{"id":2407,"date":"2025-07-10T07:05:00","date_gmt":"2025-07-09T23:05:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=2407"},"modified":"2025-07-06T15:06:27","modified_gmt":"2025-07-06T07:06:27","slug":"35%e9%a2%84%e6%9c%9f%e6%94%b6%e7%9b%8a%ef%bc%9f%e7%9c%8b%e8%bf%99%e4%b8%aa%e8%af%ba%e8%b4%9d%e5%b0%94%e5%a5%96%e5%be%97%e4%b8%bb%e7%9a%84%e9%b8%a1%e8%9b%8b%e5%88%86%e7%af%ae%e9%ab%98%e7%ba%a7","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2025\/07\/10\/35%e9%a2%84%e6%9c%9f%e6%94%b6%e7%9b%8a%ef%bc%9f%e7%9c%8b%e8%bf%99%e4%b8%aa%e8%af%ba%e8%b4%9d%e5%b0%94%e5%a5%96%e5%be%97%e4%b8%bb%e7%9a%84%e9%b8%a1%e8%9b%8b%e5%88%86%e7%af%ae%e9%ab%98%e7%ba%a7\/","title":{"rendered":"35%\u9884\u671f\u6536\u76ca\uff1f\u770b\u8fd9\u4e2a\u8bfa\u8d1d\u5c14\u5956\u5f97\u4e3b\u7684”\u9e21\u86cb\u5206\u7bee”\u9ad8\u7ea7\u73a9\u6cd5"},"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\u5e26\u6765\u4e00\u7bc7\u66f4\u6df1\u5165\u3001\u66f4\u597d\u73a9\u7684\u73b0\u4ee3\u7ec4\u5408\u7406\u8bba\uff08MPT\uff09\u5b9e\u6218\u653b\u7565\uff0c\u7528\u6700\u63a5\u5730\u6c14\u7684\u793a\u4f8b\u548c\u4ee3\u7801\uff0c\u6559\u4f60\u73a9\u8f6c\u73b0\u4ee3\u7ec4\u5408\u7406\u8bba<\/mark>\uff1a\u4ece\u6570\u636e\u83b7\u53d6\u3001Sharpe\u7b5b\u9009\u3001Riskfolio\u4f18\u5316\uff0c\u5230\u6548\u7387\u524d\u6cbf\u53ef\u89c6\u5316\uff0c\u6700\u7ec8\u5f00\u53d1\u51fa\u5e74\u531635%+\u3001\u6ce2\u52a8<20%\u7684\u6700\u4f18\u7ec4\u5408\u201c\u667a\u914d\u901a\u201dPro<\/mark>\u3002<\/pre>\n<\/blockquote>\n\n\n\n
\u4e00\u3001MPT\uff1a\u6295\u8d44\u754c\u7684\u79d1\u5b66\u9b54\u6cd5<\/strong><\/h2>\n\n\n\n
\u6211\u53d1\u73b070\u5e74\u524d\u4e00\u4e2a\u6570\u5b66\u516c\u5f0f\uff0c\u81f3\u4eca\u8ba9\u57fa\u91d1\u7ecf\u7406\u4eec\u5077\u5077\u8d5a\u94b1\u3002<\/p>\n\n\n\n
1952\u5e74\uff0c\u9a6c\u79d1\u7ef4\u8328\u5728\u83dc\u573a\u83b7\u5f97\u7075\u611f\u3002\u5f53\u5927\u5988\u8bf4”\u9e21\u86cb\u522b\u653e\u4e00\u4e2a\u7bee\u5b50”\uff0c\u4ed6\u8ffd\u95ee\uff1a”\u600e\u4e48\u653e\u6700\u7a33\u59a5\uff1f” \u4e8e\u662f\u8bde\u751f\u4e86\u73b0\u4ee3\u6295\u8d44\u7ec4\u5408\u7406\u8bba(MPT)<\/strong>\u2014\u2014\u540e\u6765\u62ff\u4e86\u8bfa\u8d1d\u5c14\u5956\