인과적 GT 신호·복리 배분 시뮬을 도입하고 운영 정합성을 맞춘다.
미래 데이터를 쓰지 않는 causal 신호/tier와 전기간 복리 포트폴리오 비교로 GT 대비 sim_sized 검증 경로를 정리하고, 일한도·매수 상한·live_buy 스케일을 제거한다. Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
xavis
402
deepcoin/ground_truth/gt_allocation.py
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402
deepcoin/ground_truth/gt_allocation.py
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"""
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GT 공통 자본 배분·포트폴리오 시뮬 엔진.
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ground_truth.allocate_gt_order_amounts · simulate_truth_portfolio ·
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matching/portfolio_sim 이 동일 규칙을 공유합니다.
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"""
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from __future__ import annotations
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from typing import Any, Callable
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from config import (
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GT_INITIAL_CASH_KRW,
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GT_MIN_ORDER_KRW,
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TRADING_FEE_RATE,
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)
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from deepcoin.ground_truth.gt_model import remaining_weight_sum
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def resolve_sell_qty(
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t: dict[str, Any],
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qty: float,
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price: float,
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sell_base_qty: float,
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weight: float,
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) -> float:
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"""
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매도 수량: amount_krw 우선, 없으면 sell_base_qty × weight.
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Args:
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t: trade dict.
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qty: 현재 보유 수량.
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price: 체결가.
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sell_base_qty: leg 첫 매도 시점 보유량.
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weight: 매도 비중.
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Returns:
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매도 수량.
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"""
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if qty <= 0 or price <= 0:
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return 0.0
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ak = t.get("amount_krw")
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if ak is not None and float(ak) > 0:
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gross_cap = float(ak)
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if gross_cap >= qty * price * 0.999:
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return qty
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return min(qty, gross_cap / price)
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return min(sell_base_qty * weight, qty)
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def allocate_order_amounts_chronological(
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trades: list[dict[str, Any]],
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*,
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initial_cash: float = GT_INITIAL_CASH_KRW,
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min_order_krw: float = GT_MIN_ORDER_KRW,
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fee_rate: float = TRADING_FEE_RATE,
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large_legs: set[int] | None = None,
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asset_pct_scale_fn: Callable[[dict[str, Any]], float] | None = None,
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causal_tier: bool = False,
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) -> tuple[list[dict[str, Any]], dict[str, Any]]:
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"""
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시각순·leg 비중·티어 스케일로 amount_krw를 배분합니다.
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causal_tier=True: 청산 완료 leg의 realized return 만으로 tier 산정 (인과적).
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Args:
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trades: trade dict (weight·leg_id·action·price).
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initial_cash: 초기 현금.
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min_order_krw: 최소 체결 원화.
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fee_rate: 수수료율.
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large_legs: 대형 leg. None이면 GT trades에서 산출(비인과).
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asset_pct_scale_fn: 매수 trade별 tier scale.
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causal_tier: 과거 청산 leg 수익률만으로 tier.
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Returns:
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(amount_krw 채워진 trades, alloc_stats).
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"""
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from config import GT_LARGE_LEG_TOP_PCT
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from deepcoin.matching.position_sizing import (
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compute_buy_amount_krw,
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large_leg_ids_from_past_returns,
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leg_asset_pct_scale,
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top_leg_ids_by_forward_return,
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)
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chron = sorted(trades, key=lambda x: x["dt"])
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if large_legs is None and not causal_tier:
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large_legs = top_leg_ids_by_forward_return(chron)
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elif large_legs is None:
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large_legs = set()
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leg_buy_idxs: dict[int, list[int]] = {}
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leg_sell_idxs: dict[int, list[int]] = {}
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for i, t in enumerate(chron):
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lid = int(t.get("leg_id", 0))
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if t["action"] == "buy":
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leg_buy_idxs.setdefault(lid, []).append(i)
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elif t["action"] == "sell":
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leg_sell_idxs.setdefault(lid, []).append(i)
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cash = float(initial_cash)
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qty = 0.0
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qty_by_leg: dict[int, float] = {}
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sell_leg: int | None = None
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sell_base_qty = 0.0
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buy_executed = 0
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buy_skipped = 0
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sell_executed = 0
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sell_skipped = 0
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buy_amounts: list[float] = []
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completed_leg_ret: dict[int, float] = {}
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leg_cost_krw: dict[int, float] = {}
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leg_proceeds_krw: dict[int, float] = {}
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for i, t in enumerate(chron):
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price = float(t["price"])
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if price <= 0:
