dsyoon
2cb67c42b3
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>
232 lines
7.1 KiB
Python
232 lines
7.1 KiB
Python
"""
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04-3: 규칙 발화별 성과 라벨링 (GT leg 청산 + forward 폴백).
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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 (
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CHART_LOOKBACK_DAYS,
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MATCH_FORWARD_BARS,
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MATCH_LABEL_MODE,
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MATCH_MAX_HOLD_DAYS,
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MATCH_PRIMARY_INTERVAL,
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SYMBOL,
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TRADING_FEE_RATE,
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)
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from deepcoin.data.mtf_bb import load_frames_from_db
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from deepcoin.matching.gt_schedule import load_gt_trade_events
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from deepcoin.ops.monitor import Monitor
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_NS_PER_DAY = 86_400 * 1_000_000_000
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def _forward_ret_vectorized(
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fire_ts_ns: np.ndarray,
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c0: np.ndarray,
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close_ts_ns: np.ndarray,
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close_px: np.ndarray,
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side: np.ndarray,
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n_bars: int,
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fee_pct: float,
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) -> tuple[np.ndarray, np.ndarray]:
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"""
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고정 N봉 forward 수익률(벡터화, 루프 최소).
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Args:
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fire_ts_ns: 발화 시각(ns).
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c0: 발화가.
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close_ts_ns, close_px: 주간격 종가 시계열.
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side: buy | sell.
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n_bars: forward 봉 수.
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fee_pct: 왕복 수수료 %p.
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Returns:
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(ret_pct, valid_mask).
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"""
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ret = np.full(len(fire_ts_ns), np.nan, dtype=float)
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valid = np.zeros(len(fire_ts_ns), dtype=bool)
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for i in range(len(fire_ts_ns)):
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idx = np.searchsorted(close_ts_ns, fire_ts_ns[i], side="right") - 1
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if idx < 0:
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continue
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end = idx + n_bars
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if end >= len(close_px):
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continue
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c_entry = c0[i]
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c_exit = float(close_px[end])
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if side[i] == "buy":
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ret[i] = (c_exit / c_entry - 1.0) * 100.0 - fee_pct
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else:
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ret[i] = (c_entry / c_exit - 1.0) * 100.0 - fee_pct
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valid[i] = True
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return ret, valid
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def _leg_gt_ret_vectorized(
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fire_ts_ns: np.ndarray,
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c0: np.ndarray,
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side: np.ndarray,
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buy_ts: np.ndarray,
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buy_px: np.ndarray,
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sell_ts: np.ndarray,
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sell_px: np.ndarray,
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max_hold_days: int,
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fee_pct: float,
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) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
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"""
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GT 이벤트 기준 leg 청산 수익률.
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매수 발화: 다음 GT 매도까지 보유.
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매도 발화: 직전 GT 매수가 대비 청산 수익.
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Args:
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fire_ts_ns, c0, side: 발화 배열.
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buy_ts, buy_px, sell_ts, sell_px: GT 이벤트.
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max_hold_days: 최대 보유 일수.
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fee_pct: 왕복 수수료 %p.
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Returns:
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(ret_pct, valid_mask, hold_days).
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"""
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n = len(fire_ts_ns)
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ret = np.full(n, np.nan, dtype=float)
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valid = np.zeros(n, dtype=bool)
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hold_days = np.full(n, np.nan, dtype=float)
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max_hold_ns = max_hold_days * _NS_PER_DAY
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buy_m = side == "buy"
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if buy_m.any() and len(sell_ts) > 0:
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t_b = fire_ts_ns[buy_m]
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idx = np.searchsorted(sell_ts, t_b, side="right")
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ok = idx < len(sell_ts)
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if ok.any():
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i_ok = np.where(buy_m)[0][ok]
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exit_ns = sell_ts[idx[ok]]
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delta = exit_ns - t_b[ok]
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within = (delta > 0) & (delta <= max_hold_ns)
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if within.any():
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ii = i_ok[within]
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exit_px = sell_px[idx[ok][within]]
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entry = c0[ii]
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ret[ii] = (exit_px / entry - 1.0) * 100.0 - fee_pct
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valid[ii] = True
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hold_days[ii] = delta[within] / _NS_PER_DAY
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sell_m = side == "sell"
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if sell_m.any() and len(buy_ts) > 0:
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t_s = fire_ts_ns[sell_m]
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idx = np.searchsorted(buy_ts, t_s, side="left") - 1
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ok = idx >= 0
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if ok.any():
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i_ok = np.where(sell_m)[0][ok]
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entry_ns = buy_ts[idx[ok]]
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delta = t_s[ok] - entry_ns
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within = (delta > 0) & (delta <= max_hold_ns)
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if within.any():
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ii = i_ok[within]
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entry_px = buy_px[idx[ok][within]]
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exit_p = c0[ii]
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ret[ii] = (exit_p / entry_px - 1.0) * 100.0 - fee_pct
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valid[ii] = True
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hold_days[ii] = delta[within] / _NS_PER_DAY
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return ret, valid, hold_days
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def label_fire_outcomes(
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fires: pd.DataFrame,
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frames: dict[int, pd.DataFrame] | None = None,
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) -> pd.DataFrame:
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"""
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각 발화의 성과를 라벨링합니다.
