MoFin 初始提交

完整数据采集+分析管道：
- market_watch.py：90行业板块采集（同花顺/东方财富）
- 市场精选推荐 cron：全市场分析+候选池+星级推荐
- price_monitor.py：持仓/自选高频价格监控
- refresh_mtf_cache.py：多周期K线缓存
- 策略评估/知识萃取管道

文档：docs/ 含完整需求+架构设计
注意：尚未配置 git remote，笑笑接手后自行配置

market_insight.py

@@ -0,0 +1,179 @@
+#!/usr/bin/env python3
+"""market_insight.py — 基于 market.json 数据生成基础洞察 + 潜力挖掘
+
+输出：更新 data/market.json 中的 insights / potential_stocks 字段
+
+策略：
+  1. 行业热点 vs 持仓匹配 → 相关影响
+  2. 资金流向异常 → 关注信号
+  3. 市场情绪 → 每日研判
+  4. 潜力挖掘 → 强势行业中寻找持仓相关标的
+"""
+
+import json
+import sys
+from datetime import datetime
+from pathlib import Path
+
+DATA_DIR = Path(__file__).parent / "data"
+
+# ── 持仓股 → 行业映射（从 stock_profiles 自动提取） ──
+
+def load_holding_industry_map():
+    """从 stock_profiles 和 portfolio 提取持仓→行业映射"""
+    try:
+        with open(DATA_DIR / "stock_profiles.json", "r", encoding="utf-8") as f:
+            profiles = json.load(f).get("profiles", [])
+
+        with open(DATA_DIR / "portfolio.json", "r", encoding="utf-8") as f:
+            portfolio = json.load(f)
+    except FileNotFoundError:
+        return {}
+
+    # 构建 code→name 映射（从 portfolio）
+    code_to_name = {}
+    for item in portfolio.get("holdings", []):
+        code_to_name[item.get("code", "")] = item.get("name", "")
+
+    # 构建行业→持仓列表
+    industry_holdings = {}
+    for p in profiles:
+        code = p.get("code", "")
+        name = p.get("name", "")
+        sector = p.get("sector", "")
+        if not sector or sector == "待补全":
+            continue
+        # 提取一级行业（取斜杠前第一个）
+        primary = sector.split("/")[0].split("（")[0].strip()
+        if primary:
+            industry_holdings.setdefault(primary, []).append({
+                "code": code,
+                "name": name,
+                "sector": sector,
+            })
+    return industry_holdings
+
+
+def generate():
+    # 加载数据
+    market_path = DATA_DIR / "market.json"
+    with open(market_path, "r", encoding="utf-8") as f:
+        market = json.load(f)
+
+    sectors = market.get("sectors", [])
+    top_gainers = market.get("top_gainers", [])
+    top_losers = market.get("top_losers", [])
+    mood = market.get("mood", "unknown")
+    up_ratio = market.get("up_ratio", 0)
+    timestamp = market.get("timestamp", "")
+
+    industry_holdings = load_holding_industry_map()
+    insights = []
+    potentials = []
+
+    # ── 洞察1：市场情绪总览 ──
+    mood_cn = {"bullish": "偏强", "neutral": "中性", "bearish": "偏弱", "unknown": "未知"}
+    insights.append(
+        f"市场情绪{mood_cn.get(mood, '未知')}，上涨占比{up_ratio}%"
+    )
+
+    # ── 洞察2：领涨行业 vs 持仓影响 ──
+    gainer_insights = []
+    for g in top_gainers[:3]:
+        name = g.get("name", "")
+        change = g.get("change", 0)
+        # 看持仓中是否有该行业
+        matched = []
+        for industry, holdings in industry_holdings.items():
+            if industry in name or name in industry:
+                matched.extend([h["name"] for h in holdings])
+        if matched:
+            gainer_insights.append(
+                f"{name}+{change}%, 关联持仓{'/'.join(matched[:3])}受益"
+            )
+        else:
+            gainer_insights.append(f"{name}+{change}%, 暂无持仓")
+    if gainer_insights:
+        insights.append("领涨板块: " + " | ".join(gainer_insights[:2]))
+
+    # ── 洞察3：领跌行业 vs 持仓风险 ──
+    loser_insights = []
+    for g in top_losers[:3]:
+        name = g.get("name", "")
+        change = g.get("change", 0)
+        matched = []
+        for industry, holdings in industry_holdings.items():
+            if industry in name or name in industry:
+                matched.extend([h["name"] for h in holdings])
+        if matched:
+            loser_insights.append(
+                f"{name}{change}%, {'/'.join(matched[:2])}需关注"
+            )
+        else:
+            loser_insights.append(f"{name}{change}%")
+    if loser_insights:
+        insights.append("风险板块: " + " | ".join(loser_insights[:3]))
+
+    # ── 洞察4：资金流向异动 ──
+    big_inflow = [s for s in sectors if s.get("net_inflow", 0) > 50]
+    big_outflow = [s for s in sectors if s.get("net_inflow", 0) < -50]
+    if big_inflow:
+        top = max(big_inflow, key=lambda s: s["net_inflow"])
+        insights.append(
+            f"资金流入最大: {top['name']} {top['net_inflow']}亿"
+        )
+    if big_outflow:
+        top = min(big_outflow, key=lambda s: s["net_inflow"])
+        insights.append(
+            f"资金流出最大: {top['name']} {top['net_inflow']}亿"
+        )
+
+    # ── 潜力股挖掘：从强势行业中找持仓或自选相关 ──
+    for g in top_gainers[:5]:
+        name = g.get("name", "")
+        change = g.get("change", 0)
+        if change < 2:
+            continue  # 只关注涨>2%的
+
+        # 找该行业指数有没有关联持仓
+        lead_stock = g.get("lead_stock", "")
+        if lead_stock:
+            potentials.append({
+                "name": lead_stock,
+                "reason": f"{name}领涨股, 板块+{change}%",
+            })
+
+        # 看持仓中是否有该行业
+        for industry, holdings in industry_holdings.items():
+            if industry in name or name in industry:
+                for h in holdings:
+                    potentials.append({
+                        "name": h["name"],
+                        "reason": f"所在行业{name}涨{change}%",
+                    })
+
+    # 去重（最多5条）
+    seen = set()
+    unique_potentials = []
+    for p in potentials:
+        key = p["name"]
+        if key not in seen:
+            seen.add(key)
+            unique_potentials.append(p)
+            if len(unique_potentials) >= 5:
+                break
+    potentials = unique_potentials
+
+    # ── 写入 market.json ──
+    market["insights"] = insights
+    market["potential_stocks"] = potentials
+    market["insight_timestamp"] = datetime.now().strftime("%Y-%m-%d %H:%M")
+
+    with open(market_path, "w", encoding="utf-8") as f:
+        json.dump(market, f, ensure_ascii=False, indent=2)
+
+    print(f"生成{len(insights)}条洞察 + {len(potentials)}条潜力挖掘")
+
+
+if __name__ == "__main__":
+    generate()