{"id":3232,"date":"2025-12-26T07:45:00","date_gmt":"2025-12-25T23:45:00","guid":{"rendered":"https:\/\/www.laoyulaoyu.com\/?p=3232"},"modified":"2025-12-13T23:48:19","modified_gmt":"2025-12-13T15:48:19","slug":"%e5%bc%80%e6%ba%90%e5%b7%a5%e5%85%b7%e6%b8%85%e5%8d%95%e9%87%8f%e5%8c%96%e5%ad%a6%e4%b9%a0%e8%b7%af%e5%be%84%ef%bc%9a%e6%99%ae%e9%80%9a%e6%8a%95%e8%b5%84%e8%80%85%e4%b8%8e%e5%ad%a6%e7%94%9f%e7%9a%84","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2025\/12\/26\/%e5%bc%80%e6%ba%90%e5%b7%a5%e5%85%b7%e6%b8%85%e5%8d%95%e9%87%8f%e5%8c%96%e5%ad%a6%e4%b9%a0%e8%b7%af%e5%be%84%ef%bc%9a%e6%99%ae%e9%80%9a%e6%8a%95%e8%b5%84%e8%80%85%e4%b8%8e%e5%ad%a6%e7%94%9f%e7%9a%84\/","title":{"rendered":"\u5f00\u6e90\u5de5\u5177\u6e05\u5355+\u91cf\u5316\u5b66\u4e60\u8def\u5f84\uff1a\u666e\u901a\u6295\u8d44\u8005\u4e0e\u5b66\u751f\u7684\u5b9e\u7528\u6307\u5357"},"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>\u65e0\u8bba\u4f60\u662f\u611f\u89c9\u5927\u5b66\u7406\u8bba\u4e0e\u5b9e\u8df5\u8131\u8282\u7684\u5b66\u5b50\uff0c\u8fd8\u662f\u5728\u6295\u8d44\u4e2d\u65f6\u5e38\u611f\u5230\u8ff7\u832b\u7684\u63a2\u7d22\u8005\uff0c\u8fd9\u7bc7\u6587\u7ae0\u90fd\u662f\u4e3a\u4f60\u5199\u7684\u3002\u4e0e\u5176\u4f9d\u8d56\u6602\u8d35\u8bfe\u7a0b\uff0c\u4e0d\u5982\u5229\u7528\u597d\u514d\u8d39\u7684\u5f00\u6e90\u5de5\u5177\u3002<\/mark>\u4eca\u5929\u5c31\u6211\u900918\u4e2a\u5b9e\u7528\u5f00\u6e90\u9879\u76ee\uff0c\u8fde\u540c\u4e00\u5957\u8fdb\u9636\u5b66\u4e60\u8def\u7ebf\u56fe\uff0c\u5168\u90e8\u5206\u4eab\u7ed9\u4f60\u3002\u5b83\u4eec\u662f\u4e13\u4e1a\u4eba\u58eb\u7684\u5f97\u529b\u52a9\u624b\uff0c\u66f4\u662f\u4f60\u6b66\u88c5\u81ea\u5df1\u3001\u6784\u5efa\u79d1\u5b66\u5206\u6790\u4f53\u7cfb\u3001\u5b9e\u73b0\u80fd\u529b\u8dc3\u5347\u7684\u6709\u6548\u8def\u5f84\u3002<\/pre>\n<\/blockquote>\n\n\n\n
\u4e00\u3001\u4f60\u7684\u56f0\u60d1\uff0c\u6211\u61c2\uff1a\u5b66\u751f\u4e0e\u6295\u8d44\u8005\u7684\u201c\u4e24\u5ea7\u5927\u5c71\u201d<\/strong><\/h2>\n\n\n\n
\u5927\u5bb6\u597d\uff0c\u6211\u662f\u8001\u4f59\u3002\u6211\u7684\u540e\u53f0\uff0c\u51e0\u4e4e\u6bcf\u5929\u90fd\u80fd\u770b\u5230\u4e24\u7c7b\u670b\u53cb\u7684\u79c1\u4fe1\uff0c\u4ed6\u4eec\u7684\u56f0\u60d1\u60ca\u4eba\u5730\u76f8\u4f3c\u3002<\/p>\n\n\n\n
\u7b2c\u4e00\u7c7b\uff0c\u662f\u8ff7\u832b\u7684\u5728\u6821\u5927\u5b66\u751f\uff08\u5305\u62ec\u7559\u5b66\u751f\uff09\u3002<\/strong>\u201c\u4f59\u54e5\uff0c\u6211\u5927\u5b66\u8bfb\u91d1\u878d\uff0c\u8bfe\u672c\u4e0a\u7684\u7406\u8bba\u611f\u89c9\u8ddf\u73b0\u5b9e\u5e02\u573a\u6709\u8ddd\u79bb\uff0c\u627e\u5b9e\u4e60\u7684\u65f6\u5019\u53d1\u73b0\u4f01\u4e1a\u9700\u8981\u7684Python\u3001SQL\u7b49\u6280\u80fd\u6211\u638c\u63e1\u5f97\u4e0d\u597d\uff0c\u5bf9\u672a\u6765\u611f\u5230\u5f88\u8ff7\u832b\u3002\u201d<\/em><\/p>\n\n\n\n
\u7b2c\u4e8c\u7c7b\uff0c\u662f\u63a2\u7d22\u4e2d\u7684\u4e2a\u4eba\u6295\u8d44\u8005\u3002<\/strong>\u201c\u8001\u4f59\u597d\uff0c\u6211\u5165\u5e02\u597d\u51e0\u5e74\uff0c\u4f46\u8d5a\u5c11\u4e8f\u591a\u3002\u6bcf\u5929\u5b66\u4e60\u7814\u7a76\uff0c\u4f46\u611f\u89c9\u8fd8\u662f\u51ed\u611f\u89c9\u5728\u51b3\u7b56\uff0c\u8ffd\u6da8\u6740\u8dcc\u548c\u8e0f\u7a7a\u65f6\u5e38\u89c4\u64cd\u4f5c\u3002\u60f3\u5b66\u70b9\u771f\u672c\u4e8b\uff0c\u53c8\u4e0d\u77e5\u9053\u4ece\u54ea\u513f\u4e0b\u624b\u3002\u201d<\/em><\/p>\n\n\n\n
