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/option_c_phase2.py
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deepcoin/matching/option_c_phase2.py
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"""
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Option C 2차 목표(+1000%) 검증 — hybrid tier 포트폴리오 WF·슬리피지·Go/No-Go.
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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 pandas as pd
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from config import (
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GT_INITIAL_CASH_KRW,
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LIVE_SLIPPAGE_PCT,
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SIM_HYBRID_MAX_MDD_PCT,
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SIM_HYBRID_PORTFOLIO_WF_MIN_RATIO,
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SIM_OPTION_C_MIN_GT_CAPTURE,
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SIM_OPTION_C_PHASE2_FEE_STRESS_RATIO,
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SIM_OPTION_C_PHASE2_TARGET_PNL_PCT,
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)
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from deepcoin.ground_truth.gt_allocation import simulate_portfolio_summary
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def apply_slippage_to_trades(
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trades: list[dict[str, Any]],
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slippage_pct: float = LIVE_SLIPPAGE_PCT,
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) -> list[dict[str, Any]]:
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"""
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체결가에 슬리피지 반영 (매수 불리·매도 불리).
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Args:
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trades: amount_krw·price·action trade dict.
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slippage_pct: 체결가 대비 % (0.05 = 0.05%).
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Returns:
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price 조정된 trade dict 복사본.
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"""
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slip = float(slippage_pct) / 100.0
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out: list[dict[str, Any]] = []
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for t in trades:
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row = dict(t)
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price = float(row.get("price") or 0)
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if price <= 0:
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out.append(row)
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continue
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action = row.get("action", "")
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if action == "buy":
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row["price"] = round(price * (1.0 + slip), 4)
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elif action == "sell":
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row["price"] = round(price * (1.0 - slip), 4)
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out.append(row)
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return out
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def walk_forward_portfolio_by_month(
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steps: list[dict[str, Any]],
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*,
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initial_cash: float = GT_INITIAL_CASH_KRW,
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) -> list[dict[str, Any]]:
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"""
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포트폴리오 step에서 월별 자산 증감률 (인과적).
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Args:
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steps: simulate_portfolio_steps 결과.
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initial_cash: 첫 달 시작 자산(이전 달 종료 없을 때).
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Returns:
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{month, pnl_pct, start_asset, end_asset} 리스트.
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"""
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if not steps:
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return []
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df = pd.DataFrame(steps)
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df["ts"] = pd.to_datetime(df["dt"])
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df = df.sort_values("ts")
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df["month"] = df["ts"].dt.to_period("M").astype(str)
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rows: list[dict[str, Any]] = []
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prev_end = float(initial_cash)
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for month in sorted(df["month"].unique()):
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grp = df[df["month"] == month]
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end_asset = float(grp["total_asset_krw"].iloc[-1])
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pnl_pct = (end_asset - prev_end) / prev_end * 100.0 if prev_end > 0 else 0.0
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rows.append(
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{
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"month": month,
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"pnl_pct": round(pnl_pct, 2),
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"start_asset_krw": round(prev_end, 0),
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"end_asset_krw": round(end_asset, 0),
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}
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)
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prev_end = end_asset
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return rows
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def walk_forward_portfolio_summary(wf_rows: list[dict[str, Any]]) -> dict[str, Any]:
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"""
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월별 포트폴리오 WF 요약.
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Args:
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wf_rows: walk_forward_portfolio_by_month 결과.
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Returns:
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months, positive_months, positive_ratio, mean_pnl_pct.
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"""
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if not wf_rows:
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return {"months": 0, "positive_ratio": 0.0, "mean_pnl_pct": 0.0}
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n = len(wf_rows)
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pos = sum(1 for r in wf_rows if float(r.get("pnl_pct") or 0) > 0)
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mean_pnl = sum(float(r.get("pnl_pct") or 0) for r in wf_rows) / n
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return {
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"months": n,
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"positive_months": pos,
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"positive_ratio": round(pos / n, 4),
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"mean_pnl_pct": round(mean_pnl, 2),
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}
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def simulate_hybrid_slippage_stress(
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sized_trades: list[dict[str, Any]],
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*,
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last_price: float | None,
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slippage_pct: float = LIVE_SLIPPAGE_PCT,
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initial_cash: float = GT_INITIAL_CASH_KRW,
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fee_rate: float,
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) -> dict[str, Any]:
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"""
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hybrid sized trades + 슬리피지 포트폴리오 요약.
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Args:
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sized_trades: build_monitor_hybrid_sized_trades 출력.
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last_price: 미청산 평가가.
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slippage_pct: 체결 슬리피지 %.
