{"data":{"cate":"post","html":"\n
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\n 深度学习微盘策略-COGRASP模型<\/a>\n 2025-12-02<\/span>\n <\/div>\n
\n 1926<\/span>\n 23<\/span>\n 45<\/span>\n <\/div>\n <\/div>\n
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\n 基于FNIRVAR模型的微盘股量化选股策略<\/a>\n 2025-12-01<\/span>\n <\/div>\n
\n 2205<\/span>\n 25<\/span>\n 41<\/span>\n <\/div>\n <\/div>\n
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\n 深度学习微盘策略-OracleMamba模型-滚动训练<\/a>\n 2025-11-30<\/span>\n <\/div>\n
\n 1506<\/span>\n 10<\/span>\n 35<\/span>\n <\/div>\n <\/div>\n
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\n 深度学习微盘策略-LSTMKNN融合模型<\/a>\n 2025-11-28<\/span>\n <\/div>\n
\n 1368<\/span>\n 13<\/span>\n 28<\/span>\n <\/div>\n <\/div>\n
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\n 机器学习多因子策略-KNN-滚动训练<\/a>\n 2025-11-28<\/span>\n <\/div>\n
\n 1140<\/span>\n 11<\/span>\n 22<\/span>\n <\/div>\n <\/div>\n
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\n 机器学习-HS300-Transformer模型策略<\/a>\n 2025-11-27<\/span>\n <\/div>\n
\n 2068<\/span>\n 28<\/span>\n 41<\/span>\n <\/div>\n <\/div>\n
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\n 自定义贝叶斯优化寻参策略的研究<\/a>\n 2025-11-26<\/span>\n <\/div>\n
\n 681<\/span>\n 12<\/span>\n 10<\/span>\n <\/div>\n <\/div>\n
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\n 深度学习微盘策略-BiLSTMTransformerCNN<\/a>\n 2025-11-24<\/span>\n <\/div>\n
\n 1496<\/span>\n 25<\/span>\n 20<\/span>\n <\/div>\n <\/div>\n
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\n 深度学习微盘策略-TabM模型-滚动训练<\/a>\n 2025-11-23<\/span>\n <\/div>\n
\n 1823<\/span>\n 19<\/span>\n 21<\/span>\n <\/div>\n <\/div>\n
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\n 深度学习微盘策略-CNN-LSTM分类模型-滚训-新因子<\/a>\n 2025-11-23<\/span>\n <\/div>\n
\n 1068<\/span>\n 12<\/span>\n 30<\/span>\n <\/div>\n <\/div>\n
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\n 深度学习微盘策略-MSTNN模型-滚动训练<\/a>\n 2025-11-21<\/span>\n <\/div>\n
\n 1532<\/span>\n 22<\/span>\n 34<\/span>\n <\/div>\n <\/div>\n
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\n 深度学习TabPFN模型微盘策略<\/a>\n 2025-11-20<\/span>\n <\/div>\n
\n 1438<\/span>\n 29<\/span>\n 34<\/span>\n <\/div>\n <\/div>\n
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\n 关于TabPFN模型的一个实验<\/a>\n 2025-11-20<\/span>\n <\/div>\n