refactor: GT·시뮬·운영 3축 정리 및 hybrid 실거래 정합

Phase C/dry-run·미사용 모듈·재생성 HTML을 제거하고, 운영 체결을
sim_causal_hybrid와 동일한 hybrid 로직으로 통합한다.

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

deepcoin/matching/position_sizing.py

@@ -217,72 +217,6 @@ def nearest_gt_leg_id(
     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 config import SIM_FEE_STRESS_MULT
-
-    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 load_gt_allocation_analysis(
     gt_trades: list[dict[str, Any]] | None = None,
 ) -> dict[str, Any]:
@@ -328,8 +262,6 @@ def gt_tier_scale_for_trade(
     """
     GT leg tier 배분 스케일 (분석 권장값 또는 config).
 
-    시뮬은 live_buy_asset_pct_scale 대신 GT와 동일 tier 정책을 사용합니다.
-
     Args:
         trade: {dt, leg_id?, action, ...}.
         gt_trades: GT trades (leg 매칭).
@@ -349,37 +281,6 @@ def gt_tier_scale_for_trade(
     return gt_tier_scale_from_analysis(int(lid), large_legs, analysis)
 
 
-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 전용 매수 tier (EV/WF·leg 상위). 시뮬은 gt_tier_scale_for_trade 사용.
-
-    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).
-    """
-    from config import LIVE_BUY_PCT_LARGE, LIVE_BUY_PCT_SMALL
-
-    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 enrich_sim_trades_with_gt_weights(
     trades: list[dict[str, Any]],
     gt_trades: list[dict[str, Any]],
@@ -504,65 +405,6 @@ def attach_gt_model_amounts(
     return enriched
 
 
-def plan_open_position_buy(
-    open_buys: list[dict[str, Any]],
-    candidate: dict[str, Any],
-    cash: float,
-    qty: float,
-    gt_trades: list[dict[str, Any]] | None = None,
-    *,
-    large_legs: set[int],
-    analysis: dict[str, Any] | None = None,
-    fee_rate: float = TRADING_FEE_RATE,
-) -> float:
-    """
-    미청산 포지션 내 다음 매수 원화 (GT tier·보유 현금 한도, 1회 상한 없음).
-
-    Args:
-        open_buys: 현재 포지션에서 이미 체결된 매수 dict.
-        candidate: 이번 매수 후보 {dt, price, rule_id, leg_id?, ...}.
-        cash: 보유 현금.
-        qty: 보유 수량.
-        gt_trades: GT leg 매칭용.
-        large_legs: 상위 leg.
-        analysis: GT 배분 분석.
-        fee_rate: 수수료율.
-
-    Returns:
-        매수 계획 원화.
-    """
-    from deepcoin.ground_truth.gt_model import leg_entry_weights
-
-    if gt_trades is None:
-        gt_trades, _, _ = load_sizing_context_from_gt()
-    if analysis is None:
-        analysis = load_gt_allocation_analysis(gt_trades)
-
-    prices = [float(t["price"]) for t in open_buys] + [float(candidate["price"])]
-    weights = leg_entry_weights(prices)
-    idx = len(open_buys)
-    w = weights[idx]
-    w_sum = sum(weights[idx:])
-    cand = dict(candidate)
-    if "leg_id" not in cand:
-        cand["leg_id"] = nearest_gt_leg_id(str(candidate["dt"]), gt_trades)
-    scale = gt_tier_scale_for_trade(
-        cand,
-        gt_trades,
-        large_legs,
-        analysis=analysis,
-    )
-    return compute_buy_amount_krw(
-        cash,
-        qty,
-        float(candidate["price"]),
-        w,
-        w_sum,
-        asset_pct_scale=scale,
-        fee_rate=fee_rate,
-    )
-
-
 def attach_dynamic_buy_amounts(
     trades: list[dict[str, Any]],
     *,