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frames: dict[int, pd.DataFrame],
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trades_path: Path | str = DEFAULT_TRADES_FILE,
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output_csv: Path | str = DEFAULT_OUTPUT_CSV,
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) -> Path:
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"""
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CSV로 타점 MTF 스냅샷 저장.
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Returns:
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저장 경로.
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"""
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data = load_ground_truth(Path(trades_path))
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if not data:
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raise FileNotFoundError(f"정답 파일 없음: {trades_path}")
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trades = data.get("trades") or []
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print(f"타점 {len(trades)}건 × {len(GENERAL_ANALYSIS_INTERVALS)} TF general_analysis")
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df = build_trade_mtf_snapshots(frames, trades)
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out = Path(output_csv)
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out.parent.mkdir(parents=True, exist_ok=True)
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df.to_csv(out, index=False, encoding="utf-8-sig")
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print(f"저장: {out} ({len(df)}행 × {len(df.columns)}열)")
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return out
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