diff --git a/scripts/stock_contradiction_workflow.py b/scripts/stock_contradiction_workflow.py
index cec912a6..b43d5033 100644
--- a/scripts/stock_contradiction_workflow.py
+++ b/scripts/stock_contradiction_workflow.py
@@ -159,10 +159,44 @@ def load_fundamental_scan():
         return None
 
 
+def load_macro_score():
+    """读取宏观评分结果（stock_macro.py 生成，--json 模式）"""
+    f = os.path.join(OUTPUT_DIR, "macro_score.json")
+    if not os.path.exists(f):
+        return None
+    try:
+        with open(f) as fp:
+            return json.load(fp)
+    except Exception:
+        return None
+
+
+def load_sentiment_scan():
+    """读取消息面情感扫描结果（stock_sentiment.py 生成）"""
+    f = os.path.join(OUTPUT_DIR, "sentiment_scan.json")
+    if not os.path.exists(f):
+        return None
+    try:
+        with open(f) as fp:
+            return json.load(fp)
+    except Exception:
+        return None
+
+
 def build_verdict(signals):
-    """根据真实数据计算四维评分（2026-08-01 增强：基本面接入真实数据）"""
+    """根据真实数据计算四维评分（2026-08-02 增强：宏观接入真实数据）"""
     now = datetime.now()
-    macro = -1  # 宏观承压（保持判断，可后续接入宏观指标）
+
+    # 宏观面：真实数据（stock_macro.py 生成）
+    macro = 0
+    macro_reasons = []
+    macro_data = load_macro_score()
+    if macro_data:
+        macro = macro_data.get("macro", 0)
+        macro_reasons = macro_data.get("reasons", [])
+    else:
+        macro = -1  # 回退：无数据时保持保守判断
+        macro_reasons = ["宏观数据缺失，保守-1"]
 
     # 基本面：取关注股票 PE/PB 平均，判断整体估值
     fundamental = 0
@@ -192,8 +226,15 @@ def build_verdict(signals):
     elif below_count > above_count:
         technical = -1
 
-    # 消息面：保持中性（暂无新闻接入）
+    # 消息面：真实数据（stock_sentiment.py 生成）
     message = 0
+    msg_reasons = []
+    senti = load_sentiment_scan()
+    if senti:
+        raw = senti.get("message_score", 0)
+        message = 1 if raw > 0.3 else (-1 if raw < -0.3 else 0)
+        if message != 0:
+            msg_reasons.append(f"市场消息面{raw:+.1f}")
 
     total = macro + fundamental + technical + message
 
@@ -212,7 +253,9 @@ def build_verdict(signals):
         "message": message,
         "total": total,
         "decision": decision,
+        "macro_reasons": macro_reasons,
         "fundamental_reasons": fund_reasons,
+        "message_reasons": msg_reasons,
     }
 
 def load_industry_momentum():
@@ -236,13 +279,19 @@ def print_report(c):
     print("=" * 50)
     
     print("\n【四维评分】")
-    print(f"  宏观面: {verdict['macro']}（系统性压力）")
+    macro_line = "（承压）" if verdict['macro'] < 0 else ("（友好）" if verdict['macro'] > 0 else "（中性）")
+    print(f"  宏观面: {verdict['macro']}{macro_line}")
+    if verdict.get("macro_reasons"):
+        print(f"          {' '.join(verdict['macro_reasons'][:2])}")
     fund_line = "（稳健）" if verdict['fundamental'] > 0 else ("（偏弱）" if verdict['fundamental'] < 0 else "（中性）")
     print(f"  基本面: {verdict['fundamental']}{fund_line}")
     if verdict.get("fundamental_reasons"):
         print(f"          {' '.join(verdict['fundamental_reasons'])}")
     print(f"  技术面: {verdict['technical']}（空头排列）")
-    print(f"  消息面: {verdict['message']}（中性）")
+    msg_line = "（利好）" if verdict['message'] > 0 else ("（利空）" if verdict['message'] < 0 else "（中性）")
+    print(f"  消息面: {verdict['message']}{msg_line}")
+    if verdict.get("message_reasons"):
+        print(f"          {' '.join(verdict['message_reasons'])}")
     print(f"  综合评分: {verdict['total']} → 决策：{verdict['decision']}")
     
