{"id":3962,"date":"2026-05-05T07:32:00","date_gmt":"2026-05-04T23:32:00","guid":{"rendered":"https:\/\/laoyulaoyu.com\/?p=3962"},"modified":"2026-04-24T23:48:36","modified_gmt":"2026-04-24T15:48:36","slug":"%e9%87%8f%e5%8c%96%e4%ba%bagpu%e6%8f%90%e9%80%9f77%e5%80%8d%e7%9a%84%e7%a5%9e%e5%99%a8%ef%bc%9aspectre%e4%bb%8e%e5%85%a5%e9%97%a8%e5%88%b0%e5%ae%9e%e6%88%98%e5%85%a8%e8%a7%a3%e6%9e%90","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2026\/05\/05\/%e9%87%8f%e5%8c%96%e4%ba%bagpu%e6%8f%90%e9%80%9f77%e5%80%8d%e7%9a%84%e7%a5%9e%e5%99%a8%ef%bc%9aspectre%e4%bb%8e%e5%85%a5%e9%97%a8%e5%88%b0%e5%ae%9e%e6%88%98%e5%85%a8%e8%a7%a3%e6%9e%90\/","title":{"rendered":"\u91cf\u5316\u4ebaGPU\u63d0\u901f77\u500d\u7684\u795e\u5668\uff1aSpectre\u4ece\u5165\u95e8\u5230\u5b9e\u6218\u5168\u89e3\u6790"},"content":{"rendered":"\n

\u4f5c\u8005\uff1a\u8001\u4f59\u635e\u9c7c<\/a><\/p>\n\n\n\n

\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>\n\n\n\n
\n
\u5199\u5728\u524d\u9762\u7684\u8bdd\uff1a<\/strong>\u4eca\u5929\u7ed9\u5927\u5bb6\u5b89\u5229\u4e00\u4e2aGPU\u52a0\u901f\u7684\u91cf\u5316\u5f00\u6e90\u5e93\uff1aSpectre\u3002\u5b83\u57fa\u4e8ePyTorch\uff0c\u80fd\u5728RTX 3090\u4e0a\u628a\u56e0\u5b50\u5206\u6790\u548c\u56de\u6d4b\u901f\u5ea6\u63d0\u534777\u500d<\/mark>\u3002\u4ece\u5b89\u88c5\u5230\u5b9e\u6218\uff0c\u6211\u5168\u7ed9\u4f60\u634b\u6e05\u695a\u4e86\u3002<\/pre>\n<\/blockquote>\n\n\n\n
\u505a\u91cf\u5316\u7684\u4eba\u90fd\u77e5\u9053\u4e00\u4e2a\u75db\u70b9\uff1a\u8dd1\u4e00\u6b21\u56de\u6d4b\uff0c\u559d\u676f\u5496\u5561\u56de\u6765\u8fd8\u6ca1\u8dd1\u5b8c\u3002\u5c24\u5176\u662f\u56e0\u5b50\u5206\u6790\u8fd9\u79cd\u9700\u8981\u904d\u5386\u51e0\u5343\u53ea\u6807\u7684\u3001\u51e0\u5341\u4e2a\u56e0\u5b50\u7684\u6d3b\u513f\uff0cCPU\u7b97\u8d77\u6765\u90a3\u662f\u771f\u7684\u6162\u3002\u4eca\u5929\u7ed9\u5927\u5bb6\u804a\u4e00\u4e2a\u8ba9\u6211\u773c\u524d\u4e00\u4eae\u7684\u5f00\u6e90\u5e93\uff1aSpectre<\/strong>\uff0c\u5b83\u57fa\u4e8ePyTorch\uff0c\u80fd\u628aGPU\u7684\u7b97\u529b\u76f4\u63a5\u704c\u8fdb\u56e0\u5b50\u5206\u6790\u548c\u7b56\u7565\u56de\u6d4b\u91cc\uff0cRTX 3090\u4e0a\u6700\u9ad8\u63d0\u901f77\u500d\u3002\u6765\uff0c\u6211\u5168\u7ed9\u4f60\u634b\u6e05\u695a\u3002<\/p>\n\n\n\n
\u4e00\u3001\u91cf\u5316\u7814\u7a76\u7684\u4e24\u5927\u6838\u5fc3\uff1a\u56e0\u5b50\u5206\u6790 & \u56de\u6d4b<\/strong><\/h2>\n\n\n\n
\u5148\u8bf4\u57fa\u7840\u3002\u91cf\u5316\u6295\u8d44\u7684\u6838\u5fc3\u6d41\u7a0b\uff0c\u8bf4\u767d\u4e86\u5c31\u4e24\u4ef6\u4e8b\uff1a<\/p>\n\n\n\n
\u56e0\u5b50\u5206\u6790<\/strong>\uff0c\u5c31\u662f\u4ece\u6d77\u91cf\u6570\u636e\u91cc\u627e\u5230\u90a3\u4e9b\u80fd\u89e3\u91ca\u8d44\u4ea7\u8868\u73b0\u5dee\u5f02\u7684\u53d8\u91cf\u3002\u6bd4\u5982\u52a8\u91cf\u3001\u6ce2\u52a8\u7387\u3001\u4ef7\u503c\u6307\u6807\uff0c\u8fd9\u4e9b\u90fd\u662f\u7ecf\u5178\u56e0\u5b50\u3002\u56e0\u5b50\u7684\u8d28\u91cf\u76f4\u63a5\u51b3\u5b9a\u4e86\u7b56\u7565\u7684\u4e0a\u9650\u3002<\/p>\n\n\n\n
\u56de\u6d4b<\/strong>\uff0c\u5c31\u662f\u62ff\u5386\u53f2\u6570\u636e\u6a21\u62df\u4f60\u7684\u7b56\u7565\u8868\u73b0\uff0c\u770b\u770b\u8fd9\u5957\u903b\u8f91\u5728\u8fc7\u53bb\u5230\u5e95\u884c\u4e0d\u884c\u3002\u5b83\u662f\u9a8c\u8bc1\u7b56\u7565\u53ef\u884c\u6027\u7684\u5173\u952e\u6b65\u9aa4\uff0c\u4e5f\u662f\u4f18\u5316\u53c2\u6570\u7684\u4e3b\u8981\u624b\u6bb5\u3002<\/p>\n\n\n\n
\u95ee\u9898\u5728\u4e8e\uff0c\u8fd9\u4e24\u4ef6\u4e8b\u90fd\u975e\u5e38\u5403\u7b97\u529b\u3002\u6570\u636e\u96c6\u52a8\u8f84\u8986\u76d6\u51e0\u5343\u53ea\u6807\u7684\u3001\u51e0\u5341\u5e74\u7684\u65f6\u95f4\u8de8\u5ea6\uff0c\u6570\u636e\u91cf\u8f7b\u677e\u4e0aGB\u3002\u7528\u4f20\u7edf\u7684CPU\u5de5\u5177\u8dd1\uff0c\u4e00\u4e2a\u590d\u6742\u56e0\u5b50\u7ec4\u5408\u53ef\u80fd\u8981\u7b49\u51e0\u5341\u5206\u949f\u751a\u81f3\u51e0\u4e2a\u5c0f\u65f6\u3002\u5728\u9ad8\u9891\u7814\u7a76\u548c\u8fed\u4ee3\u573a\u666f\u4e0b\uff0c\u8fd9\u79cd\u7b49\u5f85\u5c31\u662f\u5728\u6d6a\u8d39\u673a\u4f1a\u3002<\/p>\n\n\n\n
\u91cf\u5316\u5708\u7684\u94c1\u5f8b\uff1a\u7814\u7a76\u901f\u5ea6 = \u7b56\u7565\u8fed\u4ee3\u901f\u5ea6 = \u7ade\u4e89\u529b\u3002\u522b\u4eba\u4e00\u5929\u80fd\u6d4b100\u4e2a\u56e0\u5b50\uff0c\u4f60\u53ea\u80fd\u6d4b10\u4e2a\uff0c\u5dee\u8ddd\u5c31\u662f\u8fd9\u4e48\u62c9\u5f00\u7684\u3002<\/pre>\n\n\n\n