\u65e0\u8bba\u4f60\u662f\u60f3\u628a\u91d1\u878d\u4f5c\u4e3a\u804c\u4e1a\u7684\u672a\u6765\u4ece\u4e1a\u8005<\/strong>\uff0c\u8fd8\u662f\u60f3\u5728\u5e02\u573a\u91cc\u884c\u7a33\u81f4\u8fdc\u7684\u5f53\u524d\u5b9e\u8df5\u8005<\/strong>\uff0c\u4f60\u4eec\u90fd\u53ef\u80fd\u9762\u4e34\u540c\u6837\u7684\u95ee\u9898\uff1a\u73b0\u6709\u77e5\u8bc6\u4e0e\u5e02\u573a\u5b9e\u8df5\u7684\u5dee\u8ddd\uff0c\u4ee5\u53ca\u7f3a\u5c11\u4e00\u5957\u79d1\u5b66\u3001\u6709\u6548\u7684\u5206\u6790\u65b9\u6cd5\u3002<\/strong><\/p>\n\n\n\n
\u6602\u8d35\u7684\u57f9\u8bad\u8bfe\u4e0d\u4e00\u5b9a\u9002\u5408\u6bcf\u4e2a\u4eba\u3002\u4eca\u5929\uff0c\u8001\u4f59\u5c31\u7ed9\u4f60\u4eec\u6307\u4e00\u6761\u660e\u8def\uff0c\u4e00\u6761\u80fd\u8ba9\u5b66\u751f\u6b66\u88c5\u81ea\u5df1\u3001\u8ba9\u6295\u8d44\u8005\u63d0\u5347\u5206\u6790\u80fd\u529b\u7684\u9053\u8def\u2014\u2014\u62e5\u62b1\u5f00\u6e90\uff0c\u81ea\u5b66\u6210\u957f\uff01<\/strong><\/p>\n\n\n\n
\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u6beb\u65e0\u4fdd\u7559\u5730\u5206\u4eab18\u4e2a\u4e13\u4e1a\u4eba\u58eb\u4e5f\u5728\u4f7f\u7528\u7684\u5f00\u6e90\u91cf\u5316\u5de5\u5177\u3002\u5b83\u4eec\u4e0d\u4ec5\u80fd\u5e2e\u4f60\u6784\u5efa\u4e00\u4e2a\u8ba9\u9762\u8bd5\u5b98\u8d5e\u8d4f\u7684\u6280\u80fd\u6811\uff0c\u66f4\u80fd\u8ba9\u4f60\u5efa\u7acb\u8d77\u4e00\u5957\u79d1\u5b66\u7684\u3001\u5c5e\u4e8e\u81ea\u5df1\u7684\u6295\u8d44\u5206\u6790\u4f53\u7cfb\u3002<\/p>\n\n\n\n
\u4e8c\u300118\u4e2a\u5b9e\u7528\u5de5\u5177\uff1a\u4ece\u5165\u95e8\u5230\u8fdb\u9636\u7684\u91cf\u5316\u5b66\u4e60\u5408\u96c6<\/strong><\/h2>\n\n\n\n
\u6211\u628a\u8fd9\u4e9b\u5b9d\u85cf\u9879\u76ee\u5206\u6210\u4e86\u51e0\u4e2a\u5927\u7c7b\uff0c\u4ece\u57fa\u7840\u7814\u7a76\u5230\u590d\u6742\u884d\u751f\u54c1\uff0c\u518d\u5230\u524d\u6cbf\u7684AI\u91cf\u5316\uff0c\u5e94\u6709\u5c3d\u6709\u3002<\/p>\n\n\n\n
\u7b2c\u4e00\u68af\u961f\uff1a\u6295\u7814\u5fc5\u5907 & \u57fa\u7840\u6846\u67b6 (4\u6b3e)<\/mark><\/strong><\/p>\n\n\n\n
\u8fd9\u4e2a\u68af\u961f\u7684\u5de5\u5177\uff0c\u662f\u4f60\u6784\u5efa\u91cf\u5316\u77e5\u8bc6\u4f53\u7cfb\u7684\u201c\u5730\u57fa\u201d\u3002<\/p>\n\n\n\n
1. OpenBB – \u529f\u80fd\u5f3a\u5927\u7684\u5f00\u6e90\u6295\u7814\u5e73\u53f0<\/strong><\/h3>\n\n\n\n
\u8fd9\u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u9879\u76ee\uff01\u5b83\u51e0\u4e4e\u6db5\u76d6\u4e86\u80a1\u7968\u3001\u52a0\u5bc6\u8d27\u5e01\u3001\u5b8f\u89c2\u7ecf\u6d4e\u3001\u671f\u6743\u7b49\u6240\u6709\u4f60\u80fd\u60f3\u5230\u7684\u91d1\u878d\u6570\u636e\u548c\u7814\u7a76\u529f\u80fd\u3002\u5bf9\u4e8e\u9884\u7b97\u6709\u9650\u7684\u4e2a\u4eba\u5f00\u53d1\u8005\u6765\u8bf4\uff0c\u975e\u5e38\u6709\u5e2e\u52a9\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong>\u63d0\u53d6\u80a1\u7968\u6570\u636e<\/p>\n\n\n\n
from openbb_terminal.sdk import openbb<\/code><\/code>data = openbb.stocks.load(symbol=\"AAPL\", start=\"2023-01-01\")<\/code>print(data.head())<\/code><\/code><\/pre>\n\n\n\n
\u5ef6\u5c55\u9605\u8bfb\uff1a<\/strong>\u6211\u7684\u4e0b\u9762\u8fd9\u7bc7\u6587\u7ae0\u5c31\u662f\u4f7f\u7528OpenBB SDK\u6765\u83b7\u53d6\u548c\u5206\u6790\u671f\u6743\u6570\u636e\u3002<\/p>\n\n\n\n
