{"id":2819,"date":"2025-10-15T07:45:00","date_gmt":"2025-10-14T23:45:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=2819"},"modified":"2025-10-10T21:29:19","modified_gmt":"2025-10-10T13:29:19","slug":"%e5%91%a8%e4%ba%94%e4%b9%b0%e5%85%a5%ef%bc%8c%e5%91%a8%e4%b8%80%e6%95%b0%e9%92%b1%ef%bc%9f%e8%bf%99%e4%b8%aa%e7%ae%80%e5%8d%95%e7%9a%84%e8%bd%ae%e5%8a%a8%e7%ad%96%e7%95%a5%e8%83%bd%e8%ae%a9%e4%bd%a0","status":"publish","type":"post","link":"https:\/\/blog.laoyulaoyu.top\/index.php\/2025\/10\/15\/%e5%91%a8%e4%ba%94%e4%b9%b0%e5%85%a5%ef%bc%8c%e5%91%a8%e4%b8%80%e6%95%b0%e9%92%b1%ef%bc%9f%e8%bf%99%e4%b8%aa%e7%ae%80%e5%8d%95%e7%9a%84%e8%bd%ae%e5%8a%a8%e7%ad%96%e7%95%a5%e8%83%bd%e8%ae%a9%e4%bd%a0\/","title":{"rendered":"\u5468\u4e94\u4e70\u5165\uff0c\u5468\u4e00\u6570\u94b1\uff1f\u8fd9\u4e2a\u7b80\u5355\u7684\u8f6e\u52a8\u7b56\u7565\u80fd\u8ba9\u4f60\u8dd1\u8d6290%\u7684\u6295\u8d44\u8005"},"content":{"rendered":"\n

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

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\"\"<\/figure>\n\n\n\n
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\u5b66\u672f\u754c\u5bf9\u52a8\u91cf\u6548\u5e94\u65e9\u6709\u6df1\u5165\u7814\u7a76\u3002\u81ea\u4eceJegadeesh\u548cTitman\u53d1\u73b0\u52a8\u91cf\u6548\u5e94\u4ee5\u6765\uff0c\u8fd9\u4e00\u53ef\u4ee5\u83b7\u5f97\u8d85\u989d\u6536\u76ca\u7684\u7b56\u7565\u8d8a\u6765\u8d8a\u5e7f\u6cdb\u5730\u5e94\u7528\u5230\u6295\u8d44\u7ec4\u5408\u7ba1\u7406\u5f53\u4e2d\u3002\u7814\u7a76\u53d1\u73b0\uff0c90%\u7684\u57fa\u91d1\u7ecf\u7406\u90fd\u503e\u5411\u4f7f\u7528\u52a8\u91cf\u7b56\u7565\u8fdb\u884c\u6295\u8d44\u5b9e\u8df5\u548c\u7ec4\u5408\u914d\u7f6e<\/strong>\u3002<\/p>\n\n\n\n
\u6211\u8fd9\u4e2a\u7b56\u7565\u7684\u6838\u5fc3\u601d\u60f3\u975e\u5e38\u7b80\u5355\uff1a\u4e70\u5165\u8fd1\u671f\u8868\u73b0\u6700\u5f3a\u7684\u8d44\u4ea7\uff0c\u6bcf\u5468\u8c03\u6574\u4e00\u6b21<\/strong>\u3002\u56e0\u4e3a\u4e70\u5f3a\u52bf\u8d44\u4ea7\u6bd4\u731c\u5e02\u573a\u65b9\u5411\u806a\u660e\u591a\u4e86\u3002\u4e0d\u7528\u590d\u6742\u6a21\u578b\uff0c\u5c31\u80fd\u8ddf\u7740\u5e02\u573a\u5f3a\u52bf\u8d70\u3002<\/p>\n\n\n\n
\u5177\u4f53\u6765\u8bf4\uff0c\u5c31\u662f\u6bcf\u5468\u4e94\u9009\u62e9\u8fc7\u53bb\u4e00\u6bb5\u65f6\u95f4\u5185\u8868\u73b0\u6700\u597d\u7684\u8d44\u4ea7\uff0c\u7136\u540e\u4e70\u5165\u6301\u6709\u5230\u4e0b\u4e00\u5468\uff0c\u5982\u6b64\u5faa\u73af\u5f80\u590d\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
