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>

.env.example

@@ -8,6 +8,13 @@ COIN_TELEGRAM_CHAT_ID=
 SYMBOL=WLD
 CHART_LOOKBACK_DAYS=365
 
+# 02 Ground Truth
+GT_MIN_ORDER_KRW=5000
+GT_MAX_BUY_ORDER_KRW=100000
+GT_BUY_PCT_LARGE_LEG=1.0
+GT_BUY_PCT_SMALL_LEG=0.05
+GT_LARGE_LEG_TOP_PCT=0.2
+
 # 04 매칭
 MATCH_LABEL_MODE=leg_gt
 MATCH_HOLDOUT_RATIO=0.15
@@ -24,6 +31,8 @@ MONITOR_ALERT_KRW_AMOUNT=100000
 # 3 실거래 (오픈 시에만 1)
 LIVE_TRADING_ENABLED=0
 LIVE_ORDER_KRW=100000
+LIVE_BUY_PCT_LARGE=1.0
+LIVE_BUY_PCT_SMALL=0.05
 LIVE_DAILY_KRW_MAX=300000
 LIVE_COOLDOWN_MIN=180
 LIVE_MAX_TRADES_PER_DAY=10

config.py

@@ -202,6 +202,11 @@ GT_SELL_SPLIT_GAP_PCT = _getenv_float("GT_SELL_SPLIT_GAP_PCT", "2.5")
 GT_MARKER_SIZE_MIN = _getenv_int("GT_MARKER_SIZE_MIN", "10")
 GT_MARKER_SIZE_MAX = _getenv_int("GT_MARKER_SIZE_MAX", "32")
 GT_INITIAL_CASH_KRW = _getenv_int("GT_INITIAL_CASH_KRW", "1000000")
+GT_MIN_ORDER_KRW = _getenv_int("GT_MIN_ORDER_KRW", "5000")
+GT_MAX_BUY_ORDER_KRW = _getenv_int("GT_MAX_BUY_ORDER_KRW", "100000")
+GT_BUY_PCT_LARGE_LEG = _getenv_float("GT_BUY_PCT_LARGE_LEG", "1.0")
+GT_BUY_PCT_SMALL_LEG = _getenv_float("GT_BUY_PCT_SMALL_LEG", "0.05")
+GT_LARGE_LEG_TOP_PCT = _getenv_float("GT_LARGE_LEG_TOP_PCT", "0.2")
 TRADING_FEE_RATE = _getenv_float("TRADING_FEE_RATE", "0.0005")
 
 # --- 모니터 / API 수집 ---
@@ -335,6 +340,8 @@ LIVE_TRADING_ENABLED = _getenv("LIVE_TRADING_ENABLED", "0").strip() in (
     "yes",
 )
 LIVE_ORDER_KRW = _getenv_int("LIVE_ORDER_KRW", "100000")
+LIVE_BUY_PCT_LARGE = _getenv_float("LIVE_BUY_PCT_LARGE", "1.0")
+LIVE_BUY_PCT_SMALL = _getenv_float("LIVE_BUY_PCT_SMALL", "0.05")
 LIVE_DAILY_KRW_MAX = _getenv_int("LIVE_DAILY_KRW_MAX", "300000")
 LIVE_COOLDOWN_MIN = _getenv_int("LIVE_COOLDOWN_MIN", "180")
 LIVE_MAX_TRADES_PER_DAY = _getenv_int("LIVE_MAX_TRADES_PER_DAY", "10")

deepcoin/ground_truth/ground_truth.py

@@ -28,7 +28,12 @@ from config import (
     GT_BUY_BB_MAX,
     GT_BUY_MIN_BARS,
     GT_BUY_MIN_SWING_PCT,
+    GT_BUY_PCT_LARGE_LEG,
+    GT_BUY_PCT_SMALL_LEG,
     GT_INITIAL_CASH_KRW,
+    GT_LARGE_LEG_TOP_PCT,
+    GT_MIN_ORDER_KRW,
+    GT_MAX_BUY_ORDER_KRW,
     GT_MAX_BUYS_PER_LEG,
     GT_MAX_ROUND_TRIPS,
     TRADING_FEE_RATE,
@@ -68,6 +73,7 @@ class TradePoint:
     price: float
     memo: str
     weight: float = 1.0
+    amount_krw: float | None = None
     leg_id: int = 0
     bb_pos: float | None = None
     rsi: float | None = None
@@ -844,6 +850,12 @@ def generate_ground_truth(
     )
 
     trade_dicts = order_trades_leg_block(trades)
+    trade_dicts, alloc_stats = allocate_gt_order_amounts(
+        trade_dicts,
+        initial_cash=GT_INITIAL_CASH_KRW,
+        min_order_krw=GT_MIN_ORDER_KRW,
+        fee_rate=TRADING_FEE_RATE,
+    )
     last_close = float(df["Close"].iloc[-1])
     pnl = simulate_truth_portfolio(
         trade_dicts,
@@ -859,8 +871,13 @@ def generate_ground_truth(
     )
     _validate_leg_portfolio(trade_dicts, last_close)
 
+    from deepcoin.ground_truth.gt_model import default_model, model_to_dict
+
+    gt_model = model_to_dict(default_model())
+
     return {
         "name": "ground_truth_split_buy_peak_sell",
+        "model": gt_model,
         "method": method,
         "symbol": SYMBOL,
         "interval_min": ENTRY_INTERVAL,
@@ -893,12 +910,23 @@ def generate_ground_truth(
             "unrealized_pnl_krw": round(
                 float(pnl.get("pnl_krw", 0)) - float(pnl_realized.get("pnl_krw", 0)), 0
             ),
-            "execution_order": "leg_block",
+            "execution_order": (
+                "chronological"
+                if any(float(t.get("amount_krw") or 0) > 0 for t in trade_dicts)
+                else "leg_block"
+            ),
+            "order_amount_min_krw": GT_MIN_ORDER_KRW,
+            "order_amount_max_buy_krw": GT_MAX_BUY_ORDER_KRW,
+            "buy_pct_large_leg": GT_BUY_PCT_LARGE_LEG,
+            "buy_pct_small_leg": GT_BUY_PCT_SMALL_LEG,
+            "large_leg_top_pct": GT_LARGE_LEG_TOP_PCT,
+            **alloc_stats,
         },
         "note": (
             "저점 분할 매수(비중=삼각형), 고점 1~2회 매도. "
-            "체결 순서=leg별 매수→매도(시각순 아님). 기간말 leg는 종가 청산. "
-            "summary.pnl_pct는 미청산 포함 종가 평가, realized_pnl_pct는 체결만 반영."
+            "매수=총자산×최적비중×티어(상위 leg 대형·그 외 소형), "
+            f"현금 한도·최소 ₩{GT_MIN_ORDER_KRW:,}. "
+            "체결 순서=chronological. summary.pnl_pct는 미청산 포함 종가 평가."
         ),
         "trades": trade_dicts,
     }
@@ -921,18 +949,6 @@ def _validate_leg_portfolio(
     steps = simulate_truth_portfolio_steps(trade_dicts)
     if not steps:
         return
-    leg_ids = sorted({int(s["leg_id"]) for s in steps})
-    for lid in leg_ids:
-        leg_steps = [s for s in steps if int(s["leg_id"]) == lid]
-        sells = [s for s in leg_steps if s["action"] == "sell"]
-        if not sells:
-            continue
-        last_sell = sells[-1]
-        if float(last_sell["holding_qty"]) > 1e-4:
-            raise ValueError(
-                f"leg#{lid} 마지막 매도 후 보유 잔존 qty={last_sell['holding_qty']} "
-                "(leg 블록 체결·매도 비중 합 검토 필요)"
-            )
     final = steps[-1]
     if float(final["holding_qty"]) > 1e-2:
         raise ValueError(
@@ -943,6 +959,203 @@ def _validate_leg_portfolio(
         raise ValueError("종가 평가 후에도 미청산 보유가 남음")
 
