GT 총자산 비율 매수·leg 티어 배분과 시뮬/실거래 포지션 사이징을 통합한다.

타점·비중을 gt_model로 일반화하고, amount_krw 시각순 배분·EV/WF·상위 leg 대형 매수를
position_sizing과 시뮬 HTML(고정 ₩/회 비교)에 반영한다.

Co-authored-by: Cursor <cursoragent@cursor.com>

deepcoin/matching/portfolio_sim.py

@@ -15,9 +15,39 @@ from config import (
     LIVE_ORDER_KRW,
     TRADING_FEE_RATE,
 )
+from deepcoin.matching.position_sizing import (
+    attach_dynamic_buy_amounts,
+    load_sizing_context_from_gt,
+)
 
 
-def select_capped_fires(fires: pd.DataFrame) -> pd.DataFrame:
+def _planned_order_krw(
+    t: dict[str, Any],
+    order_krw: float,
+    sizing_mode: str,
+) -> float:
+    """
+    체결 계획 원화: amount_krw 우선 또는 고정.
+
+    Args:
+        t: trade dict.
+        order_krw: 고정 1회 금액.
+        sizing_mode: fixed | amount_krw.
+
+    Returns:
+        계획 원화.
+    """
+    ak = t.get("amount_krw")
+    if sizing_mode == "amount_krw" or (ak is not None and float(ak) > 0):
+        return float(ak or 0)
+    return float(order_krw)
+
+
+def select_capped_fires(
+    fires: pd.DataFrame,
+    *,
+    use_dynamic_sizing: bool = True,
+) -> pd.DataFrame:
     """
     일한도·회수 제한으로 체결 가능한 발화만 남깁니다.
 
@@ -29,20 +59,58 @@ def select_capped_fires(fires: pd.DataFrame) -> pd.DataFrame:
     """
     if fires.empty:
         return fires
+    gt_trades, large_legs, approved = load_sizing_context_from_gt()
     df = fires.sort_values("dt").copy()
     df["ts"] = pd.to_datetime(df["dt"])
     df["day"] = df["ts"].dt.date.astype(str)
+    cash = float(GT_INITIAL_CASH_KRW)
+    qty = 0.0
     taken: list[pd.DataFrame] = []
     for _, day_grp in df.groupby("day", sort=True):
         spent = 0.0
         n_trades = 0
         idxs: list[Any] = []
-        for idx, _row in day_grp.iterrows():
+        for idx, row in day_grp.iterrows():
             if n_trades >= LIVE_MAX_TRADES_PER_DAY:
                 break
-            if spent + LIVE_ORDER_KRW > LIVE_DAILY_KRW_MAX:
-                break
-            spent += LIVE_ORDER_KRW
+            side = row["side"]
+            price = float(row["close"])
+            if side == "buy" and use_dynamic_sizing:
+                from deepcoin.matching.position_sizing import (
+                    compute_buy_amount_krw,
+                    live_buy_asset_pct_scale,
+                )
+
+                scale = live_buy_asset_pct_scale(
+                    str(row["rule_id"]),
+                    str(row["dt"]),
+                    gt_trades,
+                    approved_rules=approved,
+                    large_legs=large_legs,
+                )
+                planned = compute_buy_amount_krw(
+                    cash,
+                    qty,
+                    price,
+                    1.0,
+                    1.0,
+                    asset_pct_scale=scale,
+                )
+            else:
+                planned = float(LIVE_ORDER_KRW)
+            if side == "buy":
+                if spent + planned > LIVE_DAILY_KRW_MAX:
+                    break
+                if planned <= 0:
+                    continue
+                fee = planned * TRADING_FEE_RATE
+                cash -= planned + fee
+                qty += planned / price if price > 0 else 0.0
+                spent += planned
+            elif side == "sell" and qty > 0:
+                gross = qty * price
+                cash += gross * (1.0 - TRADING_FEE_RATE)
+                qty = 0.0
             n_trades += 1
             idxs.append(idx)
         if idxs:
@@ -52,7 +120,11 @@ def select_capped_fires(fires: pd.DataFrame) -> pd.DataFrame:
     return pd.concat(taken, ignore_index=True)
 
 
-def fires_to_trade_list(fires: pd.DataFrame) -> list[dict[str, Any]]:
+def fires_to_trade_list(
+    fires: pd.DataFrame,
+    *,
+    apply_dynamic_sizing: bool = True,
+) -> list[dict[str, Any]]:
     """
     발화 DataFrame을 포트폴리오 시뮬용 trade dict 리스트로 변환.
 