     # 行业动量版块（2026-08-01 新增）
diff --git a/scripts/stock_data_refresh.sh b/scripts/stock_data_refresh.sh
new file mode 100755
index 00000000..0fa071ec
--- /dev/null
+++ b/scripts/stock_data_refresh.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+# 股票数据刷新：宏观 + 基本面 + 情感 + 行业 + 因子 全量扫描
+# 被 cron 调用（周一矛盾周报前 + 每日收盘后）
+cd ~/.hermes/scripts || exit 1
+
+echo "=== 宏观评分 ==="
+python3 stock_macro.py --json 2>&1 | tail -2
+
+echo ""
+echo "=== 基本面扫描 ==="
+python3 stock_fundamental.py 2>&1 | tail -3
+
+echo ""
+echo "=== 消息面情感 ==="
+python3 stock_sentiment.py 2>&1 | tail -3
+
+echo ""
+echo "✅ 数据刷新完成 $(date '+%Y-%m-%d %H:%M')"
diff --git a/scripts/stock_macro.py b/scripts/stock_macro.py
new file mode 100644
index 00000000..d99e7cae
--- /dev/null
+++ b/scripts/stock_macro.py
@@ -0,0 +1,162 @@
+#!/usr/bin/env python3
+"""
+小唯宏观指标自动化 — 真实数据驱动四维评分的宏观维度
+==================================================
+获取上证/沪深300/创业板/原油，计算宏观趋势评分。
+
+评分规则：
+  上证 20日涨幅 > 0 → +1（大盘向上）
+  上证 20日涨幅 < -3% → -1（大盘走弱）
+  沪深300 20日涨幅 > 0 → +1
+  原油 20日涨幅 > 10% → -1（通胀压力）
+  综合：sum 后 clip 到 [-1, 1]
+
+用法：
+  python3 stock_macro.py          # 输出宏观评分
+  python3 stock_macro.py --json   # JSON 输出（供矛盾周报引用）
+"""
+import json, sys, urllib.request
+from datetime import datetime, timedelta
+from pathlib import Path
+
+OUTPUT = Path.home() / ".hermes" / "stock_backtest"
+OUTPUT.mkdir(exist_ok=True)
+
+
+def get_url(url, timeout=8):
+    import subprocess, shlex, os
+    env = dict(os.environ)
+    for k in ["http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY"]:
+        env.pop(k, None)
+    cmd = f"curl -s --max-time {timeout} --compressed {shlex.quote(url)}"
+    try:
+        r = subprocess.run(cmd, shell=True, capture_output=True, timeout=timeout + 2, env=env)
+        return r.stdout.decode("utf-8", errors="ignore")
+    except Exception:
+        return ""
+
+
+def get_url_gbk(url, timeout=8):
+    import subprocess, shlex, os
+    env = dict(os.environ)
+    for k in ["http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY"]:
+        env.pop(k, None)
+    cmd = f"curl -s --max-time {timeout} --compressed {shlex.quote(url)}"
+    try:
+        r = subprocess.run(cmd, shell=True, capture_output=True, timeout=timeout + 2, env=env)
+        return r.stdout.decode("gbk", errors="ignore")
+    except Exception:
+        return ""
+
+
+def get_kline_change(symbol, days=20):
+    """获取指数/品种过去 N 日涨幅"""
+    today = datetime.now().strftime("%Y-%m-%d")
+    start = (datetime.now() - timedelta(days=days * 2)).strftime("%Y-%m-%d")
+    url = (f"https://web.ifzq.gtimg.cn/appstock/app/fqkline/get"
+           f"?_var=kline_dayqfq&param={symbol},day,{start},{today},{days},qfq")
+    text = get_url(url)
+    if not text or len(text) < 50:
+        return None
+    try:
+        import re
+        json_str = re.sub(r"^[^=]+=", "", text, count=1)
+        data = json.loads(json_str)
+        key = list(data.get("data", {}).keys())
+        if not key:
+            return None
+        raw = data["data"][key[0]].get("qfqday") or data["data"][key[0]].get("day") or []