\u300a\u8d22\u62a5\u5b63\u5fc5\u770b\uff1a\u7528\u8de8\u5f0f\u7b56\u7565\u9884\u6d4b\u82f9\u679c\u80a1\u4ef7\uff0cPython+AI \u4ee3\u7801\u5b9e\u6218\uff01<\/a>\u300b<\/p>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a<\/strong>https:\/\/github.com\/OpenBB-finance\/OpenBB<\/a><\/p>\n\n\n\n
\ud83d\udca1<\/strong>\u5c0f\u63d0\u793a\uff1a<\/strong>\u5982\u679c\u56fd\u5185\u4e0d\u80fd\u8bbf\u95eeGitHub\u7684\u540c\u5b66\uff0c\u53ef\u4ee5\u8bbf\u95eehttps:\/\/gitcode.com\/\uff0c\u7136\u540e\u5728\u9996\u9875\u641c\u7d22\u6846\u5185\u8f93\u5165\u9879\u76ee\u540d\u79f0\uff0c\u57fa\u672c\u4e0a\u80fd\u627e\u5230\u955c\u50cf\u7ad9\u70b9\u6216\u8005\u4e8c\u5f00\u4f5c\u8005\u7684\u9879\u76ee\u8fdb\u884c\u5b66\u4e60\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n

2. PyQL (QuantLib) – \u884d\u751f\u54c1\u5b9a\u4ef7\u7684\u201c\u884c\u4e1a\u6807\u6746\u201d<\/strong><\/h3>\n\n\n\n
QuantLib\u662fC++\u5199\u7684\uff0c\u662f\u4e1a\u5185\u516c\u8ba4\u7684\u884d\u751f\u54c1\u5b9a\u4ef7\u6807\u51c6\u5e93\u4e4b\u4e00\u3002PyQL\u5219\u662f\u5b83\u7684Python\u7248\u672c\uff0c\u8ba9\u4f60\u80fd\u7528\u66f4\u53cb\u597d\u7684\u65b9\u5f0f\u8c03\u7528\u8fd9\u4e2a\u5f3a\u5927\u7684\u201c\u5f15\u64ce\u201d\u3002\u5b66\u4e60\u56fa\u6536\u3001\u671f\u6743\uff0c\u4e86\u89e3QuantLib\u4f1a\u8ba9\u4f60\u53d7\u76ca\u532a\u6d45\u3002\u00a0<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong>\u6298\u6263\u56e0\u5b50<\/p>\n\n\n\n
from quantlib.time.api import Date<\/code>from quantlib.termstructures.yields.flat_forward import FlatForward<\/code><\/code>today = Date(1, 1, 2025)<\/code>curve = FlatForward(today, 0.05, \"Actual365Fixed\")<\/code>print(curve.discount(2))  # discount for 2 years<\/code><\/code><\/pre>\n\n\n\n
\u5ef6\u5c55\u9605\u8bfb\uff1a<\/strong>\u6211\u5728\u4e0b\u9762\u8fd9\u7bc7\u6587\u7ae0\u4e2d\u4e5f\u66fe\u505a\u8fc7\u4ecb\u7ecd\u8fc7QuantLib\u3002<\/p>\n\n\n\n
\u300a\u7b97\u6cd5\u4ea4\u6613\u8005\u5fc5\u987b\u4e86\u89e3\u7684\u5168\u7403\u5341\u5927Python\u5e93<\/a>\u300b<\/p>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a<\/strong>https:\/\/github.com\/enthought\/pyql<\/a><\/p>\n\n\n\n
\ud83d\udca1\u6253\u4e0d\u5f00github\u7684\u8fd8\u662f\u548c\u4e0a\u9762\u4e00\u6837\uff0c\u8bbf\u95eegitcode.com\u6765\u83b7\u53d6\u3002\u540e\u9762\u7684\u9879\u76ee\u6211\u5c31\u4e0d\u4e00\u4e00\u7d2f\u8ff0\u4e86\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n

3. gs-quant – \u6765\u81ea\u9ad8\u76db\u7684\u91cf\u5316\u5206\u6790\u5de5\u5177<\/strong><\/h3>\n\n\n\n
\u6ca1\u9519\uff0c\u5c31\u662f\u90a3\u4e2a\u9ad8\u76db\uff01\u4ed6\u4eec\u628a\u5185\u90e8\u4f7f\u7528\u7684\u90e8\u5206\u91cf\u5316\u5206\u6790\u5de5\u5177\u5305\u5f00\u6e90\u4e86\u51fa\u6765\u3002\u7528\u5b83\uff0c\u610f\u5473\u7740\u4f60\u80fd\u548c\u4e13\u4e1a\u4eba\u58eb\u7528\u4e00\u6837\u7684\u89c6\u89d2\u548c\u5de5\u5177\u6765\u5206\u6790\u5e02\u573a\u3001\u7ba1\u7406\u98ce\u9669\u3002\u8fd9\u542b\u91d1\u91cf\uff0c\u4e0d\u8a00\u800c\u55bb\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5ef6\u5c55\u9605\u8bfb\uff1a<\/strong>\u60f3\u6df1\u5165\u4e86\u89e3\u53ef\u4ee5\u770b\u770b\u6211\u5199\u7684\u8fd9\u4e00\u7bc7\u3002<\/p>\n\n\n\n