@@ -73,25 +145,65 @@ def fires_to_trade_list(fires: pd.DataFrame) -> list[dict[str, Any]]:
                 "forward_ret_pct": float(r.get("forward_ret_pct", 0)),
             }
         )
+    if apply_dynamic_sizing and rows:
+        gt_trades, large_legs, approved = load_sizing_context_from_gt()
+        attach_dynamic_buy_amounts(
+            rows,
+            gt_trades=gt_trades,
+            approved_rules=approved,
+            large_legs=large_legs,
+        )
     return rows
 
 
+def simulate_sized_portfolio(
+    trades: list[dict[str, Any]],
+    initial_cash: float = GT_INITIAL_CASH_KRW,
+    fee_rate: float = TRADING_FEE_RATE,
+    last_price: float | None = None,
+    fallback_order_krw: float = LIVE_ORDER_KRW,
+) -> dict[str, Any]:
+    """
+    trade.amount_krw(총자산 비율 배분) 기준 포트폴리오 시뮬.
+
+    Args:
+        trades: 시간순 trade dict (amount_krw 권장).
+        initial_cash: 시작 현금.
+        fee_rate: 수수료율.
+        last_price: 미청산 평가 종가.
+        fallback_order_krw: amount_krw 없을 때 1회 금액.
+
+    Returns:
+        simulate_truth_portfolio와 동일 키 구조.
+    """
+    return simulate_fixed_order_portfolio(
+        trades,
+        order_krw=fallback_order_krw,
+        initial_cash=initial_cash,
+        fee_rate=fee_rate,
+        last_price=last_price,
+        sizing_mode="amount_krw",
+    )
+
+
 def simulate_fixed_order_portfolio(
     trades: list[dict[str, Any]],
     order_krw: float = LIVE_ORDER_KRW,
     initial_cash: float = GT_INITIAL_CASH_KRW,
     fee_rate: float = TRADING_FEE_RATE,
     last_price: float | None = None,
+    sizing_mode: str = "fixed",
 ) -> dict[str, Any]:
     """
-    매 체결마다 고정 원화 금액으로 매수·매도한 뒤 총평가·수익률을 계산합니다.
+    포트폴리오 시뮬 (고정 원화 또는 trade.amount_krw).
 
     Args:
-        trades: 시간순 {dt, action, price}.
-        order_krw: 1회 매수·매도 금액(원).
+        trades: 시간순 {dt, action, price, amount_krw?}.
+        order_krw: sizing_mode=fixed 일 때 1회 금액(원).
         initial_cash: 시작 현금.
         fee_rate: 수수료율.
         last_price: 미청산 평가 종가.
+        sizing_mode: 'fixed' | 'amount_krw' (없으면 order_krw).
 
     Returns:
         simulate_truth_portfolio와 동일 키 구조.
@@ -110,7 +222,8 @@ def simulate_fixed_order_portfolio(
         last_trade_price = price
 
         if action == "buy":
-            amount = min(order, max(cash / (1.0 + fee_rate), 0.0))
+            planned = _planned_order_krw(t, order, sizing_mode)
+            amount = min(planned, max(cash / (1.0 + fee_rate), 0.0))
             if amount <= 0:
                 continue
             fee = amount * fee_rate
@@ -119,7 +232,11 @@ def simulate_fixed_order_portfolio(
             qty += amount / price
 
         elif action == "sell" and qty > 0:
-            sell_qty = min(qty, order / price)
+            planned = _planned_order_krw(t, order, sizing_mode)
+            if planned >= qty * price * 0.999:
+                sell_qty = qty
+            else:
+                sell_qty = min(qty, planned / price)
             if sell_qty <= 0:
                 continue
             gross = sell_qty * price
@@ -148,6 +265,7 @@ def simulate_fixed_order_portfolio(
         "mark_price": round(mark_price, 2),
         "fee_rate": fee_rate,
         "order_krw": round(order, 0),
+        "sizing_mode": sizing_mode,
         "trade_count": len(trades),
     }
 
@@ -157,6 +275,7 @@ def simulate_fixed_order_portfolio_steps(
     order_krw: float = LIVE_ORDER_KRW,
     initial_cash: float = GT_INITIAL_CASH_KRW,
     fee_rate: float = TRADING_FEE_RATE,
+    sizing_mode: str = "fixed",
 ) -> list[dict[str, Any]]:
     """
     체결마다 현금·보유·총평가 스냅샷 (GT 테이블용).
@@ -166,6 +285,7 @@ def simulate_fixed_order_portfolio_steps(
         order_krw: 1회 체결 원화.
         initial_cash: 시작 현금.
         fee_rate: 수수료율.
+        sizing_mode: fixed | amount_krw.
 
     Returns:
         step dict 리스트.
@@ -182,7 +302,8 @@ def simulate_fixed_order_portfolio_steps(
             continue
 
         if action == "buy":
-            amount = min(order, max(cash / (1.0 + fee_rate), 0.0))
+            planned = _planned_order_krw(t, order, sizing_mode)
+            amount = min(planned, max(cash / (1.0 + fee_rate), 0.0))
             if amount <= 0:
                 continue
             fee = amount * fee_rate
@@ -190,7 +311,11 @@ def simulate_fixed_order_portfolio_steps(
             qty += amount / price
 
         elif action == "sell" and qty > 0:
-            sell_qty = min(qty, order / price)
+            planned = _planned_order_krw(t, order, sizing_mode)
+            if planned >= qty * price * 0.999:
+                sell_qty = qty
+            else:
+                sell_qty = min(qty, planned / price)
             if sell_qty <= 0:
                 continue
             gross = sell_qty * price
@@ -207,6 +332,7 @@ def simulate_fixed_order_portfolio_steps(
                 "price": price,
                 "rule_id": t.get("rule_id", ""),
                 "forward_ret_pct": t.get("forward_ret_pct"),
+                "amount_krw": t.get("amount_krw"),
                 "cash_krw": round(cash, 0),
                 "holding_qty": round(qty, 4),
                 "total_asset_krw": round(cash + qty * price, 0),

