{"id":1770,"date":"2025-01-04T07:31:00","date_gmt":"2025-01-03T23:31:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=1770"},"modified":"2025-01-03T11:17:35","modified_gmt":"2025-01-03T03:17:35","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%86-2","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/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\/","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\u4e09\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\"><img decoding=\"async\" src=\"https:\/\/www.laoyulaoyu.com\/wp-content\/uploads\/2024\/12\/image-79.png\" alt=\"\" class=\"wp-image-3342\"\/><\/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>\u4eca\u5929\u6211\u4eec\u7ee7\u7eed\u63a2\u8ba8\u5982\u4f55\u5229\u7528\u65f6\u6001\u878d\u5408\u53d8\u6362\u5668\uff08TFT\uff09\u6a21\u578b\u6765\u9884\u6d4b\u80a1\u7968\u76841\u5206\u949f\u4ef7\u683c\u3002\u672c\u6587\u662f\u8fd9\u4e2a\u7cfb\u5217\u7684\u7b2c\u4e09\u7bc7\u6587\u7ae0\uff0c\u6211\u4eec\u5c06<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\">\u7ee7\u7eed\u63a2\u8ba8\u5982\u4f55\u589e\u5f3a\u6570\u636e\u96c6\uff0c\u5305\u62ec\u6dfb\u52a0\u54ea\u4e9b\u5173\u952e\u6280\u672f\u6307\u6807\uff0c\u4ee5\u53ca\u5982\u4f55\u8fdb\u884c\u7279\u5f81\u9884\u5904\u7406\uff0c\u4ece\u800c\u4e3a\u6a21\u578b\u8bad\u7ec3\u505a\u597d\u51c6\u5907\u3002<\/mark>\u6b64\u5916\uff0c\u6211\u8fd8\u4f1a\u8be6\u7ec6\u4ecb\u7ecdTFT\u6a21\u578b\u7684\u8bbe\u7f6e\u548c\u5b9e\u73b0\u8fc7\u7a0b\uff0c\u4ee5\u53ca\u5b83\u5728\u80a1\u7968\u4ea4\u6613\u9884\u6d4b\u4e2d\u7684\u72ec\u7279\u4f18\u52bf\u3002\uff08<em>\u5c01\u9762\u56fe\u4e3aTFT \u9884\u6d4b APPL \u80a1\u7968\u7684\u793a\u610f\uff09<\/em>\u3002<\/pre>\n<\/blockquote>\n\n\n\n<p>\u6b22\u8fce\u56de\u5230\u6211\u4eec\u4f7f\u7528\u65f6\u6001\u878d\u5408\u8f6c\u6362\u5668\u6784\u5efa\u9ad8\u9891\u80a1\u4ef7\u9884\u6d4b\u6a21\u578b\u7684\u7cfb\u5217\u6587\u7ae0\uff01\u672c\u6587\u7ed3\u675f\u65f6\uff0c\u60a8\u5c06\u77e5\u9053\u5982\u4f55\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u5229\u7528 RSI\u3001\u79fb\u52a8\u5e73\u5747\u7ebf\u548c\u652f\u6491\/\u963b\u529b\u6c34\u5e73\u7b49\u5f3a\u5927\u7684\u6280\u672f\u6307\u6807\u6765\u589e\u5f3a\u6570\u636e\u96c6\u3002<\/li>\n\n\n\n<li>\u5bf9\u7279\u5f81\u8fdb\u884c\u9884\u5904\u7406\uff0c\u521b\u5efa\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u80a1\u7968\u4ef7\u683c\u6570\u636e\u96c6\uff0c\u5e76\u5728\u6b64\u57fa\u7840\u4e0a\u8fdb\u884c\u8bad\u7ec3\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u5982\u679c\u60a8\u8fd8\u6ca1\u6709\u4e86\u89e3\u8fc7\u7b2c 1 \u90e8\u5206\u548c\u7b2c 2 \u90e8\u5206\uff0c\u8bf7\u52a1\u5fc5\u67e5\u770b\u8fd9\u4e24\u90e8\u5206\uff0c\u4ee5\u4fbf\u4e3a\u6570\u636e\u51c6\u5907\u548c\u5e02\u573a\u5fae\u89c2\u7ed3\u6784\u5206\u6790\u6253\u4e0b\u575a\u5b9e\u7684\u57fa\u7840\u3002<\/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<\/ul>\n\n\n\n<p>\u672c\u6587\u662f\u7b2c\u4e8c\u90e8\u5206\u7684\u5ef6\u7eed\uff0c\u56e0\u6b64\u5047\u5b9a\u5728\u6b64\u4e4b\u524d\u5df2\u7ecf\u8fd0\u884c\u4e86\u7b2c\u4e8c\u90e8\u5206\u63d0\u4f9b\u7684\u4ee3\u7801\u3002\u6211\u6b63\u5728\u5f00\u53d1\u4e00\u4e2a github repo\uff0c\u5176\u4e2d\u5c06\u5305\u542b\u4e0e\u8fd9\u4e00\u7cfb\u5217\u6587\u7ae0\u76f8\u5173\u7684\u6240\u6709\u4ee3\u7801\uff0c\u656c\u8bf7\u671f\u5f85\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u4e00\u3001\u6280\u672f\u5206\u6790\u6307\u6807<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><br><strong>1.1 \u79fb\u52a8\u5e73\u5747\u7ebf<\/strong><\/h3>\n\n\n\n<p>\u79fb\u52a8\u5e73\u5747\u7ebf\u662f\u6280\u672f\u5206\u6790\u7684\u57fa\u672c\u5de5\u5177\uff0c\u901a\u8fc7\u5e73\u6ed1\u4ef7\u683c\u6ce2\u52a8\u5e2e\u52a9\u4ea4\u6613\u8005\u8bc6\u522b\u8d8b\u52bf\u3002\u6211\u4eec\u4f7f\u7528\u4e24\u79cd\u7c7b\u578b\u7684\u79fb\u52a8\u5e73\u5747\u7ebf\uff1a\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u7ebf\uff08SMA\uff09\u7528\u4e8e\u5206\u6790\u957f\u671f\u8d8b\u52bf\uff0c\u800c\u6307\u6570\u79fb\u52a8\u5e73\u5747\u7ebf\uff08EMA\uff09\u5219\u7528\u4e8e\u66f4\u7075\u654f\u7684\u65e5\u5185\u5206\u6790\u3002<\/p>\n\n\n\n<p><strong>\u7b80\u5355\u79fb\u52a8\u5e73\u5747\u7ebf (SMA)\uff1a<\/strong>\u8ba1\u7b97\u6307\u5b9a\u65f6\u95f4\u6bb5\u5185\u6536\u76d8\u4ef7\u7684\u975e\u52a0\u6743\u5e73\u5747\u503c\uff0c\u63d0\u4f9b\u6574\u4f53\u8d8b\u52bf\u7684\u6e05\u6670\u89c6\u56fe\u3002\u5728\u65e5\u5185\u4ea4\u6613\u4e2d\uff0c\u65e5\u5747\u7ebf\u901a\u5e38\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u652f\u70b9&#8211;\u5982\u679c\u5904\u4e8e\u4e0a\u5347\u8d8b\u52bf\u7684\u80a1\u7968\u5f00\u76d8\u4ef7\u4f4e\u4e8e\u5747\u7ebf\uff0c\u90a3\u4e48\u8be5\u6c34\u5e73\u5728\u65e5\u5185\u89e6\u53ca\u65f6\u901a\u5e38\u4f1a\u6210\u4e3a\u963b\u529b\u4f4d\u3002\u76f8\u53cd\uff0c\u5bf9\u4e8e\u5904\u4e8e\u4e0b\u964d\u8d8b\u52bf\u7684\u80a1\u7968\uff0c\u5982\u679c\u5f00\u76d8\u4ef7\u9ad8\u4e8e\u5747\u7ebf\uff0c\u5219\u8be5\u6c34\u5e73\u5f80\u5f80\u4f1a\u5728\u65e5\u5185\u8d70\u52bf\u4e2d\u63d0\u4f9b\u652f\u6491\u3002<\/p>\n\n\n\n<p><strong>\u6307\u6570\u79fb\u52a8\u5e73\u5747\u7ebf (EMA)\uff1a<\/strong>\u5bf9\u8fd1\u671f\u4ef7\u683c\u7ed9\u4e88\u66f4\u591a\u6743\u91cd\uff0c\u4f7f\u5176\u5bf9\u5f53\u524d\u5e02\u573a\u6761\u4ef6\u53cd\u5e94\u66f4\u7075\u654f\u3002\u5bf9\u4e8e\u65e5\u5185\u4ea4\u6613\uff0cEMA \u80fd\u6709\u6548\u6307\u793a\u77ed\u671f\u8d8b\u52bf\uff0c\u5728\u4e0a\u5347\u8d8b\u52bf\u4e2d\u8d77\u652f\u6491\u4f5c\u7528\uff0c\u5728\u4e0b\u964d\u8d8b\u52bf\u4e2d\u8d77\u963b\u529b\u4f5c\u7528\u3002<\/p>\n\n\n\n<p>\u5229\u7528\u8fd9\u4e9b\u79fb\u52a8\u5e73\u5747\u7ebf\u7684\u4e00\u79cd\u6d41\u884c\u7b56\u7565\u662f &#8220;\u5747\u503c\u56de\u5f52&#8221;\uff0c\u5176\u539f\u7406\u662f\u4ef7\u683c\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u5f80\u5f80\u4f1a\u56de\u5f52\u5230\u5176\u5e73\u5747\u503c\u3002\u5e94\u7528\u8fd9\u79cd\u7b56\u7565\u7684\u65e5\u5185\u4ea4\u6613\u8005\u53ef\u80fd\u4f1a\u5728\u4ef7\u683c\u957f\u671f\u9ad8\u4e8e EMA \u65f6\u542f\u52a8\u7a7a\u5934\u5934\u5bf8\uff0c\u6216\u5728\u4ef7\u683c\u957f\u671f\u4f4e\u4e8e EMA \u65f6\u5efa\u7acb\u591a\u5934\u5934\u5bf8\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1.2 \u652f\u6491\u4f4d\u548c\u963b\u529b\u4f4d<\/strong><\/h3>\n\n\n\n<p>\u652f\u6491\u4f4d\u548c\u963b\u529b\u4f4d\u4ee3\u8868\u5173\u952e\u7684\u4ef7\u683c\u70b9\uff0c\u5e02\u573a\u5fc3\u7406\u5f80\u5f80\u4f1a\u5728\u8fd9\u4e9b\u4ef7\u683c\u70b9\u5bfc\u81f4\u8d8b\u52bf\u9006\u8f6c\u6216\u76d8\u6574\u671f\u3002\u8fd9\u4e9b\u4ef7\u4f4d\u53ef\u4ee5\u901a\u8fc7\u5386\u53f2\u4ef7\u683c\u5206\u6790\u6765\u786e\u5b9a\uff0c\u5bf9\u6bcf\u65e5\u548c\u65e5\u5185\u4ea4\u6613\u51b3\u7b56\u90fd\u5f88\u6709\u4ef7\u503c\u3002\u8ba1\u7b97\u65b9\u6cd5\u4f9d\u8d56\u4e8e\u5206\u5f62&#8211;\u4ef7\u683c\u56fe\u8868\u4e2d\u7684\u5c40\u90e8\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\u3002<\/p>\n\n\n\n<p><strong>\u652f\u6491\u4f4d\uff1a<\/strong>\u4e5f\u53eb\u5173\u952e\u4ef7\u4f4d\uff0c\u4e70\u76d8\u538b\u529b\u901a\u5e38\u4f1a\u589e\u52a0\u7684\u4ef7\u4f4d\uff0c\u53ef\u5f62\u6210 &#8220;\u5e95\u7ebf&#8221;\uff0c\u9632\u6b62\u4ef7\u683c\u8fdb\u4e00\u6b65\u4e0b\u8dcc\u3002<\/p>\n\n\n\n<p><strong>\u963b\u529b\u4f4d\uff1a<\/strong>\u5386\u53f2\u4e0a\u5356\u538b\u52a0\u5267\u7684\u4ef7\u683c\u533a\u57df\uff0c\u5f62\u6210\u9650\u5236\u5411\u4e0a\u8fd0\u52a8\u7684 &#8220;\u5929\u82b1\u677f&#8221;\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1.3 \u76f8\u5bf9\u5f3a\u5f31\u6307\u6570\uff08RSI\uff09<\/strong><\/h3>\n\n\n\n<p>\u76f8\u5bf9\u5f3a\u5f31\u6307\u6570\uff08RSI\uff09\u662f\u4e00\u79cd\u52a8\u91cf\u9707\u8361\u6307\u6807\uff0c\u53ef\u91cf\u5316\u4ef7\u683c\u5b9a\u5411\u6ce2\u52a8\u7684\u901f\u5ea6\u548c\u5e45\u5ea6\u3002\u8be5\u6307\u6807\u5728 0 \u548c 100 \u4e4b\u95f4\u9707\u8361\uff0c\u6709\u52a9\u4e8e\u4ea4\u6613\u8005\u8bc6\u522b\u6f5c\u5728\u7684\u53cd\u8f6c\u70b9\uff1a<\/p>\n\n\n\n<p><strong>RSI &gt; 70\uff1a<\/strong>\u8d85\u4e70\u4fe1\u53f7\uff0c\u8868\u660e\u53ef\u80fd\u5411\u4e0b\u4fee\u6b63\u3002<\/p>\n\n\n\n<p><strong>RSI &lt; 30\uff1a<\/strong>\u8868\u793a\u8d85\u5356\u72b6\u6001\uff0c\u6697\u793a\u4ef7\u683c\u53ef\u80fd\u4e0a\u884c<\/p>\n\n\n\n<p>\u6211\u6311\u9009\u8fd9\u4e9b\u7279\u5b9a\u6307\u6807\u662f\u57fa\u4e8e\u6211\u4e2a\u4eba\u7684\u4ea4\u6613\u5b9e\u8df5\u548c\u5bf9\u5b83\u4eec\u5728\u6211\u7684\u7b56\u7565\u4e2d\u8868\u73b0\u51fa\u7684\u7a33\u5b9a\u6027\u7684\u8ba4\u53ef\u3002\u7136\u800c\uff0c\u5728\u6280\u672f\u5206\u6790\u7684\u5e7f\u9614\u5929\u5730\u4e2d\uff0c\u8fd8\u6709\u4f17\u591a\u5353\u8d8a\u7684\u5de5\u5177\u53ef\u4f9b\u9009\u62e9\u2014\u2014\u4ece\u5e03\u6797\u5e26\u3001MACD\uff0c\u5230\u6210\u4ea4\u91cf\u6307\u6807\u548c\u5e02\u573a\u60c5\u7eea\u5206\u6790\u7b49\u3002\u4e0d\u540c\u7684\u4ea4\u6613\u98ce\u683c\u548c\u5e02\u573a\u73af\u5883\u53ef\u80fd\u8ba9\u60a8\u89c9\u5f97\u5176\u4ed6\u6307\u6807\u66f4\u52a0\u5951\u5408\u60a8\u7684\u6a21\u578b\u9700\u6c42\u3002\u5173\u952e\u5728\u4e8e\u6df1\u5165\u7406\u89e3\u6bcf\u4e2a\u6307\u6807\u7684\u957f\u5904\u4e0e\u77ed\u677f\uff0c\u5e76\u638c\u63e1\u5b83\u4eec\u5982\u4f55\u5728\u60a8\u7684\u5206\u6790\u4f53\u7cfb\u4e2d\u76f8\u4e92\u8865\u5145\u3001\u53d1\u6325\u4f5c\u7528\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u4e8c\u3001\u4ee3\u7801\u5b9e\u73b0<\/strong><\/h2>\n\n\n\n<p>\u6211\u4f1a\u5148\u5b9a\u4e49\u51e0\u4e2a\u8f85\u52a9\u51fd\u6570\uff0c\u4f7f\u4ee3\u7801\u66f4\u52a0\u7b80\u6d01\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import mplfinance as mpf\n\ndef get_daily_df(minute_df, agg_dict):\n    df_daily = resample_df(minute_df, \"D\", agg_dict)\n    return df_daily\n\ndef get_hourly_df(minute_df, agg_dict):\n    df_hourly = resample_df(minute_df, \"H\", agg_dict)\n    df_hourly&#91;\"hour\"] = df_hourly&#91;\"datetime\"].dt.hour\n    return df_hourly\n\ndef get_five_minute_df(minute_df, agg_dict):\n    df_five_minute = resample_df(minute_df, \"5T\", agg_dict)\n    return df_five_minute\n\ndef resample_df(df, resample_period, agg_dict):\n    resampled_df = df.groupby('symbol').resample(resample_period).agg(agg_dict).dropna()\n    resampled_df&#91;\"symbol\"] = resampled_df.index.get_level_values(0)\n    resampled_df&#91;\"datetime\"] = resampled_df.index.get_level_values(1)\n    resampled_df = resampled_df.reset_index(drop=True)\n    resampled_df&#91;\"date\"] = resampled_df&#91;\"datetime\"].dt.date\n    return resampled_df\n\ndef plot_bars_with_indicators(ohlc, ax, title: str, addplots=&#91;]):\n    mpf.plot(\n        ohlc.rename({'open': 'Open', 'high': 'High', 'low': 'Low', 'close': 'Close'}, axis=1),\n        type='candle',\n        ax=ax,\n        addplot=addplots,\n        axtitle=title,\n        ylabel='Price',\n        style=\"yahoo\"<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2.1 \u8ba1\u7b97 EMA \u548c SMA<\/strong><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>ema_one_minute_bars = 9 * 5 # a common value for EMA lookback period is 45 minutes\nsma_daily_bars = 50 # a common value for SMA lookback period on daily bars is 50 days\n# Calculate the EMA and SMA\ndf&#91;'EMA'] = df.groupby(&#91;'symbol', 'date'])&#91;'close'].transform(lambda x: x.ewm(span=ema_one_minute_bars).mean())\ndf_daily = get_daily_df(df, {\"close\": \"first\"}) # we take the first close of each day to not \"leak\" future data into the minute bars\ndf_daily&#91;'SMA'] = df_daily.groupby('symbol')&#91;'close'].transform(lambda x: x.rolling(window=sma_daily_bars, min_periods=1).mean()) # very important to take the rolling moving average on the daily bars to avoid data leakage from future prices into current ones\ndf = df.merge(df_daily&#91;&#91;'symbol', 'date', 'SMA']], on=&#91;'symbol', 'date'], how='left').set_index(index).rename({'SMA': 'daily_sma'})<\/code><\/pre>\n\n\n\n<p>\u6211\u4eec\u53ef\u4ee5\u5728\u56fe\u8868\u4e0a\u7ed8\u5236\u51fa\u8fd9\u4e9b\u5e73\u5747\u7ebf\u7684\u6837\u5b50\uff0c\u4f8b\u5982\u82f9\u679c\u516c\u53f8\u7684\u80a1\u7968\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># Let's plot APPLE's daily bars with 50 day sma, and the first day of trading with 45 minute ema.