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u5f53\u65f6\uff0c\u6295\u8d44\u8005\u5927\u591a\u9760\u76f4\u89c9\u6216\u5c0f\u9053\u6d88\u606f\u9009\u80a1\uff0c\u800c\u9a6c\u514b\u7ef4\u8328\u7528\u6570\u5b66\u65b9\u6cd5\u8bc1\u660e\uff1a\u901a\u8fc7\u5408\u7406\u642d\u914d\u8d44\u4ea7\uff0c\u53ef\u4ee5\u5728\u4e0d\u589e\u52a0\u98ce\u9669\u7684\u60c5\u51b5\u4e0b\u63d0\u9ad8\u56de\u62a5\u3002\u8fd9\u4e00\u53d1\u73b0\u4e0d\u4ec5\u8ba9\u4ed6\u57281990\u5e74\u62ff\u4e0b\u8bfa\u8d1d\u5c14\u7ecf\u6d4e\u5b66\u5956\uff0c\u4e5f\u5f7b\u5e95\u6539\u53d8\u4e86\u6295\u8d44\u7684\u6e38\u620f\u89c4\u5219\u3002<\/p>\n\n\n\n
\u5f88\u591a\u4eba\u4ee5\u4e3a\u4e70\u4e00\u5806\u80a1\u7968\u5c31\u662f\u5206\u6563\uff0c\u5176\u5b9e\u5927\u9519\u7279\u9519\uff01<\/strong><\/p>\n\n\n\n
MPT\u4e0d\u53ea\u662f\u201c\u5206\u6563\u6295\u8d44\u201d\uff0c\u800c\u662f\u201c\u6700\u4f73\u5206\u6563\u6295\u8d44\u201d\u3002\u6700\u5173\u952e\u7684\u4e0d\u662f\u628a\u9e21\u86cb\u653e\u6ee1\u7bee\u5b50\uff0c\u800c\u662f\u627e\u5230\u8d1f\u76f8\u5173<\/strong>\uff08Correlation\uff09\u8d44\u4ea7\u3002<\/p>\n\n\n\n
\u4e3e\u4e2a\u4f8b\u5b50\uff1a<\/strong><\/p>\n\n\n\n
\u2600 \u6674\u5929\uff1a<\/strong>\ud83c\udf66\u51b0\u6dc7\u6dcb\u516c\u53f8 \u2197\ufe0f\uff0c\u2602\ufe0f\u96e8\u4f1e\u516c\u53f8 \u2198\ufe0f\n\ud83c\udf27 \u96e8\u5929\uff1a<\/strong>\ud83c\udf66\u51b0\u6dc7\u6dcb\u516c\u53f8 \u2198\ufe0f\uff0c\u2602\ufe0f\u96e8\u4f1e\u516c\u53f8 \u2197\ufe0f<\/code><\/pre>\n\n\n\n
\u6674\u5929\u51b0\u6dc7\u6dcb\u5356\u5f97\u597d\uff0c\u96e8\u4f1e\u5356\u5f97\u5dee\uff1b\u96e8\u5929\u5219\u76f8\u53cd\u3002\u8fd9\u79cd\u201c\u8d1f\u76f8\u5173\u201d\u5c31\u662f\u5206\u6563\u6295\u8d44\u7684\u7cbe\u9ad3\u3002<\/p>\n\n\n\n
\u8fd9\u5c31\u662f\u771f\u6b63\u7684\u591a\u6837\u5316\u3002\u540c\u7406\uff0c\u5f88\u591a\u4eba\u8bef\u4ee5\u4e3a\u4e7010\u53ea\u79d1\u6280\u80a1\u5c31\u662f\u5206\u6563\u6295\u8d44\uff0c\u5176\u5b9e\u5b83\u4eec\u53ef\u80fd\u4e00\u8d77\u6da8\u8dcc\uff0c\u6839\u672c\u6ca1\u964d\u4f4e\u98ce\u9669\uff01<\/p>\n\n\n\n
\u7ec4\u522b<\/th>\u914d\u7f6e\u8d44\u4ea7<\/th>\u5178\u578b\u98ce\u9669<\/th>\u5178\u578b\u6536\u76ca<\/th><\/tr>