\u300a\u9ad8\u76db\u5185\u90e8\u91cf\u5316\u5de5\u5177\u66dd\u5149\uff0c\u514d\u8d39\u7684\u201c\u6cd5\u62c9\u5229\u201d\u4f60\u8fd8\u4e0d\u4e0a\u8f66\uff1f<\/a>\u300b<\/p>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a<\/strong>https:\/\/github.com\/goldmansachs\/gs-quant<\/a><\/p>\n\n\n\n
4. QuantPy – \u7b80\u6d01\u7684\u91cf\u5316\u5165\u95e8\u6846\u67b6<\/strong><\/h3>\n\n\n\n
\u5982\u679c\u89c9\u5f97\u4e0a\u9762\u7684\u592a\u201c\u91cd\u201d\uff0c\u53ef\u4ee5\u4eceQuantPy\u5f00\u59cb\u3002\u5b83\u5f88\u9002\u5408\u5b66\u4e60\u6295\u8d44\u7ec4\u5408\u7406\u8bba\u3001\u884d\u751f\u54c1\u57fa\u7840\u548c\u98ce\u9669\u5206\u6790\uff0c\u4ee3\u7801\u6e05\u6670\uff0c\u975e\u5e38\u9002\u5408\u521d\u5b66\u8005\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a<\/strong>https:\/\/github.com\/jsmidt\/QuantPy<\/a><\/p>\n\n\n\n
\u7b2c\u4e8c\u68af\u961f\uff1a\u7ec4\u5408\u5206\u6790 & \u7ee9\u6548\u5f52\u56e0 (3\u6b3e)<\/mark><\/strong><\/p>\n\n\n\n
\u5149\u6709\u7b56\u7565\u4e0d\u884c\uff0c\u4f60\u5f97\u77e5\u9053\u7b56\u7565\u8868\u73b0\u5982\u4f55\uff0c\u597d\u5728\u54ea\u91cc\uff0c\u5dee\u5728\u54ea\u91cc\u3002<\/p>\n\n\n\n
5. f.fn – \u6295\u8d44\u7ec4\u5408\u7ee9\u6548\u5206\u6790\u5229\u5668<\/strong><\/h3>\n\n\n\n
\"\u6ca1\u6709\u4efb\u4f55\"\/<\/figure>\n\n\n\n
\u8fd9\u662f\u6211\u4e2a\u4eba\u975e\u5e38\u559c\u6b22\u7684\u4e00\u4e2a\u5e93\u3002\u56de\u6d4b\u5b8c\u4e00\u4e2a\u7b56\u7565\uff0c\u60f3\u8ba1\u7b97\u590f\u666e\u6bd4\u7387\u3001\u6700\u5927\u56de\u64a4\u3001\u5361\u739b\u6bd4\u7387\u7b49\u4e00\u5927\u5806\u6307\u6807\uff1f\u7528ffn\uff0c\u51e0\u884c\u4ee3\u7801\u5c31\u641e\u5b9a\uff0c\u8fd8\u80fd\u753b\u51fa\u6f02\u4eae\u7684\u7ee9\u6548\u56fe\u3002<\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong>\u6295\u8d44\u7ec4\u5408\u7edf\u8ba1\u6570\u636e<\/p>\n\n\n\n
import ffn<\/code><\/code>data = ffn.get('aapl,msft', start='2025-01-01')<\/code>stats = data.calc_stats()<\/code>stats.display()<\/code><\/code><\/pre>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a<\/strong>https:\/\/github.com\/pmorissette\/ffn<\/a><\/p>\n\n\n\n
6. Finance-Python – \u6280\u672f\u5206\u6790\u4e0e\u4ea4\u6613\u65e5\u5386\u5de5\u5177<\/strong><\/h3>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5982\u679c\u4f60\u5bf9\u6280\u672f\u5206\u6790\u611f\u5174\u8da3\uff0c\u8fd9\u4e2a\u5e93\u5f88\u65b9\u4fbf\u3002\u8ba1\u7b97RSI\u3001MACD\u7b49\u6307\u6807\uff0c\u5904\u7406\u4ea4\u6613\u65e5\uff0c\u5b83\u90fd\u80fd\u5e2e\u4f60\u8f7b\u677e\u5b8c\u6210\u3002<\/strong><\/p>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong>\u8ba1\u7b97RSI<\/p>\n\n\n\n
from finance_python.ta import rsi<\/code>print(rsi([45, 46, 48, 44, 40, 42, 43]))<\/code><\/code><\/pre>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a<\/strong>\u00a0https:\/\/github.com\/alpha-miner\/Finance-Python<\/a><\/p>\n\n\n\n
7. pynance – \u8f7b\u91cf\u7ea7\u91d1\u878d\u6570\u636e\u5206\u6790<\/strong><\/h3>\n\n\n\n
\u4e00\u4e2a\u975e\u5e38\u5c0f\u5de7\u4f46\u529f\u80fd\u5f3a\u5927\u7684\u5e93\uff0c\u9002\u5408\u7528\u6765\u642d\u5efa\u6570\u636e\u5904\u7406\u6d41\u6c34\u7ebf\u548c\u5206\u6790\u6536\u76ca\u7387\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a<\/strong>https:\/\/github.com\/GriffinAustin\/pynancen<\/a><\/p>\n\n\n\n
