{"id":1794,"date":"2025-01-14T07:53:00","date_gmt":"2025-01-13T23:53:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=1794"},"modified":"2025-01-04T11:09:25","modified_gmt":"2025-01-04T03:09:25","slug":"%e3%80%82%e3%80%82%e3%80%82%e5%ae%9e%e6%88%98%e6%95%99%e5%ad%a6%ef%bc%9a%e6%9e%84%e5%bb%ba%e5%8f%af%e8%a7%a3%e9%87%8a%e7%9a%84%e5%8f%98%e6%8d%a2%e5%99%a8%e6%a8%a1%e5%9e%8b%ef%bc%8c%e7%b2%be%e5%87%86-3","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2025\/01\/14\/%e3%80%82%e3%80%82%e3%80%82%e5%ae%9e%e6%88%98%e6%95%99%e5%ad%a6%ef%bc%9a%e6%9e%84%e5%bb%ba%e5%8f%af%e8%a7%a3%e9%87%8a%e7%9a%84%e5%8f%98%e6%8d%a2%e5%99%a8%e6%a8%a1%e5%9e%8b%ef%bc%8c%e7%b2%be%e5%87%86-3\/","title":{"rendered":"\u5b9e\u6218\u6559\u5b66\uff1a\u6784\u5efa\u53ef\u89e3\u91ca\u7684\u53d8\u6362\u5668\u6a21\u578b\uff0c\u7cbe\u51c6\u9884\u6d4b\u80a1\u4ef7\u6ce2\u52a8\uff08\u56db\uff09"},"content":{"rendered":"\n<p>\u4f5c\u8005\uff1a<a href=\"https:\/\/www.laoyulaoyu.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">\u8001\u4f59\u635e\u9c7c<\/a><\/p>\n\n\n\n<p><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-cyan-bluish-gray-color\">\u539f\u521b\u4e0d\u6613\uff0c\u8f6c\u8f7d\u8bf7\u6807\u660e\u51fa\u5904\u53ca\u539f\u4f5c\u8005\u3002<\/mark><\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-158.png\" alt=\"\" class=\"wp-image-3502\" style=\"width:645px;height:auto\"\/><\/figure>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<pre class=\"wp-block-verse\"><strong>\u5199\u5728\u524d\u9762\u7684\u8bdd\uff1a<\/strong>\u6b22\u8fce\u56de\u5230\u6211\u4eec\u5229\u7528\u65f6\u6001\u878d\u5408\u53d8\u6362\u5668\uff08TFT\uff09\u8fdb\u884c\u9010\u5206\u949f\u80a1\u4ef7\u9884\u6d4b\u7cfb\u5217\u7684\u6700\u540e\u4e00\u671f\uff01\u5728\u672c\u7ae0\u4e2d\uff0c\u6211\u5c06<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\">\u8be6\u7ec6\u4ecb\u7ecd\u4ece\u6570\u636e\u51c6\u5907\u3001\u6a21\u578b\u6784\u5efa\u5230\u4ea4\u6613\u7b56\u7565\u5b9e\u65bd\u7684\u6574\u4e2a\u8fc7\u7a0b<\/mark>\uff0c\u5e76\u4f1a\u901a\u8fc7\u4ee3\u7801\u793a\u4f8b\u5206\u4eab\u4e00\u4e9b\u4ee4\u4eba\u632f\u594b\u7684\u6a21\u578b\u8bad\u7ec3\u8bc4\u4f30\u548c\u4ea4\u6613\u7b56\u7565\u5b9e\u73b0\u7684\u5b9e\u6218\u7ed3\u679c\u3002\uff08<em>\u5c01\u9762\u56fe\u4e3a\u4f7f\u7528 TFT \u9884\u6d4b\u6210\u529f\u4ea4\u6613 $TSLA<\/em>\u80a1\u7968<em>\u7684\u793a\u610f\uff09<\/em>\u3002<\/pre>\n<\/blockquote>\n\n\n\n<p id=\"ee3d\">\u9605\u8bfb\u672c\u7ae0\u8282\u7684\u4e00\u4e9b\u524d\u63d0\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u5b8c\u6210\u672c\u7cfb\u5217\u7b2c 1-3 \u7ae0\u8282\u7684\u5b66\u4e60\uff1b<\/li>\n\n\n\n<li>\u57fa\u672c\u638c\u63e1\u4e86 PyTorch Lightning\uff1b<\/li>\n\n\n\n<li>\u719f\u6089\u65f6\u95f4\u5e8f\u5217\u6307\u6807\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u524d3\u7ae0\u8282\u7684\u94fe\u63a5\u5982\u4e0b\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/blog.laoyulaoyu.top\/index.php\/2025\/01\/01\/%e5%ae%9e%e6%88%98%e6%95%99%e5%ad%a6%ef%bc%9a%e6%9e%84%e5%bb%ba%e5%8f%af%e8%a7%a3%e9%87%8a%e7%9a%84%e5%8f%98%e6%8d%a2%e5%99%a8%e6%a8%a1%e5%9e%8b%ef%bc%8c%e7%b2%be%e5%87%86%e9%a2%84%e6%b5%8b%e8%82%a1\/\" target=\"_blank\" rel=\"noreferrer noopener\">\u5b9e\u6218\u6559\u5b66\uff1a\u6784\u5efa\u53ef\u89e3\u91ca\u7684\u53d8\u6362\u5668\u6a21\u578b\uff0c\u7cbe\u51c6\u9884\u6d4b\u80a1\u4ef7\u6ce2\u52a8\uff08\u4e00\uff09<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/blog.laoyulaoyu.top\/index.php\/2025\/01\/03\/%e3%80%82%e3%80%82%e3%80%82%e5%ae%9e%e6%88%98%e6%95%99%e5%ad%a6%ef%bc%9a%e6%9e%84%e5%bb%ba%e5%8f%af%e8%a7%a3%e9%87%8a%e7%9a%84%e5%8f%98%e6%8d%a2%e5%99%a8%e6%a8%a1%e5%9e%8b%ef%bc%8c%e7%b2%be%e5%87%86\/\" target=\"_blank\" rel=\"noreferrer noopener\">\u5b9e\u6218\u6559\u5b66\uff1a\u6784\u5efa\u53ef\u89e3\u91ca\u7684\u53d8\u6362\u5668\u6a21\u578b\uff0c\u7cbe\u51c6\u9884\u6d4b\u80a1\u4ef7\u6ce2\u52a8\uff08\u4e8c\uff09<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/blog.laoyulaoyu.top\/index.php\/2025\/01\/04\/%e3%80%82%e3%80%82%e3%80%82%e5%ae%9e%e6%88%98%e6%95%99%e5%ad%a6%ef%bc%9a%e6%9e%84%e5%bb%ba%e5%8f%af%e8%a7%a3%e9%87%8a%e7%9a%84%e5%8f%98%e6%8d%a2%e5%99%a8%e6%a8%a1%e5%9e%8b%ef%bc%8c%e7%b2%be%e5%87%86-2\/\" target=\"_blank\" rel=\"noreferrer noopener\">\u5b9e\u6218\u6559\u5b66\uff1a\u6784\u5efa\u53ef\u89e3\u91ca\u7684\u53d8\u6362\u5668\u6a21\u578b\uff0c\u7cbe\u51c6\u9884\u6d4b\u80a1\u4ef7\u6ce2\u52a8\uff08\u4e09\uff09<\/a><\/li>\n<\/ul>\n\n\n\n<p id=\"531d\">\u672c\u7ae0\u8282\u7ed3\u675f\u540e\uff0c\u5e0c\u671b\u60a8\u80fd\u638c\u63e1\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u4e3a\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u8bad\u7ec3\u65f6\u6001\u878d\u5408\u53d8\u6362\u5668\uff1b<\/li>\n\n\n\n<li>\u4f7f\u7528 Tensorboard \u6709\u6548\u76d1\u63a7\u57f9\u8bad\u8fdb\u5ea6\uff1b<\/li>\n\n\n\n<li>\u7528\u65f6\u95f4\u5e8f\u5217\u6307\u6807\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\uff1b<\/li>\n\n\n\n<li>\u5229\u7528\u6a21\u578b\u9884\u6d4b\u5b9e\u65bd\u548c\u56de\u6eaf\u6d4b\u8bd5\u4ea4\u6613\u7b56\u7565\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u5728\u7b2c 3 \u90e8\u5206\u4e2d\uff0c\u6211\u4eec\u5efa\u7acb\u4e86\u8bad\u7ec3\u6570\u636e\u96c6\u3002\u73b0\u5728\uff0c\u6211\u4eec\u5c06\u5bf9\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\uff0c\u5e76\u8bc4\u4f30\u5176\u5728\u73b0\u5b9e\u4e16\u754c\u4e2d\u7684\u8868\u73b0\uff0c\u4ece\u800c\u5c06\u6240\u6709\u5185\u5bb9\u6574\u5408\u5728\u4e00\u8d77\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u4e00\u3001\u5b8c\u5584\u6570\u636e\u96c6<\/strong><\/h2>\n\n\n\n<p>\u5728\u4e0a\u4e00\u90e8\u5206\u4e2d\uff0c\u6211\u5411\u5927\u5bb6\u5c55\u793a\u4e86\u5982\u4f55\u521b\u5efa\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u96c6\uff08TimeSeriesDataset\uff09\uff0c\u8be5\u6570\u636e\u96c6\u5c06\u88ab\u6211\u4eec\u7684\u6a21\u578b\u6240\u4f7f\u7528\uff0c\u4e0b\u9762\u662f\u8be5\u90e8\u5206\u7684\u7247\u6bb5\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from pytorch_forecasting import TimeSeriesDataSet\n\nmin_prediction_length = max_prediction_length = 20 # 20 minutes\nmin_encoder_length = max_encoder_length = 240 # 4 hours\ntraining_dataset = TimeSeriesDataSet(\n    df_train.reset_index(),\n    time_idx=\"time_idx\",\n    target=\"log_return\",\n    group_ids=&#91;\"symbol\"],\n    min_encoder_length=min_encoder_length, \n    max_encoder_length=max_encoder_length,\n    min_prediction_length=min_prediction_length,\n    max_prediction_length=max_prediction_length,\n    time_varying_known_reals=&#91;\n         \"time_idx\", \"average_volume\"\n    ],\n    time_varying_known_categoricals=&#91;\n        \"day_of_the_week\", \"is_earnings_day\", \"hour\", \"minute\"\n    ],\n    time_varying_unknown_reals=&#91;\n        \"close_rank\", \"rel_volume\", \"ATR\", \"EMA_change\", \"RSI\", \"SMA_change\", \"market_cap\", \"gap\",\n        \"log_daily_key_level_above_current_price_change\", \"log_daily_key_level_below_current_price_change\",\n        \"log_hourly_key_level_above_current_price_change\", \"log_hourly_key_level_below_current_price_change\"\n    ],\n    static_categoricals=&#91;\n        \"industry\"\n    ],\n    static_reals=&#91;\n        \"shares_float\"\n    ],\n    add_relative_time_idx=True,\n    add_encoder_length=False,\n    target_normalizer=None # targets are already