\u76ee\u524d\u4e3b\u6d41\u7684Python\u91cf\u5316\u5e93\uff0c\u50cfZipline\u3001Backtrader\uff0c\u57fa\u672c\u90fd\u662fCPU\u8fd0\u7b97\uff0c\u9762\u5bf9\u5927\u89c4\u6a21\u6570\u636e\u96c6\u65f6\u6027\u80fd\u74f6\u9888\u660e\u663e\u3002Spectre\u6b63\u662f\u7784\u51c6\u4e86\u8fd9\u4e2a\u75db\u70b9\u6765\u7684\uff0c\u7528GPU\u5e76\u884c\u8ba1\u7b97\uff0c\u628a\u901f\u5ea6\u62c9\u6ee1\u3002<\/p>\n\n\n\n
\u4e8c\u3001Spectre\u662f\u4ec0\u4e48\uff1f<\/strong><\/h2>\n\n\n\n
Spectre\u662f\u4e00\u4e2a\u5f00\u6e90\u7684Python\u91cf\u5316\u4ea4\u6613\u5e93\uff0c\u4e13\u653b\u4e24\u4e2a\u65b9\u5411\uff1a\u9ad8\u6027\u80fd\u56e0\u5b50\u5206\u6790<\/strong>\u548c\u7b56\u7565\u56de\u6d4b<\/strong>\u3002\u5b83\u57fa\u4e8ePyTorch\u6784\u5efa\uff0c\u6838\u5fc3\u5356\u70b9\u662fGPU\u52a0\u901f\uff1a\u8c03\u7528\u4e00\u4e2ato_cuda()<\/code>\u65b9\u6cd5\uff0c\u5c31\u80fd\u628a\u8ba1\u7b97\u4efb\u52a1\u6254\u5230NVIDIA\u663e\u5361\u4e0a\uff0c\u5229\u7528\u6570\u5343\u4e2aCUDA\u6838\u5fc3\u5e76\u884c\u5904\u7406\uff0c\u901f\u5ea6\u76f4\u63a5\u8d77\u98de\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u548cPandas\u3001NumPy\u8fd9\u79cd\u901a\u7528\u6570\u636e\u5206\u6790\u5e93\u4e0d\u540c\uff0cSpectre\u662f\u4e13\u95e8\u4e3a\u91cf\u5316\u7814\u7a76\u91cf\u8eab\u6253\u9020\u7684\u3002\u5b83\u80fd\u9ad8\u6548\u5904\u7406OHLCV\uff08\u5f00\u76d8\u4ef7\u3001\u6700\u9ad8\u4ef7\u3001\u6700\u4f4e\u4ef7\u3001\u6536\u76d8\u4ef7\u3001\u6210\u4ea4\u91cf\uff09\u6570\u636e\uff0c\u652f\u6301\u81ea\u5b9a\u4e49\u56e0\u5b50\u521b\u5efa\uff0c\u8fd8\u80fd\u548cPyfolio\u3001Alphalens\u8fd9\u4e9b\u4e13\u4e1a\u5206\u6790\u5de5\u5177\u65e0\u7f1d\u5bf9\u63a5\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u4e09\u3001\u5e95\u5c42\u539f\u7406\uff1aDAG\u5f15\u64ce + CUDA\u5e76\u884c<\/strong><\/h2>\n\n\n\n
\u901f\u5ea6\u4e0d\u662f\u51ed\u7a7a\u6765\u7684\uff0cSpectre\u7684\u5e95\u5c42\u8bbe\u8ba1\u6709\u4e24\u4e2a\u5173\u952e\u70b9\u3002<\/p>\n\n\n\n
1. DAG\u6709\u5411\u65e0\u73af\u56fe\uff1a\u56e0\u5b50\u8ba1\u7b97\u7684\u6b63\u786e\u6253\u5f00\u65b9\u5f0f<\/strong><\/h3>\n\n\n\n
Spectre\u628a\u6bcf\u4e2a\u56e0\u5b50\uff08\u6bd4\u5982SMA\u3001RSI\uff09\u8bbe\u8ba1\u6210DAG\u56fe\u4e2d\u7684\u4e00\u4e2a\u8282\u70b9\u3002\u8282\u70b9\u4e4b\u95f4\u7684\u4f9d\u8d56\u5173\u7cfb\u51b3\u5b9a\u4e86\u8ba1\u7b97\u987a\u5e8f\uff1a\u5148\u7b97\u54ea\u4e2a\u3001\u540e\u7b97\u54ea\u4e2a\uff0c\u4e00\u76ee\u4e86\u7136\u3002\u8fd9\u79cd\u8bbe\u8ba1\u6709\u4e00\u4e2a\u975e\u5e38\u91cd\u8981\u7684\u597d\u5904\uff1a\u675c\u7edd\u524d\u89c6\u504f\u5dee<\/strong>\u3002<\/p>\n\n\n\n
\u524d\u89c6\u504f\u5dee\u662f\u91cf\u5316\u7814\u7a76\u4e2d\u7684\u5927\u5fcc\uff0c\u5c31\u662f”\u7528\u672a\u6765\u7684\u6570\u636e\u7b97\u8fc7\u53bb\u7684\u7ed3\u679c”\u3002DAG\u901a\u8fc7\u4e25\u683c\u7684\u4f9d\u8d56\u6392\u5e8f\uff0c\u786e\u4fdd\u6bcf\u4e2a\u56e0\u5b50\u53ea\u80fd\u8bbf\u95ee\u5b83\u4f9d\u8d56\u7684\u4e0a\u6e38\u6570\u636e\uff0c\u4e0d\u4f1a\u51fa\u73b0\u4fe1\u606f\u6cc4\u9732\u3002\u8fd9\u5bf9\u4fdd\u8bc1\u56de\u6d4b\u7ed3\u679c\u7684\u53ef\u9760\u6027\u81f3\u5173\u91cd\u8981\u3002<\/p>\n\n\n\n
\u4e3a\u4ec0\u4e48DAG\u8fd9\u4e48\u91cd\u8981\uff1f<\/strong>
\u4e3e\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a\u4f60\u8981\u7b97\"20\u65e5\u52a8\u91cf\u56e0\u5b50\"\uff0c\u5b83\u4f9d\u8d56\"\u6536\u76d8\u4ef7\"\u548c\"\u6536\u76ca\u7387\"\u3002DAG\u4f1a\u5148\u7b97\u6536\u76ca\u7387\uff0c\u518d\u7b97\u52a8\u91cf\uff0c\u987a\u5e8f\u4e0d\u4f1a\u641e\u53cd\u3002\u5982\u679c\u56e0\u5b50\u4e4b\u95f4\u6709\u5faa\u73af\u4f9d\u8d56\uff0cDAG\u4f1a\u76f4\u63a5\u62a5\u9519\uff0c\u800c\u4e0d\u662f\u9ed8\u9ed8\u7ed9\u51fa\u4e00\u4e2a\u6709\u504f\u5dee\u7684\u7ed3\u679c\u3002<\/pre>\n\n\n\n
2. CUDA\u5e76\u884c\u8ba1\u7b97\uff1a\u628a\u663e\u5361\u7b97\u529b\u5403\u6ee1<\/strong><\/h3>\n\n\n\n
\u8c03\u7528to_cuda()<\/code>\u540e\uff0cSpectre\u628a\u6240\u6709\u5f20\u91cf\u8fd0\u7b97\u8f6c\u79fb\u5230GPU\u4e0a\u6267\u884c\u3002RTX 3090\u670910496\u4e2aCUDA\u6838\u5fc3\uff0c\u8fd9\u610f\u5473\u7740\u540c\u4e00\u65f6\u523b\u53ef\u4ee5\u5e76\u884c\u8ba1\u7b97\u4e0a\u5343\u4e2a\u6807\u7684\u7684\u56e0\u5b50\u503c\u3002\u5bf9\u4e8eEMA\u3001RSI\u8fd9\u7c7b\u9700\u8981\u5728\u65f6\u95f4\u5e8f\u5217\u4e0a\u6eda\u52a8\u8ba1\u7b97\u7684\u56e0\u5b50\uff0cGPU\u7684\u52a0\u901f\u6548\u679c\u5c24\u4e3a\u660e\u663e\u3002<\/p>\n\n\n\n
\u6839\u636e\u5b98\u65b9\u57fa\u51c6\u6d4b\u8bd5\u6570\u636e\uff0c\u5728RTX 3090\u4e0a\u8ba1\u7b97\u590d\u6742\u56e0\u5b50\u7ec4\u5408\uff0c\u76f8\u6bd4\u7eafCPU\u8fd0\u7b97\uff0c\u52a0\u901f\u6bd4\u6700\u9ad8\u53ef\u8fbe77\u500d\u3002\u8fd9\u662f\u4ec0\u4e48\u6982\u5ff5\uff1f\u539f\u6765\u9700\u8981\u4e00\u4e2a\u5c0f\u65f6\u8dd1\u5b8c\u7684\u56e0\u5b50\u5206\u6790\uff0c\u73b0\u5728\u4e0d\u5230\u4e00\u5206\u949f\u5c31\u80fd\u51fa\u7ed3\u679c\u3002<\/p>\n\n\n\n
