feat: sector trend analysis + ETF representative monitor (DAG step_count 81->83)
- src/quant_engine/sector_trend_analysis.py: ETF proxy 기반 11개 섹터 동향 + smart money lens - src/quant_engine/etf_representative_monitor.py: ETF 대표 종목 8개 추적 + 벤치마크 연동 - tools/build_sector_trend_analysis_v1.py: SECTOR_TREND_ANALYSIS_V1 Temp JSON 생성 - tools/build_etf_representative_monitor_v1.py: ETF_REPRESENTATIVE_MONITOR_V1 Temp JSON 생성 - tools/update_workbook_sector_insights.py: Google Sheets 섹터 인사이트 동기화 - spec/41_release_dag.yaml: step_count 81->83, wave_1에 2개 신규 노드 등록 - validate_engine_harness_gate.py: CHECK_87B (SECTOR_TREND_ANALYSIS_V1) + ETF monitor DAG 스텝 추가 - render_operational_report.py: sector_trend_analysis_v1 / etf_representative_monitor_v1 / portfolio_performance_summary 섹션 추가 - gas_lib.gs: doPost + syncSectorInsightSheets_ (섹터 인사이트 GAS 동기화 엔드포인트) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
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from __future__ import annotations
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import json
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from collections import Counter, defaultdict
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from datetime import datetime
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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[2]
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def _parse_jsonish(value: Any) -> Any:
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if isinstance(value, (dict, list)):
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return value
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if isinstance(value, str) and value.strip():
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try:
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return json.loads(value)
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except Exception:
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return value
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return value
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def _load_payload(payload: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]:
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data = payload.get("data") if isinstance(payload.get("data"), dict) else {}
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hctx = data.get("_harness_context") if isinstance(data.get("_harness_context"), dict) else {}
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return data, hctx
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def _num(value: Any, default: float = 0.0) -> float:
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try:
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return float(value)
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except Exception:
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return default
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def _txt(value: Any, default: str = "") -> str:
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if value is None:
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return default
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text = str(value).strip()
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return text if text else default
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def _latest_dates(history: list[dict[str, Any]]) -> tuple[str | None, str | None]:
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dates = sorted({str(row.get("Snapshot_Date") or "") for row in history if str(row.get("Snapshot_Date") or "")})
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if not dates:
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return None, None
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latest = dates[-1]
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previous = dates[-2] if len(dates) >= 2 else None
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return latest, previous
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def _rows_by_date(history: list[dict[str, Any]], snapshot_date: str | None) -> dict[str, dict[str, Any]]:
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if not snapshot_date:
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return {}
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rows = {}
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for row in history:
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if str(row.get("Snapshot_Date") or "") != snapshot_date:
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continue
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sector = str(row.get("Sector") or "").strip()
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if sector:
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rows[sector] = row
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return rows
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def _trend_state(momentum: dict[str, Any], row: dict[str, Any], prev_row: dict[str, Any] | None) -> str:
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state = str(momentum.get("momentum_state") or "").upper()
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if state in {"RISING", "FADING", "TOPPING_OUT", "STABLE"}:
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return state
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rank = momentum.get("rank")
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prev_rank = momentum.get("prev_rank_w1") or momentum.get("prevRank") or momentum.get("rank_w1")
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delta = None
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if isinstance(rank, (int, float)) and isinstance(prev_rank, (int, float)):
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delta = prev_rank - rank
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if delta is None and prev_row is not None:
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try:
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delta = _num(prev_row.get("Sector_Score")) - _num(row.get("Sector_Score"))
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except Exception:
