feat: 리밸런싱 엔진 V1 + GAS 버그 수정 (2026-06-13)
주요 변경: - tools/build_rebalance_engine_v1.py: REBALANCE_ENGINE_V1 신규 * account_snapshot 직접 합산(_build_snap_position_map) → 소수주 분리 행 병합 * 레짐 소스 macro.REGIME_PRELIM 최우선 (GAS 와 동일) - src/gas_adapter_parts/gdf_06_rebalance.gs: runRebalanceSheet_() 신규 * Logger.log / getSpreadsheet_() 로 run_all 연동 수정 - src/gas_adapter_parts/gdc_01_fetch_fundamentals.gs * _mergePositionRecord_(): 소수주 중복 행 합산 신규 * parseInt → parseFloat (qty, availQty) - src/gas_adapter_parts/gdf_01_price_metrics.gs * 미보유 종목 SELL_READY → WATCH_EXIT_SIGNAL - spec/41_release_dag.yaml: build_rebalance_sheet 노드 추가 (step_count 63) - spec/51_formula_lifecycle_registry.yaml: REBALANCE_ENGINE_V1 등록 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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from __future__ import annotations
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import json
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import math
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import re
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from pathlib import Path
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from typing import Any
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ROOT = Path(__file__).resolve().parents[1]
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JSON_PATH = ROOT / "GatherTradingData.json"
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WORKBOOK_ROLE = "provided_raw_analysis_data_json"
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EXPECTED_SCHEMA_VERSION = "2026-05-18-json-raw-data-v1"
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REQUIRED_SHEETS = {"data_feed", "sector_flow", "macro", "event_risk", "core_satellite", "sell_priority"}
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REQUIRED_COLUMNS = {
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"data_feed": {
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"Ticker", "Name", "Flow_OK", "Frg_5D", "Inst_5D",
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"Open", "PrevClose", "High", "Low", "Volume", "AvgVolume_5D",
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"MA20", "MA60", "Ret10D", "Ret20D", "Ret60D",
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"Timing_Score_Entry", "Timing_Score_Exit", "Timing_Action",
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"Sell_Action", "Sell_Qty", "Sell_Price_Basis",
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"Sell_Execution_Window", "Sell_Order_Type", "Sell_Validation",
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"Account_Holding_Qty", "Account_Parse_Status",
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"Rule_Sell_Qty", "Rebalance_Need_KRW", "Override_Sell_Qty", "Override_Validation",
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"Final_Action", "Action_Priority", "Priority_Score",
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"Final_Rank", "Decision_Source",
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},
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"sector_flow": {
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"Sector", "Proxy_Ticker", "Proxy_Name", "Proxy_Type", "Coverage_Weight",
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"Sector_Ret5D", "Sector_Ret20D", "Sector_RS_20D",
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"SmartMoney_5D_KRW", "SmartMoney_20D_KRW", "Sector_AvgTradeValue_20D_KRW",
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"SmartMoney_5D_Norm", "Flow_Breadth_5D", "Flow_Rows_Min", "Stale_Count",
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"ETF_Liquidity_Score", "ETF_NAV_Risk", "ETF_Liquidity_Status", "ETF_Execution_Use",
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"Sector_Median_PE", "Sector_Median_PBR", "Sector_Score", "Sector_Rank",
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"Alert_Level", "Data_Quality", "Decision_Use", "Reason", "AsOfDate",
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},
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"macro": {"Symbol", "Name", "Close", "Status"},
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"event_risk": {"Date", "Event", "Impact"},
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"core_satellite": {
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"Ticker", "Name", "Open", "PrevClose", "High", "Low", "Volume",
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"AvgVolume_5D", "MA20", "MA60", "Ret10D", "Ret20D", "Ret60D",
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"Allowed_Action", "Final_Action", "Sell_Action", "Sell_Ratio_Pct",
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"Sell_Qty", "Sell_Limit_Price", "Sell_Validation", "Action_Reason",
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"Action_Params", "Cash_Preserve_Style", "Cash_Preserve_Ratio", "Cash_Preserve_Reason",
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"Candidate_Quality_Grade", "T1_Forced_Sell_Risk_Score", "T1_Forced_Sell_Risk_State",
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"Sell_Conflict_Score", "Sell_Conflict_State", "Execution_Recommendation_State",
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},
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"sell_priority": {
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"Rank", "Ticker", "Name", "Tier", "Tier_Label", "Action_Group", "Sell_Action", "Sell_Ratio_Pct",
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"Sell_Qty", "Sell_Limit_Price", "Sell_Validation", "Sell_Priority_Score", "Raw_Sell_Priority_Score",
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"Rebound_Holdback_Score", "Cash_Preserve_Style", "Cash_Preserve_Ratio",
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"Cash_Preserve_Reason", "Action_Reason", "Action_Params",
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},
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"account_snapshot": {
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"ticker", "holding_quantity", "immediate_cash", "settlement_cash_d2",
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"parse_status", "user_confirmed",
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},
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"monthly_history": {
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"Month", "Total_Asset", "Orbit_Gap_Pct", "Orbit_State",
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},
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}
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RECOMMENDED_COLUMNS = {
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"data_feed": {"AvgTradeValue_5D_KRW", "AvgTradeValue_20D_KRW", "TradeValue_Unit"},
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"core_satellite": {
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"AvgTradeValue_5D_KRW", "AvgTradeValue_20D_KRW", "TradeValue_Unit",
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"Timing_Action", "Timing_Score_Entry", "Timing_Score_Exit",
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"Candidate_Quality_Grade", "T1_Forced_Sell_Risk_Score", "T1_Forced_Sell_Risk_State",
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"Sell_Conflict_Score", "Sell_Conflict_State", "Execution_Recommendation_State",
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},
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}
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STRICT_TICKER_SHEETS = {"data_feed", "core_satellite", "sell_priority"}
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STRICT_TEXT_CODE_COLUMNS = {
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"sector_universe": {"Proxy_Ticker", "Base_Ticker", "Constituent_Code"},
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"etf_nav_manual": {"ETF_Ticker"},
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"sector_flow": {"Proxy_Ticker"},
