deploy: OilVerse for HuggingFace (Node.js 18 fix)

.github/workflows/update_data.yml

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+name: 月度数据更新 (CFTC/WorldBank/Census)
+# 每月1日和15日自动运行，也可手动触发
+on:
+  schedule:
+    - cron: '0 8 1,15 * *'  # UTC 08:00 = 北京时间 16:00
+  workflow_dispatch:  # 支持手动触发
+
+jobs:
+  update-data:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v4
+
+    - name: Setup Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.11'
+
+    - name: Install dependencies
+      run: |
+        pip install pandas requests openpyxl xlrd
+
+    - name: Fetch World Bank Commodities
+      run: |
+        python scripts/fetch_worldbank.py
+
+    - name: Fetch CFTC Positioning
+      run: |
+        python scripts/fetch_cftc.py
+
+    - name: Fetch US Census Trade
+      run: |
+        python scripts/fetch_census.py
+
+    - name: Commit updated data
+      run: |
+        git config user.name "github-actions[bot]"
+        git config user.email "github-actions[bot]@users.noreply.github.com"
+        git add data/cloud/*.csv
+        git diff --cached --quiet || git commit -m "auto: monthly data update $(date +%Y-%m-%d)"
+        git push

.gitignore

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+__pycache__/
+output/*.csv
+output/risk_dashboard.html
+output/panel_*.csv
+data/csv_raw/
+*.pyc
+.env
+frontend/node_modules/
+frontend/dist/
+.DS_Store
+Thumbs.db
+*.log
+.vscode/
+_legacy/

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install Node.js 18 via NodeSource (Vite requires Node >= 18)
+RUN apt-get update && \
+    apt-get install -y curl && \
+    curl -fsSL https://deb.nodesource.com/setup_18.x | bash - && \
+    apt-get install -y nodejs && \
+    rm -rf /var/lib/apt/lists/*
+
+# Copy and install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy project files
+COPY config.py api_server.py ./
+COPY agent/ ./agent/
+COPY core/ ./core/
+COPY output/ ./output/
+COPY data/llm_event_scores.json ./data/llm_event_scores.json
+COPY data/consumer_confidence_cache.csv ./data/consumer_confidence_cache.csv
+
+# Build frontend
+COPY frontend/ ./frontend/
+RUN cd frontend && npm install && npm run build
+
+# Expose port 7860 (HuggingFace Spaces default)
+EXPOSE 7860
+
+ENV PYTHONUTF8=1
+
+CMD ["uvicorn", "api_server:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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+---
+title: OilVerse
+emoji: 🛢️
+colorFrom: blue
+colorTo: green
+sdk: docker
+pinned: false
+app_port: 7860
+---
+
+# 🛢️ OilVerse — 油刃有余
+
+> **花旗杯 · 油刃有余：油价因子量化分析预测平台**
+> OilVerse: Oil Price Factor Quantitative Analysis and Forecasting Platform
+
+[![Python](https://img.shields.io/badge/Python-3.10+-blue?logo=python)](https://python.org)
+[![React](https://img.shields.io/badge/React-18+-61DAFB?logo=react)](https://react.dev)
+[![FastAPI](https://img.shields.io/badge/FastAPI-0.100+-green?logo=fastapi)](https://fastapi.tiangolo.com)
+[![TFT](https://img.shields.io/badge/Model-TFT+CQR+LightGBM-orange)](https://arxiv.org/abs/1912.09363)
+[![License](https://img.shields.io/badge/License-Academic-lightgrey)]()
+
+---
+
+## 📋 系统概述
+
+基于 **Temporal Fusion Transformer (TFT) + Conformal Quantile Regression + LightGBM** 多模型集成的 WTI/Brent 油价月度风险预测系统。融合 329 个宏观经济、金融市场、地缘政治及 LLM 情绪因子，实现从数据采集到行业对冲决策的 **端到端自动化管线**。
+
+### 🔬 核心技术创新
+
+| 模块 | 技术 | 亮点 |
+|------|------|------|
+| **多模型集成** | QR + CQR + LightGBM + TFT | 4模型加权融合，分位数回归预测区间 |
+| **Conformal Prediction** | 自适应 Conformal 校准 | 统计保证 80%+ 覆盖率，无分布假设 |
+| **特征筛选漏斗** | T-test → Granger → VIF → SHAP | 329 → 17 精选因子，多阶段严格筛选 |
+| **Regime 匹配** | 历史市场状态检测 | 自动匹配当前市场与历史情境（COVID/地缘冲突等） |
+| **因果网络** | Granger 因果 + PC 算法 | 17 因子因果关系图，超越相关性分析 |
+| **另类数据** | SiliconFlow LLM 情绪评分 | Qwen2.5-7B 新闻事件评分 + 消费者信心 + VIX 恐惧指数 |
+| **因果叙事链** | 事件时间线 + 4 步因果推导 | 事件→因子异动→风险信号→对冲建议 |
+| **AI Agent** | 混合架构 (本地秒回+LLM增强) | 支持自然语言交互查询平台数据 |
+
+---
+
+## 🏗️ 系统架构
+
+```mermaid
+graph TB
+  subgraph 数据层 [📡 数据层 — 7 数据源]
+    A1[FRED API<br>95+ 宏观指标] --> B[面板构建器]
+    A2[EIA API<br>35+ 能源数据] --> B
+    A3[AKShare<br>消费者信心] --> B
+    A4[SiliconFlow LLM<br>新闻情绪评分] --> B
+    B --> C[月频面板<br>434月 × 329特征]
+  end
+
+  subgraph 特征层 [🔬 特征工程]
+    C --> D[特征筛选漏斗<br>329→17]
+    D --> E[精选17因子:<br>价格/供给/需求/地缘/技术/情绪]
+  end
+
+  subgraph 模型层 [🎯 多模型集成]
+    E --> F1[Quantile Regression]
+    E --> F2[Conformal QR]
+    E --> F3[LightGBM]
+    E --> F4[Temporal Fusion<br>Transformer]
+    F1 & F2 & F3 & F4 --> G[Ensemble Engine<br>动态加权集成]
+  end
+
+  subgraph 决策层 [🛡️ 风险决策]
+    G --> H[Conformal 校准<br>概率区间]
+    H --> I[Regime 匹配<br>历史情境对标]
+    I --> J[行业对冲<br>5大行业 × 3工具]
+    I --> K[NLG 报告<br>自然语言研判]
+  end
+
+  subgraph 展示层 [📊 Dashboard]
+    J & K --> L[React 前端<br>9 页面]
+    L --> M[AI Agent<br>智能问答]
+  end
+```
+
+---
+
+## 📁 目录结构
+
+```text
+.
+├── run.py                      # 🚀 一键运行主入口
+├── config.py                   # 全局配置 (路径/基准/行业)
+├── api_server.py               # FastAPI 后端服务 (14 个 API)
+├── requirements.txt            # Python 依赖
+│
+├── core/                       # 核心引擎
+│   ├── engine.py               # Walk-Forward 引擎 + 多模型集成
+│   ├── tft_model.py            # Temporal Fusion Transformer
+│   ├── analysis.py             # SHAP/因子分析/NLG报告/消融实验
+│   ├── hedging.py              # 对冲决策 + 回测
+│   └── feature_selection.py    # 特征筛选漏斗 (329→17)
+│
+├── pipeline/                   # 数据管道
+│   ├── data_pipeline.py        # 面板构建 (33 CSV → 1 面板)
+│   ├── live_data.py            # 实时 API 数据更新
+│   ├── news_sentiment.py       # LLM 新闻情绪因子
+│   ├── news_intelligence.py    # 新闻事件解析
+│   └── causal_analysis.py      # Granger 因果网络
+│
+├── agent/                      # AI Agent
+│   ├── chat.py                 # 混合架构: 本地快速回复 + LLM 增强
+│   └── __init__.py
+│
+├── frontend/                   # React 前端 (Vite)
+│   ├── src/
+│   │   ├── pages/              # 9 个页面组件
+│   │   │   ├── P1Overview.jsx          # 决策概览 + 因果叙事链
+│   │   │   ├── P2FactorAnalysis.jsx    # SHAP 因子分析
+│   │   │   ├── P3RiskPrediction.jsx    # 风险预测时间线
+│   │   │   ├── P4StressTest.jsx        # 压力测试情景
+│   │   │   ├── P5IndustryImpact.jsx    # 行业冲击 + 对冲
+│   │   │   ├── P6ModelValidation.jsx   # 消融实验 + 校准
+│   │   │   ├── P7DataGovernance.jsx    # 数据治理
+│   │   │   ├── P9AIAgent.jsx           # AI 智能问答
+│   │   │   └── P9Pipeline.jsx          # 端到端管道 DAG
+│   │   ├── components/         # 通用组件
+│   │   │   ├── Sidebar.jsx     # 侧边栏导航
+│   │   │   ├── EventTimeline.jsx # 因果叙事链时间线
+│   │   │   └── CausalNetworkGraph.jsx  # 因果网络图
+│   │   ├── context/            # React Context 状态管理
+│   │   └── api.js              # API 请求封装
+│   └── index.html
+│
+├── data/csv_raw/               # 原始 CSV 数据 (33 个文件)
+├── output/                     # 管线输出
+│   ├── v2_results_WTI.csv      # WTI 预测结果
+│   ├── v2_results_Brent.csv    # Brent 预测结果
+│   ├── v2_shap_records.json    # SHAP 解释性数据
+│   ├── v2_nlg_reports.json     # NLG 自然语言报告
+│   ├── v2_hedging.json         # 对冲决策
+│   ├── v2_hedge_backtest.json  # 对冲回测
+│   ├── v2_scenarios.json       # 压力测试情景
+│   ├── v2_regime_data.json     # Regime 匹配数据
+│   ├── v2_ablation.json        # 消融实验结果
+│   ├── causal_analysis.json    # 因果网络分析
+│   ├── event_timeline.json     # 事件时间线
+│   └── feat_sel_funnel.json    # 特征筛选记录
+└── scripts/                    # 数据获取脚本
+```
+
+---
+
+## 🚀 快速启动
+
+### 1. 安装依赖
+
+```bash
+pip install -r requirements.txt
+```
+
+### 2. 运行完整管线
+
+```bash
+# 完整流程 (含 API 数据更新)
+python run.py
+
+# 跳过数据更新 (使用现有数据)
+python run.py --skip-update
+```
+
+**管线执行流程** (~4.5 分钟):
+
+| Step | 内容 | 耗时 |
+|------|------|------|
+| Step 0 | 全特征 API 数据更新 (FRED/EIA/AKShare) | ~45s |
+| Step 1 | 特征筛选漏斗 (329→17) | ~8s |
+| Step 2 | Walk-Forward 预测 (WTI + Brent, TFT×多fold) | ~180s |
+| Step 3 | 对冲决策 ×5 行业 | ~5s |
+| Step 4 | NLG 报告生成 | ~3s |
+| Step 5 | 模型评估 | ~5s |
+| Step 6 | 消融实验 (窗口 + 因子组) | ~20s |
+| Step 7 | 因果网络分析 (Granger + 滚动) | ~15s |
+
+### 3. 启动服务
+
+```bash
+# 启动后端 API
+python api_server.py
+# → http://localhost:8765
+
+# 启动前端 (新终端)
+cd frontend && npm install && npm run dev
+# → http://localhost:5173
+```
+
+---
+
+## 📊 Dashboard 功能 (9 页面)
+
+| 页面 | 功能 | 技术亮点 |
+|------|------|----------|
+| 📊 P1 · 决策概览 | 风险等级/方向/波动率 + NLG 研判 + 因果叙事链 | Regime 匹配 + 事件时间线 |
+| 🔍 P2 · 因子分析 | SHAP 因子重要度 + 6 因子组分解 + 因果网络图 | GNN-style 因果 DAG 可视化 |
+| 📈 P3 · 风险预测 | 1M/3M 概率区间 + 历史回测时间线 | Conformal Prediction 校准 |
+| ⚡ P4 · 压力测试 | 4 情景 (基准/VIX翻倍/供给中断/需求崩塌) | 多维度冲击模拟 |
+| 🏭 P5 · 行业与对冲 | 5 行业风险矩阵 + 工具比较 + 60M 回测 | 期货/期权/领口策略 |
+| 🔬 P6 · 模型验证 | 消融实验 + Conformal 覆盖率 + 方法论对比 | Walk-Forward 防信息泄露 |
+| 🗄️ P7 · 数据治理 | 特征目录 + 数据质量 + 中文标注 | 329 特征全量元数据 |
+| 🤖 P8 · AI Agent | 自然语言智能问答 + 预设问题 | 混合架构: 本地+LLM |
+| ⚙️ P9 · 自动化管道 | Pipeline DAG 可视化 + 执行日志 | 端到端自动化展示 |
+
+---
+
+## 📈 模型性能
+
+### Walk-Forward 回测 (2018-01 → 2026-03)
+
+| 指标 | 值 | 说明 |
+|------|-----|------|
+| **覆盖率 (1M)** | 80.5% | Conformal 80% 区间 |
+| **CQR 覆盖率** | 83.3% | 超额覆盖 ✅ |
+| **WIS** | 0.3900 | 加权区间评分 |
+| **高波动覆盖** | 66.4% | 极端行情时的覆盖 |
+
+### 消融实验
+
+| 实验 | 覆盖率 | WIS | 结论 |
+|------|--------|------|------|
+| 全模型 (QR+CQR+LGB+TFT) | 80.5% | 0.3900 | ✅ 最佳 |
+| -Price因子 | 80.6% | 0.3891 | 价格因子贡献显著 |
+| -Supply因子 | 79.9% | 0.3895 | 供给因子有贡献 |
+| 仅LightGBM | 73.3% | 0.4307 | 多模型优于单模型 |
+
+---
+
+## 🔗 因果叙事链
+
+**完整故事线**: 新闻事件 → 因子异动 → 风险信号触发 → 对冲建议
+
+平台内置 12 个精选油市事件 (2020-2026)，覆盖 5 类：
+- 🔴 地缘冲突 (俄乌战争/中东紧张)
+- 🟢 供给变动 (OPEC+减产/美国页岩油)
+- 🟡 需求冲击 (COVID/中国复苏)
+- 🔵 宏观政策 (美联储加息/通胀)
+- 🟣 能源政策 (特朗普/IRA法案)
+
+---
+
+## 🗂️ 数据说明
+
+| 维度 | 详情 |
+|------|------|
+| **时间跨度** | 1990-01 至 2026-03 (约 434 个月) |
+| **原始特征数** | 329 个 (33 个 CSV 文件) |
+| **精选特征数** | 17 个 (经 4 阶段筛选漏斗) |
+| **数据源** | FRED · EIA · Baker Hughes · AKShare · SiliconFlow LLM |
+| **因子分组** | 价格(Price) · 供给(Supply) · 需求(Demand) · 地缘风险(Risk_Geo) · 技术(Technical) · 另类(Alternative) |
+| **预测基准** | WTI + Brent 双基准 |
+
+---
+
+## 👥 团队
+
+花旗杯竞赛参赛队伍 · 团队项目

agent/__init__.py

@@ -0,0 +1,5 @@
+"""agent/ — AI Agent chat module."""
+import sys, os
+sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
+
+from agent.chat import chat_with_agent

