dsyoon
2cb67c42b3
3분~일봉 GT 타점 분석(03c), leg 체결 순서 수정, 총자산 90% 검증 루프, walk-forward Go/No-Go 시뮬, monitor·live_trader 및 reference 문서를 포함한다. Co-authored-by: Cursor <cursoragent@cursor.com>
372 lines
11 KiB
Python
372 lines
11 KiB
Python
"""
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1단계: walk-forward·민감도·실거래 한도 가정 시뮬·Go/No-Go 리포트.
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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from typing import Any
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import numpy as np
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import pandas as pd
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from config import (
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LIVE_DAILY_KRW_MAX,
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LIVE_MAX_TRADES_PER_DAY,
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LIVE_ORDER_KRW,
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LIVE_SLIPPAGE_PCT,
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MATCH_HOLDOUT_RATIO,
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MATCH_MIN_EV_VALID,
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MATCH_MIN_FIRES_HOLDOUT,
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MATCH_MIN_PROFIT_FACTOR,
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MATCH_TRAIN_RATIO,
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SIM_FEE_STRESS_MULT,
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SIM_GO_MIN_HOLDOUT_EV,
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SIM_GO_MIN_HOLDOUT_PF,
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SIM_GO_WF_POSITIVE_RATIO,
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SIM_WALK_FORWARD_MIN_MONTHS,
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TRADING_FEE_RATE,
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)
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from deepcoin.matching.select_rules import _rule_metrics, _split_train_valid_holdout
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from deepcoin.paths import (
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ANALYSIS_GT_CALIBRATION_JSON,
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MATCHING_FIRE_OUTCOMES,
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MATCHING_MATCHED_RULES,
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MATCHING_SIMULATION_HTML,
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MATCHING_SIMULATION_JSON,
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resolve_ground_truth_file,
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)
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def _fee_adjust_ret(series: pd.Series, mult: float) -> pd.Series:
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"""
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수수료 스트레스: 왕복 수수료 %p를 (mult-1)배 추가 차감.
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Args:
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series: forward_ret_pct.
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mult: 수수료 배수 (2.0 = 2배).
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Returns:
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조정된 수익률 %.
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"""
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extra = TRADING_FEE_RATE * 2 * 100 * (mult - 1.0)
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return series - extra
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def walk_forward_by_month(outcomes: pd.DataFrame) -> list[dict[str, Any]]:
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"""
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규칙·월별 EV·PF 집계.
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Args:
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outcomes: fire_outcomes.
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Returns:
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월별 행 dict 리스트.
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"""
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if outcomes.empty:
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return []
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df = outcomes.copy()
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df["ts"] = pd.to_datetime(df["dt"])
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df["month"] = df["ts"].dt.to_period("M").astype(str)
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rows: list[dict[str, Any]] = []
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for (rid, month), grp in df.groupby(["rule_id", "month"]):
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m = _rule_metrics(grp)
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rows.append(
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{
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"rule_id": rid,
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"side": grp["side"].iloc[0],
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"month": month,
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**m,
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}
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)
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return rows
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def walk_forward_summary(wf_rows: list[dict[str, Any]]) -> dict[str, Any]:
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"""
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규칙별 월별 EV 양수 비율 요약.
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Args:
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wf_rows: walk_forward_by_month 결과.
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Returns:
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rule_id → {positive_ratio, months, ...}.
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"""
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if not wf_rows:
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return {}
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df = pd.DataFrame(wf_rows)
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out: dict[str, Any] = {}
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for rid, grp in df.groupby("rule_id"):
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n = len(grp)
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pos = int((grp["ev_pct"] > 0).sum())
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out[rid] = {
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"months": n,
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"positive_months": pos,
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"positive_ratio": round(pos / n, 4) if n else 0.0,
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"mean_ev_pct": round(float(grp["ev_pct"].mean()), 4),
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}
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return out
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def simulate_live_order_cap(outcomes: pd.DataFrame) -> dict[str, Any]:
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"""
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1회·일 한도·슬리피지 가정으로 체결 가능한 발화만 집계.
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Args:
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outcomes: fire_outcomes.
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Returns:
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규칙별·전체 요약.