\u8ba1\u7b97\u573a\u666f<\/strong><\/th>CPU\u8017\u65f6<\/strong><\/th>GPU\u8017\u65f6 (RTX 3090)<\/strong><\/th>\u52a0\u901f\u500d\u6570<\/strong><\/th><\/tr>
\u7b80\u5355\u56e0\u5b50 (SMA\/RSI)<\/td>~5\u5206\u949f<\/td>~10\u79d2<\/td>~30x<\/td><\/tr>
\u4e2d\u7b49\u590d\u6742\u56e0\u5b50\u7ec4\u5408<\/td>~30\u5206\u949f<\/td>~30\u79d2<\/td>~60x<\/td><\/tr>
\u590d\u6742\u56e0\u5b50+\u81ea\u5b9a\u4e49\u56e0\u5b50<\/td>~60\u5206\u949f<\/td>~47\u79d2<\/td>~77x<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u6ce8\uff1a\u4ee5\u4e0a\u6570\u636e\u4e3a\u5b98\u65b9\u6587\u6863\u516c\u5e03\u7684\u57fa\u51c6\u6d4b\u8bd5\u7ed3\u679c\uff0c\u5b9e\u9645\u8868\u73b0\u56e0\u6570\u636e\u89c4\u6a21\u548c\u786c\u4ef6\u914d\u7f6e\u4e0d\u540c\u4f1a\u6709\u5dee\u5f02\u3002<\/p>\n\n\n\n
\u56db\u3001\u6570\u636e\u52a0\u8f7d\u4e0e\u56de\u6d4b\u5f15\u64ce<\/strong><\/h2>\n\n\n\n

\u6570\u636e\u52a0\u8f7d<\/strong><\/h3>\n\n\n\n
Spectre\u5185\u7f6e\u4e86\u591a\u79cd\u6570\u636e\u52a0\u8f7d\u5668\uff0c\u652f\u6301CSV\u3001Arrow\uff08Feather\u683c\u5f0f\uff09\u7b49\u5e38\u89c1\u6570\u636e\u683c\u5f0f\uff0c\u8fd8\u63d0\u4f9b\u4e86YahooDownloader\u53ef\u4ee5\u76f4\u63a5\u4ece\u96c5\u864e\u8d22\u7ecf\u4e0b\u8f7d\u6570\u636e\u3002\u6570\u636e\u52a0\u8f7d\u540e\uff0c\u4f1a\u88ab\u6574\u7406\u6210\u591a\u5c42\u7d22\u5f15\u7684Pandas DataFrame\uff1a\u65e5\u671f\u4e3a\u884c\u7d22\u5f15\uff0c\u6807\u7684\u4ee3\u7801\u4e3a\u5217\u7d22\u5f15\uff0c\u8fd9\u4e5f\u662f\u91cf\u5316\u5206\u6790\u5e08\u6700\u719f\u6089\u7684\u6570\u636e\u683c\u5f0f\u3002<\/p>\n\n\n\n
\u56de\u6d4b\u5f15\u64ce<\/strong><\/h3>\n\n\n\n
Spectre\u7684\u56de\u6d4b\u6a21\u5757\u91c7\u7528\u4e8b\u4ef6\u9a71\u52a8\u67b6\u6784\uff0c\u6309\u65f6\u95f4\u987a\u5e8f\u9010\u6761\u5904\u7406\u884c\u60c5\u6570\u636e\u3002\u5b83\u5185\u7f6e\u4e86\u8ba2\u5355\u7ba1\u7406\u7cfb\u7edf\uff08Blotter\uff09\uff0c\u652f\u6301\u624b\u7eed\u8d39\u3001\u6ed1\u70b9\u3001\u6b62\u635f\u7b49\u771f\u5b9e\u4ea4\u6613\u6210\u672c\u7684\u6a21\u62df\uff0c\u8ba9\u56de\u6d4b\u7ed3\u679c\u66f4\u8d34\u8fd1\u5b9e\u76d8\u8868\u73b0\u3002<\/p>\n\n\n\n
\u56de\u6d4b\u8f93\u51fa\u53ef\u4ee5\u76f4\u63a5\u5bf9\u63a5Pyfolio\u751f\u6210\u5b8c\u6574\u7684\u7ee9\u6548\u62a5\u544a\u2014\u2014\u590f\u666e\u6bd4\u7387\u3001\u6700\u5927\u56de\u64a4\u3001\u5e74\u5316\u6536\u76ca\u7b49\u6838\u5fc3\u6307\u6807\u4e00\u5e94\u4ff1\u5168\uff0c\u4e5f\u53ef\u4ee5\u7528Alphalens\u751f\u6210\u56e0\u5b50\u5206\u4f4d\u6570\u6536\u76ca\u56fe\u548cIC\u5206\u6790\u56fe\u3002<\/p>\n\n\n\n
\n
\u5b9e\u7528\u5efa\u8bae<\/strong><\/p>\n\n\n\n
\u56de\u6d4b\u65f6\u4e00\u5b9a\u8981\u52a0\u4e0a\u624b\u7eed\u8d39\u548c\u6ed1\u70b9\uff0c\u5426\u5219\u7ed3\u679c\u4f1a\u4e25\u91cd\u5931\u771f\u3002Spectre\u7684Blotter\u7cfb\u7edf\u9ed8\u8ba4\u5c31\u652f\u6301\u8fd9\u4e9b\u53c2\u6570\uff0c\u522b\u5077\u61d2\u8df3\u8fc7\u3002<\/p>\n<\/blockquote>\n\n\n\n
\u4e94\u3001\u624b\u628a\u624b\u5b89\u88c5\u6559\u7a0b<\/strong><\/h2>\n\n\n\n
\u5b89\u88c5Spectre\u4e0d\u7b97\u590d\u6742\uff0c\u4f46\u6709\u51e0\u4e2a\u4f9d\u8d56\u9879\u9700\u8981\u6ce8\u610f\uff0c\u5c24\u5176\u662fGPU\u652f\u6301\u90e8\u5206\u3002\u8001\u4f59\u4e00\u6b65\u4e00\u6b65\u5e26\u4f60\u8d70\u3002<\/p>\n\n\n\n
\u524d\u7f6e\u6761\u4ef6<\/strong><\/h3>\n\n\n\n
    \n
  • Python 3.7+<\/strong>\uff08\u5efa\u8bae3.9\u4ee5\u4e0a\uff0c\u7a33\u5b9a\u6027\u66f4\u597d\uff09<\/li>\n\n\n\n
  • PyTorch 1.3+<\/strong>\uff08\u9700\u8981CUDA\u7248\u672c\u624d\u80fd\u7528GPU\u52a0\u901f\uff09<\/li>\n\n\n\n
  • Pandas 1.0+<\/strong><\/li>\n\n\n\n
  • NVIDIA GPU<\/strong>\uff08\u5efa\u8baeRTX\u7cfb\u5217\uff0c3090\/4090\u6548\u679c\u6700\u4f73\uff09<\/li>\n<\/ul>\n\n\n\n
    \u7b2c\u4e00\u6b65\uff1a\u5b89\u88c5PyTorch\uff08CUDA\u7248\uff09<\/strong><\/h3>\n\n\n\n
    \u53bbPyTorch\u5b98\u7f51\uff0c\u6839\u636e\u4f60\u7684\u7cfb\u7edf\u3001CUDA\u7248\u672c\u9009\u62e9\u5bf9\u5e94\u7684\u5b89\u88c5\u547d\u4ee4\u3002\u4ee5CUDA 11.8\u4e3a\u4f8b\uff1a<\/p>\n\n\n\n
    # \u5b89\u88c5PyTorch CUDA\u7248\u672c<\/em>\npip install torch torchvision torchaudio --index-url https:\/\/download.pytorch.org\/whl\/cu118<\/code><\/pre>\n\n\n\n
    \u7b2c\u4e8c\u6b65\uff1a\u5b89\u88c5\u4f9d\u8d56\u5e93<\/strong><\/h3>\n\n\n\n