<\/p>\n\n\n\n
\ud83d\udd01\u80cc\u540e\u7684\u903b\u8f91\u548c\u7b56\u7565\u6838\u5fc3\u89c4\u5219<\/strong><\/h3>\n\n\n\n
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\u53cd\u5e94\u4e0d\u8db3<\/strong>\uff1a\u5e02\u573a\u5bf9\u5229\u597d\u6d88\u606f\u4e0d\u80fd\u4e00\u6b21\u5b8c\u5168\u6d88\u5316\uff0c\u4f1a\u6301\u7eed\u5728\u80a1\u4ef7\u4e0a\u4f53\u73b0\uff0c\u5f62\u6210\u6301\u7eed\u7684\u4e0a\u6da8\u8d8b\u52bf\u3002<\/p>\n\n\n\n
\u6b63\u53cd\u9988\u6548\u5e94<\/strong>\uff1a\u8d62\u5bb6\u6052\u8d62\uff0c\u8868\u73b0\u597d\u7684\u8d44\u4ea7\u4f1a\u5438\u5f15\u66f4\u591a\u6295\u8d44\u8005\u5173\u6ce8\uff0c\u5f62\u6210\u6b63\u5411\u5faa\u73af\u3002<\/p>\n\n\n\n
\u8fc7\u5ea6\u53cd\u5e94<\/strong>\uff1a\u6295\u8d44\u8005\u5f80\u5f80\u4f1a\u8fc7\u5ea6\u8ffd\u6367\u8868\u73b0\u597d\u7684\u8d44\u4ea7\uff0c\u63a8\u52a8\u5176\u4ef7\u683c\u8fdb\u4e00\u6b65\u4e0a\u6da8\u3002<\/p>\n\n\n\n
\u8fd9\u4e2a\u7b56\u7565\u6709\u51e0\u4e2a\u5173\u952e\u89c4\u5219\uff1a<\/p>\n\n\n\n
\u53ea\u5728\u5468\u4e94\u8c03\u4ed3<\/strong>\uff1a\u6bcf\u5468\u4e94\u68c0\u67e5\u6301\u4ed3\uff0c\u5356\u51fa\u4e0d\u7b26\u5408\u6761\u4ef6\u7684\u8d44\u4ea7\uff0c\u4e70\u5165\u65b0\u7684\u5f3a\u52bf\u8d44\u4ea7\u3002<\/p>\n\n\n\n
\u9009\u62e9\u8fd1\u671f\u8868\u73b0\u6700\u4f73\u7684\u8d44\u4ea7<\/strong>\uff1a\u57fa\u4e8e\u8fc7\u53bb63\u5929\uff08\u7ea63\u4e2a\u6708\uff09\u7684\u6536\u76ca\u7387\u6392\u540d\u3002<\/p>\n\n\n\n
\u53ea\u9009\u62e9\u8868\u73b0\u6b63\u9762\u7684\u8d44\u4ea7<\/strong>\uff1a\u6392\u9664\u6240\u6709\u8fd1\u671f\u6536\u76ca\u4e3a\u8d1f\u7684\u8d44\u4ea7\u3002<\/p>\n\n\n\n
\u7b49\u6743\u91cd\u5206\u914d<\/strong>\uff1a\u6bcf\u4e2a\u5165\u9009\u8d44\u4ea7\u5206\u914d\u76f8\u540c\u7684\u8d44\u91d1\u3002<\/p>\n\n\n\n
\u4e8c\u3001\u624b\u628a\u624b\u6559\u5b66<\/strong><\/h2>\n\n\n\n
\u63a5\u4e0b\u6765\uff0c\u6211\u8be6\u7ec6\u8bb2\u89e3\u5982\u4f55\u7528Python\u5b9e\u73b0\u8fd9\u4e2a\u7b56\u7565\u3002\u522b\u62c5\u5fc3\uff0c\u5373\u4f7f\u4f60\u662f\u7f16\u7a0b\u65b0\u624b\uff0c\u4e5f\u80fd\u8ddf\u7740\u4e00\u6b65\u6b65\u505a\u51fa\u6765\u3002<\/p>\n\n\n\n
\u7b2c\u4e00\u6b65\uff1a\u51c6\u5907\u5de5\u4f5c<\/strong><\/h3>\n\n\n\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u4e00\u4e9bPython\u5e93\uff1a<\/p>\n\n\n\n
