{"data":{"cate":"post","html":"\n
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\n 多任务学习一条龙框架优化修复版<\/a>\n 2026-02-11<\/span>\n <\/div>\n
\n 5285<\/span>\n 41<\/span>\n 120<\/span>\n <\/div>\n <\/div>\n
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\n AlphaCFG单因子策略示例<\/a>\n 2026-02-02<\/span>\n <\/div>\n
\n 2702<\/span>\n 29<\/span>\n 42<\/span>\n <\/div>\n <\/div>\n
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\n MCTS_DeepSeek单因子小市值策略<\/a>\n 2026-01-30<\/span>\n <\/div>\n
\n 2038<\/span>\n 5<\/span>\n 38<\/span>\n <\/div>\n <\/div>\n
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\n MCTS_DeepSeek挖因子框架示例三<\/a>\n 2026-01-26<\/span>\n <\/div>\n
\n 1476<\/span>\n 5<\/span>\n 30<\/span>\n <\/div>\n <\/div>\n
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\n AlphaGPT-Deepseek策略-截面因子挖掘示例<\/a>\n 2026-01-26<\/span>\n <\/div>\n
\n 1381<\/span>\n 13<\/span>\n 19<\/span>\n <\/div>\n <\/div>\n
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\n MCTS_DeepSeek挖因子框架示例二<\/a>\n 2026-01-25<\/span>\n <\/div>\n
\n 978<\/span>\n 3<\/span>\n 22<\/span>\n <\/div>\n <\/div>\n
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\n MCTS_DeepSeek挖因子框架示例<\/a>\n 2026-01-25<\/span>\n <\/div>\n
\n 1003<\/span>\n 2<\/span>\n 20<\/span>\n <\/div>\n <\/div>\n
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\n Alpha-DeepSeek挖掘因子示例<\/a>\n 2026-01-25<\/span>\n <\/div>\n
\n 3465<\/span>\n 9<\/span>\n 36<\/span>\n <\/div>\n <\/div>\n
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\n 深度学习微盘策略-COGRASP模型升级<\/a>\n 2026-01-23<\/span>\n <\/div>\n
\n 1871<\/span>\n 15<\/span>\n 34<\/span>\n <\/div>\n <\/div>\n
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\n AlphaGPO模型-强化学习挖掘截面因子策略<\/a>\n 2026-01-21<\/span>\n <\/div>\n
\n 1611<\/span>\n 18<\/span>\n 53<\/span>\n <\/div>\n <\/div>\n
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\n AlphaGPT-Transformer策略-截面因子挖掘<\/a>\n 2026-01-17<\/span>\n <\/div>\n
\n 1607<\/span>\n 6<\/span>\n 39<\/span>\n <\/div>\n <\/div>\n
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\n AlphaGPT-LSTM策略-截面因子挖掘示例<\/a>\n 2026-01-16<\/span>\n <\/div>\n
\n 1760<\/span>\n 16<\/span>\n 42<\/span>\n <\/div>\n <\/div>\n
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\n GPTransformer混合_AlphaGPT因子挖掘示例<\/a>\n 2026-01-14<\/span>\n <\/div>\n
\n 1349<\/span>\n 15<\/span>\n 26<\/span>\n <\/div>\n <\/div>\n
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\n 基因遗传算法AlphaGP公式因子挖掘示例<\/a>\n 2026-01-14<\/span>\n <\/div>\n
\n 1187<\/span>\n 13<\/span>\n 27<\/span>\n <\/div>\n <\/div>\n
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\n AlphaGPT因子挖掘多品种ETF优化<\/a>\n 2026-01-13<\/span>\n <\/div>\n
\n 2764<\/span>\n 42<\/span>\n 51<\/span>\n <\/div>\n <\/div>\n
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\n AlphaGPT因子挖掘代码示例<\/a>\n 2026-01-12<\/span>\n <\/div>\n
\n 2807<\/span>\n 40<\/span>\n 99<\/span>\n <\/div>\n <\/div>\n
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\n 深度学习微盘策略-DRFN模型-滚动训练-复现版<\/a>\n 2025-12-28<\/span>\n <\/div>\n
\n 1642<\/span>\n 7<\/span>\n 41<\/span>\n <\/div>\n <\/div>\n
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\n 机器学习微盘策略-Transformer融合模型-日历效应<\/a>\n 2025-12-22<\/span>\n <\/div>\n
\n 4500<\/span>\n 22<\/span>\n 67<\/span>\n <\/div>\n <\/div>\n
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\n 机器学习5折保形回归-V3微盘-框架研究-滚动训练<\/a>\n 2025-12-21<\/span>\n <\/div>\n
\n 1992<\/span>\n 18<\/span>\n 32<\/span>\n <\/div>\n <\/div>\n
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\n 机器学习微盘策略-Transformer融合模型-年度数据<\/a>\n 2025-12-21<\/span>\n <\/div>\n
\n 880<\/span>\n 0<\/span>\n 10<\/span>\n <\/div>\n <\/div>\n <\/div>\n