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delta = None
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if delta is not None:
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if delta >= 2:
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return "RISING"
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if delta <= -2:
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return "FADING"
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breadth = _num(row.get("Flow_Breadth_5D"), 0.0)
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if breadth >= 0.6:
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return "RISING"
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if breadth <= -0.6:
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return "FADING"
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return "STABLE"
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def _direction_from_flow(value: float, threshold: float = 0.0) -> str:
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if value > threshold:
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return "INFLOW"
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if value < -threshold:
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return "OUTFLOW"
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return "NEUTRAL"
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def _alignment_state(smart_money_direction: str, breadth: float, etf_return_5d: float) -> str:
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if smart_money_direction == "INFLOW" and breadth > 0 and etf_return_5d >= 0:
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return "ALIGNED_POSITIVE"
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if smart_money_direction == "OUTFLOW" and breadth < 0 and etf_return_5d <= 0:
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return "ALIGNED_NEGATIVE"
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if smart_money_direction in {"INFLOW", "OUTFLOW"} and abs(breadth) >= 0.5:
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return "FLOW_CONFIRMING"
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if smart_money_direction == "NEUTRAL" and abs(breadth) < 0.5:
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return "MIXED"
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return "DIVERGING"
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def _build_timeline(sector_history: list[dict[str, Any]]) -> list[dict[str, Any]]:
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by_date: dict[str, list[dict[str, Any]]] = defaultdict(list)
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for row in sector_history:
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snapshot_date = _txt(row.get("Snapshot_Date"))
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if snapshot_date:
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by_date[snapshot_date].append(row)
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timeline: list[dict[str, Any]] = []
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for snapshot_date in sorted(by_date):
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rows = by_date[snapshot_date]
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top = max(rows, key=lambda r: _num(r.get("Sector_Score"), 0.0)) if rows else {}
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total_smart_money = sum(_num(r.get("SmartMoney_5D_KRW"), 0.0) for r in rows)
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avg_score = round(sum(_num(r.get("Sector_Score"), 0.0) for r in rows) / len(rows), 2) if rows else 0.0
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positive_breadth = sum(1 for r in rows if _num(r.get("Flow_Breadth_5D"), 0.0) > 0)
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liquidity_warn = sum(1 for r in rows if _txt(r.get("ETF_Liquidity_Status"), "UNKNOWN") in {"WARN", "RISK", "BLOCK"})
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timeline.append({
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"snapshot_date": snapshot_date,
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"sector_count": len(rows),
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"avg_sector_score": avg_score,
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"top_sector": _txt(top.get("Sector")),
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"top_sector_score": top.get("Sector_Score", ""),
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"top_sector_rank": top.get("Sector_Rank", ""),
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"top_sector_smart_money_5d_krw": top.get("SmartMoney_5D_KRW", ""),
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"positive_breadth_count": positive_breadth,
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"liquidity_warn_count": liquidity_warn,
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"net_smart_money_5d_krw": round(total_smart_money, 2),
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})
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return timeline
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def build_sector_trend_analysis(payload: dict[str, Any]) -> dict[str, Any]:
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data, hctx = _load_payload(payload)
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sector_flow = data.get("sector_flow") if isinstance(data.get("sector_flow"), list) else []
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sector_history = data.get("sector_flow_history") if isinstance(data.get("sector_flow_history"), list) else []
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sector_flow = [r for r in sector_flow if isinstance(r, dict)]
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sector_history = [r for r in sector_history if isinstance(r, dict)]
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rotation_rows = _parse_jsonish(hctx.get("sector_rotation_momentum_json"))
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if not isinstance(rotation_rows, list):
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rotation_rows = []