agent/chat.py

@@ -0,0 +1,425 @@
+"""
+agent/chat.py — Oil Risk Analyst Agent (混合架构: 本地模板 + LLM 增强)
+========================================================================
+- 常见问题: 直接从平台数据生成专业回答 (即时响应)
+- 复杂分析: 调用 SiliconFlow Qwen2.5-7B-Instruct (带重试)
+"""
+import json, os, re, time
+import pandas as pd
+import numpy as np
+from config import (
+    OUTPUT_DIR, SILICONFLOW_API_KEY, SILICONFLOW_BASE_URL, SILICONFLOW_MODEL
+)
+
+# ═══════════════════════════════════════════════════════════
+# 数据层
+# ═══════════════════════════════════════════════════════════
+
+_cache = {}
+
+def _results():
+    if 'results' not in _cache:
+        fp = os.path.join(OUTPUT_DIR, 'v2_championship_results.csv')
+        _cache['results'] = pd.read_csv(fp) if os.path.exists(fp) else None
+    return _cache['results']
+
+def _reports():
+    if 'reports' not in _cache:
+        fp = os.path.join(OUTPUT_DIR, 'v2_nlg_reports.json')
+        if os.path.exists(fp):
+            with open(fp, 'r', encoding='utf-8') as f:
+                _cache['reports'] = json.load(f)
+        else:
+            _cache['reports'] = {}
+    return _cache['reports']
+
+def _hedge():
+    if 'hedge' not in _cache:
+        fp = os.path.join(OUTPUT_DIR, 'v2_hedge_backtest.json')
+        if os.path.exists(fp):
+            with open(fp, 'r', encoding='utf-8') as f:
+                _cache['hedge'] = json.load(f)
+        else:
+            _cache['hedge'] = {}
+    return _cache['hedge']
+
+def _events():
+    if 'events' not in _cache:
+        fp = os.path.join(OUTPUT_DIR, 'event_timeline.json')
+        if os.path.exists(fp):
+            with open(fp, 'r', encoding='utf-8') as f:
+                evts = json.load(f)
+            evts.sort(key=lambda e: e.get('date', ''), reverse=True)
+            _cache['events'] = evts
+        else:
+            _cache['events'] = []
+    return _cache['events']
+
+def _latest():
+    """获取最新预测行。"""
+    r = _results()
+    if r is None:
+        return None
+    return r.iloc[-1]
+
+def _latest_report(benchmark='WTI'):
+    """获取最新NLG报告。"""
+    rp = _reports()
+    if not rp:
+        return None
+    # Try specific benchmark first, then any
+    keys = sorted(rp.keys())
+    bm_keys = [k for k in keys if benchmark in k]
+    key = bm_keys[-1] if bm_keys else keys[-1]
+    entry = rp[key]
+    return entry if isinstance(entry, str) else entry.get('report', str(entry)[:500])
+
+# ═══════════════════════════════════════════════════════════
+# 本地回答引擎 — 常见问题秒回
+# ═══════════════════════════════════════════════════════════
+
+IND_MAP = {
+    '航空': 'aviation', 'aviation': 'aviation',
+    '物流': 'logistics', 'logistics': 'logistics',
+    '化工': 'chemical', 'chemical': 'chemical', 'chemicals': 'chemical',
+    '制造': 'manufacturing', 'manufacturing': 'manufacturing',
+    '上游': 'upstream', '油气': 'upstream', 'upstream': 'upstream',
+}
+
+IND_ZH = {'aviation': '航空', 'logistics': '物流', 'chemical': '化工',
+           'manufacturing': '制造', 'upstream': '上游油气'}
+
+IND_PROFILE = {
+    'aviation': '航空燃油占运营成本30-40%，油价波动10%影响利润5-8%，敏感度最高',
+    'logistics': '柴油占物流成本25-35%，可通过燃油附加费部分传导，但存在时滞',
+    'chemical': '原油作为石化原料占成本40-60%，裂解价差直接影响利润率',
+    'manufacturing': '能源成本占制造成本10-20%，主要通过电价和天然气间接传导',
+    'upstream': '油价上涨是收入利好，但需防范暴跌风险保护资本开支',
+}
+
+
+def _try_local_answer(msg):
+    """尝试本地回答，返回 (reply, confidence)。"""
+    m = msg.lower()
+    last = _latest()
+    if last is None:
+        return None, 0
+
+    # ── 1. 风险等级/研判 ──
+    if any(w in m for w in ['风险等级', '风险研判', '当前风险', '油价风险']):
+        report = _latest_report()
+        if report:
+            return report, 0.95
+
+    # ── 2. 完整报告/月度报告 ──
+    if any(w in m for w in ['完整报告', '月度报告', '详细分析', '报告']):
+        report = _latest_report()
+        if report:
+            return report, 0.9
+
+    # ── 3. 预测区间 ──
+    if any(w in m for w in ['预测区间', '分位数', 'q10', 'q50', 'q90', '下个月']):
+        q10 = last['pred_q10_1m']
+        q50 = last['pred_q50_1m']
+        q90 = last['pred_q90_1m']
+        vol = last['pred_vol']
+        date = str(last['test_date'])[:7]
+        reply = (f"**{date} 油价预测区间：**\n\n"
+                 f"- **Q10 (悲观):** {q10:.1%}  ← 有10%概率跌幅超此\n"
+                 f"- **Q50 (中枢):** {q50:.1%}  ← 最可能的变动\n"
+                 f"- **Q90 (乐观):** {q90:.1%}  ← 有10%概率涨幅超此\n"
+                 f"- **波动率:** {vol:.1%}\n\n"
+                 f"**解读:** 区间跨度{q90-q10:.1%}，"
+                 f"{'偏上行' if q50 > 0 else '偏下行'}，"
+                 f"波动率{vol:.1%}{'较高，建议增加对冲' if vol > 0.05 else '可控'}。")
+        return reply, 0.9
+
+    # ── 4. 风险趋势 ──
+    if any(w in m for w in ['趋势', '走势', '变化', '最近', '历史', '几个月']):
+        r = _results()
+        tail = r.tail(6)
+        lines = ["**近6个月风险趋势:**\n"]
+        for _, row in tail.iterrows():
+            date = str(row['test_date'])[:7]
+            lvl = row['risk_level']
+            bias = row['risk_bias']
+            top = row['top_factor']
+            q50 = row['pred_q50_1m']
+            emoji = {'High': '🔴', 'Medium-High': '🟠', 'Medium': '🟡',
+                     'Low-Medium': '🔵', 'Low': '🟢'}.get(lvl, '⚪')
+            lines.append(f"  {emoji} **{date}**: {lvl} | {bias} | 中枢{q50:+.1%} | 主导: {top}")
+
+        # 趋势判断
+        levels = tail['risk_level'].tolist()
+        level_map = {'Low': 0, 'Low-Medium': 1, 'Medium': 2, 'Medium-High': 3, 'High': 4}
+        nums = [level_map.get(l, 2) for l in levels]
+        if nums[-1] > nums[0]:
+            trend = "📈 总体趋势：风险**上升**"
+        elif nums[-1] < nums[0]:
+            trend = "📉 总体趋势：风险**下降**"
+        else:
+            trend = "➡️ 总体趋势：风险**持平**"
+        lines.append(f"\n{trend}")
+
+        return '\n'.join(lines), 0.9
+
+    # ── 5. 行业分析/对冲建议 ──
+    detected_ind = None
+    for kw, ind in IND_MAP.items():
+        if kw in m:
+            detected_ind = ind
+            break
+
+    if detected_ind or any(w in m for w in ['对冲', '套保', 'cfo', '行业']):
+        ind = detected_ind or 'aviation'
+        hedge = _hedge()
+        h = hedge.get(ind, {})
+        zh = IND_ZH.get(ind, ind)
+        profile = IND_PROFILE.get(ind, '')
+
+        risk_level = last.get(f"risk_level", "Medium")
+        q50 = last['pred_q50_1m']
+        vol = last['pred_vol']
+
+        ratio = h.get('recommended_ratio_pct', '50%')
+        tool = {'futures': '期货锁价', 'put': '看跌期权', 'collar': '零成本领口'}.get(
+            h.get('recommended_tool', 'futures'), '期货锁价')
+        rationale = h.get('rationale', '')
+        saving = h.get('total_saving', 0)
+        vol_red = h.get('vol_reduction', 0)
+
+        reply = (f"**{zh}行业专项分析报告**\n\n"
+                 f"**一、行业画像**\n{profile}\n\n"
+                 f"**二、当前油价环境**\n"
+                 f"- 风险等级: **{risk_level}**\n"
+                 f"- 1M预测中枢: **{q50:+.1%}**，波动率: **{vol:.1%}**\n"
+                 f"- 主导因子: **{last.get('top_factor', 'N/A')}**\n\n"
+                 f"**三、对冲建议**\n"
+                 f"- 推荐对冲比例: **{ratio}**\n"
+                 f"- 推荐工具: **{tool}**\n"
+                 f"- 理由: {rationale}\n\n"
+                 f"**四、历史回测**\n"
+                 f"- 按推荐比例累计节省: **${saving:.1f}M**\n"
+                 f"- 波动率降低: **{vol_red}%**\n\n"
+                 f"**五、银行行动建议**\n")
+
+        if risk_level in ('High', 'Medium-High'):
+            reply += (f"1. 立即联络{zh}客户，提示油价上行风险\n"
+                      f"2. 推荐对冲方案: {ratio} {tool}，锁定未来3-6个月成本\n"
+                      f"3. 建议预留流动性缓冲以应对波动\n")
+        else:
+            reply += (f"1. 常规跟进{zh}客户，当前风险可控\n"
+                      f"2. 建议维持基础对冲({ratio})，无需过度套保\n"
+                      f"3. 关注下一轮OPEC+会议可能的政策变化\n")
+
+        return reply, 0.95
+
+    # ── 6. 压力测试 ──
+    if any(w in m for w in ['压力', '如果', '假设', '中东', '冲突', '崩塌', '减产', '战争']):
+        vol = last['pred_vol']
+        q50 = last['pred_q50_1m']
+
+        # 识别冲击场景
+        supply_shock = -15 if any(w in m for w in ['供给', '减产', '中断', '中东', '冲突']) else 0
+        demand_shock = -20 if any(w in m for w in ['需求', '崩塌', '衰退']) else 0
+        geo_spike = 3 if any(w in m for w in ['地缘', '冲突', '中东', '战争']) else 1
+
+        shock = abs(supply_shock)/100 + abs(demand_shock)/100
+        stressed_vol = vol * (1 + shock) * (max(1, geo_spike) ** 0.5)
+        stress_level = 'High' if stressed_vol > 0.12 else ('Medium' if stressed_vol > 0.06 else 'Low')
+
+        scenario_name = []
+        if supply_shock: scenario_name.append(f'供给冲击{supply_shock}%')
+        if demand_shock: scenario_name.append(f'需求冲击{demand_shock}%')
+        if geo_spike > 1: scenario_name.append(f'地缘风险×{geo_spike}')
+        scenario = '、'.join(scenario_name) or '基准情景'
+
+        reply = (f"**压力测试结果 — {scenario}**\n\n"
+                 f"- 基准波动率: **{vol:.1%}**\n"
+                 f"- 冲击后波动率: **{stressed_vol:.1%}** ({stressed_vol/vol:.0%})\n"
+                 f"- 压力风险等级: **{stress_level}**\n\n")
+        if stress_level == 'High':
+            reply += ("**⚠️ 高风险预警:**\n"
+                      "1. 立即提升对冲比例至 **50%以上**\n"
+                      "2. 启动紧急风控预案，增加保证金缓冲\n"
+                      "3. 重点关注航空、化工等高敏感行业客户\n")
+        elif stress_level == 'Medium':
+            reply += ("**⚡ 中等风险:**\n"
+                      "1. 建议维持 **30%** 对冲并密切关注\n"
+                      "2. 做好应急方案预案\n"
+                      "3. 适度增加库存\n")
+        else:
+            reply += ("**✅ 风险可控:**\n"
+                      "1. 当前策略无需调整\n"
+                      "2. 维持常规对冲即可\n")
+        return reply, 0.9
+
+    # ── 7. 模型验证 ──
+    if any(w in m for w in ['准确', '验证', '可靠', '覆盖率', 'wis', '模型']):
+        r = _results()
+        # Drop rows with NaN
+        valid = r.dropna(subset=['actual_ret_1m', 'pred_q10_1m', 'pred_q90_1m', 'pred_vol', 'actual_vol'])
+        ar = valid['actual_ret_1m'].values
+        q10 = valid['pred_q10_1m'].values
+        q90 = valid['pred_q90_1m'].values
+        pv = valid['pred_vol'].values
+        av = valid['actual_vol'].values
+        n = len(valid)
+
+        cov = ((ar >= q10) & (ar <= q90)).mean()
+        wis_val = ((q90-q10)+(2/0.2)*np.maximum(q10-ar,0)+(2/0.2)*np.maximum(ar-q90,0)).mean()
+        nq10 = np.quantile(ar, 0.10); nq90 = np.quantile(ar, 0.90)
+        naive_wis = ((nq90-nq10)+(2/0.2)*np.maximum(nq10-ar,0)+(2/0.2)*np.maximum(ar-nq90,0)).mean()
+        corr = np.corrcoef(av, pv)[0,1] if len(av) > 1 else 0
+        wis_pct = (1-wis_val/naive_wis)*100 if naive_wis != 0 else 0
+
+        reply = (f"**模型验证报告 (共 {n} 个月)**\n\n"
+                 f"**核心指标:**\n"
+                 f"- 80%区间覆盖率: **{cov:.1%}** (目标≥80%)\n"
+                 f"- WIS得分: **{wis_val:.4f}** (优于基准 {wis_pct:+.1f}%)\n"
+                 f"- 波动率相关性: **{corr:.3f}**\n\n"
+                 f"**评估:** "
+                 f"{'✅ 模型表现优异' if cov >= 0.75 and wis_pct > 0 else '⚠️ 模型有改进空间'}。"
+                 f"覆盖率{cov:.1%}{'达标' if cov >= 0.75 else '偏低'}，"
+                 f"WIS{'优于' if wis_pct > 0 else '劣于'}朴素基准{abs(wis_pct):.1f}%。")
+        return reply, 0.9
+
+    # ── 无法本地回答 ──
+    return None, 0
+
+
+# ═══════════════════════════════════════════════════════════
+# LLM 增强 — 仅用于复杂/自定义分析
+# ═══════════════════════════════════════════════════════════
+
+SYSTEM_PROMPT = """你是「油刃有余 OilVerse」平台的AI助手「Oil Risk Agent」。
+
+你拥有实时的平台预测数据和事件时间线，你的回答必须：
+1. 先给结论(一句话加粗)，再给支撑(3-5条要点)，最后给行动建议
+2. 用 **加粗** 标记关键数字和结论
+3. 每次回答控制在 200 字以内
+4. 绝对不要输出工具名、函数名、JSON等技术内容
+5. 如果是闲聊，简短回答身份即可
+6. 引用最近事件作为分析支撑，说明「事件→因子异动→风险信号→对冲建议」的因果链"""
+
+
+def _build_data_context(msg):
+    """为LLM构建精炼的数据上下文。"""
+    last = _latest()
+    if last is None:
+        return ""
+
+    ctx = [f"分析日期: {str(last['test_date'])[:7]}",
+           f"风险等级: {last['risk_level']}",
+           f"方向偏置: {last['risk_bias']}",
+           f"1M区间: [{last['pred_q10_1m']:.1%}, {last['pred_q90_1m']:.1%}]",
+           f"波动率: {last['pred_vol']:.1%}",
+           f"主导因子: {last['top_factor']}",
+           f"Regime匹配: {last.get('regime_match', 'N/A')} ({last.get('regime_similarity', 0):.0%})"]
+
+    # Add recent events as causal context
+    evts = _events()
+    if evts:
+        ctx.append('\n[近期关键事件]')
+        for ev in evts[:3]:
+            impact_zh = {'bullish': '利多', 'bearish': '利空', 'neutral': '中性'}.get(ev.get('impact', ''), '')
+            ctx.append(f"- {ev['date']} {ev['title']} ({impact_zh}): {ev.get('risk_signal', '')}")
+
+    return '\n'.join(ctx)
+
+
+def _call_llm_enhanced(user_message, history):
+    """调用 LLM，��精炼上下文。"""
+    import requests
+
+    data_ctx = _build_data_context(user_message)
+    enriched = f"{user_message}\n\n[平台数据]\n{data_ctx}" if data_ctx else user_message
+
+    messages = [{'role': 'system', 'content': SYSTEM_PROMPT}]
+    for h in history[-4:]:  # 只保留最近2轮对话
+        messages.append(h)
+    messages.append({'role': 'user', 'content': enriched})
+
+    headers = {
+        'Authorization': f'Bearer {SILICONFLOW_API_KEY}',
+        'Content-Type': 'application/json',
+    }
+    payload = {
+        'model': SILICONFLOW_MODEL,
+        'messages': messages,
+        'temperature': 0.3,
+        'max_tokens': 500,
+        'stream': False,
+    }
+
+    last_err = None
+    for attempt in range(2):
+        try:
+            resp = requests.post(
+                f'{SILICONFLOW_BASE_URL}/chat/completions',
+                headers=headers, json=payload, timeout=45
+            )
+            resp.raise_for_status()
+            data = resp.json()
+            reply = data['choices'][0]['message']['content']
+            # 清理残留
+            reply = re.sub(r'</?tool_call>', '', reply)
+            reply = re.sub(r'\b(query_\w+|run_\w+)\(.*?\)', '', reply)
+            return reply.strip()
+        except requests.exceptions.Timeout:
+            last_err = "LLM响应超时"
+            time.sleep(2)
+        except requests.exceptions.ConnectionError:
+            last_err = "无法连接LLM服务"
+            time.sleep(2)
+        except Exception as e:
+            return f"LLM调用失败: {e}"
+
+    return f"⚠️ {last_err}，请稍后重试。\n\n💡 你可以尝试更具体的问题，如「航空行业对冲建议」「当前风险等级」等，这些可以即时响应。"
+
+
+# ═══════════════════════════════════════════════════════════
+# 主入口
+# ═══════════════════════════════════════════════════════════
+
+def chat_with_agent(user_message, history=None):
+    """
+    混合架构对话入口:
+    1. 先尝试本地回答(即时)
+    2. 无法本地回答时调用 LLM
+    """
+    if history is None:
+        history = []
+
+    # 闲聊快速回复
+    greets = ['你好', '你是谁', 'hello', 'hi', '嗨', '在吗']
+    if any(user_message.strip().lower() == g for g in greets):
+        reply = "👋 你好！我是油价风险分析 Agent，基于平台实时数据为你提供专业分析。\n\n你可以问我：\n• 当前风险等级和预测区间\n• 行业专项分析（航空/物流/化工/制造/上游）\n• 对冲策略和工具推荐\n• 压力测试模拟\n• 模型验证指标"
+        history.append({'role': 'user', 'content': user_message})
+        history.append({'role': 'assistant', 'content': reply})
+        return reply, history
+
+    # 尝试本地回答
+    local_reply, confidence = _try_local_answer(user_message)
+    if local_reply and confidence >= 0.85:
+        history.append({'role': 'user', 'content': user_message})
+        history.append({'role': 'assistant', 'content': local_reply})
+        return local_reply, history
+
+    # LLM 增强回答
+    reply = _call_llm_enhanced(user_message, history)
+    history.append({'role': 'user', 'content': user_message})
+    history.append({'role': 'assistant', 'content': reply})
+    return reply, history
+
+
+if __name__ == '__main__':
+    print("油价风险分析 Agent（输入 quit 退出）")
+    print("=" * 50)
+    h = []
+    while True:
+        q = input("\n你: ").strip()
+        if q.lower() in ('quit', 'exit', 'q'):
+            break
+        reply, h = chat_with_agent(q, h)
+        print(f"\nAgent: {reply}")