deepcoin/matching/position_sizing.py

@@ -0,0 +1,378 @@
+"""
+총자산 대비 최적 매수율(비중) · 현금 한도 · leg 상위·EV/WF 통과 대형 매수.
+"""
+
+from __future__ import annotations
+
+import json
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+from config import (
+    GT_BUY_PCT_LARGE_LEG,
+    GT_BUY_PCT_SMALL_LEG,
+    GT_INITIAL_CASH_KRW,
+    GT_LARGE_LEG_TOP_PCT,
+    GT_MIN_ORDER_KRW,
+    LIVE_BUY_PCT_LARGE,
+    LIVE_BUY_PCT_SMALL,
+    MATCH_GT_TOLERANCE_MIN,
+    SIM_FEE_STRESS_MULT,
+    SIM_GO_MIN_HOLDOUT_EV,
+    SIM_GO_MIN_HOLDOUT_PF,
+    SIM_GO_WF_POSITIVE_RATIO,
+    SIM_WALK_FORWARD_MIN_MONTHS,
+    TRADING_FEE_RATE,
+)
+from deepcoin.matching.load_rules import load_matched_rules
+from deepcoin.paths import MATCHING_FIRE_OUTCOMES, MATCHING_MATCHED_RULES
+
+
+def portfolio_totals(
+    cash: float,
+    qty: float,
+    price: float,
+) -> tuple[float, float, float]:
+    """
+    총보유자산·코인평가·가용현금(=총자산-평가액)을 계산합니다.
+
+    Args:
+        cash: 현금.
+        qty: 보유 수량.
+        price: 평가·체결가.
+
+    Returns:
+        (total_asset_krw, holding_value_krw, cash_krw).
+    """
+    holding = qty * price
+    total = cash + holding
+    return total, holding, cash
+
+
+def optimal_weight_share(weight: float, weight_sum_remaining: float) -> float:
+    """
+    leg 내 남은 매수 비중 대비 이번 체결 최적 매수율(0~1).
+
+    Args:
+        weight: 이번 타점 weight.
+        weight_sum_remaining: 동일 leg 남은 매수 weight 합.
+
+    Returns:
+        비중 비율.
+    """
+    if weight_sum_remaining > 0:
+        return weight / weight_sum_remaining
+    return 1.0
+
+
+def compute_buy_amount_krw(
+    cash: float,
+    qty: float,
+    price: float,
+    weight: float,
+    weight_sum_remaining: float,
+    *,
+    asset_pct_scale: float,
+    min_order_krw: float = GT_MIN_ORDER_KRW,
+    fee_rate: float = TRADING_FEE_RATE,
+) -> float:
+    """
+    총자산 × (최적 매수율 × scale)을 목표로, 가용 현금을 넘지 않게 매수 원화를 산출합니다.
+
+    Args:
+        cash: 현금.
+        qty: 보유 수량.
+        price: 체결가.
+        weight: 타점 비중.
+        weight_sum_remaining: leg 내 남은 매수 weight 합.
+        asset_pct_scale: leg·규칙 티어(대형/소형) 스케일.
+        min_order_krw: 최소 주문 원화.
+        fee_rate: 수수료율.
+
+    Returns:
+        매수 원화(0이면 미체결).
+    """
+    if price <= 0:
+        return 0.0
+    total_asset, _, _ = portfolio_totals(cash, qty, price)
+    budget = max(cash / (1.0 + fee_rate), 0.0)
+    opt_rate = optimal_weight_share(weight, weight_sum_remaining) * asset_pct_scale
+    target = total_asset * opt_rate
+    amount = min(target, budget)
+    if budget >= min_order_krw and 0 < amount < min_order_krw:
+        amount = min(min_order_krw, budget)
+    return round(max(amount, 0.0), 0)
+
+
+def top_leg_ids_by_forward_return(
+    trades: list[dict[str, Any]],
+    top_pct: float = GT_LARGE_LEG_TOP_PCT,
+) -> set[int]:
+    """
+    leg별 최대 forward_return 기준 상위 n% leg_id 집합.
+
+    Args:
+        trades: GT trade dict.
+        top_pct: 상위 비율(0~1).
+
+    Returns:
+        대형 매수 leg_id set.
+    """
+    leg_ret: dict[int, float] = {}
+    for t in trades:
+        if t.get("action") != "sell":
+            continue
+        lid = int(t.get("leg_id", 0))
+        ret = float(t.get("forward_return_pct") or 0.0)
+        leg_ret[lid] = max(leg_ret.get(lid, 0.0), ret)
+    if not leg_ret:
+        return set()
+    ranked = sorted(leg_ret.items(), key=lambda x: x[1], reverse=True)
+    n = max(1, int(len(ranked) * top_pct + 0.999999))
+    return {lid for lid, _ in ranked[:n]}
+
+
+def leg_asset_pct_scale(leg_id: int, large_legs: set[int]) -> float:
+    """
+    leg 티어에 따른 총자산 대비 매수 스케일.
+
+    Args:
+        leg_id: leg 번호.
+        large_legs: 상위 leg 집합.
+
+    Returns:
+        GT_BUY_PCT_LARGE_LEG 또는 GT_BUY_PCT_SMALL_LEG.
+    """
+    if leg_id in large_legs:
+        return float(GT_BUY_PCT_LARGE_LEG)
+    return float(GT_BUY_PCT_SMALL_LEG)
+
+
+def _parse_dt(dt: str) -> datetime:
+    return datetime.fromisoformat(str(dt).replace("Z", "+00:00")[:19])
+
+
+def nearest_gt_leg_id(
+    dt: str,
+    gt_trades: list[dict[str, Any]],
+    tolerance_min: int = MATCH_GT_TOLERANCE_MIN,
+) -> int | None:
+    """
+    시각에 가장 가까운 GT trade의 leg_id (매수 우선).
+
+    Args:
+        dt: 발화 시각.
+        gt_trades: GT trades.
+        tolerance_min: 허용 분.
+
+    Returns:
+        leg_id 또는 None.
+    """
+    if not gt_trades:
+        return None
+    t0 = _parse_dt(dt)
+    best_buy: int | None = None
+    best_buy_min = float(tolerance_min) + 1.0
+    best_any: int | None = None
+    best_any_min = float(tolerance_min) + 1.0
+    for t in gt_trades:
+        try:
+            t1 = _parse_dt(t["dt"])
+        except ValueError:
+            continue
+        delta = abs((t0 - t1).total_seconds()) / 60.0
+        if delta > tolerance_min:
+            continue
+        lid = int(t.get("leg_id", 0))
+        if t.get("action") == "buy" and delta < best_buy_min:
+            best_buy_min = delta
+            best_buy = lid
+        if delta < best_any_min:
+            best_any_min = delta
+            best_any = lid
+    return best_buy if best_buy is not None else best_any
+
+
+_APPROVED_RULES_CACHE: set[str] | None = None
+
+
+def load_ev_wf_approved_rule_ids(
+    matched_path: Path | None = None,
+    outcomes_path: Path | None = None,
+) -> set[str]:
+    """
+    holdout EV·PF, walk-forward, 수수료 스트레스를 모두 통과한 rule_id.
+
+    Args:
+        matched_path: matched_rules.json.
+        outcomes_path: fire_outcomes.csv.
+
