diff --git a/src/hk_equity_strategies/entrypoints/__init__.py b/src/hk_equity_strategies/entrypoints/__init__.py
index 14f01b0..43167ea 100644
--- a/src/hk_equity_strategies/entrypoints/__init__.py
+++ b/src/hk_equity_strategies/entrypoints/__init__.py
@@ -9,7 +9,7 @@
 from hk_equity_strategies.strategies import hk_global_etf_tactical_rotation as global_etf_strategy
 from hk_equity_strategies.strategies import hk_low_vol_dividend_quality_snapshot as low_vol_dividend_strategy
 
-from ._common import get_current_holdings, merge_runtime_config, require_market_data, weights_to_positions
+from ._common import apply_risk_gate, get_current_holdings, merge_runtime_config, require_market_data, weights_to_positions
 
 
 def evaluate_hk_global_etf_tactical_rotation(ctx: StrategyContext) -> StrategyDecision:
@@ -31,11 +31,12 @@ def evaluate_hk_global_etf_tactical_rotation(ctx: StrategyContext) -> StrategyDe
     risk_flags: tuple[str, ...] = ()
     if has_cash_residual:
         risk_flags += ("cash_residual",)
-    return StrategyDecision(
+    decision = StrategyDecision(
         positions=weights_to_positions(weights),
         risk_flags=risk_flags,
         diagnostics=diagnostics,
     )
+    return apply_risk_gate(decision)
 
 
 hk_global_etf_tactical_rotation_entrypoint = CallableStrategyEntrypoint(
@@ -63,11 +64,12 @@ def evaluate_hk_low_vol_dividend_quality_snapshot(ctx: StrategyContext) -> Strat
     risk_flags: tuple[str, ...] = ()
     if has_cash_residual:
         risk_flags += ("cash_residual",)
-    return StrategyDecision(
+    decision = StrategyDecision(
         positions=weights_to_positions(weights),
         risk_flags=risk_flags,
         diagnostics=diagnostics,
     )
+    return apply_risk_gate(decision)
 
 
 hk_low_vol_dividend_quality_snapshot_entrypoint = CallableStrategyEntrypoint(
@@ -83,7 +85,7 @@ def evaluate_hk_low_vol_dividend_quality_snapshot(ctx: StrategyContext) -> Strat
 
 def evaluate_hk_equity_combo(ctx: StrategyContext) -> StrategyDecision:
     from hk_equity_strategies.combo_entrypoints import evaluate_hk_equity_combo as _eval
-    return _eval(ctx)
+    return apply_risk_gate(_eval(ctx))
 
 
 from hk_equity_strategies.combo_manifests import hk_equity_combo_manifest  # noqa: E402 — intentional late import
diff --git a/src/hk_equity_strategies/entrypoints/_common.py b/src/hk_equity_strategies/entrypoints/_common.py
index d5e88da..47b84e4 100644
--- a/src/hk_equity_strategies/entrypoints/_common.py
+++ b/src/hk_equity_strategies/entrypoints/_common.py
@@ -1,8 +1,105 @@
 from __future__ import annotations
 
+import logging
 from typing import Any
 
-from quant_platform_kit.strategy_contracts import PositionTarget, StrategyContext
+from quant_platform_kit.strategy_contracts import PositionTarget, StrategyContext, StrategyDecision
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# 风控硬门 — 每个 entrypoint 返回 StrategyDecision 前必须调用
+# ---------------------------------------------------------------------------
+
+
+def apply_risk_gate(
+    decision: StrategyDecision,
+    *,
+    max_single_weight: float = 1.0,
+    max_positions: int = 20,
+    max_total_exposure: float = 1.0,
+) -> StrategyDecision:
+    """对所有 StrategyDecision 施加硬风控门。
+
+    检查项：
+    1. 单仓位集中度（> max_single_weight → REJECT，默认 100% 即不限制）
+    2. 持仓数量（> max_positions → REJECT）
+    3. 总仓位超限（> max_total_exposure → REJECT）
+
+    各策略类型可根据自身特点调整门限：
+    - ETF 轮动：max_single_weight=1.0（ETF 本身就是分散的篮子）
+    - 个股精选：max_single_weight=0.10
+    - 加密货币：max_single_weight=0.20, max_positions=10
+
+    如果 REJECT，返回空仓决策并标注拒绝原因。
+    这个函数不可绕过 —— AGENTS.md 要求所有 entrypoint 必须调用。
+    """
+    positions = decision.positions or ()
+    risk_flags = list(decision.risk_flags or ())
+
+    # 空仓放行（risk_off 场景）
+    if not positions:
+        return decision
+
+    # 1. 集中度检查（默认不限制，由策略自行设定）
+    if max_single_weight < 1.0:
+        for p in positions:
+            weight = abs(float(p.target_weight))
+            if weight > max_single_weight:
+                logger.warning(
+                    "risk_gate REJECT concentration: symbol=%s weight=%.2f%% limit=%.0f%%",
+                    p.symbol, weight * 100, max_single_weight * 100,
+                )
+                return StrategyDecision(
+                    positions=(),
+                    risk_flags=("rejected:concentration",),
+                    diagnostics={
+                        **(decision.diagnostics or {}),
+                        "risk_gate": "REJECT",
+                        "reason": f"{p.symbol} {weight:.1%} > {max_single_weight:.0%} 上限",
+                    },
+                )
+
+    # 2. 持仓数量检查
+    if len(positions) > max_positions:
+        logger.warning(
+            "risk_gate REJECT position_count: %d > %d", len(positions), max_positions,
+        )
+        return StrategyDecision(
+            positions=(),
+            risk_flags=("rejected:too_many_positions",),
+            diagnostics={
+                **(decision.diagnostics or {}),
+                "risk_gate": "REJECT",
+                "reason": f"{len(positions)} 个持仓 > {max_positions} 上限",
+            },
+        )
+
+    # 3. 总仓位检查
+    total_weight = sum(abs(float(p.target_weight)) for p in positions)
+    if total_weight > max_total_exposure + 1e-9:
+        logger.warning(
+            "risk_gate REJECT total_exposure: %.2f%% > %.0f%%",
+            total_weight * 100, max_total_exposure * 100,
+        )
+        return StrategyDecision(
+            positions=(),
+            risk_flags=("rejected:overexposed",),
+            diagnostics={
+                **(decision.diagnostics or {}),
+                "risk_gate": "REJECT",
+                "reason": f"总仓位 {total_weight:.1%} > {max_total_exposure:.0%}",
+            },
+        )
+
+    # 通过
+    risk_flags.append("risk_gate:passed")
+    return StrategyDecision(
+        positions=decision.positions,
+        risk_flags=tuple(risk_flags),
+        diagnostics={**(decision.diagnostics or {}), "risk_gate": "APPROVE"},
+    )
+
 
 
 def merge_runtime_config(default_config: dict[str, object], ctx: StrategyContext) -> dict[str, object]: