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69bb755f37
* 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
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
import pandas as pd
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import pytest
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from qlib.contrib.strategy.cost_control import SoftTopkStrategy
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class MockPosition:
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def __init__(self, weights):
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self.weights = weights
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def get_stock_weight_dict(self, only_stock=True):
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return self.weights
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def test_soft_topk_logic():
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# Initial: A=0.8, B=0.2 (Total=1.0). Target Risk=0.95.
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# Scores: A and B are low, C and D are topk.
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scores = pd.Series({"C": 0.9, "D": 0.8, "A": 0.1, "B": 0.1})
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current_pos = MockPosition({"A": 0.8, "B": 0.2})
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topk = 2
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risk_degree = 0.95
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impact_limit = 0.1 # Max change per step
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def create_test_strategy(impact_limit_value):
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strat = SoftTopkStrategy.__new__(SoftTopkStrategy)
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strat.topk = topk
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strat.risk_degree = risk_degree
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strat.trade_impact_limit = impact_limit_value
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return strat
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# 1. With impact limit: Expect deterministic sell and limited buy
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strat_i = create_test_strategy(impact_limit)
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res_i = strat_i.generate_target_weight_position(scores, current_pos, None, None)
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# A should be exactly 0.8 - 0.1 = 0.7
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assert abs(res_i["A"] - 0.7) < 1e-8
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# B should be exactly 0.2 - 0.1 = 0.1
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assert abs(res_i["B"] - 0.1) < 1e-8
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# Total sells = 0.2 released. New budget = 0.2 + (0.95 - 1.0) = 0.15.
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# C and D share 0.15 -> 0.075 each.
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assert abs(res_i["C"] - 0.075) < 1e-8
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assert abs(res_i["D"] - 0.075) < 1e-8
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# 2. Without impact limit: Expect full liquidation and full target fill
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strat_c = create_test_strategy(1.0)
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res_c = strat_c.generate_target_weight_position(scores, current_pos, None, None)
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# A, B not in topk -> Liquidated
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assert "A" not in res_c and "B" not in res_c
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# C, D should reach ideal_per_stock (0.95/2 = 0.475)
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assert abs(res_c["C"] - 0.475) < 1e-8
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assert abs(res_c["D"] - 0.475) < 1e-8
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if __name__ == "__main__":
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pytest.main([__file__])
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