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continue
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leg_id = int(t.get("leg_id", 0))
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weight = float(t.get("weight", 1.0))
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if t["action"] == "buy":
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w_sum = remaining_weight_sum(chron, leg_id, i)
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if causal_tier:
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large_now = large_leg_ids_from_past_returns(
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completed_leg_ret, GT_LARGE_LEG_TOP_PCT
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)
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scale = leg_asset_pct_scale(leg_id, large_now)
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elif asset_pct_scale_fn is not None:
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scale = asset_pct_scale_fn(t)
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else:
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scale = leg_asset_pct_scale(leg_id, large_legs)
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amount = compute_buy_amount_krw(
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cash,
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qty,
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price,
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weight,
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w_sum,
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asset_pct_scale=scale,
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min_order_krw=min_order_krw,
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fee_rate=fee_rate,
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)
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if amount <= 0:
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t["amount_krw"] = 0
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buy_skipped += 1
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continue
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t["amount_krw"] = amount
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fee = amount * fee_rate
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cash -= amount + fee
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bought_qty = amount / price
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qty += bought_qty
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qty_by_leg[leg_id] = qty_by_leg.get(leg_id, 0.0) + bought_qty
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leg_cost_krw[leg_id] = leg_cost_krw.get(leg_id, 0.0) + amount + fee
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buy_executed += 1
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buy_amounts.append(amount)
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sell_leg = None
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elif t["action"] == "sell":
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leg_qty = qty_by_leg.get(leg_id, 0.0)
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if leg_qty <= 1e-12:
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sell_skipped += 1
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continue
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if sell_leg != leg_id:
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sell_leg = leg_id
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sell_base_qty = leg_qty
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rem_sells = [j for j in leg_sell_idxs.get(leg_id, []) if j >= i]
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is_last_leg_sell = bool(rem_sells) and i == rem_sells[-1]
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if is_last_leg_sell:
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sell_qty = leg_qty
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gross = sell_qty * price
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else:
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gross = sell_base_qty * weight * price
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if gross >= min_order_krw:
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gross = max(min_order_krw, gross)
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gross = min(gross, leg_qty * price)
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if gross <= 0:
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sell_skipped += 1
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continue
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sell_qty = leg_qty if is_last_leg_sell else gross / price
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t["amount_krw"] = round(gross, 0)
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fee = gross * fee_rate
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cash += gross - fee
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leg_proceeds_krw[leg_id] = leg_proceeds_krw.get(leg_id, 0.0) + (gross - fee)
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leg_qty -= sell_qty
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qty_by_leg[leg_id] = max(leg_qty, 0.0)
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qty = max(qty - sell_qty, 0.0)
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if qty < 1e-12:
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qty = 0.0
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sell_executed += 1
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if causal_tier and leg_qty <= 1e-12:
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cost = leg_cost_krw.pop(leg_id, 0.0)
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proceeds = leg_proceeds_krw.pop(leg_id, 0.0)
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if cost > 0:
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completed_leg_ret[leg_id] = (proceeds - cost) / cost * 100.0
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stats: dict[str, Any] = {
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"buy_executed": buy_executed,
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"buy_skipped": buy_skipped,
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"sell_executed": sell_executed,
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"sell_skipped": sell_skipped,
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"buy_total_krw": round(sum(buy_amounts), 0),
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"large_leg_count": len(large_legs),
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}
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if buy_amounts:
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stats["buy_amount_avg_krw"] = round(sum(buy_amounts) / len(buy_amounts), 0)
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stats["buy_amount_min_krw"] = round(min(buy_amounts), 0)
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stats["buy_amount_max_krw"] = round(max(buy_amounts), 0)
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return trades, stats
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def simulate_portfolio_steps(
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trades: list[dict[str, Any]],
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*,
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initial_cash: float = GT_INITIAL_CASH_KRW,
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fee_rate: float = TRADING_FEE_RATE,
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use_amount_krw: bool = True,
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) -> list[dict[str, Any]]:
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"""
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체결마다 현금·보유·총평가 스냅샷.
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Args:
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trades: 시각순 trade dict (amount_krw·weight·leg_id).
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initial_cash: 시작 현금.
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fee_rate: 수수료율.
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use_amount_krw: True면 amount_krw 기준 체결.
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Returns:
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step dict 리스트.