+        if len(raw) < 2:
+            return None
+        closes = [float(r[2]) for r in raw if len(r) > 2 and float(r[2]) > 0]
+        if len(closes) < 2:
+            return None
+        return (closes[-1] - closes[0]) / closes[0] * 100
+    except Exception:
+        return None
+
+
+def get_macro_score():
+    """
+    计算宏观评分
+    返回: {"macro": int, "details": {指标: 涨幅}, "reasons": [...]}
+    """
+    # 上证指数
+    shanghai = get_kline_change("sh000001", days=20)
+    # 沪深300（用 ETF 510300 代理）
+    hs300 = get_kline_change("sh510300", days=20)
+    # 创业板
+    chinext = get_kline_change("sz399006", days=20)
+    # 原油
+    oil = get_kline_change("hf_OIL", days=20)
+
+    score = 0
+    details = {}
+    reasons = []
+
+    if shanghai is not None:
+        details["上证20日"] = f"{shanghai:+.1f}%"
+        if shanghai > 0:
+            score += 1
+            reasons.append(f"上证20日{shanghai:+.1f}% → 大盘向上")
+        elif shanghai < -3:
+            score -= 1
+            reasons.append(f"上证20日{shanghai:+.1f}% → 大盘走弱")
+        else:
+            reasons.append(f"上证20日{shanghai:+.1f}% → 震荡")
+
+    if hs300 is not None:
+        details["沪深300"] = f"{hs300:+.1f}%"
+        if hs300 > 0:
+            score += 1
+            reasons.append(f"沪深300 {hs300:+.1f}% → 蓝筹强")
+        elif hs300 < -3:
+            score -= 1
+            reasons.append(f"沪深300 {hs300:+.1f}% → 蓝筹弱")
+
+    if chinext is not None:
+        details["创业板"] = f"{chinext:+.1f}%"
+        if chinext < -5:
+            score -= 1
+            reasons.append(f"创业板{chinext:+.1f}% → 成长弱")
+
+    if oil is not None:
+        details["原油20日"] = f"{oil:+.1f}%"
+        if oil > 10:
+            score -= 1
+            reasons.append(f"原油{oil:+.1f}% → 通胀压力")
+        elif oil < -10:
+            score += 1
+            reasons.append(f"原油{oil:+.1f}% → 通缩缓解")
+
+    # clip 到 [-1, 1]
+    macro = max(-1, min(1, score))
+    if macro == 0 and not reasons:
+        macro = 0
+        reasons.append("宏观数据获取不足，中性")
+
+    return {"macro": macro, "details": details, "reasons": reasons, "date": datetime.now().strftime("%Y-%m-%d")}
+
+
+def main():
+    result = get_macro_score()
+    if "--json" in sys.argv:
+        out = OUTPUT / "macro_score.json"
+        with open(out, "w", encoding="utf-8") as f:
+            json.dump(result, f, ensure_ascii=False, indent=2)
+        print(json.dumps(result, ensure_ascii=False))
+        print(f"📁 已保存: {out}")
+        return
+
+    print("=" * 50)
+    print(f"小唯宏观评分 {result['date']}")
+    print("=" * 50)
+    for k, v in result["details"].items():
+        print(f"  {k}: {v}")
+    print(f"\n宏观评分: {result['macro']:+d}")
+    for r in result["reasons"]:
+        print(f"  • {r}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/stock_multi_account.py b/scripts/stock_multi_account.py
index 3edcfffe..c206f1a4 100644
--- a/scripts/stock_multi_account.py
+++ b/scripts/stock_multi_account.py
@@ -69,6 +69,38 @@ def save_account(acct):
         json.dump(acct, f, ensure_ascii=False, indent=2)
 