\u968f\u673a\u5206\u6563<\/td>\u540c\u4e00\u884c\u4e1a\u591a\u53ea\u80a1\u7968<\/td>\u884c\u4e1a\u6574\u4f53\u4e0b\u8dcc\u98ce\u9669\u9ad8<\/td>\u4e0e\u5e02\u573a\u540c\u6b65<\/td><\/tr>
\u6700\u4f18\u591a\u6837\u5316<\/td>\u884c\u4e1aA + \u884c\u4e1aB + \u5927\u5b97\u5546\u54c1<\/td>\u4e0d\u540c\u884c\u4e1a\/\u8d44\u4ea7\u95f4\u8d1f\u76f8\u5173\u964d\u98ce<\/td>\u66f4\u5e73\u6ed1\u7684\u6536\u76ca\u66f2\u7ebf<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
MPT\u867d\u7136\u7ecf\u5178\uff0c\u4f46\u8fd940\u591a\u5e74\u6765\u4e5f\u5728\u4e0d\u65ad\u8fdb\u5316\uff1a<\/p>\n\n\n\n
    \n
  • \u5957\u5229\u5b9a\u4ef7\u7406\u8bba\uff08APT\uff09<\/strong>\uff1a\u8ba4\u4e3a\u6536\u76ca\u7531\u591a\u4e2a\u56e0\u7d20\u9a71\u52a8\uff0c\u5982\u5e02\u573a\u8d8b\u52bf\uff0c\u63a8\u52a8\u4e86\u667a\u80fdbeta\u7b56\u7565\u3002<\/li>\n\n\n\n
  • \u7ecf\u6d4e\u5468\u671f<\/strong>\uff1a\u6839\u636e\u7ecf\u6d4e\u5468\u671f\u8c03\u6574\u6295\u8d44\u7ec4\u5408\uff0c\u6bd4\u5982\u7528OECD\u6307\u6807\u9884\u6d4b\u5e02\u573a\u8d8b\u52bf\u3002<\/li>\n\n\n\n
  • ARCH\u6a21\u578b<\/strong>\uff1a\u5173\u6ce8\u6ce2\u52a8\u6027\u53d8\u5316\uff0c\u63d0\u9192\u6211\u4eec\u5728\u5371\u673a\u65f6\u5206\u6563\u6548\u679c\u53ef\u80fd\u51cf\u5f31\u3002<\/li>\n\n\n\n
  • \u4f18\u5316\u9677\u9631<\/strong>\uff1a\u4f18\u5316\u7ed3\u679c\u4f9d\u8d56\u8f93\u5165\u6570\u636e\uff0c\u9700\u8c28\u614e\u9009\u62e9\u4ee5\u907f\u514d\u9519\u8fc7\u6b21\u4f18\u7ec4\u5408\u3002<\/li>\n\n\n\n
  • \u957f\u671f vs \u77ed\u671f<\/strong>\uff1a\u957f\u671f\u6295\u8d44\u8005\u7684\u98ce\u9669-\u56de\u62a5\u6743\u8861\u4e0d\u540c\uff0c\u9700\u8003\u8651\u6295\u8d44\u671f\u9650\u3002<\/li>\n<\/ul>\n\n\n\n
    \u8fd9\u4e9b\u8fdb\u5c55\u8ba9MPT\u66f4\u9002\u5e94\u73b0\u4ee3\u5e02\u573a\uff0c\u4f46\u6838\u5fc3\u7406\u5ff5\u4e0d\u53d8\uff1a\u79d1\u5b66\u5206\u6563\uff0c\u7406\u6027\u6295\u8d44\u3002<\/strong><\/p>\n\n\n\n
    \u4e8c\u3001\u624b\u628a\u624b\u6559\u5b66\uff1a\u8ba9MPT\u6d3b\u8d77\u6765<\/strong><\/h2>\n\n\n\n
    \u73b0\u5728\uff0c\u6211\u4eec\u7528\u4ee3\u7801\u628aMPT\u53d8\u6210\u73b0\u5b9e\uff01\u4ee5\u4e0b\u662f\u5b9e\u73b0\u6b65\u9aa4\uff0c\u4e0b\u9762\u7684\u4ee3\u7801\u57fa\u4e8e\u6fb3\u5927\u5229\u4e9a\u80a1\u5e02\u6570\u636e\uff0c\u7a0d\u4f5c\u8c03\u6574\u4e5f\u80fd\u7528\u5728A\u80a1\u6216\u6e2f\u80a1\u3002<\/p>\n\n\n\n