 
+def allocate_gt_order_amounts(
+    trades: list[dict[str, Any]],
+    initial_cash: float = GT_INITIAL_CASH_KRW,
+    min_order_krw: float = GT_MIN_ORDER_KRW,
+    max_buy_krw: float = GT_MAX_BUY_ORDER_KRW,
+    fee_rate: float = TRADING_FEE_RATE,
+) -> tuple[list[dict[str, Any]], dict[str, Any]]:
+    """
+    GT 각 타점에 amount_krw를 시각순·총자산·비중(최적 매수율)으로 배분합니다.
+
+    매수: 총보유자산 × (leg 비중 share × 티어 스케일), 상한=가용 현금.
+    leg 상위 GT_LARGE_LEG_TOP_PCT는 GT_BUY_PCT_LARGE_LEG, 그 외는 GT_BUY_PCT_SMALL_LEG.
+    매도 후 현금 증가분은 다음 매수부터 자동 반영(시각순 복리).
+
+    Args:
+        trades: trade dict 리스트(시각순 정렬 전).
+        initial_cash: 초기 현금.
+        min_order_krw: 매수·매도 최소 원화 금액.
+        max_buy_krw: 매수 1회 상한(가용 현금·비중 배분 후 캡).
+        fee_rate: 수수료율.
+
+    Returns:
+        (동일 dict 참조, amount_krw 채움), alloc_stats 요약.
+    """
+    from deepcoin.matching.position_sizing import (
+        compute_buy_amount_krw,
+        leg_asset_pct_scale,
+        top_leg_ids_by_forward_return,
+    )
+
+    chron = sorted(trades, key=lambda x: x["dt"])
+    large_legs = top_leg_ids_by_forward_return(chron)
+    leg_buy_idxs: dict[int, list[int]] = {}
+    leg_sell_idxs: dict[int, list[int]] = {}
+    for i, t in enumerate(chron):
+        lid = int(t.get("leg_id", 0))
+        if t["action"] == "buy":
+            leg_buy_idxs.setdefault(lid, []).append(i)
+        elif t["action"] == "sell":
+            leg_sell_idxs.setdefault(lid, []).append(i)
+
+    cash = float(initial_cash)
+    qty = 0.0
+    qty_by_leg: dict[int, float] = {}
+    sell_leg: int | None = None
+    sell_base_qty = 0.0
+    buy_executed = 0
+    buy_skipped = 0
+    sell_executed = 0
+    sell_skipped = 0
+    buy_amounts: list[float] = []
+
+    for i, t in enumerate(chron):
+        price = float(t["price"])
+        if price <= 0:
+            continue
+        leg_id = int(t.get("leg_id", 0))
+        weight = float(t.get("weight", 1.0))
+
+        if t["action"] == "buy":
+            rem = [j for j in leg_buy_idxs.get(leg_id, []) if j >= i]
+            w_sum = sum(float(chron[j].get("weight", 1.0)) for j in rem)
+            w_share = (
+                weight / w_sum if w_sum > 0 else 1.0 / max(len(rem), 1)
+            )
+            scale = leg_asset_pct_scale(leg_id, large_legs)
+            amount = compute_buy_amount_krw(
+                cash,
+                qty,
+                price,
+                weight,
+                w_sum,
+                asset_pct_scale=scale,
+                min_order_krw=min_order_krw,
+                fee_rate=fee_rate,
+            )
+            if amount <= 0:
+                t["amount_krw"] = 0
+                buy_skipped += 1
+                continue
+            t["amount_krw"] = amount
+            fee = amount * fee_rate
+            cash -= amount + fee
+            bought_qty = amount / price
+            qty += bought_qty
+            qty_by_leg[leg_id] = qty_by_leg.get(leg_id, 0.0) + bought_qty
+            buy_executed += 1
+            buy_amounts.append(amount)
+            sell_leg = None
+
+        elif t["action"] == "sell":
+            leg_qty = qty_by_leg.get(leg_id, 0.0)
+            if leg_qty <= 1e-12:
+                sell_skipped += 1
+                continue
+            if sell_leg != leg_id:
+                sell_leg = leg_id
+                sell_base_qty = leg_qty
+            rem_sells = [j for j in leg_sell_idxs.get(leg_id, []) if j >= i]
+            is_last_leg_sell = bool(rem_sells) and i == rem_sells[-1]
+            if is_last_leg_sell:
+                sell_qty = leg_qty
+                gross = sell_qty * price
+            else:
+                gross = sell_base_qty * weight * price
+                if gross >= min_order_krw:
+                    gross = max(min_order_krw, gross)
+                gross = min(gross, leg_qty * price)
+            if gross <= 0:
+                sell_skipped += 1
+                continue
+            if not is_last_leg_sell:
+                sell_qty = gross / price
+            else:
+                sell_qty = leg_qty
+            t["amount_krw"] = round(gross, 0)
+            fee = gross * fee_rate
+            cash += gross - fee
+            leg_qty -= sell_qty
+            qty_by_leg[leg_id] = max(leg_qty, 0.0)
+            qty = max(qty - sell_qty, 0.0)
+            if qty < 1e-12:
+                qty = 0.0
+            sell_executed += 1
+
+    stats: dict[str, Any] = {
+        "buy_executed": buy_executed,
+        "buy_skipped": buy_skipped,
+        "sell_executed": sell_executed,
+        "sell_skipped": sell_skipped,
+        "buy_total_krw": round(sum(buy_amounts), 0),
+        "large_leg_count": len(large_legs),
+        "large_leg_top_pct": GT_LARGE_LEG_TOP_PCT,
+    }
+    if buy_amounts:
+        stats["buy_amount_avg_krw"] = round(sum(buy_amounts) / len(buy_amounts), 0)
+        stats["buy_amount_min_krw"] = round(min(buy_amounts), 0)
+        stats["buy_amount_max_krw"] = round(max(buy_amounts), 0)
+    return trades, stats
+
+
+def _resolve_sell_qty(
+    t: dict[str, Any],
+    qty: float,
+    price: float,
+    sell_base_qty: float,
+    weight: float,
+) -> float:
+    """
+    매도 수량: amount_krw가 보유 전량에 가깝으면 전량, 아니면 weight 비중.
+
+    Args:
+        t: trade dict.
+        qty: 현재 보유 수량.
+        price: 체결가.
+        sell_base_qty: leg 첫 매도 시점 보유량.
+        weight: 매도 비중.
+
+    Returns:
+        매도 수량.
+    """
+    if qty <= 0 or price <= 0:
+        return 0.0
+    ak = t.get("amount_krw")
+    if ak is not None and float(ak) > 0:
+        gross_cap = float(ak)
+        if gross_cap >= qty * price * 0.999:
+            return qty
+        return min(qty, gross_cap / price)
+    return min(sell_base_qty * weight, qty)
+
+
+def _trade_buy_amount(
+    t: dict[str, Any],
+    cash: float,
+    leg_budget: float,
+    current_leg: int | None,
+    leg_id: int,
+    fee_rate: float,
+) -> tuple[float, float, int | None]:
+    """
+    매수 체결 원화: amount_krw 우선, 없으면 leg_budget*weight.
+
+    Returns:
+        (amount, new_leg_budget, new_current_leg).
+    """
+    weight = float(t.get("weight", 1.0))
+    if t.get("amount_krw") is not None and float(t["amount_krw"]) > 0:
+        amount = min(float(t["amount_krw"]), max(cash / (1.0 + fee_rate), 0.0))
+        return amount, leg_budget, current_leg
+    if leg_id != current_leg:
+        current_leg = leg_id
+        leg_budget = cash
+    amount = leg_budget * weight
+    return amount, leg_budget, current_leg
+
+
 def order_trades_leg_block(
     trades: list[TradePoint] | list[dict[str, Any]],
 ) -> list[dict[str, Any]]:
@@ -996,8 +1209,12 @@ def _truth_simulation_rows(
     Returns:
         dict 행 리스트.
     """
-    if chronological:
-        return order_trades_chronological(trades)
+    rows = [t if isinstance(t, dict) else asdict(t) for t in trades]
+    use_chrono = chronological or any(
+        float(r.get("amount_krw") or 0) > 0 for r in rows
+    )
+    if use_chrono:
+        return sorted(rows, key=lambda x: x["dt"])
     return order_trades_leg_block(trades)
 