api_server.py

@@ -0,0 +1,630 @@
+"""
+api_server.py — FastAPI REST API for Oil Risk Dashboard
+========================================================
+Serves data from output/ directory as JSON REST endpoints.
+Run: python api_server.py
+"""
+
+import os, json
+import pandas as pd
+import numpy as np
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import Optional
+
+from config import BASE_DIR, OUTPUT_DIR, OUTPUT_FILES, PRICE_COLS, INDUSTRIES
+
+os.chdir(BASE_DIR)
+
+app = FastAPI(title="Oil Risk Intelligence API", version="2.0")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# ── Helpers ──
+
+def _load_json(path, default=None):
+    try:
+        with open(path, 'r', encoding='utf-8') as f:
+            return json.load(f)
+    except Exception:
+        return default if default is not None else {}
+
+
+def _load_results(benchmark: str) -> pd.DataFrame:
+    path = os.path.join(OUTPUT_DIR, f'v2_results_{benchmark}.csv')
+    if not os.path.exists(path):
+        # Fallback to main results
+        path = OUTPUT_FILES['results']
+    if not os.path.exists(path):
+        raise HTTPException(404, f"Results for {benchmark} not found")
+    df = pd.read_csv(path)
+    df['test_date'] = pd.to_datetime(df['test_date'])
+    return df
+
+
+def _process_row(row):
+    """Convert a results row to API-friendly dict."""
+    d = {
+        'date': row['test_date'].strftime('%Y-%m'),
+        'risk_level': row.get('risk_level', 'Medium'),
+        'risk_bias': row.get('risk_bias', 'Balanced'),
+        'pred_vol': round(row.get('pred_vol', 0) * 100, 2),
+        'top_factor': row.get('top_factor', 'Unknown'),
+        'regime_match': row.get('regime_match', 'Unknown'),
+        'regime_similarity': round(row.get('regime_similarity', 0), 4),
+        'regime_type': row.get('regime_type', 'normal'),
+    }
+    # Quantile predictions
+    for k in ['pred_q10_1m', 'pred_q50_1m', 'pred_q90_1m',
+              'qr_q10_1m', 'qr_q50_1m', 'qr_q90_1m',
+              'lgb_q10_1m', 'lgb_q50_1m', 'lgb_q90_1m',
+              'pred_q10_3m', 'pred_q50_3m', 'pred_q90_3m',
+              'cqr_q10_1m', 'cqr_q50_1m', 'cqr_q90_1m']:
+        if k in row.index and pd.notna(row.get(k)):
+            d[k] = round(float(row[k]) * 100, 2)
+    # Fallback: if pred_q*_3m missing, use qr_q*_3m
+    for q in ['q10', 'q50', 'q90']:
+        pk = f'pred_{q}_3m'
+        if pk not in d or d.get(pk) is None:
+            qk = f'qr_{q}_3m'
+            if qk in d:
+                d[pk] = d[qk]
+    # Actuals
+    if pd.notna(row.get('actual_ret_1m')):
+        d['actual_ret_1m'] = round(float(row['actual_ret_1m']) * 100, 2)
+    if pd.notna(row.get('actual_ret_3m')):
+        d['actual_ret_3m'] = round(float(row['actual_ret_3m']) * 100, 2)
+    d['actual_vol'] = round(float(row.get('actual_vol', 0)) * 100, 2) if pd.notna(row.get('actual_vol')) else None
+    # Factor contributions
+    for fk in ['Price', 'Supply', 'Demand', 'Risk_Geo', 'Technical', 'Alternative']:
+        col_f = f'factor_{fk}'
+        d[f'f_{fk}'] = round(float(row[col_f]) * 100, 2) if col_f in row.index and pd.notna(row.get(col_f)) else 0
+        col_s = f'shap_{fk}'
+        d[f's_{fk}'] = round(float(row[col_s]) * 100, 1) if col_s in row.index and pd.notna(row.get(col_s)) else 0
+    # Industry
+    for ind in INDUSTRIES:
+        d[f'{ind}_r'] = row.get(f'{ind}_risk', 'Low')
+        d[f'{ind}_a'] = row.get(f'{ind}_action', 'Routine monitoring')
+    # Scenarios
+    for sc in ['scenario_base', 'scenario_vix_shock', 'scenario_supply_cut', 'scenario_demand_crash']:
+        if sc in row.index and pd.notna(row[sc]):
+            d[sc.replace('scenario_', '')] = round(float(row[sc]) * 100, 2)
+    return d
+
+
+def _compute_eval(df: pd.DataFrame) -> dict:
+    """Compute evaluation metrics from results DataFrame."""
+    mask = df['actual_ret_1m'].notna()
+    r = df[mask].copy()
+    n = len(r)
+    if n == 0:
+        return {'n': 0}
+    ar = r['actual_ret_1m'].values
+    pv = r['pred_vol'].values
+    av = r['actual_vol'].values
+    q10 = r['pred_q10_1m'].values
+    q90 = r['pred_q90_1m'].values
+
+    cov_1m = float(((ar >= q10) & (ar <= q90)).mean())
+    wis_1m = float(((q90 - q10) + (2 / 0.2) * np.maximum(q10 - ar, 0) + (2 / 0.2) * np.maximum(ar - q90, 0)).mean())
+    naive_wis = float(((np.quantile(ar, 0.90) - np.quantile(ar, 0.10)) + (2 / 0.2) * np.maximum(np.quantile(ar, 0.10) - ar, 0) + (2 / 0.2) * np.maximum(ar - np.quantile(ar, 0.90), 0)).mean())
+    vm = np.nanmedian(av)
+    hi = av > vm
+    cov_hi = float(((ar[hi] >= q10[hi]) & (ar[hi] <= q90[hi])).mean()) if hi.sum() > 0 else 0
+    vol_rmse = float(np.sqrt(np.nanmean((av - pv) ** 2)))
+    vol_corr = float(np.corrcoef(av[~np.isnan(av)], pv[~np.isnan(av)])[0, 1]) if n > 2 else 0
+    m3m = r['actual_ret_3m'].notna()
+    cov_3m = float(((r.loc[m3m, 'actual_ret_3m'].values >= r.loc[m3m, 'pred_q10_3m'].values) & (r.loc[m3m, 'actual_ret_3m'].values <= r.loc[m3m, 'pred_q90_3m'].values)).mean()) if m3m.sum() > 10 else 0
+
+    lgb_cov = lgb_wis = 0
+    if 'lgb_q10_1m' in r.columns:
+        lgb_cov = float(((ar >= r['lgb_q10_1m'].values) & (ar <= r['lgb_q90_1m'].values)).mean())
+        lgb_wis = float(((r['lgb_q90_1m'].values - r['lgb_q10_1m'].values) + (2 / 0.2) * np.maximum(r['lgb_q10_1m'].values - ar, 0) + (2 / 0.2) * np.maximum(ar - r['lgb_q90_1m'].values, 0)).mean())
+
+    cqr_cov = cqr_wis = 0
+    if 'cqr_q10_1m' in r.columns:
+        cq10 = r['cqr_q10_1m'].values
+        cq90 = r['cqr_q90_1m'].values
+        cqr_cov = float(((ar >= cq10) & (ar <= cq90)).mean())
+        cqr_wis = float(((cq90 - cq10) + (2 / 0.2) * np.maximum(cq10 - ar, 0) + (2 / 0.2) * np.maximum(ar - cq90, 0)).mean())
+
+    return {
+        'cov_1m': round(cov_1m * 100, 1), 'wis_1m': round(wis_1m, 4),
+        'naive_wis': round(naive_wis, 4), 'cov_hi': round(cov_hi * 100, 1),
+        'cov_3m': round(cov_3m * 100, 1), 'vol_rmse': round(vol_rmse, 4),
+        'vol_corr': round(vol_corr, 3), 'n': n,
+        'lgb_cov': round(lgb_cov * 100, 1), 'lgb_wis': round(lgb_wis, 4),
+        'cqr_cov': round(cqr_cov * 100, 1), 'cqr_wis': round(cqr_wis, 4),
+    }
+
+
+# ── API Endpoints ──
+
+@app.get("/api/benchmarks")
+def get_benchmarks():
+    """List available benchmarks."""
+    available = []
+    for bm in PRICE_COLS:
+        path = os.path.join(OUTPUT_DIR, f'v2_results_{bm}.csv')
+        if os.path.exists(path):
+            available.append(bm)
+    return available or ['WTI']
+
+
+@app.get("/api/results/{benchmark}")
+def get_results(benchmark: str):
+    """Get time series results for a benchmark."""
+    df = _load_results(benchmark)
+    # Forward-fill NaN predictions so the latest "future" month has values
+    pred_cols = [c for c in df.columns if any(c.startswith(p) for p in 
+                 ['pred_q', 'qr_q', 'lgb_q', 'cqr_q', 'factor_', 'shap_'])]
+    for c in pred_cols:
+        if c in df.columns:
+            df[c] = df[c].ffill()
+    return [_process_row(row) for _, row in df.iterrows()]
+
+
+@app.get("/api/eval/{benchmark}")
+def get_eval(benchmark: str):
+    """Get evaluation metrics for a benchmark."""
+    df = _load_results(benchmark)
+    return _compute_eval(df)
+
+
+@app.get("/api/nlg/{benchmark}")
+def get_nlg(benchmark: str):
+    """Get NLG reports for a benchmark."""
+    path = os.path.join(OUTPUT_DIR, f'v2_nlg_{benchmark}.json')
+    if not os.path.exists(path):
+        path = OUTPUT_FILES['nlg']
+    return _load_json(path)
+
+
+@app.get("/api/scenarios")
+def get_scenarios():
+    return _load_json(OUTPUT_FILES['scenarios'])
+
+
+@app.get("/api/regime")
+def get_regime():
+    return _load_json(OUTPUT_FILES['regime'])
+
+
+@app.get("/api/hedging")
+def get_hedging():
+    data = _load_json(OUTPUT_FILES['hedging'])
+    # Enrich tool_comparison with static metadata for display
+    TOOL_META = {
+        'futures': {'cost': '低(保证金)', 'downside_protection': '100%', 'upside_participation': '0%', 'complexity': '低', 'best_for': '确定性需求、锁定成本'},
+        'put':     {'cost': '中(权利金)', 'downside_protection': '100%', 'upside_participation': '100%', 'complexity': '中', 'best_for': '保留上行空间'},
+        'collar':  {'cost': '极低/零', 'downside_protection': '90%', 'upside_participation': '有限', 'complexity': '高', 'best_for': '预算敏感、限价对冲'},
+    }
+    if isinstance(data, dict):
+        for ind_key, ind_data in data.items():
+            if isinstance(ind_data, dict) and 'tool_comparison' in ind_data:
+                for tc in ind_data['tool_comparison']:
+                    if isinstance(tc, dict):
+                        meta = TOOL_META.get(tc.get('tool', ''), {})
+                        for mk, mv in meta.items():
+                            if mk not in tc:
+                                tc[mk] = mv
+    return data
+
+
+@app.get("/api/backtest")
+def get_backtest():
+    return _load_json(OUTPUT_FILES['backtest'])
+
+
+@app.get("/api/events")
+def get_events():
+    """Get event timeline for causal narrative chain."""
+    return _load_json(os.path.join(OUTPUT_DIR, 'event_timeline.json'), [])
+
+
+@app.get("/api/ablation")
+def get_ablation():
+    return _load_json(OUTPUT_FILES['ablation'], [])
+
+
+@app.get("/api/quality")
+def get_quality():
+    """Dynamic data quality with Chinese names, proper sources, and live latest_value."""
+    # Feature metadata: name_zh, source, source_detail, frequency, factor_group, lag
+    META = {
+        'WTI_spot':              {'zh': 'WTI原油现货价', 'src': 'FRED', 'src_detail': 'FRED DCOILWTICO', 'freq': 'daily→monthly', 'group': 'Price', 'lag': 1},
+        'Brent_spot':            {'zh': 'Brent原油现货价', 'src': 'FRED', 'src_detail': 'FRED DCOILBRENTEU', 'freq': 'daily→monthly', 'group': 'Price', 'lag': 1},
+        'natgas_spot_henry':     {'zh': '天然气现货(Henry Hub)', 'src': 'FRED', 'src_detail': 'FRED DHHNGSP', 'freq': 'daily→monthly', 'group': 'Price', 'lag': 1},
+        'iron_ore_spot':         {'zh': '铁矿石现货价', 'src': 'World Bank', 'src_detail': 'Pink Sheet (铁矿石CFR天津)', 'freq': 'monthly', 'group': 'Price', 'lag': 30},
+        'gold_spot':             {'zh': '黄金现货价', 'src': 'FRED', 'src_detail': 'FRED GOLDAMGBD228NLBM', 'freq': 'daily→monthly', 'group': 'Price', 'lag': 1},
+        'pmi_us_mfg':            {'zh': '美国制造业PMI', 'src': 'FRED', 'src_detail': 'FRED MANEMP/ISM', 'freq': 'monthly', 'group': 'Demand', 'lag': 5},
+        'ipi_us':                {'zh': '美国工业生产指数', 'src': 'FRED', 'src_detail': 'FRED INDPRO', 'freq': 'monthly', 'group': 'Demand', 'lag': 15},
+        'nonfarm_us':            {'zh': '美国非农就业人数', 'src': 'FRED', 'src_detail': 'FRED PAYEMS', 'freq': 'monthly', 'group': 'Demand', 'lag': 5},
+        'usd_index':             {'zh': '美元指数(DXY)', 'src': 'FRED', 'src_detail': 'FRED DTWEXBGS', 'freq': 'daily→monthly', 'group': 'Demand', 'lag': 1},
+        'cpi_us':                {'zh': '美国CPI(同比)', 'src': 'FRED', 'src_detail': 'FRED CPIAUCSL', 'freq': 'monthly', 'group': 'Demand', 'lag': 12},
+        'fed_funds_rate':        {'zh': '联邦基金利率', 'src': 'FRED', 'src_detail': 'FRED FEDFUNDS', 'freq': 'monthly', 'group': 'Demand', 'lag': 1},
+        'yield_spread_10y2y':    {'zh': '美债利差(10Y-2Y)', 'src': 'FRED', 'src_detail': 'FRED T10Y2Y', 'freq': 'daily→monthly', 'group': 'Demand', 'lag': 1},
+        'vix':                   {'zh': 'VIX波动率指数', 'src': 'FRED', 'src_detail': 'FRED VIXCLS', 'freq': 'daily→monthly', 'group': 'Risk', 'lag': 1},
+        'gpr_index':             {'zh': '地缘政治风险指数(GPR)', 'src': 'GPR', 'src_detail': 'Caldara & Iacoviello', 'freq': 'monthly', 'group': 'Risk', 'lag': 30},
+        'us_oil_inventory_total':{'zh': '美国原油商业库存', 'src': 'EIA', 'src_detail': 'EIA WCESTUS1', 'freq': 'weekly→monthly', 'group': 'Supply', 'lag': 5},
+        'us_crude_production':   {'zh': '美国原油产量', 'src': 'EIA', 'src_detail': 'EIA MCRFPUS2', 'freq': 'monthly', 'group': 'Supply', 'lag': 60},
+        'rig_count_us_new':      {'zh': '美国石油钻井数', 'src': 'Baker Hughes', 'src_detail': 'Baker Hughes Rig Count', 'freq': 'weekly→monthly', 'group': 'Supply', 'lag': 3},
+        'supply_saudi':          {'zh': '沙特原油产量', 'src': 'OPEC', 'src_detail': 'OPEC MOMR', 'freq': 'monthly', 'group': 'Supply', 'lag': 15},
+        # Derived features (computed from source features)
+        'vix_lag1':              {'zh': 'VIX滞后1期', 'src': '派生计算', 'src_detail': 'VIX t-1', 'freq': 'monthly', 'group': 'Risk_Geo', 'lag': 0},
+        'vix_lag2':              {'zh': 'VIX滞后2期', 'src': '派生计算', 'src_detail': 'VIX t-2', 'freq': 'monthly', 'group': 'Risk_Geo', 'lag': 0},
+        'geo_shock_count':       {'zh': '地缘冲击事件数', 'src': '派生计算', 'src_detail': 'GPR超阈值计数', 'freq': 'monthly', 'group': 'Risk_Geo', 'lag': 0},
+        'geo_active_events':     {'zh': '活跃地缘事件数', 'src': '派生计算', 'src_detail': '事件时间线活跃计数', 'freq': 'monthly', 'group': 'Risk_Geo', 'lag': 0},
+        'mom1m_lag1':            {'zh': '油价动量(1M滞后)', 'src': '派生计算', 'src_detail': 'WTI月收益率 t-1', 'freq': 'monthly', 'group': 'Technical', 'lag': 0},
+        'hist_vol_12m':          {'zh': '12月历史波动率', 'src': '派生计算', 'src_detail': 'WTI 12M 滚动std', 'freq': 'monthly', 'group': 'Technical', 'lag': 0},
+        'rsi12m':                {'zh': '12月RSI指标', 'src': '派生计算', 'src_detail': 'WTI 12M RSI', 'freq': 'monthly', 'group': 'Technical', 'lag': 0},
+    }
+
+    # Try to read actual panel data for live values
+    panel = None
+    for pp in ['output/panel_monthly_live.csv', 'output/panel_monthly.csv']:
+        if os.path.exists(pp):
+            try:
+                panel = pd.read_csv(pp, index_col=0, parse_dates=True)
+            except:
+                pass
+            break
+
+    # Also try the static quality report as fallback
+    static = _load_json(OUTPUT_FILES.get('quality', ''), {})
+
+    result = {}
+    from config import FEATURES
+    all_feats = list(META.keys())
+    # Also add any features in FEATURES list not in META
+    for f in FEATURES:
+        if f not in all_feats:
+            all_feats.append(f)
+
+    for feat in all_feats:
+        meta = META.get(feat, {})
+        sq = static.get(feat, {})
+
+        # Get latest_value from panel
+        latest_value = None
+        total_months = 0
+        missing = 0
+        missing_rate = 0.0
+        first_valid = None
+        last_valid = None
+        staleness = None
+        status = 'OK'
+
+        if panel is not None and feat in panel.columns:
+            series = panel[feat].dropna()
+            total_months = len(panel)
+            missing = int(panel[feat].isna().sum())
+            missing_rate = round(missing / total_months, 3) if total_months > 0 else 0
+            if len(series) > 0:
+                latest_value = round(float(series.iloc[-1]), 4)
+                first_valid = str(series.index[0])[:10]
+                last_valid = str(series.index[-1])[:10]
+                staleness = (pd.Timestamp.now() - series.index[-1]).days
+        else:
+            # Fall back to static data
+            latest_value = sq.get('latest_value')
+            total_months = sq.get('total_months', 0)
+            missing = sq.get('missing', 0)
+            missing_rate = sq.get('missing_rate', 0)
+            first_valid = sq.get('first_valid')
+            last_valid = sq.get('last_valid')
+            staleness = sq.get('staleness_days')
+
+        # Determine status
+        if missing_rate > 0.3:
+            status = 'HIGH_MISSING'
+        elif staleness and staleness > 60:
+            status = 'STALE'
+        else:
+            status = 'OK'
+
+        result[feat] = {
+            'name_zh': meta.get('zh', feat),
+            'source': meta.get('src', sq.get('source', 'CSV')),
+            'source_detail': meta.get('src_detail', ''),
+            'factor_group': meta.get('group', sq.get('factor_group', 'Other')),
+            'frequency': meta.get('freq', sq.get('frequency', 'monthly')),
+            'release_lag_days': meta.get('lag', sq.get('release_lag_days', 0)),
+            'total_months': total_months,
+            'missing': missing,
+            'missing_rate': missing_rate,
+            'first_valid': first_valid,
+            'last_valid': last_valid,
+            'staleness_days': staleness,
+            'latest_value': latest_value,
+            'status': status,
+        }
+
+    return result
+
+
+@app.get("/api/lineage")
+def get_lineage():
+    lineage = _load_json(OUTPUT_FILES.get('lineage', ''), {})
+    # Enrich sources if present
+    if 'sources' in lineage:
+        src = lineage['sources']
+        # Fix CSV source name
+        if 'CSV' in src:
+            src['Baker Hughes'] = {'name': 'Baker Hughes Rig Count', 'url': 'https://rigcount.bakerhughes.com/', 'type': '公开CSV', 'features_count': 1}
+            src['World Bank'] = {'name': 'World Bank Pink Sheet', 'url': 'https://www.worldbank.org/en/research/commodity-markets', 'type': '公开Excel', 'features_count': 1}
+            src['OPEC'] = {'name': 'OPEC Monthly Oil Market Report', 'url': 'https://www.opec.org/opec_web/en/', 'type': '公开PDF/CSV', 'features_count': 1}
+            src['GPR'] = {'name': 'Geopolitical Risk Index', 'url': 'https://www.matteoiacoviello.com/gpr.htm', 'type': '公开CSV', 'features_count': 1}
+            del src['CSV']
+    return lineage
+
+
+@app.get("/api/feat_sel")
+def get_feat_sel():
+    return _load_json(OUTPUT_FILES['feat_sel'])
+
+
+@app.get("/api/causal")
+def get_causal():
+    return _load_json(os.path.join(OUTPUT_DIR, 'causal_analysis.json'), {})
+
+
+# ── AI Agent Chat ──
+
+class ChatRequest(BaseModel):
+    message: str
+    session_id: Optional[str] = None
+
+_sessions = {}
+
+@app.post("/api/chat")
+async def chat(req: ChatRequest):
+    """AI Agent chat endpoint."""
+    try:
+        from agent.chat import chat_with_agent
+        session_id = req.session_id or 'default'
+        history = _sessions.get(session_id, [])
+        reply, history = chat_with_agent(req.message, history)
+        _sessions[session_id] = history
+        return {"reply": reply}
+    except Exception as e:
+        import traceback
+        tb = traceback.format_exc()
+        print(f"[AGENT ERROR] {type(e).__name__}: {e}\n{tb}")
+        error_msg = f"⚠️ Agent 调用出错 ({type(e).__name__}): {str(e)}"
+        if "timeout" in str(e).lower() or "connect" in str(e).lower():
+            error_msg += "\n🔄 LLM 服务暂时不可用，请稍后重试。"
+        return {"reply": error_msg}
+
+
+# ═══════════════════════════════════════════════════════════
+# Live Oil Prices — AKShare real-time daily data
+# ═══════════════════════════════════════════════════════════
+
+import time as _time
+
+_price_cache = {"data": None, "ts": 0}
+_news_cache = {"data": None, "ts": 0}
+CACHE_TTL = 3600  # 1 hour cache
+
+
+def _fetch_live_prices():
+    """Fetch latest oil prices from AKShare (daily frequency)."""
+    now = _time.time()
+    if _price_cache["data"] and (now - _price_cache["ts"]) < CACHE_TTL:
+        return _price_cache["data"]
+
+    try:
+        import akshare as ak
+
+        result = {}
+
+        # WTI Crude (CL)
+        try:
+            df_cl = ak.futures_foreign_hist(symbol='CL')
+            if len(df_cl) >= 2:
+                cur = df_cl.iloc[-1]
+                prev = df_cl.iloc[-2]
+                price = float(cur['close'])
+                change = round(price - float(prev['close']), 2)
+                pct = round(change / float(prev['close']) * 100, 2)
+                result['wti'] = {
+                    'price': price, 'change': change, 'pct': pct,
+                    'date': str(cur['date'])[:10]
+                }
+        except Exception as e:
+            print(f"[LIVE] WTI fetch failed: {e}")
+
+        # Brent Crude — estimate from WTI + typical spread (~$3-5)
+        if 'wti' in result:
+            wti_p = result['wti']['price']
+            # Use historical Brent-WTI spread
+            brent_spread = 3.8
+            brent_price = round(wti_p + brent_spread, 2)
+            brent_change = result['wti']['change']
+            brent_pct = round(brent_change / (brent_price - brent_change) * 100, 2)
+            result['brent'] = {
+                'price': brent_price, 'change': brent_change, 'pct': brent_pct,
+                'date': result['wti']['date'],
+                'note': 'estimated_from_spread'
+            }
+
+        # Natural Gas (NG - Henry Hub)
+        try:
+            df_ng = ak.futures_foreign_hist(symbol='NG')
+            if len(df_ng) >= 2:
+                cur = df_ng.iloc[-1]
+                prev = df_ng.iloc[-2]
+                price = float(cur['close'])
+                change = round(price - float(prev['close']), 2)
+                pct = round(change / float(prev['close']) * 100, 2)
+                result['natgas'] = {
+                    'price': price, 'change': change, 'pct': pct,
+                    'date': str(cur['date'])[:10]
+                }
+        except Exception as e:
+            print(f"[LIVE] NG fetch failed: {e}")
+
+        if result:
+            _price_cache["data"] = result
+            _price_cache["ts"] = now
+            print(f"[LIVE] Prices updated: WTI=${result.get('wti',{}).get('price','?')}, "
+                  f"Brent=${result.get('brent',{}).get('price','?')}, "
+                  f"NG=${result.get('natgas',{}).get('price','?')}")
+
+        return result
+
+    except Exception as e:
+        print(f"[LIVE] Price fetch error: {e}")
+        return {}
+
+
+def _fetch_live_news():
+    """Fetch latest oil-related news from Google News RSS."""
+    now = _time.time()
+    if _news_cache["data"] and (now - _news_cache["ts"]) < CACHE_TTL:
+        return _news_cache["data"]
+
+    import urllib.request
+    import re
+    from datetime import datetime
+
+    news_items = []
+
+    # Google News RSS for oil price
+    feeds = [
+        ("https://news.google.com/rss/search?q=oil+price+crude+OPEC&hl=en-US&gl=US&ceid=US:en", "en"),
+        ("https://news.google.com/rss/search?q=油价+原油+OPEC&hl=zh-CN&gl=CN&ceid=CN:zh-Hans", "zh"),
+    ]
+
+    for feed_url, lang in feeds:
+        try:
+            req = urllib.request.Request(feed_url, headers={'User-Agent': 'Mozilla/5.0'})
+            with urllib.request.urlopen(req, timeout=8) as resp:
+                data = resp.read().decode('utf-8', errors='replace')
+
+            # Parse XML items
+            items = re.findall(r'<item>(.*?)</item>', data, re.DOTALL)
+
+            for item_xml in items[:8]:
+                title = re.search(r'<title>(.*?)</title>', item_xml)
+                source = re.search(r'<source[^>]*>(.*?)</source>', item_xml)
+                pub_date = re.search(r'<pubDate>(.*?)</pubDate>', item_xml)
+
+                if title:
+                    title_text = title.group(1).strip()
+                    # Clean HTML entities
+                    title_text = title_text.replace('&amp;', '&').replace('&lt;', '<').replace('&gt;', '>').replace('&#39;', "'").replace('&quot;', '"')
+
+                    src = source.group(1).strip() if source else 'News'
+
+                    # Parse date
+                    date_str = ''
+                    if pub_date:
+                        try:
+                            dt = datetime.strptime(pub_date.group(1).strip()[:25],
+                                                   '%a, %d %b %Y %H:%M:%S')
+                            date_str = dt.strftime('%m-%d %H:%M')
+                        except:
+                            date_str = pub_date.group(1).strip()[:16]
+
+                    # Auto-tag based on keywords
+                    tag = _auto_tag(title_text)
+
+                    news_items.append({
+                        'text': title_text,
+                        'src': src,
+                        'time': date_str,
+                        'tag': tag,
+                        'lang': lang,
+                    })
+
+        except Exception as e:
+            print(f"[NEWS] Feed fetch error ({lang}): {e}")
+
+    if news_items:
+        _news_cache["data"] = news_items[:15]
+        _news_cache["ts"] = now
+        print(f"[NEWS] Fetched {len(news_items)} news items")
+
+    return news_items[:15]
+
+
+def _auto_tag(text: str) -> str:
+    """Auto-tag news based on keywords."""
+    t = text.lower()
+    if any(w in t for w in ['opec', 'supply', 'production', 'output', 'barrel',
+                            '产量', '减产', '增产', '供给', '库存', 'inventory']):
+        return '供给'
+    if any(w in t for w in ['demand', 'consumption', 'growth', 'recession',
+                            '需求', '消费', '增长', '衰退', 'gdp']):
+        return '需求'
+    if any(w in t for w in ['war', 'sanction', 'iran', 'russia', 'conflict', 'military',
+                            '制裁', '冲突', '地缘', '战争', 'tariff', '关税', 'trump']):
+        return '地缘'
+    if any(w in t for w in ['fed', 'rate', 'inflation', 'dollar', 'central bank',
+                            '利率', '通胀', '美联储', '央行', '美元']):
+        return '宏观'
+    if any(w in t for w in ['renewable', 'ev', 'solar', 'wind', 'transition', 'climate',
+                            '新能源', '碳', '气候', '转型']):
+        return '政策'
+    return '市场'
+
+
+@app.get("/api/live-prices")
+def api_live_prices():
+    """Return latest daily oil prices (WTI, Brent, Natural Gas)."""
+    data = _fetch_live_prices()
+    if not data:
+        raise HTTPException(status_code=503, detail="Unable to fetch live prices")
+    return data
+
+
+@app.get("/api/live-news")
+def api_live_news():
+    """Return latest oil-related news from RSS feeds."""
+    data = _fetch_live_news()
+    return {"items": data}
+
+
+# ═══════════════════════════════════════════════════════════
+# Static file serving (for Docker / HuggingFace deployment)
+# ═══════════════════════════════════════════════════════════
+
+_frontend_dist = os.path.join(BASE_DIR, 'frontend', 'dist')
+if os.path.isdir(_frontend_dist):
+    from fastapi.staticfiles import StaticFiles
+    from fastapi.responses import FileResponse
+
+    # Serve static assets (JS/CSS/images)
+    app.mount("/assets", StaticFiles(directory=os.path.join(_frontend_dist, "assets")), name="static-assets")
+
+    # Serve favicon and other root files
+    @app.get("/favicon.svg")
+    async def favicon():
+        return FileResponse(os.path.join(_frontend_dist, "favicon.svg"))
+
+    # Catch-all: serve index.html for SPA routing
+    @app.get("/{full_path:path}")
+    async def serve_spa(full_path: str):
+        file_path = os.path.join(_frontend_dist, full_path)
+        if os.path.isfile(file_path):
+            return FileResponse(file_path)
+        return FileResponse(os.path.join(_frontend_dist, "index.html"))
+
+
+if __name__ == '__main__':
+    import uvicorn
+    port = int(os.environ.get("PORT", 8765))
+    print("=" * 60)
+    print(f"油刃有余 OilVerse API — http://localhost:{port}")
+    print("=" * 60)
+    uvicorn.run(app, host="0.0.0.0", port=port)

config.py

@@ -0,0 +1,95 @@
+"""
+config.py — 全局常量、特征定义、路径、API Key
+=================================================
+所有模块共享的配置集中在此。
+"""
+import os
+
+# ── 工作目录 ──
+BASE_DIR = os.environ.get('OILVERSE_BASE_DIR', os.path.dirname(os.path.abspath(__file__)))
+OUTPUT_DIR = os.path.join(BASE_DIR, 'output')
+DATA_DIR = os.path.join(BASE_DIR, 'data', 'csv_raw')
+
+# ── API Keys ──
+FRED_API_KEY = 'fc02a6e6a359a4cc16f0f1752d258011'
+EIA_API_KEY = '9Nv5PhLREMmmKeo0zJ2U3Zu21Bntf8DfhEKBpi55'
+SILICONFLOW_API_KEY = 'sk-cgllfrsuchzzwerkxcegkhbroboqcnpuubhreyrfudfstqjv'
+SILICONFLOW_BASE_URL = 'https://api.siliconflow.cn/v1'
+SILICONFLOW_MODEL = 'Qwen/Qwen2.5-7B-Instruct'
+
+# ── 价格列 ──
+PRICE_COLS = {
+    'WTI':   'WTI_spot',
+    'Brent': 'Brent_spot',
+}
+PRICE_COL = 'WTI_spot'  # backward compat
+
+# ── 特征列表 (20个，含GPR + 新闻情绪) ──
+FEATURES = [
+    'Brent_spot', 'natgas_spot_henry', 'iron_ore_spot',              # Price
+    'rig_count_us_new', 'supply_saudi', 'us_oil_inventory_total',    # Supply
+    'pmi_us_mfg', 'usd_index', 'nonfarm_us', 'ipi_us',             # Demand
+    'vix_lag1', 'vix_lag2', 'geo_shock_count', 'geo_active_events',  # Risk+Geo
+    'mom1m_lag1', 'hist_vol_12m', 'rsi12m',                          # Technical
+    'news_oil_sentiment', 'news_geo_tone', 'news_article_volume',    # Alternative (GDELT)
+]
+
+# ── 因子分组 ──
+FACTOR_GROUPS = {
+    'Price':       ['Brent_spot', 'natgas_spot_henry', 'iron_ore_spot'],
+    'Supply':      ['rig_count_us_new', 'supply_saudi', 'us_oil_inventory_total'],
+    'Demand':      ['pmi_us_mfg', 'usd_index', 'nonfarm_us', 'ipi_us'],
+    'Risk_Geo':    ['vix_lag1', 'vix_lag2', 'geo_shock_count', 'geo_active_events'],
+    'Technical':   ['mom1m_lag1', 'hist_vol_12m', 'rsi12m'],
+    'Alternative': ['news_oil_sentiment', 'news_geo_tone', 'news_article_volume'],
+}
+
+# ── Walk-Forward 参数 ──
+TRAIN_WINDOW = 120
+MAX_FEATURES = 10
+MIN_TRAIN_SAMPLES = 20
+
+# ── Regime 签名 (历史时期特征向量) ──
+REGIME_SIGNATURES = {
+    '2008 金融危机':   {'supply_stress': 0.1,  'demand_stress': 0.6,  'geopolitical_stress': 0.1,  'price_momentum': 0.15, 'vol_level': 0.25},
+    '2014 页岩油冲击': {'supply_stress': 0.5,  'demand_stress': 0.2,  'geopolitical_stress': 0.05, 'price_momentum': 0.2,  'vol_level': 0.12},
+    '2020 COVID':     {'supply_stress': 0.3,  'demand_stress': 0.5,  'geopolitical_stress': 0.05, 'price_momentum': 0.1,  'vol_level': 0.30},
+    '2022 俄乌冲突':   {'supply_stress': 0.3,  'demand_stress': 0.1,  'geopolitical_stress': 0.5,  'price_momentum': 0.05, 'vol_level': 0.15},
+    '2023 OPEC减产':  {'supply_stress': 0.5,  'demand_stress': 0.15, 'geopolitical_stress': 0.15, 'price_momentum': 0.1,  'vol_level': 0.08},
+    '常态/低波动':     {'supply_stress': 0.15, 'demand_stress': 0.15, 'geopolitical_stress': 0.1,  'price_momentum': 0.1,  'vol_level': 0.04},
+}
+
+REGIME_TYPE_MAP = {
+    '2008 金融危机': 'demand_collapse',
+    '2014 页岩油冲击': 'supply_glut',
+    '2020 COVID': 'demand_collapse',
+    '2022 俄乌冲突': 'geopolitical',
+    '2023 OPEC减产': 'supply_cut',
+    '常态/低波动': 'normal',
+}
+
+# ── 行业映射 ──
+INDUSTRIES = ['Aviation', 'Logistics', 'Chemicals', 'Manufacturing', 'Upstream_OG']
+INDUSTRY_ZH = {
+    'Aviation': '航空', 'Logistics': '物流', 'Chemicals': '化工',
+    'Manufacturing': '制造', 'Upstream_OG': '上游油气',
+}
+
+# ── 输出文件路径 ──
+OUTPUT_FILES = {
+    'results':     os.path.join(OUTPUT_DIR, 'v2_championship_results.csv'),
+    'shap':        os.path.join(OUTPUT_DIR, 'v2_shap_records.json'),
+    'nlg':         os.path.join(OUTPUT_DIR, 'v2_nlg_reports.json'),
+    'scenarios':   os.path.join(OUTPUT_DIR, 'v2_scenarios.json'),
+    'regime':      os.path.join(OUTPUT_DIR, 'v2_regime_data.json'),
+    'ablation':    os.path.join(OUTPUT_DIR, 'v2_ablation.json'),
+    'hedging':     os.path.join(OUTPUT_DIR, 'v2_hedging.json'),
+    'backtest':    os.path.join(OUTPUT_DIR, 'v2_hedge_backtest.json'),
+    'feat_sel':    os.path.join(OUTPUT_DIR, 'v2_feature_selection.json'),
+    'dashboard':   os.path.join(OUTPUT_DIR, 'risk_dashboard.html'),
+    'panel_live':  os.path.join(OUTPUT_DIR, 'panel_monthly_live.csv'),
+    'lineage':     os.path.join(OUTPUT_DIR, 'data_lineage.json'),
+    'quality':     os.path.join(OUTPUT_DIR, 'data_quality.json'),
+    'causal':      os.path.join(OUTPUT_DIR, 'causal_analysis.json'),
+    'events':      os.path.join(OUTPUT_DIR, 'event_timeline.json'),
+}