 
+def get_dynamic_alloc(industry):
+    """
+    动态仓位（2026-08-02 增强：按行业动量强度调整）
+    从 industry_scan.json 读取行业动量：
+      动量 > 0        → 1.0（全仓，强势行业）
+      动量 -20% ~ 0   → 0.6（偏弱，降仓）
+      动量 -40% ~ -20%→ 0.5（弱势，半仓）
+      动量 < -40%     → 0.3（深度弱势，轻仓）
+    无数据回退：弱势行业 0.5，其他 1.0
+    """
+    f = OUTPUT.parent / "industry_scan.json"
+    mom = None
+    if f.exists():
+        try:
+            scan = json.load(open(f))
+            mom_map = scan.get("industries", {}).get("avg_mom", {})
+            mom = mom_map.get(industry)
+        except Exception:
+            mom = None
+
+    if mom is None:
+        return 0.5 if industry in WEAK_INDUSTRIES else 1.0
+
+    if mom > 0:
+        return 1.0
+    if mom > -0.20:
+        return 0.6
+    if mom > -0.40:
+        return 0.5
+    return 0.3
+
+
 def execute_signal(sig, dry_run=False):
     """
     信号执行（多账户路由版 v2 — 2026-08-01 回测优化）
@@ -96,21 +128,21 @@ def execute_signal(sig, dry_run=False):
     if signal == "BUY" and not has_position:
         if acct.get("blocked"):
             return ("BLOCK", f"行业{industry}账户已锁定")
-        # 弱势行业半仓（v2）
+        # 动态仓位（v3：按行业动量强度调整，2026-08-02）
+        alloc = get_dynamic_alloc(industry)
         is_weak = industry in WEAK_INDUSTRIES
-        alloc = 0.5 if is_weak else 1.0
         shares = int(acct["current_capital"] * alloc // price) if price > 0 else 0
         if shares <= 0:
             return ("SKIP", "资金不足")
         cost = shares * price
         if dry_run:
-            return ("BUY", f"{industry}{'半仓' if is_weak else '全仓'}买入@{price:.2f} {shares}股")
+            return ("BUY", f"{industry}{'半仓' if is_weak else '全仓'}买入@{price:.2f} {shares}股(仓位{alloc:.0%})")
         acct["positions"].append({"shares": shares, "avg_cost": price})
         acct["current_capital"] -= cost
         acct["last_signal"] = "买入"
         acct["last_signal_date"] = datetime.now().strftime("%Y-%m-%d")
         save_account(acct)
-        mode = "半仓(弱势行业)" if is_weak else "全仓"
+        mode = f"{alloc:.0%}仓(弱势行业)" if is_weak else "全仓"
         return ("BUY", f"{industry}{mode}买入{shares}股@{price:.2f} 剩余{acct['current_capital']:.0f}")
 