 
@@ -1037,7 +1254,9 @@ def simulate_truth_portfolio_steps(
                 current_leg = leg_id
                 leg_budget = cash
                 sell_leg = None
-            amount = leg_budget * weight
+            amount, leg_budget, current_leg = _trade_buy_amount(
+                t, cash, leg_budget, current_leg, leg_id, fee_rate
+            )
             if amount <= 0:
                 continue
             fee = amount * fee_rate
@@ -1054,7 +1273,7 @@ def simulate_truth_portfolio_steps(
             if leg_id != sell_leg:
                 sell_leg = leg_id
                 sell_base_qty = qty
-            sell_qty = min(sell_base_qty * weight, qty)
+            sell_qty = _resolve_sell_qty(t, qty, price, sell_base_qty, weight)
             if sell_qty <= 0:
                 continue
             gross = sell_qty * price
@@ -1071,6 +1290,7 @@ def simulate_truth_portfolio_steps(
                 "action": action,
                 "price": price,
                 "weight": weight,
+                "amount_krw": t.get("amount_krw"),
                 "leg_id": leg_id,
                 "cash_krw": round(cash, 0),
                 "holding_qty": round(qty, 4),
@@ -1130,7 +1350,9 @@ def simulate_truth_portfolio(
                 current_leg = leg_id
                 leg_budget = cash
                 sell_leg = None
-            amount = leg_budget * weight
+            amount, leg_budget, current_leg = _trade_buy_amount(
+                t, cash, leg_budget, current_leg, leg_id, fee_rate
+            )
             if amount <= 0:
                 continue
             fee = amount * fee_rate
@@ -1148,7 +1370,7 @@ def simulate_truth_portfolio(
             if leg_id != sell_leg:
                 sell_leg = leg_id
                 sell_base_qty = qty
-            sell_qty = min(sell_base_qty * weight, qty)
+            sell_qty = _resolve_sell_qty(t, qty, price, sell_base_qty, weight)
             if sell_qty <= 0:
                 continue
             gross = sell_qty * price

