refactor: implement deterministic budget allocation in SoftTopkStrategy (#2077)

* refactor: implement deterministic budget allocation in SoftTopkStrategy

* style: fix formatting issues using black

* fix: remove unused imports and pass pylint

* refactor: simplify SoftTopkStrategy impact limit

* style: relocate test files per maintainer request

tests/backtest/test_soft_topk_strategy.py

@@ -0,0 +1,56 @@
+import pandas as pd
+import pytest
+from qlib.contrib.strategy.cost_control import SoftTopkStrategy
+
+
+class MockPosition:
+    def __init__(self, weights):
+        self.weights = weights
+
+    def get_stock_weight_dict(self, only_stock=True):
+        return self.weights
+
+
+def test_soft_topk_logic():
+    # Initial: A=0.8, B=0.2 (Total=1.0). Target Risk=0.95.
+    # Scores: A and B are low, C and D are topk.
+    scores = pd.Series({"C": 0.9, "D": 0.8, "A": 0.1, "B": 0.1})
+    current_pos = MockPosition({"A": 0.8, "B": 0.2})
+
+    topk = 2
+    risk_degree = 0.95
+    impact_limit = 0.1  # Max change per step
+
+    def create_test_strategy(impact_limit_value):
+        strat = SoftTopkStrategy.__new__(SoftTopkStrategy)
+        strat.topk = topk
+        strat.risk_degree = risk_degree
+        strat.trade_impact_limit = impact_limit_value
+        return strat
+
+    # 1. With impact limit: Expect deterministic sell and limited buy
+    strat_i = create_test_strategy(impact_limit)
+    res_i = strat_i.generate_target_weight_position(scores, current_pos, None, None)
+
+    # A should be exactly 0.8 - 0.1 = 0.7
+    assert abs(res_i["A"] - 0.7) < 1e-8
+    # B should be exactly 0.2 - 0.1 = 0.1
+    assert abs(res_i["B"] - 0.1) < 1e-8
+    # Total sells = 0.2 released. New budget = 0.2 + (0.95 - 1.0) = 0.15.
+    # C and D share 0.15 -> 0.075 each.
+    assert abs(res_i["C"] - 0.075) < 1e-8
+    assert abs(res_i["D"] - 0.075) < 1e-8
+
+    # 2. Without impact limit: Expect full liquidation and full target fill
+    strat_c = create_test_strategy(1.0)
+    res_c = strat_c.generate_target_weight_position(scores, current_pos, None, None)
+
+    # A, B not in topk -> Liquidated
+    assert "A" not in res_c and "B" not in res_c
+    # C, D should reach ideal_per_stock (0.95/2 = 0.475)
+    assert abs(res_c["C"] - 0.475) < 1e-8
+    assert abs(res_c["D"] - 0.475) < 1e-8
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])

tests/backtest/test_soft_topk_strategy_cold_start.py

@@ -0,0 +1,38 @@
+import pandas as pd
+import pytest
+
+from qlib.contrib.strategy.cost_control import SoftTopkStrategy
+
+
+class MockPosition:
+    def __init__(self, weights):
+        self.weights = weights
+
+    def get_stock_weight_dict(self, only_stock=True):
+        return self.weights
+
+
+def create_test_strategy(topk, risk_degree, impact_limit):
+    strat = SoftTopkStrategy.__new__(SoftTopkStrategy)
+    strat.topk = topk
+    strat.risk_degree = risk_degree
+    strat.trade_impact_limit = impact_limit
+    return strat
+
+
+@pytest.mark.parametrize(
+    ("impact_limit", "expected_fill"),
+    [
+        (0.1, 0.1),
+        (1.0, 0.475),
+    ],
+)
+def test_soft_topk_cold_start_impact_limit(impact_limit, expected_fill):
+    scores = pd.Series({"C": 0.9, "D": 0.8, "A": 0.1, "B": 0.1})
+    current_pos = MockPosition({})
+
+    strat = create_test_strategy(topk=2, risk_degree=0.95, impact_limit=impact_limit)
+    res = strat.generate_target_weight_position(scores, current_pos, None, None)
+
+    assert abs(res["C"] - expected_fill) < 1e-8
+    assert abs(res["D"] - expected_fill) < 1e-8