diff --git a/strategy_lab.py b/strategy_lab.py
index 95916f11..7d4ef765 100644
--- a/strategy_lab.py
+++ b/strategy_lab.py
@@ -515,9 +515,9 @@ STRATEGIES.update({
     "v_next3": _v81_conviction("v_next3", "v8.1+信念叠乘+行业牛杠杆",
         "v8.1波段基座; DNA×2 + 行业ADX>25×2 + flow_delta>0×2 叠乘, 封顶×4",
         "多因子共振的票才是极品配最重仓; 行业ADX>25=行业级确认牛, 比>=20更精准",
-        {"model": "叠乘封顶×4: DNA×2 + 行业ADX>25×2 + flow_delta>0×2",
+        {"model": "叠乘封顶×4: DNA×2 + 行业ADX>25×2 + flow_delta>0×2 + 新闻正向×2",
          "stack": True, "cap": 4.0, "dna_mult": 2.0, "sector_adx_min": 25, "sector_mult": 2.0,
-         "flow_delta_min": 0, "flow_mult": 2.0}),
+         "flow_delta_min": 0, "flow_mult": 2.0, "news_mult": 2.0, "news_days": 3}),
 })
 
 STRATEGY_DESCRIPTIONS.update({
@@ -528,7 +528,7 @@ STRATEGY_DESCRIPTIONS.update({
         "evidence": "5年全参与+48.3%(年化8.8%)/回撤4.1%。×2票69%胜率/+14.23% vs ×1票48%/+5.99%。规则经逆向验证103/103笔精确复现(平铺×2)。",
     },
     "v_next3": {
-        "title": "v8.1+信念叠乘+行业牛杠杆（DNA×2·行业ADX>25×2·资金加速度×2, 封顶×4）【当前最优】",
+        "title": "v8.1+信念叠乘+行业牛杠杆+新闻因子（DNA×2·行业ADX>25×2·资金加速度×2·新闻正向×2, 封顶×4）【当前最优】",
         "algorithm": "v8.1波段出场基座 + 信念因子叠乘重仓: 动量基因DNA×2、行业确认牛(行业ADX>25)×2、资金加速度(flow_delta>0)×2; 多因子同时命中则叠乘, 封顶×4; 均不命中×1。基准3仓, 单票最大4倍基准仓。",
         "rationale": "v_next平铺只区分'有没有信念', 叠乘区分'信念有多强'——多因子共振的票是极品, 配最重仓位。行业ADX>25是行业级确认牛(比≥20更严格), 对应生产端行业牛杠杆(ADX>25升一档仓位)。",
         "evidence": "5年103笔/57.3%胜率, 全参与+55.6%(年化9.1%)/回撤4.1%, 收益/回撤比12.4全场最优。boost分布×1:56笔/×2:37笔/×4:10笔。规则经逆向验证102/103笔精确复现。",
@@ -744,6 +744,87 @@ def flow_ctx(code, date, bars_dates, idx):
 # ══════════════════════════════════════════════════════
 _WEEKLY_CTX = {}  # code -> sorted [(date, close, ma10w, ma20w)]
 
+# ══════════════════════════════════════════════════════
+# 新闻情绪上下文（stock_news 表，同花顺+东财）
+# ══════════════════════════════════════════════════════
+_NEWS_CTX = {}   # code -> sorted [(date_str, sentiment)]
+_NEWS_DATES = []  # sorted dates
+_SECTOR_NEWS_CTX = {}  # sector -> sorted [(date_str, sentiment)]
+
+def prepare_news_context(start_date, end_date):
+    """加载个股新闻情绪（关键词分类）"""
+    global _NEWS_CTX, _NEWS_DATES, _SECTOR_NEWS_CTX
+    _NEWS_CTX, _SECTOR_NEWS_CTX = {}, {}
+    conn = sqlite3.connect(DB_PATH)
+    try:
+        rows = conn.execute("""
+            SELECT code, SUBSTR(date,1,10) as d, title, content 
+            FROM stock_news WHERE date >= ? AND date <= ?
+            ORDER BY code, date
+        """, (start_date, end_date)).fetchall()
+    except sqlite3.OperationalError:
+        rows = []
+    finally:
+        conn.close()
+    
+    POS = ['涨停','大涨','预增','中标','增持','回购','利好','创新高','突破','净买入','资金流入','机构买入','看多','上调','回暖','复苏','放量']
+    NEG = ['跌停','大跌','预亏','减持','亏损','利空','立案','风险','下调','净卖出','资金流出','机构卖出','看空','退市','下滑','萎缩']
+    
+    from collections import defaultdict
+    by_code = defaultdict(list)
+    for code, d, title, content in rows:
+        text = (title or '') + ' ' + (content or '')
+        pos = sum(1 for kw in POS if kw in text)
+        neg = sum(1 for kw in NEG if kw in text)
+        if pos > neg:
+            sent = 'positive'
+        elif neg > pos:
+            sent = 'negative'
+        else:
+            continue  # 中性不存
+        by_code[code].append((d, sent))
+    
+    for code, lst in by_code.items():
+        _NEWS_CTX[code] = lst
+    
+    # 行业聚合
+    try:
+        conn = sqlite3.connect(DB_PATH)
+        sector_map = dict(conn.execute("SELECT code, sector FROM stock_sectors_em").fetchall())
+        conn.close()
+        sector_news = defaultdict(list)
+        for code, lst in by_code.items():
+            sec = sector_map.get(code)
+            if sec:
+                for d, sent in lst:
+                    sector_news[sec].append((d, sent))
+        for sec, lst in sector_news.items():
+            _SECTOR_NEWS_CTX[sec] = sorted(lst)
+    except:
+        pass
+    
+    _NEWS_DATES = sorted({d for lst in by_code.values() for d, _ in lst})
+
+def news_ctx(code, date, days=3):
+    """返回个股近N日新闻情绪统计"""
+    from datetime import datetime, timedelta
+    d_end = date
+    d_start = (datetime.strptime(date, '%Y-%m-%d') - timedelta(days=days)).strftime('%Y-%m-%d')
+    lst = _NEWS_CTX.get(code, [])
+    pos = sum(1 for d, s in lst if d_start <= d < d_end and s == 'positive')
+    neg = sum(1 for d, s in lst if d_start <= d < d_end and s == 'negative')
+    return {'pos': pos, 'neg': neg, 'total': pos + neg}
+
+def sector_news_ctx(sector, date, days=3):
+    """返回行业近N日新闻情绪统计"""
+    from datetime import datetime, timedelta
+    d_end = date
+    d_start = (datetime.strptime(date, '%Y-%m-%d') - timedelta(days=days)).strftime('%Y-%m-%d')
+    lst = _SECTOR_NEWS_CTX.get(sector, [])
+    pos = sum(1 for d, s in lst if d_start <= d < d_end and s == 'positive')
+    neg = sum(1 for d, s in lst if d_start <= d < d_end and s == 'negative')
+    return {'pos': pos, 'neg': neg, 'total': pos + neg}
+
 def prepare_weekly_context(start_date, end_date):
     """加载 stock_weekly，计算每周 MA10/MA20"""
     global _WEEKLY_CTX
@@ -924,6 +1005,7 @@ def run_backtest(strategy_version, start_date, end_date, capital=913000, save=Tr
     prepare_sector_context(start_date, end_date)
     prepare_flow_context(fetch_start, end_date)
     prepare_weekly_context(fetch_start, end_date)
+    prepare_news_context(start_date, end_date)
 