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MATCH_LABEL_MODE=leg_gt: GT 다음 매도/직전 매수 기준.
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미충족 분은 forward N봉으로 폴백.
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Args:
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fires: rule_fires.
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frames: OHLCV (폴백용).
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Returns:
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fire_outcomes (+ forward_ret_pct, label_method, hold_days, ...).
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"""
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if fires.empty:
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return fires.copy()
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fee_pct = TRADING_FEE_RATE * 2 * 100
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fts = pd.to_datetime(fires["dt"])
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fire_ts_ns = fts.values.astype("datetime64[ns]").astype(np.int64)
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c0 = fires["close"].astype(float).values
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side = fires["side"].astype(str).values
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buy_ts, buy_px, sell_ts, sell_px = load_gt_trade_events()
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label_method = np.full(len(fires), "", dtype=object)
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hold_days = np.full(len(fires), np.nan, dtype=float)
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ret = np.full(len(fires), np.nan, dtype=float)
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if MATCH_LABEL_MODE == "leg_gt":
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ret, valid, hd = _leg_gt_ret_vectorized(
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fire_ts_ns,
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c0,
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side,
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buy_ts,
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buy_px,
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sell_ts,
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sell_px,
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MATCH_MAX_HOLD_DAYS,
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fee_pct,
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)
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label_method[valid] = "leg_gt"
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hold_days = hd
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need_fb = ~np.isfinite(ret)
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if need_fb.any():
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if frames is None:
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mon = Monitor(cooldown_file=None)
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frames = load_frames_from_db(mon, SYMBOL, lookback_days=CHART_LOOKBACK_DAYS)
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if need_fb.any() and frames is not None:
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raw = frames.get(MATCH_PRIMARY_INTERVAL)
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if raw is not None and not raw.empty:
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px = raw.copy()
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if not isinstance(px.index, pd.DatetimeIndex):
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px.index = pd.to_datetime(px.index)
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px = px.sort_index()
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col = "close" if "close" in px.columns else "Close"
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close_px = px[col].astype(float).values
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close_ts_ns = px.index.astype(np.int64).values
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fb_ret, fb_ok = _forward_ret_vectorized(
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fire_ts_ns[need_fb],
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c0[need_fb],
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close_ts_ns,
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close_px,
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side[need_fb],
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MATCH_FORWARD_BARS,
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fee_pct,
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)
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ret[need_fb] = np.where(fb_ok, fb_ret, np.nan)
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fb_idx = np.where(need_fb)[0][fb_ok]
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label_method[fb_idx] = f"forward_{MATCH_FORWARD_BARS}"
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out = fires.copy()
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out["forward_ret_pct"] = np.round(ret, 4)
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out["win"] = (ret > 0).astype(int)
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out["label_method"] = label_method
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out["hold_days"] = np.round(hold_days, 2)
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out["forward_bars"] = MATCH_FORWARD_BARS
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out = out[np.isfinite(out["forward_ret_pct"])].reset_index(drop=True)
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leg_n = int((out["label_method"] == "leg_gt").sum())
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fb_n = int(out["label_method"].astype(str).str.startswith("forward").sum())
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print(
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f"[04-3] 성과 라벨: {len(out):,}건 "
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f"(mode={MATCH_LABEL_MODE}, leg_gt={leg_n:,}, forward폴백={fb_n:,}, "
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f"수수료 {fee_pct:.3f}%p)"
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)
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return out
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