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"""
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if outcomes.empty:
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return {"rules": {}, "note": "발화 없음"}
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df = outcomes.sort_values("dt").copy()
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df["ts"] = pd.to_datetime(df["dt"])
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df["day"] = df["ts"].dt.date.astype(str)
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slip = LIVE_SLIPPAGE_PCT
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taken_rows: list[pd.DataFrame] = []
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for day, day_grp in df.groupby("day", sort=True):
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spent = 0.0
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n_trades = 0
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taken_idx: list[int] = []
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for idx, row in day_grp.iterrows():
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if n_trades >= LIVE_MAX_TRADES_PER_DAY:
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break
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if spent + LIVE_ORDER_KRW > LIVE_DAILY_KRW_MAX:
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break
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spent += LIVE_ORDER_KRW
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n_trades += 1
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taken_idx.append(idx)
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if taken_idx:
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taken_rows.append(day_grp.loc[taken_idx])
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if not taken_rows:
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return {"rules": {}, "taken_count": 0}
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taken = pd.concat(taken_rows, ignore_index=True)
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taken["adj_ret_pct"] = taken["forward_ret_pct"] - slip
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by_rule: dict[str, Any] = {}
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for rid, grp in taken.groupby("rule_id"):
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g = grp.copy()
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g["forward_ret_pct"] = g["adj_ret_pct"]
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by_rule[rid] = {
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"taken_count": int(len(grp)),
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"total_count": int((df["rule_id"] == rid).sum()),
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"metrics": _rule_metrics(g),
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}
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return {
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"assumptions": {
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"order_krw": LIVE_ORDER_KRW,
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"daily_krw_max": LIVE_DAILY_KRW_MAX,
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"slippage_pct": slip,
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},
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"taken_count": int(len(taken)),
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"total_count": int(len(df)),
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"rules": by_rule,
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"portfolio_adj_ev_pct": round(float(taken["adj_ret_pct"].mean()), 4),
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}
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def evaluate_go_no_go(
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matched: dict[str, Any],
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wf_summary: dict[str, Any],
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fee_stress: dict[str, Any],
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live_cap: dict[str, Any],
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) -> dict[str, Any]:
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"""
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monitor_rules·holdout·walk-forward·수수료 스트레스 기준 Go/No-Go.
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Args:
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matched: matched_rules.json 내용.
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wf_summary: walk_forward_summary.
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fee_stress: 규칙별 fee 2x EV.
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live_cap: simulate_live_order_cap.
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Returns:
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go, checks, monitor_rules 판정.
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"""
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rules = matched.get("monitor_rules") or matched.get("selected") or []
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checks: list[dict[str, Any]] = []
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all_go = True
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for rule in rules:
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rid = rule["rule_id"]
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h = rule.get("metrics", {}).get("holdout", {})
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ev_h = float(h.get("ev_pct", -999))
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pf_h = float(h.get("profit_factor", 0))
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wf = wf_summary.get(rid, {})
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wf_ratio = float(wf.get("positive_ratio", 0))
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wf_months = int(wf.get("months", 0))
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stress_ev = fee_stress.get(rid, {}).get("ev_pct", -999)
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c_holdout = ev_h >= SIM_GO_MIN_HOLDOUT_EV and pf_h >= SIM_GO_MIN_HOLDOUT_PF
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c_wf = wf_months >= SIM_WALK_FORWARD_MIN_MONTHS and wf_ratio >= SIM_GO_WF_POSITIVE_RATIO
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c_fee = stress_ev >= SIM_GO_MIN_HOLDOUT_EV
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ok = c_holdout and c_wf and c_fee
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if not ok:
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all_go = False
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checks.append(
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{
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"rule_id": rid,
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"side": rule.get("side"),
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"pass": ok,
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"holdout_ev": ev_h,
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"holdout_pf": pf_h,
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"wf_positive_ratio": wf_ratio,
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"fee_stress_ev": stress_ev,
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}
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)
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return {
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"go": all_go and len(checks) > 0,
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"checks": checks,
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"live_cap_taken_ratio": round(
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live_cap.get("taken_count", 0) / max(live_cap.get("total_count", 1), 1),
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4,
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),
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}
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def build_simulation_report(
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outcomes_path: Path | None = None,
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matched_path: Path | None = None,
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) -> dict[str, Any]:
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"""
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시뮬레이션 리포트 dict 생성.
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Args:
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outcomes_path: fire_outcomes.csv.
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matched_path: matched_rules.json.
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Returns:
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simulation_report 전체 dict.