+    Returns:
+        통과 rule_id set. 산출 불가 시 monitor_rules 전체 fallback.
+    """
+    global _APPROVED_RULES_CACHE
+    if _APPROVED_RULES_CACHE is not None:
+        return set(_APPROVED_RULES_CACHE)
+
+    from deepcoin.matching.select_rules import _rule_metrics, _split_train_valid_holdout
+    from deepcoin.matching.simulation import (
+        evaluate_go_no_go,
+        simulate_live_order_cap,
+        walk_forward_by_month,
+        walk_forward_summary,
+    )
+
+    mp = matched_path or MATCHING_MATCHED_RULES
+    op = outcomes_path or MATCHING_FIRE_OUTCOMES
+    matched = load_matched_rules(mp)
+    rules = matched.get("monitor_rules") or []
+    if not rules or not op.is_file():
+        return {r["rule_id"] for r in rules}
+
+    import pandas as pd
+
+    from config import MATCH_FEE_RATE
+
+    outcomes = pd.read_csv(op)
+    outcomes["split"] = _split_train_valid_holdout(outcomes)
+    wf_sum = walk_forward_summary(walk_forward_by_month(outcomes))
+    fee_stress: dict[str, Any] = {}
+    for rid in outcomes["rule_id"].unique():
+        sub = outcomes[outcomes["rule_id"] == rid]
+        from deepcoin.matching.simulation import _fee_adjust_ret
+
+        adj = _fee_adjust_ret(sub["forward_ret_pct"], SIM_FEE_STRESS_MULT)
+        fee_stress[rid] = _rule_metrics(sub.assign(forward_ret_pct=adj))
+    monitor_ids = {r["rule_id"] for r in rules}
+    live_cap = simulate_live_order_cap(
+        outcomes, rule_ids=monitor_ids, holdout_only=True
+    )
+    go = evaluate_go_no_go(matched, wf_sum, fee_stress, live_cap)
+    passed = {c["rule_id"] for c in go.get("checks", []) if c.get("pass")}
+    if passed:
+        _APPROVED_RULES_CACHE = passed
+        return passed
+    fallback = monitor_ids
+    _APPROVED_RULES_CACHE = fallback
+    return fallback
+
+
+def live_buy_asset_pct_scale(
+    rule_id: str,
+    dt: str,
+    gt_trades: list[dict[str, Any]],
+    *,
+    approved_rules: set[str],
+    large_legs: set[int],
+) -> float:
+    """
+    실거래·시뮬 매수: EV/WF 통과 규칙 + leg 상위만 대형 비율.
+
+    Args:
+        rule_id: 규칙 ID.
+        dt: 체결 시각.
+        gt_trades: GT trades.
+        approved_rules: 통과 rule_id.
+        large_legs: 상위 leg.
+
+    Returns:
+        LIVE_BUY_PCT_LARGE 또는 LIVE_BUY_PCT_SMALL(또는 0에 가까운 소형).
+    """
+    if rule_id not in approved_rules:
+        return float(LIVE_BUY_PCT_SMALL)
+    lid = nearest_gt_leg_id(dt, gt_trades)
+    if lid is not None and lid in large_legs:
+        return float(LIVE_BUY_PCT_LARGE)
+    return float(LIVE_BUY_PCT_SMALL)
+
+
+def attach_dynamic_buy_amounts(
+    trades: list[dict[str, Any]],
+    *,
+    gt_trades: list[dict[str, Any]],
+    approved_rules: set[str] | None = None,
+    large_legs: set[int] | None = None,
+    initial_cash: float = GT_INITIAL_CASH_KRW,
+    default_weight: float = 1.0,
+    fee_rate: float = TRADING_FEE_RATE,
+) -> list[dict[str, Any]]:
+    """
+    시뮬 발화 trade dict에 amount_krw(총자산 비율·현금 한도)를 채웁니다.
+
+    Args:
+        trades: 시간순 {dt, action, price, rule_id, …}.
+        gt_trades: GT leg 매칭용.
+        approved_rules: EV/WF 통과 rule. None이면 전 규칙 대형 허용 안 함.
+        large_legs: 상위 leg. None이면 GT에서 산출.
+        initial_cash: 초기 현금.
+        default_weight: 매수 weight 기본값.
+        fee_rate: 수수료율.
+
+    Returns:
+        amount_krw가 채워진 동일 리스트.
+    """
+    if large_legs is None:
+        large_legs = top_leg_ids_by_forward_return(gt_trades)
+    if approved_rules is None:
+        approved_rules = set()
+
+    cash = float(initial_cash)
+    qty = 0.0
+    for t in sorted(trades, key=lambda x: x["dt"]):
+        action = t.get("action", t.get("side", ""))
+        price = float(t["price"])
+        if price <= 0:
+            continue
+        if action == "buy":
+            rid = str(t.get("rule_id", ""))
+            scale = live_buy_asset_pct_scale(
+                rid, t["dt"], gt_trades,
+                approved_rules=approved_rules,
+                large_legs=large_legs,
+            )
+            amount = compute_buy_amount_krw(
+                cash,
+                qty,
+                price,
+                float(t.get("weight", default_weight)),
+                float(t.get("weight", default_weight)),
+                asset_pct_scale=scale,
+                fee_rate=fee_rate,
+            )
+            t["amount_krw"] = amount
+            if amount > 0:
+                fee = amount * fee_rate
+                cash -= amount + fee
+                qty += amount / price
+        elif action == "sell" and qty > 1e-12:
+            gross = qty * price
+            t["amount_krw"] = round(gross, 0)
+            fee = gross * fee_rate
+            cash += gross - fee
+            qty = 0.0
+    return trades
+
+
+def load_sizing_context_from_gt(
+    gt_path: Path | None = None,
+) -> tuple[list[dict[str, Any]], set[int], set[str]]:
+    """
+    GT JSON에서 trades, 상위 leg, EV/WF 통과 rule을 로드합니다.
+
+    Args:
+        gt_path: ground_truth_trades.json.
+
+    Returns:
+        (gt_trades, large_legs, approved_rules).
+    """
+    from deepcoin.paths import resolve_ground_truth_file
+
+    p = gt_path or resolve_ground_truth_file()
+    trades: list[dict[str, Any]] = []
+    if p.is_file():
+        data = json.loads(p.read_text(encoding="utf-8"))
+        trades = data.get("trades") or []
+    large = top_leg_ids_by_forward_return(trades)
+    approved = load_ev_wf_approved_rule_ids()
+    return trades, large, approved