\u7b2c\u4e09\u68af\u961f\uff1a\u671f\u6743 & \u884d\u751f\u54c1\u5b9a\u4ef7 (6\u6b3e)<\/strong><\/mark><\/p>\n\n\n\n
\u8fd9\u90e8\u5206\u662f\u91cf\u5316\u91d1\u878d\u4e2d\u8f83\u4e3a\u6df1\u5165\u7684\u9886\u57df\uff0c\u4e5f\u662f\u6784\u7b51\u6838\u5fc3\u7ade\u4e89\u529b\u7684\u5173\u952e\u3002<\/p>\n\n\n\n
8. vollib – B-S\u6a21\u578b\u4e0e\u9690\u542b\u6ce2\u52a8\u7387<\/strong><\/h3>\n\n\n\n
\u7814\u7a76\u671f\u6743\uff0c\u79bb\u4e0d\u5f00\u6ce2\u52a8\u7387\u3002\u8fd9\u4e2a\u5e93\u4e13\u95e8\u7528\u6765\u5904\u7406\u5404\u79cdB-S\u6a21\u578b\uff08\u4ee5\u53ca\u5176\u53d8\u79cd\uff09\u7684\u8ba1\u7b97\u548c\u9690\u542b\u6ce2\u52a8\u7387\u7684\u6c42\u89e3\uff0c\u662f\u671f\u6743\u5b66\u4e60\u8005\u7684\u5b9e\u7528\u5de5\u5177\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u793a\u4f8b\uff1a<\/strong>Black\u2013Scholes price<\/strong><\/p>\n\n\n\n
from vollib.black_scholes.implied_volatility import implied_volatility<\/code>price = 10<\/code>S = 100<\/code>K = 95<\/code>t = 0.5<\/code>r = 0.01<\/code>flag = 'c'<\/code>iv = implied_volatility(price, S, K, t, r, flag)<\/code><\/code><\/pre>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a<\/strong>\u00a0https:\/\/github.com\/vollib\/vollib<\/a><\/p>\n\n\n\n
9. FinancePy – \u5168\u80fd\u578b\u884d\u751f\u54c1\u5b9a\u4ef7\u5e93<\/strong><\/h3>\n\n\n\n
\u8fd9\u4e2a\u5e93\u7684\u8986\u76d6\u9762\u975e\u5e38\u5e7f\uff0c\u80a1\u7968\u3001\u5916\u6c47\u3001\u4fe1\u7528\u3001\u56fa\u6536\u2026\u2026\u51e0\u4e4e\u6240\u6709\u7c7b\u578b\u7684\u884d\u751f\u54c1\u5b9a\u4ef7\u6a21\u578b\u5b83\u90fd\u6709\u3002\u5b66\u4e60\u4ef7\u503c\u5f88\u9ad8\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u793a\u4f8b\uff1a\u6b27\u5f0f\u671f\u6743\u5b9a\u4ef7<\/strong><\/p>\n\n\n\n
from\u00a0financepy.products.equity.equity_option import EquityOption\n\nopt\u00a0= EquityOption(is_call=True, strike=100, expiry=1.0)\nprice\u00a0= opt.value(spot=105, volatility=0.2, rate=0.03)\nprint(price)<\/code><\/pre>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a\u00a0<\/strong>https:\/\/github.com\/domokane\/FinancePy<\/a><\/p>\n\n\n\n
10. pysabr – SABR\u6a21\u578b\u4e13\u9879\u5de5\u5177<\/strong><\/h3>\n\n\n\n
SABR\u6a21\u578b\u662f\u5229\u7387\u884d\u751f\u54c1\u548c\u6ce2\u52a8\u7387\u66f2\u9762\u5efa\u6a21\u4e2d\u5e94\u7528\u5f88\u5e7f\u7684\u6a21\u578b\u4e4b\u4e00\u3002\u8fd9\u4e2a\u5e93\u5c31\u662f\u5b83\u7684Python\u5b9e\u73b0\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a\u00a0<\/strong>https:\/\/github.com\/ynouri\/pysabr<\/a><\/p>\n\n\n\n
11. willowtree – \u201c\u67f3\u6811\u201d\u683c\u70b9\u5b9a\u4ef7\u5f15\u64ce<\/strong><\/h3>\n\n\n\n
\u4f20\u7edf\u7684\u4e8c\u53c9\u6811\u3001\u4e09\u53c9\u6811\u6a21\u578b\u4f60\u53ef\u80fd\u542c\u8fc7\uff0c\u4f46\u8fd9\u4e2a\u5e93\u5b9e\u73b0\u4e86\u4e00\u79cd\u66f4\u9ad8\u7ea7\u7684\u201c\u67f3\u6811\u201d\u683c\u70b9\u6a21\u578b\uff0c\u662f\u5b66\u4e60\u9ad8\u7ea7\u5b9a\u4ef7\u65b9\u6cd5\u7684\u597d\u6750\u6599\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a\u00a0<\/strong>https:\/\/github.com\/federicomariamassari\/willowtree<\/a><\/p>\n\n\n\n