normalized\n)<\/code><\/pre>\n\n\n\n<p>\u521d\u6b65\u5b9e\u9a8c\u5bf9\u56de\u62a5\u7387\u9884\u6d4b\u7684\u63a2\u7d22\u63ed\u793a\u4e86\u4e00\u4e2a\u91d1\u878d\u9884\u6d4b\u9886\u57df\u7684\u666e\u904d\u96be\u9898\uff1a\u6a21\u578b\u503e\u5411\u4e8e\u5728\u5404\u4e2a\u65f6\u95f4\u70b9\u4e0a\u9884\u6d4b\u51fa\u63a5\u8fd1\u96f6\u7684\u7ed3\u679c\u3002\u8fd9\u79cd\u884c\u4e3a\u6e90\u4e8e\u6bcf\u5206\u949f\u6536\u76ca\u7387\u7684\u7edf\u8ba1\u7279\u6027\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u6536\u76ca\u7387\uff08\u5c24\u5176\u662f\u5bf9\u6570\u6536\u76ca\u7387\uff09\u4ee5\u96f6\u4e3a\u4e2d\u5fc3\uff1b<\/li>\n\n\n\n<li>\u8fd1\u4f3c\u6b63\u6001\u5206\u5e03\uff1b<\/li>\n\n\n\n<li>\u4ee5\u4e00\u5206\u949f\u7684\u9891\u7387\u663e\u793a\u6781\u5c0f\u7684\u6ce2\u52a8\uff08\u901a\u5e38\u662f\u767e\u5206\u6bd4\u7684\u51e0\u5206\u4e4b\u51e0\uff09\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u6211\u8f6c\u800c\u9884\u6d4b\u539f\u59cb\u4ef7\u683c\u6c34\u5e73\uff0c\u800c\u4e0d\u662f\u56de\u62a5\u7387\u3002\u4ee5\u4e0b\u662f\u6211\u4eec\u6539\u8fdb\u540e\u7684\u6570\u636e\u96c6\u914d\u7f6e\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>min_prediction_length = 10 # predict 10 minutes into the future\nmax_prediction_length = 10\nmin_encoder_length = 120 # 2 hours\nmax_encoder_length = 120\ntraining_dataset = TimeSeriesDataSet(\n    df_train.reset_index(),\n    time_idx=\"time_idx\",\n    target=\"close\",\n    group_ids=&#91;\"symbol\"],\n    min_encoder_length=min_encoder_length, \n    max_encoder_length=max_encoder_length,\n    min_prediction_length=min_prediction_length,\n    max_prediction_length=max_prediction_length,\n    time_varying_known_reals=&#91;\n         \"time_idx\", \"average_volume\"\n    ],\n    time_varying_known_categoricals=&#91;\n        \"day_of_the_week\", \"is_earnings_day\", \"hour\", \"minute\"\n    ],\n    time_varying_unknown_reals=&#91;\n        \"open\", \"high\", \"low\", \"close\", \"rel_volume\", \"ATR\", \"EMA\", \"RSI\", \"SMA\", \"market_cap\", \"gap\",\n        \"daily_key_level_above_current_price\", \"daily_key_level_below_current_price\", \"hourly_key_level_above_current_price\", \"hourly_key_level_below_current_price\"\n    ],\n    static_categoricals=&#91;\n        \"industry\"\n    ],\n    static_reals=&#91;\n        \"shares_float\"\n    ],\n    lags={\n        \"close\": &#91;60, 120, 180], # previous close price\n    },\n    add_target_scales=True,\n    add_relative_time_idx=True,\n    add_encoder_length=False,\n    target_normalizer=GroupNormalizer(\n        groups=&#91;\"symbol\"], transformation=\"softplus\"\n    ),\n    # scalers=scalers\n)<\/code><\/pre>\n\n\n\n<p><strong>\u4e3b\u8981\u7684\u6539\u8fdb\u5305\u62ec\uff1a<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u7f29\u77ed\u5e8f\u5217\u957f\u5ea6\uff1a<\/strong>\u7f16\u7801\u5668\u957f\u5ea6\u4ece 240 \u6761\u7f29\u77ed\u81f3 120 \u6761\uff0c\u9884\u6d4b\u65f6\u95f4\u4ece 20 \u5206\u949f\u7f29\u77ed\u4e3a 10 \u5206\u949f\u3002\u4f18\u70b9\u6a21\u578b\u5360\u7528\u7a7a\u95f4\u66f4\u5c0f\uff0c\u7531\u4e8e\u9884\u6d4b\u65f6\u95f4\u66f4\u77ed\uff0c\u6307\u6807\u6027\u80fd\u53ef\u80fd\u66f4\u597d\u3002<\/li>\n\n\n\n<li><strong>\u76ee\u6807\u5de5\u7a0b\uff1a<\/strong>\u5c06\u76ee\u6807\u4ece\u5bf9\u6570\u6536\u76ca\u7387\u6539\u4e3a\u539f\u59cb\u6536\u76d8\u4ef7\uff1b\u4f7f\u7528\u5e26\u6709\u8f6f\u52a0\u8f6c\u6362\u7684 GroupNormalizer \u5bf9\u6bcf\u53ea\u80a1\u7968\u7684\u4ef7\u683c\u8fdb\u884c\u5f52\u4e00\u5316\u5904\u7406\uff1b\u65b0\u589e 60\u3001120 \u548c 180 \u5206\u949f\u5386\u53f2\u6536\u76d8\u4ef7\u6ede\u540e\u529f\u80fd\u3002<\/li>\n\n\n\n<li><strong>\u529f\u80fd\u589e\u5f3a\uff1a<\/strong>\u7528\u539f\u59cb OHLC\uff08\u5f00\u76d8\u4ef7\u3001\u6700\u9ad8\u4ef7\u3001\u6700\u4f4e\u4ef7\u3001\u6536\u76d8\u4ef7\uff09\u4ef7\u683c\u53d6\u4ee3\u4e4b\u524d\u7684 close_rank \u529f\u80fd\uff1b\u4fdd\u6301\u5b8c\u6574\u7684\u4ef7\u683c\u8d70\u52bf\u4fe1\u606f\uff0c\u540c\u65f6\u53ef\u80fd\u63d0\u9ad8\u6a21\u578b\u7684\u53ef\u89e3\u91ca\u6027\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u8fd9\u4e9b\u6539\u8fdb\u65e8\u5728\u4e3a\u6a21\u578b\u63d0\u4f9b\u66f4\u76f4\u63a5\u7684\u4ef7\u683c\u4fe1\u606f\uff0c\u540c\u65f6\u4fdd\u6301\u53ef\u63a7\u7684\u8ba1\u7b97\u91cf\u3002\u7531\u4e8e\u5728\u91d1\u878d\u5e02\u573a\u4e2d\u9884\u6d4b\u672a\u6765\u7684\u96be\u5ea6\u5448\u6307\u6570\u7ea7\u589e\u957f\uff0c\u56e0\u6b64\u7f29\u77ed\u9884\u6d4b\u8303\u56f4\u4e5f\u5e94\u80fd\u5e26\u6765\u66f4\u51c6\u786e\u7684\u9884\u6d4b\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u4e8c\u3001\u5efa\u7acb\u8bad\u7ec3\u901a\u9053<\/strong><br><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2.1 \u6570\u636e\u52a0\u8f7d\u5668\u914d\u7f6e<\/strong><\/h3>\n\n\n\n<p>\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u4e3a\u8bad\u7ec3\u548c\u9a8c\u8bc1\u914d\u7f6e\u6570\u636e\u52a0\u8f7d\u5668\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>batch_size = 128\ntrain_dataloader = training_dataset.to_dataloader(train=True, batch_size=batch_size, num_workers=0)\nvalidation = TimeSeriesDataSet.from_dataset(training_dataset, df_val.reset_index(), predict=True, stop_randomization=True)\nval_dataloader = validation.to_dataloader(train=False, batch_size=batch_size * 10, num_workers=0)<\/code><\/pre>\n\n\n\n<p>\u6570\u636e\u52a0\u8f7d\u5668\u8bbe\u7f6e\u5305\u62ec\u4e24\u4e2a\u5173\u952e\u914d\u7f6e\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u542f\u7528\u8bad\u7ec3\u52a0\u8f7d\u5668 (<code>train=True<\/code>)\uff1b<\/li>\n\n\n\n<li>\u9a8c\u8bc1\u52a0\u8f7d\u5668\uff0c\u53ea\u8bc4\u4f30\u6bcf\u4e2a\u65f6\u95f4\u5e8f\u5217\u7684\u6700\u540e\u4e00\u4e2a\u6837\u672c(<code>predict=True<\/code>)\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u8fd9\u79cd\u9a8c\u8bc1\u65b9\u6cd5\u5927\u5927\u52a0\u5feb\u4e86\u6211\u4eec\u7684\u8bc4\u4f30\u8fc7\u7a0b\uff0c\u540c\u65f6\u8fd8\u4e3a\u6211\u4eec\u7684\u6d4b\u8bd5\u5e93\u5b58\u63d0\u4f9b\u4e86\u6709\u610f\u4e49\u7684\u6307\u6807\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2.2 Pytorch Lightning Trainer \u8bbe\u7f6e<\/strong><\/h3>\n\n\n\n<p>\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528 PyTorch Lightning \u914d\u7f6e\u6211\u4eec\u7684\u8bad\u7ec3\u57fa\u7840\u67b6\u6784\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from lightning.pytorch.callbacks import ModelCheckpoint\nimport lightning as pl\n\nlr_logger = LearningRateMonitor()  # log the learning rate\nlogger = TensorBoardLogger(\"lightning_logs\")  # logging results to a tensorboard\n\ncheckpoint_callback = ModelCheckpoint(\n    filename='stock_forecasting-{epoch:02d}-{val_loss:.2f}',\n    save_top_k=3,\n    save_last=True,\n    monitor=\"val_loss\",\n    mode=\"min\",\n    every_n_train_steps=200,\n)\n\ntrainer = pl.Trainer(\n    max_epochs=5,\n    accelerator=\"cuda\",\n    enable_model_summary=True,\n    gradient_clip_val=0.1,\n    #fast_dev_run=True,  # uncomment to check that the model or dataset has no serious bugs\n    callbacks=&#91;lr_logger, checkpoint_callback],\n    val_check_interval=100,\n    logger=logger,\n)<\/code><\/pre>\n\n\n\n<p>\u8bad\u7ec3\u8bbe\u7f6e\u5305\u62ec\u51e0\u4e2a\u91cd\u8981\u7ec4\u6210\u90e8\u5206\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u65e5\u5fd7\u914d\u7f6e\uff1a \u96c6\u6210 TensorBoard\uff0c\u5b9e\u73b0\u8bad\u7ec3\u6307\u6807\u7684\u53ef\u89c6\u5316 &#8211; \u5b66\u4e60\u7387\u76d1\u63a7\uff08\u5bf9\u672a\u6765\u7684\u5b66\u4e60\u7387\u8c03\u5ea6\u5b9e\u9a8c\u975e\u5e38\u6709\u7528\uff09\uff1b<\/li>\n\n\n\n<li>\u6a21\u578b\u68c0\u67e5\u70b9\uff1a\u6bcf 200 \u4e2a\u8bad\u7ec3\u6b65\u9aa4\u4fdd\u5b58\u6a21\u578b\u5feb\u7167 &#8211; \u6839\u636e\u9a8c\u8bc1\u635f\u5931\u4fdd\u7559\u524d 3 \u4e2a\u6a21\u578b &#8211; \u4fdd\u7559\u6700\u65b0\u6a21\u578b\u4f5c\u4e3a\u5907\u4efd\uff1b<\/li>\n\n\n\n<li>\u8bad\u7ec3\u53c2\u6570\uff1a5 \u4e2a\u8bad\u7ec3\u5386\u5143 &#8211; \u68af\u5ea6\u526a\u5207\u4e3a 0.1\uff0c\u4ee5\u9632\u6b62\u68af\u5ea6\u7206\u70b8 &#8211; \u6bcf 100 \u4e2a\u8bad\u7ec3\u6b65\u9a8c\u8bc1 &#8211; GPU \u52a0\u901f\uff08CPU \u8bad\u7ec3\u65f6\u66f4\u6539\u4e3a &#8220;cpu&#8221;\uff0cMac M1\/M2 \u65f6\u66f4\u6539\u4e3a &#8220;mps&#8221;\uff09 &#8211; GPU \u52a0\u901f\uff08CPU \u8bad\u7ec3\u65f6\u66f4\u6539\u4e3a &#8220;cpu&#8221;\uff0cMac M1\/M2 \u65f6\u66f4\u6539\u4e3a &#8220;mps\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u63d0\u793a\uff1a\u53d6\u6d88 fast_dev_run=True \u4ee5\u6267\u884c\u5feb\u901f\u8c03\u8bd5\u8fd0\u884c\uff0c\u5728\u5168\u9762\u8bad\u7ec3\u524d\u9a8c\u8bc1\u60a8\u7684\u8bbe\u7f6e\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2.3 \u521d\u59cb\u5316\u65f6\u7a7a\u878d\u5408\u8f6c\u6362\u5668<\/strong><\/h3>\n\n\n\n<p>\u73b0\u5728\uff0c\u6211\u4eec\u5c06\u7528\u8d85\u53c2\u6570\u6765\u914d\u7f6e TFT \u6a21\u578b\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from pytorch_forecasting.metrics import QuantileLoss\n\ntft = TemporalFusionTransformer.from_dataset(\n    training_dataset,\n    learning_rate=0.02,\n    hidden_size=128,\n    lstm_layers=2,\n    attention_head_size=4,\n    dropout=0.1,\n    hidden_continuous_size=128,\n    loss=QuantileLoss(&#91;0.3, 0.5, 0.7]), # 3 quantiles for uncertainty estimation\n    log_interval=10,  # uncomment for learning rate finder and otherwise, e.g. to 10 for logging every 10 batches\n    reduce_on_plateau_patience=100,\n    optimizer=\"ranger\"\n)\nprint(f\"Number of parameters in network: {tft.size()\/1e3:.1f}k\")<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-preformatted\">GPU available: True (cuda), used: True<br>TPU available: False, using: 0 TPU cores<br>HPU available: False, using: 0 HPUs<br><br>Number of parameters in network: 3168.2k<\/pre>\n\n\n\n<p>\u6211\u4eec\u7684\u6a21\u578b\u914d\u7f6e\u4ea7\u751f\u4e86\u7ea6 320 \u4e07\u4e2a\u53c2\u6570\uff0c\u5728\u6a21\u578b\u5bb9\u91cf\u548c\u8ba1\u7b97\u6548\u7387\u4e4b\u95f4\u53d6\u5f97\u4e86\u5e73\u8861\u3002\u8ba9\u6211\u4eec\u6765\u770b\u770b\u6bcf\u4e2a\u5173\u952e\u8d85\u53c2\u6570\u53ca\u5176\u5f71\u54cd\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Learning Rate (0.02)<\/strong>&nbsp;\u2014&nbsp;\u6a21\u578b\u6536\u655b\u7684\u5173\u952e\u53c2\u6570\u3002 \u6211\u5efa\u8bae\u5728\u7b2c 2 \u4e2a\u5386\u5143\u540e\u6267\u884c\u5b66\u4e60\u7387\u8c03\u5ea6\u7a0b\u5e8f\uff0c\u53ef\u8003\u8651\u4f7f\u7528 ReduceLROnPlateau \u6216 cosine annealing\u3002<\/li>\n\n\n\n<li><strong>Hidden Size (128) \u2014&nbsp;<\/strong>\u9690\u85cf\u5927\u5c0f (128) &#8211; \u63a7\u5236\u6a21\u578b\u5b66\u4e60\u6a21\u5f0f\u7684\u80fd\u529b\u3002 512 \u4ee5\u4e0a\u7684\u503c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6d88\u8d39\u7ea7 GPU \u7684\u5185\u5b58\u95ee\u9898\uff0c\u76ee\u524d\u7684\u8bbe\u7f6e\u5728\u6027\u80fd\u4e0e\u5185\u5b58\u4f7f\u7528\u4e4b\u95f4\u53d6\u5f97\u4e86\u826f\u597d\u7684\u5e73\u8861\u3002<\/li>\n\n\n\n<li><strong>LSTM Layers (2) \u2014&nbsp;<\/strong>\u6839\u636e\u7ecf\u9a8c\uff0c\u8be5\u4efb\u52a1\u7684\u6700\u4f73\u5c42\u6570\uff0c\u989d\u5916\u5c42\u6570\u7684\u6536\u76ca\u9012\u51cf\u3002\u6709\u52a9\u4e8e\u6355\u6349\u77ed\u671f\u548c\u4e2d\u671f\u4f9d\u8d56\u5173\u7cfb\u3002<\/li>\n\n\n\n<li><strong>Attention Heads (4) \u2014&nbsp;<\/strong>\u901a\u8fc7\u5b9e\u9a8c\u53d1\u73b0\u662f\u6700\u4f73\u7684\uff0c\u53ef\u8ba9\u6a21\u578b\u540c\u65f6\u5173\u6ce8\u4e0d\u540c\u7684\u65f6\u95f4\u6a21\u5f0f\u3002 \u66f4\u591a\u7684\u6ce8\u610f\u5934\u5e76\u4e0d\u80fd\u663e\u8457\u63d0\u9ad8\u6027\u80fd\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u9644\u52a0\u53c2\u6570\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Dropout (0.1)\uff1a\u9632\u6b62\u8fc7\u5ea6\u62df\u5408\uff1b<\/li>\n\n\n\n<li>Hidden Continuous Size (128)\uff1a\u5339\u914d\u5e73\u8861\u67b6\u6784\u7684\u9690\u85cf\u5927\u5c0f\uff1b<\/li>\n\n\n\n<li>\u4f18\u5316\u5668\uff1a\u4f7f\u7528 Ranger\uff08RAdam + LookAhead\uff09\uff1b<\/li>\n\n\n\n<li>Patience (100)\uff1a\u5982\u679c\u4f7f\u7528\u9ad8\u539f\u8c03\u5ea6\u7a0b\u5e8f\uff0c\u5b66\u4e60\u7387\u964d\u4f4e\u524d\u7684\u6b65\u9aa4\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u8be5\u6a21\u578b\u7684 320 \u4e07\u4e2a\u53c2\u6570\u8868\u660e\uff0c\u8be5\u7f51\u7edc\u89c4\u6a21\u9002\u4e2d\uff0c\u53ef\u4ee5\u5728\u5927\u591a\u6570 GPU \u4e0a\u9ad8\u6548\u5730\u8fdb\u884c\u8bad\u7ec3\uff0c\u540c\u65f6\u4fdd\u6301\u8db3\u591f\u7684\u5bb9\u91cf\u6765\u6355\u6349\u590d\u6742\u7684\u5e02\u573a\u6a21\u5f0f\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2.4 The Loss<\/strong><\/h3>\n\n\n\n<p>\u6211\u4eec\u7684\u6a21\u578b\u4f7f\u7528 Quantile Loss\uff08\u91cf\u5b50\u635f\u5931\u6cd5\uff09\u548c three regression heads&nbsp;\uff08\u03b1 = 0.3\u30010.5\u30010.7\uff09\u6765\u751f\u6210\u9884\u6d4b\u533a\u95f4\u3002\u6bcf\u4e2a\u9884\u6d4b\u503c \u0177 \u548c\u5b9e\u9645\u503c y \u7684\u635f\u5931\u51fd\u6570\u5b9a\u4e49\u4e3a\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-159.png\" alt=\"\" class=\"wp-image-3503\" style=\"width:474px;height:auto\"\/><\/figure>\n\n\n\n<p id=\"fad9\">\u8fd9\u4e00&nbsp; loss function \u521b\u5efa\u4e86\u4e09\u4e2a\u4e0d\u540c\u7684\u9884\u6d4b\u76ee\u6807\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Lower Bound (\u03b1 = 0.3)<\/strong>&nbsp;&#8211; \u76ee\u6807\u662f\u5728 70% \u7684\u60c5\u51b5\u4e0b\u4f4e\u4e8e\u5b9e\u9645\u4ef7\u683c &#8211; \u5bf9\u4f4e\u4f30\u7684\u60e9\u7f5a\u6bd4\u5bf9\u9ad8\u4f30\u7684\u60e9\u7f5a\u66f4\u91cd &#8211; \u5f62\u6210\u7f6e\u4fe1\u5ea6\u4e0b\u9650\u3002<\/li>\n\n\n\n<li><strong>Central Prediction (\u03b1 = 0.5) &#8211;&nbsp;<\/strong>\u7b49\u540c\u4e8e\u5e73\u5747\u7edd\u5bf9\u8bef\u5dee (MAE) &#8211; \u63d0\u4f9b\u6211\u4eec\u7684\u6700\u4f73\u70b9\u4f30\u8ba1\u503c &#8211; \u5bf9\u9ad8\u4f30\u548c\u4f4e\u4f30\u8fdb\u884c\u5e73\u8861\u60e9\u7f5a\u3002<\/li>\n\n\n\n<li><strong>Upper Bound (\u03b1 = 0.7) &#8211;&nbsp;<\/strong>\u76ee\u6807\u662f\u5728 70% \u7684\u60c5\u51b5\u4e0b\u9ad8\u4e8e\u5b9e\u9645\u4ef7\u683c &#8211; \u5bf9\u9ad8\u4f30\u7684\u60e9\u7f5a\u6bd4\u5bf9\u4f4e\u4f30\u7684\u60e9\u7f5a\u66f4\u91cd &#8211; \u5f62\u6210\u7f6e\u4fe1\u5ea6\u4e0a\u8fb9\u754c\u3002<\/li>\n<\/ul>\n\n\n\n<p>final loss \u662f\u6240\u6709\u91cf\u5316\u503c\u548c\u65f6\u95f4\u6b65\u957f\u7684\u5e73\u5747\u503c\uff0c\u521b\u5efa\u7684\u6a21\u578b\u53ef\u4e3a\u6bcf\u4e2a\u9884\u6d4b\u4ef7\u683c\u63d0\u4f9b\u70b9\u9884\u6d4b\u548c\u7f6e\u4fe1\u533a\u95f4\u3002\u8fd9\u79cd\u4e0d\u786e\u5b9a\u6027\u4f30\u8ba1\u5bf9\u4ea4\u6613\u5e94\u7528\u7279\u522b\u6709\u4ef7\u503c\uff0c\u56e0\u4e3a\u5b83\u6709\u52a9\u4e8e\u91cf\u5316\u9884\u6d4b\u7f6e\u4fe1\u5ea6\u548c\u6f5c\u5728\u7684\u4ef7\u683c\u8303\u56f4\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u4e09\u3001\u6a21\u578b\u8bad\u7ec3<\/strong><\/h2>\n\n\n\n<p>\u73b0\u5728\u51c6\u5907\u5f00\u59cb\u8bad\u7ec3\u8fc7\u7a0b\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>trainer.fit(\n    tft,\n    train_dataloaders=train_dataloader,\n    val_dataloaders=val_dataloader,\n)<\/code><\/pre>\n\n\n\n<p>\u8fd9\u5c06\u6253\u5370\u53ef\u8bad\u7ec3\u548c\u51bb\u7ed3\u7684\u6a21\u578b\u53c2\u6570\uff0c\u5c31\u50cf\u8fd9\u6837\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: &#91;0]\n\n   | Name                               | Type                            | Params | Mode \n------------------------------------------------------------------------------------------------\n0  | loss                               | QuantileLoss                    | 0      | train\n1  | logging_metrics                    | ModuleList                      | 0      | train\n2  | input_embeddings                   | MultiEmbedding                  | 1.0 K  | train\n3  | prescalers                         | ModuleDict                      | 6.1 K  | train\n4  | static_variable_selection          | VariableSelectionNetwork        | 201 K  | train\n5  | encoder_variable_selection         | VariableSelectionNetwork        | 1.5 M  | train\n6  | decoder_variable_selection         | VariableSelectionNetwork        | 410 K  | train\n7  | static_context_variable_selection  | GatedResidualNetwork            | 66.3 K | train\n8  | static_context_initial_hidden_lstm | GatedResidualNetwork            | 66.3 K | train\n9  | static_context_initial_cell_lstm   | GatedResidualNetwork            | 66.3 K | train\n10 | static_context_enrichment          | GatedResidualNetwork            | 66.3 K | train\n11 | lstm_encoder                       | LSTM                            | 264 K  | train\n12 | lstm_decoder                       | LSTM                            | 264 K  | train\n13 | post_lstm_gate_encoder             | GatedLinearUnit                 | 33.0 K | train\n14 | post_lstm_add_norm_encoder         | AddNorm                         | 256    | train\n15 | static_enrichment                  | GatedResidualNetwork            | 82.7 K | train\n16 | multihead_attn                     | InterpretableMultiHeadAttention | 41.2 K | train\n17 | post_attn_gate_norm                | GateAddNorm                     | 33.3 K | train\n18 | pos_wise_ff                        | GatedResidualNetwork            | 66.3 K | train\n19 | pre_output_gate_norm               | GateAddNorm                     | 33.3 K | train\n20 | output_layer                       | Linear                          | 387    | train\n------------------------------------------------------------------------------------------------\n3.2 M     Trainable params\n0         Non-trainable params\n3.2 M     Total params\n12.673    Total estimated model params size (MB)\n526       Modules in train mode\n0         Modules in eval mode<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3.1 \u6a21\u578b\u67b6\u6784\u6982\u8ff0<\/strong><\/h3>\n\n\n\n<p>\u8ba9\u6211\u4eec\u6765\u770b\u770b\u5176\u6838\u5fc3\u7ec4\u6210\u90e8\u5206\u6709\u54ea\u4e9b\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u7528\u4e8e\u7279\u5f81\u5904\u7406\u7684\u8f93\u5165\u5d4c\u5165\u548c\u9884\u5206\u7ea7\u5668\uff1b<\/li>\n\n\n\n<li>\u7528\u4e8e\u9759\u6001\u3001\u7f16\u7801\u5668\u548c\u89e3\u7801\u5668\u8f93\u5165\u7684\u53ef\u53d8\u9009\u62e9\u7f51\u7edc\uff1b<\/li>\n\n\n\n<li>\u5177\u6709\u6ce8\u610f\u529b\u673a\u5236\u7684 LSTM \u7f16\u7801\u5668-\u89e3\u7801\u5668\u67b6\u6784\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u5176\u603b\u53c2\u65703.2M\uff0c\u6700\u5927\u7684\u7ec4\u4ef6\u5305\u62ec\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u7f16\u7801\u5668\u53d8\u91cf\u9009\u62e9\uff08~1.5M \u4e2a\u53c2\u6570\uff09<\/li>\n\n\n\n<li>\u89e3\u7801\u5668\u53d8\u91cf\u9009\u62e9\uff08~410K \u4e2a\u53c2\u6570\uff09<\/li>\n\n\n\n<li>LSTM \u7f16\u7801\u5668\/\u89e3\u7801\u5668\uff08\u5404\u6709 264K \u4e2a\u53c2\u6570\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>\u91cd\u8981\u63d0\u793a\uff1a<\/strong> \u9a8c\u8bc1\u6240\u6709\u6a21\u5757\u662f\u5426\u5728\u6700\u53f3\u8fb9\u4e00\u5217\u663e\u793a &#8220;<em>train mode<\/em>\uff08\u8bad\u7ec3\u6a21\u5f0f\uff09&#8221;\u3002\u5728\u8bad\u7ec3\u671f\u95f4\uff0c\u4efb\u4f55\u6a21\u5757\u5904\u4e8e &#8220;<em>eval mode<\/em>\uff08\u8bc4\u4f30\u6a21\u5f0f\uff09&#8221;\u90fd\u53ef\u80fd\u8868\u660e\u5b58\u5728\u914d\u7f6e\u95ee\u9898\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3.2 \u7528 TensorBoard \u76d1\u63a7\u8bad\u7ec3\u8fdb\u5ea6<\/strong><\/h3>\n\n\n\n<p>1. \u5b9e\u65f6\u53ef\u89c6\u5316\u8bad\u7ec3\uff1a\u5982\u679c\u5c1a\u672a\u5b89\u88c5\uff0c\u8bf7\u5b89\u88c5 TensorBoard\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>pip install tensorboard<\/code><\/pre>\n\n\n\n<p>2. \u542f\u52a8 TensorBoard\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>tensorboard --logdir=lightning_logs<\/code><\/pre>\n\n\n\n<p>3.\u5728\u6d4f\u89c8\u5668\u4e2d\u6253\u5f00 http:\/\/localhost:6006\uff0c\u8bbf\u95ee TensorBoard \u7528\u6237\u754c\u9762\u3002<\/p>\n\n\n\n<p>\u76d1\u6d4b\u7684\u5173\u952e\u6307\u6807\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Training loss\uff08\u57f9\u8bad\u635f\u5931)<\/li>\n\n\n\n<li>Validation loss(\u9a8c\u8bc1\u635f\u5931)<\/li>\n\n\n\n<li>MAPE (\u5e73\u5747\u7edd\u5bf9\u767e\u5206\u6bd4\u8bef\u5dee)<\/li>\n\n\n\n<li>Learning rate(\u5b66\u4e60\u7387)<\/li>\n\n\n\n<li>Quantile predictions (0.3, 0.5, 0.7)<\/li>\n<\/ul>\n\n\n\n<p>\u901a\u8fc7 TensorBoard \u754c\u9762\uff0c\u6211\u4eec\u53ef\u4ee5\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u5b9e\u65f6\u8ddf\u8e2a\u6a21\u578b\u6536\u655b\uff1b<\/li>\n\n\n\n<li>\u6bd4\u8f83\u4e0d\u540c\u7684\u8bad\u7ec3\u8fd0\u884c\uff1b<\/li>\n\n\n\n<li>\u53ca\u65e9\u53d1\u73b0\u6f5c\u5728\u95ee\u9898\uff1b<\/li>\n\n\n\n<li>\u5bfc\u51fa\u6307\u6807\u7528\u4e8e\u62a5\u544a\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u63d0\u793a\uff1a\u8003\u8651\u4f7f\u7528\u4e0d\u540c\u7684\u8d85\u53c2\u6570\u8bbe\u7f6e\u591a\u4e2a TensorBoard \u8fd0\u884c\uff0c\u4ee5\u5e76\u884c\u6bd4\u8f83\u5176\u6027\u80fd\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-160.png\" alt=\"\" class=\"wp-image-3504\"\/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-161.png\" alt=\"\" class=\"wp-image-3505\"\/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-162.png\" alt=\"\" class=\"wp-image-3506\"\/><\/figure>\n\n\n\n<p>\u89c2\u5bdf TensorBoard \u4e2d\u7684\u8bad\u7ec3\u66f2\u7ebf\uff0c\u6211\u4eec\u53d1\u73b0\u5728 2000 \u6b65\u5de6\u53f3\u5c31\u51fa\u73b0\u4e86\u65e9\u671f\u6536\u655b\u3002\u603b\u7ed3\u5982\u4e0b\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u8bad\u7ec3\u548c\u9a8c\u8bc1\u635f\u5931\u5747\u8fbe\u5230\u9ad8\u5cf0\uff1b<\/li>\n\n\n\n<li>\u5feb\u901f\u8d8b\u5e73\u8868\u660e\u6211\u4eec\u5df2\u7ecf\u8fbe\u5230\u4e86\u5c40\u90e8\u6700\u4f73\u503c\uff1b<\/li>\n\n\n\n<li>\u8fd9\u79cd\u6536\u655b\u6a21\u5f0f\u5728\u91d1\u878d\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u4e2d\u975e\u5e38\u5178\u578b\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u5e73\u53f0\u521d\u671f\u53cd\u5e94\u7684\u73b0\u8c61\u53ef\u80fd\u8bf4\u660e\u4e86\u51e0\u4e2a\u95ee\u9898\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u6a21\u578b\u5df2\u7ecf\u638c\u63e1\u4e86\u6570\u636e\u4e2d\u53ef\u9884\u6d4b\u7684\u6a21\u5f0f\uff1b<\/li>\n\n\n\n<li>\u5206\u949f\u7ea7\u4ef7\u683c\u8d70\u52bf\u7684\u53ef\u9884\u6d4b\u6027\u53ef\u80fd\u5b58\u5728\u4e0a\u9650\uff1b<\/li>\n\n\n\n<li>\u6211\u4eec\u53ef\u80fd\u4f1a\u53d7\u76ca\u4e8e\u5b66\u4e60\u7387\u8c03\u6574\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u6f5c\u5728\u7684\u6539\u8fdb\u63aa\u65bd\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u6267\u884c\u5b66\u4e60\u7387\u8c03\u5ea6\u7a0b\u5e8f\uff0c\u5728\u8fbe\u5230\u5cf0\u503c\u540e\u964d\u4f4e\u5b66\u4e60\u7387\uff1b<\/li>\n\n\n\n<li>\u5c1d\u8bd5\u4f7f\u7528\u4e0d\u540c\u7684\u4f18\u5316\u5668\uff0c\u5982 