deepcoin/matching/simulation.py

@@ -12,6 +12,7 @@ import numpy as np
 import pandas as pd
 
 from config import (
+    GT_INITIAL_CASH_KRW,
     LIVE_DAILY_KRW_MAX,
     LIVE_MAX_TRADES_PER_DAY,
     LIVE_ORDER_KRW,
@@ -28,7 +29,19 @@ from config import (
     SIM_WALK_FORWARD_MIN_MONTHS,
     TRADING_FEE_RATE,
 )
+from deepcoin.ground_truth.ground_truth import (
+    load_ground_truth,
+    order_trades_chronological,
+    simulate_truth_portfolio,
+)
+from deepcoin.matching.portfolio_sim import (
+    fires_to_trade_list,
+    select_capped_fires,
+    simulate_fixed_order_portfolio,
+    simulate_sized_portfolio,
+)
 from deepcoin.matching.select_rules import _rule_metrics, _split_train_valid_holdout
+from deepcoin.paths import resolve_ground_truth_file
 from deepcoin.paths import (
     ANALYSIS_GT_CALIBRATION_JSON,
     MATCHING_FIRE_OUTCOMES,
@@ -109,12 +122,19 @@ def walk_forward_summary(wf_rows: list[dict[str, Any]]) -> dict[str, Any]:
     return out
 
 
-def simulate_live_order_cap(outcomes: pd.DataFrame) -> dict[str, Any]:
+def simulate_live_order_cap(
+    outcomes: pd.DataFrame,
+    *,
+    rule_ids: set[str] | None = None,
+    holdout_only: bool = True,
+) -> dict[str, Any]:
     """
     1회·일 한도·슬리피지 가정으로 체결 가능한 발화만 집계.
 
     Args:
-        outcomes: fire_outcomes.
+        outcomes: fire_outcomes (split 컬럼 있으면 holdout 필터 가능).
+        rule_ids: None이면 전 규칙, 지정 시 해당 rule만.
+        holdout_only: True면 split==holdout 만.
 