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}
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STRICT_NUMERIC_COLUMNS = {
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"data_feed": {
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"Close", "ATR20", "Frg_5D", "Inst_5D", "Indiv_5D", "Flow_Rows",
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"AvgTradeValue_5D_KRW", "AvgTradeValue_20D_KRW",
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"Timing_Score_Entry", "Timing_Score_Exit", "Sell_Ratio_Pct",
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"Account_Holding_Qty", "Account_Avg_Cost", "Account_Market_Value",
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"Rule_Sell_Qty", "Rebalance_Target_Cash_Pct", "Rebalance_Need_KRW",
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"Override_Sell_Qty", "Action_Priority", "Priority_Score", "Final_Rank",
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},
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"core_satellite": {
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"Close", "ATR20", "Frg_5D", "Inst_5D", "Indiv_5D", "Flow_Rows",
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"AvgTradeValue_5D_KRW", "AvgTradeValue_20D_KRW", "RS_Rank_20D", "RS_Pct_20D",
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"Timing_Score_Entry", "Timing_Score_Exit", "T1_Forced_Sell_Risk_Score", "Sell_Conflict_Score",
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},
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"sell_priority": {
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"Sell_Ratio_Pct", "Sell_Qty", "Sell_Limit_Price", "Sell_Priority_Score",
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"Raw_Sell_Priority_Score", "Rebound_Holdback_Score", "Cash_Preserve_Ratio",
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},
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"sector_flow": {
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"Coverage_Weight", "Sector_Ret5D", "Sector_Ret20D", "Sector_RS_20D",
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"SmartMoney_5D_KRW", "SmartMoney_20D_KRW", "Sector_AvgTradeValue_20D_KRW",
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"SmartMoney_5D_Norm", "Flow_Breadth_5D", "Flow_Rows_Min", "Stale_Count",
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"ETF_Liquidity_Score", "Sector_Score", "Sector_Rank",
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},
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"sector_universe": {"Weight"},
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"sector_flow_history": {
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"Sector_Score", "Sector_Rank", "SmartMoney_5D_KRW", "SmartMoney_20D_KRW",
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"Flow_Breadth_5D",
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},
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"monthly_history": {
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"Total_Asset", "MoM_Return_Pct", "YTD_Return_Pct", "Orbit_Gap_Pct",
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},
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}
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ERROR_VALUE_RE = re.compile(r"^#(?:VALUE!|NUM!|REF!|DIV/0!|NAME\?|N/A)")
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def is_number(value: Any) -> bool:
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if value in (None, "") or isinstance(value, bool):
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return False
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try:
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return math.isfinite(float(value))
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except (TypeError, ValueError):
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return False
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def rows_for(data: dict[str, Any], sheet: str) -> list[dict[str, Any]]:
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value = data.get(sheet)
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if isinstance(value, list):
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return [row for row in value if isinstance(row, dict)]
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return []
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def validate_required(data: dict[str, Any], errors: list[str], warnings: list[str]) -> None:
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missing = sorted(REQUIRED_SHEETS - set(data))
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if missing:
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errors.append(f"missing required json sheets: {missing}; found={list(data)}")
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for sheet, required in REQUIRED_COLUMNS.items():
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if sheet not in data:
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continue
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if not isinstance(data[sheet], list):
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if sheet == "settings" and isinstance(data[sheet], dict):
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continue
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errors.append(f"{sheet} must be a list of row objects")
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continue
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rows = rows_for(data, sheet)
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if not rows:
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errors.append(f"{sheet} has no data rows")
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continue
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columns = set().union(*(row.keys() for row in rows[:5]))
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missing_cols = sorted(required - columns)
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if missing_cols:
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errors.append(f"{sheet} missing required columns: {missing_cols}")
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recommended = RECOMMENDED_COLUMNS.get(sheet, set())
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missing_recommended = sorted(recommended - columns)
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if missing_recommended:
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warnings.append(f"{sheet} missing recommended columns: {missing_recommended}")
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def validate_values(data: dict[str, Any], errors: list[str]) -> None:
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for sheet, rows in ((name, rows_for(data, name)) for name in data):
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for idx, row in enumerate(rows, start=1):
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for key, value in row.items():
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if isinstance(value, str) and ERROR_VALUE_RE.match(value):
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errors.append(f"{sheet}[{idx}].{key} has error value {value}")
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if sheet in STRICT_TICKER_SHEETS:
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bad = []
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for idx, row in enumerate(rows, start=1):
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text = str(row.get("Ticker") or "").strip()
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if text and not re.fullmatch(r"\d{6}|\d{4}[A-Z]\d", text):
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bad.append((idx, text))
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if bad:
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errors.append(f"{sheet}.Ticker invalid samples: {bad[:5]}")