deepcoin/ground_truth/gt_model.py

@@ -0,0 +1,228 @@
+"""
+Ground Truth 타점·비중·자본 배분 모델 (일반화 명세).
+
+타점 생성(ground_truth.py)과 자본 배분(position_sizing.py)의 공통 언어.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+from config import (
+    GT_BUY_BB_MAX,
+    GT_BUY_MIN_BARS,
+    GT_BUY_MIN_SWING_PCT,
+    GT_BUY_PCT_LARGE_LEG,
+    GT_BUY_PCT_SMALL_LEG,
+    GT_LARGE_LEG_TOP_PCT,
+    GT_MAX_BUYS_PER_LEG,
+    GT_MAX_SELLS_PER_LEG,
+    GT_MIN_ORDER_KRW,
+    GT_SELL_SPLIT_GAP_PCT,
+    GT_SELECTION_MODE,
+)
+
+
+@dataclass(frozen=True)
+class GtEntrySpec:
+    """
+    매수 타점(leg 내 분할) 규칙.
+
+    Attributes:
+        pivot_kind: ZigZag 저점(trough).
+        price_field: 체결가 = 봉 Low.
+        weight_rule: 저가일수록 큰 비중 (1/price 정규화).
+        max_per_leg: leg당 최대 매수 횟수.
+        min_bars_gap: 분할 매수 최소 봉 간격.
+    """
+
+    pivot_kind: str = "trough"
+    price_field: str = "Low"
+    weight_rule: str = "inverse_price_normalized"
+    max_per_leg: int = GT_MAX_BUYS_PER_LEG
+    min_bars_gap: int = GT_BUY_MIN_BARS
+    bb_filter: str = f"bb_pos <= {GT_BUY_BB_MAX}"
+
+
+@dataclass(frozen=True)
+class GtExitSpec:
+    """
+    매도 타점(leg 내 분할) 규칙.
+
+    Attributes:
+        pivot_kind: major swing 고점(peak).
+        price_field: 체결가 = 봉 High.
+        split_weights: 2회 분할 시 (65%, 35%).
+        split_gap_pct: 2차 고점 인정 최소 괴리(%).
+    """
+
+    pivot_kind: str = "peak"
+    price_field: str = "High"
+    split_weights: tuple[float, float] = (0.65, 0.35)
+    split_gap_pct: float = GT_SELL_SPLIT_GAP_PCT
+    max_per_leg: int = GT_MAX_SELLS_PER_LEG
+
+
+@dataclass(frozen=True)
+class GtCapitalSpec:
+    """
+    체결 원화(amount_krw) 배분 규칙.
+
+    Attributes:
+        buy_formula: 총자산 × 최적매수율, 상한=가용현금.
+        optimal_buy_rate: leg 내 남은 weight 비중.
+        large_leg_top_pct: 수익률 상위 leg 비율.
+        pct_large: 상위 leg 총자산 배분 스케일.
+        pct_small: 그 외 leg 스케일.
+        min_order_krw: 최소 체결 원화.
+    """
+
+    buy_formula: str = "min(total_asset * w_share * tier_scale, cash/(1+fee))"
+    optimal_buy_rate: str = "weight / sum(remaining_buy_weights_in_leg)"
+    large_leg_top_pct: float = GT_LARGE_LEG_TOP_PCT
+    pct_large: float = GT_BUY_PCT_LARGE_LEG
+    pct_small: float = GT_BUY_PCT_SMALL_LEG
+    min_order_krw: float = float(GT_MIN_ORDER_KRW)
+    sell_formula: str = "leg_qty * sell_weight * price (last sell = full leg_qty)"
+
+
+@dataclass
+class GroundTruthModel:
+    """
+    GT 전체 모델 (타점 + 비중 + 자본).
+
+    Attributes:
+        selection_mode: 타점 생성 모드.
+        entry: 매수 명세.
+        exit: 매도 명세.
+        capital: 자본 배분 명세.
+        leg: leg 정의.
+    """
+
+    selection_mode: str = GT_SELECTION_MODE
+    entry: GtEntrySpec = field(default_factory=GtEntrySpec)
+    exit: GtExitSpec = field(default_factory=GtExitSpec)
+    capital: GtCapitalSpec = field(default_factory=GtCapitalSpec)
+    leg_definition: str = (
+        "이전 고점 매도 ~ 다음 고점 매도 구간 = leg_id; "
+        "기간말 잔여 구간은 마지막 leg"
+    )
+    execution_order_chrono: str = (
+        "amount_krw 배분·summary.pnl_pct = 시각순 체결(매도 후 현금 → 다음 매수 반영)"
+    )
+    execution_order_leg_block: str = (
+        "JSON 저장 순서 = leg별 매수 전량 → 매도 전량 (차트·테이블 leg 정합)"
+    )
+
+
+def default_model() -> GroundTruthModel:
+    """현재 config 기준 GT 모델."""
+    return GroundTruthModel()
+
+
+def compute_buy_weights_inverse_price(prices: list[float]) -> list[float]:
+    """
+    저점 매수 비중: score_i = 1/price_i → 합=1 정규화.
+
+    Args:
+        prices: leg 내 매수 후보 가격.
+
+    Returns:
+        weight 리스트 (합 ≈ 1).
+    """
+    if not prices:
+        return []
+    scores = [1.0 / max(p, 1e-9) for p in prices]
+    s = sum(scores)
+    return [x / s for x in scores] if s > 0 else [1.0 / len(prices)] * len(prices)
+
+
+def sell_split_weights(n_sells: int) -> list[float]:
+    """
+    leg 매도 비중.
+
+    Args:
+        n_sells: 매도 횟수(1 또는 2).
+
+    Returns:
+        weight 리스트.
+    """
+    spec = GtExitSpec()
+    if n_sells >= 2:
+        return list(spec.split_weights)
+    return [1.0]
+
+
+def model_to_dict(model: GroundTruthModel | None = None) -> dict[str, Any]:
+    """
+    JSON·리포트용 모델 dict.
+
+    Args:
+        model: None이면 default.
+
+    Returns:
+        직렬화 dict.
+    """
+    m = model or default_model()
+    return {
+        "selection_mode": m.selection_mode,
+        "leg_definition": m.leg_definition,
+        "entry": {
+            "pivot": m.entry.pivot_kind,
+            "price": m.entry.price_field,
+            "weight_rule": m.entry.weight_rule,
+            "weight_formula": "w_i = (1/price_i) / sum(1/price_j)",
+            "max_buys_per_leg": m.entry.max_per_leg,
+            "min_bars_between_buys": m.entry.min_bars_gap,
+            "bb_filter": m.entry.bb_filter,
+        },
+        "exit": {
+            "pivot": m.exit.pivot_kind,
+            "price": m.exit.price_field,
+            "weight_rule": "fixed_split_or_full",
+            "weights_two_sell": list(m.exit.split_weights),
+            "split_gap_pct": m.exit.split_gap_pct,
+            "max_sells_per_leg": m.exit.max_per_leg,
+        },
+        "capital": {
+            "buy": m.capital.buy_formula,
+            "optimal_buy_rate": m.capital.optimal_buy_rate,
+            "large_leg_top_pct": m.capital.large_leg_top_pct,
+            "pct_large_leg": m.capital.pct_large,
+            "pct_small_leg": m.capital.pct_small,
+            "min_order_krw": m.capital.min_order_krw,
+            "sell": m.capital.sell_formula,
+        },
+        "execution": {
+            "chrono": m.execution_order_chrono,
+            "leg_block_json": m.execution_order_leg_block,
+        },
+    }
+
+
+def summarize_leg_weights(trades: list[dict[str, Any]]) -> dict[str, Any]:
+    """
+    leg별 매수·매도 비중 합 검증용 요약.
+
+    Args:
+        trades: GT trade dict.
+
+    Returns:
+        leg_id → {buy_sum, sell_sum, n_buy, n_sell}.
+    """
+    legs: dict[int, dict[str, Any]] = {}
+    for t in trades:
+        lid = int(t.get("leg_id", 0))
+        legs.setdefault(
+            lid,
+            {"buy_sum": 0.0, "sell_sum": 0.0, "n_buy": 0, "n_sell": 0},
+        )
+        w = float(t.get("weight", 0))
+        if t.get("action") == "buy":
+            legs[lid]["buy_sum"] += w
+            legs[lid]["n_buy"] += 1
+        elif t.get("action") == "sell":
+            legs[lid]["sell_sum"] += w
+            legs[lid]["n_sell"] += 1
+    return legs

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,

deepcoin/ops/chart_report.py

@@ -37,7 +37,13 @@ CHART_REPORT_CSS = """
     .table-scroll { max-height: 480px; overflow-y: auto; border: 1px solid #e2e8f0; border-radius: 8px; }
     .pass { color: #16a34a; font-weight: 600; }
     .fail { color: #dc2626; font-weight: 600; }
-    .cards-group-title { font-size: 0.82rem; color: #475569; margin: 14px 0 6px; font-weight: 600; }
+    .summary-cards { margin: 16px 0; }
+    .summary-cards .cards-row-block { display: block; width: 100%; margin-bottom: 14px; }
+    .summary-cards .cards-row-block:last-child { margin-bottom: 0; }
+    .cards-group-title {
+      font-size: 0.82rem; color: #475569; margin: 0 0 8px; font-weight: 600;
+      display: block;
+    }
 """
 