    \u6b65\u9aa41\uff1a\u6536\u96c6\u548c\u51c6\u5907\u6570\u636e<\/strong><\/h3>\n\n\n\n
    \u6211\u4eec\u9700\u8981\u5386\u53f2\u80a1\u4ef7\u6570\u636e\u6765\u5206\u6790\u3002\u4ee5\u4e0b\u4ee3\u7801\u83b7\u53d6\u8fc7\u53bb5\u5e74\u7684\u80a1\u7968\u6536\u76d8\u4ef7\uff1a<\/p>\n\n\n\n
    # \u5b9a\u4e49\u65e5\u671f\u8303\u56f4\uff08\u4ece\u4eca\u5929\u8d77 5 \u5e74\u524d\uff09\nfrom datetime import datetime, timedelta\nimport yfinance as yf\nend_date = datetime.now().strftime('%Y-%m-%d')\nstart_date = (datetime.now() - timedelta(days=5*365)).strftime('%Y-%m-%d')\n\n# \u4e0b\u8f7d\u6570\u636e\ndata = yf.download(all_assets, start=start_date, end=end_date, auto_adjust=False)\ndata = data.loc[:, ('Adj Close', slice(None))]<\/code><\/pre>\n\n\n\n
    \u4e3a\u4ec0\u4e48\u90095\u5e74\uff1f\u592a\u77ed\u6570\u636e\u4e0d\u53ef\u9760\uff0c\u592a\u957f\u53ef\u80fd\u8fc7\u65f6\uff0c5\u5e74\u662f\u201c\u521a\u521a\u597d\u201d\u7684\u65f6\u95f4\u6bb5\u3002<\/p>\n\n\n\n
    \u6b65\u9aa42\uff1a\u8ba1\u7b97\u6bcf\u65e5\u6536\u76ca\u7387<\/strong><\/h3>\n\n\n\n
    \u63a5\u4e0b\u6765\uff0c\u628a\u80a1\u4ef7\u53d8\u5316\u8f6c\u4e3a\u6536\u76ca\u7387\uff0c\u5e76\u5e74\u5ea6\u5316\uff1a<\/p>\n\n\n\n
    # \u8ba1\u7b97\u6bcf\u65e5\u6536\u76ca\nreturns = data.pct_change().dropna()\n\n# \u5e74\u5316\u6536\u76ca\u548c\u6807\u51c6\u5dee\ntrading_days_per_year = 252\nannualized_returns = returns.mean() * trading_days_per_year\nannualized_volatility = returns.std() * np.sqrt(trading_days_per_year)<\/code><\/pre>\n\n\n\n
    \u8fd9\u91cc\u628a\u6bcf\u65e5\u6536\u76ca\u7387\u4e58\u4ee5252\uff08\u4e00\u5e74\u4ea4\u6613\u65e5\u6570\uff09\uff0c\u6ce2\u52a8\u7387\u7528\u5e73\u65b9\u6839\u7f29\u653e\uff0c\u8fd9\u662f\u91d1\u878d\u5b66\u7684\u57fa\u672c\u539f\u7406\u3002<\/p>\n\n\n\n
    \u6b65\u9aa43\uff1a\u8ba1\u7b97\u590f\u666e\u6bd4\u7387<\/strong><\/h3>\n\n\n\n
    \u795e\u5947\u516c\u5f0f\uff1a\u6027\u4ef7\u6bd4 = \uff08\u6536\u76ca – \u4fdd\u672c\u6536\u76ca\uff09\/ \u6ce2\u52a8\u98ce\u9669<\/strong><\/p>\n\n\n\n
