refactor: 归档策略进化模块+新建评估页面+API

- 归档 evolution/ + meta_growth/meta_watchdog/ab_research_daily
- docs/evolution-archive-readme.md: 归档说明(旧模块功能+替代方案)
- server.py: 新增 /api/research/effectiveness + effectiveness/summary + recommendation_log + execution_log
- static/effectiveness.html: 新评估页面(概览/详细评估/推荐记录/执行记录)
- 策略进化改为人驱动闭环(评估→用户决策→调整)

server.py

@@ -2409,4 +2409,102 @@ def api_docs_read():
 if __name__ == "__main__":
     port = int(os.environ.get("PORT", 8899))
     print(f"🚀 MoFin Dashboard → http://0.0.0.0:{port}")
-    app.run(host="0.0.0.0", port=port, debug=False)
+    app.run(host="0.0.0.0", port=port, debug=False)
+
+# ── 策略评估 API ────────────────────────────────────────
+@app.route("/api/research/effectiveness")
+def api_research_effectiveness():
+    """策略到期评估结果查询
+    
+    GET /api/research/effectiveness                → 全部评估记录
+    GET /api/research/effectiveness?code=600262    → 指定股票
+    GET /api/research/effectiveness?source=v_next  → 指定策略来源
+    """
+    import sqlite3
+    code = request.args.get("code", "")
+    source = request.args.get("source", "")
+    
+    db = sqlite3.connect("/home/hmo/MoFin/data/mofin.db", timeout=30)
+    db.row_factory = sqlite3.Row
+    
+    query = "SELECT * FROM strategy_effectiveness WHERE 1=1"
+    params = []
+    if code:
+        query += " AND code=?"
+        params.append(code)
+    if source:
+        query += " AND strategy_source=?"
+        params.append(source)
+    query += " ORDER BY created_at DESC LIMIT 100"
+    
+    rows = db.execute(query, params).fetchall()
+    db.close()
+    
+    return jsonify([dict(r) for r in rows])
+
+
+@app.route("/api/research/effectiveness/summary")
+def api_research_effectiveness_summary():
+    """策略评估汇总：按策略来源分组统计"""
+    import sqlite3
+    db = sqlite3.connect("/home/hmo/MoFin/data/mofin.db", timeout=30)
+    db.row_factory = sqlite3.Row
+    
+    summary = db.execute("""
+        SELECT strategy_source,
+               COUNT(*) as total,
+               SUM(CASE WHEN buy_zone_accuracy='effective' THEN 1 ELSE 0 END) as buy_effective,
+               SUM(CASE WHEN stop_loss_accuracy='effective' THEN 1 ELSE 0 END) as sl_effective,
+               SUM(CASE WHEN take_profit_accuracy='effective' THEN 1 ELSE 0 END) as tp_effective,
+               AVG(CASE WHEN time_accuracy='on_time' THEN 1.0 WHEN time_accuracy='slightly_late' THEN 0.7 ELSE 0.3 END) as time_score
+        FROM strategy_effectiveness
+        GROUP BY strategy_source
+        ORDER BY total DESC
+    """).fetchall()
+    db.close()
+    
+    return jsonify([dict(r) for r in summary])
+
+
+@app.route("/api/research/recommendation_log")
+def api_research_recommendation_log():
+    """推荐历史查询"""
+    import sqlite3
+    code = request.args.get("code", "")
+    limit = int(request.args.get("limit", "50"))
+    
+    db = sqlite3.connect("/home/hmo/MoFin/data/mofin.db", timeout=30)
+    db.row_factory = sqlite3.Row
+    
+    query = "SELECT * FROM recommendation_log WHERE 1=1"
+    params = []
+    if code:
+        query += " AND code=?"
+        params.append(code)
+    query += f" ORDER BY recommend_time DESC LIMIT {limit}"
+    
+    rows = db.execute(query, params).fetchall()
+    db.close()
+    return jsonify([dict(r) for r in rows])
+
+
+@app.route("/api/research/execution_log")
+def api_research_execution_log():
+    """执行历史查询"""
+    import sqlite3
+    code = request.args.get("code", "")
+    limit = int(request.args.get("limit", "50"))
+    
+    db = sqlite3.connect("/home/hmo/MoFin/data/mofin.db", timeout=30)
+    db.row_factory = sqlite3.Row
+    
+    query = "SELECT * FROM execution_log WHERE 1=1"
+    params = []
+    if code:
+        query += " AND code=?"
+        params.append(code)
+    query += f" ORDER BY execute_time DESC LIMIT {limit}"
+    
+    rows = db.execute(query, params).fetchall()
+    db.close()
+    return jsonify([dict(r) for r in rows])