 
@@ -116,6 +122,28 @@ def card_html(label: str, value: str) -> str:
     return f'<div class="card"><span>{label}</span><b>{value}</b></div>'
 
 
+def stacked_summary_cards_html(
+    title: str,
+    cards_inner: str,
+) -> str:
+    """
+    제목 한 줄 + 카드 flex 한 줄을 세로 블록으로 묶습니다.
+
+    Args:
+        title: cards-group-title 텍스트(HTML 허용).
+        cards_inner: .cards 안에 넣을 card div 문자열.
+
+    Returns:
+        cards-row-block HTML.
+    """
+    return (
+        '<div class="cards-row-block">'
+        f'<p class="cards-group-title">{title}</p>'
+        f'<div class="cards">{cards_inner}</div>'
+        "</div>"
+    )
+
+
 def wrap_chart_report_page(
     page_title: str,
     heading: str,
@@ -135,7 +163,7 @@ def wrap_chart_report_page(
         meta_line: 기간·추세 등.
         note_html: 안내 박스.
         legend_html: 차트 범례 설명.
-        cards_html: .cards 내부 HTML.
+        cards_html: .cards 내부 HTML 또는 .summary-cards 블록 전체.
         chart_html: plotly embed.
         sections_html: h2·테이블·criteria 등 본문 하단.
 
@@ -154,7 +182,7 @@ def wrap_chart_report_page(
   <p class="meta">{meta_line}</p>
   {note_html}
   <div class="legend-box">{legend_html}</div>
-  <div class="cards">{cards_html}</div>
+  {cards_html if "summary-cards" in cards_html else f'<div class="cards">{cards_html}</div>'}
   <div class="chart-wrap">{chart_html}</div>
   {sections_html}
 </body>

deepcoin/ops/live_trader.py

@@ -19,9 +19,18 @@ from config import (
     LIVE_ORDER_KRW,
     LIVE_TRADING_ENABLED,
     SYMBOL,
+    TRADING_FEE_RATE,
 )
+from deepcoin.ground_truth.ground_truth import load_ground_truth
 from deepcoin.matching.live_eval import evaluate_live_rules
 from deepcoin.matching.load_rules import load_monitor_rules
+from deepcoin.matching.position_sizing import (
+    compute_buy_amount_krw,
+    live_buy_asset_pct_scale,
+    load_ev_wf_approved_rule_ids,
+    top_leg_ids_by_forward_return,
+)
+from deepcoin.paths import resolve_ground_truth_file
 from deepcoin.ops.alert_message import build_rule_alert_message
 from deepcoin.ops.monitor import Monitor
 from deepcoin.paths import LIVE_TRADES_LOG
@@ -40,6 +49,17 @@ class LiveTrader(Monitor):
         self._day_spent_krw: float = 0.0
         self._day_trades: int = 0
         self._day_pnl_krw: float = 0.0
+        self._gt_trades: list[dict] = []
+        self._large_legs: set[int] = set()
+        self._approved_rules: set[str] = set()
+        self._load_sizing_context()
+
+    def _load_sizing_context(self) -> None:
+        """GT leg·EV/WF 통과 규칙 캐시."""
+        gt = load_ground_truth(resolve_ground_truth_file()) or {}
+        self._gt_trades = gt.get("trades") or []
+        self._large_legs = top_leg_ids_by_forward_return(self._gt_trades)
+        self._approved_rules = load_ev_wf_approved_rule_ids()
 
     def _reset_day_if_needed(self) -> None:
         """날짜 변경 시 일별 한도 카운터 초기화."""
@@ -61,7 +81,7 @@ class LiveTrader(Monitor):
         with LIVE_TRADES_LOG.open("a", encoding="utf-8") as f:
             f.write(json.dumps(record, ensure_ascii=False) + "\n")
 
-    def _can_trade(self, rule_id: str) -> tuple[bool, str]:
+    def _can_trade(self, rule_id: str, planned_krw: float | None = None) -> tuple[bool, str]:
         """
         일·쿨다운·손실 한도 검사.
 
@@ -74,7 +94,8 @@ class LiveTrader(Monitor):
         self._reset_day_if_needed()
         if self._day_trades >= LIVE_MAX_TRADES_PER_DAY:
             return False, "일 최대 거래 수 초과"
-        if self._day_spent_krw + LIVE_ORDER_KRW > LIVE_DAILY_KRW_MAX:
+        need = float(planned_krw if planned_krw is not None else LIVE_ORDER_KRW)
+        if self._day_spent_krw + need > LIVE_DAILY_KRW_MAX:
             return False, "일 주문 한도 초과"
         if self._day_pnl_krw <= -abs(LIVE_DAILY_LOSS_LIMIT_KRW):
             return False, "일 손실 한도 초과"
@@ -83,6 +104,46 @@ class LiveTrader(Monitor):
             return False, f"규칙 쿨다운({LIVE_COOLDOWN_MIN}분)"
         return True, ""
 
+    def _resolve_buy_amount_krw(self, hit: dict[str, Any]) -> float:
+        """
+        총자산·현금·EV/WF·leg 티어로 매수 원화 산출.
+
+        Args:
+            hit: evaluate_live_rules 항목.
+
+        Returns:
+            매수 원화.
+        """
+        rid = hit["rule_id"]
+        if rid not in self._approved_rules:
+            return 0.0
+        price = float(hit["close"])
+        cash = 0.0
+        qty = 0.0
+        try:
+            bal = self.load_balances_dict()
+            sym = bal.get(SYMBOL, {})
+            cash = float(sym.get("available_krw") or sym.get("krw") or 0)
+            qty = float(sym.get("balance") or 0)
+        except Exception:
+            return 0.0
+        scale = live_buy_asset_pct_scale(
+            rid,
+            hit["dt"],
+            self._gt_trades,
+            approved_rules=self._approved_rules,
+            large_legs=self._large_legs,
+        )
+        return compute_buy_amount_krw(
+            cash,
+            qty,
+            price,
+            1.0,
+            1.0,
+            asset_pct_scale=scale,
+            fee_rate=TRADING_FEE_RATE,
+        )
+
     def _execute_order(self, hit: dict[str, Any]) -> dict[str, Any]:
         """
         매수·매도 주문 실행 또는 드라이런.
@@ -95,7 +156,22 @@ class LiveTrader(Monitor):
         """
         side = hit["side"]
         price = float(hit["close"])
-        amount_krw = float(LIVE_ORDER_KRW)
+        if side == "buy":
+            amount_krw = self._resolve_buy_amount_krw(hit)
+            if amount_krw <= 0:
+                return {
+                    "ts": datetime.now().isoformat(timespec="seconds"),
+                    "rule_id": hit["rule_id"],
+                    "side": side,
+                    "signal_dt": hit["dt"],
+                    "price": price,
+                    "amount_krw": 0,
+                    "live_enabled": LIVE_TRADING_ENABLED,
+                    "ok": False,
+                    "message": "매수 스킵(EV/WF·leg·현금)",
+                }
+        else:
+            amount_krw = float(LIVE_ORDER_KRW)
         record: dict[str, Any] = {
             "ts": datetime.now().isoformat(timespec="seconds"),
             "rule_id": hit["rule_id"],
@@ -163,11 +239,23 @@ class LiveTrader(Monitor):
 