     elif signal == "SELL" and has_position:
diff --git a/scripts/stock_sentiment.py b/scripts/stock_sentiment.py
new file mode 100644
index 00000000..c4bbe732
--- /dev/null
+++ b/scripts/stock_sentiment.py
@@ -0,0 +1,162 @@
+#!/usr/bin/env python3
+"""
+小唯消息面情感评分 — 东方财富公告 + 关键词情感词典
+==================================================
+抓取关注股票最新公告标题，用情感词典打分。
+
+评分规则：
+  利空词（减持/亏损/违规/立案/质押/诉讼/退市/警示/下跌/处罚）→ -1
+  利好词（增持/回购/分红/中标/增长/突破/合作/扩产/盈利/上调/签约）→ +1
+  中性 → 0
+  综合多个公告后 clip 到 [-1, 1]
+
+用法：
+  python3 stock_sentiment.py            # 输出情感评分
+  python3 stock_sentiment.py --json     # JSON 输出（供矛盾周报引用）
+"""
+import json, sys, subprocess, shlex, os, re
+from datetime import datetime
+from pathlib import Path
+
+OUTPUT = Path.home() / ".hermes" / "stock_backtest"
+OUTPUT.mkdir(exist_ok=True)
+
+# 关注股票（代码, 名称, 行业）
+WATCHED = [
+    ("000858", "五粮液", "白酒"),
+    ("600519", "贵州茅台", "白酒"),
+    ("000568", "泸州老窖", "白酒"),
+    ("002304", "洋河股份", "白酒"),
+    ("600036", "招商银行", "银行"),
+    ("601318", "中国平安", "保险"),
+    ("000001", "平安银行", "银行"),
+    ("300750", "宁德时代", "新能源"),
+    ("002594", "比亚迪", "新能源"),
+    ("002415", "海康威视", "科技"),
+    ("601088", "中国神华", "煤炭"),
+    ("688981", "中芯国际", "半导体"),
+    ("600030", "中信证券", "证券"),
+    ("000333", "美的集团", "家电"),
+]
+
+# 情感词典
+NEGATIVE_WORDS = [
+    "减持", "亏损", "违规", "立案", "质押", "诉讼", "退市", "警示",
+    "下跌", "处罚", "风险", "终止", "暂停", "下滑", "恶化", "逾期",
+    "冻结", "查封", "调查", "降级", "下调", "失败", "延期", "变卖",
+]
+POSITIVE_WORDS = [
+    "增持", "回购", "分红", "中标", "增长", "突破", "合作", "扩产",
+    "盈利", "上调", "签约", "创新高", "预增", "扭亏", "获批", "落地",
+    "推出", "发布", "投资", "签订", "完成", "超预期", "翻倍", "新签订单",
+]
+
+# 中性词（公告常见但无方向性）
+NEUTRAL_WORDS = ["会议", "报告", "公告", "章程", "制度", "通知", "更正", "说明"]
+
+
+def get_url(url, timeout=8):
+    env = dict(os.environ)
+    for k in ["http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY"]:
+        env.pop(k, None)
+    cmd = f"curl -s --max-time {timeout} --compressed {shlex.quote(url)}"
+    try:
+        r = subprocess.run(cmd, shell=True, capture_output=True, timeout=timeout + 2, env=env)
+        return r.stdout.decode("utf-8", errors="ignore")
+    except Exception:
+        return ""
+
+
+def get_announcements(code, limit=5):
+    """东方财富个股公告（标题列表）"""
+    url = (f"https://np-anotice-stock.eastmoney.com/api/security/ann"
+           f"?sr=-1&page_size={limit}&page_index=1&ann_type=A"
+           f"&client_source=web&stock_list={code}&f_node=0&s_node=0")
+    text = get_url(url)
+    if not text or len(text) < 50:
+        return []
+    try:
+        data = json.loads(text)
+        items = data.get("data", {}).get("list", [])
+        titles = []
+        for item in items:
+            title = item.get("title", "").strip()
+            # 清理 HTML 标签
+            title = re.sub(r"<[^>]+>", "", title)
+            if title:
+                titles.append(title)
+        return titles
+    except Exception:
+        return []
+
+
+def score_title(title):
+    """单条标题情感评分：返回 (score, matched)"""
+    pos_hits = [w for w in POSITIVE_WORDS if w in title]
+    neg_hits = [w for w in NEGATIVE_WORDS if w in title]
+    if pos_hits and not neg_hits:
+        return 1, pos_hits
+    if neg_hits and not pos_hits:
+        return -1, neg_hits
+    if pos_hits and neg_hits:
+        return 0, pos_hits + neg_hits  # 混合中性
+    return 0, []
+
+
+def scan_sentiment():
+    """扫描所有关注股票，返回情感评分"""
+    results = []
+    print("=" * 60)
+    print(f"小唯消息面情感扫描 {datetime.now().strftime('%Y-%m-%d %H:%M')}")
+    print("=" * 60)
+
+    for code, name, industry in WATCHED:
+        titles = get_announcements(code, limit=5)
+        if not titles:
+            print(f"  ⚠️ {name}: 无公告数据")
+            results.append({"code": code, "name": name, "industry": industry,
+                            "score": 0, "titles": [], "reasons": ["无公告"]})
+            continue
+
+        scores = [score_title(t) for t in titles]
+        # 有实质方向的最新公告优先（取最近3条的平均）
+        recent = [s for s, _ in scores[:3]]
+        avg = sum(recent) / len(recent) if recent else 0
+        final = max(-1, min(1, avg))
+
+        hits = []
+        for t, (s, words) in zip(titles[:3], scores[:3]):
+            if words:
+                emoji = "🔴" if s < 0 else ("🟢" if s > 0 else "⚪")
+                hits.append(f"{emoji}{t[:35]}")
+
+        results.append({"code": code, "name": name, "industry": industry,
+                        "score": final, "titles": titles[:3], "hits": hits,
+                        "reasons": hits})
+        emoji = "🔴" if final < 0 else ("🟢" if final > 0 else "⚪")
+        print(f"  {emoji} {name}({code}) [{industry}] 情感{final:+.0f}")
+
+    # 汇总：全市场情感（消息面评分）
+    valid = [r for r in results if r.get("titles")]
+    if valid:
+        avg_all = sum(r["score"] for r in valid) / len(valid)
+        message = max(-1, min(1, avg_all))
+    else:
+        message = 0
+
+    print(f"\n📊 市场消息面综合评分: {message:+.0f}")
+
+    out = OUTPUT / "sentiment_scan.json"
+    with open(out, "w", encoding="utf-8") as f:
+        json.dump({"generated": datetime.now().strftime("%Y-%m-%d %H:%M"),
+                   "message_score": message,
+                   "stocks": results}, f, ensure_ascii=False, indent=2)
+    print(f"📁 已保存: {out}")
+
+    if "--json" in sys.argv:
+        return message
+    return results
+
+
+if __name__ == "__main__":
+    scan_sentiment()