    \u4e13\u4e1a\u8bcd\u53eb”\u590f\u666e\u6bd4\u7387”\uff0c\u6211\u7ba1\u5b83\u53eb“\u6295\u8d44\u6027\u4ef7\u6bd4”<\/strong>\uff0c\u590f\u666e\u6bd4\u7387\u8861\u91cf\u6bcf\u5355\u4f4d\u98ce\u9669\u5e26\u6765\u7684\u989d\u5916\u56de\u62a5\uff1a<\/p>\n\n\n\n
    risk_free_rate = 0.04  # \u5047\u8bbe\u65e0\u98ce\u9669\u5229\u73874%\nsharpe_ratios = (annualized_returns - risk_free_rate) \/ annualized_volatility\n\n# \u6309\u590f\u666e\u6bd4\u7387\u9009\u62e9\u524d 12 \u53ea\u80a1\u7968\ntop_n = 12\ntop_stocks = sharpe_ratios.sort_values(ascending=False).head(top_n).index.tolist()<\/code><\/pre>\n\n\n\n
    \u4ee5\u4e0b\u662f\u6fb3\u5927\u5229\u4e9a\u80a1\u5e02\u524d12\u53ea\u80a1\u7968\u7684\u590f\u666e\u6bd4\u7387\uff08\u793a\u4f8b\u6570\u636e\uff09\uff1a<\/p>\n\n\n\n
    \u80a1\u7968\u4ee3\u7801<\/th>\u516c\u53f8\u540d\u79f0<\/th>\u590f\u666e\u6bd4\u7387<\/th><\/tr>
    TLX.AX<\/td>Telix Pharmaceuticals<\/td>1.17<\/td><\/tr>
    CBA.AX<\/td>Commonwealth Bank<\/td>1.02<\/td><\/tr>
    CPU.AX<\/td>Computershare<\/td>0.93<\/td><\/tr>
    NAB.AX<\/td>National Australia Bank<\/td>0.87<\/td><\/tr>
    WTC.AX<\/td>WiseTech Global<\/td>0.85<\/td><\/tr>
    PLS.AX<\/td>Pilbara Minerals<\/td>0.82<\/td><\/tr>
    BXB.AX<\/td>Brambles<\/td>0.77<\/td><\/tr>
    QBE.AX<\/td>QBE Insurance<\/td>0.76<\/td><\/tr>
    ALL.AX<\/td>Aristocrat Leisure<\/td>0.74<\/td><\/tr>
    WBC.AX<\/td>Westpac Banking<\/td>0.72<\/td><\/tr>
    WES.AX<\/td>Wesfarmers<\/td>0.69<\/td><\/tr>
    ANZ.AX<\/td>ANZ Banking Group<\/td>0.67<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    \u666e\u6bd4\u7387\u544a\u8bc9\u6211\u4eec\uff0c\u6211\u4eec\u627f\u62c5\u6bcf\u5355\u4f4d\u98ce\u9669\u80fd\u83b7\u5f97\u591a\u5c11\u989d\u5916\u56de\u62a5\u3002\u751f\u7269\u79d1\u6280\u516c\u53f8Telix\u4ee51.17\u9886\u5148\uff0c\u4f46\u94f6\u884c\u80a1\u4e5f\u8868\u73b0\u4e0d\u4fd7\u3002<\/p>\n\n\n\n
    \u6b65\u9aa44\uff1a\u6784\u5efa\u6295\u8d44\u7ec4\u5408<\/strong><\/h3>\n\n\n\n
    \u6211\u4eec\u7528riskfolio\u5e93\u521b\u5efa\u6295\u8d44\u7ec4\u5408\u5bf9\u8c61\uff0c\u8ba1\u7b97\u9884\u671f\u6536\u76ca\u7387\u548c\u534f\u65b9\u5dee\u77e9\u9635\uff1a<\/p>\n\n\n\n