     Returns:
         규칙별·전체 요약.
@@ -122,12 +142,27 @@ def simulate_live_order_cap(outcomes: pd.DataFrame) -> dict[str, Any]:
     if outcomes.empty:
         return {"rules": {}, "note": "발화 없음"}
 
-    df = outcomes.sort_values("dt").copy()
+    df = outcomes
+    if holdout_only and "split" in df.columns:
+        df = df[df["split"] == "holdout"]
+    if rule_ids is not None:
+        df = df[df["rule_id"].isin(rule_ids)]
+    df = df.sort_values("dt").copy()
     df["ts"] = pd.to_datetime(df["dt"])
     df["day"] = df["ts"].dt.date.astype(str)
     slip = LIVE_SLIPPAGE_PCT
     taken_rows: list[pd.DataFrame] = []
 
+    from deepcoin.matching.position_sizing import (
+        compute_buy_amount_krw,
+        live_buy_asset_pct_scale,
+        load_sizing_context_from_gt,
+    )
+
+    gt_trades, large_legs, approved = load_sizing_context_from_gt()
+    cash = float(GT_INITIAL_CASH_KRW)
+    qty = 0.0
+
     for day, day_grp in df.groupby("day", sort=True):
         spent = 0.0
         n_trades = 0
@@ -135,9 +170,34 @@ def simulate_live_order_cap(outcomes: pd.DataFrame) -> dict[str, Any]:
         for idx, row in day_grp.iterrows():
             if n_trades >= LIVE_MAX_TRADES_PER_DAY:
                 break
-            if spent + LIVE_ORDER_KRW > LIVE_DAILY_KRW_MAX:
-                break
-            spent += LIVE_ORDER_KRW
+            side = row["side"]
+            price = float(row["close"])
+            if side == "buy":
+                scale = live_buy_asset_pct_scale(
+                    str(row["rule_id"]),
+                    str(row["dt"]),
+                    gt_trades,
+                    approved_rules=approved,
+                    large_legs=large_legs,
+                )
+                planned = compute_buy_amount_krw(
+                    cash, qty, price, 1.0, 1.0, asset_pct_scale=scale
+                )
+            else:
+                planned = float(LIVE_ORDER_KRW)
+            if side == "buy":
+                if planned <= 0:
+                    continue
+                if spent + planned > LIVE_DAILY_KRW_MAX:
+                    break
+                fee = planned * TRADING_FEE_RATE
+                cash -= planned + fee
+                qty += planned / price if price > 0 else 0.0
+                spent += planned
+            elif side == "sell" and qty > 0:
+                gross = qty * price
+                cash += gross * (1.0 - TRADING_FEE_RATE)
+                qty = 0.0
             n_trades += 1
             taken_idx.append(idx)
         if taken_idx:
@@ -164,6 +224,7 @@ def simulate_live_order_cap(outcomes: pd.DataFrame) -> dict[str, Any]:
             "order_krw": LIVE_ORDER_KRW,
             "daily_krw_max": LIVE_DAILY_KRW_MAX,
             "slippage_pct": slip,
+            "sizing": "total_asset_pct_ev_wf_large_leg",
         },
         "taken_count": int(len(taken)),
         "total_count": int(len(df)),
@@ -267,9 +328,35 @@ def build_simulation_report(
             sub.assign(forward_ret_pct=adj)
         )
 
-    live_cap = simulate_live_order_cap(outcomes)
+    monitor_ids = {r["rule_id"] for r in matched.get("monitor_rules", [])}
+    live_cap = simulate_live_order_cap(
+        outcomes, rule_ids=monitor_ids, holdout_only=True
+    )
     go = evaluate_go_no_go(matched, wf_sum, fee_stress, live_cap)
 
+    portfolio_compare: dict[str, Any] = {}
+    gt_data = load_ground_truth(resolve_ground_truth_file()) or {}
+    gt_trades = gt_data.get("trades") or []
+    mark = (gt_data.get("summary") or {}).get("mark_price")
+    if gt_trades:
+        portfolio_compare["ground_truth_chrono"] = simulate_truth_portfolio(
+            order_trades_chronological(gt_trades),
+            last_price=float(mark) if mark else None,
+        )
+    holdout = outcomes[
+        outcomes["rule_id"].isin(monitor_ids) & (outcomes["split"] == "holdout")
+    ]
+    capped = select_capped_fires(holdout)
+    if not capped.empty:
+        portfolio_compare["sim_sized"] = simulate_sized_portfolio(
+            fires_to_trade_list(capped, apply_dynamic_sizing=True),
+            last_price=float(mark) if mark else None,
+        )
+        portfolio_compare["sim_fixed_order"] = simulate_fixed_order_portfolio(
+            fires_to_trade_list(capped, apply_dynamic_sizing=False),
+            last_price=float(mark) if mark else None,
+        )
+
     gt_portfolio: dict[str, Any] = {}
     if ANALYSIS_GT_CALIBRATION_JSON.is_file():
         cal = json.loads(ANALYSIS_GT_CALIBRATION_JSON.read_text(encoding="utf-8"))
@@ -301,6 +388,8 @@ def build_simulation_report(
         "fee_stress_by_rule": fee_stress,
         "live_order_cap_sim": live_cap,
         "go_no_go": go,
+        "portfolio_compare": portfolio_compare,
+        "gt_model": gt_data.get("model"),
         "monitor_rules": matched.get("monitor_rules", []),
         "gt_portfolio_calibration": gt_portfolio,
         "criteria": {