import pandas as pd\nimport numpy as np\nimport yfinance as yf\nimport matplotlib.pyplot as plt\n\n# \u8bbe\u7f6e\u4e2d\u6587\u663e\u793a\nplt.rcParams['font.sans-serif'] = ['SimHei']\nplt.rcParams['axes.unicode_minus'] = False<\/pre>\n\n\n\n
\u7b2c\u4e8c\u6b65\uff1a\u8bbe\u7f6e\u7b56\u7565\u53c2\u6570<\/strong><\/h3>\n\n\n\n
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\u5728\u56fd\u5185\u53ef\u4ee5\u7528A\u80a1\u5927\u76d8\u80a1\u5982\u8d35\u5dde\u8305\u53f0\u3001\u5b81\u5fb7\u65f6\u4ee3\u3001\u4e2d\u56fd\u5e73\u5b89\u3001\u6bd4\u4e9a\u8fea\u3001\u817e\u8baf\u63a7\u80a1\u3001\u963f\u91cc\u5df4\u5df4\u3002<\/p>\n\n\n\n
# \u7b56\u7565\u53c2\u6570\u8bbe\u7f6e\nTICKERS = [\"AAPL\", \"MSFT\", \"AMZN\", \"GOOGL\", \"TSLA\", \"NVDA\"]  # \u80a1\u7968\u6c60\nSTART_DATE = \"2020-01-01\"    # \u56de\u6d4b\u5f00\u59cb\u65e5\u671f\nEND_DATE = \"2025-07-01\"      # \u56de\u6d4b\u7ed3\u675f\u65e5\u671f\nMOM_DAYS = 63                # \u52a8\u91cf\u8ba1\u7b97\u5468\u671f\uff0c63\u5929\u7ea6\u7b49\u4e8e3\u4e2a\u6708\nTOP_N = 2                    # \u6bcf\u6b21\u9009\u62e9\u7684\u8d44\u4ea7\u6570\u91cf\nCOST_BPS = 5                 # \u4ea4\u6613\u6210\u672c\uff0c\u4e07\u5206\u4e4b\u4e94<\/pre>\n\n\n\n
\u6bcf\u4e2a\u8d44\u4ea7\u7528\u8fc7\u53bb\u4e00\u6bb5\u65f6\u95f4\u6da8\u5e45\u6253\u5206\u3002\u65f6\u95f4\u7a97\u53ef\u8c03\uff0c\u7528\u5929\u6570\u3002 \u53c2\u6570\u662f\u5f3a\u52bf\u5929\u6570\uff1a63\u5929\uff0c\u5927\u69823\u4e2a\u6708\u3002 \u4f60\u4e5f\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u8c03\u6574\u8fd9\u4e9b\u53c2\u6570\u3002<\/p>\n\n\n\n
\u7b2c\u4e09\u6b65\uff1a\u83b7\u53d6\u6570\u636e<\/strong><\/h3>\n\n\n\n
\u4eceYahoo Finance\u62c9\u5f00\u76d8\u548c\u6536\u76d8\u4ef7\u3002\u6570\u636e\u5bf9\u9f50\uff0c\u586b\u7a7a\u3002<\/p>\n\n\n\n
# \u4e0b\u8f7d\u5386\u53f2\u6570\u636e\ndata = {}\nfor t in TICKERS:\n    df = yf.download(t, start=START_DATE, end=END_DATE, progress=False)\n    data[t] = df\n\n# \u6574\u7406\u5f00\u76d8\u4ef7\u548c\u6536\u76d8\u4ef7\u6570\u636e\nopens = pd.concat([data[t][\"Open\"] for t in TICKERS], axis=1)\ncloses = pd.concat([data[t][\"Close\"] for t in TICKERS], axis=1)\nopens.columns = TICKERS\ncloses.columns = TICKERS<\/pre>\n\n\n\n
\u7b2c\u56db\u6b65\uff1a\u8ba1\u7b97\u52a8\u91cf\u4fe1\u53f7<\/strong><\/h3>\n\n\n\n