    Spectre\u4e0d\u81ea\u5e26\u8fd9\u4e9b\u4f9d\u8d56\uff0c\u9700\u8981\u624b\u52a8\u5b89\u88c5\uff1a<\/p>\n\n\n\n
    pip install pyarrow pandas tqdm plotly requests beautifulsoup4 lxml<\/code><\/pre>\n\n\n\n
    \u7b2c\u4e09\u6b65\uff1a\u5b89\u88c5Spectre<\/strong><\/h3>\n\n\n\n
    \u4eceGitHub\u5b89\u88c5\uff0c\u7528--no-deps<\/code>\u8df3\u8fc7\u81ea\u52a8\u4f9d\u8d56\uff0c\u907f\u514d\u7248\u672c\u51b2\u7a81\uff1a<\/p>\n\n\n\n
    pip install --no-deps git+https:\/\/github.com\/Heerozh\/spectre.git<\/code><\/pre>\n\n\n\n
    \u7b2c\u56db\u6b65\uff1a\u9a8c\u8bc1\u5b89\u88c5<\/strong><\/h3>\n\n\n\n
    import\u00a0spectre\nprint(spectre.__version__) \u00a0# \u5e94\u8f93\u51fa\u7c7b\u4f3c '0.3.1'<\/em>\n\n# \u68c0\u67e5CUDA\u662f\u5426\u53ef\u7528<\/em>\nimport\u00a0torch\nprint(torch.cuda.is_available()) \u00a0# True\u8868\u793aGPU\u53ef\u7528<\/em><\/code><\/pre>\n\n\n\n
    \u7b2c\u4e94\u6b65\uff1a\u51c6\u5907\u6570\u636e<\/strong><\/h3>\n\n\n\n
    \u53ef\u4ee5\u7528yfinance\u4e0b\u8f7d\u5386\u53f2\u6570\u636e\uff0c\u5e76\u8f6c\u5b58\u4e3aArrow\u683c\u5f0f\uff08\u52a0\u8f7d\u66f4\u5feb\uff09\uff1a<\/p>\n\n\n\n
    import\u00a0yfinance\u00a0as\u00a0yf\nimport\u00a0pyarrow\u00a0as\u00a0pa\nimport\u00a0pyarrow.feather\u00a0as\u00a0feather\nimport\u00a0pandas\u00a0as\u00a0pd\n\n# \u5b9a\u4e49\u6807\u7684\u6c60<\/em>\ntickers = ['AAPL',\u00a0'MSFT',\u00a0'GOOGL']\n\n# \u4e0b\u8f7d\u6570\u636e<\/em>\ndata = yf.download(tickers, start='2010-01-01', end='2023-12-31')\ndata = data.stack(level=1).reset_index().rename(columns={'level_1':\u00a0'symbol'})\ndata.columns = [col.lower()\u00a0for\u00a0col\u00a0in\u00a0data.columns]\n\n# \u5b58\u4e3aArrow\u683c\u5f0f<\/em>\ntable =pa.Table.from_pandas(data)\nfeather.write_feather(table,\u00a0'yahoo_data.feather')<\/code><\/pre>\n\n\n\n
    \u5e38\u89c1\u5751<\/strong>
    1. GPU\u9a71\u52a8\u5fc5\u987b\u66f4\u65b0\u5230\u6700\u65b0\u7248\u672c\uff0c\u5426\u5219\u53ef\u80fdCUDA\u4e0d\u53ef\u7528\uff1b2. \u5927\u6570\u636e\u96c6\u5efa\u8bae\u5206\u5757\u52a0\u8f7d\uff0c\u907f\u514d\u663e\u5b58\u6ea2\u51fa\uff1b3. \u5982\u679c\u6ca1\u6709NVIDIA\u663e\u5361\uff0cSpectre\u4f1a\u81ea\u52a8\u964d\u7ea7\u4e3aCPU\u6a21\u5f0f\uff0c\u4f46\u901f\u5ea6\u4f18\u52bf\u5c31\u6ca1\u4e86\u3002<\/pre>\n\n\n\n
    \u516d\u3001\u5b9e\u6218\u6f14\u7ec3\uff1a\u4ece\u56e0\u5b50\u8ba1\u7b97\u5230\u5b8c\u6574\u56de\u6d4b<\/strong><\/h2>\n\n\n\n
    \u5b89\u88c5\u597d\u4e86\uff0c\u6211\u4eec\u6765\u8dd1\u51e0\u4e2a\u5b9e\u9645\u4f8b\u5b50\u3002\u4ece\u7b80\u5355\u5230\u590d\u6742\uff0c\u4e00\u6b65\u6b65\u6765\u3002<\/p>\n\n\n\n
    \u573a\u666f1\uff1a\u57fa\u7840\u56e0\u5b50\u8ba1\u7b97<\/strong><\/h3>\n\n\n\n
    \u52a0\u8f7d\u5386\u53f2\u6570\u636e\uff0c\u8ba1\u7b9750\u65e5\u5747\u7ebf\u548c14\u65e5RSI\uff1a<\/p>\n\n\n\n
    from\u00a0spectre\u00a0import\u00a0factors\nfrom\u00a0spectre.data\u00a0import\u00a0ArrowLoader\n\n# \u52a0\u8f7d\u6570\u636e<\/em>\nloader = ArrowLoader('yahoo_data.feather')\nengine = factors.FactorEngine(loader)\n\n# \u5207\u6362\u5230GPU\u6a21\u5f0f<\/em>\nengine.to_cuda()\n\n# \u6dfb\u52a0\u5185\u7f6e\u56e0\u5b50<\/em>\nengine.add(factors.SMA(50),\u00a0'sma50') \u00a0# 50\u65e5\u5747\u7ebf<\/em>engine.add(factors.RSI(14),\u00a0'rsi14') \u00a0# 14\u65e5RSI<\/em>\n\n# \u8fd0\u884c\u56e0\u5b50\u8ba1\u7b97<\/em>\ndf = engine.run('2020-01-01',\u00a0'2023-12-31')\nprint(df.head())<\/code><\/pre>\n\n\n\n
    \u8f93\u51fa\u7684DataFrame\u5305\u542b\u6bcf\u4e2a\u6807\u7684\u5728\u6bcf\u4e2a\u65e5\u671f\u7684\u56e0\u5b50\u503c\u3002\u5728\u5b9e\u9645\u4f7f\u7528\u4e2d\uff0c\u4f60\u53ef\u4ee5\u7528RSI\u7b5b\u9009\u8d85\u4e70\u533a\u57df\uff0c\u7528\u5747\u7ebf\u5224\u65ad\u8d8b\u52bf\u65b9\u5411\uff0c\u8fd9\u4e9b\u90fd\u662f\u5e38\u89c1\u7684\u56e0\u5b50\u5e94\u7528\u65b9\u5f0f\u3002<\/p>\n\n\n\n
    \u573a\u666f2\uff1a\u81ea\u5b9a\u4e49\u56e0\u5b50<\/strong><\/h3>\n\n\n\n
    \u5185\u7f6e\u56e0\u5b50\u4e0d\u591f\u7528\uff1f\u4f60\u53ef\u4ee5\u81ea\u5df1\u9020\u3002\u4e0b\u9762\u662f\u4e00\u4e2a”\u52a8\u91cf-\u6ce2\u52a8\u7387\u590d\u5408\u56e0\u5b50”\u7684\u4f8b\u5b50\u2014\u2014\u8861\u91cf\u7684\u662f\u98ce\u9669\u8c03\u6574\u540e\u7684\u52a8\u91cf\u8868\u73b0\uff1a<\/p>\n\n\n\n
