hybrid DD tier와 Option C 2차(+1000%) 검증을 추가하고 실거래 사이징을 정합한다.
인과 GT leg 엔진·drawdown tier·train 캘리브레이션, Phase 2 Go/No-Go 및 시뮬 리포트를 반영한다. Co-authored-by: Cursor <cursoragent@cursor.com>
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xavis
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deepcoin/matching/live_sizing.py
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214
deepcoin/matching/live_sizing.py
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"""
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실거래 매수 사이징 — 시뮬(sim_tier_enhanced)과 동일 인과 tier·weight 정책.
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"""
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from __future__ import annotations
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import json
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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 config import (
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GT_SIGNAL_CAUSAL,
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TRADING_FEE_RATE,
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)
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from deepcoin.ground_truth.causal_gt_hybrid import (
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_attach_drawdown_to_buys,
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_bar_index_at,
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_close_series_from_df,
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_drawdown_pct_at_index,
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hybrid_tier_scale,
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)
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from deepcoin.ground_truth.gt_model import leg_entry_weights, remaining_weight_sum
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from deepcoin.matching.position_sizing import compute_buy_amount_krw
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from deepcoin.paths import OPS_STATE_DIR
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LIVE_SIZING_STATE_JSON = OPS_STATE_DIR / "live_sizing_state.json"
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class LivePositionState:
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"""
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미청산 leg·과거 leg 수익·매수 weight 추적 (시뮬 enrich/causal tier 정합).
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"""
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def __init__(self) -> None:
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"""빈 포지션 상태."""
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self.current_leg_id: int = 0
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self.open_buys: list[dict[str, Any]] = []
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self.completed_leg_ret: dict[int, float] = {}
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self.leg_cost_krw: float = 0.0
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self.leg_proceeds_krw: float = 0.0
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@classmethod
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def load(cls, path: Path | None = None) -> LivePositionState:
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"""
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디스크에서 상태 복원.
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Args:
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path: JSON 경로. None이면 기본 경로.
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Returns:
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LivePositionState 인스턴스.
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"""
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p = path or LIVE_SIZING_STATE_JSON
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st = cls()
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if not p.is_file():
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return st
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try:
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data = json.loads(p.read_text(encoding="utf-8"))
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except (json.JSONDecodeError, OSError):
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return st
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st.current_leg_id = int(data.get("current_leg_id") or 0)
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st.open_buys = list(data.get("open_buys") or [])
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st.completed_leg_ret = {
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int(k): float(v) for k, v in (data.get("completed_leg_ret") or {}).items()
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}
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st.leg_cost_krw = float(data.get("leg_cost_krw") or 0.0)
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st.leg_proceeds_krw = float(data.get("leg_proceeds_krw") or 0.0)
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return st
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def save(self, path: Path | None = None) -> None:
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"""
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상태를 디스크에 저장.
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Args:
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path: JSON 경로. None이면 기본 경로.
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"""
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p = path or LIVE_SIZING_STATE_JSON
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p.parent.mkdir(parents=True, exist_ok=True)
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payload = {
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"current_leg_id": self.current_leg_id,
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"open_buys": self.open_buys,
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"completed_leg_ret": self.completed_leg_ret,
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"leg_cost_krw": round(self.leg_cost_krw, 0),
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"leg_proceeds_krw": round(self.leg_proceeds_krw, 0),
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}
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p.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
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def _start_new_leg_if_needed(self) -> None:
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"""포지션 없을 때 새 leg 시작."""
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if not self.open_buys:
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self.current_leg_id += 1
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self.leg_cost_krw = 0.0
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self.leg_proceeds_krw = 0.0
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def record_buy(self, dt: str, price: float, amount_krw: float, fee: float) -> None:
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"""
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체결 매수 기록.
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Args:
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dt: 체결 시각.
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price: 체결가.
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amount_krw: 매수 원화.
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fee: 수수료.
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"""
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self._start_new_leg_if_needed()
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self.open_buys.append({"dt": dt, "price": price, "amount_krw": amount_krw})
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self.leg_cost_krw += amount_krw + fee
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def record_sell(self, amount_krw: float, fee: float, *, full_close: bool) -> None:
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"""
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체결 매도 기록.
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Args:
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amount_krw: 매도 원화(총액).
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fee: 수수료.
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full_close: leg 전량 청산 여부.
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"""
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net = amount_krw - fee
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self.leg_proceeds_krw += net
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if full_close and self.leg_cost_krw > 0:
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ret_pct = (self.leg_proceeds_krw - self.leg_cost_krw) / self.leg_cost_krw * 100.0
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self.completed_leg_ret[self.current_leg_id] = ret_pct
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self.open_buys = []
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self.leg_cost_krw = 0.0
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self.leg_proceeds_krw = 0.0
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def plan_buy_amount_krw(
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self,
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dt: str,
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price: float,
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cash: float,
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qty: float,
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df: pd.DataFrame | None = None,
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*,
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enhanced: bool = True,
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fee_rate: float = TRADING_FEE_RATE,
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) -> float:
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"""
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시뮬과 동일 tier·weight로 매수 원화 산출.
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Args:
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dt: 신호 시각.
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price: 종가.
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cash: 가용 원화.
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qty: 보유 수량.
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df: OHLC (drawdown).
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enhanced: conviction·medium tier 사용.
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fee_rate: 수수료율.
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Returns:
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매수 원화.
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"""
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self._start_new_leg_if_needed()
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prices = [float(b["price"]) for b in self.open_buys] + [price]
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weights = leg_entry_weights(prices)
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idx = len(self.open_buys)
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weight = float(weights[idx])
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w_sum = float(sum(weights[idx:]))
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trade: dict[str, Any] = {
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"dt": dt,
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"action": "buy",
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"price": price,
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"leg_id": self.current_leg_id,
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"weight": round(weight, 4),
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}
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if df is not None and not df.empty:
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attached = _attach_drawdown_to_buys([trade], df)
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if attached:
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trade = attached[0]
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from deepcoin.ground_truth.hybrid_dd_calibrate import load_hybrid_dd_params
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dd_params = load_hybrid_dd_params()
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scale = hybrid_tier_scale(
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trade,
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completed_leg_ret=self.completed_leg_ret,
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enhanced=enhanced,
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dd_large_pct=dd_params.get("dd_large_pct"),
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dd_medium_pct=dd_params.get("dd_medium_pct"),
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)
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return compute_buy_amount_krw(
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cash,
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qty,
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price,
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weight,
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w_sum,
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asset_pct_scale=scale,
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fee_rate=fee_rate,
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ignore_weight_split=bool(trade.get("conviction_buy")),
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)
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def drawdown_pct_from_df(df: pd.DataFrame, dt: str) -> float:
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"""
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bar 시점 drawdown % (인과적).
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Args:
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df: DatetimeIndex OHLC.
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dt: 시각 문자열.
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Returns:
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drawdown %.
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"""
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if df.empty:
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return 0.0
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close_s = _close_series_from_df(df)
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bar_idx = _bar_index_at(df, dt)
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return _drawdown_pct_at_index(close_s, bar_idx)
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def live_sizing_enabled() -> bool:
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"""실거래 사이징을 시뮬 인과 tier와 정합할지."""
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return bool(GT_SIGNAL_CAUSAL)
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