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"""
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rows = sorted(trades, key=lambda x: x["dt"])
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cash = float(initial_cash)
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qty = 0.0
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qty_by_leg: dict[int, float] = {}
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sell_leg: int | None = None
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sell_base_qty = 0.0
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leg_budget = 0.0
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current_leg: int | None = None
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steps: list[dict[str, Any]] = []
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for t in rows:
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action = t.get("action", t.get("side", ""))
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price = float(t["price"])
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if price <= 0:
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continue
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weight = float(t.get("weight", 1.0))
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leg_id = int(t.get("leg_id", 0))
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if action == "buy":
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if use_amount_krw and t.get("amount_krw") is not None and float(t["amount_krw"]) > 0:
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amount = min(float(t["amount_krw"]), max(cash / (1.0 + fee_rate), 0.0))
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else:
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if leg_id != current_leg:
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current_leg = leg_id
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leg_budget = cash
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amount = min(leg_budget * weight, max(cash / (1.0 + fee_rate), 0.0))
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if amount <= 0:
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continue
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fee = amount * fee_rate
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cash -= amount + fee
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bought = amount / price
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qty += bought
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qty_by_leg[leg_id] = qty_by_leg.get(leg_id, 0.0) + bought
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sell_leg = None
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elif action == "sell" and qty > 0:
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leg_qty = qty_by_leg.get(leg_id, qty)
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if sell_leg != leg_id:
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sell_leg = leg_id
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sell_base_qty = leg_qty
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sell_qty = resolve_sell_qty(t, leg_qty, price, sell_base_qty, weight)
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if sell_qty <= 0:
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continue
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gross = sell_qty * price
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fee = gross * fee_rate
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cash += gross - fee
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leg_qty -= sell_qty
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qty_by_leg[leg_id] = max(leg_qty, 0.0)
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qty -= sell_qty
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if qty < 1e-12:
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qty = 0.0
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steps.append(
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{
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"dt": t["dt"],
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"action": action,
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"price": price,
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"weight": weight,
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"leg_id": leg_id,
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"amount_krw": t.get("amount_krw"),
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"cash_krw": round(cash, 0),
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"holding_qty": round(qty, 6),
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"total_asset_krw": round(cash + qty * price, 0),
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}
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)
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return steps
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def compute_drawdown_metrics(steps: list[dict[str, Any]]) -> dict[str, Any]:
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"""
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equity curve 기준 최대 낙폭·고점 대비 하락.
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Args:
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steps: simulate_portfolio_steps 결과.
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Returns:
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max_drawdown_pct, peak_asset_krw, trough_after_peak_krw.
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"""
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if not steps:
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return {
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"max_drawdown_pct": 0.0,
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"peak_asset_krw": 0.0,
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"trough_asset_krw": 0.0,
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}
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assets = [float(s["total_asset_krw"]) for s in steps]
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peak = assets[0]
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max_dd = 0.0
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peak_at = assets[0]
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trough_at = assets[0]
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for a in assets:
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if a > peak:
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peak = a
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dd = (peak - a) / peak * 100.0 if peak > 0 else 0.0
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if dd > max_dd:
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max_dd = dd
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peak_at = peak
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trough_at = a
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return {
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"max_drawdown_pct": round(max_dd, 2),
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"peak_asset_krw": round(peak_at, 0),
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"trough_asset_krw": round(trough_at, 0),
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}
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def simulate_portfolio_summary(
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trades: list[dict[str, Any]],
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*,
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initial_cash: float = GT_INITIAL_CASH_KRW,
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fee_rate: float = TRADING_FEE_RATE,
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last_price: float | None = None,
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use_amount_krw: bool = True,
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) -> dict[str, Any]:
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"""
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포트폴리오 시뮬 요약 + MDD.
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Args:
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trades: trade dict 리스트.
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initial_cash: 시작 현금.
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fee_rate: 수수료율.
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last_price: 미청산 평가가.
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use_amount_krw: amount_krw 체결 사용.
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Returns:
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pnl·fee·MDD 포함 dict.
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"""
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steps = simulate_portfolio_steps(
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trades,
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initial_cash=initial_cash,
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fee_rate=fee_rate,
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use_amount_krw=use_amount_krw,
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)
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if not steps:
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return {
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"initial_cash_krw": round(initial_cash, 0),
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"final_asset_krw": round(initial_cash, 0),
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"pnl_krw": 0.0,
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"pnl_pct": 0.0,
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"trade_count": len(trades),
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"max_drawdown_pct": 0.0,
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}
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last_step = steps[-1]
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cash = float(last_step["cash_krw"])
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qty = float(last_step["holding_qty"])
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mark = float(last_price if last_price is not None else last_step["price"])
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holding_value = qty * mark
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final_asset = cash + holding_value
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pnl = final_asset - initial_cash
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pnl_pct = pnl / initial_cash * 100.0 if initial_cash else 0.0
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fees = 0.0
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for t in sorted(trades, key=lambda x: x["dt"]):
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ak = float(t.get("amount_krw") or 0)
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if ak <= 0:
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continue
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fees += ak * fee_rate
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dd = compute_drawdown_metrics(steps)
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return {
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"initial_cash_krw": round(initial_cash, 0),
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"final_asset_krw": round(final_asset, 0),
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"pnl_krw": round(pnl, 0),
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"pnl_pct": round(pnl_pct, 2),
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"total_fees_krw": round(fees, 0),
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"cash_krw": round(cash, 0),
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"holding_qty": round(qty, 6),
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"holding_value_krw": round(holding_value, 0),
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"mark_price": round(mark, 2),
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"fee_rate": fee_rate,
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"trade_count": len(trades),
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**dd,
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}
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