    from\u00a0spectre.factors\u00a0import\u00a0Factor, LogReturns, EMA\n\nclass\u00a0MomentumVol(Factor):\n\u00a0 \u00a0 inputs = [LogReturns(1)]\n\u00a0 \u00a0 window =\u00a020\n\n\u00a0 \u00a0\u00a0def\u00a0compute(self, close):\n\u00a0 \u00a0 \u00a0 \u00a0 ret = self.inputs[0]\n\u00a0 \u00a0 \u00a0 \u00a0 mom = EMA(ret,\u00a020) \u00a0# \u6536\u76ca\u7387\u7684\u6307\u6570\u79fb\u52a8\u5e73\u5747<\/em>\n\u00a0 \u00a0 \u00a0 \u00a0 vol = mom.rolling_std(20) \u00a0# \u6ce2\u52a8\u7387<\/em>\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0return\u00a0mom \/ vol \u00a0# \u98ce\u9669\u8c03\u6574\u540e\u7684\u52a8\u91cf<\/em>\n\nengine.add(MomentumVol(),\u00a0'mom_vol')\ndf = engine.run('2020-01-01',\u00a0'2023-12-31')<\/code><\/pre>\n\n\n\n
    \u8fd9\u4e2a\u56e0\u5b50\u7684\u542b\u4e49\u5f88\u76f4\u89c2\uff1a\u52a8\u91cf\u9ad8\u4f46\u6ce2\u52a8\u7387\u4f4e\u7684\u6807\u7684\u5f97\u5206\u66f4\u9ad8\uff0c\u9002\u5408\u7528\u6765\u7b5b\u9009\u98ce\u9669\u6536\u76ca\u6bd4\u8f83\u597d\u7684\u54c1\u79cd\u3002\u5728\u7ec4\u5408\u6784\u5efa\u4e2d\uff0c\u8fd9\u79cd\u56e0\u5b50\u53ef\u4ee5\u4f5c\u4e3a\u6743\u91cd\u5206\u914d\u7684\u53c2\u8003\u4f9d\u636e\u3002<\/p>\n\n\n\n
    \u573a\u666f3\uff1a\u5b8c\u6574\u56de\u6d4b\u6d41\u7a0b<\/strong><\/h3>\n\n\n\n
    \u56e0\u5b50\u7b97\u5b8c\u4e86\uff0c\u6765\u8dd1\u4e00\u4e2a\u5b8c\u6574\u7684\u7b56\u7565\u56de\u6d4b\u2014\u2014\u52a8\u91cf\u8f6e\u52a8\u7b56\u7565\uff0c\u6bcf\u6708\u5e95\u8c03\u4ed3\uff1a<\/p>\n\n\n\n
    from\u00a0spectre.trading\u00a0import\u00a0CustomAlgorithm, Backtest\nfrom\u00a0spectre.portfolio\u00a0import\u00a0Blotter\n\nclassMomentumStrategy(CustomAlgorithm):\n\u00a0 \u00a0\u00a0definitialize(self):\n\u00a0 \u00a0 \u00a0 \u00a0 self.add(factors.Momentum(252),\u00a0'mom')\n\u00a0 \u00a0 \u00a0 \u00a0 self.schedule_rebalance('month_end')\n\n\u00a0 \u00a0\u00a0defrebalance(self):\n\u00a0 \u00a0 \u00a0 \u00a0 mom = self.get_values('mom')\n\u00a0 \u00a0 \u00a0 \u00a0 longs = mom[mom >\u00a00].index\n\u00a0 \u00a0 \u00a0 \u00a0 self.portfolio.set_target_percent(\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {sym:\u00a01\/len(longs)\u00a0for\u00a0sym\u00a0in\u00a0longs}\n\u00a0 \u00a0 \u00a0 \u00a0 )\n\n\u00a0 \u00a0\u00a0defterminate(self):\n\u00a0 \u00a0 \u00a0 \u00a0 self.portfolio.plot_returns()\n\n# \u8fd0\u884c\u56de\u6d4b<\/em>\nloader = ArrowLoader('yahoo_data.feather')\nalgo = MomentumStrategy(loader)\nbacktest = Backtest(algo)\nresults = backtest.run('2014-01-01',\u00a0'2018-12-31')\nprint(results.summary())<\/code><\/pre>\n\n\n\n
    \u52a0\u4e0a\u624b\u7eed\u8d39\u548c\u6ed1\u70b9\u4e5f\u5f88\u7b80\u5355\uff1a<\/p>\n\n\n\n
    blotter = Blotter(commission=0.001, slippage=0.0005)\nalgo.blotter = blotter<\/code><\/pre>\n\n\n\n
    \u56de\u6d4b\u7ed3\u679c\u4f1a\u8f93\u51fa\u590f\u666e\u6bd4\u7387\u3001\u5e74\u5316\u6536\u76ca\u3001\u6700\u5927\u56de\u64a4\u7b49\u6838\u5fc3\u7ee9\u6548\u6307\u6807\u3002\u5982\u679c\u9700\u8981\u66f4\u6df1\u5165\u7684\u56e0\u5b50\u5206\u6790\uff0c\u53ef\u4ee5\u5bf9\u63a5Alphalens\uff1a<\/p>\n\n\n\n
    import\u00a0alphalens\u00a0as\u00a0al\n\nfactor_data = engine.run(\n\u00a0 \u00a0\u00a0'2014-01-01',\u00a0'2018-12-31',\n\u00a0 \u00a0 forward_returns_periods=[1,\u00a05,\u00a021]\n)\nal.tears.create_full_tear_sheet(factor_data)<\/code><\/pre>\n\n\n\n
    \u8fd9\u4f1a\u751f\u6210\u5b8c\u6574\u7684\u56e0\u5b50\u5206\u6790\u62a5\u544a\uff0c\u5305\u62ec\u5206\u4f4d\u6570\u6536\u76ca\u56fe\u3001IC\u503c\u8d70\u52bf\u7b49\uff0c\u662f\u4e13\u4e1a\u6295\u7814\u62a5\u544a\u7684\u6807\u51c6\u914d\u7f6e\u3002<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \u4e03\u3001\u8fdb\u9636\u73a9\u6cd5\uff1a\u4e0e\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u96c6\u6210<\/strong><\/h2>\n\n\n\n
    Spectre\u57fa\u4e8ePyTorch\uff0c\u8fd9\u610f\u5473\u7740\u4f60\u53ef\u4ee5\u76f4\u63a5\u628a\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u5d4c\u5165\u56e0\u5b50\u8ba1\u7b97\u6d41\u7a0b\u3002\u6bd4\u5982\u7528\u5168\u8fde\u63a5\u7f51\u7edc\u9884\u6d4b\u6536\u76ca\uff1a<\/p>\n\n\n\n
    import\u00a0torch.nn\u00a0as\u00a0nn\n\nclass\u00a0NeuralFactor(Factor):\n\u00a0 \u00a0\u00a0def\u00a0compute(self, inputs):\n\u00a0 \u00a0 \u00a0 \u00a0 model = nn.Sequential(\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 nn.Linear(10,\u00a05),\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 nn.ReLU(),\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 nn.Linear(5,\u00a01)\n\u00a0 \u00a0 \u00a0 \u00a0 )\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0return\u00a0model(inputs) \u00a0# \u7b80\u5316\u793a\u4f8b\uff0c\u5b9e\u9645\u9700\u5355\u72ec\u8bad\u7ec3<\/em>\n\nengine.add(NeuralFactor(inputs=[factors.SMA(10), factors.RSI(14)]),\u00a0'neural_pred')<\/code><\/pre>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \u8fd9\u6253\u5f00\u4e86AI\u9a71\u52a8\u56e0\u5b50\u7684\u60f3\u8c61\u7a7a\u95f4\uff1a\u7528\u795e\u7ecf\u7f51\u7edc\u9884\u6d4b\u6536\u76ca\u3001\u7528\u5f3a\u5316\u5b66\u4e60\u4f18\u5316\u8c03\u4ed3\u903b\u8f91\uff0c\u8fd9\u4e9b\u90fd\u662f\u4f20\u7edf\u56e0\u5b50\u5e93\u505a\u4e0d\u5230\u7684\u4e8b\u60c5\u3002\u5bf9\u4e8e\u6709ML\u80cc\u666f\u7684\u91cf\u5316\u56e2\u961f\u6765\u8bf4\uff0cSpectre\u7684PyTorch\u57fa\u56e0\u662f\u4e00\u4e2a\u5de8\u5927\u7684\u52a0\u5206\u9879\u3002<\/p>\n\n\n\n