12. financial-engineering – \u8499\u7279\u5361\u6d1b\u6a21\u578b\u5408\u96c6<\/strong><\/h3>\n\n\n\n
\u91cc\u9762\u5168\u662f\u5404\u79cd\u7528\u8499\u7279\u5361\u6d1b\u65b9\u6cd5\u7ed9\u5947\u5f02\u671f\u6743\u5b9a\u4ef7\u7684\u6e05\u6670\u4ee3\u7801\u3002\u60f3\u6df1\u5165\u7406\u89e3\u968f\u673a\u8fc7\u7a0b\u548c\u6a21\u62df\uff0c\u770b\u5b83\u5c31\u5bf9\u4e86\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u793a\u4f8b\uff1a\u7b80\u5355\u591a\u9009<\/strong><\/p>\n\n\n\n
import\u00a0numpy as np\n\nS0, K, r, sigma, T =\u00a0100,\u00a0100,\u00a00.05,\u00a00.2,\u00a01\nN\u00a0=\u00a01_00000\nST\u00a0= S0 * np.exp((r -\u00a00.5*sigma**2)*T + sigma*np.random.randn(N)*np.sqrt(T))\nprice\u00a0= np.exp(-r*T) * np.maximum(ST - K,\u00a00).mean()\nprint(price)<\/code><\/pre>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a<\/strong>\u00a0https:\/\/github.com\/federicomariamassari\/financial-engineering<\/a><\/p>\n\n\n\n
13. optlib – \u7eaf\u7cb9\u7684\u671f\u6743\u5b9a\u4ef7\u5b66\u4e60\u5e93<\/strong><\/h3>\n\n\n\n
\u4e00\u4e2a\u4ee3\u7801\u6e05\u6670\u3001\u4e13\u6ce8\u6559\u5b66\u7684\u671f\u6743\u5b9a\u4ef7\u5e93\uff0c\u9002\u5408\u7528\u6765\u7406\u89e3\u671f\u6743\u5b9a\u4ef7\u7684\u5e95\u5c42\u903b\u8f91\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a\u00a0<\/strong>https:\/\/github.com\/dbrojas\/optlib<\/a><\/p>\n\n\n\n
\u7b2c\u56db\u68af\u961f\uff1aAI\u8d4b\u80fd & \u524d\u6cbf\u6a21\u578b (4\u6b3e)<\/mark><\/strong><\/p>\n\n\n\n
\u6b22\u8fce\u6765\u5230\u91cf\u5316\u7684\u65b0\u9886\u57df\uff01AI\u548c\u673a\u5668\u5b66\u4e60\u6b63\u5728\u4e3a\u8fd9\u4e2a\u884c\u4e1a\u5e26\u6765\u65b0\u601d\u8def\u3002<\/p>\n\n\n\n
14. tf-quant-finance – \u8c37\u6b4c\u51fa\u54c1\u7684AI\u91cf\u5316\u5de5\u5177<\/strong><\/h3>\n\n\n\n
\u5b83\u628a\u5f3a\u5927\u7684TensorFlow\u6846\u67b6\u7528\u5230\u4e86\u91cf\u5316\u91d1\u878d\u4e0a\u3002\u968f\u673a\u8fc7\u7a0b\u3001\u6ce2\u52a8\u7387\u66f2\u9762\u3001\u8499\u7279\u5361\u6d1b\u6a21\u62df\u2026\u2026\u6240\u6709\u8fd9\u4e9b\u90fd\u53ef\u4ee5\u7528GPU\u52a0\u901f\uff0c\u5e76\u4e14\u652f\u6301\u81ea\u52a8\u5fae\u5206\u3002\u8fd9\u662f\u901a\u5f80\u673a\u5668\u5b66\u4e60\u9a71\u52a8\u91cf\u5316\u6a21\u578b\u7684\u5927\u95e8\uff0c\u95e8\u540e\u6709\u5de8\u5927\u7684\u5b9d\u85cf\u5728\u7b49\u4f60\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a\u00a0<\/strong>https:\/\/github.com\/google\/tf-quant-finance<\/a><\/p>\n\n\n\n
15. Q-Fin – \u6570\u5b66\u91d1\u878d\u6a21\u578b\u5e93<\/strong><\/h3>\n\n\n\n
\u4e13\u6ce8\u4e8e\u968f\u673a\u8fc7\u7a0b\u5efa\u6a21\u548c\u884d\u751f\u54c1\u5b9a\u4ef7\uff0c\u6570\u5b66\u5473\u9053\u5f88\u6d53\uff0c\u9002\u5408\u60f3\u628a\u7406\u8bba\u548c\u4ee3\u7801\u7ed3\u5408\u8d77\u6765\u7684\u540c\u5b66\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a<\/strong>https:\/\/github.com\/RomanMichaelPaolucci\/Q-Fin<\/a><\/p>\n\n\n\n
16. <\/strong>OpenStock<\/strong> – GitHub\u4e0a6K+\u661f\u6807\u7684\u5168\u529f\u80fd\u5de5\u5177<\/strong><\/h3>\n\n\n\n