core/__init__.py

@@ -0,0 +1,8 @@
+"""core/ — ML prediction engine modules."""
+import sys, os
+sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
+
+from core.engine import load_panel, run_walk_forward
+from core.analysis import apply_industry_rules, generate_all_reports, evaluate_results, run_ablation
+from core.hedging import compute_all_industry_hedges, backtest_hedging
+from core.feature_selection import run_feature_funnel

core/analysis.py

@@ -0,0 +1,395 @@
+"""
+analysis.py — NLG报告、行业影响、评估指标、消融实验
+====================================================
+从 v2_championship.py 拆出的分析与评估模块。
+"""
+
+import pandas as pd
+import numpy as np
+from sklearn.linear_model import QuantileRegressor
+from sklearn.preprocessing import StandardScaler
+
+from config import FACTOR_GROUPS, INDUSTRIES, INDUSTRY_ZH
+
+
+# ═══════════════════════════════════════════════════════════
+# INDUSTRY IMPACT RULE ENGINE
+# ═══════════════════════════════════════════════════════════
+
+def apply_industry_rules(rec):
+    """基于风险等级、偏置、因子等推断各行业的风险和建议。"""
+    risk_level = rec.get('risk_level', 'Low')
+    risk_bias = rec.get('risk_bias', 'Balanced')
+    vol_ratio = rec.get('vol_ratio', 1.0)
+    top_fac = rec.get('top_factor', 'Unknown')
+
+    is_high = risk_level == 'High'
+    is_medium = risk_level == 'Medium'
+    is_upward = risk_bias == 'Upward'
+    is_downward = risk_bias == 'Downward'
+    wide = vol_ratio > 1.3
+
+    rules = {}
+    for industry in INDUSTRIES:
+        ind_risk = 'Low'
+        ind_action = 'Routine monitoring'
+
+        if industry == 'Aviation':
+            if is_high and is_upward:
+                ind_risk, ind_action = 'High', 'Increase hedging coverage; review fuel cost budget'
+            elif is_high:
+                ind_risk, ind_action = 'High', 'Elevated volatility; prepare contingency liquidity'
+            elif is_upward:
+                ind_risk, ind_action = 'Medium-High', 'Monitor fuel cost exposure; consider forward contracts'
+            elif is_medium:
+                ind_risk, ind_action = 'Medium', 'Review quarterly fuel hedging strategy'
+            if top_fac == 'Demand': ind_action += '; demand-driven → cost pressure may persist'
+            elif top_fac == 'Supply': ind_action += '; supply-driven → watch OPEC decisions'
+            elif top_fac == 'Risk_Geo': ind_action += '; geopolitical risk → event monitoring'
+
+        elif industry == 'Logistics':
+            if is_high and is_upward:
+                ind_risk, ind_action = 'Medium-High', 'Review transport cost pass-through; working capital buffer'
+            elif is_high:
+                ind_risk, ind_action = 'Medium', 'Monitor diesel/freight cost exposure'
+            elif is_medium and is_upward:
+                ind_risk, ind_action = 'Medium', 'Review fuel surcharge mechanisms'
+
+        elif industry == 'Chemicals':
+            if is_upward and top_fac == 'Supply':
+                ind_risk, ind_action = 'High', 'Feedstock cost pressure; margin compression likely'
+            elif is_high:
+                ind_risk, ind_action = 'Medium-High', 'Monitor naphtha/ethylene spread; review procurement'
+            elif wide:
+                ind_risk, ind_action = 'Medium', 'Profit uncertainty elevated; scenario planning advised'
+
+        elif industry == 'Manufacturing':
+            if is_high and is_upward:
+                ind_risk, ind_action = 'High', 'Energy cost surge risk; review energy hedging'
+            elif is_high:
+                ind_risk, ind_action = 'Medium', 'Elevated input cost volatility'
+            elif is_medium:
+                ind_risk, ind_action = 'Medium', 'Monitor energy procurement costs'
+
+        elif industry == 'Upstream_OG':
+            if is_downward and is_high:
+                ind_risk, ind_action = 'High', 'Revenue decline risk; review covenant compliance & liquidity'
+            elif is_downward:
+                ind_risk, ind_action = 'Medium-High', 'Downside tail expanding; monitor cash flow coverage'
+            elif is_upward:
+                ind_risk, ind_action = 'Low', 'Revenue tailwind; capex commitment review advised'
+            else:
+                ind_risk, ind_action = 'Low-Medium', 'Balanced outlook'
+
+        rules[f'{industry}_risk'] = ind_risk
+        rules[f'{industry}_action'] = ind_action
+    return rules
+
+
+# ═══════════════════════════════════════════════════════════
+# REGIME ECONOMIC NARRATIVES
+# ═══════════════════════════════════════════════════════════
+
+REGIME_NARRATIVES = {
+    '2008 金融危机': {
+        'history': '2008年全球金融危机期间，油价从$147暴跌至$32，降幅78%。需求端崩塌是主因——全球GDP收缩、贸易锐减、制造业PMI普遍跌破40。',
+        'implication': '需求崩塌格局下，下游成本端企业短期受益于低油价，但总需求萎缩拖累整体营收。上游企业面临最大冲击。',
+        'hedge_advice': '上游企业应���即锁定远期销售价格；下游企业可逢低建仓，锁定低价原料。',
+    },
+    '2014 页岩油冲击': {
+        'history': '2014-16年美国页岩油产量爆发（+400万桶/日），叠加OPEC拒绝减产，油价从$110跌至$26。供给过剩主导。',
+        'implication': '供给过剩格局持续时间长（18个月以上），下游企业成本优势可持续；上游企业需要重组债务、缩减资本开支。',
+        'hedge_advice': '重点关注远期曲线结构（contango加深），利用期货锁价窗口。下游企业延长采购合约期限。',
+    },
+    '2020 COVID': {
+        'history': '2020年COVID-19导致全球需求暴减2000万桶/日，WTI期货历史性跌至负值。需求冲击+仓储危机双重打击。',
+        'implication': '极端需求冲击下，航空业客运量降90%+，物流链中断。但复苏速度可能超预期——V型反弹是历史常态。',
+        'hedge_advice': '短期：保持现金流弹性，避免过度套保。中期：关注OPEC+协调减产信号，逢低建立多头头寸。',
+    },
+    '2022 俄乌冲突': {
+        'history': '2022年俄乌冲突导致俄罗斯原油出口受制裁，供给缺口+地缘溢价推升布伦特至$130+。地缘政治+供给双重冲击。',
+        'implication': '地缘驱动的价格飙升通常突然但短暂（3-6个月），随后制裁适应和替代供给逐步消化溢价。',
+        'hedge_advice': '事件驱动行情中，期权策略优于期货——买入看涨期权锁定上限成本，保留价格回落的收益空间。',
+    },
+    '2023 OPEC减产': {
+        'history': '2023年OPEC+主动减产200万桶/日，托底油价在$70-90区间。供给管理型市场，价格波动率较低。',
+        'implication': 'OPEC减产格局下，价格区间可预测性较高，但下行风险来自减产执行率下滑和非OPEC增产。',
+        'hedge_advice': '低波动环境适合使用零成本领（collar）策略，锁定窄价格带。',
+    },
+    '常态/低波动': {
+        'history': '油价处于常态波动区间，无明显单一因子主导。市场处于供需基本平衡状态。',
+        'implication': '常态下关注结构性变化信号——OPEC会议决策、美国钻井数趋势、中国PMI走向。',
+        'hedge_advice': '常态下对冲比例可适当降低（25-40%），使用低成本期货锁价即可。',
+    },
+}
+
+
+# ═══════════════════════════════════════════════════════════
+# NLG REPORT GENERATION (ENHANCED)
+# ═══════════════════════════════════════════════════════════
+
+def generate_nlg_report(row):
+    """生成单月深度风险研判报告，含经济学叙事和对冲建议。"""
+    date = pd.Timestamp(row['test_date']).strftime('%Y年%m月')
+    rl = row['risk_level']
+    rb = row['risk_bias']
+    top = row.get('top_factor', 'Unknown')
+    q10 = row['pred_q10_1m'] * 100
+    q50 = row['pred_q50_1m'] * 100
+    q90 = row['pred_q90_1m'] * 100
+    vol = row['pred_vol'] * 100
+
+    rl_zh = {'Low': '低', 'Medium': '中等', 'High': '高'}.get(rl, rl)
+    rb_zh = {'Upward': '偏上行', 'Downward': '偏下行', 'Balanced': '均衡'}.get(rb, rb)
+    factor_zh = {
+        'Price': '价格联动', 'Supply': '供给端', 'Demand': '需求端',
+        'Risk_Geo': '地缘政治/风险', 'Technical': '技术面',
+    }.get(top, top)
+
+    # ── Part 1: 核心判断 ──
+    summary = (
+        f"【{date}油价风险研判】\n"
+        f"■ 核心判断：风险等级{rl_zh}，方向{rb_zh}，由{factor_zh}因子主导。\n"
+        f"■ 1M预测区间：[{q10:+.1f}%, {q90:+.1f}%]，中枢{q50:+.1f}%，波动率{vol:.1f}%。\n"
+    )
+
+    # 3M
+    if pd.notna(row.get('pred_q10_3m')):
+        summary += f"■ 3M预测区间：[{row['pred_q10_3m']*100:+.1f}%, {row['pred_q90_3m']*100:+.1f}%]，中枢{row['pred_q50_3m']*100:+.1f}%。\n"
+
+    # CQR
+    cqr_lo = row.get('cqr_q10_1m', None)
+    if cqr_lo is not None and pd.notna(cqr_lo):
+        summary += f"■ CQR校准区间：[{cqr_lo*100:+.1f}%, {row['cqr_q90_1m']*100:+.1f}%]（分布自由覆盖保证）。\n"
+
+    # ── Part 2: Regime 经济学叙事 ──
+    regime = row.get('regime_match', '')
+    regime_sim = row.get('regime_similarity', 0)
+    if regime and regime != 'Unknown':
+        summary += f"\n▶ 格局识别：当前最接近「{regime}」（相似度{regime_sim*100:.0f}%）\n"
+        narr = REGIME_NARRATIVES.get(regime, {})
+        if narr:
+            summary += f"  历史参照：{narr['history']}\n"
+            summary += f"  当前启示：{narr['implication']}\n"
+            summary += f"  对冲建议：{narr['hedge_advice']}\n"
+
+    # ── Part 3: 行业影响 ──
+    high_risk = []
+    med_risk = []
+    for ind in INDUSTRIES:
+        risk = str(row.get(f'{ind}_risk', 'Low'))
+        if 'High' in risk:
+            high_risk.append(INDUSTRY_ZH.get(ind, ind))
+        elif 'Medium' in risk:
+            med_risk.append(INDUSTRY_ZH.get(ind, ind))
+    if high_risk:
+        summary += f"\n▶ 高风险行业：{'、'.join(high_risk)}——建议提升套保覆盖率至60-80%。\n"
+    if med_risk:
+        summary += f"▶ 中风险行业：{'、'.join(med_risk)}——建议维持25-50%套保覆盖。\n"
+    if not high_risk and not med_risk:
+        summary += f"\n▶ 各行业风险均处于可控水平，建议维持常规套保比例。\n"
+
+    # ── Part 4: 压力测试 ──
+    base = row.get('scenario_base', 0) * 100
+    vix = row.get('scenario_vix_shock', 0) * 100
+    supply = row.get('scenario_supply_cut', 0) * 100
+    demand = row.get('scenario_demand_crash', 0) * 100
+    worst = min(supply, demand)
+    summary += (
+        f"\n▶ 压力测试：基准{base:+.1f}% | VIX翻倍{vix:+.1f}% | "
+        f"供给中断{supply:+.1f}% | 需求崩塌{demand:+.1f}%\n"
+        f"  最大下行风险：{worst:+.1f}%，建议预留相应流动性缓冲。\n"
+    )
+
+    return summary
+
+
+def generate_all_reports(results):
+    """为所有月份生成 NLG 报告。"""
+    reports = {}
+    for _, row in results.iterrows():
+        dt = pd.Timestamp(row['test_date']).strftime('%Y-%m')
+        reports[dt] = generate_nlg_report(row)
+    return reports
+
+
+# ═══════════════════════════════════════════════════════════
+# EVALUATION METRICS
+# ═══════════════════════════════════════════════════════════
+
+def evaluate_results(results):
+    """计算全面的评估指标并打印。"""
+    # 只评估有实际值的月份（排除 live forecast）
+    eval_mask = results['actual_ret_1m'].notna()
+    results = results[eval_mask].copy()
+    ar = results['actual_ret_1m'].values
+    av = results['actual_vol'].values
+    n = len(results)
+
+    print(f"\n{'='*65}")
+    print("V2 CHAMPIONSHIP — EVALUATION")
+    print("=" * 65)
+
+    # 1M Interval
+    print(f"\n--- 1M INTERVAL ---")
+    models = [('QR (vol-adapt)', 'pred'), ('LightGBM', 'lgb')]
+    if 'cqr_q10_1m' in results.columns:
+        models.append(('Conformal QR', 'cqr'))
+    for model_name, prefix in models:
+        q10 = results[f'{prefix}_q10_1m'].values
+        q90 = results[f'{prefix}_q90_1m'].values
+        cov = ((ar >= q10) & (ar <= q90)).mean()
+        wis = ((q90-q10) + (2/0.2)*np.maximum(q10-ar, 0) + (2/0.2)*np.maximum(ar-q90, 0)).mean()
+        vm = np.median(av)
+        hi = av > vm
+        cov_hi = ((ar[hi] >= q10[hi]) & (ar[hi] <= q90[hi])).mean() if hi.sum() > 0 else 0
+        print(f"  {model_name:<18} Cov={cov:.1%} HiCov={cov_hi:.1%} WIS={wis:.4f}")
+
+    nq10 = np.full(n, np.quantile(ar, 0.10))
+    nq90 = np.full(n, np.quantile(ar, 0.90))
+    naive_wis = ((nq90-nq10) + (2/0.2)*np.maximum(nq10-ar, 0) + (2/0.2)*np.maximum(ar-nq90, 0)).mean()
+    print(f"  {'Naive':<18} WIS={naive_wis:.4f}")
+
+    # 3M
+    print(f"\n--- 3M INTERVAL ---")
+    m3 = results['actual_ret_3m'].notna()
+    if m3.sum() > 10:
+        ar3 = results.loc[m3, 'actual_ret_3m'].values
+        q10_3 = results.loc[m3, 'pred_q10_3m'].values
+        q90_3 = results.loc[m3, 'pred_q90_3m'].values
+        cov3 = ((ar3 >= q10_3) & (ar3 <= q90_3)).mean()
+        wis3 = ((q90_3-q10_3) + (2/0.2)*np.maximum(q10_3-ar3, 0) + (2/0.2)*np.maximum(ar3-q90_3, 0)).mean()
+        print(f"  QR 3M (vol-adapt)  Cov={cov3:.1%} WIS={wis3:.4f} n={m3.sum()}")
+
+    # Vol
+    print(f"\n--- VOL SCORE ---")
+    pv = results['pred_vol'].values
+    bl = results['baseline_ewma'].values
+    for nm, prd in [('V2 BL+Resid', pv), ('EWMA', bl)]:
+        from sklearn.metrics import mean_squared_error
+        rmse = np.sqrt(mean_squared_error(av, prd))
+        corr = np.corrcoef(av, prd)[0, 1]
+        print(f"  {nm:<18} RMSE={rmse:.4f} corr={corr:+.3f}")
+
+    # Risk levels
+    print(f"\n--- RISK LEVELS ---")
+    for lvl in ['Low', 'Medium', 'High']:
+        mask = results['risk_level'] == lvl
+        if mask.sum() > 0:
+            print(f"  {lvl:<8}: vol={av[mask].mean():.4f} n={mask.sum()}")
+
+    # Factor frequency
+    print(f"\n--- FACTOR FREQ ---")
+    if 'top_factor' in results.columns:
+        for fac, cnt in results['top_factor'].value_counts().items():
+            print(f"  {fac:<12}: {cnt} ({cnt/n:.1%})")
+
+    # SHAP
+    print(f"\n--- SHAP (avg) ---")
+    for g in FACTOR_GROUPS:
+        col = f'shap_{g}'
+        if col in results.columns:
+            print(f"  {g:<12}: {results[col].abs().mean():.4f}")
+
+    # Scenario
+    print(f"\n--- SCENARIO (latest) ---")
+    lat = results.iloc[-1]
+    print(f"  Base:         {lat['scenario_base']*100:+.1f}%")
+    print(f"  VIX shock:    {lat['scenario_vix_shock']*100:+.1f}%")
+    print(f"  Supply cut:   {lat['scenario_supply_cut']*100:+.1f}%")
+    print(f"  Demand crash: {lat['scenario_demand_crash']*100:+.1f}%")
+
+    # NLG sample
+    print(f"\n--- NLG REPORT (latest) ---")
+    print(generate_nlg_report(results.iloc[-1]))
+
+
+# ═══════════════════════════════════════════════════════════
+# ABLATION EXPERIMENTS
+# ═══════════════════════════════════════════════════════════
+
+def _run_qr_eval(panel, feat_list, train_window=120):
+    """内部辅助：对给定特征子集跑 walk-forward QR 并返回 (cov, wis, n)。"""
+    hits, totals, wis_list = 0, 0, []
+    for i in range(train_window, len(panel) - 1):
+        train_df = panel.iloc[max(0, i - train_window):i]
+        test_df = panel.iloc[i:i + 1]
+        avail = [f for f in feat_list if f in train_df.columns and train_df[f].notna().mean() > 0.8]
+        if len(avail) < 2:
+            continue
+        X_tr = train_df[avail].fillna(train_df[avail].median())
+        X_te = test_df[avail].fillna(train_df[avail].median())
+        sc = StandardScaler()
+        X_tr_s = sc.fit_transform(X_tr)
+        X_te_s = sc.transform(X_te)
+        y = train_df['target_ret_1m'].dropna()
+        mask = y.index.isin(X_tr.index)
+        y = y[mask]
+        X_tr_s = X_tr_s[:len(y)]
+        actual = panel['target_ret_1m'].iloc[i]
+        if np.isnan(actual):
+            continue
+        try:
+            qr10 = QuantileRegressor(quantile=0.10, alpha=0.1, solver='highs')
+            qr90 = QuantileRegressor(quantile=0.90, alpha=0.1, solver='highs')
+            qr10.fit(X_tr_s, y)
+            qr90.fit(X_tr_s, y)
+            p10, p90 = qr10.predict(X_te_s)[0], qr90.predict(X_te_s)[0]
+            if p10 > p90: p10, p90 = p90, p10
+            hit = 1 if p10 <= actual <= p90 else 0
+            hits += hit
+            totals += 1
+            wis_list.append((p90-p10) + (2/0.2)*max(p10-actual, 0) + (2/0.2)*max(actual-p90, 0))
+        except:
+            continue
+    cov = hits / totals if totals > 0 else 0
+    wis = np.mean(wis_list) if wis_list else 999
+    return cov, wis, totals
+
+
+def run_ablation(panel, features):
+    """运行消融实验：训练窗口 + 因子组 leave-one-out。"""
+    print(f"\n--- ABLATION EXPERIMENTS ---")
+    ablation_results = []
+
+    # ── Part 1: Window ablation ──
+    print("  [窗口消融]")
+    for w in [84, 120, 180]:
+        cov, wis, n_ab = _run_qr_eval(panel, features, train_window=w)
+        ablation_results.append({
+            'type': 'window', 'param': w, 'param_label': f'{w}月',
+            'cov': round(cov, 3), 'wis': round(wis, 4), 'n': n_ab
+        })
+        print(f"    Window={w:>3}: Cov={cov:.1%} WIS={wis:.4f} n={n_ab}")
+
+    # ── Part 2: Factor-group leave-one-out ──
+    print("  [因子组消融 — Leave-One-Out]")
+    # Baseline: all factors
+    base_cov, base_wis, base_n = _run_qr_eval(panel, features)
+    ablation_results.append({
+        'type': 'factor_group', 'param': 'ALL', 'param_label': '全部因子',
+        'cov': round(base_cov, 3), 'wis': round(base_wis, 4), 'n': base_n
+    })
+    print(f"    ALL (baseline): Cov={base_cov:.1%} WIS={base_wis:.4f}")
+
+    group_zh = {'Price': '价格联动', 'Supply': '供给端', 'Demand': '需求端',
+                'Risk_Geo': '地缘/风险', 'Technical': '技术面'}
+
+    for group, members in FACTOR_GROUPS.items():
+        # Remove this group's features
+        reduced = [f for f in features if f not in members]
+        if len(reduced) < 2:
+            continue
+        cov, wis, n_ab = _run_qr_eval(panel, reduced)
+        delta_cov = cov - base_cov
+        delta_wis = wis - base_wis
+        ablation_results.append({
+            'type': 'factor_group', 'param': group,
+            'param_label': f'去除{group_zh.get(group, group)}',
+            'cov': round(cov, 3), 'wis': round(wis, 4), 'n': n_ab,
+            'delta_cov': round(delta_cov, 3), 'delta_wis': round(delta_wis, 4),
+        })
+        print(f"    -{group:<12}: Cov={cov:.1%} (Δ{delta_cov:+.1%}) "
+              f"WIS={wis:.4f} (Δ{delta_wis:+.4f})")
+
+    return ablation_results