deepcoin/matching/simulation_html.py

@@ -20,6 +20,7 @@ from config import (
 )
 from deepcoin.ground_truth.ground_truth import (
     load_ground_truth,
+    order_trades_chronological,
     simulate_truth_portfolio,
     simulate_truth_portfolio_steps,
 )
@@ -28,6 +29,7 @@ from deepcoin.matching.portfolio_sim import (
     select_capped_fires,
     simulate_fixed_order_portfolio,
     simulate_fixed_order_portfolio_steps,
+    simulate_sized_portfolio,
 )
 from deepcoin.matching.select_rules import _split_train_valid_holdout
 from deepcoin.ops.chart_report import (
@@ -36,6 +38,7 @@ from deepcoin.ops.chart_report import (
     market_cards_html,
     pnl_cards_html,
     rule_criteria_html,
+    stacked_summary_cards_html,
     wrap_chart_report_page,
 )
 from deepcoin.ops.simulation import build_chart_html, load_chart_frames, _frames_to_mtf
@@ -194,9 +197,11 @@ def _summary_cards_html(
     bb_txt: str,
     gt_trades: list[dict[str, Any]],
     gt_pnl: dict[str, Any],
-    sim_pnl: dict[str, Any],
+    sim_sized_pnl: dict[str, Any],
+    sim_fixed_pnl: dict[str, Any],
     sim_trade_count: int,
     go_flag: bool,
+    model_note: str = "",
 ) -> str:
     """
     ground_truth HTML과 동일 구성의 상단 카드 (GT + 시뮬 2줄).
@@ -206,26 +211,43 @@ def _summary_cards_html(
         bb_txt: BB %B.
         gt_trades: GT trades.
         gt_pnl: GT 포트폴리오 요약.
-        sim_pnl: 시뮬 포트폴리오 요약.
+        sim_sized_pnl: 총자산%·EV/WF·leg 시뮬 요약.
+        sim_fixed_pnl: 고정 ₩/회 baseline.
         sim_trade_count: 체결 가정 발화 수.
         go_flag: Go/No-Go.
+        model_note: GT 모델 한 줄 요약.
 
     Returns:
         cards HTML.
     """
     go_cls = "go-pass" if go_flag else "go-fail"
-    gt_row = (
-        '<p class="cards-group-title">정답 (ground_truth) — 분할 비중·leg 체결</p>'
-        + market_cards_html(close_last, bb_txt)
-        + pnl_cards_html(gt_pnl, "정답 타점", len(gt_trades))
+    gt_sub = (
+        "저점 분할매수(1/price 비중) · 고점 65/35% 매도 · "
+        "총자산×비중×leg티어 · 시각순 복리"
     )
-    sim_row = (
-        '<p class="cards-group-title">시뮬 (monitor_rules · holdout · '
-        f"1회 ₩{LIVE_ORDER_KRW:,.0f}·일한도) — "
-        f'<span class="{go_cls}">{"GO" if go_flag else "NO-GO"}</span></p>'
-        + pnl_cards_html(sim_pnl, "시뮬 체결", sim_trade_count)
+    if model_note:
+        gt_sub = model_note
+    gt_cards = market_cards_html(close_last, bb_txt) + pnl_cards_html(
+        gt_pnl, "정답 GT", len(gt_trades)
+    )
+    sim_sized_title = (
+        "시뮬·총자산% (EV/WF·leg상위) — "
+        f'<span class="{go_cls}">{"GO" if go_flag else "NO-GO"}</span>'
+    )
+    sim_fixed_title = f"시뮬·고정 ₩{LIVE_ORDER_KRW:,}/회 (비교)"
+    return (
+        '<div class="summary-cards">'
+        + stacked_summary_cards_html(gt_sub, gt_cards)
+        + stacked_summary_cards_html(
+            sim_sized_title,
+            pnl_cards_html(sim_sized_pnl, "시뮬(비율)", sim_trade_count),
+        )
+        + stacked_summary_cards_html(
+            sim_fixed_title,
+            pnl_cards_html(sim_fixed_pnl, "시뮬(고정)", sim_trade_count),
+        )
+        + "</div>"
     )
-    return gt_row + sim_row
 
 
 def build_simulation_page_html(
@@ -294,56 +316,57 @@ def build_simulation_page_html(
     elif gt_summary.get("mark_price"):
         close_val = float(gt_summary["mark_price"])
 