         for hit in fired:
             rid = hit["rule_id"]
-            ok, reason = self._can_trade(rid)
+            if hit["side"] == "buy" and hit["rule_id"] not in self._approved_rules:
+                print(f"  [{hit['side']}] {rid} @ {hit['dt']}")
+                print("    skip: EV/WF 미통과 규칙")
+                continue
+            planned = (
+                self._resolve_buy_amount_krw(hit)
+                if hit["side"] == "buy"
+                else float(LIVE_ORDER_KRW)
+            )
+            ok, reason = self._can_trade(rid, planned)
             print(f"  [{hit['side']}] {rid} @ {hit['dt']}")
             if not ok:
                 print(f"    skip: {reason}")
                 continue
+            if hit["side"] == "buy" and planned <= 0:
+                print("    skip: 매수금액 0")
+                continue
             log = self._execute_order(hit)
             self._append_log(log)
             print(f"    order: {log['message']} ok={log['ok']}")

deepcoin/ops/simulation.py

@@ -28,6 +28,8 @@ from config import (
     GT_INITIAL_CASH_KRW,
     GT_MARKER_SIZE_MAX,
     GT_MARKER_SIZE_MIN,
+    GT_MAX_BUY_ORDER_KRW,
+    LIVE_ORDER_KRW,
     MACD_FAST,
     MACD_SIGNAL,
     MACD_SLOW,
@@ -58,16 +60,94 @@ def interval_chart_label(interval_min: int) -> str:
     return f"{interval_min}분봉"
 
 
-def _marker_sizes(trades: list[dict], action: str) -> list[float]:
-    """비중(weight, 0~1)에 비례한 삼각형 크기."""
-    pts = [t for t in trades if t.get("action") == action]
+def _marker_hover_text(
+    label: str,
+    t: dict,
+    *,
+    default_order_krw: float | None = None,
+    extra_lines: list[str] | None = None,
+) -> str:
+    """
+    차트 마커 툴팁: 체결가(price)와 체결 원화(amount_krw)를 함께 표시.
+
+    Args:
+        label: 정답/시뮬 매수·매도 라벨.
+        t: trade dict (price, amount_krw, weight, memo …).
+        default_order_krw: amount_krw 없을 때 표시할 기본 원화(시뮬 고정 주문).
+        extra_lines: 툴팁 하단 추가 줄.
+
+    Returns:
+        hovertext HTML 줄바꿈 문자열.
+    """
+    action = t.get("action", t.get("side", ""))
+    amt_label = "매수금액" if action == "buy" else "매도금액"
+    lines = [
+        label,
+        str(t.get("dt", ""))[:16],
+        f"체결가 ₩{float(t['price']):,.0f}",
+    ]
+    ak = t.get("amount_krw")
+    if ak is not None and float(ak) > 0:
+        lines.append(f"{amt_label} ₩{float(ak):,.0f}")
+    elif default_order_krw is not None and action == "buy":
+        lines.append(f"{amt_label} ₩{float(default_order_krw):,.0f}")
+    else:
+        lines.append(f"{amt_label} (미배분)")
+    if t.get("weight") is not None:
+        lines.append(f"비중 {float(t.get('weight', 1)) * 100:.0f}%")
+    if extra_lines:
+        lines.extend(extra_lines)
+    memo = t.get("memo", "")
+    if memo:
+        lines.append(str(memo))
+    rule_id = t.get("rule_id", "")
+    if rule_id:
+        lines.append(str(rule_id))
+    return "<br>".join(lines)
+
+
+def _trade_amount_krw(t: dict) -> float:
+    """
+    마커 크기·툴팁용 체결 원화. amount_krw 없으면 비중×상한으로 추정.
+
+    Args:
+        t: trade dict.
+
+    Returns:
+        원화 금액(0 이상).
+    """
+    ak = t.get("amount_krw")
+    if ak is not None and float(ak) > 0:
+        return float(ak)
+    return max(float(t.get("weight", 1.0)), 0.05) * float(GT_MAX_BUY_ORDER_KRW)
+
+
+def _marker_sizes(pts: list[dict]) -> list[float]:
+    """
+    체결 원화(amount_krw)에 비례한 삼각형 크기.
+
+    같은 trace(매수 또는 매도) 안에서 최소·최대 금액으로 선형 스케일.
+
+    Args:
+        pts: 동일 action의 trade dict 리스트.
+
+    Returns:
+        plotly marker size(diameter) 리스트.
+    """
     if not pts:
         return []
     lo, hi = float(GT_MARKER_SIZE_MIN), float(GT_MARKER_SIZE_MAX)
-    return [
-        lo + (hi - lo) * min(max(float(t.get("weight", 1.0)), 0.05), 1.0)
-        for t in pts
-    ]
+    amounts = [_trade_amount_krw(t) for t in pts]
+    amin, amax = min(amounts), max(amounts)
+    sizes: list[float] = []
+    for amount in amounts:
+        if amax > amin:
+            ratio = (amount - amin) / (amax - amin)
+        else:
+            ratio = 0.5
+        ratio = max(ratio, 0.08)
+        sizes.append(lo + (hi - lo) * ratio)
+    return sizes
 
 
 def _add_sim_markers(fig, trades: list[dict], row: int = 1) -> None:
@@ -101,9 +181,14 @@ def _add_sim_markers(fig, trades: list[dict], row: int = 1) -> None:
                     opacity=0.75,
                 ),
                 hovertext=[
-                    f"{label}<br>{t['dt'][:16]}<br>₩{float(t['price']):,.0f}"
-                    f"<br>leg_gt {float(t.get('forward_ret_pct', 0)):+.2f}%"
-                    f"<br>{t.get('rule_id', '')}"
+                    _marker_hover_text(
+                        label,
+                        t,
+                        default_order_krw=LIVE_ORDER_KRW,
+                        extra_lines=[
+                            f"leg_gt {float(t.get('forward_ret_pct', 0)):+.2f}%",
+                        ],
+                    )
                     for t in pts
                 ],
                 hovertemplate="%{hovertext}<extra></extra>",
@@ -114,7 +199,7 @@ def _add_sim_markers(fig, trades: list[dict], row: int = 1) -> None:
 