\ndf_daily = get_daily_df(df, {\"open\": \"first\", \"high\": \"max\", \"low\": \"min\", \"close\": \"last\", \"SMA\": \"mean\"})\ndf_daily.set_index(pd.to_datetime(df_daily&#91;\"date\"]), inplace=True)\naapl_daily = df_daily&#91;df_daily&#91;\"symbol\"] == \"AAPL\"]&#91;&#91;\"open\", \"high\", \"low\", \"close\", \"SMA\"]]\naapl_daily_sma = aapl_daily&#91;\"SMA\"]\naapl_minute = df&#91;df&#91;\"symbol\"] == \"AAPL\"]\naapl_minute = aapl_minute&#91;aapl_minute&#91;\"date\"] == df&#91;\"date\"].iloc&#91;0]] # Plot the first day\naapl_minute_ema = aapl_minute&#91;\"EMA\"]\nfig, (ax_daily, ax_minute) = plt.subplots(1, 2, figsize=(18, 8))\ndaily_addplot = mpf.make_addplot(aapl_daily_sma, panel=0, color='orange', ax=ax_daily)\nminute_addplot = mpf.make_addplot(aapl_minute_ema, panel=0, color='orange', ax=ax_minute)\nplot_bars_with_indicators(aapl_daily, ax_daily, title=\"AAPL Daily bars with SMA\", addplots=&#91;daily_addplot])\nplot_bars_with_indicators(aapl_minute, ax_minute, title=\"AAPL First Day of Trading with EMA\", addplots=&#91;minute_addplot])\nplt.show()<\/code><\/pre>\n\n\n\n<p>AAPL\u56fe\u8868\u4e0a\u7684\u79fb\u52a8\u5e73\u5747\u7ebf\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-80.png\" alt=\"\" class=\"wp-image-3345\"\/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><br><strong>2.2 \u8ba1\u7b97\u652f\u6491\u7ebf\u548c\u963b\u529b\u7ebf\uff08\u5173\u952e\u6c34\u5e73\uff09<\/strong><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code># resistances column holds the resistance levels (from the past) for each day\ndf_daily&#91;\"resistances\"] = df_daily.progress_apply(\n    lambda x: historic_resistances(\n        df_daily,\n        x&#91;\"symbol\"],\n        x&#91;\"date\"],\n        peak_rank_w_pct=0.03, # group peaks that are within 3% of the stock price\n        strong_peak_prominence_pct=0.05, # strong peaks have a prominence of at least 5%\n        strong_peak_distance=10, # strong peaks are at least 10 bars away from each other\n        peak_distance=5, # peaks are at least 5 bars away from each other\n    ), axis=1\n)\ndf_daily&#91;\"supports\"] = df_daily.progress_apply(\n    lambda x: historic_supports(\n        df_daily,\n        x&#91;\"symbol\"],\n        x&#91;\"date\"],\n        trough_rank_w_pct=0.03,\n        strong_trough_prominence_pct=0.05,\n        strong_trough_distance=10,\n        trough_distance=5,\n    ), axis=1)\ndf_daily&#91;\"resistances\"] = df_daily.apply(lambda row: row&#91;\"resistances\"] if row&#91;\"resistances\"]&#91;0] &gt; 0 else &#91;], axis=1)\ndf_daily&#91;\"supports\"] = df_daily.apply(lambda row: row&#91;\"supports\"] if row&#91;\"supports\"]&#91;0] &gt; 0 else &#91;], axis=1)<\/code><\/pre>\n\n\n\n<p>\u53ef\u4ee5\u7ed8\u5236\u51e0\u4e2a\u793a\u4f8b\uff0c\u770b\u770b\u6211\u4eec\u7684\u5173\u952e\u6c34\u5e73\u662f\u5426\u5408\u7406\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>def plot_with_resistances_and_supports(df, ax):\n    # Create a list to hold resistance line data\n    addplot = &#91;]\n\n    last_resistances = df.iloc&#91;-1]&#91;\"resistances\"]\n    last_supports = df.iloc&#91;-1]&#91;\"supports\"]\n    for resistance in last_resistances:\n        addplot.append(mpf.make_addplot(&#91;resistance] * len(df), color='red', ax=ax))\n    for support in last_supports:\n        addplot.append(mpf.make_addplot(&#91;support] * len(df), color='green', ax=ax))\n\n\n    # Plot the candlestick chart with resistance lines\n    mpf.plot(df, type='candle', addplot=addplot, style='charles',\n             volume=False, ax=ax)\n\n# plotting support and resistance for stocks\nsymbols = df_daily.sample(2)&#91;\"symbol\"].values # sample 2 stock symbols\nfor symbol in symbols:\n    daily = df_daily&#91;df_daily&#91;\"symbol\"] == symbol]\n    fig, ax = plt.subplots(figsize=(18, 8))\n    ax.set_title(f\"{symbol} daily bars with resistance (red) and support (green) lines\")\n    plot_with_resistances_and_supports(daily,  ax)<\/code><\/pre>\n\n\n\n<p>\u5e26\u6709\u963b\u529b\u7ebf\uff08\u7ea2\u8272\uff09\u548c\u652f\u6491\u7ebf\uff08\u7eff\u8272\uff09\u7684 $JBLU \u65e5\u7ebf\u6761\u5f62\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-81.png\" alt=\"\" class=\"wp-image-3346\"\/><\/figure>\n\n\n\n<p>\u5e26\u6709\u963b\u529b\u7ebf\uff08\u7ea2\u8272\uff09\u548c\u652f\u6491\u7ebf\uff08\u7eff\u8272\uff09\u7684 $CRM \u65e5\u7ebf\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-82.png\" alt=\"\" class=\"wp-image-3347\"\/><\/figure>\n\n\n\n<p>\u4e00\u65e6\u80a1\u7968\u6536\u76d8\u4ef7\u9ad8\u4e8e\u963b\u529b\u7ebf\u6216\u4f4e\u4e8e\u652f\u6491\u7ebf\uff0c\u652f\u6491\u4ef7\u5c31\u4f1a\u53d8\u6210\u963b\u529b\u4ef7\uff0c\u53cd\u4e4b\u4ea6\u7136\u3002\u4ea4\u6613\u8005\u4e0d\u628a\u8fd9\u4e9b\u6c34\u5e73\u5f53\u4f5c &#8220;\u963b\u529b &#8220;\u548c &#8220;\u652f\u6491 &#8220;\u6c34\u5e73\uff0c\u800c\u662f\u79f0\u4e4b\u4e3a &#8220;\u5173\u952e\u6c34\u5e73&#8221;\u3002\u5173\u952e\u6c34\u5e73\u4e4b\u6240\u4ee5\u91cd\u8981\uff0c\u662f\u56e0\u4e3a\u5b83\u4eec\u66f4\u6709\u53ef\u80fd\u6210\u4e3a\u652f\u70b9\u3002<\/p>\n\n\n\n<p>\u6211\u9009\u62e9\u5c06\u8fd9\u4e9b\u6c34\u4f4d\u79f0\u4e3a &#8220;\u5173\u952e\u6c34\u4f4d&#8221;\uff0c\u800c\u4e0d\u533a\u5206\u652f\u6491\u4f4d\u548c\u963b\u529b\u4f4d\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>df_daily&#91;\"key_levels\"] = df_daily.apply(lambda x: x&#91;\"resistances\"] + x&#91;\"supports\"], axis=1)\ndf_daily.reset_index(inplace=True, drop=True)\nindex = df.index\ndf = df.merge(df_daily&#91;&#91;'symbol', 'date', 'key_levels']], on=&#91;'symbol', 'date'], how='left').set_index(index)\ndf.set_index(index, inplace=True)<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2.3 \u8ba1\u7b97 RSI<\/strong><\/h3>\n\n\n\n<p>RSI \u662f\u4e00\u4e2a\u5f88\u597d\u7684\u52a8\u91cf\u6307\u6807\u3002\u5b83\u6d4b\u91cf\u7684\u662f\u6307\u5b9a\u56de\u6eaf\u7a97\u53e3\u4e2d\u5e73\u5747\u7eff\u8272\uff08\u4e0a\u6da8\uff09\u67f1\u548c\u7ea2\u8272\uff08\u4e0b\u8dcc\uff09\u67f1\u4e4b\u95f4\u7684\u6bd4\u7387\u3002\u5bf9\u4e8e\u6211\u7684\u6a21\u578b\uff0c\u6211\u9009\u62e9\u7684\u56de\u770b\u7a97\u53e3\u5927\u5c0f\u4e3a 30 \u6761\uff08\u4ee3\u8868 30 \u5206\u949f\uff09\uff0c\u56e0\u4e3a\u6211\u4eec\u8981\u5904\u7406\u7684\u662f\u9ad8\u9891\u9884\u6d4b\u3002\u4e0d\u8fc7\uff0c\u7406\u60f3\u7684\u7a97\u53e3\u5927\u5c0f\u53ef\u80fd\u6709\u6240\u4e0d\u540c\uff0c\u56e0\u4e3a\u6211\u6ca1\u6709\u5c1d\u8bd5\u8c03\u6574\u8fd9\u4e9b\u8d85\u53c2\u6570\u3002\u8fd9\u53ea\u9700\u4f7f\u7528 pandas-ta \u8f6f\u4ef6\u5305\u5373\u53ef\u5b9e\u73b0\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code><code>import pandas_ta as ta<\/code><code>WINDOW_SIZE = 30<\/code><code>df&#91;'RSI'] = df.groupby('symbol')&#91;'close'].transform(<\/code><code>  lambda x: ta.rsi(x, window=WINDOW_SIZE)<\/code><code>).fillna(50) # fillna 50 to represent neutral RSI<\/code><\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2.4 \u7f3a\u53e3<\/strong><\/h3>\n\n\n\n<p>\u7f3a\u53e3\u662f\u80a1\u5e02\u4e2d\u5e38\u89c1\u7684\u73b0\u8c61\u3002\u7f3a\u53e3\u662f\u56fe\u8868\u4e0a\u4ef7\u683c\u4e4b\u95f4\u7684\u65ad\u88c2\uff0c\u5f53\u80a1\u7968\u4ef7\u683c\u6025\u5267\u4e0a\u6da8\u6216\u4e0b\u8dcc\uff0c\u800c\u4e2d\u95f4\u6ca1\u6709\u4ea4\u6613\u65f6\uff0c\u5c31\u4f1a\u51fa\u73b0\u7f3a\u53e3\u3002\u9020\u6210\u7f3a\u53e3\u7684\u539f\u56e0\u6709\u5f88\u591a\uff0c\u5982\u65b0\u95fb\u3001\u76c8\u5229\u62a5\u544a\u6216\u5e02\u573a\u60c5\u7eea\u3002\u4ea4\u6613\u8005\u901a\u5e38\u4f1a\u5bfb\u627e\u7f3a\u53e3\uff0c\u56e0\u4e3a\u5b83\u4eec\u53ef\u4ee5\u63d0\u4f9b\u826f\u597d\u7684\u4ea4\u6613\u673a\u4f1a\u3002\u7f3a\u53e3\u6709\u56db\u79cd\u7c7b\u578b\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u5e38\u89c1\u7f1d\u9699\uff1a<\/strong>\u8fd9\u4e9b\u5dee\u8ddd\u901a\u5e38\u8f83\u5c0f\uff0c\u901a\u5e38\u5f88\u5feb\u5c31\u80fd\u586b\u8865\u3002<\/li>\n\n\n\n<li><strong>\u7a81\u7834\u7f3a\u53e3\uff1a<\/strong>\u5f53\u4ef7\u683c\u7a81\u7834\u4ea4\u6613\u533a\u95f4\u65f6\uff0c\u5c31\u4f1a\u51fa\u73b0\u8fd9\u79cd\u7f3a\u53e3\u3002<\/li>\n\n\n\n<li><strong>\u5931\u63a7\u7f3a\u53e3\uff1a<\/strong>\u8fd9\u4e9b\u7f3a\u53e3\u51fa\u73b0\u5728\u5f3a\u52bf\u8d8b\u52bf\u4e2d\uff0c\u8868\u660e\u8d8b\u52bf\u53ef\u80fd\u4f1a\u6301\u7eed\u3002<\/li>\n\n\n\n<li><strong>\u8870\u7aed\u7f3a\u53e3\uff1a<\/strong>\u8fd9\u4e9b\u7f3a\u53e3\u51fa\u73b0\u5728\u8d8b\u52bf\u7684\u672b\u7aef\uff0c\u9884\u793a\u7740\u8d8b\u52bf\u6709\u53ef\u80fd\u9006\u8f6c\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u7f3a\u53e3\u5206\u6790\u7740\u91cd\u770b\u7684\u662f\u6bcf\u65e5\u7f3a\u53e3\uff0c\u5373\u524d\u4e00\u5929\u6536\u76d8\u4ef7\u4e0e\u5f53\u5929\u5f00\u76d8\u4ef7\u4e4b\u95f4\u7684\u5dee\u989d\u3002\u7531\u4e8e\u6295\u8d44\u8005\u5bf9\u76c8\u5229\u62a5\u544a\u7684\u53cd\u5e94\u4f1a\u4f7f\u80a1\u7968\u5728\u76d8\u524d\u6216\u76d8\u540e\u53d1\u751f\u53d8\u52a8\uff0c\u56e0\u6b64\u7f3a\u53e3\u5728\u76c8\u5229\u540e\u975e\u5e38\u5e38\u89c1\u3002\u4ee5\u8df3\u7a7a\u7f3a\u53e3\u5f00\u76d8\u7684\u80a1\u7968\u901a\u5e38\u4f1a\u5438\u5f15\u5927\u91cf\u65e5\u5185\u4ea4\u6613\u8005\u548c\u6295\u673a\u8005\uff0c\u4ece\u800c\u4f7f\u4ef7\u683c\u8d70\u52bf\u66f4\u52a0\u96be\u4ee5\u9884\u6d4b\u3002\u6211\u4eec\u5c06\u628a\u5f53\u5929\u7684\u5f00\u76d8\u8df3\u7a7a\u7f3a\u53e3\uff08\u6536\u76d8\u4ef7\u7684\u5bf9\u6570\u767e\u5206\u6bd4\uff09\u4f5c\u4e3a\u4e00\u4e2a\u7279\u5f81\u6dfb\u52a0\u5230\u6a21\u578b\u4e2d\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>daily_df = get_daily_df(df, {\n    \"close\": \"last\",\n    \"open\": \"first\",\n    \"high\": \"max\",\n    \"low\": \"min\",\n    \"volume\": \"sum\",\n    \"average_volume\": \"first\"\n}).reset_index() \ndaily_df&#91;\"previous_close\"] = daily_df.groupby(\"symbol\")&#91;\"close\"].shift(1).fillna(daily_df&#91;\"close\"])\ndaily_df&#91;\"gap\"] = np.log((daily_df&#91;\"open\"] - daily_df&#91;\"previous_close\"]) \/ daily_df&#91;\"previous_close\"])\ndf = df.merge(daily_df&#91;&#91;\"symbol\", \"date\", \"gap\"]], on=&#91;\"symbol\", \"date\"], how=\"left\").set_index(df_index)<\/code><\/pre>\n\n\n\n<p>\u5728\u672c\u7cfb\u5217\u7684\u6700\u540e\u4e00\u4e2a\u4e13\u9898\uff08\u4e0b\u4e00\u7bc7\uff09\u4e2d\uff0c\u6211\u4eec\u5c06\u4ee5\u7c7b\u4f3c\u4e8e\u65e5\u7ebf\u5173\u952e\u6c34\u5e73\u7684\u65b9\u5f0f\u6dfb\u52a0\u5c0f\u65f6\u5173\u952e\u6c34\u5e73\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#multi time frame analysis - find support and resistance on different time frames. We will use the key levels from daily hourly and 5 minute time frames.\nhourly_df = get_hourly_df(df, {\"close\": \"last\", \"high\": \"max\", \"low\": \"min\"})\nhourly_df&#91;\"resistances\"] = hourly_df.progress_apply(\n    lambda x: historic_resistances(\n        df_daily,\n        x&#91;\"symbol\"],\n        x&#91;\"date\"],\n        peak_rank_w_pct=0.005, # group peaks that are within 0.5% of the stock price\n        strong_peak_prominence_pct=0.02, # strong peaks have a prominence of at least 2%\n        strong_peak_distance=96, # strong peaks are at least 96 hours away from each other\n        peak_distance=4, # peaks are at least 4 hours away from each other\n        include_high=False\n    ), axis=1\n)\nhourly_df&#91;\"supports\"] = hourly_df.progress_apply(\n    lambda x: historic_supports(\n        df_daily,\n        x&#91;\"symbol\"],\n        x&#91;\"date\"],\n        trough_rank_w_pct=0.005,\n        strong_trough_prominence_pct=0.02,\n        strong_trough_distance=96,\n        trough_distance=4,\n        include_low=False\n    ), axis=1\n)\nhourly_df&#91;\"key_levels\"] = hourly_df.apply(lambda x: x&#91;\"resistances\"] + x&#91;\"supports\"], axis=1)\nhourly_df&#91;\"date\"] = pd.to_datetime(hourly_df&#91;\"date\"])\ndf&#91;\"date\"] = pd.to_datetime(df&#91;\"date\"])\ndf = df.merge(hourly_df&#91;&#91;\"symbol\", \"date\", \"hour\" , \"key_levels\"]], on=&#91;\"symbol\", \"date\", \"hour\"], how=\"left\", suffixes=(\"\",\"_hourly\")).set_index(df_index)<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u4e09\u3001\u6570\u636e\u9884\u5904\u7406<\/strong><\/h2>\n\n\n\n<p>\u73b0\u5728\u6211\u4eec\u6709\u4e86\u7279\u5f81 DataFrame\uff0c\u9700\u8981\u5bf9\u5176\u8fdb\u884c\u9884\u5904\u7406\uff0c\u4ee5\u4fbf\u6a21\u578b\u80fd\u591f\u6709\u6548\u5730\u4ece\u4e2d\u5b66\u4e60\u3002\u91d1\u878d\u65f6\u95f4\u5e8f\u5217\u5efa\u6a21\u7684\u4e00\u4e2a\u5173\u952e\u6b65\u9aa4\u662f\u5c06\u539f\u59cb\u4ef7\u683c\u6570\u636e\u8f6c\u6362\u4e3a\u5bf9\u6570\u6536\u76ca\u3002\u5bf9\u6570\u6536\u76ca\u8ba1\u7b97\u516c\u5f0f\u4e3a\uff1a<\/p>\n\n\n\n<p><strong>log_returns<\/strong>&nbsp;= np.log(price_t \/ price_t-1)<\/p>\n\n\n\n<p>\u867d\u7136\u6211\u4e0d\u4f1a\u5728\u8fd9\u91cc\u6df1\u5165\u63a2\u8ba8\u6570\u5b66\u7406\u8bba\uff0c\u4f46\u5bf9\u6570\u6536\u76ca\u7387\u4e0e\u539f\u59cb\u4ef7\u683c\u76f8\u6bd4\u6709\u51e0\u4e2a\u5173\u952e\u4f18\u52bf\uff1a<\/p>\n\n\n\n<p><strong>\u9759\u6001\u6027\uff1a<\/strong>\u5bf9\u6570\u6536\u76ca\u901a\u5e38\u6bd4\u539f\u59cb\u4ef7\u683c\u66f4\u7a33\u5b9a\uff0c\u56e0\u6b64\u66f4\u9002\u5408\u7edf\u8ba1\u5efa\u6a21\uff1b<br><strong>\u5bf9\u79f0\u6027\uff1a<\/strong>\u6536\u76ca\u56f4\u7ed5\u96f6\u5bf9\u79f0\u5206\u5e03\uff0c\u6709\u52a9\u4e8e\u6a21\u578b\u8bad\u7ec3\uff1b<br><strong>\u52a0\u6cd5\u5c5e\u6027\uff1a<\/strong>\u4e0e\u539f\u59cb\u4ef7\u683c\u6bd4\u4e0d\u540c\uff0c\u8fde\u7eed\u65f6\u671f\u7684\u6536\u76ca\u53ef\u4ee5\u76f8\u52a0\uff1b<br><strong>\u89c4\u6a21\u72ec\u7acb\u6027\uff1a<\/strong>\u5bf9\u6570\u6536\u76ca\u7387\u66f4\u6613\u4e8e\u6bd4\u8f83\u4e0d\u540c\u80a1\u7968\uff0c\u65e0\u8bba\u5176\u4ef7\u683c\u6c34\u5e73\u5982\u4f55\u3002<\/p>\n\n\n\n<p>\u9884\u5904\u7406\u9636\u6bb5\u8fd8\u5305\u62ec\u6211\u4eec\u5c06\u8981\u5b9e\u65bd\u7684\u5176\u4ed6\u51e0\u4e2a\u5173\u952e\u6b65\u9aa4\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u5904\u7406\u7f3a\u5931\u503c<\/li>\n\n\n\n<li>\u529f\u80fd\u7f29\u653e<\/li>\n\n\n\n<li>\u521b\u5efa\u7b26\u5408\u65f6\u95f4\u987a\u5e8f\u7684\u8bad\u7ec3\/\u9a8c\u8bc1\/\u6d4b\u8bd5\u5206\u533a<\/li>\n\n\n\n<li>\u5bf9 TFT \u6a21\u578b\u7684\u6570\u636e\u8fdb\u884c\u6392\u5e8f<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3.1 \u521b\u5efa\u65f6\u95f4\u6307\u6570<\/strong><\/h3>\n\n\n\n<p>pytorch_forecasting \u8f6f\u4ef6\u5305\u8981\u6c42\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u96c6\u4e2d\u7684\u6bcf\u4e00\u884c\u90fd\u6709\u4e00\u4e2a\u552f\u4e00\u7684\u6574\u6570\u7d22\u5f15\uff0c\u4ee3\u8868\u5176\u5728\u5e8f\u5217\u4e2d\u7684\u4f4d\u7f6e\u3002\u7531\u4e8e\u6211\u4eec\u6709\u591a\u4e2a\u65f6\u95f4\u5e8f\u5217\uff08\u6bcf\u53ea\u80a1\u7968\u4e00\u4e2a\uff09\uff0c\u56e0\u6b64\u6211\u4eec\u6309\u7ec4\u8ba1\u7b97\u8be5\u6307\u6570\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>df&#91;\"month\"] = df&#91;\"date\"].dt.month\ndf&#91;\"day\"] = df&#91;\"date\"].dt.day\n\ndef create_time_idx(group):\n    # Use pd.factorize to create a continuous index for each symbol's time series\n    group&#91;'time_idx'] = pd.factorize(group.index)&#91;0]\n    return group\n\ndf_index = df.index\ndf = df.groupby('symbol').apply(create_time_idx).reset_index(drop=True).set_index(df_index)<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3.2 \u5904\u7406\u5173\u952e\u6c34\u5e73\uff08Key Levels\uff09<\/strong><\/h3>\n\n\n\n<p>\u5173\u952e\u6c34\u5e73\u76ee\u524d\u4ee5\u5217\u8868\u5f62\u5f0f\u5b58\u50a8\uff0c\u6a21\u578b\u65e0\u6cd5\u76f4\u63a5\u5904\u7406\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u6211\u4eec\u4e3a\u6700\u8fd1\u7684\u652f\u6491\u4f4d\u548c\u963b\u529b\u4f4d\u521b\u5efa\u4e86\u5355\u72ec\u7684\u65e5\u7ebf\u548c\u5c0f\u65f6\u7ebf\u5217\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>def find_closest_resistance(row, col_name=\"key_levels\"):\n    resistances = &#91;level for level in row&#91;col_name] if level &gt; row&#91;\"close\"]]\n    if not resistances:\n        return row&#91;\"high\"]\n    return min(resistances)\n\ndef find_closest_support(row, col_name=\"key_levels\"):\n    supports = &#91;level for level in row&#91;col_name] if level &lt; row&#91;\"close\"]]\n    if not supports:\n        return row&#91;\"low\"]\n    return max(supports)\n\ndf&#91;\"daily_key_level_above_current_price\"] = df.apply(\n    find_closest_resistance,\n    axis=1\n)\ndf&#91;\"hourly_key_level_above_current_price\"] = df.apply(\n    find_closest_resistance,\n    axis=1,\n    col_name=\"key_levels_hourly\"\n)\ndf&#91;\"daily_key_level_below_current_price\"] = df.apply(\n    find_closest_support,\n    axis=1\n)\ndf&#91;\"hourly_key_level_below_current_price\"] = df.apply(\n    find_closest_support,\n    axis=1,\n    col_name=\"key_levels_hourly\"\n)<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3.3 \u7279\u5f81\u5f52\u4e00\u5316<\/strong><\/h3>\n\n\n\n<p>\u4e3a\u4e86\u4f7f\u4e0d\u540c\u4ef7\u683c\u533a\u95f4\u7684\u80a1\u7968\u7279\u5f81\u6b63\u5e38\u5316\uff0c\u6211\u4eec\u5c06\u5173\u952e\u4ef7\u4f4d\u3001EMA \u548c SMA \u8f6c\u6362\u4e3a\u4e0e\u5f53\u524d\u4ef7\u683c\u7684\u767e\u5206\u6bd4\u5dee\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>df&#91;\"daily_key_level_above_current_price_change\"] = df&#91;\"daily_key_level_above_current_price\"] \/ df&#91;\"close\"] - 1\ndf&#91;\"daily_key_level_below_current_price_change\"] = df&#91;\"close\"] \/ df&#91;\"daily_key_level_below_current_price\"] - 1\ndf&#91;\"hourly_key_level_above_current_price_change\"] = df&#91;\"hourly_key_level_above_current_price\"] \/ df&#91;\"close\"] - 1\ndf&#91;\"hourly_key_level_below_current_price_change\"] = df&#91;\"close\"] \/ df&#91;\"hourly_key_level_below_current_price\"] - 1\ndf&#91;\"EMA_change\"] = df&#91;\"EMA\"] \/ df&#91;\"close\"] - 1\ndf&#91;\"SMA_change\"] = df&#91;\"SMA\"] \/ df&#91;\"close\"] - 1<\/code><\/pre>\n\n\n\n<p>\u5728\u4f7f\u7528\u4e86\u539f\u59cb\u5f62\u5f0f\u7684\u6536\u76d8\u4ef7\u5217\u4e4b\u540e\uff0c\u6211\u5c06&nbsp;<code>close<\/code>\u8f6c\u6362\u4e3a\u5bf9\u6570\u6536\u76ca\u7387\uff0c\u5e76\u5c06\u5176\u7f29\u653e 100\uff08\u51fa\u4e8e\u6570\u503c\u7a33\u5b9a\u6027\u8003\u8651\uff0c\u56e0\u4e3a\u5206\u949f\u7ea7\u6536\u76ca\u7387\u975e\u5e38\u5c0f\uff09\uff0c\u7136\u540e\u53bb\u6389\u5f00\u76d8\u4ef7\u3001\u6700\u9ad8\u4ef7\u548c\u6700\u4f4e\u4ef7\uff0c\u4ee3\u4e4b\u4ee5close_rank\u3002\u8ba1\u7b97\u516c\u5f0f\u4e3a(close-low) \/ (high-low)\uff08\u6536\u76d8\u4ef7-\u6700\u4f4e\u4ef7\uff09\/\uff08\u6700\u9ad8\u4ef7-\u6700\u4f4e\u4ef7\uff09\u3002\u8fd9\u6837\uff0c\u6211\u5c31\u53ef\u4ee5\u5c06\u5f00\u76d8\u4ef7-\u9ad8\u4ef7-\u4f4e\u4ef7-\u6536\u76d8\u4ef7\u67f1\u72b6\u56fe\u4e2d\u7684\u4fe1\u606f\u63d0\u70bc\u4e3a\u5355\u4e00\u6307\u6807\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">df[\"close_rank\"] = (df[\"close\"] - df[\"low\"]) \/ (df[\"high\"] - df[\"low\"]) # rank of the close price in the daily range\ndf[\"log_return\"] = np.log(df.groupby(\"symbol\")[\"close\"].pct_change() + 1) * 100 # transform close prices to log returns\ndf[\"log_return\"] = df[\"log_return\"].fillna(0) # fill NaNs with 