crypto_predictor
1bd9d25f60
- 删除测试脚本: test_2026_classification.py, test_lagllama.py, test_timesfm.py, test_ttm.py - 删除2019相关文件: analyze_2019_*, apply_2019_*, dlinear_2019_*, three_models_2019_* - 添加集成模型相关代码和分析脚本 - 添加N-BEATS模型和CatBoost分类模型
58 lines
1.3 KiB
JSON
58 lines
1.3 KiB
JSON
{
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"测试期间": {
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"开始日期": "2026-01-01 00:00:00",
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"结束日期": "2026-01-30 00:00:00",
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"总天数": 22,
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"总样本数": 495
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},
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"单模型性能": {
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"DLinear": {
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"准确率": "53.33%",
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"预测上涨比例": "47.1%"
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},
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"N-BEATS": {
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"准确率": "49.90%",
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"预测上涨比例": "73.5%"
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},
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"TTM-R2": {
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"准确率": "51.31%",
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"预测上涨比例": "23.6%"
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}
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},
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"集成策略性能": {
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"双模型交集": {
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"准确率": "53.39%",
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"覆盖率": "47.68%",
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"有效预测数": 236
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},
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"三模型投票": {
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"准确率": "52.93%",
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"覆盖率": "100.00%",
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"有效预测数": 495
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}
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},
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"DLinear概率分布": {
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"0.4-0.5区间": "262个 (52.9%)",
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"0.5-0.6区间": "229个 (46.3%)",
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"0.6-0.7区间": "4个 (0.8%)",
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"平均概率": "0.501"
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},
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"最佳/最差交易日": {
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"双模型最佳日": {
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"日期": "2026-01-11",
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"准确率": "83.3%"
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},
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"双模型最差日": {
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"日期": "2026-01-23",
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"准确率": "28.6%"
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},
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"三模型最佳日": {
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"日期": "2026-01-28",
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"准确率": "80.0%"
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},
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"三模型最差日": {
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"日期": "2026-01-23",
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"准确率": "29.2%"
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}
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}
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} |