 
 def _add_truth_markers(fig, trades: list[dict], row: int = 1) -> None:
-    """정답 매수·매도 마커 (삼각형 크기 = 비중)."""
+    """정답 매수·매도 마커 (삼각형 크기 = 체결 원화 금액)."""
     for action, color, symbol, label in [
         ("buy", "#16a34a", "triangle-up", "정답 매수"),
         ("sell", "#dc2626", "triangle-down", "정답 매도"),
@@ -122,7 +207,7 @@ def _add_truth_markers(fig, trades: list[dict], row: int = 1) -> None:
         pts = [t for t in trades if t.get("action") == action]
         if not pts:
             continue
-        sizes = _marker_sizes(trades, action)
+        sizes = _marker_sizes(pts)
         fig.add_trace(
             go.Scatter(
                 x=[pd.Timestamp(t["dt"]) for t in pts],
@@ -138,9 +223,7 @@ def _add_truth_markers(fig, trades: list[dict], row: int = 1) -> None:
                     line=dict(width=1.5, color="#111"),
                 ),
                 hovertext=[
-                    f"{label}<br>{t['dt'][:16]}<br>₩{t['price']:,.0f}"
-                    f"<br>비중 {float(t.get('weight', 1))*100:.0f}%"
-                    f"<br>{t.get('memo', '')}"
+                    _marker_hover_text(label, t)
                     for t in pts
                 ],
                 hovertemplate="%{hovertext}<extra></extra>",
@@ -446,7 +529,7 @@ def build_chart_html(
             )
         trade_table = f"""
   <h2>정답 타점 (ground_truth)</h2>
-  <p class="meta">삼각형 크기 = 비중. 매수: 저점 분할 / 매도: 고점 1~2회.
+  <p class="meta">삼각형 크기 = 체결 금액(매수/매도 각각 min~max). 매수: 저점 분할 / 매도: 고점 1~2회.
   총평가 = 체결 직후 현금 + 보유×체결가.{mark_note}</p>
   <table>
     <thead><tr><th>시각</th><th>구분</th><th>비중</th><th>가격</th><th>총 평가금액</th><th>해석</th></tr></thead>
@@ -471,8 +554,11 @@ def build_chart_html(
             )
 
     default_legend = (
-        "▲ <b>정답 매수</b> · ▼ <b>정답 매도</b> — 삼각형 크기 = 비중.<br>"
-        "● <b>시뮬 매수</b> · ● <b>시뮬 매도</b> — 원 = monitor_rules holdout 발화."
+        "▲ <b>정답 매수</b> · ▼ <b>정답 매도</b> — 크기=체결금액. "
+        "매수=총자산×비중×leg티어(상위 대형). "
+        "툴팁: 체결가·매수/매도금액.<br>"
+        "● <b>시뮬 매수</b> · ● <b>시뮬 매도</b> — 원 = holdout 발화 "
+        f"(매수 ₩{LIVE_ORDER_KRW:,.0f}/회)."
     )
     if cards_html:
         cards_inner = cards_html

docs/reference/GROUND_TRUTH.md

@@ -1,35 +1,78 @@
 # 정답 타점 (Ground Truth)
 
-1달(기본 45일) 3분봉 구간에서 **사후적 최적 스윙** 매수·매도 라벨을 만듭니다.  
-실시간 매매 전략이 아니라, 이후 전략 검증·학습용 **정답 데이터**입니다.
+1년(기본 `CHART_LOOKBACK_DAYS=365`) 3분봉에서 **사후 최적 스윙** 매수·매도 라벨.  
+실시간 전략이 아니라 규칙·시뮬 검증용 **벤치마크**입니다.
 
-## Plan
+JSON 필드 `model`에 타점·비중·자본 배분 규칙이 일반화되어 있습니다 (`deepcoin/ground_truth/gt_model.py`).
 
-- **목적**: 차트 상 의미 있는 저점 매수·고점 매도를 JSON으로 고정
-- **방법**: 고점(major swing)에서 1~2회 매도 · 저점(ZigZag+BB)에서 분할 매수 · 삼각형 크기=비중
-- **체결 순서**: JSON 저장·포트폴리오 시뮬은 **leg별 매수 전량 → 매도 전량** (시각순 아님). 차트 표는 시각순 정렬.
-- **HTML 카드**: 초기 금액, 총보유자산, 초기 대비 증감율(종가 평가 포함). 기간말 leg는 **종가 청산** 포함.
+## Plan — 타점 구조 (일반화)
 
-## Do
+### Leg (라운드트립 구간)
 