    \u516b\u3001Spectre vs \u4e3b\u6d41\u91cf\u5316\u5e93\u5bf9\u6bd4<\/strong><\/h2>\n\n\n\n
    \u5149\u8bf4\u4e0d\u7ec3\u5047\u628a\u5f0f\uff0c\u6211\u628aSpectre\u548c\u51e0\u4e2a\u4e3b\u6d41Python\u91cf\u5316\u5e93\u653e\u5728\u4e00\u8d77\u5bf9\u6bd4\u4e00\u4e0b\uff1a<\/p>\n\n\n\n
    \u7279\u6027<\/strong><\/th>Spectre<\/strong><\/th>Zipline<\/strong><\/th>Backtrader<\/strong><\/th><\/tr>
    \u8ba1\u7b97\u67b6\u6784<\/td>GPU (CUDA)<\/td>CPU<\/td>CPU<\/td><\/tr>
    \u52a0\u901f\u6bd4 (vs CPU)<\/td>\u6700\u9ad877x<\/td>1x<\/td>1x<\/td><\/tr>
    \u56e0\u5b50\u5f15\u64ce<\/td>DAG\u6709\u5411\u65e0\u73af\u56fe<\/td>\u65e0\u72ec\u7acb\u56e0\u5b50\u5f15\u64ce<\/td>\u6307\u6807\u5e93<\/td><\/tr>
    \u81ea\u5b9a\u4e49\u56e0\u5b50<\/td>\u652f\u6301<\/td>\u6709\u9650\u652f\u6301<\/td>\u652f\u6301<\/td><\/tr>
    ML\u96c6\u6210<\/td>\u539f\u751fPyTorch<\/td>\u9700\u5916\u90e8\u96c6\u6210<\/td>\u9700\u5916\u90e8\u96c6\u6210<\/td><\/tr>
    \u524d\u89c6\u504f\u5dee\u9632\u62a4<\/td>DAG\u81ea\u52a8\u9632\u62a4<\/td>\u90e8\u5206\u652f\u6301<\/td>\u9700\u624b\u52a8\u5904\u7406<\/td><\/tr>
    \u6570\u636e\u683c\u5f0f<\/td>CSV\/Arrow\/Quandl<\/td>Bundle\u683c\u5f0f<\/td>CSV\/Pandas<\/td><\/tr>
    \u624b\u7eed\u8d39\/\u6ed1\u70b9\u6a21\u62df<\/td>\u5185\u7f6eBlotter<\/td>\u5185\u7f6e<\/td>\u5185\u7f6e<\/td><\/tr>
    \u5206\u6790\u5de5\u5177\u5bf9\u63a5<\/td>Pyfolio\/Alphalens<\/td>Pyfolio<\/td>\u9700\u81ea\u884c\u5bf9\u63a5<\/td><\/tr>
    \u9879\u76ee\u7ef4\u62a4\u72b6\u6001<\/td>\u793e\u533a\u7ef4\u62a4\u4e2d<\/td>\u5df2\u505c\u6b62\u66f4\u65b0<\/td>\u793e\u533a\u7ef4\u62a4\u4e2d<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    \u4ece\u5bf9\u6bd4\u53ef\u4ee5\u770b\u51fa\uff0cSpectre\u5728\u901f\u5ea6\u548cML\u96c6\u6210\u65b9\u9762\u6709\u660e\u663e\u4f18\u52bf\uff0c\u4f46\u5728\u751f\u6001\u6210\u719f\u5ea6\u548c\u7ef4\u62a4\u6d3b\u8dc3\u5ea6\u4e0a\u8fd8\u6709\u5dee\u8ddd\u3002\u9009\u62e9\u54ea\u4e2a\u5de5\u5177\uff0c\u53d6\u51b3\u4e8e\u4f60\u7684\u5177\u4f53\u9700\u6c42\u548c\u786c\u4ef6\u6761\u4ef6\u3002<\/p>\n\n\n\n
    \u4e5d\u3001Spectre\u7684\u4e94\u5927\u4f18\u52bf<\/strong><\/h2>\n\n\n\n
    \u7528\u4e86\u8fd9\u4e48\u4e45\uff0c\u6211\u628aSpectre\u6700\u6838\u5fc3\u7684\u51e0\u4e2a\u4f18\u52bf\u603b\u7ed3\u4e00\u4e0b\uff1a<\/p>\n\n\n\n
    1. \u901f\u5ea6\u78be\u538b\uff0c\u6ca1\u6709\u5bf9\u624b<\/strong><\/h3>\n\n\n\n
    GPU\u52a0\u901f\u5e26\u6765\u7684\u901f\u5ea6\u63d0\u5347\u662f\u5b9e\u5b9e\u5728\u5728\u7684\u300277\u500d\u7684\u52a0\u901f\u6bd4\u610f\u5473\u7740\u4f60\u539f\u6765\u7b49\u4e00\u4e2a\u4e0b\u5348\u7684\u7ed3\u679c\uff0c\u73b0\u5728\u51e0\u5206\u949f\u5c31\u80fd\u62ff\u5230\u3002\u7814\u7a76\u6548\u7387\u7684\u63d0\u5347\u76f4\u63a5\u8f6c\u5316\u4e3a\u7b56\u7565\u8fed\u4ee3\u7684\u6548\u7387\uff0c\u6700\u7ec8\u4f53\u73b0\u5728\u4e1a\u7ee9\u4e0a\u3002<\/p>\n\n\n\n
    2. \u786c\u4ef6\u6210\u672c\u4f4e\uff0c\u6027\u4ef7\u6bd4\u9ad8<\/strong><\/h3>\n\n\n\n
    \u4e00\u5f20\u6d88\u8d39\u7ea7RTX 3090\/4090\u5c31\u80fd\u8dd1\u8d77\u6765\uff0c\u4e0d\u9700\u8981\u6602\u8d35\u7684\u4e91\u8ba1\u7b97\u96c6\u7fa4\u3002\u4e2a\u4eba\u6295\u8d44\u8005\u548c\u4e2d\u5c0f\u56e2\u961f\u90fd\u80fd\u8d1f\u62c5\u5f97\u8d77\uff0c\u628a\u539f\u6765\u53ea\u6709\u5927\u673a\u6784\u624d\u6709\u7684\u7b97\u529b\u62c9\u5230\u4e86\u666e\u901a\u4eba\u7684\u684c\u9762\u3002<\/p>\n\n\n\n
    3. \u751f\u6001\u517c\u5bb9\uff0c\u65e0\u7f1d\u8854\u63a5<\/strong><\/h3>\n\n\n\n
    \u57fa\u4e8ePyTorch\uff0c\u5929\u7136\u5bf9\u63a5\u6df1\u5ea6\u5b66\u4e60\u751f\u6001\uff1b\u517c\u5bb9Pyfolio\u548cAlphalens\uff0c\u4ece\u56e0\u5b50\u7814\u7a76\u5230\u7ee9\u6548\u5206\u6790\u4e00\u6761\u9f99\u3002\u4e0d\u9700\u8981\u5728\u4e0d\u540c\u5de5\u5177\u4e4b\u95f4\u53cd\u590d\u5207\u6362\uff0c\u7814\u7a76\u6d41\u7a0b\u987a\u7545\u5f88\u591a\u3002<\/p>\n\n\n\n