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"""
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op = outcomes_path or MATCHING_FIRE_OUTCOMES
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mp = matched_path or MATCHING_MATCHED_RULES
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if not op.is_file():
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raise FileNotFoundError(f"fire_outcomes 없음: {op} — 04_match_rules.py 먼저 실행")
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outcomes = pd.read_csv(op)
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matched: dict[str, Any] = {}
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if mp.is_file():
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matched = json.loads(mp.read_text(encoding="utf-8"))
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outcomes["split"] = _split_train_valid_holdout(outcomes)
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wf_rows = walk_forward_by_month(outcomes)
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wf_sum = walk_forward_summary(wf_rows)
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fee_stress: dict[str, Any] = {}
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for rid in outcomes["rule_id"].unique():
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sub = outcomes[outcomes["rule_id"] == rid]
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adj = _fee_adjust_ret(sub["forward_ret_pct"], SIM_FEE_STRESS_MULT)
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fee_stress[rid] = _rule_metrics(
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sub.assign(forward_ret_pct=adj)
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)
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live_cap = simulate_live_order_cap(outcomes)
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go = evaluate_go_no_go(matched, wf_sum, fee_stress, live_cap)
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gt_portfolio: dict[str, Any] = {}
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if ANALYSIS_GT_CALIBRATION_JSON.is_file():
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cal = json.loads(ANALYSIS_GT_CALIBRATION_JSON.read_text(encoding="utf-8"))
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gt_portfolio = cal.get("final", {})
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else:
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from deepcoin.ground_truth.ground_truth import load_ground_truth
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from deepcoin.matching.gt_asset_calibration import (
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portfolio_asset_ratio,
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)
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gt_data = load_ground_truth(resolve_ground_truth_file()) or {}
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trades = gt_data.get("trades") or []
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mark = (gt_data.get("summary") or {}).get("mark_price")
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if trades:
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gt_portfolio = {
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"portfolio": portfolio_asset_ratio(trades, set(), mark),
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"note": "캘리브레이션 미실행 — scripts/04_calibrate_gt_assets.py",
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}
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summaries = matched.get("all_rule_summaries") or matched.get("monitor_rules") or []
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return {
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"label_mode": matched.get("label_mode"),
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"train_ratio": MATCH_TRAIN_RATIO,
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"holdout_ratio": MATCH_HOLDOUT_RATIO,
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"outcomes_rows": int(len(outcomes)),
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"walk_forward": wf_rows,
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"walk_forward_summary": wf_sum,
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"fee_stress_mult": SIM_FEE_STRESS_MULT,
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"fee_stress_by_rule": fee_stress,
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"live_order_cap_sim": live_cap,
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"go_no_go": go,
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"monitor_rules": matched.get("monitor_rules", []),
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"gt_portfolio_calibration": gt_portfolio,
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"criteria": {
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"min_holdout_ev": SIM_GO_MIN_HOLDOUT_EV,
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"min_holdout_pf": SIM_GO_MIN_HOLDOUT_PF,
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"wf_positive_ratio": SIM_GO_WF_POSITIVE_RATIO,
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"wf_min_months": SIM_WALK_FORWARD_MIN_MONTHS,
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},
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}
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def write_simulation_html(report: dict[str, Any], out_path: Path) -> Path:
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"""
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simulation_report.html 저장 (ground_truth 차트 동일 스타일).
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Args:
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report: build_simulation_report 결과.
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out_path: HTML 경로.
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Returns:
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out_path.
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"""
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from deepcoin.matching.simulation_html import write_simulation_report_html
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return write_simulation_report_html(report, out_path)
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def run_simulation_report(
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outcomes_path: Path | None = None,
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matched_path: Path | None = None,
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) -> dict[str, Any]:
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"""
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시뮬 리포트 생성·저장·요약 출력.
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Args:
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outcomes_path: fire_outcomes.csv.
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matched_path: matched_rules.json.
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Returns:
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report dict.
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"""
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report = build_simulation_report(outcomes_path, matched_path)
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MATCHING_SIMULATION_JSON.parent.mkdir(parents=True, exist_ok=True)
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MATCHING_SIMULATION_JSON.write_text(
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json.dumps(report, ensure_ascii=False, indent=2),
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encoding="utf-8",
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)
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write_simulation_html(report, MATCHING_SIMULATION_HTML)
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go = report["go_no_go"]["go"]
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print(f"[시뮬] 저장: {MATCHING_SIMULATION_JSON}")
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print(f"[시뮬] 저장: {MATCHING_SIMULATION_HTML}")
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print(f"[시뮬] Go/No-Go: {'GO' if go else 'NO-GO'}")
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for c in report["go_no_go"].get("checks", []):
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mark = "OK" if c["pass"] else "NG"
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print(
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f" [{mark}] {c['rule_id']}: holdout EV={c['holdout_ev']} "
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f"WF+={c['wf_positive_ratio']} fee2x EV={c['fee_stress_ev']}"
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)
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cal = report.get("gt_portfolio_calibration") or {}
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port = cal.get("portfolio") or {}
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if port.get("asset_ratio") is not None:
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met = cal.get("targets_met", port.get("target_met_90"))
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print(
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f"[시뮬] GT 총자산 대비 leg subset 비율: {port['asset_ratio']:.2%} "
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f"({port.get('legs_covered')}/{port.get('legs_total')} leg) "
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f"목표90%={'달성' if met else '미달'}"
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)
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return report
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