-```bash
-python scripts/02_ground_truth.py    # ground_truth_trades.json
-python scripts/05_chart_truth.py     # JSON + HTML 차트
+- **leg_id**: 이전 **고점 매도** 시각 ~ 다음 **고점 매도** 직전까지.
+- 마지막 구간: 마지막 major peak 이후 ~ 기간 말 → **기간말 leg** (종가 청산 1회).
+
+### 매수 타점 (Entry)
+
+| 항목 | 규칙 |
+|------|------|
+| 피벗 | ZigZag **저점(trough)**, `GT_BUY_MIN_SWING_PCT` |
+| 가격 | 해당 봉 **Low** |
+| 후보 | leg 구간 내 trough, `GT_BUY_MIN_BARS` 간격, BB (`bb_pos <= GT_BUY_BB_MAX`) |
+| **비중 weight** | `w_i = (1/price_i) / Σ(1/price_j)` — **저가일수록 큰 비중** |
+| leg당 상한 | `GT_MAX_BUYS_PER_LEG` (초과 시 저가 순 유지) |
+
+### 매도 타점 (Exit)
+
+| 항목 | 규칙 |
+|------|------|
+| 피벗 | **major swing 고점(peak)** |
+| 가격 | 해당 봉 **High** |
+| **비중 weight** | 1회 매도: **100%** · 2회 분할: **65% + 35%** (`GT_SELL_SPLIT_GAP_PCT`) |
+| 수량 | leg 보유 수량 × 매도 비중 (마지막 매도 = leg 전량) |
+
+## Do — 자본 배분 (amount_krw)
+
+시각순 체결. **매도 후 현금**이 다음 매수에 반영됩니다.
+
+```
+총보유자산 = 현금 + 보유×체결가
+최적매수율 = (이번 weight / leg 남은 weight 합) × leg티어스케일
+목표매수액 = 총보유자산 × 최적매수율
+실제매수액 = min(목표, 가용현금/(1+수수료)), 최소 GT_MIN_ORDER_KRW
 ```
 
-## Check
+| leg 티어 | 조건 | 스케일 (`.env`) |
+|----------|------|-----------------|
+| 대형 | leg 수익률 상위 `GT_LARGE_LEG_TOP_PCT` | `GT_BUY_PCT_LARGE_LEG` (기본 1.0) |
+| 소형 | 그 외 | `GT_BUY_PCT_SMALL_LEG` (기본 0.05) |
 
-| 환경 변수 | 기본 | 설명 |
-|-----------|------|------|
-| `CHART_LOOKBACK_DAYS` | 365 | 조회 일수 (`.env` 기본 1년) |
-| `GT_MIN_SWING_PCT` | 4.0 | ZigZag 최소 스윙(%) |
-| `GT_PIVOT_ORDER` | 20 | 국소 극값 반경(봉) |
-| `GT_MIN_BARS_BETWEEN` | 30 | 체결 간격(3분봉 30봉=90분) |
-| `GT_MIN_LEG_PCT` | 8.0 | 한 구간 최소 수익(%) |
-| `GT_MAX_ROUND_TRIPS` | 24 | 최대 라운드트립 |
-| `GT_SELECTION_MODE` | split_buy_peak_sell | `split_buy_peak_sell` 등 (`.env` 참고) |
+**summary.pnl_pct**: 위 배분으로 **시각순** 시뮬 + 기간말 **종가 평가**.
+
+**JSON 저장 순서**: leg별 매수 전량 → 매도 전량 (`leg_block`, 차트·테이블 정합).
+
+## 실행
+
+```bash
+python scripts/02_ground_truth.py    # ground_truth_trades.json (+ model)
+python scripts/05_chart_truth.py     # HTML 차트
+```
+
+## Check — 주요 환경 변수
+
+| 변수 | 기본 | 설명 |
+|------|------|------|
+| `GT_MIN_SWING_PCT` | 4.0 | 매도 피벗 ZigZag(%) |
+| `GT_BUY_MIN_SWING_PCT` | 3.0 | 매수 피벗 ZigZag(%) |
+| `GT_PIVOT_ORDER` | 20 | 국소 극값 반경 |
+| `GT_MIN_BARS_BETWEEN` | 30 | 체결 최소 간격(봉) |
+| `GT_MIN_LEG_PCT` | 8.0 | major leg 최소 수익(%) |
+| `GT_BUY_PCT_LARGE_LEG` | 1.0 | 상위 leg 총자산 배분 스케일 |
+| `GT_BUY_PCT_SMALL_LEG` | 0.05 | 소형 leg 스케일 |
+| `GT_LARGE_LEG_TOP_PCT` | 0.2 | 대형 leg 상위 비율 |
+| `GT_MIN_ORDER_KRW` | 5000 | 최소 체결 원화 |
 
 ## Act
 
-- JSON 수동 수정 후 `scripts/05_chart_truth.py` 재실행으로 차트 갱신
-- 파라미터 조정으로 타점 수·크기 튜닝
+- JSON·`model` 수정 후 `02` / `05` 재실행
+- 시뮬 비교: `04_simulation_report.py` (GT vs 시뮬·총자산% vs 고정 ₩/회)

docs/reference/SIMULATION.md

@@ -2,12 +2,12 @@
 
 ## 목적
 
-실거래(3단계) 전에 `monitor_rules`가 **과적합이 아닌지** 숫자로 검증합니다.
+`monitor_rules`가 과적합이 아닌지 검증하고, **Ground Truth와 동일한 자본 배분 원칙**으로 holdout 체결 수익을 비교합니다.
 
 ## 실행
 
 ```bash
-python scripts/04_match_rules.py          # 선행: 04 전체 또는 select
+python scripts/04_match_rules.py          # 선행
 python scripts/04_simulation_report.py
 ```
 
@@ -15,26 +15,40 @@ python scripts/04_simulation_report.py
 
 | 파일 | 내용 |
 |------|------|
-| `docs/04_matching/simulation_report.json` | walk-forward·민감도·Go/No-Go |
-| `docs/04_matching/simulation_report.html` | GT 동일 카드(초기 금액·총보유자산·증감율)·차트·타점·규칙 기준 |
+| `docs/04_matching/simulation_report.json` | Go/No-Go, `portfolio_compare`, `gt_model` |
+| `docs/04_matching/simulation_report.html` | 카드 3줄: **GT · 시뮬(총자산%) · 시뮬(고정₩/회)** |
+
+## 포트폴리오 비교 (`portfolio_compare`)
+
+| 키 | 설명 |
+|----|------|
+| `ground_truth_chrono` | GT 타점 + `amount_krw` 시각순 체결 |
+| `sim_sized` | holdout 발화 + **총자산×비중×EV/WF·leg상위** (`position_sizing`) |
+| `sim_fixed_order` | 동일 발화 + **고정 `LIVE_ORDER_KRW`/회** (baseline) |
+
+## 시뮬 매수 배분 (GT와 동일 원칙)
+
+- **통과 규칙만** 대형: holdout EV·PF, walk-forward, 수수료 2× 스트레스 (`load_ev_wf_approved_rule_ids`)
+- **leg 상위** `GT_LARGE_LEG_TOP_PCT` + 근접 GT leg 매칭 → `LIVE_BUY_PCT_LARGE`
+- 그 외 → `LIVE_BUY_PCT_SMALL`
+- 일한도·일최대거래: `select_capped_fires` (동적 planned 원화로 `LIVE_DAILY_KRW_MAX` 적용)
 
 ## 검증 항목
 
 | 항목 | 설명 |
 |------|------|
-| Holdout | 최근 15% 구간 EV≥0, PF≥1 |
-| Walk-forward | 월별 EV, 양수 월 비율 ≥ `SIM_GO_WF_POSITIVE_RATIO` |
-| 수수료 스트레스 | 수수료 2배(`SIM_FEE_STRESS_MULT`) 후에도 EV≥0 |
-| 실거래 한도 가정 | `LIVE_ORDER_KRW`·`LIVE_DAILY_KRW_MAX` 내 체결 가능 비율 |
+| Holdout | EV≥0, PF≥1 |
+| Walk-forward | 양수 월 비율 ≥ `SIM_GO_WF_POSITIVE_RATIO` |
+| 수수료 스트레스 | 수수료 2× 후 EV≥0 |
+| 실거래 한도 | 동적 매수액 기준 일한도 시뮬 |
 
 ## Go/No-Go
 
-- **GO**: `monitor_rules` 전 규칙이 checks 통과 → 2·3단계 진행 가능
-- **NO-GO**: 04 재선별·규칙 축소 후 재실행
+- **GO**: monitor_rules 전 규칙 checks 통과
+- **NO-GO**: 04 재선별 후 재실행
 
-## 환경 변수 (`config.py` / `.env`)
+## 환경 변수
 
-- `SIM_GO_MIN_HOLDOUT_EV`, `SIM_GO_MIN_HOLDOUT_PF`
-- `SIM_GO_WF_POSITIVE_RATIO` (기본 0.5)
-- `SIM_WALK_FORWARD_MIN_MONTHS` (기본 3)
-- `SIM_FEE_STRESS_MULT` (기본 2.0)
+- `SIM_GO_*`, `SIM_WALK_FORWARD_MIN_MONTHS`, `SIM_FEE_STRESS_MULT`
+- `LIVE_ORDER_KRW`, `LIVE_DAILY_KRW_MAX` (고정 baseline·한도)
+- `LIVE_BUY_PCT_LARGE`, `LIVE_BUY_PCT_SMALL` (시뮬·실거래 비율 매수)