core/engine.py

@@ -0,0 +1,632 @@
+"""
+engine.py — Walk-Forward 预测引擎
+=====================================
+核心组件：
+  1. 数据加载与目标构建
+  2. Walk-Forward 主循环
+     ├─ 特征选择 (MI)
+     ├─ 波动率预测 (EWMA + Ridge residual)
+     ├─ 区间预测 (QR + LightGBM) × 1M/3M
+     ├─ Vol-Adaptive 宽度调整
+     ├─ LightGBM 特征重要性
+     ├─ 风险等级 / 偏置推断
+     ├─ Regime 检测 (余弦相似度)
+     └─ 因子归因 (Ridge)
+"""
+
+import pandas as pd
+import numpy as np
+import warnings
+from sklearn.linear_model import Ridge, QuantileRegressor
+from sklearn.preprocessing import StandardScaler
+from sklearn.feature_selection import mutual_info_regression
+import lightgbm as lgb
+
+# TFT (Temporal Fusion Transformer) — frontier deep learning model
+try:
+    from core.tft_model import tft_predict_step
+    _TFT_AVAILABLE = True
+except ImportError:
+    _TFT_AVAILABLE = False
+
+from config import (
+    FEATURES, FACTOR_GROUPS, PRICE_COL, TRAIN_WINDOW,
+    MAX_FEATURES, MIN_TRAIN_SAMPLES,
+    REGIME_SIGNATURES, REGIME_TYPE_MAP,
+)
+
+warnings.filterwarnings('ignore')
+
+
+# ═══════════════════════════════════════════════════════════
+# DATA LOADING
+# ═══════════════════════════════════════════════════════════
+
+def load_panel(panel_path, price_col=None):
+    """加载月度面板并构建目标变量和地缘特征。
+
+    Args:
+        price_col: 可选，指定价格列（默认 config.PRICE_COL）。
+                   当切换目标时（如 Brent），自动从特征列表中移除该列。
+    """
+    if price_col is None:
+        price_col = PRICE_COL
+
+    monthly = pd.read_csv(panel_path, index_col=0, parse_dates=True)
+
+    # Geopolitical features
+    try:
+        geo = pd.read_csv('data/csv_raw/geopolitical_shocks.csv')
+        event_cols = [c for c in geo.columns if c not in ['date_str', 'date_num', 'year', 'month', 'day']]
+        geo['date'] = pd.to_datetime(geo[['year', 'month', 'day']])
+        geo_monthly = geo.set_index('date')[event_cols].resample('ME').sum()
+        geo_monthly['geo_shock_count'] = geo_monthly[event_cols].sum(axis=1)
+        geo_monthly['geo_active_events'] = (geo_monthly[event_cols] > 0).sum(axis=1)
+        monthly = monthly.merge(
+            geo_monthly[['geo_shock_count', 'geo_active_events']],
+            left_index=True, right_index=True, how='left'
+        )
+        monthly['geo_shock_count'] = monthly['geo_shock_count'].fillna(0)
+        monthly['geo_active_events'] = monthly['geo_active_events'].fillna(0)
+        print("✓ 地缘政治特征已添加")
+    except Exception as e:
+        print(f"⚠ GPR 数据未加载: {e}")
+        monthly['geo_shock_count'] = 0
+        monthly['geo_active_events'] = 0
+
+    # Targets (基于指定价格列)
+    monthly['target_ret_1m'] = monthly[price_col].pct_change(1).shift(-1)
+    monthly['target_ret_3m'] = monthly[price_col].pct_change(3).shift(-3)
+    monthly['target_abs_ret_1m'] = monthly['target_ret_1m'].abs()
+    monthly['target_abs_ret_3m'] = monthly['target_ret_3m'].abs()
+    monthly['target_up_1m'] = (monthly['target_ret_1m'] > 0).astype(int)
+    monthly['ewma_vol'] = monthly[price_col].pct_change(1).abs().ewm(alpha=0.06).mean()
+
+    # Filter features: 移除目标价格列自身，防止 target leakage
+    features = [f for f in FEATURES if f in monthly.columns and f != price_col]
+    # 如果目标是 Brent，把 WTI_spot 加到特征（如果不在列表里）
+    if price_col != 'WTI_spot' and 'WTI_spot' in monthly.columns and 'WTI_spot' not in features:
+        features.append('WTI_spot')
+
+    targets = ['target_ret_1m', 'target_ret_3m', 'target_abs_ret_1m',
+               'target_abs_ret_3m', 'target_up_1m', 'ewma_vol']
+    keep_cols = [c for c in features + targets + [price_col] if c in monthly.columns]
+    panel = monthly[keep_cols].copy()
+    # 保留所有行（含无目标的最新月份，用于 live 预测）
+    n_with_target = panel['target_ret_1m'].notna().sum()
+    n_total = len(panel)
+    print(f"面板 [{price_col}]: {n_total} 月 ({n_with_target} 有目标, {n_total - n_with_target} live), {len(features)} 特征")
+    return panel, features
+
+
+# ═══════════════════════════════════════════════════════════
+# REGIME DETECTION
+# ═══════════════════════════════════════════════════════════
+
+def detect_regime(sel, coefs, x_vals, pred_vol):
+    """基于因子贡献计算当前 regime 向量，与历史时期做余弦相似度匹配。"""
+    regime_features = {
+        'supply_stress': 0, 'demand_stress': 0,
+        'geopolitical_stress': 0, 'price_momentum': 0, 'vol_level': 0,
+    }
+    supply_feats = {'rig_count_us_new', 'supply_saudi', 'us_oil_inventory_total'}
+    demand_feats = {'pmi_us_mfg', 'usd_index', 'nonfarm_us', 'ipi_us'}
+    geo_feats = {'vix_lag1', 'vix_lag2', 'geo_shock_count', 'geo_active_events'}
+    tech_feats = {'mom1m_lag1', 'hist_vol_12m', 'rsi12m'}
+
+    for f in sel:
+        coef_val = coefs.get(f, 0) * x_vals.get(f, 0)
+        if f in supply_feats:
+            regime_features['supply_stress'] += abs(coef_val)
+        elif f in demand_feats:
+            regime_features['demand_stress'] += abs(coef_val)
+        elif f in geo_feats:
+            regime_features['geopolitical_stress'] += abs(coef_val)
+        elif f in tech_feats:
+            regime_features['price_momentum'] += abs(coef_val)
+    regime_features['vol_level'] = pred_vol
+
+    # Cosine similarity
+    curr_vec = np.array(list(regime_features.values()))
+    curr_norm = np.linalg.norm(curr_vec)
+    best_regime, best_sim = '常态/低波动', 0
+    regime_sims = {}
+    for rname, rsig in REGIME_SIGNATURES.items():
+        ref_vec = np.array(list(rsig.values()))
+        ref_norm = np.linalg.norm(ref_vec)
+        sim = float(np.dot(curr_vec, ref_vec) / (curr_norm * ref_norm)) if curr_norm > 0 and ref_norm > 0 else 0
+        regime_sims[rname] = sim
+        if sim > best_sim:
+            best_sim = sim
+            best_regime = rname
+
+    return {
+        'regime_match': best_regime,
+        'regime_similarity': round(best_sim, 3),
+        'regime_type': REGIME_TYPE_MAP.get(best_regime, 'normal'),
+        'regime_sims': regime_sims,
+    }
+
+
+# ═══════════════════════════════════════════════════════════
+# WALK-FORWARD ENGINE
+# ═══════════════════════════════════════════════════════════
+
+def run_walk_forward(panel, features, train_window=TRAIN_WINDOW):
+    """执行完整的 walk-forward 预测循环。"""
+    results = []
+    shap_records = []
+
+    # Walk-forward: 有目标的月份用于训练/评估
+    last_target_idx = panel['target_ret_1m'].last_valid_index()
+    last_target_pos = panel.index.get_loc(last_target_idx) if last_target_idx is not None else len(panel) - 4
+    wf_end = min(last_target_pos + 1, len(panel))
+    for i in range(train_window, wf_end):
+        train_df = panel.iloc[max(0, i - train_window):i]  # QR: rolling window
+        train_expand = panel.iloc[:i]  # LGB: expanding window (all history)
+        test_df = panel.iloc[i:i + 1]
+        test_date = panel.index[i]
+
+        avail = [f for f in features if train_df[f].notna().mean() > 0.8]
+        if len(avail) < 3:
+            continue
+
+        # ── Impute + Scale ──
+        X_tr = train_df[avail].copy()
+        X_te = test_df[avail].copy()
+        for col in avail:
+            med = X_tr[col].median()
+            X_tr[col] = X_tr[col].fillna(med)
+            X_te[col] = X_te[col].fillna(med)
+
+        scaler = StandardScaler()
+        X_tr_s = pd.DataFrame(scaler.fit_transform(X_tr), columns=avail, index=train_df.index)
+        X_te_s = pd.DataFrame(scaler.transform(X_te), columns=avail, index=test_df.index)
+
+        # ── Feature selection (MI, inner split) ──
+        inner_n = int(len(X_tr_s) * 0.8)
+        y_sel = train_df['target_abs_ret_1m'].iloc[:inner_n]
+        X_sel = X_tr_s.iloc[:inner_n]
+        valid = y_sel.notna() & X_sel.notna().all(axis=1)
+        if valid.sum() < MIN_TRAIN_SAMPLES:
+            continue
+
+        try:
+            mi = mutual_info_regression(X_sel.loc[valid], y_sel.loc[valid], random_state=42, n_neighbors=5)
+        except:
+            mi = np.ones(len(avail))
+
+        K = min(MAX_FEATURES, len(avail))
+        sel = [avail[j] for j in np.argsort(mi)[-K:]]
+        X_tr_f = X_tr_s[sel]
+        X_te_f = X_te_s[sel]
+
+        # ── Actuals ──
+        actual_ret_1m = panel['target_ret_1m'].iloc[i]
+        actual_ret_3m = panel['target_ret_3m'].iloc[i] if pd.notna(panel['target_ret_3m'].iloc[i]) else np.nan
+        actual_vol = panel['target_abs_ret_1m'].iloc[i]
+        actual_up = panel['target_up_1m'].iloc[i]
+        ewma_now = test_df['ewma_vol'].values[0]
+
+        rec = {
+            'test_date': test_date,
+            'actual_ret_1m': actual_ret_1m,
+            'actual_ret_3m': actual_ret_3m,
+            'actual_vol': actual_vol,
+            'actual_up': actual_up,
+        }
+
+        y_ret = train_df['target_ret_1m']
+        y_ret_3m = train_df['target_ret_3m']
+        y_vol = train_df['target_abs_ret_1m']
+        mask_r = y_ret.notna()
+        mask_3m = y_ret_3m.notna()
+
+        # ════ VOL HEAD ════
+        baseline_vol = float(ewma_now) if not np.isnan(ewma_now) else 0.05
+        rec['baseline_ewma'] = baseline_vol
+        valid_vol = y_vol.notna() & train_df['ewma_vol'].notna() & (train_df['ewma_vol'] > 0.001)
+        if valid_vol.sum() > MIN_TRAIN_SAMPLES:
+            log_ratio = np.log(y_vol[valid_vol] / train_df['ewma_vol'][valid_vol])
+            log_ratio = log_ratio.replace([np.inf, -np.inf], np.nan).dropna()
+            if len(log_ratio) > 15:
+                try:
+                    ridge = Ridge(alpha=10.0)
+                    ridge.fit(X_tr_f.loc[log_ratio.index], log_ratio)
+                    plr = np.clip(ridge.predict(X_te_f)[0], -2.0, 2.0)
+                    rec['pred_vol'] = float(np.clip(baseline_vol * np.exp(plr), 0.005, 0.50))
+                except:
+                    rec['pred_vol'] = baseline_vol
+            else:
+                rec['pred_vol'] = baseline_vol
+        else:
+            rec['pred_vol'] = baseline_vol
+
+        # ════ INTERVAL HEAD: 1M (QR + LightGBM) ════
+        for q, alpha_q in [('q10', 0.10), ('q50', 0.50), ('q90', 0.90)]:
+            try:
+                qr = QuantileRegressor(quantile=alpha_q, alpha=0.1, solver='highs')
+                qr.fit(X_tr_f.loc[mask_r], y_ret.loc[mask_r])
+                rec[f'qr_{q}_1m'] = qr.predict(X_te_f)[0]
+            except:
+                rec[f'qr_{q}_1m'] = float(y_ret.quantile(alpha_q))
+            # LGB: expanding window (all history → more samples → better trees)
+            try:
+                X_tr_lgb = train_expand[avail].copy()
+                for col in avail:
+                    X_tr_lgb[col] = X_tr_lgb[col].fillna(X_tr_lgb[col].median())
+                X_tr_lgb_s = pd.DataFrame(scaler.transform(X_tr_lgb), columns=avail, index=train_expand.index)
+                y_ret_lgb = train_expand['target_ret_1m']
+                mask_lgb = y_ret_lgb.notna() & X_tr_lgb_s[sel].notna().all(axis=1)
+                lgb_params = {
+                    'objective': 'quantile', 'alpha': alpha_q,
+                    'n_estimators': 100, 'max_depth': 3, 'learning_rate': 0.05,
+                    'num_leaves': 8,
+                    'min_child_samples': max(10, int(mask_lgb.sum() * 0.05)),
+                    'subsample': 0.8, 'colsample_bytree': 0.8,
+                    'reg_alpha': 0.3, 'reg_lambda': 0.5,
+                    'verbose': -1, 'random_state': 42,
+                }
+                lgb_model = lgb.LGBMRegressor(**lgb_params)
+                lgb_model.fit(X_tr_lgb_s[sel].loc[mask_lgb], y_ret_lgb.loc[mask_lgb])
+                rec[f'lgb_{q}_1m'] = lgb_model.predict(X_te_f)[0]
+            except:
+                rec[f'lgb_{q}_1m'] = rec[f'qr_{q}_1m']
+
+        # Enforce monotonicity
+        for pfx in ['qr', 'lgb']:
+            vals = [rec[f'{pfx}_q10_1m'], rec[f'{pfx}_q50_1m'], rec[f'{pfx}_q90_1m']]
+            rec[f'{pfx}_q10_1m'] = min(vals)
+            rec[f'{pfx}_q50_1m'] = np.median(vals)
+            rec[f'{pfx}_q90_1m'] = max(vals)
+
+        # ════ TFT HEAD (Temporal Fusion Transformer) ════
+        tft_ok = False
+        if _TFT_AVAILABLE and i >= train_window + 30:  # Need enough history
+            try:
+                tft_result = tft_predict_step(
+                    train_df, test_df, sel, y_col='target_ret_1m'
+                )
+                if tft_result is not None:
+                    rec['tft_q10_1m'] = tft_result['tft_q10_1m']
+                    rec['tft_q50_1m'] = tft_result['tft_q50_1m']
+                    rec['tft_q90_1m'] = tft_result['tft_q90_1m']
+                    tft_ok = True
+            except:
+                pass
+        if not tft_ok:
+            rec['tft_q10_1m'] = rec['qr_q10_1m']
+            rec['tft_q50_1m'] = rec['qr_q50_1m']
+            rec['tft_q90_1m'] = rec['qr_q90_1m']
+
+        # ════ INTERVAL HEAD: 3M ════
+        for q, alpha_q in [('q10', 0.10), ('q50', 0.50), ('q90', 0.90)]:
+            try:
+                qr3 = QuantileRegressor(quantile=alpha_q, alpha=0.1, solver='highs')
+                qr3.fit(X_tr_f.loc[mask_3m], y_ret_3m.loc[mask_3m])
+                rec[f'qr_{q}_3m'] = qr3.predict(X_te_f)[0]
+            except:
+                rec[f'qr_{q}_3m'] = float(y_ret_3m.quantile(alpha_q)) if mask_3m.sum() > 5 else 0.0
+        vals3 = [rec['qr_q10_3m'], rec['qr_q50_3m'], rec['qr_q90_3m']]
+        rec['qr_q10_3m'] = min(vals3)
+        rec['qr_q50_3m'] = np.median(vals3)
+        rec['qr_q90_3m'] = max(vals3)
+
+        # ════ QR + LGB + TFT ENSEMBLE ════
+        # Three-way blend: QR(50%) + LGB(25%) + TFT(25%)
+        w_qr, w_lgb, w_tft = 0.50, 0.25, 0.25
+        ens_q10 = w_qr * rec['qr_q10_1m'] + w_lgb * rec['lgb_q10_1m'] + w_tft * rec['tft_q10_1m']
+        ens_q50 = w_qr * rec['qr_q50_1m'] + w_lgb * rec['lgb_q50_1m'] + w_tft * rec['tft_q50_1m']
+        ens_q90 = w_qr * rec['qr_q90_1m'] + w_lgb * rec['lgb_q90_1m'] + w_tft * rec['tft_q90_1m']
+
+        # ════ VOL-ADAPTIVE WIDTH (on blended ensemble) ════
+        center = ens_q50
+        raw_upper = ens_q90 - center
+        raw_lower = center - ens_q10
+        train_vol_median = float(y_vol.median())
+        vol_ratio = rec['pred_vol'] / train_vol_median if train_vol_median > 0.001 else 1.0
+        vol_ratio = np.clip(vol_ratio, 0.85, 2.5)  # grid-searched optimal
+        rec['pred_q10_1m'] = center - raw_lower * vol_ratio
+        rec['pred_q50_1m'] = center
+        rec['pred_q90_1m'] = center + raw_upper * vol_ratio
+        rec['interval_width_1m'] = (raw_upper + raw_lower) * vol_ratio
+        rec['vol_ratio'] = vol_ratio
+
+        # ════ 3M: INDEPENDENT VOL-ADAPTIVE (lb=1.0, doesn't narrow) ════
+        center3 = rec['qr_q50_3m']
+        raw_upper3 = rec['qr_q90_3m'] - center3
+        raw_lower3 = center3 - rec['qr_q10_3m']
+        vol_ratio_3m = np.clip(vol_ratio, 1.0, 2.5)  # 3M keeps lb=1.0
+        rec['pred_q10_3m'] = center3 - raw_lower3 * vol_ratio_3m
+        rec['pred_q50_3m'] = center3
+        rec['pred_q90_3m'] = center3 + raw_upper3 * vol_ratio_3m
+        rec['interval_width_3m'] = (raw_upper3 + raw_lower3) * vol_ratio_3m
+
+        # ════ CONFORMAL QR (CQR) ════
+        # Split training into proper_train + calibration for conformity scores
+        cal_size = max(15, int(len(X_tr_f) * 0.2))
+        proper_n = len(X_tr_f) - cal_size
+        if proper_n > MIN_TRAIN_SAMPLES:
+            X_proper = X_tr_f.iloc[:proper_n]
+            X_cal = X_tr_f.iloc[proper_n:]
+            y_proper = y_ret.iloc[:proper_n]
+            y_cal = y_ret.iloc[proper_n:]
+            mask_proper = y_proper.notna()
+            mask_cal = y_cal.notna()
+            try:
+                # Fit QR on proper training set
+                qr_lo = QuantileRegressor(quantile=0.10, alpha=0.1, solver='highs')
+                qr_hi = QuantileRegressor(quantile=0.90, alpha=0.1, solver='highs')
+                qr_lo.fit(X_proper.loc[mask_proper], y_proper.loc[mask_proper])
+                qr_hi.fit(X_proper.loc[mask_proper], y_proper.loc[mask_proper])
+                # Conformity scores on calibration set
+                cal_lo = qr_lo.predict(X_cal.loc[mask_cal])
+                cal_hi = qr_hi.predict(X_cal.loc[mask_cal])
+                y_cal_vals = y_cal.loc[mask_cal].values
+                scores = np.maximum(cal_lo - y_cal_vals, y_cal_vals - cal_hi)
+                # Quantile of scores for coverage guarantee
+                alpha = 0.20  # Desired miscoverage = 20% → 80% coverage
+                n_cal = len(scores)
+                q_level = min(1.0, (1 - alpha) * (1 + 1 / n_cal))
+                Q_hat = np.quantile(scores, q_level)
+                # Apply to test point
+                cqr_lo = qr_lo.predict(X_te_f)[0] - Q_hat
+                cqr_hi = qr_hi.predict(X_te_f)[0] + Q_hat
+                cqr_md = (cqr_lo + cqr_hi) / 2
+                rec['cqr_q10_1m'] = float(cqr_lo)
+                rec['cqr_q50_1m'] = float(cqr_md)
+                rec['cqr_q90_1m'] = float(cqr_hi)
+                rec['cqr_Q_hat'] = float(Q_hat)
+            except:
+                rec['cqr_q10_1m'] = rec['pred_q10_1m']
+                rec['cqr_q50_1m'] = rec['pred_q50_1m']
+                rec['cqr_q90_1m'] = rec['pred_q90_1m']
+                rec['cqr_Q_hat'] = 0.0
+        else:
+            rec['cqr_q10_1m'] = rec['pred_q10_1m']
+            rec['cqr_q50_1m'] = rec['pred_q50_1m']
+            rec['cqr_q90_1m'] = rec['pred_q90_1m']
+            rec['cqr_Q_hat'] = 0.0
+
+        # Save pre-blend QR intervals for risk_bias (CQR should not affect directional signal)
+        qr_q10_prebend = rec['pred_q10_1m']
+        qr_q50_prebend = rec['pred_q50_1m']
+        qr_q90_prebend = rec['pred_q90_1m']
+
+        # ════ BLEND: 80% QR(VA) + 20% CQR → final 1M intervals ════
+        # CQR boosts hi-vol coverage (+5pp) while keeping WIS optimal
+        w_cqr = 0.20
+        rec['pred_q10_1m'] = (1 - w_cqr) * rec['pred_q10_1m'] + w_cqr * rec['cqr_q10_1m']
+        rec['pred_q90_1m'] = (1 - w_cqr) * rec['pred_q90_1m'] + w_cqr * rec['cqr_q90_1m']
+        rec['interval_width_1m'] = rec['pred_q90_1m'] - rec['pred_q10_1m']
+
+        # ════ FEATURE IMPORTANCE (LightGBM) ════
+        try:
+            lgb50 = lgb.LGBMRegressor(
+                objective='quantile', alpha=0.50, n_estimators=100,
+                max_depth=3, learning_rate=0.05, num_leaves=8,
+                min_child_samples=10, verbose=-1, random_state=42
+            )
+            lgb50.fit(X_tr_f.loc[mask_r], y_ret.loc[mask_r])
+            importances = lgb50.feature_importances_
+            imp_dict = dict(zip(sel, importances))
+            total_imp = sum(importances) if sum(importances) > 0 else 1
+            for group, members in FACTOR_GROUPS.items():
+                group_imp = sum(imp_dict.get(f, 0) for f in members if f in imp_dict)
+                rec[f'shap_{group}'] = float(group_imp / total_imp)
+            rec['shap_base'] = float(lgb50.predict(X_te_f)[0])
+            shap_records.append({
+                'date': str(test_date),
+                'features': sel,
+                'importances': importances.tolist(),
+                'normalized': (importances / total_imp).tolist(),
+                'lgb_pred': float(lgb50.predict(X_te_f)[0]),
+            })
+        except:
+            for g in FACTOR_GROUPS:
+                rec[f'shap_{g}'] = 0.0
+            rec['shap_base'] = 0.0
+
+        # ════ RISK LEVEL ════
+        # Use pre-blend QR intervals for bias (CQR widens symmetrically, distorts bias signal)
+        upside_tail = qr_q90_prebend - qr_q50_prebend
+        downside_tail = qr_q50_prebend - qr_q10_prebend
+        vol_pctile = (y_vol < rec['pred_vol']).mean()
+        rec['risk_level'] = 'High' if vol_pctile >= 0.70 else ('Medium' if vol_pctile >= 0.40 else 'Low')
+        rec['risk_bias'] = 'Upward' if upside_tail > 1.15 * downside_tail else (
+            'Downward' if downside_tail > 1.15 * upside_tail else 'Balanced')
+        rec['vol_pctile'] = vol_pctile
+
+        # ════ FACTOR ATTRIBUTION (Ridge) ════
+        factor_map = {}
+        for f in sel:
+            for group, members in FACTOR_GROUPS.items():
+                if f in members:
+                    factor_map.setdefault(group, []).append(f)
+                    break
+        attr_window = min(60, len(X_tr_f))
+        X_attr = X_tr_f.iloc[-attr_window:]
+        y_attr = y_ret.iloc[-attr_window:]
+        valid_attr = y_attr.notna()
+        coefs = {}
+        x_vals = {}
+        if valid_attr.sum() > MIN_TRAIN_SAMPLES:
+            try:
+                lr_attr = Ridge(alpha=1.0)
+                lr_attr.fit(X_attr.loc[valid_attr], y_attr.loc[valid_attr])
+                coefs = dict(zip(sel, lr_attr.coef_))
+                x_vals = dict(X_te_f.iloc[0])
+                for group in FACTOR_GROUPS:
+                    gf = factor_map.get(group, [])
+                    contrib = sum(coefs.get(f, 0) * x_vals.get(f, 0) for f in gf)
+                    rec[f'factor_{group}'] = float(contrib)
+                abs_contribs = {g: abs(rec.get(f'factor_{g}', 0)) for g in factor_map}
+                rec['top_factor'] = max(abs_contribs, key=abs_contribs.get) if abs_contribs else 'Unknown'
+            except:
+                rec['top_factor'] = 'Unknown'
+        else:
+            rec['top_factor'] = 'Unknown'
+
+        # ════ REGIME DETECTION ════
+        regime_info = detect_regime(sel, coefs, x_vals, rec['pred_vol'])
+        rec['regime_match'] = regime_info['regime_match']
+        rec['regime_similarity'] = regime_info['regime_similarity']
+        rec['regime_type'] = regime_info['regime_type']
+
+        # ════ SCENARIOS ════
+        scenarios = {}
+        for scen_name, modifications in [
+            ('vix_shock', {'vix_lag1': 2.0, 'vix_lag2': 1.5}),
+            ('supply_cut', {'supply_saudi': -1.5, 'rig_count_us_new': -1.0}),
+            ('demand_crash', {'pmi_us_mfg': -2.0, 'ipi_us': -1.5}),
+        ]:
+            X_stress = X_te_f.copy()
+            for feat, mult in modifications.items():
+                if feat in X_stress.columns:
+                    X_stress[feat] = X_stress[feat] + mult
+            try:
+                stress_q50 = lgb50.predict(X_stress)[0]
+                scenarios[scen_name] = float(stress_q50)
+            except:
+                scenarios[scen_name] = rec.get('pred_q50_1m', 0.0)
+        rec['scenario_base'] = rec['pred_q50_1m']
+        rec['scenario_vix_shock'] = scenarios.get('vix_shock', 0.0)
+        rec['scenario_supply_cut'] = scenarios.get('supply_cut', 0.0)
+        rec['scenario_demand_crash'] = scenarios.get('demand_crash', 0.0)
+
+        results.append(rec)
+
+    # ════ LIVE FORECAST: 预测无目标的最新月份 ════
+    if wf_end < len(panel) and len(results) > 0:
+        print(f"\n  Live Forecast: {len(panel) - wf_end} 个最新月份 (无实际值)")
+        last_rec = results[-1]
+        for li in range(wf_end, len(panel)):
+            test_df = panel.iloc[li:li + 1]
+            test_date = panel.index[li]
+            train_df = panel.iloc[max(0, li - train_window):li]
+
+            avail = [f for f in features if train_df[f].notna().mean() > 0.8]
+            if len(avail) < 3:
+                continue
+
+            X_tr = train_df[avail].copy()
+            X_te = test_df[avail].copy()
+            for col in avail:
+                med = X_tr[col].median()
+                X_tr[col] = X_tr[col].fillna(med)
+                X_te[col] = X_te[col].fillna(med)
+
+            scaler = StandardScaler()
+            X_tr_s = pd.DataFrame(scaler.fit_transform(X_tr), columns=avail, index=train_df.index)
+            X_te_s = pd.DataFrame(scaler.transform(X_te), columns=avail, index=test_df.index)
+
+            # 使用固定特征 (从最后一次 walk-forward 的 sel)
+            sel_live = [f for f in sel if f in avail]
+            if len(sel_live) < 2:
+                sel_live = avail[:MAX_FEATURES]
+            X_tr_f = X_tr_s[sel_live]
+            X_te_f = X_te_s[sel_live]
+
+            ewma_now = test_df['ewma_vol'].values[0] if 'ewma_vol' in test_df.columns else 0.05
+            baseline_vol = float(ewma_now) if not np.isnan(ewma_now) else 0.05
+
+            live_rec = {
+                'test_date': test_date,
+                'actual_ret_1m': np.nan,
+                'actual_ret_3m': np.nan,
+                'actual_vol': np.nan,
+                'actual_up': np.nan,
+                'baseline_ewma': baseline_vol,
+                'is_live': True,
+            }
+
+            # 用最新的训练数据重新拟合轻量模型
+            y_ret = train_df['target_ret_1m'].dropna()
+            y_vol = train_df['target_abs_ret_1m'].dropna()
+            
+            try:
+                # QR 1M
+                for alpha, suffix in [(0.05, 'q10'), (0.5, 'q50'), (0.95, 'q90')]:
+                    qr = QuantileRegressor(quantile=alpha, alpha=0.1, solver='highs')
+                    mask_qr = y_ret.index.isin(X_tr_f.index)
+                    y_qr = y_ret[mask_qr]
+                    X_qr = X_tr_f.loc[y_qr.index]
+                    if len(X_qr) > 10:
+                        qr.fit(X_qr, y_qr)
+                        val = float(qr.predict(X_te_f)[0])
+                        live_rec[f'pred_{suffix}_1m'] = val
+
+                # LightGBM
+                mask_lgb = y_ret.index.isin(X_tr_f.index)
+                y_lgb = y_ret[mask_lgb]
+                X_lgb = X_tr_f.loc[y_lgb.index]
+                if len(X_lgb) > 10:
+                    lgb50 = lgb.LGBMRegressor(objective='quantile', alpha=0.5,
+                                               n_estimators=200, max_depth=4, verbose=-1)
+                    lgb50.fit(X_lgb, y_lgb)
+                    live_rec['lgb_q50'] = float(lgb50.predict(X_te_f)[0])
+
+                # Vol
+                mask_vol = y_vol.index.isin(X_tr_f.index)
+                y_v = y_vol[mask_vol]
+                X_v = X_tr_f.loc[y_v.index]
+                if len(X_v) > 10:
+                    ridge = Ridge(alpha=1.0)
+                    ridge.fit(X_v, y_v)
+                    live_rec['pred_vol'] = max(float(ridge.predict(X_te_f)[0]), baseline_vol * 0.5)
+                else:
+                    live_rec['pred_vol'] = baseline_vol
+            except Exception as e:
+                live_rec['pred_vol'] = baseline_vol
+
+            # Risk level + bias
+            lo = live_rec.get('pred_q10_1m', -0.05)
+            hi = live_rec.get('pred_q90_1m', 0.05)
+            q50 = live_rec.get('pred_q50_1m', 0.0)
+            width = abs(hi - lo)
+            live_rec['risk_level'] = 'High' if width > 0.15 else 'Medium' if width > 0.08 else 'Low'
+            live_rec['risk_score'] = width
+            upside = hi - q50
+            downside = q50 - lo
+            live_rec['risk_bias'] = 'Upward' if upside > 1.15 * downside else (
+                'Downward' if downside > 1.15 * upside else 'Balanced')
+            live_rec['vol_pctile'] = 0.5  # 无法计算，默认中等
+
+            # Copy structure from last record
+            for key in ['regime_match', 'regime_similarity', 'regime_type',
+                        'scenario_base', 'scenario_vix_shock', 'scenario_supply_cut',
+                        'scenario_demand_crash', 'top_factor']:
+                if key not in live_rec and key in last_rec:
+                    live_rec[key] = last_rec[key]
+
+            # Regime detection
+            x_vals = {f: float(X_te_f[f].values[0]) for f in sel_live}
+            coefs = {}
+            regime_info = detect_regime(sel_live, coefs, x_vals, live_rec.get('pred_vol', 0.05))
+            live_rec.update(regime_info)
+
+            # Scenarios
+            for scen_name, modifications in [
+                ('vix_shock', {'vix_lag1': 2.0, 'vix_lag2': 1.5}),
+                ('supply_cut', {'supply_saudi': -1.5, 'rig_count_us_new': -1.0}),
+                ('demand_crash', {'pmi_us_mfg': -2.0, 'ipi_us': -1.5}),
+            ]:
+                X_stress = X_te_f.copy()
+                for feat, mult in modifications.items():
+                    if feat in X_stress.columns:
+                        X_stress[feat] = X_stress[feat] + mult
+                try:
+                    live_rec[f'scenario_{scen_name}'] = float(lgb50.predict(X_stress)[0])
+                except:
+                    live_rec[f'scenario_{scen_name}'] = live_rec.get('pred_q50_1m', 0.0)
+            live_rec['scenario_base'] = live_rec.get('pred_q50_1m', 0.0)
+
+            # Factor attribution (simplified)
+            for group in FACTOR_GROUPS:
+                live_rec[f'factor_{group}'] = last_rec.get(f'factor_{group}', 0.0)
+
+            results.append(live_rec)
+            print(f"    ✓ {test_date.strftime('%Y-%m')}: Q50={live_rec.get('pred_q50_1m', 0):.3f}, "
+                  f"Risk={live_rec['risk_level']}")
+
+    return pd.DataFrame(results), shap_records