\u8fd9\u79cd\u8ba1\u7b97\u52a8\u91cf\u7684\u65b9\u6cd5\u662f\u91d1\u878d\u9886\u57df\u7684\u5e38\u89c1\u505a\u6cd5\uff0c\u5373\u4f7f\u7528\u4e00\u5b9a\u65f6\u95f4\u5185\u7684\u6536\u76ca\u7387\u6765\u8861\u91cf\u8d44\u4ea7\u7684\u52a8\u91cf\u5f3a\u5f31\u3002<\/p>\n\n\n\n
# \u8ba1\u7b97\u52a8\u91cf\uff1a\u4f7f\u7528\u6536\u76ca\u7387\u4f5c\u4e3a\u52a8\u91cf\u6307\u6807\nmomentum = closes.pct_change(MOM_DAYS)\n\n# \u786e\u5b9a\u8c03\u4ed3\u65e5\u671f\uff08\u6bcf\u5468\u4e94\uff09\nis_friday = closes.index.weekday == 4\nrebalance_dates = closes.index[is_friday]\n\n# \u6784\u5efa\u6295\u8d44\u7ec4\u5408\nportfolio = pd.Series(index=closes.index, dtype=object)\n\nfor date in rebalance_dates:\n    # \u9009\u62e9\u8868\u73b0\u6700\u597d\u7684TOP_N\u4e2a\u8d44\u4ea7\n    top_assets = momentum.loc[date].nlargest(TOP_N)\n    # \u53ea\u9009\u62e9\u52a8\u91cf\u4e3a\u6b62\u7684\u8d44\u4ea7\n    winners = top_assets[top_assets > 0].index.tolist()\n    \n    if len(winners) == 0:\n        # \u6ca1\u6709\u6b63\u52a8\u91cf\u8d44\u4ea7\uff0c\u6301\u6709\u73b0\u91d1\n        portfolio.loc[date] = [\"CASH\"]\n    else:\n        portfolio.loc[date] = winners\n\n# \u586b\u5145\u975e\u8c03\u4ed3\u65e5\u7684\u6301\u4ed3\nportfolio = portfolio.ffill()<\/pre>\n\n\n\n
\u7b2c\u4e94\u6b65\uff1a\u8ba1\u7b97\u6536\u76ca<\/strong><\/h3>\n\n\n\n
\u56de\u62a5\u662f\u6309\u5f00\u76d8\u4ef7\u8ba1\u7b97\u7684\uff0c\u5373\u4ee5\u4eca\u5929\u5f00\u76d8\u4ef7\u4e70\u5165\uff0c\u4ee5\u660e\u5929\u5f00\u76d8\u4ef7\u5356\u51fa\u3002\u8fd9\u907f\u514d\u4e86\u524d\u77bb\u504f\u5dee\u5e76\u4e0e\u5b9e\u9645\u518d\u5e73\u8861\u7684\u53d1\u751f\u65b9\u5f0f\u76f8\u5339\u914d\u3002<\/p>\n\n\n\n
# \u8ba1\u7b97\u5f00\u76d8\u4ef7\u5230\u5f00\u76d8\u4ef7\u7684\u6536\u76ca\u7387\uff08\u907f\u514d\u4f7f\u7528\u672a\u6765\u6570\u636e\uff09\nopen_ret = opens.pct_change().fillna(0)\nopen_ret[\"CASH\"] = 0.0  # \u73b0\u91d1\u6536\u76ca\u7387\u4e3a0\n\n# \u6784\u5efa\u6743\u91cd\u77e9\u9635\nassets = opens.columns.tolist() + [\"CASH\"]\nweights = pd.DataFrame(0.0, index=opens.index, columns=assets)\n\nfor date, selected_assets in portfolio.items():\n    weight = 1.0 \/ len(selected_assets)  # \u7b49\u6743\u91cd\n    for asset in selected_assets:\n        weights.at[date, asset] = weight\n\n# \u5411\u524d\u586b\u5145\u6743\u91cd\nweights = weights.ffill()\n\n# \u8ba1\u7b97\u7b56\u7565\u6536\u76ca\nstrategy_returns = (weights.shift() * open_ret).sum(axis=1)\n\n# \u8003\u8651\u4ea4\u6613\u6210\u672c\nrebalance_days = (weights != weights.shift()).any(axis=1)\nstrategy_returns[rebalance_days] -= COST_BPS \/ 10000<\/pre>\n\n\n\n
\u6bcf\u6b21\u91cd\u65b0\u5e73\u8861\u65f6\uff0c\u6211\u4eec\u4f1a\u5c06\u8d44\u91d1\u5e73\u5747\u5206\u914d\u7ed9\u6240\u9009\u80a1\u7968\u3002\u5982\u679c\u7b26\u5408\u6761\u4ef6\u7684\u80a1\u7968\u6570\u91cf\u5c11\u4e8e\u76ee\u6807\u6570\u91cf\uff0c\u5219\u5269\u4f59\u7684\u6743\u91cd\u5c06\u8f6c\u6362\u4e3a\u73b0\u91d1\u3002<\/p>\n\n\n\n
\u7b2c\u516d\u6b65\uff1a\u7ed8\u5236\u6536\u76ca\u66f2\u7ebf<\/strong><\/h3>\n\n\n\n
\u6211\u4eec\u5c06\u8f6e\u6362\u7b56\u7565\u548c\u4e2a\u4eba\u4e70\u5165\u5e76\u6301\u6709\u6536\u76ca\u8fdb\u884c\u4e86\u56fe\u5f62\u5316\u6bd4\u8f83\u3002<\/p>\n\n\n\n
# \u8ba1\u7b97\u7b56\u7565\u51c0\u503c\u66f2\u7ebf\nequity_curve = (1 + strategy_returns).cumprod()\n\n# \u8ba1\u7b97\u7b49\u6743\u91cd\u57fa\u51c6\nequal_weight_returns = open_ret[TICKERS].mean(axis=1)\nequal_weight_curve = (1 + equal_weight_returns).cumprod()\n\n# \u7ed8\u5236\u6536\u76ca\u66f2\u7ebf\u5bf9\u6bd4\u56fe\nplt.figure(figsize=(12, 6))\nplt.plot(equity_curve, label='\u52a8\u91cf\u8f6e\u52a8\u7b56\u7565', linewidth=2)\nplt.plot(equal_weight_curve, label='\u7b49\u6743\u91cd\u7ec4\u5408', linestyle='--')\nplt.title('\u52a8\u91cf\u8f6e\u52a8\u7b56\u7565 vs \u7b49\u6743\u91cd\u7ec4\u5408')\nplt.xlabel('\u65e5\u671f')\nplt.ylabel('\u51c0\u503c')\nplt.legend()\nplt.grid(True)\nplt.show()<\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u4ece\u4e0a\u56fe\u4e2d\u53ef\u4ee5\u770b\u5230\uff0c\u52a8\u91cf\u8f6e\u52a8\u7b56\u7565\u7684\u51c0\u503c\u66f2\u7ebf\u660e\u663e\u8dd1\u8d62\u4e86\u7b49\u6743\u91cd\u7ec4\u5408\uff0c\u4f53\u73b0\u4e86\u7b56\u7565\u7684\u6709\u6548\u6027\u3002<\/p>\n\n\n\n
\u4e09\u3001\u7b56\u7565\u6548\u679c\u6570\u636e\u5206\u6790<\/strong><\/h2>\n\n\n\n
\u6211\u4eec\u4f1a\u5c06\u6295\u8d44\u7ec4\u5408\u6301\u6709\u91cf\u968f\u65f6\u95f4\u7684\u53d8\u5316\u8fdb\u884c\u53ef\u89c6\u5316\uff0c\u4ee5\u663e\u793a\u6bcf\u5468\u9009\u62e9\u4e86\u54ea\u4e9b\u8d44\u4ea7\u3002<\/p>\n\n\n\n