AdaBelief \u6216\u5e26\u6709\u70ed\u8eab\u529f\u80fd\u7684 Adam\uff1b<\/li>\n\n\n\n<li>\u8003\u8651\u589e\u52a0\u66f4\u591a\u7684\u529f\u80fd\u6216\u4ee5\u4e0d\u540c\u7684\u65b9\u5f0f\u8bbe\u8ba1\u73b0\u6709\u529f\u80fd\uff1b<\/li>\n\n\n\n<li>\u8c03\u6574\u91cf\u5316\u635f\u5931\u53c2\u6570\uff0c\u5173\u6ce8\u4e0d\u540c\u7684\u9884\u6d4b\u533a\u95f4\u3002<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u56db\u3001\u6a21\u578b\u8bc4\u4f30<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><br><strong>4.1 \u52a0\u8f7d\u9a8c\u8bc1<\/strong><\/h3>\n\n\n\n<p>\u53ef\u4ee5\u901a\u8fc7\u4e24\u79cd\u65b9\u5f0f\u52a0\u8f7d\u6027\u80fd\u6700\u4f73\u7684\u6a21\u578b\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># Option 1: Load best model from training\nmodel_path = checkpoint_callback.best_model_path\n\n# Option 2: Load specific checkpoint\nmodel_path = \"path_to_checkpoint.ckpt\"\n\n# Load the model\nbest_tft = TemporalFusionTransformer.load_from_checkpoint(model_path)<\/code><\/pre>\n\n\n\n<p>\u6211\u4eec\u53ef\u4ee5\u5728\u9a8c\u8bc1\u96c6\u548c\u8bad\u7ec3\u96c6\u7684\u6700\u540e\u4e00\u4e2a\u6837\u672c\u4e0a\u8c03\u7528\u8be5\u6a21\u578b\uff0c\u5e76\u8ba1\u7b97 MAPE\uff08\u5e73\u5747\u7edd\u5bf9\u8bef\u5dee\u767e\u5206\u6bd4\uff09\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from pytorch_forecasting import MAPE\n\ntest_dataset = TimeSeriesDataSet.from_dataset(training_dataset, df_test.reset_index(), predict=True, stop_randomization=True)\ntest_dataloader = test_dataset.to_dataloader(train=False, batch_size=batch_size, num_workers=0)\npredictions = best_tft.predict(test_dataloader, return_y=True, trainer_kwargs=dict(accelerator=\"cuda\"))\nprint(f\"test set MAPE: {MAPE()(predictions.output, predictions.y)}\")\nprint(f\"validation set MAPE: {MAPE()(best_tft.predict(val_dataloader, return_y=True).output, best_tft.predict(val_dataloader, return_y=True).y)}\")<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-preformatted\">test set MAPE: 0.02121799625456333\nvalidation set MAPE: 0.01347196102142334<\/pre>\n\n\n\n<p>\u7ed3\u679c\u8f93\u51fa\u4e86\u9a8c\u8bc1&nbsp; MAPE: 1.34%\u548c\u6d4b\u8bd5 MAPE: 2.12%\u3002\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\u4e4b\u95f4\u7684\u6027\u80fd\u5dee\u8ddd\uff08\u7ea6 0.8%\uff09\u53ef\u80fd\u662f\u7531\u4e8e\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u8bad\u7ec3\u6570\u636e\u7684\u65f6\u95f4\u8ddd\u79bb\uff1b<\/li>\n\n\n\n<li>\u6d4b\u8bd5\u671f\u5185\u5e02\u573a\u5236\u5ea6\u7684\u6f5c\u5728\u53d8\u5316\uff1b<\/li>\n\n\n\n<li>\u9a8c\u8bc1\u6837\u672c\u91cf\u6709\u9650\uff0810 \u4e2a\u5b9e\u4f8b\uff09\u3002<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4.2 \u5982\u4f55\u8bc4\u4f30\u6a21\u578b<\/strong><\/h3>\n\n\n\n<p>\u8fd8\u8bb0\u5f97 TFT \u7684\u6700\u5927\u4f18\u52bf\u4e4b\u4e00\u662f\u5176\u53ef\u89e3\u91ca\u6027\u5417\uff1f\u4e0b\u9762\u6211\u4eec\u5c31\u6765\u770b\u770b\u5b83\u662f\u5982\u4f55\u53d1\u6325\u4f5c\u7528\u7684\u3002\u6211\u4eec\u53ef\u4ee5\u76f4\u89c2\u5730\u770b\u5230\u6bcf\u4e2a\u65f6\u95f4\u6b65\u5728\u6ce8\u610f\u529b\u4e2d\u7684\u91cd\u8981\u6027\uff0c\u4ee5\u53ca\u9759\u6001\u548c\u52a8\u6001\u53d8\u91cf\u7684\u91cd\u8981\u6027\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>raw_predictions = best_tft.predict(val_dataloader, mode=\"raw\", return_x=True)\ninterpretation = best_tft.interpret_output(raw_predictions.output, reduction=\"sum\")\nbest_tft.plot_interpretation(interpretation)<\/code><\/pre>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-163.png\" alt=\"\" class=\"wp-image-3507\"\/><\/figure>\n\n\n\n<p>\u9759\u6001\u53d8\u91cf\u7684\u91cd\u8981\u6027\u56fe\u793a\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-164.png\" alt=\"\" class=\"wp-image-3508\" style=\"width:597px;height:auto\"\/><\/figure>\n\n\n\n<p>\u52a8\u6001\u7f16\u7801\u5668\u53d8\u91cf\uff08\u672a\u77e5\u6570\uff09\u7684\u91cd\u8981\u6027\u56fe\u793a\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-165.png\" alt=\"\" class=\"wp-image-3509\" style=\"width:597px;height:auto\"\/><\/figure>\n\n\n\n<p>\u52a8\u6001\u89e3\u7801\u5668\u53d8\u91cf\uff08\u5df2\u77e5\uff09\u7684\u91cd\u8981\u6027\u56fe\u793a\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-166.png\" alt=\"\" class=\"wp-image-3510\" style=\"width:597px;height:auto\"\/><\/figure>\n\n\n\n<p>\u7279\u5f81\u7406\u89e3\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u5927\u591a\u6570\u5de5\u7a0b\u7279\u5f81\u663e\u793a\u51fa\u5bf9\u9884\u6d4b\u7684\u8d21\u732e\u3002<\/li>\n\n\n\n<li>\u503c\u5f97\u6ce8\u610f\u7684\u4f8b\u5916\u60c5\u51b5\uff1ais_earnings_day \u7531\u4e8e\u5f88\u5c11\u51fa\u73b0\uff0c\u5176\u91cd\u8981\u6027\u8f83\u4f4e\u3002<\/li>\n\n\n\n<li>\u5efa\u8bae\u9488\u5bf9\u7279\u5b9a\u4e8b\u4ef6\uff08\u5982\u6536\u76ca\u65e5\uff09\u5efa\u7acb\u4e13\u95e8\u6a21\u578b\u7684\u53ef\u80fd\u6027\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u6539\u8fdb\u5efa\u8bae\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u8003\u8651\u9488\u5bf9\u4e0d\u540c\u7684\u5e02\u573a\u6761\u4ef6\u91c7\u7528\u4e0d\u540c\u7684\u6a21\u5f0f\u3002<\/li>\n\n\n\n<li>\u589e\u52a0\u9a8c\u8bc1\u96c6\u7684\u5927\u5c0f\uff0c\u4ee5\u83b7\u5f97\u66f4\u53ef\u9760\u7684\u6307\u6807\u3002<\/li>\n\n\n\n<li>\u8c03\u67e5\u7f55\u89c1\u4f46\u91cd\u8981\u4e8b\u4ef6\u7684\u7279\u5f81\u5de5\u7a0b\u3002<\/li>\n\n\n\n<li>\u76d1\u6d4b\u57f9\u8bad\u671f\u548c\u6d4b\u8bd5\u671f\u4e4b\u95f4\u7684\u6982\u5ff5\u504f\u79fb\u3002<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u4e94\u3001\u9884\u6d4b\u53ef\u89c6\u5316<\/strong><\/h2>\n\n\n\n<p>\u4e3a\u4e86\u76f4\u89c2\u5730\u663e\u793a\u6a21\u578b\u7684\u9884\u6d4b\u7ed3\u679c\uff0c\u6211\u4eec\u9996\u5148\u8981\u91cd\u65b0\u914d\u7f6e\u6d4b\u8bd5\u6570\u636e\u96c6\uff0c\u4f7f\u5176\u5305\u62ec\u6240\u6709timesteps\uff0c\u800c\u4e0d\u4ec5\u4ec5\u662f\u6700\u540e\u4e00\u4e2atimesteps\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>test_dataset = TimeSeriesDataSet.from_dataset(training_dataset, df_test.reset_index(), predict=False, stop_randomization=True)\ntest_dataloader = test_dataset.to_dataloader(train=False, batch_size=batch_size, num_workers=0)<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>5.1 \u5229\u7528\u9884\u6d4b\u533a\u95f4\u521b\u5efa\u53ef\u89c6\u5316<\/strong><\/h3>\n\n\n\n<p>\u4e0b\u9762\u7684\u51fd\u6570\u53ef\u4ee5\u901a\u8fc7\u8721\u70db\u56fe\u548c\u4e0d\u786e\u5b9a\u6027\u5e26\u76f4\u89c2\u5730\u663e\u793a\u6211\u4eec\u7684\u9884\u6d4b\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>for i in range(5):\n    symbol = \"TSLA\"\n    first_test_time_idx = test_dataset.filter(lambda x: x.symbol == symbol)&#91;0]&#91;0]&#91;\"encoder_time_idx_start\"].item()\n    first_test_time_idx += np.random.randint(0, df_test.time_idx.max() - first_test_time_idx)\n    date = df_test&#91;df_test&#91;\"time_idx\"] == first_test_time_idx]&#91;\"date\"].values&#91;0]\n    