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concentration_rows = _parse_jsonish(hctx.get("sector_concentration_json"))
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if not isinstance(concentration_rows, list):
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concentration_rows = []
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momentum_map: dict[str, dict[str, Any]] = {}
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for row in rotation_rows:
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if isinstance(row, dict):
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sec = str(row.get("sector") or "").strip()
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if sec:
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momentum_map[sec] = row
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concentration_map: dict[str, dict[str, Any]] = {}
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for row in concentration_rows:
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if isinstance(row, dict):
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sec = str(row.get("sector") or "").strip()
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if sec:
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concentration_map[sec] = row
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latest_date, previous_date = _latest_dates(sector_history)
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latest_rows = _rows_by_date(sector_history, latest_date)
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prev_rows = _rows_by_date(sector_history, previous_date)
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timeline = _build_timeline(sector_history)
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rows: list[dict[str, Any]] = []
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for row in sorted(sector_flow, key=lambda r: (_num(r.get("Sector_Rank"), 999), -abs(_num(r.get("SmartMoney_5D_KRW"), 0.0)))):
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sector = str(row.get("Sector") or "").strip()
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if not sector:
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continue
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hist_latest = latest_rows.get(sector, {})
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hist_prev = prev_rows.get(sector)
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mom = momentum_map.get(sector, {})
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conc = concentration_map.get(sector, {})
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proxy_ticker = _txt(row.get("Proxy_Ticker"))
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proxy_name = _txt(row.get("Proxy_Name"))
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proxy_type = _txt(row.get("Proxy_Type"), "UNKNOWN")
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etf_code = _txt(row.get("ETF_Code"), proxy_ticker)
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etf_execution_use = _txt(row.get("ETF_Execution_Use"))
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etf_liquidity_status = _txt(row.get("ETF_Liquidity_Status"), "UNKNOWN")
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etf_nav_risk = _txt(row.get("ETF_NAV_Risk"), "UNKNOWN")
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etf_liquidity_score = row.get("ETF_Liquidity_Score", "")
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data_quality = _txt(row.get("Data_Quality"))
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stale_count = int(_num(row.get("Stale_Count"), 0.0))
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smart_money_5d_krw = _num(row.get("SmartMoney_5D_KRW"), 0.0)
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smart_money_20d_krw = _num(row.get("SmartMoney_20D_KRW"), 0.0)
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smart_money_5d_norm = _num(row.get("SmartMoney_5D_Norm"), 0.0)
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smart_money_20d_norm = _num(row.get("SmartMoney_20D_Norm"), 0.0)
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flow_breadth_5d = _num(row.get("Flow_Breadth_5D"), 0.0)
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etf_ret5d = _num(row.get("ETF_Ret5D"), 0.0)
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etf_ret20d = _num(row.get("ETF_Ret20D"), 0.0)
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rank = _num(hist_latest.get("Sector_Rank") if hist_latest else row.get("Sector_Rank"), 0)
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prev_rank_w1 = _num(mom.get("prev_rank_w1") or mom.get("prevRank") or (hist_prev.get("Sector_Rank") if hist_prev else None), 0)
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prev_rank_w2 = _num(mom.get("prev_rank_w2") or mom.get("prevRankW2"), 0)
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current_score = _num(hist_latest.get("Sector_Score") if hist_latest else row.get("Sector_Score"), 0)
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prev_score = _num(hist_prev.get("Sector_Score") if hist_prev else None, 0)
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state = _trend_state(mom, row, hist_prev)
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proxy_confidence = "HIGH"
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if proxy_type != "ETF":
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proxy_confidence = "MEDIUM"
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if etf_liquidity_status in {"WARN", "RISK", "BLOCK"} or etf_nav_risk not in {"", "OK", "NONE", "NAV_DATA_OK"}:
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proxy_confidence = "LOW" if proxy_confidence == "MEDIUM" else "MEDIUM"
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if stale_count > 0 or data_quality not in {"A", "AA", "AAA"}:
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proxy_confidence = "LOW"
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smart_money_direction = _direction_from_flow(smart_money_5d_krw)
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liquidity_direction = "FLOW_EXPANSION" if flow_breadth_5d >= 0.5 and smart_money_5d_krw > 0 else (