# \u6301\u6709\u65f6\u95f4\u7ebf\nfig, ax = plt.subplots(figsize=(13,2))\nidx_map = {t:i for i,t in enumerate(TICKERS+['USD'])}\n\nif TOP_N == 1:\n    hold = basket.map(lambda x: x[0])\n    hold_idx = hold.map(idx_map)\n    ax.step(basket.index, hold_idx, where=\"post\", linewidth=2)\nelse:\n    for pos in range(TOP_N):\n        series = basket.map(lambda x: x[pos] if pos < len(x) else 'USD')\n        series_idx = series.map(idx_map)\n        ax.step(basket.index, series_idx, where=\"post\", linewidth=2)\n\nax.set_yticks(list(idx_map.values()))\nax.set_yticklabels(list(idx_map.keys()))\nax.set_xlabel(\"Date\")\nax.set_title(\"Holding Timeline\")\nax.grid(axis=\"y\", alpha=0.3)\nplt.tight_layout()\nplt.show()<\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u6700\u540e\uff0c\u4e3a\u4e86\u5168\u9762\u8bc4\u4f30\u7b56\u7565\u8868\u73b0\uff0c\u6211\u4eec\u8ba1\u7b97\u51e0\u4e2a\u5173\u952e\u6307\u6807\uff1a<\/p>\n\n\n\n
# \u8ba1\u7b97\u5e74\u5316\u6536\u76ca\u7387
years = len(strategy_returns) \/ 252
cagr = equity_curve.iloc[-1] ** (1 \/ years) - 1

# \u8ba1\u7b97\u5e74\u5316\u6ce2\u52a8\u7387
volatility = strategy_returns.std() * np.sqrt(252)

# \u8ba1\u7b97\u590f\u666e\u6bd4\u7387
sharpe = strategy_returns.mean() \/ strategy_returns.std() * np.sqrt(252)

# \u8ba1\u7b97\u6700\u5927\u56de\u64a4
max_drawdown = (equity_curve \/ equity_curve.cummax() - 1).min()

print(f\"\u5e74\u5316\u6536\u76ca\u7387: {cagr:.2%}\")
print(f\"\u5e74\u5316\u6ce2\u52a8\u7387: {volatility:.2%}\")
print(f\"\u590f\u666e\u6bd4\u7387: {sharpe:.2f}\")
print(f\"\u6700\u5927\u56de\u64a4: {max_drawdown:.2%}\")<\/pre>\n\n\n\n
\u56de\u6d4b\u7ed3\u679c\u5982\u4e0b\u8868\u6240\u793a\uff1a<\/p>\n\n\n\n
\u6307\u6807<\/th>\u52a8\u91cf\u8f6e\u52a8\u7b56\u7565<\/th>\u7b49\u6743\u91cd\u7ec4\u5408<\/th><\/tr><\/thead>
\u5e74\u5316\u6536\u76ca\u7387<\/td>56.83%<\/strong><\/td>18.30%<\/td><\/tr>
\u5e74\u5316\u6ce2\u52a8\u7387<\/td>37.93%<\/strong><\/td>20.10%<\/td><\/tr>
\u590f\u666e\u6bd4\u7387<\/td>1.38<\/strong><\/td>0.91<\/td><\/tr>
\u6700\u5927\u56de\u64a4<\/td>-54.75%<\/strong><\/td>-40.50%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u4ece\u8fd9\u4e9b\u6307\u6807\u53ef\u4ee5\u770b\u51fa\uff0c\u52a8\u91cf\u8f6e\u52a8\u7b56\u7565\u5728\u6536\u76ca\u65b9\u9762\u8868\u73b0\u6781\u5176\u51fa\u8272\uff0c\u5e74\u5316\u6536\u76ca\u7387\u8fbe\u523056.83%<\/strong>\uff0c\u8fdc\u8d85\u7b49\u6743\u91cd\u7ec4\u5408\u3002\u867d\u7136\u6ce2\u52a8\u7387\u548c\u6700\u5927\u56de\u64a4\u76f8\u5bf9\u8f83\u9ad8\uff0c\u4f46\u590f\u666e\u6bd4\u73871.38<\/strong>\u8868\u660e\u7b56\u7565\u7684\u98ce\u9669\u8c03\u6574\u540e\u6536\u76ca\u4f9d\u7136\u4f18\u79c0\u3002<\/p>\n\n\n\n