OpenStock\u7684\u6280\u672f\u6808\u76f8\u5f53\u73b0\u4ee3\uff1aNext.js15+React19+TypeScript\uff0c\u524d\u7aef\u7528Tailwind CSS\u548cshadcn\/ui\u6253\u9020\uff0c\u989c\u503c\u5728\u7ebf\u3002\u800c\u4e14\u8fd9\u4e2a\u91cf\u5316\u5de5\u5177\u5305\u62ec\u4e86\u5b9e\u65f6\u884c\u60c5\u3001\u81ea\u9009\u80a1\u7ba1\u7406\u3001\u6280\u672f\u6307\u6807\u3001\u65b0\u95fb\u8d44\u8baf\u3001\u8d22\u62a5\u6570\u636e\uff0c\u8be5\u6709\u7684\u5b83\u90fd\u6709\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5ef6\u5c55\u9605\u8bfb\uff1a<\/strong>\u5b8c\u6574\u7248\u4ecb\u7ecd<\/strong>\u770b\u4e0b\u9762\ud83d\udc47<\/p>\n\n\n\n
\u300a\u5b8c\u5168\u5f00\u6e90\u7684\u4ea4\u6613\u7cfb\u7edf\u6846\u67b6\u5206\u4eab\uff0c\u529f\u80fd\u5bf9\u6807\u673a\u6784\u7ea7\u5de5\u5177\u300b<\/p>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a<\/strong>https:\/\/github.com\/Open-Dev-Society\/OpenStock<\/a><\/p>\n\n\n\n
17. Qlib – \u5fae\u8f6f\u7684AI\u91cf\u5316\u6295\u8d44\u5e73\u53f0<\/strong><\/h3>\n\n\n\n
\u8fd9\u662f\u5fae\u8f6f\u4e9a\u6d32\u7814\u7a76\u9662\u63a8\u51fa\u7684\uff0c\u4e13\u95e8\u9762\u5411AI\u91cf\u5316\u6295\u8d44\u3002\u5b83\u63d0\u4f9b\u4e86\u4e00\u6574\u5957\u4ece\u6570\u636e\u5904\u7406\u3001\u6a21\u578b\u8bad\u7ec3\u5230\u56de\u6d4b\u7684\u6d41\u6c34\u7ebf\uff0c\u7279\u522b\u9002\u5408\u60f3\u505aA\u80a1\u56e0\u5b50\u6316\u6398\u548c\u673a\u5668\u5b66\u4e60\u7b56\u7565\u7684\u540c\u5b66\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5ef6\u5c55\u9605\u8bfb\uff1a<\/strong>\u6df1\u5165\u4e86\u89e3\u53ef\u4ee5\u770b\u4e0b\u9762\ud83d\udc47\u8fd9\u7bc7<\/p>\n\n\n\n
\u300aAI + Qlib \u98a0\u8986\u91cf\u5316\u6295\u8d44\uff1f\u63ed\u79d8\u5fae\u8f6f\u5f00\u6e90\u5e73\u53f0\u5e26\u6765\u7684\u667a\u80fd\u4ea4\u6613\u9769\u547d<\/a>\u300b<\/p>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a<\/strong>https:\/\/github.com\/microsoft\/qlib<\/a><\/p>\n\n\n\n
18. vn.py – \u56fd\u5185\u77e5\u540d\u7684\u5f00\u6e90\u91cf\u5316\u4ea4\u6613\u5e73\u53f0<\/strong><\/h3>\n\n\n\n
\u5728\u56fd\u5185\u505a\u91cf\u5316\u4e0d\u80fd\u4e0d\u77e5\u9053\u5b83\uff0cvnpy\u63d0\u4f9b\u4e86\u4e00\u6574\u5957\u4ea4\u6613\u63a5\u53e3\u548c\u7b56\u7565\u6846\u67b6\uff0c\u652f\u6301\u6a21\u62df\u548c\u5b9e\u76d8\u4ea4\u6613\uff0c\u793e\u533a\u975e\u5e38\u6d3b\u8dc3\uff0c\u662f\u628a\u7b56\u7565\u4ece\u7814\u7a76\u63a8\u5411\u5b9e\u8df5\u7684\u597d\u5e2e\u624b\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5ef6\u5c55\u9605\u8bfb\uff1a<\/strong>\u5728\u4e0b\u9762\u8fd9\u7bc7\u91cc\u9762\u7528\u5230\u4e86vn.py\u7684\u9884\u8ba1\u7b97\u6982\u5ff5\u3002<\/strong><\/p>\n\n\n\n
\u300a\u4e09\u6b65\u81ea\u5236\u91cf\u5316\u9009\u80a1\u5de5\u5177\uff0c\u8f7b\u677e\u53d1\u73b0\u6a2a\u76d8\u7a81\u7834\u725b\u80a1<\/a>\u300b<\/p>\n\n\n\n
\u5f00\u6e90\u9879\u76ee\u5730\u5740\uff1a<\/strong>https:\/\/github.com\/vnpy\/vnpy<\/a><\/p>\n\n\n\n
\u5f53\u7136\u9664\u4e86\u4ee5\u4e0a\u7684AI\u5de5\u5177\u5916\uff0c\u8fd8\u6709\u6bd4\u5982rd-agent\u3001abu\u3001RQAlpha<\/strong>\u7b49\u56fd\u5185\u5916\u7684\u4f18\u79c0\u9879\u76ee\uff0c\u5982\u4e0b\u8868\u6240\u793a\uff0c\u5404\u6709\u6240\u957f\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u7531\u4e8e\u53d7\u7bc7\u5e45\u6240\u9650\uff0c\u6211\u5c31\u4e0d\u5728\u672c\u6587\u4e2d\u5c55\u5f00\u8bf4\u660e\u4e86\u3002\u6709\u5174\u8da3\u7684\u670b\u53cb\u53ef\u4ee5\u770b\u6211\u5199\u7684\u8fd9\u7bc7\u6587\u7ae0\uff1a<\/p>\n\n\n\n