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"FLOW_DECAY" if flow_breadth_5d <= -0.5 and smart_money_5d_krw < 0 else "FLOW_MIXED"
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)
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alignment_state = _alignment_state(smart_money_direction, flow_breadth_5d, etf_ret5d)
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rows.append({
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"sector": sector,
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"proxy_ticker": proxy_ticker,
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"proxy_name": proxy_name,
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"proxy_type": proxy_type,
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"etf_code": etf_code,
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"etf_execution_use": etf_execution_use,
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"etf_liquidity_score": etf_liquidity_score,
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"etf_liquidity_status": etf_liquidity_status,
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"etf_nav_risk": etf_nav_risk,
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"proxy_confidence": proxy_confidence,
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"rank": int(rank) if rank else row.get("Sector_Rank"),
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"prev_rank_w1": int(prev_rank_w1) if prev_rank_w1 else mom.get("prev_rank_w1", mom.get("prevRank", "")),
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"prev_rank_w2": int(prev_rank_w2) if prev_rank_w2 else mom.get("prev_rank_w2", mom.get("prevRankW2", "")),
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"rank_delta_w1": mom.get("rank_delta_w1", (int(prev_rank_w1) - int(rank)) if prev_rank_w1 and rank else ""),
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"rank_delta_w2": mom.get("rank_delta_w2", (int(prev_rank_w2) - int(rank)) if prev_rank_w2 and rank else ""),
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"sector_score": current_score if current_score else row.get("Sector_Score", ""),
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"score_delta": round(current_score - prev_score, 2) if prev_score else "",
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"sector_ret5d": row.get("Sector_Ret5D", ""),
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"sector_ret20d": row.get("Sector_Ret20D", ""),
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"smart_money_5d_krw": row.get("SmartMoney_5D_KRW", ""),
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"smart_money_20d_krw": row.get("SmartMoney_20D_KRW", ""),
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"flow_breadth_5d": row.get("Flow_Breadth_5D", ""),
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"alert_level": row.get("Alert_Level", ""),
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"decision_use": row.get("Decision_Use", ""),
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"data_quality": data_quality,
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"stale_count": stale_count,
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"smart_money_direction": smart_money_direction,
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"liquidity_direction": liquidity_direction,
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"flow_alignment_state": alignment_state,
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"momentum_state": state,
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"concentration_weight_pct": conc.get("weight_pct", row.get("Coverage_Weight", "")),
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"etf_return_5d": row.get("ETF_Ret5D", ""),
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"etf_return_10d": row.get("ETF_Ret10D", ""),
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"etf_return_20d": row.get("ETF_Ret20D", ""),
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"sector_etf_ret_gap_5d": round(_num(row.get("Sector_Ret5D"), 0.0) - etf_ret5d, 2),
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"sector_etf_ret_gap_20d": round(_num(row.get("Sector_Ret20D"), 0.0) - etf_ret20d, 2),
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"smart_money_5d_norm": smart_money_5d_norm,
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"smart_money_20d_norm": smart_money_20d_norm,
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"smart_money_5d_krw_raw": smart_money_5d_krw,
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"smart_money_20d_krw_raw": smart_money_20d_krw,
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"flow_breadth_5d_raw": flow_breadth_5d,
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})
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def _take_top(items: list[dict[str, Any]], key: str, reverse: bool = True, n: int = 3) -> list[str]:
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ranked = sorted(
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[r for r in items if isinstance(r.get(key), (int, float))],
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key=lambda r: r.get(key, 0),
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reverse=reverse,
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)
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return [str(r.get("sector") or "") for r in ranked[:n] if str(r.get("sector") or "")]
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rising = sum(1 for r in rows if r.get("momentum_state") == "RISING")
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fading = sum(1 for r in rows if r.get("momentum_state") == "FADING")
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stable = sum(1 for r in rows if r.get("momentum_state") == "STABLE")
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topping = sum(1 for r in rows if r.get("momentum_state") == "TOPPING_OUT")
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breadth_positive = sum(1 for r in rows if _num(r.get("flow_breadth_5d"), 0.0) > 0)