    4. \u5f00\u6e90\u514d\u8d39\uff0c\u6ca1\u6709\u9501\u5b9a<\/strong><\/h3>\n\n\n\n
    MIT\u534f\u8bae\u5f00\u6e90\uff0c\u4ee3\u7801\u900f\u660e\uff0c\u53ef\u4ee5\u81ea\u7531\u4fee\u6539\u548c\u6269\u5c55\u3002\u5bf9\u4e8e\u6709\u5b9a\u5236\u5316\u9700\u6c42\u7684\u56e2\u961f\u6765\u8bf4\uff0c\u8fd9\u610f\u5473\u7740\u4e0d\u4f1a\u88ab\u56f0\u5728\u4efb\u4f55\u5546\u4e1a\u8f6f\u4ef6\u7684\u751f\u6001\u91cc\u3002<\/p>\n\n\n\n
    5. \u524d\u89c6\u504f\u5dee\u9632\u62a4\uff0c\u56de\u6d4b\u66f4\u53ef\u4fe1<\/strong><\/h3>\n\n\n\n
    DAG\u56e0\u5b50\u5f15\u64ce\u81ea\u52a8\u5904\u7406\u4f9d\u8d56\u5173\u7cfb\u548c\u65f6\u5e8f\u504f\u79fb\uff0c\u51cf\u5c11\u4e86\u4eba\u4e3a\u5931\u8bef\u5bfc\u81f4\u56de\u6d4b\u5931\u771f\u7684\u98ce\u9669\u3002\u5728\u91cf\u5316\u7814\u7a76\u4e2d\uff0c\u4e00\u4e2a\u6ca1\u6709\u504f\u5dee\u7684\u56de\u6d4b\u7ed3\u679c\u6bd4\u4e00\u4e2a\u6f02\u4eae\u4f46\u5931\u771f\u7684\u7ed3\u679c\u91cd\u8981\u4e00\u767e\u500d\u3002<\/p>\n\n\n\n
    \u5341\u3001\u5ba2\u89c2\u8bf4\u7f3a\u70b9\uff1aSpectre\u7684\u516d\u4e2a\u77ed\u677f<\/strong><\/h2>\n\n\n\n
    \u8bf4\u5b8c\u597d\u7684\uff0c\u4e5f\u5f97\u8bf4\u8bf4\u4e0d\u8db3\u3002\u505a\u51b3\u7b56\u5fc5\u987b\u5168\u9762\u4e86\u89e3\uff0c\u4e0d\u80fd\u53ea\u542c\u597d\u8bdd\u3002<\/p>\n\n\n\n
    \u77ed\u677f<\/strong><\/th>\u5177\u4f53\u8868\u73b0<\/strong><\/th>\u5f71\u54cd\u7a0b\u5ea6<\/strong><\/th><\/tr>
    GPU\u786c\u4ef6\u4f9d\u8d56<\/td>\u6ca1\u6709NVIDIA\u663e\u5361\u5c31\u53ea\u80fd\u7528CPU\uff0c\u901f\u5ea6\u4f18\u52bf\u5f52\u96f6<\/td>\u9ad8<\/td><\/tr>
    \u7ef4\u62a4\u8282\u594f\u504f\u6162<\/td>\u6700\u8fd1\u4e00\u6b21\u5927\u7248\u672c\u66f4\u65b0\u57282020\u5e74\uff0c\u540e\u7eed\u4e3b\u8981\u662f\u5c0f\u4fee\u5c0f\u8865<\/td>\u4e2d\u9ad8<\/td><\/tr>
    \u6570\u636e\u683c\u5f0f\u6709\u9650<\/td>\u5bf9tick\u7ea7\u7b49\u975e\u6807\u6570\u636e\u683c\u5f0f\u652f\u6301\u4e0d\u8db3\uff0c\u9700\u81ea\u5199Loader<\/td>\u4e2d<\/td><\/tr>
    \u5b66\u4e60\u95e8\u69db\u4e0d\u4f4e<\/td>\u9700\u8981Python+\u91cf\u5316+GPU\u7f16\u7a0b\u57fa\u7840\uff0c\u65b0\u624b\u5165\u95e8\u6709\u96be\u5ea6<\/td>\u4e2d<\/td><\/tr>
    \u8d85\u5927\u89c4\u6a21\u53d7\u9650<\/td>\u4e07\u53ea\u4ee5\u4e0a\u6807\u7684\u7684\u56e0\u5b50\u8ba1\u7b97\u4f1a\u9047\u5230\u663e\u5b58\u74f6\u9888\uff0c\u539f\u751f\u4e0d\u652f\u6301\u5206\u5e03\u5f0f<\/td>\u4e2d<\/td><\/tr>
    \u7f3a\u5c11\u4f01\u4e1a\u7ea7\u7279\u6027<\/td>\u65e0\u5408\u89c4\u68c0\u67e5\u3001\u65e0\u5b9e\u76d8\u63a5\u53e3\uff0c\u4e0d\u9002\u5408\u76f4\u63a5\u7528\u4e8e\u751f\u4ea7\u73af\u5883<\/td>\u4e2d\u4f4e<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    \u7279\u522b\u63d0\u9192<\/strong>
    \u7ef4\u62a4\u8282\u594f\u662f\u6700\u503c\u5f97\u5173\u6ce8\u7684\u70b9\u3002\u91cf\u5316\u9886\u57df\u6570\u636e\u6807\u51c6\u548c\u76d1\u7ba1\u8981\u6c42\u53d8\u5316\u5f88\u5feb\uff0c\u4e00\u4e2a\u957f\u671f\u4e0d\u66f4\u65b0\u7684\u5de5\u5177\u53ef\u80fd\u5728\u65b0\u73af\u5883\u4e0b\u51fa\u73b0\u517c\u5bb9\u6027\u95ee\u9898\u3002\u5efa\u8bae\u5728\u751f\u4ea7\u4f7f\u7528\u524d\u505a\u597d\u5145\u5206\u6d4b\u8bd5\uff0c\u5173\u6ce8\u793e\u533a\u52a8\u6001\u3002<\/pre>\n\n\n\n
    \u5341\u4e00\u3001\u5b9e\u9645\u5e94\u7528\u573a\u666f\uff1a\u8c01\u9002\u5408\u7528Spectre\uff1f<\/strong><\/h2>\n\n\n\n
    \u8bf4\u4e86\u8fd9\u4e48\u591a\u4f18\u7f3a\u70b9\uff0c\u90a3\u5230\u5e95\u8c01\u8be5\u7528Spectre\uff1f\u6211\u6309\u573a\u666f\u62c6\u4e00\u4e0b\uff1a<\/p>\n\n\n\n
    \u573a\u666fA\uff1a\u91cf\u5316\u6295\u7814\u56e2\u961f<\/strong><\/h3>\n\n\n\n
    \u5982\u679c\u4f60\u662f\u505a\u56e0\u5b50\u7814\u7a76\u7684\u6295\u7814\u56e2\u961f\uff0c\u6bcf\u5929\u9700\u8981\u6d4b\u8bd5\u5927\u91cf\u56e0\u5b50\u7ec4\u5408\uff0cSpectre\u7684GPU\u52a0\u901f\u80fd\u628a\u7814\u7a76\u6548\u7387\u63d0\u5347\u4e00\u4e2a\u91cf\u7ea7\u3002\u7279\u522b\u662f\u5728\u5e02\u573a\u6ce2\u52a8\u52a0\u5927\u7684\u65f6\u5019\uff0c\u5feb\u901f\u8fed\u4ee3\u56e0\u5b50\u7b56\u7565\u7684\u80fd\u529b\u5c31\u662f\u7ade\u4e89\u529b\u3002<\/p>\n\n\n\n
    \u573a\u666fB\uff1a\u4e2a\u4eba\u91cf\u5316\u5f00\u53d1\u8005<\/strong><\/h3>\n\n\n\n
    \u624b\u91cc\u6709\u4e00\u5f20RTX\u663e\u5361\u7684\u72ec\u7acb\u7814\u7a76\u8005\uff0cSpectre\u8ba9\u4f60\u5728\u5bb6\u5c31\u80fd\u62e5\u6709\u63a5\u8fd1\u673a\u6784\u7ea7\u522b\u7684\u56de\u6d4b\u80fd\u529b\u3002\u914d\u5408yfinance\u514d\u8d39\u6570\u636e\uff0c\u96f6\u6210\u672c\u642d\u5efa\u4e13\u4e1a\u91cf\u5316\u7814\u7a76\u73af\u5883\u3002<\/p>\n\n\n\n