     conn = sqlite3.connect(DB_PATH)
     stocks = conn.execute("""
@@ -1288,6 +1370,8 @@ def run_backtest(strategy_version, start_date, end_date, capital=913000, save=Tr
             _sec_m = conv.get('sector_mult', 2.0)
             _flo_t = conv.get('flow_delta_min', 0)
             _flo_m = conv.get('flow_mult', 2.0)
+            _news_m = conv.get('news_mult', 0)  # 新闻因子倍数（0=不启用）
+            _news_days = conv.get('news_days', 3)
             _cap = conv.get('cap')
             _stack = conv.get('stack', True)
             for t in trades:
@@ -1296,6 +1380,15 @@ def run_backtest(strategy_version, start_date, end_date, capital=913000, save=Tr
                 _sec_hit = _sa is not None and _sa > _sec_t
                 _fd = _f.get('flow_delta')
                 _flo_hit = _fd is not None and _fd > _flo_t
+                # 新闻因子（个股+行业）
+                _news_hit = False
+                if _news_m > 0:
+                    _code = t.get('code')
+                    _date = t.get('entry_date')
+                    _sector = _STOCK_SECTOR.get(_code, '')
+                    _sn = news_ctx(_code, _date, _news_days)
+                    _ssn = sector_news_ctx(_sector, _date, _news_days) if _sector else {'pos': 0, 'neg': 0}
+                    _news_hit = (_sn['pos'] + _ssn['pos']) > (_sn['neg'] + _ssn['neg']) and (_sn['pos'] + _ssn['pos']) >= 1
                 if _stack:
                     b = 1.0
                     if t.get('dna'):
@@ -1304,16 +1397,29 @@ def run_backtest(strategy_version, start_date, end_date, capital=913000, save=Tr
                         b *= _sec_m
                     if _flo_hit:
                         b *= _flo_m
+                    if _news_hit:
+                        b *= _news_m
                     if _cap:
                         b = min(b, _cap)
                 else:
-                    b = _dna_m if (t.get('dna') or _sec_hit or _flo_hit) else 1.0
+                    b = _dna_m if (t.get('dna') or _sec_hit or _flo_hit or _news_hit) else 1.0
                 t['boost'] = b
             summary['conviction_model'] = conv.get('model', '')
         else:
             boost_k = cfg.get('exit', {}).get('dna_boost', 2.5)
+            _news_m = cfg.get('news_mult', 0)
+            _news_days = cfg.get('news_days', 3)
             for t in trades:
-                t['boost'] = boost_k if t.get('dna') else 1.0
+                b = boost_k if t.get('dna') else 1.0
+                if _news_m > 0:
+                    _code = t.get('code')
+                    _date = t.get('entry_date')
+                    _sector = _STOCK_SECTOR.get(_code, '')
+                    _sn = news_ctx(_code, _date, _news_days)
+                    _ssn = sector_news_ctx(_sector, _date, _news_days) if _sector else {'pos': 0, 'neg': 0}
+                    if (_sn['pos'] + _ssn['pos']) > (_sn['neg'] + _ssn['neg']) and (_sn['pos'] + _ssn['pos']) >= 1:
+                        b *= _news_m
+                t['boost'] = b
         # 集中仓位（该策略最优激进仓位）
         slots = STRATEGY_SIZING.get(strategy_version, 10)
         summary['portfolio'] = portfolio_sim(trades, capital, slots)