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etf_proxy_count = sum(1 for r in rows if str(r.get("proxy_type") or "").upper() == "ETF")
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liquidity_warn_count = sum(1 for r in rows if str(r.get("etf_liquidity_status") or "").upper() in {"WARN", "RISK", "BLOCK"})
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nav_risk_count = sum(1 for r in rows if str(r.get("etf_nav_risk") or "").upper() not in {"", "OK", "NONE", "NAV_DATA_OK"})
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low_confidence_count = sum(1 for r in rows if str(r.get("proxy_confidence") or "").upper() == "LOW")
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smart_money_inflow_count = sum(1 for r in rows if str(r.get("smart_money_direction") or "") == "INFLOW")
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smart_money_outflow_count = sum(1 for r in rows if str(r.get("smart_money_direction") or "") == "OUTFLOW")
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flow_aligned_count = sum(1 for r in rows if str(r.get("flow_alignment_state") or "").startswith("ALIGNED"))
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flow_diverging_count = sum(1 for r in rows if str(r.get("flow_alignment_state") or "") == "DIVERGING")
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top_inflow = _take_top(rows, "smart_money_5d_krw", True, 3)
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outflow_warning = [
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r["sector"]
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for r in sorted(rows, key=lambda r: _num(r.get("smart_money_5d_krw"), 0.0))
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if _num(r.get("smart_money_5d_krw"), 0.0) < 0 or str(r.get("alert_level") or "").upper().startswith("OUTFLOW")
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][:3]
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strong_smart_money = [
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r["sector"]
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for r in sorted(rows, key=lambda r: _num(r.get("smart_money_5d_krw"), 0.0), reverse=True)
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if _num(r.get("smart_money_5d_krw"), 0.0) > 0 and _num(r.get("flow_breadth_5d"), 0.0) >= 0
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][:3]
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conc_rows_sorted = sorted(concentration_rows, key=lambda r: _num(r.get("weight_pct"), 0.0), reverse=True)
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top_sector = conc_rows_sorted[0] if conc_rows_sorted else {}
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top2_sum = round(sum(_num(r.get("weight_pct"), 0.0) for r in conc_rows_sorted[:2]), 2) if conc_rows_sorted else 0.0
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top1_weight = round(_num(top_sector.get("weight_pct"), 0.0), 2) if top_sector else 0.0
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if fading > rising and top1_weight >= 60:
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posture = "DEFENSIVE_CONCENTRATED"
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elif liquidity_warn_count >= max(1, len(rows) // 3) or nav_risk_count >= max(1, len(rows) // 4):
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posture = "ETF_PROXY_RISK"
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elif rising >= fading and breadth_positive >= max(1, len(rows) // 2):
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posture = "RISK_ON_ROTATION"
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elif smart_money_inflow_count > smart_money_outflow_count and flow_aligned_count >= max(1, len(rows) // 3):
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posture = "SMART_MONEY_CONFIRMED"
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else:
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posture = "BALANCED_ROTATION"
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gate = "PASS" if rows else "DATA_MISSING"
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if not latest_date:
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gate = "WARN"
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result = {
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"formula_id": "SECTOR_TREND_ANALYSIS_V1",
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"gate": gate,
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"latest_snapshot_date": latest_date,
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"previous_snapshot_date": previous_date,
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"sector_count": len(rows),
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"summary": {
|
||||
"rising_count": rising,
|
||||
"fading_count": fading,
|
||||
"stable_count": stable,
|
||||
"topping_out_count": topping,
|
||||
"positive_breadth_count": breadth_positive,
|
||||
"etf_proxy_count": etf_proxy_count,
|
||||
"liquidity_warn_count": liquidity_warn_count,
|
||||
"nav_risk_count": nav_risk_count,
|
||||
"low_proxy_confidence_count": low_confidence_count,
|
||||
"smart_money_inflow_count": smart_money_inflow_count,
|
||||
"smart_money_outflow_count": smart_money_outflow_count,
|
||||
"flow_aligned_count": flow_aligned_count,
|
||||
"flow_diverging_count": flow_diverging_count,
|
||||
"top_inflow_sectors": top_inflow,
|
||||
"outflow_warning_sectors": outflow_warning,
|
||||
"strong_smart_money_sectors": strong_smart_money,
|
||||
"trend_posture": posture,
|
||||
},
|
||||
"concentration": {
|
||||
"top_sector": top_sector.get("sector", ""),
|
||||
"top_sector_weight_pct": top1_weight,
|
||||
"top2_weight_pct": top2_sum,
|
||||
"concentration_gate": top_sector.get("gate", ""),
|
||||
},
|
||||
"rows": rows,
|
||||
"timeline": timeline,
|
||||
"source": {
|
||||
"sector_flow_rows": len(sector_flow),
|
||||
"sector_flow_history_rows": len(sector_history),
|
||||
"sector_rotation_momentum_rows": len(rotation_rows),
|
||||
"sector_concentration_rows": len(concentration_rows),
|
||||
"proxy_coverage_pct": round((etf_proxy_count / len(rows)) * 100.0, 2) if rows else 0.0,
|
||||
},
|
||||
}
|
||||
return result
|
||||
Reference in New Issue
Block a user