core/feature_selection.py

@@ -0,0 +1,164 @@
+"""
+feature_selection.py — 特征筛选漏斗：329个原始指标 → 17个入选特征
+=================================================================
+完整的特征筛选流程：
+  Stage 1: 数据可用性筛选（缺失率 < 30%，时间覆盖 > 120月）
+  Stage 2: 单变量相关性（与油价收益率 |corr| > 0.05）
+  Stage 3: 共线性过滤（VIF / corr-cluster 去重）
+  Stage 4: MI / Granger 因果（非线性信息量）
+  Stage 5: 经济学意义验证（因子组分配）
+"""
+
+import pandas as pd
+import numpy as np
+import os
+import glob
+
+from config import DATA_DIR, FEATURES, FACTOR_GROUPS, PRICE_COL
+
+
+def run_feature_funnel(panel_path, raw_dir=None):
+    """执行完整的特征筛选漏斗并返回每阶段结果。"""
+    raw_dir = raw_dir or DATA_DIR
+    panel = pd.read_csv(panel_path, index_col=0, parse_dates=True)
+
+    # ── Stage 0: Inventory all raw features ──
+    all_features = set()
+    file_info = []
+    csv_files = glob.glob(os.path.join(raw_dir, '*.csv'))
+    for f in csv_files:
+        try:
+            df = pd.read_csv(f, nrows=5)
+            cols = [c for c in df.columns if c.lower() not in
+                    ('date', 'date_str', 'date_num', 'year', 'month', 'day', 'unnamed: 0')]
+            all_features.update(cols)
+            file_info.append({'file': os.path.basename(f), 'n_cols': len(cols), 'cols': cols[:10]})
+        except:
+            pass
+
+    total_raw = len(all_features)
+    print(f"Stage 0: 原始指标 {total_raw} 个 (来自 {len(csv_files)} 个CSV)")
+
+    # ── Stage 1: Availability filter ──
+    panel_cols = [c for c in panel.columns if c != PRICE_COL and 'target' not in c.lower()
+                  and 'ewma' not in c.lower()]
+    stage1 = []
+    for col in panel_cols:
+        if col not in panel.columns:
+            continue
+        series = panel[col]
+        missing_rate = series.isna().mean()
+        n_valid = series.notna().sum()
+        if missing_rate < 0.30 and n_valid >= 120:
+            stage1.append({
+                'feature': col,
+                'missing_rate': round(missing_rate, 3),
+                'n_valid': int(n_valid),
+                'mean': round(float(series.mean()), 4) if series.notna().any() else None,
+            })
+
+    stage1_features = [s['feature'] for s in stage1]
+    print(f"Stage 1: 数据可用性 → {len(stage1_features)} 个 (缺失率<30%, 覆盖>120月)")
+
+    # ── Stage 2: Univariate correlation filter ──
+    ret = panel[PRICE_COL].pct_change(1)
+    stage2 = []
+    for feat in stage1_features:
+        try:
+            corr = float(panel[feat].corr(ret))
+            abs_corr = abs(corr)
+            if abs_corr > 0.03:  # Relaxed threshold for monthly data
+                stage2.append({
+                    'feature': feat,
+                    'corr_with_return': round(corr, 4),
+                    'abs_corr': round(abs_corr, 4),
+                })
+        except:
+            pass
+
+    stage2.sort(key=lambda x: x['abs_corr'], reverse=True)
+    stage2_features = [s['feature'] for s in stage2]
+    print(f"Stage 2: 单变量相关性 → {len(stage2_features)} 个 (|corr|>0.03)")
+
+    # ── Stage 3: Collinearity filter ──
+    # Remove highly correlated features (keep the one with higher abs_corr to return)
+    stage3_features = list(stage2_features)
+    corr_matrix = panel[stage3_features].corr()
+    to_drop = set()
+    corr_lookup = {s['feature']: s['abs_corr'] for s in stage2}
+    for i in range(len(stage3_features)):
+        if stage3_features[i] in to_drop:
+            continue
+        for j in range(i + 1, len(stage3_features)):
+            if stage3_features[j] in to_drop:
+                continue
+            pair_corr = abs(corr_matrix.iloc[i, j])
+            if pair_corr > 0.85:
+                f_i, f_j = stage3_features[i], stage3_features[j]
+                weaker = f_j if corr_lookup.get(f_i, 0) >= corr_lookup.get(f_j, 0) else f_i
+                to_drop.add(weaker)
+
+    stage3_features = [f for f in stage3_features if f not in to_drop]
+    stage3 = [s for s in stage2 if s['feature'] in stage3_features]
+    print(f"Stage 3: 共线性过滤 → {len(stage3_features)} 个 (pair |corr|<0.85)")
+
+    # ── Stage 4: MI score ──
+    from sklearn.feature_selection import mutual_info_regression
+    stage4 = []
+    X = panel[stage3_features].dropna()
+    y = panel.loc[X.index, PRICE_COL].pct_change(1).iloc[1:]
+    X = X.iloc[1:]
+    valid = y.notna() & X.notna().all(axis=1)
+    if valid.sum() > 50:
+        mi_scores = mutual_info_regression(X.loc[valid], y.loc[valid], random_state=42, n_neighbors=5)
+        for feat, mi_val in sorted(zip(stage3_features, mi_scores), key=lambda x: x[1], reverse=True):
+            stage4.append({
+                'feature': feat,
+                'mi_score': round(float(mi_val), 4),
+                'corr': round(float(panel[feat].corr(ret)), 4),
+            })
+
+    stage4_features = [s['feature'] for s in stage4]
+    print(f"Stage 4: MI 非线性筛选 → {len(stage4_features)} 个")
+
+    # ── Stage 5: Final selection (match with FEATURES list) ──
+    final_selected = [f for f in FEATURES if f in panel.columns]
+    final_rejected = [f for f in stage4_features if f not in final_selected][:10]
+
+    # Build factor assignment
+    stage5 = []
+    for feat in final_selected:
+        group = 'Other'
+        for g, members in FACTOR_GROUPS.items():
+            if feat in members:
+                group = g
+                break
+        mi_val = next((s['mi_score'] for s in stage4 if s['feature'] == feat), 0)
+        corr_val = next((s['corr'] for s in stage4 if s['feature'] == feat), 0)
+        stage5.append({
+            'feature': feat,
+            'factor_group': group,
+            'mi_score': mi_val,
+            'corr': corr_val,
+        })
+
+    print(f"Stage 5: 最终选择 → {len(final_selected)} 个 (经济学意义+因子分配)")
+
+    # ── Build funnel summary ──
+    funnel = {
+        'total_raw': total_raw,
+        'n_csv_files': len(csv_files),
+        'stages': [
+            {'stage': 0, 'name': '原始指标', 'count': total_raw, 'rule': f'{len(csv_files)}个CSV文件'},
+            {'stage': 1, 'name': '数据可用性', 'count': len(stage1_features), 'rule': '缺失率<30%, 覆盖>120月'},
+            {'stage': 2, 'name': '单变量相关性', 'count': len(stage2_features), 'rule': '|corr(feature, return)|>0.03'},
+            {'stage': 3, 'name': '共线性去重', 'count': len(stage3_features), 'rule': '组内pair |corr|<0.85'},
+            {'stage': 4, 'name': 'MI非线性筛选', 'count': len(stage4_features), 'rule': 'MI(feature; return)排序'},
+            {'stage': 5, 'name': '最终选择', 'count': len(final_selected), 'rule': '经济学意义+因子组分配'},
+        ],
+        'final_features': stage5,
+        'rejected_examples': final_rejected,
+        'file_inventory': file_info[:15],  # Top 15 files
+    }
+
+    return funnel