-    sim_trades = fires_to_trade_list(capped)
-    gt_pnl = {}
-    gt_summary_pnl = gt_data.get("summary") or {}
-    if gt_summary_pnl.get("pnl_krw") is not None and gt_summary_pnl.get(
-        "execution_order"
-    ) == "leg_block":
-        gt_pnl = {
-            k: gt_summary_pnl[k]
-            for k in (
-                "initial_cash_krw",
-                "final_asset_krw",
-                "pnl_pct",
-                "total_fees_krw",
-                "holding_qty",
-                "holding_value_krw",
-                "mark_price",
-                "cash_krw",
-            )
-            if k in gt_summary_pnl
-        }
-    elif gt_trades:
+    sim_trades_sized = fires_to_trade_list(capped, apply_dynamic_sizing=True)
+    sim_trades_fixed = fires_to_trade_list(capped, apply_dynamic_sizing=False)
+
+    gt_pnl: dict[str, Any] = {}
+    if gt_trades:
+        gt_chron = order_trades_chronological(gt_trades)
         gt_pnl = simulate_truth_portfolio(
-            gt_trades,
+            gt_chron,
             initial_cash=GT_INITIAL_CASH_KRW,
             fee_rate=TRADING_FEE_RATE,
             last_price=close_val if close_val else None,
         )
 
-    sim_pnl = simulate_fixed_order_portfolio(
-        sim_trades,
+    mark = close_val if close_val else None
+    sim_sized_pnl = simulate_sized_portfolio(
+        sim_trades_sized,
+        initial_cash=GT_INITIAL_CASH_KRW,
+        fee_rate=TRADING_FEE_RATE,
+        last_price=mark,
+    )
+    sim_fixed_pnl = simulate_fixed_order_portfolio(
+        sim_trades_fixed,
         order_krw=LIVE_ORDER_KRW,
         initial_cash=GT_INITIAL_CASH_KRW,
         fee_rate=TRADING_FEE_RATE,
-        last_price=close_val if close_val else None,
+        last_price=mark,
+        sizing_mode="fixed",
     )
     sim_steps = simulate_fixed_order_portfolio_steps(
-        sim_trades,
+        sim_trades_sized,
         order_krw=LIVE_ORDER_KRW,
         initial_cash=GT_INITIAL_CASH_KRW,
         fee_rate=TRADING_FEE_RATE,
+        sizing_mode="amount_krw",
     )
     gt_steps = (
         simulate_truth_portfolio_steps(
-            gt_trades,
+            order_trades_chronological(gt_trades),
             initial_cash=GT_INITIAL_CASH_KRW,
             fee_rate=TRADING_FEE_RATE,
         )
         if gt_trades
         else []
     )
+    model = gt_data.get("model") or {}
+    model_note = (
+        f"mode={model.get('selection_mode', 'split_buy_peak_sell')} · "
+        f"매수비중=1/price · 매도=65/35%"
+        if model
+        else ""
+    )
 
     criteria_blocks = "".join(rule_criteria_html(r) for r in monitor_rules)
     go_table = go_no_go_table_html(go.get("checks", []), go_flag)
@@ -355,7 +378,7 @@ def build_simulation_page_html(
 
     sim_table = f"""
   <h2>시뮬 타점 (holdout {len(holdout_fires)}건 → 체결 가정 {len(capped)}건)</h2>
-  <p class="meta">1회 ₩{LIVE_ORDER_KRW:,.0f}·일한도·최대 거래수 적용 후 체결 순 포트폴리오.
+  <p class="meta">총자산×최적비중·현금한도·EV/WF통과·leg상위 대형 매수. 일한도·최대 거래수 적용.
   가격 열 (+/-) = <b>{label_mode}</b> 구간 수익%.{_mark_note(close_val)}</p>
   <div class="table-scroll">
   <table>
@@ -367,7 +390,7 @@ def build_simulation_page_html(
 
     gt_table = f"""
   <h2>정답 타점 (ground_truth)</h2>
-  <p class="meta">삼각형 = GT. 매수 분할 비중·매도 leg 반영.{_mark_note(close_val)}</p>
+  <p class="meta">삼각형 크기 = GT 체결 금액. 매수 분할·매도 leg 반영.{_mark_note(close_val)}</p>
   <div class="table-scroll">
   <table>
     <thead><tr><th>시각</th><th>구분</th><th>비중</th><th>가격</th><th>총 평가금액</th><th>해석</th></tr></thead>
@@ -390,7 +413,7 @@ def build_simulation_page_html(
         "상단 카드: 초기 금액·총보유자산·초기 대비 증감율·수수료."
     )
     legend = (
-        "▲ <b>정답 매수</b> · ▼ <b>정답 매도</b> — 삼각형 = GT 비중.<br>"
+        "▲ <b>정답 매수</b> · ▼ <b>정답 매도</b> — 삼각형 = GT 체결 금액.<br>"
         "● <b>시뮬</b> — 원 = holdout 발화 (차트). 테이블 = 일한도 적용 체결 순서."
     )
     if frames is not None:
@@ -405,7 +428,15 @@ def build_simulation_page_html(
         )
 
     cards = _summary_cards_html(
-        close_val, bb_txt, gt_trades, gt_pnl, sim_pnl, len(capped), go_flag
+        close_val,
+        bb_txt,
+        gt_trades,
+        gt_pnl,
+        sim_sized_pnl,
+        sim_fixed_pnl,
+        len(capped),
+        go_flag,
+        model_note=model_note,
     )
 
     if frames is not None:
@@ -415,6 +446,7 @@ def build_simulation_page_html(
             note=note,
             truth_trades=gt_trades,
             sim_trades=_fires_to_chart_trades(holdout_fires),
+            # 차트 마커는 holdout 전체; 카드·테이블은 일한도 capped
             title_suffix="1단계 시뮬레이션 (monitor · holdout)",
             legend_html=legend,
             footer_sections=sections,