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for col in sorted(STRICT_TEXT_CODE_COLUMNS.get(sheet, set())):
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bad = []
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for idx, row in enumerate(rows, start=1):
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text = str(row.get(col) or "").strip()
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if text and not re.fullmatch(r"\d{6}|\d{4}[A-Z]\d", text):
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bad.append((idx, text))
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if bad:
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errors.append(f"{sheet}.{col} invalid code samples: {bad[:5]}")
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for col in sorted(STRICT_NUMERIC_COLUMNS.get(sheet, set())):
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bad = []
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for idx, row in enumerate(rows, start=1):
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value = row.get(col)
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if value in (None, ""):
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continue
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if not is_number(value):
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bad.append((idx, value))
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if bad:
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errors.append(f"{sheet}.{col} non-numeric samples: {bad[:5]}")
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df_rows = rows_for(data, "data_feed")
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if df_rows:
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ranks = []
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final_rows = []
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bad_sell_qty = []
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for idx, row in enumerate(df_rows, start=1):
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if row.get("Sell_Validation") == "NO_HOLDING_QTY" and row.get("Sell_Qty") not in (None, ""):
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bad_sell_qty.append((idx, row.get("Sell_Qty")))
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if row.get("Final_Action") not in (None, ""):
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final_rows.append(idx)
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if is_number(row.get("Final_Rank")):
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ranks.append(int(float(row["Final_Rank"])))
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if bad_sell_qty:
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errors.append(f"data_feed.Sell_Qty must be blank when Sell_Validation=NO_HOLDING_QTY; samples={bad_sell_qty[:5]}")
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if final_rows and sorted(ranks) != list(range(1, len(final_rows) + 1)):
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errors.append(f"data_feed.Final_Rank must be contiguous 1-based; found={sorted(ranks)}, expected_count={len(final_rows)}")
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status_rows = rows_for(data, "core_satellite_status")
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for row in status_rows[:1]:
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if row.get("Status") == "COMPLETE":
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if not (is_number(row.get("Processed_Count")) and is_number(row.get("Universe_Count"))):
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errors.append("core_satellite_status COMPLETE but counts are not numeric")
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elif int(float(row["Processed_Count"])) != int(float(row["Universe_Count"])):
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errors.append("core_satellite_status COMPLETE but processed != universe")
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if not (is_number(row.get("Coverage_Pct")) and float(row["Coverage_Pct"]) >= 99.9):
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errors.append(f"core_satellite_status COMPLETE but coverage < 99.9: {row.get('Coverage_Pct')}")
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def main() -> int:
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errors: list[str] = []
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warnings: list[str] = []
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if not JSON_PATH.exists():
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errors.append(f"missing json: {JSON_PATH}")
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else:
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try:
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payload = json.loads(JSON_PATH.read_text(encoding="utf-8"))
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except json.JSONDecodeError as exc:
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errors.append(f"invalid json: {exc}")
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payload = {}
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metadata = payload.get("metadata") if isinstance(payload, dict) else None
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data = payload.get("data") if isinstance(payload, dict) else None
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if not isinstance(metadata, dict):
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errors.append("metadata must be an object")
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else:
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if metadata.get("schema_version") != EXPECTED_SCHEMA_VERSION:
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errors.append(f"metadata.schema_version must be {EXPECTED_SCHEMA_VERSION}; found={metadata.get('schema_version')}")
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if not isinstance(data, dict):
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errors.append("data must be an object keyed by sheet name")
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else:
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has_harness = "_harness_context" in data
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harness_missing_flag = metadata.get("harness_context_missing") if isinstance(metadata, dict) else None
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if has_harness and harness_missing_flag not in (None, ""):
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errors.append("metadata.harness_context_missing must be null/empty when data._harness_context exists")
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if not has_harness and harness_missing_flag in (None, ""):
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errors.append("data._harness_context missing but metadata.harness_context_missing is null/empty")
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validate_required(data, errors, warnings)
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validate_values(data, errors)
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if errors:
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print("DATA SAMPLE JSON VALIDATION FAIL")
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for err in errors:
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print(f"- {err}")
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return 1
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print(f"DATA SAMPLE JSON VALIDATION OK: {JSON_PATH.name} role={WORKBOOK_ROLE}")
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for warning in warnings:
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print(f"DATA SAMPLE JSON VALIDATION WARN: {warning}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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Block a user