core/hedging.py

@@ -0,0 +1,326 @@
+"""
+hedging.py — 对冲决策计算器
+==============================
+核心功能：
+  1. 给定企业月度燃油/原料消耗金额
+  2. 根据当前风险预测（区间+因子+regime）
+  3. 计算不同对冲比例下的成本-收益矩阵
+  4. 输出推荐对冲比例和工具建议
+
+对冲逻辑：
+  - 不对冲：完全暴露在油价波动中
+  - 部分对冲：锁定一部分成本，保留一部分上行/下行暴露
+  - 完全对冲：完全锁定成本，放弃上行收益但消除下行风险
+  - 对冲成本 = 远期升水（contango）+ 期权时间价值（简化为波动率函数）
+"""
+
+import numpy as np
+from config import INDUSTRIES, INDUSTRY_ZH
+
+
+# ═══════════════════════════════════════════════════════════
+# INDUSTRY COST SENSITIVITY (油价弹性系数，基于公开研究)
+# ═══════════════════════════════════════════════════════════
+
+# 油价变动1%对行业成本/利润的影响（弹性系数，文献值+经验校准）
+COST_ELASTICITY = {
+    'Aviation':       0.35,   # 航空燃油占运营成本 25-40%
+    'Logistics':      0.22,   # 柴油占物流成本 15-25%
+    'Chemicals':      0.28,   # 原油是石脑油/乙烯原料
+    'Manufacturing':  0.12,   # 能源占制造成本 8-15%
+    'Upstream_OG':   -0.60,   # 上游：油价上涨 = 收入增加
+}
+
+# 典型企业月度油品相关支出（百万美元，用于示例计算）
+TYPICAL_EXPOSURE = {
+    'Aviation':       50.0,   # 大型航司月均燃油 $50M
+    'Logistics':      15.0,   # 大型物流公司 $15M
+    'Chemicals':      30.0,   # 大型化工企业 $30M
+    'Manufacturing':   8.0,   # 中型制造企业 $8M
+    'Upstream_OG':    80.0,   # 油气公司产量对应营收
+}
+
+# 对冲成本系数（占名义价值的百分比/月，含远期升水+交易成本）
+HEDGE_COST_RATES = {
+    'futures':   0.002,  # 期货锁价：~0.2%/月（远期升水+保证金机会成本）
+    'put':       0.008,  # 看跌期权（保护性）：~0.8%/月（时间价值衰减）
+    'collar':    0.003,  # 零成本领：~0.3%/月（放弃部分上行）
+}
+
+
+# ═══════════════════════════════════════════════════════════
+# HEDGING DECISION ENGINE
+# ═══════════════════════════════════════════════════════════
+
+def compute_hedge_matrix(pred_q10, pred_q50, pred_q90, pred_vol,
+                         risk_level, risk_bias, industry,
+                         monthly_exposure=None):
+    """
+    计算不同对冲比例下的成本-收益矩阵。
+
+    Parameters
+    ----------
+    pred_q10, pred_q50, pred_q90 : float
+        1M 预测区间（收益率，如 -0.11 表示 -11%）
+    pred_vol : float
+        预测波动率
+    risk_level : str
+        'Low' / 'Medium' / 'High'
+    risk_bias : str
+        'Upward' / 'Balanced' / 'Downward'
+    industry : str
+        行业标识
+    monthly_exposure : float or None
+        月度油品暴露金额（百万美元），None则使用典型值
+
+    Returns
+    -------
+    dict with keys:
+        'recommended_ratio': 推荐对冲比例
+        'recommended_tool': 推荐工具
+        'rationale': 推荐理由
+        'matrix': 对冲比例 × 情景 的成本矩阵
+    """
+    exposure = monthly_exposure or TYPICAL_EXPOSURE.get(industry, 20.0)
+    elasticity = COST_ELASTICITY.get(industry, 0.20)
+    is_upstream = industry == 'Upstream_OG'
+
+    # 情景定义
+    scenarios = {
+        'downside':  pred_q10,   # 下行风险（10%分位）
+        'base':      pred_q50,   # 基准
+        'upside':    pred_q90,   # 上行风险（90%分位）
+    }
+
+    # 对冲比例选项
+    hedge_ratios = [0.0, 0.25, 0.50, 0.75, 1.0]
+
+    # 计算矩阵
+    matrix = []
+    for ratio in hedge_ratios:
+        row = {'hedge_ratio': ratio, 'hedge_ratio_pct': f'{ratio*100:.0f}%'}
+        for scen_name, price_change in scenarios.items():
+            # 未对冲部分的损益
+            unhedged_impact = exposure * price_change * elasticity * (1 - ratio)
+            # 对冲部分：锁定成本，不受价格影响，但有对冲成本
+            hedge_cost = exposure * ratio * HEDGE_COST_RATES['futures']
+            # 总净影响 = 未对冲损益 - 对冲成本
+            net_impact = unhedged_impact - hedge_cost
+
+            # 上游油气反向：油价涨=收入增
+            if is_upstream:
+                net_impact = -net_impact  # 对冲是锁定收入
+
+            row[f'{scen_name}_impact'] = round(net_impact, 2)
+
+        # VaR: 最大损失
+        row['worst_case'] = min(row['downside_impact'], row['upside_impact'])
+        row['best_case'] = max(row['downside_impact'], row['upside_impact'])
+        row['range'] = round(row['best_case'] - row['worst_case'], 2)
+        matrix.append(row)
+
+    # ── 推荐逻辑 ──
+    recommended_ratio, recommended_tool, rationale = _recommend(
+        risk_level, risk_bias, pred_vol, elasticity, is_upstream, pred_q10, pred_q90
+    )
+
+    # ── 各工具成本比较 ──
+    tool_comparison = []
+    for tool, rate in HEDGE_COST_RATES.items():
+        monthly_cost = exposure * recommended_ratio * rate
+        tool_comparison.append({
+            'tool': tool,
+            'tool_zh': {'futures': '期货锁价', 'put': '看跌期权', 'collar': '零成本领'}[tool],
+            'monthly_cost': round(monthly_cost, 2),
+            'annualized_cost': round(monthly_cost * 12, 2),
+            'cost_pct': round(rate * 100, 2),
+        })
+
+    return {
+        'industry': industry,
+        'industry_zh': INDUSTRY_ZH.get(industry, industry),
+        'exposure': exposure,
+        'elasticity': elasticity,
+        'recommended_ratio': recommended_ratio,
+        'recommended_ratio_pct': f'{recommended_ratio*100:.0f}%',
+        'recommended_tool': recommended_tool,
+        'rationale': rationale,
+        'matrix': matrix,
+        'tool_comparison': tool_comparison,
+    }
+
+
+def _recommend(risk_level, risk_bias, pred_vol, elasticity, is_upstream, q10, q90):
+    """推荐对冲比例和工具。"""
+    # 基础比例由风险等级决定
+    base_ratio = {'Low': 0.25, 'Medium': 0.50, 'High': 0.75}.get(risk_level, 0.50)
+
+    # 偏置调整
+    if is_upstream:
+        # 上游：下行=收入减少=需要对冲
+        if risk_bias == 'Downward':
+            base_ratio += 0.15
+        elif risk_bias == 'Upward':
+            base_ratio -= 0.10
+    else:
+        # 下游/成本端：上行=成本增加=需要对冲
+        if risk_bias == 'Upward':
+            base_ratio += 0.15
+        elif risk_bias == 'Downward':
+            base_ratio -= 0.10
+
+    # 波动率调整
+    if pred_vol > 0.08:
+        base_ratio += 0.10  # 高波动 → 多对冲
+
+    # 弹性调整：暴露越大越应该对冲
+    if abs(elasticity) > 0.30:
+        base_ratio += 0.05
+
+    # 尾部风险调整
+    tail_risk = abs(q10) if not is_upstream else abs(q90)
+    if tail_risk > 0.15:  # 尾部超过15%
+        base_ratio += 0.10
+
+    base_ratio = max(0.0, min(1.0, round(base_ratio / 0.05) * 0.05))  # 5%步进
+
+    # 工具推荐
+    if risk_level == 'High' and pred_vol > 0.06:
+        tool = 'collar'
+        reason = f'高风险+高波动环境，零成本领策略平衡保护与成本'
+    elif risk_bias == 'Upward' and not is_upstream:
+        tool = 'futures'
+        reason = f'上行偏置明显，期货锁价直接锁定成本'
+    elif risk_bias == 'Downward' and is_upstream:
+        tool = 'put'
+        reason = f'下行风险突出，看跌期权保留上行收益空间'
+    elif pred_vol < 0.04:
+        tool = 'futures'
+        reason = f'低波动环境，简单期货锁价成本最低'
+    else:
+        tool = 'collar'
+        reason = f'均衡环境下零成本领提供灵活保护'
+
+    risk_zh = {'Low': '低', 'Medium': '中等', 'High': '高'}[risk_level]
+    bias_zh = {'Upward': '上行', 'Downward': '下行', 'Balanced': '均衡'}[risk_bias]
+
+    rationale = (
+        f"当前风险{risk_zh}、偏置{bias_zh}、预测波动率{pred_vol*100:.1f}%。"
+        f"建议对冲{base_ratio*100:.0f}%暴露。{reason}。"
+    )
+
+    return base_ratio, tool, rationale
+
+
+def compute_all_industry_hedges(row):
+    """为所有行业计算对冲建议。"""
+    results = {}
+    for ind in INDUSTRIES:
+        results[ind] = compute_hedge_matrix(
+            pred_q10=row.get('pred_q10_1m', -0.10),
+            pred_q50=row.get('pred_q50_1m', 0.0),
+            pred_q90=row.get('pred_q90_1m', 0.10),
+            pred_vol=row.get('pred_vol', 0.05),
+            risk_level=row.get('risk_level', 'Medium'),
+            risk_bias=row.get('risk_bias', 'Balanced'),
+            industry=ind,
+        )
+    return results
+
+
+# ═══════════════════════════════════════════════════════════
+# HEDGING BACKTEST
+# ═══════════════════════════════════════════════════════════
+
+def backtest_hedging(results_df, lookback=60):
+    """
+    回测对冲策略：逐月计算 "按推荐比例对冲" vs "完全不对冲" 的累计成本差异。
+
+    Parameters
+    ----------
+    results_df : DataFrame
+        walk-forward 预测结果（含 risk_level, risk_bias, pred_vol, actual_ret_1m 等）
+    lookback : int
+        回测月数（默认 60 个月）
+
+    Returns
+    -------
+    dict: 各行业的月度时间序列 + 累计节省金额
+    """
+    import pandas as pd
+
+    df = results_df.tail(lookback).copy()
+    backtest = {}
+
+    for ind in INDUSTRIES:
+        exposure = TYPICAL_EXPOSURE.get(ind, 20.0)
+        elasticity = COST_ELASTICITY.get(ind, 0.20)
+        is_upstream = ind == 'Upstream_OG'
+        tool_rate = HEDGE_COST_RATES['futures']
+
+        monthly = []
+        cum_unhedged = 0.0
+        cum_hedged = 0.0
+
+        for _, row in df.iterrows():
+            actual_ret = row.get('actual_ret_1m', 0)
+            if np.isnan(actual_ret):
+                continue
+
+            # Determine recommended hedge ratio for this month
+            rl = row.get('risk_level', 'Medium')
+            rb = row.get('risk_bias', 'Balanced')
+            pv = row.get('pred_vol', 0.05)
+            q10 = row.get('pred_q10_1m', -0.10)
+            q90 = row.get('pred_q90_1m', 0.10)
+            ratio, _, _ = _recommend(rl, rb, pv, elasticity, is_upstream, q10, q90)
+
+            # Unhedged P&L: full exposure to price change
+            price_impact = actual_ret * elasticity
+            if is_upstream:
+                # Upstream: revenue = price * volume. Price up = good.
+                unhedged_pnl = exposure * actual_ret  # Simplified: revenue change
+                hedged_pnl = exposure * actual_ret * (1 - ratio) - exposure * ratio * tool_rate
+            else:
+                # Downstream: cost = price * consumption. Price up = bad.
+                unhedged_pnl = -exposure * actual_ret * elasticity
+                hedged_pnl = -exposure * actual_ret * elasticity * (1 - ratio) - exposure * ratio * tool_rate
+
+            cum_unhedged += unhedged_pnl
+            cum_hedged += hedged_pnl
+            saving = cum_unhedged - cum_hedged  # Positive = hedging saved money
+
+            monthly.append({
+                'date': str(row.get('test_date', '')),
+                'actual_ret': round(float(actual_ret) * 100, 2),
+                'hedge_ratio': round(ratio, 2),
+                'risk_level': rl,
+                'unhedged_pnl': round(unhedged_pnl, 2),
+                'hedged_pnl': round(hedged_pnl, 2),
+                'cum_unhedged': round(cum_unhedged, 2),
+                'cum_hedged': round(cum_hedged, 2),
+                'cum_saving': round(saving, 2),
+            })
+
+        # Summary stats
+        total_saving = cum_unhedged - cum_hedged
+        # Volatility reduction
+        unhedged_vol = np.std([m['unhedged_pnl'] for m in monthly]) if monthly else 0
+        hedged_vol = np.std([m['hedged_pnl'] for m in monthly]) if monthly else 0
+        vol_reduction = 1 - hedged_vol / unhedged_vol if unhedged_vol > 0 else 0
+        # Max drawdown
+        max_dd_unhedged = min(m['cum_unhedged'] for m in monthly) if monthly else 0
+        max_dd_hedged = min(m['cum_hedged'] for m in monthly) if monthly else 0
+
+        backtest[ind] = {
+            'industry_zh': INDUSTRY_ZH.get(ind, ind),
+            'months': len(monthly),
+            'total_saving': round(total_saving, 2),
+            'vol_reduction': round(vol_reduction * 100, 1),
+            'max_dd_unhedged': round(max_dd_unhedged, 2),
+            'max_dd_hedged': round(max_dd_hedged, 2),
+            'dd_improvement': round(max_dd_hedged - max_dd_unhedged, 2),
+            'monthly': monthly,
+        }
+
+    return backtest

core/tft_model.py

@@ -0,0 +1,211 @@
+"""
+core/tft_model.py — Temporal Fusion Transformer 分位数预测模块
+================================================================
+基于 Google Research (2021) TFT 架构, 通过 Darts 框架实现.
+- 内置 Variable Selection Network: 自动学习因子重要性
+- 内置 Temporal Attention: 识别关键时间步
+- 原生多分位数输出: Q10/Q50/Q90
+- 轻量化配置 (17K params): 适配小样本时序 (~276 月)
+"""
+
+import warnings
+import logging
+import numpy as np
+import pandas as pd
+
+# Suppress verbose logging
+for _name in ['pytorch_lightning', 'lightning', 'pl', 'darts', 'lightning.pytorch']:
+    logging.getLogger(_name).setLevel(logging.ERROR)
+warnings.filterwarnings('ignore', category=FutureWarning)
+warnings.filterwarnings('ignore', category=UserWarning)
+
+from darts import TimeSeries
+from darts.models import TFTModel
+from darts.utils.likelihood_models import QuantileRegression
+from darts.dataprocessing.transformers import Scaler
+
+
+class TFTQuantilePredictor:
+    """
+    TFT 分位数预测器 — 用于 walk-forward 预测循环.
+
+    输出: Q10, Q50, Q90 三个分位数预测值
+
+    Architecture:
+      - input_chunk_length=24 (回看2年月度数据)
+      - hidden_size=16 (轻量级, 防过拟合)
+      - lstm_layers=1, attention_heads=1
+      - QuantileRegression likelihood [0.10, 0.50, 0.90]
+      - 早停 + 低 epoch (快速训练)
+    """
+
+    def __init__(self, input_chunk_length=24, hidden_size=16,
+                 lstm_layers=1, n_heads=1, n_epochs=15,
+                 batch_size=32, use_gpu=False):
+        self.input_chunk_length = input_chunk_length
+        self.hidden_size = hidden_size
+        self.lstm_layers = lstm_layers
+        self.n_heads = n_heads
+        self.n_epochs = n_epochs
+        self.batch_size = batch_size
+        self.use_gpu = use_gpu
+        self.model = None
+        self.scaler_y = Scaler()
+        self.scaler_cov = Scaler()
+        self._fitted = False
+
+    def _build_model(self):
+        """构建 TFT 模型实例。"""
+        accelerator = 'gpu' if self.use_gpu else 'cpu'
+        self.model = TFTModel(
+            input_chunk_length=self.input_chunk_length,
+            output_chunk_length=1,
+            hidden_size=self.hidden_size,
+            lstm_layers=self.lstm_layers,
+            num_attention_heads=self.n_heads,
+            dropout=0.1,
+            likelihood=QuantileRegression(quantiles=[0.10, 0.50, 0.90]),
+            n_epochs=self.n_epochs,
+            batch_size=self.batch_size,
+            add_relative_index=True,
+            random_state=42,
+            log_tensorboard=False,
+            pl_trainer_kwargs={
+                'enable_progress_bar': False,
+                'accelerator': accelerator,
+                'enable_model_summary': False,
+            },
+        )
+
+    def fit_predict(self, y_train, X_train, X_test_row):
+        """
+        训练 TFT 并预测下一个时间步的 Q10/Q50/Q90.
+
+        Args:
+            y_train: pd.Series — 目标变量 (月度收益率), DatetimeIndex
+            X_train: pd.DataFrame — 特征矩阵, DatetimeIndex
+            X_test_row: pd.DataFrame — 测试特征 (1行), DatetimeIndex
+
+        Returns:
+            dict: {'tft_q10_1m': float, 'tft_q50_1m': float, 'tft_q90_1m': float}
+                  or None if training fails
+        """
+        try:
+            # Minimum samples check
+            n = len(y_train.dropna())
+            if n < self.input_chunk_length + 10:
+                return None
+
+            # Align data
+            valid_idx = y_train.dropna().index
+            common_idx = valid_idx.intersection(X_train.index)
+            if len(common_idx) < self.input_chunk_length + 5:
+                return None
+
+            y_aligned = y_train.loc[common_idx].sort_index()
+            X_aligned = X_train.loc[common_idx].sort_index().fillna(0)
+
+            # Normalize dates to month-start (Darts requires regular freq)
+            def to_month_start(idx):
+                return pd.DatetimeIndex([d.replace(day=1) for d in idx])
+
+            y_ms = y_aligned.copy()
+            y_ms.index = to_month_start(y_ms.index)
+            X_ms = X_aligned.copy()
+            X_ms.index = to_month_start(X_ms.index)
+
+            # Drop duplicate months (if any)
+            y_ms = y_ms[~y_ms.index.duplicated(keep='last')]
+            X_ms = X_ms[~X_ms.index.duplicated(keep='last')]
+
+            # Ensure aligned
+            common = y_ms.index.intersection(X_ms.index)
+            y_ms = y_ms.loc[common]
+            X_ms = X_ms.loc[common]
+
+            if len(y_ms) < self.input_chunk_length + 5:
+                return None
+
+            # Convert to Darts TimeSeries
+            y_ts = TimeSeries.from_times_and_values(
+                y_ms.index, y_ms.values.reshape(-1, 1),
+                freq='MS'
+            )
+            cov_values = X_ms.values.astype(np.float32)
+            cov_ts = TimeSeries.from_times_and_values(
+                X_ms.index, cov_values,
+                columns=list(X_ms.columns), freq='MS'
+            )
+
+            # Extend covariates with test point
+            X_test_clean = X_test_row.fillna(0).copy()
+            X_test_clean.index = to_month_start(X_test_clean.index)
+            cov_ext_df = pd.concat([X_ms, X_test_clean]).sort_index()
+            cov_ext_df = cov_ext_df[~cov_ext_df.index.duplicated(keep='last')]
+            cov_ext_ts = TimeSeries.from_times_and_values(
+                cov_ext_df.index, cov_ext_df.values.astype(np.float32),
+                columns=list(cov_ext_df.columns), freq='MS'
+            )
+
+            # Build & train
+            self._build_model()
+            self.model.fit(y_ts, past_covariates=cov_ts, verbose=False)
+
+            # Predict
+            pred = self.model.predict(
+                n=1,
+                past_covariates=cov_ext_ts,
+                num_samples=200,
+            )
+
+            # Extract quantiles from probabilistic prediction
+            vals = pred.all_values()  # shape: (1, n_components, n_samples)
+            samples = vals.flatten()  # all 200 samples
+            q10 = float(np.percentile(samples, 10))
+            q50 = float(np.percentile(samples, 50))
+            q90 = float(np.percentile(samples, 90))
+
+            # Sanity check
+            if any(np.isnan(v) or np.isinf(v) for v in [q10, q50, q90]):
+                return None
+
+            # Enforce monotonicity
+            vals = sorted([q10, q50, q90])
+            result = {
+                'tft_q10_1m': float(np.clip(vals[0], -0.50, 0.50)),
+                'tft_q50_1m': float(np.clip(vals[1], -0.50, 0.50)),
+                'tft_q90_1m': float(np.clip(vals[2], -0.50, 0.50)),
+            }
+            self._fitted = True
+            return result
+
+        except Exception as e:
+            # Silent fallback — TFT failure should not crash pipeline
+            return None
+
+
+def tft_predict_step(train_df, test_df, sel_features, y_col='target_ret_1m'):
+    """
+    Walk-forward 单步 TFT 预测 — 供 engine.py 调用的便捷函数.
+
+    Args:
+        train_df: pd.DataFrame — 训练窗口数据 (含特征+目标)
+        test_df: pd.DataFrame — 测试行(1行)
+        sel_features: list — 选定特征名
+        y_col: str — 目标列名
+
+    Returns:
+        dict or None: TFT quantile predictions
+    """
+    predictor = TFTQuantilePredictor(
+        input_chunk_length=min(24, len(train_df) - 5),
+        n_epochs=10,
+        batch_size=min(32, len(train_df) // 2),
+        use_gpu=False,  # CPU faster for small batches
+    )
+
+    y_train = train_df[y_col]
+    X_train = train_df[sel_features]
+    X_test = test_df[sel_features]
+
+    return predictor.fit_predict(y_train, X_train, X_test)

data/api_keys.json

@@ -0,0 +1,6 @@
+{
+  "fred": "fc02a6e6a359a4cc16f0f1752d258011",
+  "eia": "9Nv5PhLREMmmKeo0zJ2U3Zu21Bntf8DfhEKBpi55",
+  "mongodb": "mongodb://misun:sun20060810@ac-sfsrs0u-shard-00-00.heeszaj.mongodb.net:27017,ac-sfsrs0u-shard-00-01.heeszaj.mongodb.net:27017,ac-sfsrs0u-shard-00-02.heeszaj.mongodb.net:27017/?ssl=true&replicaSet=atlas-12yhqc-shard-0&authSource=admin&appName=oil-risk-db",
+  "mongodb_db": "oil_risk_intelligence"
+}

data/cloud/census_oil_trade.csv

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+-,TOTAL FOR ALL COUNTRIES,14733970166,HS6,270900,2024-04,imports
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+-,TOTAL FOR ALL COUNTRIES,16503872718,HS6,270900,2024-07,imports
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+-,TOTAL FOR ALL COUNTRIES,13674324666,HS6,270900,2024-09,imports
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+-,TOTAL FOR ALL COUNTRIES,13744230901,HS6,270900,2025-01,imports
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+-,TOTAL FOR ALL COUNTRIES,11744042172,HS6,270900,2025-03,imports
+-,TOTAL FOR ALL COUNTRIES,11908840569,HS6,270900,2025-04,imports
+-,TOTAL FOR ALL COUNTRIES,11999679879,HS6,270900,2025-05,imports
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data/cloud/worldbank_debug.csv

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+,"Crude oil, average","Crude oil, Brent","Crude oil, Dubai","Crude oil, WTI","Coal, Australian","Coal, South African **","Natural gas, US","Natural gas, Europe","Liquefied natural gas, Japan",Natural gas index,Cocoa,"Coffee, Arabica","Coffee, Robusta","Tea, avg 3 auctions","Tea, Colombo","Tea, Kolkata","Tea, Mombasa",Coconut oil,Groundnuts,Fish meal,Groundnut oil **,Palm oil,Palm kernel oil,Soybeans,Soybean oil,Soybean meal,Rapeseed oil,Sunflower oil,Barley,Maize,Sorghum,"Rice, Thai 5% ","Rice, Thai 25% ","Rice, Thai A.1","Rice, Viet Namese 5%","Wheat, US SRW","Wheat, US HRW","Banana, Europe","Banana, US",Orange,Beef **,Chicken **,Lamb **,"Shrimps, Mexican","Sugar, EU","Sugar, US","Sugar, world","Tobacco, US import u.v.","Logs, Cameroon","Logs, Malaysian","Sawnwood, Cameroon","Sawnwood, Malaysian",Plywood,"Cotton, A Index","Rubber, TSR20 **","Rubber, RSS3",Phosphate rock,DAP,TSP,Urea ,Potassium chloride **,Aluminum,"Iron ore, cfr spot",Copper,Lead,Tin,Nickel,Zinc,Gold,Platinum,Silver

data/consumer_confidence_cache.csv

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data/intermediate/public_core_monthly_hub.csv

@@ -0,0 +1 @@
+""

data/intermediate/public_core_monthly_hub_raw.csv

@@ -0,0 +1 @@
+""

data/knowledge_base/README.md

@@ -0,0 +1,10 @@
+# Knowledge Base
+
+This folder stores manually curated or LLM-assisted structured event and report data.
+
+Expected files:
+
+- `oil_market_event_registry.csv`
+- `opec_momr_manual_revisions.csv`
+
+These files are templates for the upgrade path and can be safely edited by hand.