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initial_cash: 시작 현금.
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fee_rate: 수수료율.
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Returns:
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simulate_portfolio_summary 결과.
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"""
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slipped = apply_slippage_to_trades(sized_trades, slippage_pct)
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result = simulate_portfolio_summary(
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slipped,
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initial_cash=initial_cash,
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fee_rate=fee_rate,
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last_price=last_price,
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use_amount_krw=True,
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)
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result["slippage_pct"] = slippage_pct
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result["sizing_mode"] = "hybrid_slippage_stress"
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return result
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def evaluate_option_c_phase2_go(
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hybrid_go: dict[str, Any],
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hybrid_full: dict[str, Any],
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hybrid_holdout: dict[str, Any],
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hybrid_fee_stress: dict[str, Any],
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hybrid_slippage: dict[str, Any],
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portfolio_wf_summary: dict[str, Any],
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gt_pnl_pct: float,
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) -> dict[str, Any]:
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"""
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Option C 2차(+1000%) Go/No-Go.
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Args:
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hybrid_go: 1차 hybrid tier Go 결과.
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hybrid_full: 전기간 hybrid 포트폴리오.
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hybrid_holdout: holdout 구간 증감.
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hybrid_fee_stress: 수수료 2x hybrid.
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hybrid_slippage: 슬리피지 반영 hybrid.
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portfolio_wf_summary: 월별 포트폴리오 WF.
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gt_pnl_pct: GT 전기간 PnL %.
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Returns:
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go, checks, targets.
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"""
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full_pnl = float(hybrid_full.get("pnl_pct", 0))
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capture = full_pnl / gt_pnl_pct if abs(gt_pnl_pct) > 1e-6 else 0.0
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ho_pnl = float(hybrid_holdout.get("pnl_pct", -999))
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mdd = float(hybrid_full.get("max_drawdown_pct", 999))
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fee_pnl = float(hybrid_fee_stress.get("pnl_pct", -999))
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slip_pnl = float(hybrid_slippage.get("pnl_pct", -999))
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wf_ratio = float(portfolio_wf_summary.get("positive_ratio", 0))
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c_phase1 = bool(hybrid_go.get("go"))
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c_pnl = full_pnl >= SIM_OPTION_C_PHASE2_TARGET_PNL_PCT
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c_capture = capture >= SIM_OPTION_C_MIN_GT_CAPTURE
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c_holdout = ho_pnl > 0.0
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c_mdd = mdd <= SIM_HYBRID_MAX_MDD_PCT
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c_fee = fee_pnl >= SIM_OPTION_C_PHASE2_TARGET_PNL_PCT * SIM_OPTION_C_PHASE2_FEE_STRESS_RATIO
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c_slip = slip_pnl > 0.0
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c_wf = wf_ratio >= SIM_HYBRID_PORTFOLIO_WF_MIN_RATIO
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all_go = (
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c_phase1
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and c_pnl
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and c_capture
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and c_holdout
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and c_mdd
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and c_fee
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and c_slip
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and c_wf
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)
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return {
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"go": all_go,
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"gt_capture_ratio": round(capture, 4),
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"targets": {
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"phase2_pnl_pct": SIM_OPTION_C_PHASE2_TARGET_PNL_PCT,
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"min_gt_capture": SIM_OPTION_C_MIN_GT_CAPTURE,
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"portfolio_wf_min_ratio": SIM_HYBRID_PORTFOLIO_WF_MIN_RATIO,
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},
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"checks": [
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{"name": "phase1_hybrid_go", "pass": c_phase1},
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{
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"name": "full_pnl_1000pct",
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"pass": c_pnl,
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"value": full_pnl,
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},
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{
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"name": "gt_capture_23pct",
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"pass": c_capture,
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"value": round(capture, 4),
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},
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{"name": "holdout_pnl_positive", "pass": c_holdout, "value": ho_pnl},
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{"name": "max_mdd", "pass": c_mdd, "value": mdd},
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{
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"name": "fee_stress_ratio",
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"pass": c_fee,
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"value": fee_pnl,
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"threshold": round(
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SIM_OPTION_C_PHASE2_TARGET_PNL_PCT * SIM_OPTION_C_PHASE2_FEE_STRESS_RATIO,
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2,
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),
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},
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{
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"name": "slippage_stress_positive",
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"pass": c_slip,
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"value": slip_pnl,
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"note": "체결가 슬리피지 반영 후에도 흑자",
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},
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{
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"name": "portfolio_wf_positive_ratio",
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"pass": c_wf,
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"value": wf_ratio,
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},
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],
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}
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