    \u573a\u666fC\uff1aAI+\u91cf\u5316\u878d\u5408\u56e2\u961f<\/strong><\/h3>\n\n\n\n
    PyTorch\u57fa\u56e0\u8ba9Spectre\u6210\u4e3aAI\u56e0\u5b50\u7814\u7a76\u7684\u5929\u7136\u5e73\u53f0\u3002\u4f60\u53ef\u4ee5\u76f4\u63a5\u5728\u56e0\u5b50\u8ba1\u7b97\u6d41\u7a0b\u4e2d\u5d4c\u5165\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\uff0c\u5b9e\u73b0\u4f20\u7edf\u56e0\u5b50\u548cAI\u56e0\u5b50\u7684\u6df7\u5408\u7814\u7a76\uff0c\u8fd9\u662f\u4f20\u7edf\u91cf\u5316\u5e93\u505a\u4e0d\u5230\u7684\u3002<\/p>\n\n\n\n
    \u573a\u666fD\uff1a\u98ce\u9669\u7ba1\u7406\u4e0e\u538b\u529b\u6d4b\u8bd5<\/strong><\/h3>\n\n\n\n
    \u4e0d\u53ea\u662f\u505a\u7b56\u7565\uff0cSpectre\u7684\u9ad8\u6027\u80fd\u4e5f\u9002\u5408\u505a\u5927\u89c4\u6a21\u98ce\u9669\u6a21\u62df\u3002\u6bd4\u5982\u6a21\u62df\u4e0d\u540c\u5e02\u573a\u73af\u5883\u4e0b\u7684\u7ec4\u5408\u8868\u73b0\uff0c\u8bc4\u4f30\u6781\u7aef\u884c\u60c5\u4e0b\u7684\u98ce\u9669\u655e\u53e3\uff0c\u8fd9\u4e9b\u573a\u666f\u90fd\u9700\u8981\u5927\u91cf\u8ba1\u7b97\u3002<\/p>\n\n\n\n
    \u8001\u4f59\u5efa\u8bae<\/strong>
    \u5982\u679c\u4f60\u53ea\u662f\u505a\u7b80\u5355\u7684\u7b56\u7565\u9a8c\u8bc1\uff0cZipline\u6216Backtrader\u5c31\u591f\u7528\u4e86\u3002\u4f46\u5982\u679c\u4f60\u9700\u8981\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u3001\u9ad8\u9891\u56e0\u5b50\u8ba1\u7b97\u3001\u6216\u8005\u60f3\u628aML\u878d\u5165\u91cf\u5316\u6d41\u7a0b\uff0cSpectre\u503c\u5f97\u8ba4\u771f\u5c1d\u8bd5\u3002\u4ece\u5c0f\u7684\u6570\u636e\u96c6\u5f00\u59cb\u8dd1\uff0c\u9a8c\u8bc1\u6548\u679c\u540e\u518d\u9010\u6b65\u6269\u5927\u89c4\u6a21\u3002<\/pre>\n\n\n\n
    \u5341\u4e8c\u3001\u672a\u6765\u5c55\u671b\uff1aGPU\u91cf\u5316\u751f\u6001\u4f1a\u8d70\u5411\u54ea\u91cc\uff1f<\/strong><\/h2>\n\n\n\n
    \u7ad9\u5728\u66f4\u5927\u7684\u89c6\u89d2\u770b\uff0cSpectre\u4ee3\u8868\u7684GPU\u52a0\u901f\u91cf\u5316\u53ea\u662f\u5f00\u59cb\u3002\u968f\u7740GPU\u7b97\u529b\u6301\u7eed\u589e\u957f\uff08RTX 5090\u5df2\u7ecf\u7bad\u5728\u5f26\u4e0a\uff09\u3001\u663e\u5b58\u8d8a\u6765\u8d8a\u5927\u3001CUDA\u751f\u6001\u8d8a\u6765\u8d8a\u5b8c\u5584\uff0c\u91cf\u5316\u7814\u7a76\u7684\u8ba1\u7b97\u8303\u5f0f\u6b63\u5728\u53d1\u751f\u6839\u672c\u6027\u53d8\u5316\u3002<\/p>\n\n\n\n
    \u51e0\u4e2a\u503c\u5f97\u5173\u6ce8\u7684\u8d8b\u52bf\uff1a<\/p>\n\n\n\n
    \u8d8b\u52bf\u4e00\uff1aGPU\u5c06\u6210\u4e3a\u91cf\u5316\u7814\u7a76\u7684\u6807\u914d\u3002<\/strong>\u5c31\u50cf10\u5e74\u524dGPU\u6539\u53d8\u4e86\u6df1\u5ea6\u5b66\u4e60\u4e00\u6837\uff0c\u5b83\u6b63\u5728\u6539\u53d8\u91cf\u5316\u7814\u7a76\u7684\u65b9\u5f0f\u3002\u672a\u6765\u4e0d\u4f1a\u7528GPU\u52a0\u901f\u7684\u91cf\u5316\u56e2\u961f\uff0c\u5c31\u50cf\u4eca\u5929\u8fd8\u5728\u7528CPU\u8bad\u7ec3\u795e\u7ecf\u7f51\u7edc\u4e00\u6837\u4f4e\u6548\u3002<\/p>\n\n\n\n
    \u8d8b\u52bf\u4e8c\uff1aAI\u56e0\u5b50\u5c06\u6210\u4e3a\u4e3b\u6d41\u3002<\/strong>\u4f20\u7edf\u56e0\u5b50\u6316\u6398\u9760\u4eba\u5de5\uff0c\u6548\u7387\u4f4e\u3001\u8986\u76d6\u7a84\u3002\u7528\u795e\u7ecf\u7f51\u7edc\u81ea\u52a8\u53d1\u73b0\u56e0\u5b50\uff0c\u518d\u7528GPU\u9ad8\u901f\u9a8c\u8bc1\uff0c\u8fd9\u4e2a\u95ed\u73af\u4f1a\u8d8a\u6765\u8d8a\u6210\u719f\u3002Spectre\u7684PyTorch\u57fa\u7840\u8ba9\u5b83\u5728\u8fd9\u4e2a\u65b9\u5411\u4e0a\u6709\u5929\u7136\u4f18\u52bf\u3002<\/p>\n\n\n\n
    \u8d8b\u52bf\u4e09\uff1a\u5f00\u6e90\u91cf\u5316\u751f\u6001\u4f1a\u52a0\u901f\u6210\u719f\u3002<\/strong>Spectre\u867d\u7136\u6709\u7ef4\u62a4\u8282\u594f\u7684\u95ee\u9898\uff0c\u4f46\u5b83\u8bc1\u660e\u4e86GPU\u91cf\u5316\u5de5\u5177\u7684\u53ef\u884c\u6027\u3002\u672a\u6765\u4e00\u5b9a\u4f1a\u6709\u66f4\u591a\u7c7b\u4f3c\u7684\u5f00\u6e90\u9879\u76ee\u51fa\u73b0\uff0c\u529f\u80fd\u66f4\u5b8c\u5584\u3001\u793e\u533a\u66f4\u6d3b\u8dc3\u3002<\/p>\n\n\n\n
    \u5199\u5728\u6700\u540e<\/strong><\/h2>\n\n\n\n
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    \u5de5\u5177\u6c38\u8fdc\u662f\u624b\u6bb5\uff0c\u7b56\u7565\u624d\u662f\u6838\u5fc3\u3002\u518d\u5feb\u7684\u8ba1\u7b97\u901f\u5ea6\uff0c\u4e5f\u6551\u4e0d\u4e86\u4e00\u4e2a\u903b\u8f91\u6709\u95ee\u9898\u7684\u7b56\u7565\u3002\u4f46\u4e00\u4e2a\u597d\u7b56\u7565\u914d\u4e0a\u8db3\u591f\u5feb\u7684\u9a8c\u8bc1\u5de5\u5177\uff0c\u786e\u5b9e\u80fd\u5e2e\u4f60\u66f4\u5feb\u5730\u627e\u5230\u5bf9\u7684\u65b9\u5411\u3002<\/p>\n\n\n\n
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