\u300a\u8fd95\u4e2a\u5f00\u6e90\u91cf\u5316AI\u7092\u80a1\u5de5\u5177\uff0c\u8ba9\u6563\u6237\u4e5f\u80fd\u79d2\u53d8\u91cf\u5316\u5927\u795e\uff01\uff08\u9644\u771f\u5b9e\u4ea4\u6613\u6848\u4f8b\uff09<\/a>\u300b<\/p>\n\n\n\n
\u4e09\u3001\u4ece\u5165\u95e8\u5230\u5b9e\u8df5\uff1a\u201c3\u9636\u664b\u7ea7\u201d\u8def\u7ebf\u56fe<\/strong><\/h2>\n\n\n\n
\u6784\u5efa\u77e5\u8bc6\u4f53\u7cfb\u9700\u8981\u8010\u5fc3\u3002\u4e0d\u8981\u76f8\u4fe1\u4e0d\u5207\u5b9e\u9645\u7684\u201c\u901f\u6210\u201d\u8bf4\u6cd5\u3002\u6211\u7ed9\u4f60\u8bbe\u8ba1\u7684\uff0c\u662f\u4e00\u4e2a\u66f4\u79d1\u5b66\u3001\u66f4\u624e\u5b9e\u7684\u201c\u4e09\u9636\u664b\u7ea7\u201d\u8def\u7ebf\u56fe\u3002\u4f60\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u8282\u594f\uff0c\u4e00\u6b65\u4e00\u4e2a\u811a\u5370\u5730\u5f80\u524d\u8d70\u3002<\/p>\n\n\n\n
\u9636\u6bb5<\/th>\u9636\u6bb5\u76ee\u6807<\/th>\u6838\u5fc3\u5de5\u5177<\/th>\u4ea7\u51fa\u6210\u679c<\/th><\/tr><\/thead>
\u7b2c\u4e00\u9636\u6bb5\uff1a\u5960\u5b9a\u6839\u57fa<\/strong><\/td>\u638c\u63e1\u91cf\u5316\u91d1\u878d\u7684\u201c\u901a\u7528\u8bed\u8a00\u201d\uff0c\u80fd\u542c\u61c2\u3001\u4f1a\u8868\u8fbe\u3002<\/td>QuantPy, vollib, Pandas<\/td>\u80fd\u7528Python\u5904\u7406\u91d1\u878d\u6570\u636e\uff1b\u80fd\u72ec\u7acb\u8ba1\u7b97\u671f\u6743\u4ef7\u683c\u548c\u5e38\u7528\u6280\u672f\u6307\u6807\u3002<\/td><\/tr>
\u7b2c\u4e8c\u9636\u6bb5\uff1a\u6784\u7b51\u6838\u5fc3<\/strong><\/td>\u8dd1\u901a\u4e00\u4e2a\u5b8c\u6574\u7684\u6295\u7814\u5c0f\u5faa\u73af\uff0c\u4ece\u6570\u636e\u5230\u56de\u6d4b\u3002<\/td>OpenBB, ffn, PyQL (\u57fa\u7840)<\/td>\u80fd\u83b7\u53d6\u6570\u636e\u3001\u5206\u6790\u7ec4\u5408\u7ee9\u6548\uff0c\u5e76\u5bf9\u6807\u51c6\u884d\u751f\u54c1\u8fdb\u884c\u5b9a\u4ef7\u3002<\/td><\/tr>
\u7b2c\u4e09\u9636\u6bb5\uff1a\u8fc8\u5411\u9ad8\u9636<\/strong><\/td>\u63a2\u7d22\u884c\u4e1a\u524d\u6cbf\uff0c\u6df1\u5165\u4e13\u9879\u9886\u57df\uff0c\u5177\u5907\u5b9e\u8df5\u6f5c\u529b\u3002<\/td>tf-quant-finance, Qlib, vnpy<\/td>\u80fd\u642d\u5efaAI\u91cf\u5316\u7b56\u7565\u56de\u6d4b\uff1b\u4e86\u89e3\u4ea4\u6613\u63a5\u53e3\uff0c\u5b8c\u6210\u4e00\u4e2a\u6709\u6df1\u5ea6\u7684\u4e2a\u4eba\u9879\u76ee\u3002<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u56db\u3001\u603b\u7ed3\uff1a\u8df3\u51fa\u8212\u9002\u533a\uff0c\u5f00\u59cb\u52a8\u624b\u5b9e\u8df5<\/strong><\/h2>\n\n\n\n
\u4eca\u5929\u8fd9\u7bc7\u6587\u7ae0\uff0c\u65e0\u8bba\u4f60\u662f\u4e3a\u524d\u9014\u89c4\u5212\u7684\u5b66\u751f\uff0c\u8fd8\u662f\u4e3a\u6295\u8d44\u80fd\u529b\u7126\u8651\u7684\u670b\u53cb\uff0c\u6211\u5e0c\u671b\u5b83\u80fd\u6210\u4e3a\u4f60\u7684\u201c\u7834\u5c40\u4e4b\u4f5c<\/strong>\u201d\u3002\u901a\u5f80\u4e13\u4e1a\u548c\u6210\u957f\u7684\u9053\u8def\u4ece\u6765\u6ca1\u6709\u6377\u5f84\uff0c\u4f46\u4e00\u5b9a\u6709\u66f4\u806a\u660e\u7684\u8def\u5f84\u3002\u653e\u5f03\u5e7b\u60f3\uff0c\u62e5\u62b1\u5f00\u6e90\uff0c\u7528AI\u3001\u4ee3\u7801\u548c\u6570\u636e\u6784\u5efa\u4f60\u7684\u6838\u5fc3\u7ade\u4e89\u529b\u3002\u8fd9\uff0c\u624d\u662f\u5f53\u4e0b\u8fd9\u4e2a\u65f6\u4ee3\u7a00\u7f3a\u4e14\u5bcc\u6709\u4ef7\u503c\u7684\u80fd\u529b\u3002<\/p>\n\n\n\n