0<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3.4 \u5206\u7c7b\u7279\u5f81<\/strong><\/h3>\n\n\n\n<p>\u65f6\u6001\u878d\u5408\u8f6c\u6362\u5668\u662f\u4e3a\u652f\u6301\u5206\u7c7b\u7279\u5f81\u800c\u6784\u5efa\u7684\uff0c\u56e0\u6b64\u6211\u4eec\u9700\u8981\u5c06\u76f8\u5173\u5217\u8f6c\u6362\u4e3a\u5206\u7c7b pandas \u7c7b\u578b\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># process categorical variables\ndf&#91;\"month\"] = df&#91;\"month\"].astype(str).astype(\"category\")\ndf&#91;\"hour\"] = df&#91;\"hour\"].astype(str).astype(\"category\")\ndf&#91;\"minute\"] = df&#91;\"minute\"].astype(str).astype(\"category\")\ndf&#91;\"industry\"] = df&#91;\"symbol\"].apply(lambda x: fundamental_data&#91;x]&#91;\"industry\"]).astype(\"category\")\ndf&#91;\"day_of_the_week\"] = df&#91;\"date\"].dt.dayofweek.astype(str).astype(\"category\")\ndf&#91;\"is_earnings_day\"] = df&#91;\"is_earnings_day\"].apply(lambda x: \"yes\" if x else \"no\").astype(\"category\")<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3.5 \u6570\u636e\u7b5b\u9009<\/strong><\/h3>\n\n\n\n<p>\u4e3a\u4e86\u964d\u4f4e\u6210\u672c\uff0c\u6211\u5c06\u5bf9\u6570\u636e\u8fdb\u884c\u7b5b\u9009\uff0c\u53ea\u5305\u542b\u4ea4\u6613\u91cf\u6700\u5927\u7684 20 \u53ea\u80a1\u7968\uff0c\u8fd9\u6837\u6a21\u578b\u8bad\u7ec3\u5c31\u80fd\u82b1\u8d39\u5408\u7406\u7684\u65f6\u95f4\u548c\u8ba1\u7b97\u91cf\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>LIMIT_STOCKS = 20\ntop_20_average_volume_stocks = df.groupby(\"symbol\")&#91;\"average_volume\"].mean().nlargest(LIMIT_STOCKS).index\ndf = df&#91;df&#91;\"symbol\"].isin(top_20_average_volume_stocks)]\nprint(f\"Top 20 stocks by average volume: {top_20_average_volume_stocks}\")<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code>Top 20 stocks by average volume: Index(&#91;'NVDA', 'TSLA', 'AMD', 'PLTR', 'F', 'SOFI', 'AAPL', 'RIVN', 'INTC',\n   'AAL', 'PFE', 'CLSK', 'T', 'AMZN', 'CCL', 'UBER', 'MU', 'WFC', 'CMCSA',\n       'GOOG'],\n      dtype='object', name='symbol')<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3.6 \u6570\u636e\u5206\u5272<\/strong><\/h3>\n\n\n\n<p>\u5c06\u6570\u636e\u5206\u4e3a\u8bad\u7ec3\u96c6\u3001\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>TRAIN_PERIOD_END = \"2024-06-01\"\nVAL_PERIOD_END = \"2024-06-10\"\ndf_train = df&#91;df&#91;\"date\"] &lt; TRAIN_PERIOD_END]\ndf_val = df&#91;(df&#91;\"date\"] &gt;= TRAIN_PERIOD_END) &amp; (df&#91;\"date\"] &lt; VAL_PERIOD_END)]\ndf_test = df&#91;df&#91;\"date\"] &gt;= VAL_PERIOD_END]\nprint(f\"Total train rows: {len(df_train)}, Total validation rows: {len(df_val)}, Total test rows: {len(df_test)}\")<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code>Total train rows: 803394, Total validation rows: 38997, Total test rows: 199218<\/code><\/pre>\n\n\n\n<p>\u8fd9\u6837\u6211\u4eec\u5c31\u6709\u4e86 5 \u4e2a\u6708\u7684\u8bad\u7ec3\u6570\u636e\u300110 \u5929\u7684\u9a8c\u8bc1\u6570\u636e\u548c\u5927\u7ea6 1.5 \u4e2a\u6708\u7684\u6d4b\u8bd5\u6570\u636e\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u56db\u3001\u65f6\u7a7a\u878d\u5408\u8f6c\u6362\u5668\u8bbe\u7f6e<\/strong><\/h2>\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-83.png\" alt=\"\" class=\"wp-image-3348\"\/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><br><strong>4.1 \u4e86\u89e3\u67b6\u6784<\/strong><\/h3>\n\n\n\n<p>\u65f6\u6001\u878d\u5408\u53d8\u6362\u5668\uff08Temporal Fusion Transformer\uff0cTFT\uff09\u662f\u4e00\u79cd\u590d\u6742\u7684\u6df1\u5ea6\u5b66\u4e60\u67b6\u6784\uff0c\u8c37\u6b4c\u7814\u7a76\u9662\u5728\u8bba\u6587\u300aTemporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting\u300b\u4e2d\u4ecb\u7ecd\u4e86\u5b83\u3002\u6709\u5174\u8da3\u7684\u670b\u53cb\u53ef\u4ee5\u53bb\u5ef6\u5c55\u9605\u8bfb\u4e0b\u3002<\/p>\n\n\n\n<p>\u5730\u5740\uff1a<a href=\"https:\/\/arxiv.org\/abs\/1912.09363\">https:\/\/arxiv.org\/abs\/1912.09363<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4.2 \u4e3b\u8981\u7ec4\u4ef6\u548c\u529f\u80fd<\/strong><\/h3>\n\n\n\n<p><strong>1. \u591a\u7c7b\u578b\u53d8\u91cf\u5904\u7406<\/strong><\/p>\n\n\n\n<p><strong>\u9759\u6001\u53d8\u91cf\uff1a<\/strong>\u884c\u4e1a\u90e8\u95e8\u6216\u516c\u53f8\u89c4\u6a21\u7b49\u4e0d\u53d8\u7279\u5f81\u3002<\/p>\n\n\n\n<p><strong>\u65f6\u53d8\u5df2\u77e5\uff1a<\/strong>\u672a\u6765\u5df2\u77e5\u7279\u5f81\uff0c\u5982\u65e5\u5386\u4e8b\u4ef6\u6216\u9884\u5b9a\u6536\u76ca\u65e5\u671f\u3002<\/p>\n\n\n\n<p><strong>\u65f6\u53d8\u672a\u77e5\uff1a<\/strong>\u6211\u4eec\u9700\u8981\u9884\u6d4b\u7684\u7279\u5f81\uff0c\u5982\u4ef7\u683c\u8d70\u52bf\u548c\u6210\u4ea4\u91cf\u3002<\/p>\n\n\n\n<p><strong>2. \u53ef\u89e3\u91ca\u7684\u591a\u5934\u6ce8\u610f\u529b<\/strong><\/p>\n\n\n\n<p>\u4e0e\u4f20\u7edf\u4e0d\u540c\uff0cTFT \u4f7f\u7528\u4e00\u79cd\u4e13\u95e8\u7684\u6ce8\u610f\u529b\u673a\u5236\uff0c\u4f7f\u6211\u4eec\u53ef\u4ee5\uff1a<\/p>\n\n\n\n<p>\u53ef\u89c6\u5316\u5386\u53f2\u65f6\u95f4\u70b9\u5bf9\u6bcf\u4e2a\u9884\u6d4b\u7684\u5f71\u54cd\u3002<\/p>\n\n\n\n<p>\u4e86\u89e3\u4e0d\u540c\u9884\u6d4b\u8303\u56f4\u5185\u7279\u5f81\u7684\u91cd\u8981\u6027\u3002<\/p>\n\n\n\n<p>\u786e\u5b9a\u6a21\u578b\u5b66\u4e60\u8bc6\u522b\u7684\u65f6\u95f4\u6a21\u5f0f\u3002<\/p>\n\n\n\n<p><strong>3. \u53d8\u91cf\u9009\u62e9\u7f51\u7edc\uff08\u6a21\u578b\u81ea\u52a8\u5b66\u4e60\uff09<\/strong><\/p>\n\n\n\n<p>\u54ea\u4e9b\u7279\u5f81\u5bf9\u6bcf\u4e2a\u9884\u6d4b\u6b65\u9aa4\u90fd\u5f88\u91cd\u8981\u3002<\/p>\n\n\n\n<p>\u7279\u5f81\u7684\u91cd\u8981\u6027\u5728\u4e0d\u540c\u9884\u6d4b\u8303\u56f4\u5185\u5982\u4f55\u53d8\u5316\u3002<\/p>\n\n\n\n<p>\u4f55\u65f6\u66f4\u4f9d\u8d56\u8fd1\u671f\u6570\u636e\uff0c\u4f55\u65f6\u66f4\u4f9d\u8d56\u5386\u53f2\u6570\u636e\u3002<\/p>\n\n\n\n<p><strong>4. \u591a\u5730\u5e73\u7ebf\u9884\u6d4b<\/strong><\/p>\n\n\n\n<p>\u4e0e GPT \u9884\u6d4b\u4e0b\u4e00\u4e2a\u4ee4\u724c\u7684\u65b9\u6cd5\u7c7b\u4f3c\uff0cTFT \u4e5f\u80fd\u9884\u6d4b\u4e0b\u4e00\u4e2a\u4ee4\u724c\uff1a<\/p>\n\n\n\n<p>\u4ee5\u81ea\u52a8\u56de\u5f52\u65b9\u5f0f\u9884\u6d4b\u672a\u6765\u7684\u591a\u4e2a\u65f6\u95f4\u6b65\u957f\u3002<\/p>\n\n\n\n<p>\u8003\u8651\u4e0d\u540c\u65f6\u95f4\u8de8\u5ea6\u7684\u4e0d\u786e\u5b9a\u6027\u6c34\u5e73\u3002<\/p>\n\n\n\n<p>\u4e3a\u6bcf\u6b21\u9884\u6d4b\u63d0\u4f9b\u91cf\u5316\u9884\u6d4b\uff08\u7f6e\u4fe1\uff09\u533a\u95f4\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4.3 \u5728\u80a1\u7968\u4ea4\u6613\u4e2d\u7684\u5b9e\u9645\u5e94\u7528<\/strong><\/h3>\n\n\n\n<p>\u4e8b\u5b9e\u4e0a TFT \u67b6\u6784\u7279\u522b\u9002\u5408\u80a1\u7968\u9884\u6d4b\uff0c\u56e0\u4e3a\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u5b83\u53ef\u4ee5\u540c\u65f6\u5904\u7406\u6280\u672f\u6307\u6807\uff08\u65f6\u53d8\uff09\u548c\u57fa\u672c\u6570\u636e\uff08\u9759\u6001\uff09\u3002<\/li>\n\n\n\n<li>\u5173\u6ce8\u673a\u5236\u6709\u52a9\u4e8e\u8bc6\u522b\u76f8\u5173\u5386\u53f2\u6a21\u5f0f\uff0c\u7c7b\u4f3c\u4e8e\u4ea4\u6613\u5458\u5bfb\u627e\u56fe\u8868\u6a21\u5f0f\u7684\u65b9\u5f0f<\/li>\n\n\n\n<li>\u5b9a\u91cf\u9884\u6d4b\u6709\u52a9\u4e8e\u8bc4\u4f30\u98ce\u9669\u548c\u6f5c\u5728\u7684\u4ef7\u683c\u8303\u56f4\uff0c\u5bf9\u4ed3\u4f4d\u5927\u5c0f\u81f3\u5173\u91cd\u8981\u3002\u4e5f\u53ef\u7528\u4e8e\u5957\u5229\u4ea4\u6613\u3002<\/li>\n\n\n\n<li>\u53ef\u89e3\u91ca\u6027\u6709\u52a9\u4e8e\u9a8c\u8bc1\u6a21\u578b\u662f\u5426\u5728\u5b66\u4e60\u6709\u610f\u4e49\u7684\u6a21\u5f0f\uff0c\u800c\u4e0d\u662f\u566a\u97f3\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u73b0\u5728\u8ba9\u6211\u4eec\u5efa\u7acb\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u96c6\uff08TimeSeriesDataset\uff09\uff0c\u5b83\u662f\u8bad\u7ec3 TFT \u6a21\u578b\u7684\u57fa\u7840\u3002\u8fd9\u79cd\u6570\u636e\u96c6\u7ed3\u6784\u4e13\u95e8\u8bbe\u8ba1\u7528\u4e8e\u5904\u7406\u65f6\u95f4\u6570\u636e\u7684\u590d\u6742\u8981\u6c42\uff0c\u540c\u65f6\u4fdd\u6301\u9ad8\u6548\u7684\u6279\u5904\u7406\u80fd\u529b\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># if you haven't yet, run pip install pytorch_forecasting lightning\nfrom pytorch_forecasting import Baseline, TemporalFusionTransformer, TimeSeriesDataSet\nfrom pytorch_forecasting.data import GroupNormalizer<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code>min_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>\u8ba9\u6211\u4eec\u6765\u5206\u6790\u4e00\u4e0b TimeSeriesDataSet \u914d\u7f6e\u7684\u6bcf\u4e2a\u53c2\u6570\uff1a<\/p>\n\n\n\n<p><strong>1. \u6838\u5fc3\u53c2\u6570<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><code>group_ids=[\"symbol\"]<\/code>: \u6807\u8bc6\u6570\u636e\u96c6\u4e2d\u4e0d\u540c\u7684\u65f6\u95f4\u5e8f\u5217\u3002\u5728\u6211\u4eec\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u6709 20 \u79cd\u4e0d\u540c\u7684\u80a1\u7968\uff0c\u6bcf\u4e2a\u80a1\u7968\u4ee3\u7801\u4ee3\u8868\u4e00\u4e2a\u72ec\u7acb\u7684\u65f6\u95f4\u5e8f\u5217\uff0c\u6a21\u578b\u5c06\u5b66\u4e60\u5982\u4f55\u9884\u6d4b\u3002<\/li>\n\n\n\n<li><code>time_idx=\"time_idx\"<\/code>: \u4ee3\u8868\u6570\u636e\u70b9\u7684\u987a\u5e8f\u6392\u5217\u3002\u8fd9\u4e2a\u6574\u6570\u7d22\u5f15\u5fc5\u987b\u5728\u6bcf\u53ea\u80a1\u7968\u7684\u65f6\u95f4\u5e8f\u5217\u4e2d\u4fdd\u6301\u8fde\u7eed\uff0c\u5e76\u5728\u9884\u5904\u7406\u6b65\u9aa4\u4e2d\u521b\u5efa\uff0c\u4ee5\u6ee1\u8db3\u8fd9\u4e00\u8981\u6c42\u3002<\/li>\n\n\n\n<li><code>target=\"log_return\"<\/code>: \u6307\u5b9a\u6211\u4eec\u7684\u9884\u6d4b\u76ee\u6807\uff0c\u5728\u672c\u4f8b\u4e2d\u5c31\u662f\u6211\u4eec\u4e4b\u524d\u8ba1\u7b97\u7684\u5bf9\u6570\u6536\u76ca\u3002\u6a21\u578b\u5c06\u5c1d\u8bd5\u5728\u9884\u6d4b\u8303\u56f4\u5185\u7684\u6bcf\u4e00\u6b65\u9884\u6d4b\u8fd9\u4e2a\u503c\u3002<\/li>\n<\/ul>\n\n\n\n<p><strong>2. \u5e8f\u5217\u957f\u5ea6\u53c2\u6570<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><code>min_encoder_length=240<\/code>&nbsp;and&nbsp;<code>max_encoder_length=240<\/code>: \u8bbe\u7f6e\u6a21\u578b\u5728\u8fdb\u884c\u9884\u6d4b\u65f6\u80fd\u770b\u5230\u591a\u5c11\u5386\u53f2\u6570\u636e\u3002\u6211\u4eec\u5c06\u5176\u56fa\u5b9a\u4e3a\u6b63\u597d 4 \u5c0f\u65f6\uff08240 \u5206\u949f\uff09\u7684\u6570\u636e\uff0c\u4ee5\u786e\u4fdd\u6bcf\u6b21\u9884\u6d4b\u7684\u4e0a\u4e0b\u6587\u4e00\u81f4\u3002<\/li>\n\n\n\n<li><code>min_prediction_length=20<\/code>&nbsp;and&nbsp;<code>max_prediction_length=20<\/code>: \u5b9a\u4e49 20 \u5206\u949f\u7684\u9884\u6d4b\u8303\u56f4\u3002\u5728\u5b9e\u9645\u9884\u6d4b\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u4f1a\u4f7f\u7528\u6700\u5927\u957f\u5ea6\u3002<\/li>\n<\/ul>\n\n\n\n<p><strong>3. \u7279\u5f81\u5206\u7c7b<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><code>time_varying_known_reals=[\"time_idx\", \"average_volume\"]<\/code>: \u8fd9\u4e9b\u662f\u6211\u4eec\u9884\u5148\u77e5\u9053\u7684\u6570\u5b57\u7279\u5f81\uff0c\u751a\u81f3\u662f\u672a\u6765\u65e5\u671f\u7684\u6570\u5b57\u7279\u5f81\u3002\u5b83\u4eec\u6709\u52a9\u4e8e\u6a21\u578b\u7406\u89e3\u65f6\u95f4\u6a21\u5f0f\uff0c\u5e76\u7eb3\u5165\u5df2\u77e5\u7684\u672a\u6765\u4fe1\u606f\u3002<\/li>\n\n\n\n<li><code>time_varying_known_categoricals=[\"day_of_the_week\", \"is_earnings_day\", \"hour\", \"minute\"]<\/code>: \u8fd9\u4e9b\u662f\u6211\u4eec\u4e8b\u5148\u77e5\u9053\u7684\u5206\u7c7b\u7279\u5f81\uff0c\u5982\u65e5\u5386\u4fe1\u606f\u548c\u9884\u5b9a\u4e8b\u4ef6\u3002\u5b83\u4eec\u6709\u52a9\u4e8e\u6a21\u578b\u8bc6\u522b\u5468\u671f\u6027\u6a21\u5f0f\u548c\u7279\u6b8a\u5e02\u573a\u6761\u4ef6\u3002<\/li>\n\n\n\n<li><code>time_varying_unknown_reals<\/code>: \u5305\u542b\u6211\u4eec\u7684\u6280\u672f\u6307\u6807\u3001\u5e02\u573a\u6570\u636e\u548c\u5f52\u4e00\u5316\u5173\u952e\u6c34\u5e73\u3002\u8fd9\u4e9b\u90fd\u662f\u6211\u4eec\u4e8b\u5148\u4e0d\u77e5\u9053\u7684\u7279\u5f81\uff0c\u5fc5\u987b\u5bf9\u672a\u6765\u7684\u65f6\u95f4\u6233\u8fdb\u884c\u9884\u6d4b\u6216\u4f30\u7b97\u3002<\/li>\n\n\n\n<li><code>static_categoricals=[\"industry\"]<\/code>&nbsp;and&nbsp;<code>static_reals=[\"shares_float\"]<\/code>: \u8fd9\u4e9b\u7279\u5f81\u5728\u6574\u4e2a\u65f6\u95f4\u5e8f\u5217\u4e2d\u5bf9\u6bcf\u53ea\u80a1\u7968\u4fdd\u6301\u4e0d\u53d8\u3002\u5b83\u4eec\u6709\u52a9\u4e8e\u6a21\u578b\u6839\u636e\u6bcf\u53ea\u80a1\u7968\u7684\u57fa\u672c\u7279\u5f81\u8c03\u6574\u9884\u6d4b\u3002<\/li>\n<\/ul>\n\n\n\n<p><strong>4. \u9ad8\u7ea7\u529f\u80fd<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><code>add_relative_time_idx=True<\/code>:\u6dfb\u52a0\u5f52\u4e00\u5316\u65f6\u95f4\u7d22\u5f15\u7279\u5f81\uff0c\u5e2e\u52a9\u6a21\u578b\u7406\u89e3\u5e8f\u5217\u4e2d\u7684\u76f8\u5bf9\u65f6\u95f4\u4f4d\u7f6e\u3002<\/li>\n\n\n\n<li><code>add_encoder_length=False<\/code>: \u7531\u4e8e\u6211\u4eec\u4f7f\u7528\u7684\u662f 240 \u5206\u949f\u7684\u56fa\u5b9a\u957f\u5ea6\u5e8f\u5217\uff0c\u56e0\u6b64\u5df2\u7981\u7528\u6dfb\u52a0\u5e8f\u5217\u957f\u5ea6\u529f\u80fd\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u8be5\u914d\u7f6e\u521b\u5efa\u4e86\u4e00\u4e2a\u6570\u636e\u96c6\uff0c\u4e3a\u6211\u4eec\u7684\u6a21\u578b\u63d0\u4f9b 4 \u5c0f\u65f6\u7684\u5386\u53f2\u6570\u636e\uff0c\u7528\u4e8e\u9884\u6d4b\u672a\u6765 20 \u5206\u949f\u7684\u6536\u76ca\u3002pytorch_forecasting \u8f6f\u4ef6\u5305\u4f1a\u81ea\u52a8\u5904\u7406\u6240\u6709\u5fc5\u8981\u7684\u7f29\u653e\u548c\u7279\u5f81\u5f52\u4e00\u5316\uff0c\u4f7f\u6211\u4eec\u66f4\u5bb9\u6613\u4e13\u6ce8\u4e8e\u6a21\u578b\u67b6\u6784\u548c\u4ea4\u6613\u7b56\u7565\uff0c\u800c\u4e0d\u662f\u6570\u636e\u9884\u5904\u7406\u3002\u8fd9\u4e5f\u662f\u4f7f\u7528\u8be5\u8f6f\u4ef6\u5305\u7684\u4e3b\u8981\u4f18\u52bf\u4e4b\u4e00&#8211;\u5b83\u53ef\u4ee5\u62bd\u8c61\u6389\u6240\u6709\u7684\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u51c6\u5907\u5de5\u4f5c\uff0c\u786e\u4fdd\u6211\u4eec\u7684\u7279\u5f81\u5f97\u5230\u6b63\u786e\u7684\u7f29\u653e\u548c\u5f52\u4e00\u5316\uff0c\u4ece\u800c\u5b9e\u73b0\u6700\u4f73\u7684\u6a21\u578b\u8bad\u7ec3\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u4e94\u3001\u89c2\u70b9\u603b\u7ed3<\/strong><\/h2>\n\n\n\n<p>\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u4f7f\u7528\u65f6\u6001\u878d\u5408\u8f6c\u6362\u5668\u6784\u5efa\u80a1\u7968\u4ef7\u683c\u9884\u6d4b\u6a21\u578b\u7684\u57fa\u672c\u57fa\u7840\u5de5\u4f5c\u3002\u6211\u4eec\u4ece\u79fb\u52a8\u5e73\u5747\u7ebf\u3001\u652f\u6491\u4f4d\/\u963b\u529b\u4f4d\u548c RSI \u7b49\u57fa\u672c\u6280\u672f\u6307\u6807\u5165\u624b\uff0c\u5c06\u539f\u59cb\u4ef7\u683c\u6570\u636e\u8f6c\u6362\u4e3a\u6709\u610f\u4e49\u7684\u7279\u5f81\uff0c\u5e76\u4f7f\u7528 pytorch_forecasting \u5efa\u7acb\u4e86\u590d\u6742\u7684\u6570\u636e\u96c6\u7ed3\u6784\u3002TFT \u67b6\u6784\u5c24\u5176\u9002\u5408\u8fd9\u9879\u4efb\u52a1\uff0c\u56e0\u4e3a\u5b83\u53ef\u4ee5\u5904\u7406\u591a\u79cd\u7c7b\u578b\u7684\u7279\u5f81\uff0c\u540c\u65f6\u63d0\u4f9b\u5e26\u6709\u7f6e\u4fe1\u533a\u95f4\u7684\u53ef\u89e3\u91ca\u9884\u6d4b\u3002<\/p>\n\n\n\n<p><strong>\u80a1\u7968\u4ef7\u683c\u9884\u6d4b\u7684\u590d\u6742\u6027\u548c\u4e0d\u786e\u5b9a\u6027<\/strong>\uff1a\u4f5c\u8005\u5f3a\u8c03\u4e86\u80a1\u7968\u4ef7\u683c\u9884\u6d4b\u7684\u56f0\u96be\uff0c\u5e76\u63d0\u9192\u8bfb\u8005\u8fd9\u4e9b\u9884\u6d4b\u4e0d\u5e94\u88ab\u89c6\u4e3a\u6295\u8d44\u5efa\u8bae\u3002<\/p>\n\n\n\n<p><strong>\u6280\u672f\u6307\u6807\u7684\u91cd\u8981\u6027<\/strong>\uff1a\u6280\u672f\u6307\u6807\u5982\u79fb\u52a8\u5e73\u5747\u7ebf\u3001RSI\u548c\u652f\u6491\u963b\u529b\u6c34\u5e73\u5728\u6784\u5efa\u9884\u6d4b\u6a21\u578b\u4e2d\u8d77\u7740\u5173\u952e\u4f5c\u7528\u3002<\/p>\n\n\n\n<p><strong>\u6570\u636e\u9884\u5904\u7406\u7684\u5fc5\u8981\u6027<\/strong>\uff1a\u6b63\u786e\u7684\u6570\u636e\u9884\u5904\u7406\uff0c\u5305\u62ec\u5c06\u4ef7\u683c\u6570\u636e\u8f6c\u6362\u4e3a\u5bf9\u6570\u6536\u76ca\u7387\u3001\u5f52\u4e00\u5316\u7279\u5f81\u4ee5\u53ca\u5904\u7406\u5206\u7c7b\u7279\u5f81\uff0c\u5bf9\u4e8e\u63d0\u9ad8\u6a21\u578b\u6027\u80fd\u81f3\u5173\u91cd\u8981\u3002<\/p>\n\n\n\n<p><strong>\u65f6\u6001\u878d\u5408\u53d8\u6362\u5668\uff08TFT\uff09\u7684\u4f18\u52bf<\/strong>\uff1aTFT\u6a21\u578b\u80fd\u591f\u5904\u7406\u591a\u79cd\u7c7b\u578b\u7684\u7279\u5f81\uff0c\u63d0\u4f9b\u53ef\u89e3\u91ca\u7684\u9884\u6d4b\u7ed3\u679c\uff0c\u5e76\u4e14\u80fd\u591f\u8fdb\u884c\u591a\u5730\u5e73\u7ebf\u9884\u6d4b\uff0c\u8fd9\u4f7f\u5f97\u5b83\u5728\u80a1\u7968\u4ea4\u6613\u9884\u6d4b\u4e2d\u5177\u6709\u72ec\u7279\u7684\u4f18\u52bf\u3002<\/p>\n\n\n\n<p><strong>\u6a21\u578b\u89e3\u91ca\u6027\u548c\u98ce\u9669\u7ba1\u7406<\/strong>\uff1a\u672c\u6587\u5f3a\u8c03\u4e86\u6a21\u578b\u9884\u6d4b\u7684\u89e3\u91ca\u6027\uff0c\u4ee5\u53ca\u5982\u4f55\u4f7f\u7528\u7f6e\u4fe1\u533a\u95f4\u6765\u7ba1\u7406\u4ea4\u6613\u98ce\u9669\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u516d\u3001\u9884\u544a<\/strong><\/h2>\n\n\n\n<p id=\"fe49\">\u73b0\u5728\u6211\u4eec\u7684\u7279\u5f81\u5de5\u7a0b\u7ba1\u9053\u548c\u6570\u636e\u96c6\u7ed3\u6784\u5df2\u7ecf\u5c31\u4f4d\uff0c\u4e5f\u5df2\u7ecf\u51c6\u5907\u597d\u7528\u673a\u5668\u5b66\u4e60\u6765\u5b9e\u73b0\u6211\u4eec\u7684\u4ea4\u6613\u7b56\u7565\u3002\u5728\u7b2c 4 \u90e8\u5206\uff08\u6700\u540e\u7ae0\u8282\uff09\u4e2d\uff0c\u6211\u4eec\u5c06\u63a2\u8ba8\u6240\u6709\u7ec4\u4ef6\u662f\u5982\u4f55\u534f\u540c\u5de5\u4f5c\u7684\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u5982\u4f55\u4f7f\u7528 Tensorboard \u5728\u8bad\u7ec3\u65f6\u95f4\u5185\u8bad\u7ec3\u6a21\u578b\u5e76\u76d1\u63a7\u4e0d\u540c\u6307\u6807\u3002<\/li>\n\n\n\n<li>\u5982\u4f55\u4f7f\u7528 pytorch_forecasting \u7684\u5185\u7f6e\u529f\u80fd\u6765\u89e3\u91ca\u6a21\u578b\u7684\u9884\u6d4b\u548c\u5173\u6ce8\u6a21\u5f0f\u3002<\/li>\n\n\n\n<li>\u6839\u636e\u4e0d\u540c\u6307\u6807\u8bc4\u4f30\u6a21\u578b\u3002<\/li>\n\n\n\n<li>\u5b9e\u65bd\u6700\u57fa\u672c\u7684\u4ea4\u6613\u7b56\u7565\u5e76\u8fdb\u884c\u56de\u6eaf\u6d4b\u8bd5\u3002<\/li>\n<\/ul>\n\n\n\n<p><em>\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<\/em><\/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\/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\/\">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\u4e09\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-1770","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\/1770","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=1770"}],"version-history":[{"count":2,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/1770\/revisions"}],"predecessor-version":[{"id":1861,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/1770\/revisions\/1861"}],"wp:attachment":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/media?parent=1770"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/categories?post=1770"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/tags?post=1770"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}