diff --git a/qlib/contrib/strategy/signal_strategy.py b/qlib/contrib/strategy/signal_strategy.py
index bad19ddfdc9..5397827acd8 100644
--- a/qlib/contrib/strategy/signal_strategy.py
+++ b/qlib/contrib/strategy/signal_strategy.py
@@ -263,7 +263,7 @@ def filter_stock(li):
         # buy new stock
         # note the current has been changed
         # current_stock_list = current_temp.get_stock_list()
-        value = cash * self.risk_degree / len(buy) if len(buy) > 0 else 0
+        value = cash * self.get_risk_degree(trade_step) / len(buy) if len(buy) > 0 else 0
 
         # open_cost should be considered in the real trading environment, while the backtest in evaluate.py does not
         # consider it as the aim of demo is to accomplish same strategy as evaluate.py, so comment out this line
diff --git a/tests/backtest/test_topk_dropout_strategy.py b/tests/backtest/test_topk_dropout_strategy.py
new file mode 100644
index 00000000000..c345a9a7ee5
--- /dev/null
+++ b/tests/backtest/test_topk_dropout_strategy.py
@@ -0,0 +1,78 @@
+import pandas as pd
+
+from qlib.backtest.decision import Order
+from qlib.backtest.position import Position
+from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
+
+
+class MockInfra:
+    def __init__(self, values):
+        self.values = values
+
+    def get(self, name):
+        return self.values[name]
+
+
+class MockAccount:
+    def __init__(self, position):
+        self.current_position = position
+
+
+class MockCalendar:
+    def get_trade_step(self):
+        return 0
+
+    def get_step_time(self, trade_step=None, shift=0):
+        if shift:
+            return pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01")
+        return pd.Timestamp("2020-01-02"), pd.Timestamp("2020-01-02")
+
+
+class MockSignal:
+    def get_signal(self, start_time, end_time):
+        return pd.Series({"A": 1.0})
+
+
+class MockExchange:
+    def is_stock_tradable(self, stock_id, start_time, end_time, direction=None):
+        return True
+
+    def get_deal_price(self, stock_id, start_time, end_time, direction):
+        return 10.0
+
+    def get_factor(self, stock_id, start_time, end_time):
+        return 1.0
+
+    def round_amount_by_trade_unit(self, amount, factor):
+        return amount
+
+
+class DynamicRiskTopkDropoutStrategy(TopkDropoutStrategy):
+    def get_risk_degree(self, trade_step=None):
+        return 0.25
+
+
+def test_topk_dropout_uses_dynamic_risk_degree_for_buy_budget():
+    strategy = DynamicRiskTopkDropoutStrategy.__new__(DynamicRiskTopkDropoutStrategy)
+    strategy.signal = MockSignal()
+    strategy.risk_degree = 0.95
+    strategy.topk = 1
+    strategy.n_drop = 1
+    strategy.method_sell = "bottom"
+    strategy.method_buy = "top"
+    strategy.hold_thresh = 1
+    strategy.only_tradable = False
+    strategy.forbid_all_trade_at_limit = True
+    strategy.level_infra = MockInfra({"trade_calendar": MockCalendar()})
+    strategy.common_infra = MockInfra(
+        {"trade_account": MockAccount(Position(cash=1000.0))}
+    )
+    strategy._trade_exchange = MockExchange()
+
+    decision = strategy.generate_trade_decision()
+    orders = decision.get_decision()
+
+    assert len(orders) == 1
+    assert orders[0].stock_id == "A"
+    assert orders[0].direction == Order.BUY
+    assert orders[0].amount == 25.0