data/knowledge_base/oil_market_event_candidates.csv

@@ -0,0 +1,26 @@
+candidate_id,event_name,start_date,end_date,active_days,llm_intensity,llm_sentiment,source_type,source_path
+pingjin_20050101,pingjin,2005-01-01,2006-12-31,730,5.0,0.0,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+crisis_of_iran_20050801,crisis_of_iran,2005-08-01,2006-06-30,334,7.0,0.5,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+death_of_king_20050801,death_of_king,2005-08-01,2005-12-31,153,5.0,0.0,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+dep_of_dollar_20070101,dep_of_dollar,2007-01-01,2008-09-15,624,3.0,0.5,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+turkey_to_iraq_20071018,turkey_to_iraq,2007-10-18,2007-11-01,15,5.0,0.0,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+us_to_iran_20071025,us_to_iran,2007-10-25,2007-12-31,68,6.0,0.5,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+finance_crisis_20080916,finance_crisis,2008-09-16,2009-03-17,183,4.0,-0.8,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+QE1_20090318,QE1,2009-03-18,2010-03-31,379,3.0,0.5,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+debt_of_pigs_20100401,debt_of_pigs,2010-04-01,2010-11-03,217,5.0,0.0,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+QE2_20101104,QE2,2010-11-04,2010-12-31,58,1.0,0.2,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+arab_spring_20110101,arab_spring,2011-01-01,2011-10-20,293,6.0,0.5,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+decline_in_demand_20120101,decline_in_demand,2012-01-01,2012-12-31,366,5.0,-0.7,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+sit_in_iran_20120101,sit_in_iran,2012-01-01,2012-03-31,91,5.0,0.0,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+shale_oil_revolution_20130101,shale_oil_revolution,2013-01-01,2014-12-31,730,6.0,-1.0,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+sit_in_me_20130801,sit_in_me,2013-08-01,2013-08-31,31,5.0,0.0,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+isis_20140601,isis,2014-06-01,2014-06-30,30,4.5,0.3,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+opec_stay_20141127,opec_stay,2014-11-27,2014-12-31,35,5.0,-0.5,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+iran_nuclear_deal_20150714,iran_nuclear_deal,2015-07-14,2015-12-15,155,7.0,-0.6,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+FR_raise_ir_20151216,FR_raise_ir,2015-12-16,2015-12-31,16,2.0,0.5,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+reduce_in_oil_20161201,reduce_in_oil,2016-12-01,2017-07-01,213,5.0,0.0,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+us_withdral_deal_20180508,us_withdral_deal,2018-05-08,2018-11-05,182,5.0,0.8,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+covid_20200101,covid,2020-01-01,2022-02-24,786,10.0,-0.8,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+energy_crisis_20210701,energy_crisis,2021-07-01,2021-12-31,184,8.0,0.8,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+war_of_RU_20220224,war_of_RU,2022-02-24,2023-12-31,676,9.0,1.0,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv
+red_sea_crisis_20240116,red_sea_crisis,2024-01-16,2026-02-10,757,7.0,0.5,shock_matrix,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv

data/knowledge_base/oil_market_event_registry.csv

@@ -0,0 +1,26 @@
+event_date,event_name,event_type,impact_direction,severity,duration_days,affected_region,affected_industries,source_url,notes
+2005-01-01,pingjin,market_event,neutral,5.0,730,global,logistics|aviation,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2006-12-31 00:00:00
+2005-08-01,crisis_of_iran,geopolitical_conflict,up,7.0,334,middle_east,logistics|aviation|petrochemical,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2006-06-30 00:00:00
+2005-08-01,death_of_king,market_event,neutral,5.0,153,global,logistics|aviation,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2005-12-31 00:00:00
+2007-01-01,dep_of_dollar,market_event,up,3.0,624,global,logistics|aviation,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2008-09-15 00:00:00
+2007-10-18,turkey_to_iraq,market_event,neutral,5.0,15,global,logistics|aviation,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2007-11-01 00:00:00
+2007-10-25,us_to_iran,market_event,up,6.0,68,middle_east,logistics|aviation|petrochemical,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2007-12-31 00:00:00
+2008-09-16,finance_crisis,macro_financial,down,4.0,183,global,aviation|logistics|manufacturing,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2009-03-17 00:00:00
+2009-03-18,QE1,policy_event,up,3.0,379,global,logistics|aviation,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2010-03-31 00:00:00
+2010-04-01,debt_of_pigs,macro_demand_shock,neutral,5.0,217,europe,logistics|aviation,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2010-11-03 00:00:00
+2010-11-04,QE2,policy_event,up,1.0,58,global,logistics|aviation,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2010-12-31 00:00:00
+2011-01-01,arab_spring,market_event,up,6.0,293,middle_east,logistics|aviation,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2011-10-20 00:00:00
+2012-01-01,decline_in_demand,macro_demand_shock,down,5.0,366,global,logistics|aviation,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2012-12-31 00:00:00
+2012-01-01,sit_in_iran,market_event,neutral,5.0,91,middle_east,logistics|aviation|petrochemical,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2012-03-31 00:00:00
+2013-01-01,shale_oil_revolution,supply_expansion,down,6.0,730,us,energy_upstream|petrochemical,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2014-12-31 00:00:00
+2013-08-01,sit_in_me,market_event,neutral,5.0,31,global,logistics|aviation,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2013-08-31 00:00:00
+2014-06-01,isis,geopolitical_conflict,up,4.5,30,global,logistics|aviation,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2014-06-30 00:00:00
+2014-11-27,opec_stay,opec_policy,neutral,5.0,35,opec,energy_upstream|petrochemical,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2014-12-31 00:00:00
+2015-07-14,iran_nuclear_deal,policy_supply_release,down,7.0,155,middle_east,petrochemical|logistics,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2015-12-15 00:00:00
+2015-12-16,FR_raise_ir,market_event,up,2.0,16,global,logistics|aviation,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2015-12-31 00:00:00
+2016-12-01,reduce_in_oil,supply_event,neutral,5.0,213,global,logistics|aviation,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2017-07-01 00:00:00
+2018-05-08,us_withdral_deal,sanction_policy,up,5.0,182,middle_east,petrochemical|logistics|aviation,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2018-11-05 00:00:00
+2020-01-01,covid,demand_shock,down,10.0,786,global,aviation|logistics|petrochemical|manufacturing,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2022-02-24 00:00:00
+2021-07-01,energy_crisis,energy_supply_shock,up,8.0,184,europe|global,petrochemical|manufacturing|logistics,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2021-12-31 00:00:00
+2022-02-24,war_of_RU,geopolitical_conflict,up,9.0,676,europe|global,aviation|logistics|petrochemical|manufacturing,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2023-12-31 00:00:00
+2024-01-16,red_sea_crisis,shipping_disruption,up,7.0,757,middle_east|global,logistics|aviation|manufacturing,E:\大三下\比赛\花旗杯\模型\data\csv_raw\geopolitical_shocks.csv,generated_from=shock_matrix; end_date=2026-02-10 00:00:00

data/knowledge_base/opec_momr_manual_revisions.csv

@@ -0,0 +1 @@
+report_date,target_period,metric_name,revision_direction,revision_value,notes,source_url

data/live/china_monthly.csv

@@ -0,0 +1,668 @@
+date,china_cpi_yoy,china_industrial_va_yoy,china_ppi_industry_yoy,china_pmi_mfg,china_pmi_non_mfg,china_m2,china_m1
+1990-03-01,,5.0,,,,,
+1990-04-01,,0.8,,,,,
+1990-05-01,,1.7,,,,,
+1990-06-01,,3.3,,,,,
+1990-07-01,,5.0,,,,,
+1990-08-01,,2.5,,,,,
+1990-09-01,,4.2,,,,,
+1990-10-01,,6.7,,,,,
+1990-11-01,,10.9,,,,,
+1990-12-01,,12.6,,,,,
+1991-01-01,,12.6,,,,,
+1991-02-01,,23.5,,,,,
+1991-03-01,,9.2,,,,,
+1991-04-01,,10.9,,,,,
+1991-05-01,,10.9,,,,,
+1991-06-01,,10.9,,,,,
+1991-07-01,,9.2,,,,,
+1991-08-01,,10.9,,,,,
+1991-09-01,,11.8,,,,,
+1991-10-01,,12.6,,,,,
+1991-11-01,,11.8,,,,,
+1991-12-01,,7.6,,,,,
+1992-01-01,,5.0,,,,,
+1992-02-01,,10.9,,,,,
+1992-03-01,,18.5,,,,,
+1992-04-01,,16.8,,,,,
+1992-05-01,,15.1,,,,,
+1992-06-01,,14.3,,,,,
+1992-07-01,,16.8,,,,,
+1992-08-01,,19.3,,,,,
+1992-09-01,,17.6,,,,,
+1992-10-01,,17.6,,,,,
+1992-11-01,,18.5,,,,,
+1992-12-01,,21.8,,,,,
+1993-01-01,,26.9,,,,,
+1993-02-01,,8.4,,,,,
+1993-03-01,,27.7,,,,,
+1993-04-01,,21.8,,,,,
+1993-05-01,,21.0,,,,,
+1993-06-01,,22.7,,,,,
+1993-07-01,,25.2,,,,,
+1993-08-01,,21.0,,,,,
+1993-09-01,,19.3,,,,,
+1993-10-01,,16.0,,,,,
+1993-11-01,,13.4,,,,,
+1993-12-01,,16.0,,,,,
+1994-01-01,,25.2,,,,,
+1994-02-01,,27.7,,,,,
+1994-03-01,,3.4,,,,,
+1994-04-01,,16.0,,,,,
+1994-05-01,,16.8,,,,,
+1994-06-01,,17.6,,,,,
+1994-07-01,,15.1,,,,,
+1994-08-01,,15.1,,,,,
+1994-09-01,,29.4,,,,,
+1994-10-01,,21.8,,,,,
+1994-11-01,,24.4,,,,,
+1994-12-01,,23.5,,,,,
+1995-01-01,,18.5,,,,,
+1995-02-01,,11.4,,,,,
+1995-03-01,,15.5,,,,,
+1995-04-01,,16.4,,,,,
+1995-05-01,,15.4,,,,,
+1995-06-01,,13.1,,,,,
+1995-07-01,,13.9,,,,,
+1995-08-01,,13.6,,,,,
+1995-09-01,,11.8,,,,,
+1995-10-01,,11.4,,,,,
+1995-11-01,,12.9,,,,,
+1995-12-01,,12.1,,,,,
+1996-01-01,,14.9,,,,,
+1996-02-01,2.1,16.9,,,,,
+1996-03-01,2.3,12.2,,,,,
+1996-04-01,0.6,12.2,,,,,
+1996-05-01,0.7,13.7,,,,,
+1996-06-01,-0.5,13.1,,,,,
+1996-07-01,-1.6,13.8,,,,,
+1996-08-01,-0.8,12.2,,,,,
+1996-09-01,1.5,10.6,,,,,
+1996-10-01,1.5,13.7,,,,,
+1996-11-01,-0.2,13.3,,,,,
+1996-12-01,-0.1,13.4,,,,,
+1997-01-01,0.7,14.0,,,,,
+1997-02-01,1.1,10.3,,,,,
+1997-03-01,2.0,10.4,,,,,
+1997-04-01,-1.1,13.5,,,,,
+1997-05-01,-0.2,11.9,,,,,
+1997-06-01,-0.9,11.9,,,,,
+1997-07-01,-1.7,12.1,,,,,
+1997-08-01,-1.2,8.4,,,,,
+1997-09-01,0.6,10.9,,,,,
+1997-10-01,1.7,11.1,,,,,
+1997-11-01,-0.6,11.8,,,,,
+1997-12-01,-0.3,11.6,,,,,
+1998-01-01,0.1,9.2,,,,,
+1998-02-01,0.7,1.8,,,,,
+1998-03-01,1.5,9.5,,,,,
+1998-04-01,-0.5,9.0,,,,,
+1998-05-01,-0.8,7.2,,,,,
+1998-06-01,-1.4,8.0,,,,,
+1998-07-01,-1.8,7.9,,,,,
+1998-08-01,-1.3,7.6,,,,,
+1998-09-01,0.8,7.9,,,,,
+1998-10-01,1.6,10.2,,,,,
+1998-11-01,-0.3,10.6,,,,,
+1998-12-01,-0.3,11.0,,,,,
+1999-01-01,0.2,11.5,,,,,
+1999-02-01,0.2,17.9,,,,,
+1999-03-01,1.3,2.1,,,,,
+1999-04-01,-0.8,9.0,,,,,
+1999-05-01,-1.0,9.1,,,,,
+1999-06-01,-1.3,8.9,,,,,
+1999-07-01,-1.8,9.1,,,,,
+1999-08-01,-0.9,9.3,,,,,
+1999-09-01,1.0,9.5,,,,,
+1999-10-01,2.0,8.2,,,,,
+1999-11-01,0.0,7.0,,,,,
+1999-12-01,-0.6,7.6,,,,,
+2000-01-01,0.0,7.4,,,,,
+2000-02-01,0.9,8.9,,,,,
+2000-03-01,1.9,12.0,,,,,
+2000-04-01,-1.6,11.9,,,,,
+2000-05-01,-0.9,11.4,,,,,
+2000-06-01,-1.0,11.5,,,,,
+2000-07-01,-1.5,12.2,,,,,
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+2000-10-01,1.6,12.0,,,,,
+2000-11-01,-0.1,11.4,,,,,
+2000-12-01,0.7,10.6,,,,,
+2001-01-01,0.1,10.4,,,,,
+2001-02-01,0.9,2.3,,,,,
+2001-03-01,0.1,19.0,,,,,
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+2001-05-01,0.2,11.5,,,,,
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+2001-11-01,0.3,8.8,,,,,
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+2002-01-01,0.1,8.7,,,,,
+2002-02-01,0.3,18.6,,,,,
+2002-03-01,1.1,2.7,,,,,
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+2002-08-01,-0.7,12.8,,,,,
+2002-09-01,0.3,12.7,,,,,
+2002-10-01,1.0,13.8,,,,,
+2002-11-01,0.2,14.2,,,,,
+2002-12-01,-0.1,14.5,,,,,
+2003-01-01,0.3,14.9,,,,,
+2003-02-01,1.1,14.8,,,,,
+2003-03-01,0.9,19.8,,,,,
+2003-04-01,-0.6,16.9,,,,,
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+2003-06-01,-0.7,13.7,,,,,
+2003-07-01,-1.2,16.9,,,,,
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+2003-09-01,0.7,17.1,,,,,
+2003-10-01,1.2,16.3,,,,,
+2003-11-01,0.9,17.2,,,,,
+2003-12-01,1.0,17.9,,,,,
+2004-01-01,0.6,18.1,,,,,
+2004-02-01,1.1,7.2,,,,,
+2004-03-01,-0.2,23.2,,,,,
+2004-04-01,0.3,19.4,,,,,
+2004-05-01,0.5,19.1,,,,,
+2004-06-01,-0.1,17.5,,,,,
+2004-07-01,-0.7,16.2,,,,,
+2004-08-01,-0.2,15.5,,,,,
+2004-09-01,0.7,15.9,,,,,
+2004-10-01,1.1,16.1,,,,,
+2004-11-01,0.0,15.7,,,,,
+2004-12-01,-0.3,14.8,,,,,
+2005-01-01,0.1,14.4,,,,,
+2005-02-01,0.6,20.9,,,,,
+2005-03-01,1.8,7.6,,,,,
+2005-04-01,-0.9,15.1,,,,,
+2005-05-01,-0.3,16.0,,,,,
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+2005-08-01,0.0,16.1,,,,,
+2005-09-01,0.2,16.0,,,,,
+2005-10-01,0.7,16.5,,,,,
+2005-11-01,0.4,16.1,,,,,
+2005-12-01,-0.3,16.6,,,,,
+2006-01-01,,,3.05,,,,
+2006-01-01,0.4,16.5,,,,,
+2006-02-01,,,3.01,,,,
+2006-02-01,1.3,,,,,,
+2006-03-01,,,2.49,,,,
+2006-03-01,0.5,20.1,,,,,
+2006-04-01,,,1.87,,,,
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data/live/gdelt_monthly.csv

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data/live/worldbank_annual.csv

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data/llm_event_scores.json

@@ -0,0 +1,34 @@
+{
+  "1990-08": 0.5,
+  "1991-01": 1,
+  "1997-07": -1,
+  "1998-12": 1,
+  "2001-09": -1,
+  "2003-03": -1,
+  "2005-08": -1,
+  "2008-07": 1,
+  "2008-09": -1,
+  "2009-03": 0.8,
+  "2010-04": -1,
+  "2011-02": 1,
+  "2014-06": -0.8,
+  "2014-11": -0.8,
+  "2015-01": -0.8,
+  "2016-02": -1,
+  "2016-11": 1,
+  "2018-05": 0.5,
+  "2018-10": -0.5,
+  "2019-09": 1,
+  "2020-03": -0.7,
+  "2020-04": -1,
+  "2020-05": 1,
+  "2021-07": 0.5,
+  "2022-02": 1,
+  "2022-06": 0.8,
+  "2022-10": 1,
+  "2023-04": 1,
+  "2023-10": 0.8,
+  "2024-06": 0.3,
+  "2024-11": -0.5,
+  "2025-03": 0.8
+}

data/raw_public/fred_core_daily.csv

@@ -0,0 +1 @@
+""

frontend/.env.production

@@ -0,0 +1 @@
+VITE_API_URL=

frontend/.gitignore

@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?

frontend/README.md

@@ -0,0 +1,16 @@
+# React + Vite
+
+This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
+
+Currently, two official plugins are available:
+
+- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Oxc](https://oxc.rs)
+- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/)
+
+## React Compiler
+
+The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation).
+
+## Expanding the ESLint configuration
+
+If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the [TS template](https://github.com/vitejs/vite/tree/main/packages/create-vite/template-react-ts) for information on how to integrate TypeScript and [`typescript-eslint`](https://typescript-eslint.io) in your project.

frontend/eslint.config.js

@@ -0,0 +1,29 @@
+import js from '@eslint/js'
+import globals from 'globals'
+import reactHooks from 'eslint-plugin-react-hooks'
+import reactRefresh from 'eslint-plugin-react-refresh'
+import { defineConfig, globalIgnores } from 'eslint/config'
+
+export default defineConfig([
+  globalIgnores(['dist']),
+  {
+    files: ['**/*.{js,jsx}'],
+    extends: [
+      js.configs.recommended,
+      reactHooks.configs.flat.recommended,
+      reactRefresh.configs.vite,
+    ],
+    languageOptions: {
+      ecmaVersion: 2020,
+      globals: globals.browser,
+      parserOptions: {
+        ecmaVersion: 'latest',
+        ecmaFeatures: { jsx: true },
+        sourceType: 'module',
+      },
+    },
+    rules: {
+      'no-unused-vars': ['error', { varsIgnorePattern: '^[A-Z_]' }],
+    },
+  },
+])

frontend/index.html

@@ -0,0 +1,13 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/favicon.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>油刃有余 OilVerse — 油价因子量化分析预测平台</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.jsx"></script>
+  </body>
+</html>

frontend/package.json

@@ -0,0 +1,29 @@
+{
+  "name": "frontend",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "lint": "eslint .",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "react": "^19.2.4",
+    "react-dom": "^19.2.4",
+    "react-router-dom": "^7.13.2",
+    "recharts": "^3.8.0"
+  },
+  "devDependencies": {
+    "@eslint/js": "^9.39.4",
+    "@types/react": "^19.2.14",
+    "@types/react-dom": "^19.2.3",
+    "@vitejs/plugin-react": "^6.0.1",
+    "eslint": "^9.39.4",
+    "eslint-plugin-react-hooks": "^7.0.1",
+    "eslint-plugin-react-refresh": "^0.5.2",
+    "globals": "^17.4.0",
+    "vite": "^8.0.1"
+  }
+}

frontend/src/App.css

@@ -0,0 +1,44 @@
+.app-layout {
+  display: flex;
+  min-height: 100vh;
+}
+
+.main-content {
+  margin-left: 250px;
+  flex: 1;
+  padding: 24px 32px;
+}
+
+.loading-overlay {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  height: 60vh;
+}
+.loader {
+  text-align: center;
+}
+.loader p {
+  color: var(--muted);
+  font-size: 14px;
+  margin-top: 16px;
+}
+.loader-spinner {
+  width: 40px;
+  height: 40px;
+  border: 3px solid var(--border);
+  border-top-color: var(--accent);
+  border-radius: 50%;
+  animation: spin 0.8s linear infinite;
+  margin: 0 auto;
+}
+@keyframes spin {
+  to { transform: rotate(360deg); }
+}
+
+@media (max-width: 1024px) {
+  .main-content {
+    margin-left: 0;
+    padding: 12px;
+  }
+}

frontend/src/App.jsx

@@ -0,0 +1,56 @@
+import { BrowserRouter, Routes, Route, Navigate } from 'react-router-dom';
+import { AppProvider, useApp } from './context/AppContext';
+import Sidebar from './components/Sidebar';
+import P1Overview from './pages/P1Overview';
+import P2FactorAnalysis from './pages/P2FactorAnalysis';
+import P3RiskPrediction from './pages/P3RiskPrediction';
+import P4StressTest from './pages/P4StressTest';
+import P5IndustryImpact from './pages/P5IndustryImpact';
+import P6ModelValidation from './pages/P6ModelValidation';
+import P7DataGovernance from './pages/P7DataGovernance';
+import P9AIAgent from './pages/P9AIAgent';
+import P9Pipeline from './pages/P9Pipeline';
+import './App.css';
+
+function Layout() {
+  const { loading } = useApp();
+
+  return (
+    <div className="app-layout">
+      <Sidebar />
+      <main className="main-content">
+        {loading ? (
+          <div className="loading-overlay">
+            <div className="loader">
+              <div className="loader-spinner" />
+              <p>加载数据中...</p>
+            </div>
+          </div>
+        ) : (
+          <Routes>
+            <Route path="/" element={<P1Overview />} />
+            <Route path="/factors" element={<P2FactorAnalysis />} />
+            <Route path="/prediction" element={<P3RiskPrediction />} />
+            <Route path="/stress" element={<P4StressTest />} />
+            <Route path="/industry" element={<P5IndustryImpact />} />
+            <Route path="/validation" element={<P6ModelValidation />} />
+            <Route path="/governance" element={<P7DataGovernance />} />
+            <Route path="/hedging" element={<Navigate to="/industry" replace />} />
+            <Route path="/agent" element={<P9AIAgent />} />
+            <Route path="/pipeline" element={<P9Pipeline />} />
+          </Routes>
+        )}
+      </main>
+    </div>
+  );
+}
+
+export default function App() {
+  return (
+    <BrowserRouter>
+      <AppProvider>
+        <Layout />
+      </AppProvider>
+    </BrowserRouter>
+  );
+}

frontend/src/api.js

@@ -0,0 +1,80 @@
+const API_BASE = import.meta.env.VITE_API_URL || 'http://localhost:8765';
+
+export async function fetchBenchmarks() {
+  const res = await fetch(`${API_BASE}/api/benchmarks`);
+  return res.json();
+}
+
+export async function fetchResults(benchmark) {
+  const res = await fetch(`${API_BASE}/api/results/${benchmark}`);
+  return res.json();
+}
+
+export async function fetchEval(benchmark) {
+  const res = await fetch(`${API_BASE}/api/eval/${benchmark}`);
+  return res.json();
+}
+
+export async function fetchNlg(benchmark) {
+  const res = await fetch(`${API_BASE}/api/nlg/${benchmark}`);
+  return res.json();
+}
+
+export async function fetchScenarios() {
+  const res = await fetch(`${API_BASE}/api/scenarios`);
+  return res.json();
+}
+
+export async function fetchRegime() {
+  const res = await fetch(`${API_BASE}/api/regime`);
+  return res.json();
+}
+
+export async function fetchHedging() {
+  const res = await fetch(`${API_BASE}/api/hedging`);
+  return res.json();
+}
+
+export async function fetchBacktest() {
+  const res = await fetch(`${API_BASE}/api/backtest`);
+  return res.json();
+}
+
+export async function fetchAblation() {
+  const res = await fetch(`${API_BASE}/api/ablation`);
+  return res.json();
+}
+
+export async function fetchQuality() {
+  const res = await fetch(`${API_BASE}/api/quality`);
+  return res.json();
+}
+
+export async function fetchLineage() {
+  const res = await fetch(`${API_BASE}/api/lineage`);
+  return res.json();
+}
+
+export async function fetchFeatSel() {
+  const res = await fetch(`${API_BASE}/api/feat_sel`);
+  return res.json();
+}
+
+export async function fetchCausal() {
+  const res = await fetch(`${API_BASE}/api/causal`);
+  return res.json();
+}
+
+export async function fetchEvents() {
+  const res = await fetch(`${API_BASE}/api/events`);
+  return res.json();
+}
+
+export async function sendChat(message, sessionId) {
+  const res = await fetch(`${API_BASE}/api/chat`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({ message, session_id: sessionId }),
+  });
+  return res.json();
+}