    # \u6784\u5efa\u6295\u8d44\u7ec4\u5408\u5bf9\u8c61\nimport riskfolio as rp\nport = rp.Portfolio(returns=returns)\n\n# \u9009\u62e9\u65b9\u6cd5\u5e76\u4f30\u8ba1\u8f93\u5165\u53c2\u6570\nmethod_mu = 'hist' # \u6839\u636e\u5386\u53f2\u6570\u636e\u4f30\u8ba1\u9884\u671f\u6536\u76ca\u7684\u65b9\u6cd5\nmethod_cov = 'hist' # \u6839\u636e\u5386\u53f2\u6570\u636e\u4f30\u8ba1\u534f\u65b9\u5dee\u77e9\u9635\u7684\u65b9\u6cd5\n\nport.assets_stats(method_mu=method_mu, method_cov=method_cov)<\/code><\/pre>\n\n\n\n
    \u8fd9\u5c31\u50cf\u7ed9\u6295\u8d44\u7ec4\u5408\u88c5\u4e0a\u201c\u53d1\u52a8\u673a\u201d\uff0c\u51c6\u5907\u4f18\u5316\uff01<\/p>\n\n\n\n
    \u6b65\u9aa45\uff1a\u4f18\u5316\u6295\u8d44\u7ec4\u5408<\/strong><\/h3>\n\n\n\n
    \u73b0\u5728\uff0c\u627e\u5230\u6700\u5927\u5316\u590f\u666e\u6bd4\u7387\u7684\u6295\u8d44\u7ec4\u5408\uff1a<\/p>\n\n\n\n
    w = port.optimization(model='Classic', rm='MV', obj='Sharpe', rf=0, hist=True)<\/code><\/pre>\n\n\n\n
    \u8fd9\u5c31\u662f\u8bfa\u8d1d\u5c14\u5956\u7684\u9b54\u529b\uff01<\/strong>\u6211\u4eec\u544a\u8bc9\u7b97\u6cd5\uff1a\u201c\u627e\u5230\u6700\u5927\u5316\u590f\u666e\u6bd4\u7387\u7684\u6295\u8d44\u7ec4\u5408\u6743\u91cd\u3002\u201d \u4f18\u5316\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n\n\n\n
    \u80a1\u7968\u4ee3\u7801<\/th>\u516c\u53f8\u540d\u79f0<\/th>\u6743\u91cd (%)<\/th><\/tr>
    CBA.AX<\/td>Commonwealth Bank<\/td>29.40<\/td><\/tr>
    CPU.AX<\/td>Computershare<\/td>17.62<\/td><\/tr>
    BXB.AX<\/td>Brambles<\/td>16.71<\/td><\/tr>
    TLX.AX<\/td>Telix Pharmaceuticals<\/td>16.57<\/td><\/tr>
    PLS.AX<\/td>Pilbara Minerals<\/td>7.88<\/td><\/tr>
    WTC.AX<\/td>WiseTech Global<\/td>7.17<\/td><\/tr>
    QBE.AX<\/td>QBE Insurance<\/td>4.65<\/td><\/tr>
    NAB.AX<\/td>National Australia Bank<\/td>0.00<\/td><\/tr>
    ALL.AX<\/td>Aristocrat Leisure<\/td>0.00<\/td><\/tr>
    WBC.AX<\/td>Westpac Banking<\/td>0.00<\/td><\/tr>
    WES.AX<\/td>Wesfarmers<\/td>0.00<\/td><\/tr>