first_predict_point_time_idx = first_test_time_idx + min_encoder_length\n    prediction = best_tft.predict(\n        test_dataset.filter(lambda x: (x.symbol == symbol) &amp; (x.time_idx_first_prediction == first_predict_point_time_idx)),\n        mode=\"raw\",\n        return_x=True,\n    )\n    candles = df_test&#91;(df_test&#91;\"symbol\"] == symbol) &amp; (df_test&#91;\"time_idx\"] &gt;= first_test_time_idx) &amp; (df_test&#91;\"time_idx\"] &lt; first_predict_point_time_idx+max_prediction_length)]\n    fig, ax = plt.subplots(figsize=(12, 6))\n    ax.set_title(f\"{symbol} stock price prediction on {date}\")\n    prediction_quantile_30 = prediction.output.prediction&#91;:, :, 0].cpu().numpy().squeeze(0)\n    prediction_quantile_50 = prediction.output.prediction&#91;:, :, 1].cpu().numpy().squeeze(0)\n    prediction_quantile_70 = prediction.output.prediction&#91;:, :, 2].cpu().numpy().squeeze(0)\n    adjust_predictions = prediction_quantile_50&#91;0] - candles.iloc&#91;-max_prediction_length]&#91;\"open\"]\n    prediction_quantile_50 -= adjust_predictions\n    prediction_quantile_30 -= adjust_predictions\n    prediction_quantile_70 -= adjust_predictions\n    quantile_30 = np.concatenate(&#91;np.array(&#91;np.nan]*min_encoder_length), prediction_quantile_30])\n    quantile_50 = np.concatenate(&#91;np.array(&#91;np.nan]*min_encoder_length), prediction_quantile_50])\n    quantile_70 = np.concatenate(&#91;np.array(&#91;np.nan]*min_encoder_length), prediction_quantile_70])\n    addplots = &#91;\n        mpf.make_addplot(quantile_50, type=\"line\", color=\"orange\", width=2, ax=ax),  # Middle line bold\n    ]\n    \n    mpf.plot(\n        candles,\n        ax=ax,\n        type=\"candle\",\n        addplot=addplots,\n    )\n    \n    ax.fill_between(\n        range(len(quantile_50)),  # x values\n        quantile_30,  # bottom quantile (30)\n        quantile_70,  # top quantile (70)\n        color=\"orange\", alpha=0.3  # fill color and transparency\n    )\n    plt.show()<\/code><\/pre>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-167.png\" alt=\"\" class=\"wp-image-3511\"\/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-168.png\" alt=\"\" class=\"wp-image-3512\"\/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-169.png\" alt=\"\" class=\"wp-image-3513\"\/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><br><strong>5.2 \u7406\u89e3\u53ef\u89c6\u5316<\/strong><\/h3>\n\n\n\n<p id=\"e5d2\">\u56fe\u8868\u663e\u793a\u4e86\u5982\u4e0b\u4fe1\u606f\u9700\u8981\u5927\u5bb6\u5173\u6ce8\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u7f16\u7801\u5668\u671f\u95f4\u7684\u5386\u53f2\u8721\u70db\u56fe\u3002<\/li>\n\n\n\n<li>\u6a59\u7ebf\u663e\u793a\u9884\u6d4b\u4e2d\u503c\uff080.5 \u91cf\u7ea7\uff09\u3002<\/li>\n\n\n\n<li>\u6a59\u8272\u9634\u5f71\u533a\u57df\u663e\u793a\u9884\u6d4b\u533a\u95f4\uff080.3 \u81f3 0.7 \u91cf\u7ea7\uff09\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u91cd\u8981\u7684\u6a21\u578b\u9650\u5236\uff1a\u6211\u4eec\u6240\u91c7\u7528\u7684\u9884\u6d4b\u8c03\u6574\u662f\u9488\u5bf9\u4e00\u4e2a\u6839\u672c\u95ee\u9898\u7684\u4e34\u65f6\u53d8\u901a\u65b9\u6cd5\uff0c\u5373\u6a21\u578b\u503e\u5411\u4e8e\u9884\u6d4b\u4e0e\u6700\u540e\u5df2\u77e5\u4ef7\u683c\u8131\u8282\u7684\u7b2c\u4e00\u4e2a\u65f6\u95f4\u6b65\u503c\u3002<\/p>\n\n\n\n<p>\u867d\u7136\u8fd9\u79cd\u8c03\u6574\u4f7f\u6211\u4eec\u7684\u53ef\u89c6\u5316\u6548\u679c\u66f4\u5177\u53ef\u89e3\u91ca\u6027\uff0c\u4f46\u5b83\u63a9\u76d6\u4e86\u4e00\u4e2a\u9700\u8981\u89e3\u51b3\u7684\u8bad\u7ec3\u95ee\u9898\u3002\u8fd9\u91cc\u6709\u4e00\u4e9b\u6f5c\u5728\u7684\u89e3\u51b3\u65b9\u6848\u53ef\u4f9b\u63a2\u7d22\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u8bad\u7ec3\u6539\u8fdb\uff1a\u589e\u52a0\u8fde\u7eed\u6027\u635f\u5931\u9879\uff0c\u4ee5\u60e9\u7f5a\u7f16\u7801\u5668\u548c\u89e3\u7801\u5668\u4e4b\u95f4\u7684\u5de8\u5927\u5dee\u8ddd &#8211; \u589e\u52a0\u635f\u5931\u51fd\u6570\u4e2d\u7b2c\u4e00\u6b65\u9884\u6d4b\u8bef\u5dee\u7684\u6743\u91cd &#8211; \u5728\u89e3\u7801\u5668\u4e2d\u5c06\u6700\u540e\u5df2\u77e5\u4ef7\u683c\u4f5c\u4e3a\u7279\u6b8a\u529f\u80fd\u5305\u62ec\u5728\u5185<\/li>\n\n\n\n<li>\u7ed3\u6784\u6539\u8fdb\uff1a\u589e\u52a0\u4e0a\u4e00\u4e2a\u7f16\u7801\u5668\u65f6\u95f4\u6b65\u7684\u5269\u4f59\u8fde\u63a5\u3002<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u516d\u3001\u56de\u6d4b\u57fa\u4e8e\u91cf\u503c\u7684\u4ea4\u6613\u7b56\u7565<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><br><strong>6.1 \u751f\u6210\u5b8c\u6574\u7684\u6d4b\u8bd5\u96c6\u9884\u6d4b<\/strong><\/h3>\n\n\n\n<p>\u9996\u5148\u9700\u8981\u5bf9\u6574\u4e2a\u6d4b\u8bd5\u96c6\u8fdb\u884c\u9884\u6d4b\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># Generate predictions (note: this is computationally intensive)\npredictions = best_tft.predict(\n    test_dataloader,\n    mode=\"quantiles\",\n    return_index=True,\n    trainer_kwargs=dict(accelerator=\"cuda\")\n)<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code># Create DataFrame with predictions\npredictions_df = pd.DataFrame({\n    'symbol': predictions.index&#91;'symbol'],     # the symbol column from index_df\n    'time_idx': predictions.index&#91;'time_idx'],   # the time index column from index_df\n    'predictions': list(predictions.output.cpu().numpy())   # store each &#91;10, 3] array as a list in the DataFrame\n})\n\n# Adjust predictions to match current price levels\ndef adjust_predictions(row):\n    if type(row&#91;\"predictions\"]) == float:\n        return row\n    adjust_scale = row&#91;\"predictions\"]&#91;0]&#91;1] - row&#91;\"open\"]\n    row&#91;\"predictions\"] = row&#91;\"predictions\"] - adjust_scale\n\n    return row\n\n# Process predictions\ndf_index = df_test.index\npredictions_df = df_test.merge(predictions_df, on=&#91;'symbol', 'time_idx'], how='left').set_index(df_index)\npredictions_df = predictions_df.apply(adjust_predictions, axis=1)\npredictions_df = predictions_df&#91;~predictions_df&#91;\"predictions\"].isna()]\n\n# Calculate predicted and actual changes\npredictions_df&#91;\"change_prediction_q_50\"] = predictions_df&#91;\"predictions\"].apply(lambda x: x&#91;-1]&#91;1] - x&#91;0]&#91;1])\npredictions_df&#91;\"change_prediction_q_30\"] = predictions_df&#91;\"predictions\"].apply(lambda x: x&#91;-1]&#91;0] - x&#91;0]&#91;0])\npredictions_df&#91;\"change_prediction_q_70\"] = predictions_df&#91;\"predictions\"].apply(lambda x: x&#91;-1]&#91;2] - x&#91;0]&#91;2])\npredictions_df&#91;\"change_actual\"] = predictions_df&#91;\"close\"].shift(-10) - predictions_df&#91;\"open\"]<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code>predictions_df&#91;\"direction_matches\"] = predictions_df.apply(lambda x: np.sign(x&#91;\"change_prediction_q_50\"]) == np.sign(x&#91;\"change_actual\"]), axis=1)\ndirection_accuracy = predictions_df&#91;\"direction_matches\"].mean()\nprint(f\"Directional accuracy: {direction_accuracy}\")<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code>Directional accuracy:\uff08\u65b9\u5411\u7cbe\u5ea6\uff09\uff1a 0.45370907583920433<\/code><\/pre>\n\n\n\n<p>45.37% \u7684\u65b9\u5411\u51c6\u786e\u7387\u8868\u660e\uff0c\u4ec5\u4ec5\u4f7f\u7528\u7b80\u5355\u7684\u65b9\u5411\u9884\u6d4b\u662f\u4e0d\u591f\u7684\u3002\u6211\u4eec\u5c06\u5229\u7528\u6a21\u578b\u7684\u91cf\u5316\u9884\u6d4b\u6765\u521b\u5efa\u4e00\u4e2a\u66f4\u590d\u6742\u7684\u7b56\u7565\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>6.2 \u4ea4\u6613\u7b56\u7565\u8bbe\u8ba1<\/strong><\/h3>\n\n\n\n<p>\u6211\u4eec\u7684\u7b56\u7565\u662f\u901a\u8fc7\u91cf\u5316\u9884\u6d4b\u6765\u5229\u7528\u6a21\u578b\u7684\u4e0d\u786e\u5b9a\u6027\u4f30\u8ba1\uff0c\u5176\u4e70\u5165\u6761\u4ef6\u5982\u4e0b\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u591a\u5934\u5165\u573a\uff1a\u5982\u679c\u8fc7\u53bb 5 \u4e2a\u9884\u6d4b\u5206\u949f\u5185\u4efb\u4f55 0.3 \u5206\u4f4d\u6570 &gt; \u5f53\u524d\u4ef7\u683c &#8211; \u6b62\u76c8\uff1a\u6700\u9ad8 0.5 \u5206\u4f4d\u6570\u4ef7\u683c &#8211; \u6b62\u635f\uff1a\u6700\u4f4e 0.3 \u5206\u4f4d\u6570\u4ef7\u683c\uff1b<\/li>\n\n\n\n<li>\u505a\u7a7a\u5165\u573a\uff1a\u5982\u679c\u8fc7\u53bb 5 \u5206\u949f\u9884\u6d4b\u4e2d\u7684\u4efb\u4f55 0.7 \u5206\u4f4d\u6570 &lt; \u5f53\u524d\u4ef7\u683c &#8211; \u6b62\u76c8\uff1a\u6700\u4f4e 0.5 \u5206\u4f4d\u6570\u4ef7\u683c &#8211; \u6b62\u635f\uff1a \u6700\u9ad8 0.7 \u5206\u4f4d\u6570\u4ef7\u683c\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u9000\u51fa\u6761\u4ef6\u4e3a\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Hit take profit<\/li>\n\n\n\n<li>Hit stop loss<\/li>\n\n\n\n<li>Time-based exit after 10 minutes\uff0810 \u5206\u949f\u540e\u5b9a\u65f6\u9000\u51fa\uff09<\/li>\n<\/ul>\n\n\n\n<p>\u6211\u4e0d\u6253\u7b97\u5728\u8fd9\u91cc\u63d0\u4f9b\u56de\u6eaf\u6d4b\u8bd5\u4ee3\u7801\uff0c\u56e0\u4e3a\u5b83\u5b9e\u5728\u592a\u957f\u4e86\u3002\u4f46\u6839\u636e\u6211\u4e4b\u524d\u521b\u5efa\u7684 predictions_df\uff0c\u8fd9\u5f88\u5bb9\u6613\u5b9e\u73b0\uff0c\u5176\u4e2d\u6bcf\u4e00\u884c\u90fd\u662f\u4e00\u4e2a\u6761\u5f62\u56fe\uff0c\u6709\u4e00\u5217\u662f\u9884\u6d4b\u7684\u672a\u6765 10 \u5206\u949f\u7684\u6570\u636e\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>6.3 \u56de\u6eaf\u6d4b\u8bd5\u7ed3\u679c<\/strong><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>Portfolio Statistics:\n- Initial Capital: $10,000\n- Total PnL: $1,547.89 (15.48% return)\n- Average PnL per Stock: $154.79\n- Maximum Drawdown: $223.97\n- Sharpe Ratio: 0.11\n- Win Rate: 35.81%\n- Total Trades: 4,160\n\nIndividual Stock Performance:\n1. PLUG: $493.77\n2. SOFI: $287.35\n3. FFIE: $211.26\n4. NVDA: $199.52\n5. NIO: $167.89\n6. TSLA: $55.23\n7. AMD: $49.51\n8. F: $40.00\n9. PLTR: $35.09\n10. AAPL: $8.29<\/code><\/pre>\n\n\n\n<p>\u5982\u4e0a\u9762\u7684\u56de\u6d4b\u7ed3\u679c\u5f97\u51fa\u5982\u4e0b\u5206\u6790\u7ed3\u679c\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u6218\u7565\u56de\u62a5\u738715.48%\uff1b<\/li>\n\n\n\n<li>\u4e70\u5165\u5e76\u6301\u6709\u57fa\u51c6\u4e3a7%\uff1b<\/li>\n\n\n\n<li>\u8d85\u989d\u6536\u76ca\u4e3a 8.48%\uff1b<\/li>\n\n\n\n<li>\u80dc\u7387\u8f83\u4f4e\uff0835.81%\uff09\uff0c\u4f46\u603b\u4f53\u56de\u62a5\u7387\u8f83\u9ad8\uff0c\u8868\u660e\u4ed3\u4f4d\u5927\u5c0f\u6709\u6548\uff1b<\/li>\n\n\n\n<li>\u4e0d\u540c\u80a1\u7968\u7684\u8868\u73b0\u5dee\u5f02\u663e\u8457\uff0c\u589e\u957f\u578b\u80a1\u7968\uff08PLUG\u3001SOFI\uff09\u8868\u73b0\u6700\u4f73\uff1b<\/li>\n\n\n\n<li>\u5176\u5c40\u9650\u6027\u4e3a\u5047\u5b9a\u516c\u5f00\u4ef7\u683c\u5b8c\u5168\u6267\u884c\uff0c\u4e14\u4e0d\u5305\u62ec\u4ea4\u6613\u8d39\u7528\u3002<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>6.4 \u4ea4\u6613\u53ef\u89c6\u5316<\/strong><\/h3>\n\n\n\n<p>\u4e0b\u56fe\u4e3aAAPL\u7684\u4e8f\u635f\u4ea4\u6613\u60c5\u51b5\u5982\u56fe\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-170.png\" alt=\"\" class=\"wp-image-3514\"\/><\/figure>\n\n\n\n<p>F\u7f8e\u5143\u7684\u4e8f\u635f\u4ea4\u6613\u60c5\u51b5\u5982\u56fe\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-171.png\" alt=\"\" class=\"wp-image-3515\"\/><\/figure>\n\n\n\n<p>\u5728 $FFIE \u4ea4\u6613\u4e2d\u76c8\u5229\u7684\u4ea4\u6613\u60c5\u51b5\u5982\u56fe\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-172.png\" alt=\"\" class=\"wp-image-3516\"\/><\/figure>\n\n\n\n<p>\u5728 $TSLA \u4ea4\u6613\u4e2d\u76c8\u5229\u7684\u4ea4\u6613\u60c5\u51b5\u5982\u56fe\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-173.png\" alt=\"\" class=\"wp-image-3517\"\/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><br><strong>\u4e03\u3001\u9879\u76ee\u603b\u7ed3<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><br><strong>7.1 \u9879\u76ee\u6458\u8981<\/strong><\/h3>\n\n\n\n<p>\u672c\u7cfb\u5217\u63a2\u8ba8\u4e86\u5982\u4f55\u5229\u7528\u65f6\u6001\u878d\u5408\u8f6c\u6362\u5668\u5f00\u53d1\u5148\u8fdb\u7684\u7b97\u6cd5\u4ea4\u6613\u7cfb\u7edf\uff0c\u8fdb\u884c\u5206\u949f\u7ea7\u80a1\u4ef7\u9884\u6d4b\u3002\u901a\u8fc7\u5c06\u73b0\u4ee3\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u4e0e\u4f20\u7edf\u4ea4\u6613\u7406\u5ff5\u76f8\u7ed3\u5408\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u79cd\u7b56\u7565\uff0c\u5176\u6027\u80fd\u4f18\u4e8e\u6807\u51c6\u57fa\u51c6\uff0c\u540c\u65f6\u8fd8\u80fd\u63d0\u4f9b\u53ef\u89e3\u91ca\u7684\u9884\u6d4b\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>7.2 \u4e3b\u8981\u6210\u679c<\/strong><\/h3>\n\n\n\n<p><strong>Data Engineering\uff08\u6570\u636e\u5de5\u7a0b\uff09<\/strong>&#8211; \u521b\u5efa\u5168\u9762\u7684\u5206\u949f\u7ea7\u80a1\u7968\u4ef7\u683c\u6570\u636e\u96c6 &#8211; \u5f00\u53d1\u4e30\u5bcc\u7684\u529f\u80fd\u96c6\uff0c\u5305\u62ec\uff1a &#8211; \u6280\u672f\u6307\u6807\uff08ATR\u3001EMA\u3001RSI\u3001SMA\uff09 &#8211; \u5e02\u573a\u52a8\u6001\uff08\u6ce2\u52a8\u7387\u3001\u6210\u4ea4\u91cf\u6982\u51b5\uff09 &#8211; \u4e8b\u4ef6\u6807\u8bb0\uff08\u6536\u76ca\u65e5\u671f\uff09 &#8211; \u4ef7\u683c\u6c34\u5e73\uff08\u652f\u6491\u4f4d\/\u963b\u529b\u4f4d\uff09\u3002<\/p>\n\n\n\n<p id=\"7057\"><strong>Model Architecture\uff08\u6a21\u578b\u67b6\u6784\uff09 <\/strong>&#8211; \u6210\u529f\u5b9e\u65bd\u4e86\u5e26\u6709\u91cf\u5316\u9884\u6d4b\u7684 TFT &#8211; \u901a\u8fc7\u591a\u4e2a\u9884\u6d4b\u5934\u5b9e\u73b0\u4e86\u6709\u6548\u7684\u4e0d\u786e\u5b9a\u6027\u4f30\u8ba1 &#8211; \u5229\u7528\u6a21\u578b\u7684\u53ef\u89e3\u91ca\u6027\u8fdb\u884c\u7279\u5f81\u91cd\u8981\u6027\u5206\u6790 &#8211; \u4f18\u5316\u4e86\u91d1\u878d\u65f6\u95f4\u5e8f\u5217\u7684\u8d85\u53c2\u6570\u3002<\/p>\n\n\n\n<p id=\"f9da\"><strong>Trading System\uff08\u4ea4\u6613\u7cfb\u7edf\uff09<\/strong> &#8211; \u5229\u7528\u57fa\u4e8e\u91cf\u503c\u7684\u9884\u6d4b\u5f00\u53d1\u4e86\u4e00\u79cd\u65b0\u9896\u7684\u7b56\u7565 &#8211; \u76f8\u5bf9\u4e8e 7% \u7684\u4e70\u5165\u5e76\u6301\u6709\u57fa\u51c6\uff0c\u5b9e\u73b0\u4e86 15.48% \u7684\u56de\u62a5\u7387 &#8211; \u5c3d\u7ba1\u65b9\u5411\u51c6\u786e\u7387\u4f4e\u4e8e 50%\uff0c\u4f46\u4ecd\u4ea7\u751f\u4e86\u76c8\u5229\u4ea4\u6613 &#8211; \u5728\u591a\u53ea\u80a1\u7968\u4e0a\u5c55\u793a\u4e86\u6709\u6548\u6027\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>7.3 \u91cd\u8981\u89c1\u89e3<\/strong><\/h3>\n\n\n\n<p><strong>\u6a21\u578b\u884c\u4e3a\uff1a<\/strong>45% \u7684\u65b9\u5411\u51c6\u786e\u7387\u51f8\u663e\u4e86\u70b9\u9884\u6d4b\u7684\u5c40\u9650\u6027 &#8211; \u4e8b\u5b9e\u8bc1\u660e\uff0c\u4e0d\u786e\u5b9a\u6027\u4f30\u8ba1\u6bd4\u65b9\u5411\u9884\u6d4b\u66f4\u6709\u4ef7\u503c &#8211; \u7279\u5f81\u91cd\u8981\u6027\u4e0e\u91d1\u878d\u9886\u57df\u77e5\u8bc6\u76f8\u4e00\u81f4\u3002<\/p>\n\n\n\n<p><strong>\u4ea4\u6613\u8868\u73b0\uff1a<\/strong>\u5c3d\u7ba1\u80dc\u7387\u8f83\u4f4e\uff0835.81%\uff09\uff0c\u4f46\u7b56\u7565\u4ecd\u80fd\u76c8\u5229 &#8211; \u4e0d\u540c\u80a1\u7968\u7684\u8868\u73b0\u5dee\u5f02\u663e\u8457 &#8211; \u57fa\u4e8e\u91cf\u503c\u7684\u8fdb\u5165\/\u9000\u51fa\u89c4\u5219\u7684\u6709\u6548\u6027\u3002<\/p>\n\n\n\n<p><strong>\u6280\u672f\u6311\u6218\uff1a<\/strong>\u65e9\u671f\u8bad\u7ec3\u6536\u655b\u8868\u660e\u6a21\u578b\u5bb9\u91cf\u6709\u9650 &#8211; \u7f16\u7801\u5668\u548c\u89e3\u7801\u5668\u4e4b\u95f4\u7684\u4ef7\u683c\u6c34\u5e73\u4e0d\u8fde\u7eed\u6027 &#8211; \u5206\u949f\u7ea7\u9884\u6d4b\u7684\u8ba1\u7b97\u8981\u6c42\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>7.4 \u672a\u6765\u53d1\u5c55\u65b9\u5411<\/strong><\/h3>\n\n\n\n<p><strong>\u589e\u5f3a\u67b6\u6784\uff1a<\/strong>\u5728\u7f16\u7801\u5668\u548c\u89e3\u7801\u5668\u4e4b\u95f4\u589e\u52a0\u8fde\u7eed\u6027\u7ea6\u675f &#8211; \u63a2\u7d22\u66f4\u5927\u7684\u6a21\u578b\u5bb9\u91cf\u3002<\/p>\n\n\n\n<p><strong>\u8bad\u7ec3\u4f18\u5316\uff1a<\/strong>\u5229\u7528\u5b66\u4e60\u7387\u8c03\u5ea6\u5ef6\u957f\u8bad\u7ec3\u65f6\u95f4 &#8211; \u5b9e\u65bd\u81ea\u9002\u5e94\u635f\u5931\u51fd\u6570\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>7.5 \u89c2\u70b9\u603b\u7ed3<\/strong><\/h3>\n\n\n\n<p>\u672c\u9879\u76ee\u5145\u5206\u5c55\u73b0\u4e86\u73b0\u4ee3\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u4e0e\u91cf\u5316\u4ea4\u6613\u7b56\u7565\u878d\u5408\u6240\u8574\u542b\u7684\u5de8\u5927\u6f5c\u529b\u3002\u5c3d\u7ba1\u521d\u6b65\u5b9e\u65bd\u5df2\u53d6\u5f97\u4ee4\u4eba\u9f13\u821e\u7684\u6210\u6548\uff0c\u4f46\u6a21\u578b\u67b6\u6784\u4e0e\u4ea4\u6613\u7b56\u7565\u4ecd\u6709\u5e7f\u9614\u7684\u4f18\u5316\u4f59\u5730\u3002\u6b64\u5916\uff0c\u91cf\u5316\u65b9\u6cd5\u7684\u6210\u529f\u5b9e\u8df5\u63ed\u793a\u4e86\u4e00\u4e2a\u91cd\u8981\u8d8b\u52bf\uff1a\u5728\u91d1\u878d\u5e94\u7528\u4e2d\uff0c\u91cd\u89c6\u4e0d\u786e\u5b9a\u6027\u4f30\u8ba1\u800c\u975e\u5355\u4e00\u7684\u70b9\u9884\u6d4b\u53ef\u80fd\u66f4\u5177\u5b9e\u9645\u4ef7\u503c\u3002<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u80a1\u7968\u4ef7\u683c\u9884\u6d4b\u7684\u6311\u6218<\/strong>\uff1a\u91d1\u878d\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u7684\u4e0d\u786e\u5b9a\u6027\uff0c\u7279\u522b\u662f\u5728\u5206\u949f\u7ea7\u522b\u7684\u80a1\u7968\u4ef7\u683c\u9884\u6d4b\u4e2d\uff0c\u6a21\u578b\u9700\u8981\u80fd\u591f\u5904\u7406\u6536\u76ca\u7387\u7684\u7edf\u8ba1\u7279\u6027\u548c\u6781\u5c0f\u7684\u6ce2\u52a8\u3002<\/li>\n\n\n\n<li><strong>\u6a21\u578b\u8bad\u7ec3\u4e0e\u8bc4\u4f30<\/strong>\uff1a\u5927\u5bb6\u53ef\u4ee5\u770b\u5230\u4f7f\u7528PyTorch Lightning\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3\u7684\u4fbf\u5229\u6027\uff0c\u6211\u901a\u8fc7TensorBoard\u5b9e\u65f6\u76d1\u63a7\u8bad\u7ec3\u6307\u6807\uff0c\u4ee5\u53ca\u5982\u4f55\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u7684\u91cd\u8981\u6027\u3002<\/li>\n\n\n\n<li><strong>\u7279\u5f81\u5de5\u7a0b\u7684\u91cd\u8981\u6027<\/strong>\uff1a\u901a\u8fc7\u8c03\u6574\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf\uff0c\u6539\u8fdb\u4e86\u6a21\u578b\u7684\u9884\u6d4b\u80fd\u529b\uff0c\u5e76\u5f3a\u8c03\u4e86\u7279\u5f81\u9009\u62e9\u548c\u9884\u5904\u7406\u5728\u6a21\u578b\u6027\u80fd\u4e2d\u7684\u4f5c\u7528\u3002<\/li>\n\n\n\n<li><strong>\u6a21\u578b\u7684\u53ef\u89e3\u91ca\u6027<\/strong>\uff1a\u6587\u7ae0\u901a\u8fc7\u5206\u6790\u7279\u5f81\u91cd\u8981\u6027\u548c\u6a21\u578b\u7684\u6ce8\u610f\u529b\u673a\u5236\uff0c\u5c55\u793a\u4e86TFT\u6a21\u578b\u7684\u53ef\u89e3\u91ca\u6027\uff0c\u5e2e\u52a9\u7406\u89e3\u6a21\u578b\u9884\u6d4b\u7684\u4f9d\u636e\u3002<\/li>\n\n\n\n<li><strong>\u4ea4\u6613\u7b56\u7565\u7684\u5b9e\u8df5\u5e94\u7528<\/strong>\uff1a\u901a\u8fc7\u56de\u6d4b\uff0c\u6211\u7ed9\u5927\u5bb6\u5c55\u793a\u4e86\u5982\u4f55\u5c06\u6a21\u578b\u9884\u6d4b\u5e94\u7528\u4e8e\u4ea4\u6613\u7b56\u7565\uff0c\u5e76\u5206\u6790\u4e86\u7b56\u7565\u7684\u56de\u62a5\u548c\u98ce\u9669\uff0c\u5982\u6700\u5927\u56de\u64a4\u548c\u80dc\u7387\u3002<\/li>\n\n\n\n<li><strong>\u6a21\u578b\u548c\u7b56\u7565\u7684\u6539\u8fdb\u65b9\u5411<\/strong>\uff1a\u8ba8\u8bba\u6a21\u578b\u8bad\u7ec3\u548c\u4ea4\u6613\u7b56\u7565\u53ef\u80fd\u7684\u6539\u8fdb\u63aa\u65bd\uff0c\u5305\u62ec\u5b66\u4e60\u7387\u8c03\u6574\u3001\u7279\u5b9a\u5e02\u573a\u6761\u4ef6\u7684\u6a21\u578b\u3001\u4ee5\u53ca\u66f4\u5927\u89c4\u6a21\u7684\u9a8c\u8bc1\u96c6\u3002<\/li>\n\n\n\n<li><strong>\u9879\u76ee\u7684\u6574\u4f53\u8bc4\u4f30<\/strong>\uff1a\u603b\u7ed3\u9879\u76ee\u7684\u6210\u5c31\u548c\u6311\u6218\uff0c\u5e76\u63d0\u51fa\u4e86\u672a\u6765\u53ef\u80fd\u7684\u7814\u7a76\u65b9\u5411\uff0c\u5982\u6a21\u578b\u67b6\u6784\u7684\u589e\u5f3a\u548c\u8bad\u7ec3\u4f18\u5316\u3002<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u516b\u3001\u611f\u8c22<\/strong><\/h2>\n\n\n\n<p>\u6211\u60f3\u501f\u6b64\u673a\u4f1a\u611f\u8c22\u6240\u6709\u4ece\u59cb\u81f3\u7ec8\u5173\u6ce8\u8fd9\u4e2a\u7cfb\u5217\u7684\u4eba\u3002\u5f53\u6211\u5f00\u59cb\u5206\u4eab\u8fd9\u4e2a\u9879\u76ee\u65f6\uff0c\u6211\u4ece\u672a\u60f3\u5230\u4f1a\u5f97\u5230\u5982\u6b64\u70ed\u70c8\u7684\u56de\u5e94\u548c\u652f\u6301\u3002\u5173\u6ce8\u8005\u3001\u8bc4\u8bba\u548c\u7559\u8a00\u7684\u6570\u91cf\u7740\u5b9e\u8ba9\u4eba\u60ed\u6127\u3002<\/p>\n\n\n\n<p>\u611f\u8c22\u60a8\u9605\u8bfb\u5230\u6700\u540e\uff0c\u5e0c\u671b\u672c\u6587\u80fd\u7ed9\u60a8\u5e26\u6765\u65b0\u7684\u6536\u83b7\u3002\u795d\u60a8\u6295\u8d44\u987a\u5229\uff01\u5982\u679c\u5bf9\u6587\u4e2d\u7684\u5185\u5bb9\u6709\u4efb\u4f55\u7591\u95ee\uff0c\u8bf7\u7ed9\u6211\u7559\u8a00\uff0c\u5fc5\u590d\u3002<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"has-text-align-center\" id=\"a1c6\">\u672c\u6587\u5185\u5bb9\u4ec5\u9650\u6280\u672f\u63a2\u8ba8\u548c\u5b66\u4e60\uff0c\u4e0d\u6784\u6210\u4efb\u4f55\u6295\u8d44\u5efa\u8bae<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u4f5c\u8005\uff1a\u8001\u4f59\u635e\u9c7c \u539f\u521b\u4e0d\u6613\uff0c\u8f6c\u8f7d\u8bf7\u6807\u660e\u51fa\u5904\u53ca\u539f\u4f5c\u8005\u3002&#8230;<\/p>\n<div class=\"more-link-wrapper\"><a class=\"more-link\" href=\"https:\/\/laoyulaoyu.com\/index.php\/2025\/01\/14\/%e3%80%82%e3%80%82%e3%80%82%e5%ae%9e%e6%88%98%e6%95%99%e5%ad%a6%ef%bc%9a%e6%9e%84%e5%bb%ba%e5%8f%af%e8%a7%a3%e9%87%8a%e7%9a%84%e5%8f%98%e6%8d%a2%e5%99%a8%e6%a8%a1%e5%9e%8b%ef%bc%8c%e7%b2%be%e5%87%86-3\/\">Continue reading<span class=\"screen-reader-text\">\u5b9e\u6218\u6559\u5b66\uff1a\u6784\u5efa\u53ef\u89e3\u91ca\u7684\u53d8\u6362\u5668\u6a21\u578b\uff0c\u7cbe\u51c6\u9884\u6d4b\u80a1\u4ef7\u6ce2\u52a8\uff08\u56db\uff09<\/span><\/a><\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[5,6],"class_list":["post-1794","post","type-post","status-publish","format-standard","hentry","category-aiinvest","tag-ai","tag-6","entry"],"_links":{"self":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/1794","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/comments?post=1794"}],"version-history":[{"count":2,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/1794\/revisions"}],"predecessor-version":[{"id":1864,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/1794\/revisions\/1864"}],"wp:attachment":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/media?parent=1794"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/categories?post=1794"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/tags?post=1794"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}