\u56db\u3001\u7b56\u7565\u7684\u5c40\u9650\u4e0e\u6539\u8fdb<\/strong><\/h2>\n\n\n\n
\u867d\u7136\u8fd9\u4e2a\u7b56\u7565\u8868\u73b0\u4e0d\u9519\uff0c\u4f46\u4efb\u4f55\u7b56\u7565\u90fd\u4e0d\u662f\u5b8c\u7f8e\u7684\u3002\u8fd9\u4e2a\u52a8\u91cf\u8f6e\u52a8\u7b56\u7565\u4e3b\u8981\u6709\u4ee5\u4e0b\u5c40\u9650\u6027\uff1a<\/p>\n\n\n\n
4.1 \u52a8\u91cf\u5d29\u6e83\u98ce\u9669<\/strong><\/h3>\n\n\n\n
\u5728\u6781\u7aef\u5e02\u573a\u73af\u5883\u4e0b\uff0c\u5c24\u5176\u662f\u5728\u5e02\u573a\u6301\u7eed\u4e0b\u8dcc\u540e\u7a81\u7136\u53cd\u5f39\uff0c\u5e76\u4e14\u4e8b\u524d\u5e02\u573a\u6ce2\u52a8\u7387\u5904\u4e8e\u8f83\u9ad8\u6c34\u5e73\u65f6\uff0c\u52a8\u91cf\u7b56\u7565\u7684\u6536\u76ca\u53ef\u80fd\u51fa\u73b0\u6781\u7aef\u635f\u5931\uff0c\u8fd9\u79cd\u73b0\u8c61\u88ab\u5b66\u672f\u754c\u79f0\u4e3a”\u52a8\u91cf\u5d29\u6e83<\/strong>“\u3002<\/p>\n\n\n\n
2008-2009\u5e74\u5168\u7403\u91d1\u878d\u5371\u673a\u671f\u95f4\uff0c\u4f20\u7edf\u52a8\u91cf\u7b56\u7565\u5c31\u7ecf\u5386\u4e86\u663e\u8457\u7684\u5d29\u76d8\u3002<\/p>\n\n\n\n
4.2 \u6539\u8fdb\u65b9\u6cd5<\/strong><\/h3>\n\n\n\n
\u6ce2\u52a8\u7387\u62e9\u65f6<\/strong>\uff1a\u5f53\u5e02\u573a\u6ce2\u52a8\u7387\u8fc7\u9ad8\u65f6\uff0c\u6682\u505c\u52a8\u91cf\u7b56\u7565\u3002\u7814\u7a76\u8868\u660e\uff0c\u57fa\u4e8e\u6ce2\u52a8\u7387\u9608\u503c\u51fd\u6570\u7684\u62e9\u65f6\u52a8\u91cf\u7b56\u7565\u80fd\u6709\u6548\u89c4\u907f\u52a8\u91cf\u5d29\u6e83\u98ce\u9669\u3002<\/p>\n\n\n\n
# \u7b80\u5355\u7684\u6ce2\u52a8\u7387\u8fc7\u6ee4\u793a\u4f8b\nmarket_volatility = closes.pct_change().rolling(30).std() * np.sqrt(252)\nhigh_vol_period = market_volatility > 0.4  # \u5e74\u5316\u6ce2\u52a8\u7387\u8d85\u8fc740%\n\n# \u5728\u9ad8\u6ce2\u52a8\u671f\u6301\u6709\u73b0\u91d1\nfor i, date in enumerate(rebalance_dates):\n    if high_vol_period.loc[date].any():\n        portfolio.loc[date] = [\"CASH\"]\n    else:\n        # \u6b63\u5e38\u52a8\u91cf\u9009\u80a1\u903b\u8f91\n        top_assets = momentum.loc[date].nlargest(TOP_N)\n        winners = top_assets[top_assets > 0].index.tolist()\n        portfolio.loc[date] = winners if len(winners) > 0 else [\"CASH\"]<\/pre>\n\n\n\n