    ANZ.AX<\/td>ANZ Banking Group<\/td>0.00<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    \u60ca\u8bb6\u5417\uff1f\u53ea\u67096\u53ea\u80a1\u7968\u6709\u6743\u91cd\uff01\u8fd9\u662f\u56e0\u4e3a\u4f18\u5316\u5668\u53d1\u73b0\u5176\u4ed6\u80a1\u7968\u5728\u98ce\u9669-\u56de\u62a5\u7ec4\u5408\u4e0a\u201c\u591a\u4f59\u201d\uff0c\u7528\u6570\u5b66\u7b5b\u9009\u6389\u4e86\u201c\u5197\u4f59\u201d\u8d44\u4ea7\uff0c\u7559\u4e0b\u6700\u80fd\u4e92\u8865\u7684\u3002<\/p>\n\n\n\n
    \u6b65\u9aa46\uff1a\u53ef\u89c6\u5316\u6548\u7387\u524d\u6cbf<\/strong><\/h3>\n\n\n\n
    \u53ef\u89c6\u5316\u201c\u6548\u7387\u524d\u6cbf\u201d\u662fMPT\u7684\u7cbe\u9ad3\uff0c\u5c55\u793a\u98ce\u9669\u548c\u56de\u62a5\u7684\u6700\u4f73\u7ec4\u5408\uff1a<\/p>\n\n\n\n
    import matplotlib.pyplot as plt\n# \u6784\u5efa\u6548\u7387\u524d\u6cbf\nef = port.efficient_frontier(model='Classic', rm='MV', points=50, rf=rf, hist=True)\nplt.plot(ef['Volatility'], ef['Return'])\nplt.scatter(w.volatility, w.returns)\nplt.title('\u6548\u7387\u524d\u6cbf\u4e0e\u6700\u4f18\u70b9')\nplt.xlabel('\u5e74\u5316\u6ce2\u52a8')\nplt.ylabel('\u5e74\u5316\u6536\u76ca')\nplt.show()<\/code><\/pre>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \u4e0a\u56fe\u751f\u6210\u4e86\u4e00\u6761\u66f2\u7ebf\uff0c\u6807\u51fa\u6700\u4f73\u6295\u8d44\u7ec4\u5408\uff08\u7ea2\u661f\uff09\u3002<\/p>\n\n\n\n
    \u4e09\u3001\u5b9e\u6218\u7ed3\u679c&\u5b9e\u7528\u5efa\u8bae<\/strong><\/h2>\n\n\n\n
    \u4e0b\u9762\u662f\u5b9e\u6218\u540e\u7684\u6570\u636e\u7ed3\u679c\uff1a<\/p>\n\n\n\n
    \ud83d\udd0d \u4f18\u5316\u6295\u8d44\u7ec4\u5408\u8868\u73b0\uff1a
    \ud83d\udcc8 \u9884\u671f\u5e74\u5316\u6536\u76ca\u7387\uff1a35.49% 
    \u26a0\ufe0f \u5e74\u5316\u6ce2\u52a8\u7387\uff1a18.87% 
    \ud83c\udfaf \u590f\u666e\u6bd4\u7387\uff1a1.67 
    \ud83d\ude80 \u8d85\u989d\u6536\u76ca\uff08\u5bf9\u6bd4 4.00% \u65e0\u98ce\u9669\u6536\u76ca\u7387\uff09\uff1a31.49%<\/pre>\n\n\n\n
    \u6027\u4ef7\u6bd4\uff08\u590f\u666e\u6bd4\u7387\uff091.67\u610f\u5473\u7740\u4ec0\u4e48\uff1f
    <1.0\uff1a\u98ce\u9669\u56de\u62a5\u4e0d\u4f73\uff1b1.0-2.0\uff1a\u4f18\u79c0\u8868\u73b0\uff1b2.0\uff1a\u6781\u7f55\u89c1\u3002<\/p>\n\n\n\n
    MPT\u7684\u5b9e\u7528\u5efa\u8bae<\/strong><\/p>\n\n\n\n
    1. \u8981\u505a\u7684\u4e8b\uff1a<\/strong><\/p>\n\n\n\n