{"id":1073592,"date":"2025-01-08T11:27:04","date_gmt":"2025-01-08T03:27:04","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1073592.html"},"modified":"2025-01-08T11:27:06","modified_gmt":"2025-01-08T03:27:06","slug":"python%e5%a6%82%e4%bd%95%e6%af%94%e5%af%b9%e6%89%80%e6%9c%89a%e8%82%a1%e7%9a%84%e5%90%97-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1073592.html","title":{"rendered":"python\u5982\u4f55\u6bd4\u5bf9\u6240\u6709a\u80a1\u7684\u5417"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25103501\/92c5d1f0-a7f4-40e5-887f-e34fa9ec4b83.webp\" alt=\"python\u5982\u4f55\u6bd4\u5bf9\u6240\u6709a\u80a1\u7684\u5417\" \/><\/p>\n<p><p> <strong>Python\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u6bd4\u5bf9\u6240\u6709A\u80a1<\/strong>\uff0c<strong>\u83b7\u53d6\u6570\u636e\u3001\u6e05\u6d17\u4e0e\u5904\u7406\u6570\u636e\u3001\u8fdb\u884c\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316<\/strong>\u3002\u5176\u4e2d\u6700\u91cd\u8981\u7684\u4e00\u70b9\u662f\u83b7\u53d6\u6570\u636e\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u91d1\u878d\u6570\u636eAPI\uff08\u5982Tushare\uff09\u6765\u83b7\u53d6\u6240\u6709A\u80a1\u7684\u5386\u53f2\u6570\u636e\u3002<\/p>\n<\/p>\n<p><h2>\u4e00\u3001\u83b7\u53d6\u6570\u636e<\/h2>\n<\/p>\n<p><h3>1. \u4f7f\u7528Tushare\u83b7\u53d6A\u80a1\u6570\u636e<\/h3>\n<\/p>\n<p><p>Tushare\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u91d1\u878d\u6570\u636e\u63a5\u53e3\u5305\uff0c\u53ef\u4ee5\u83b7\u53d6\u5404\u79cd\u91d1\u878d\u5e02\u573a\u7684\u6570\u636e\uff0c\u5305\u62ec\u80a1\u7968\u3001\u57fa\u91d1\u3001\u6307\u6570\u7b49\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u5b89\u88c5Tushare\u5e76\u6ce8\u518c\u4e00\u4e2a\u8d26\u53f7\u83b7\u53d6API\u5bc6\u94a5\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">pip install tushare<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u6ce8\u518c\u5e76\u83b7\u53d6API\u5bc6\u94a5\u540e\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u83b7\u53d6\u6240\u6709A\u80a1\u7684\u5217\u8868\u548c\u5386\u53f2\u6570\u636e\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import tushare as ts<\/p>\n<h2><strong>\u8bbe\u7f6e\u4f60\u7684Tushare API\u5bc6\u94a5<\/strong><\/h2>\n<p>ts.set_token(&#39;your_api_key&#39;)<\/p>\n<h2><strong>\u521d\u59cb\u5316pro\u63a5\u53e3<\/strong><\/h2>\n<p>pro = ts.pro_api()<\/p>\n<h2><strong>\u83b7\u53d6\u6240\u6709A\u80a1\u7684\u57fa\u7840\u4fe1\u606f<\/strong><\/h2>\n<p>stock_info = pro.stock_basic(exchange=&#39;&#39;, list_status=&#39;L&#39;, fields=&#39;ts_code,symbol,name,area,industry,list_date&#39;)<\/p>\n<h2><strong>\u6253\u5370\u524d\u4e94\u884c\u6570\u636e<\/strong><\/h2>\n<p>print(stock_info.head())<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>2. \u83b7\u53d6\u5386\u53f2\u6570\u636e<\/h3>\n<\/p>\n<p><p>\u83b7\u53d6\u6240\u6709A\u80a1\u7684\u5217\u8868\u540e\uff0c\u60a8\u53ef\u4ee5\u904d\u5386\u8fd9\u4e9b\u80a1\u7968\u4ee3\u7801\uff0c\u5e76\u83b7\u53d6\u6bcf\u53ea\u80a1\u7968\u7684\u5386\u53f2\u6570\u636e\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<h2><strong>\u521b\u5efa\u4e00\u4e2a\u7a7a\u7684DataFrame\u6765\u5b58\u50a8\u6240\u6709\u80a1\u7968\u7684\u5386\u53f2\u6570\u636e<\/strong><\/h2>\n<p>all_stock_data = pd.DataFrame()<\/p>\n<h2><strong>\u904d\u5386\u6240\u6709\u80a1\u7968\u4ee3\u7801<\/strong><\/h2>\n<p>for ts_code in stock_info[&#39;ts_code&#39;]:<\/p>\n<p>    # \u83b7\u53d6\u6bcf\u53ea\u80a1\u7968\u7684\u65e5\u7ebf\u884c\u60c5\u6570\u636e<\/p>\n<p>    df = pro.d<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>ly(ts_code=ts_code, start_date=&#39;20210101&#39;, end_date=&#39;20211231&#39;)<\/p>\n<p>    # \u5c06\u6570\u636e\u8ffd\u52a0\u5230all_stock_data\u4e2d<\/p>\n<p>    all_stock_data = pd.concat([all_stock_data, df])<\/p>\n<h2><strong>\u6253\u5370\u524d\u4e94\u884c\u6570\u636e<\/strong><\/h2>\n<p>print(all_stock_data.head())<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e8c\u3001\u6e05\u6d17\u4e0e\u5904\u7406\u6570\u636e<\/h2>\n<\/p>\n<p><p>\u5728\u83b7\u53d6\u6570\u636e\u540e\uff0c\u6570\u636e\u901a\u5e38\u662f\u6742\u4e71\u7684\uff0c\u9700\u8981\u8fdb\u884c\u6e05\u6d17\u548c\u5904\u7406\u3002\u6e05\u6d17\u6570\u636e\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u91cd\u590d\u503c\u3001\u6570\u636e\u683c\u5f0f\u8f6c\u6362\u7b49\u3002<\/p>\n<\/p>\n<p><h3>1. \u5904\u7406\u7f3a\u5931\u503c<\/h3>\n<\/p>\n<p><p>\u7f3a\u5931\u503c\u53ef\u80fd\u4f1a\u5f71\u54cd\u6570\u636e\u5206\u6790\u7684\u7ed3\u679c\uff0c\u56e0\u6b64\u9700\u8981\u5904\u7406\u8fd9\u4e9b\u7f3a\u5931\u503c\u3002\u5e38\u89c1\u7684\u5904\u7406\u65b9\u6cd5\u5305\u62ec\u5220\u9664\u3001\u586b\u5145\u6216\u63d2\u503c\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c<\/p>\n<p>all_stock_data.dropna(inplace=True)<\/p>\n<h2><strong>\u6216\u8005\u4f7f\u7528\u63d2\u503c\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n<p>all_stock_data.interpolate(inplace=True)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>2. \u5904\u7406\u91cd\u590d\u503c<\/h3>\n<\/p>\n<p><p>\u91cd\u590d\u503c\u4e5f\u4f1a\u5f71\u54cd\u6570\u636e\u5206\u6790\u7684\u51c6\u786e\u6027\uff0c\u9700\u8981\u5220\u9664\u8fd9\u4e9b\u91cd\u590d\u503c\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u5220\u9664\u91cd\u590d\u503c<\/p>\n<p>all_stock_data.drop_duplicates(inplace=True)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u4e09\u3001\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316<\/h2>\n<\/p>\n<p><p>\u6570\u636e\u6e05\u6d17\u5b8c\u6210\u540e\uff0c\u60a8\u53ef\u4ee5\u8fdb\u884c\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316\uff0c\u4ee5\u83b7\u53d6\u66f4\u591a\u7684\u5e02\u573a\u6d1e\u5bdf\u3002<\/p>\n<\/p>\n<p><h3>1. \u6570\u636e\u5206\u6790<\/h3>\n<\/p>\n<p><p>\u60a8\u53ef\u4ee5\u8ba1\u7b97\u4e00\u4e9b\u5e38\u89c1\u7684\u91d1\u878d\u6307\u6807\uff0c\u5982\u6536\u76ca\u7387\u3001\u6ce2\u52a8\u7387\u3001\u5e02\u76c8\u7387\u7b49\uff0c\u4ee5\u5bf9\u6bd4\u4e0d\u540c\u80a1\u7968\u7684\u8868\u73b0\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u8ba1\u7b97\u65e5\u6536\u76ca\u7387<\/p>\n<p>all_stock_data[&#39;pct_change&#39;] = all_stock_data[&#39;close&#39;].pct_change()<\/p>\n<h2><strong>\u8ba1\u7b97\u7d2f\u79ef\u6536\u76ca\u7387<\/strong><\/h2>\n<p>all_stock_data[&#39;cum_return&#39;] = (1 + all_stock_data[&#39;pct_change&#39;]).cumprod()<\/p>\n<h2><strong>\u6253\u5370\u524d\u4e94\u884c\u6570\u636e<\/strong><\/h2>\n<p>print(all_stock_data.head())<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>2. \u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n<p><p>\u53ef\u89c6\u5316\u53ef\u4ee5\u5e2e\u52a9\u66f4\u76f4\u89c2\u5730\u7406\u89e3\u6570\u636e\u3002\u5e38\u89c1\u7684\u53ef\u89c6\u5316\u5de5\u5177\u5305\u62ecMatplotlib\u3001Seaborn\u7b49\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>import seaborn as sns<\/p>\n<h2><strong>\u7ed8\u5236\u7d2f\u79ef\u6536\u76ca\u7387\u66f2\u7ebf<\/strong><\/h2>\n<p>plt.figure(figsize=(10, 6))<\/p>\n<p>for ts_code in stock_info[&#39;ts_code&#39;].unique()[:10]:  # \u4ec5\u7ed8\u5236\u524d10\u53ea\u80a1\u7968<\/p>\n<p>    stock_data = all_stock_data[all_stock_data[&#39;ts_code&#39;] == ts_code]<\/p>\n<p>    plt.plot(stock_data[&#39;trade_date&#39;], stock_data[&#39;cum_return&#39;], label=ts_code)<\/p>\n<p>plt.xlabel(&#39;Trade Date&#39;)<\/p>\n<p>plt.ylabel(&#39;Cumulative Return&#39;)<\/p>\n<p>plt.title(&#39;Cumulative Return of A-Shares&#39;)<\/p>\n<p>plt.legend()<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u56db\u3001\u4f7f\u7528<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u8fdb\u884c\u9884\u6d4b<\/h2>\n<\/p>\n<p><p>\u9664\u4e86\u57fa\u672c\u7684\u6570\u636e\u5206\u6790\uff0c\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u6a21\u578b\u6765\u9884\u6d4b\u80a1\u7968\u4ef7\u683c\u3002\u5e38\u89c1\u7684\u6a21\u578b\u5305\u62ec\u7ebf\u6027\u56de\u5f52\u3001\u968f\u673a\u68ee\u6797\u3001LSTM\u7b49\u3002<\/p>\n<\/p>\n<p><h3>1. \u51c6\u5907\u6570\u636e<\/h3>\n<\/p>\n<p><p>\u5728\u5e94\u7528\u673a\u5668\u5b66\u4e60\u6a21\u578b\u4e4b\u524d\uff0c\u9700\u8981\u51c6\u5907\u8bad\u7ec3\u6570\u636e\u548c\u6d4b\u8bd5\u6570\u636e\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.model_selection import train_test_split<\/p>\n<h2><strong>\u9009\u62e9\u7279\u5f81\u548c\u6807\u7b7e<\/strong><\/h2>\n<p>X = all_stock_data[[&#39;open&#39;, &#39;high&#39;, &#39;low&#39;, &#39;volume&#39;]]  # \u7279\u5f81<\/p>\n<p>y = all_stock_data[&#39;close&#39;]  # \u6807\u7b7e<\/p>\n<h2><strong>\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n<p>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>2. \u8bad\u7ec3\u6a21\u578b<\/h3>\n<\/p>\n<p><p>\u4ee5\u7ebf\u6027\u56de\u5f52\u4e3a\u4f8b\uff0c\u8bad\u7ec3\u6a21\u578b\u5e76\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.linear_model import LinearRegression<\/p>\n<h2><strong>\u521d\u59cb\u5316\u6a21\u578b<\/strong><\/h2>\n<p>model = LinearRegression()<\/p>\n<h2><strong>\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n<p>model.fit(X_train, y_train)<\/p>\n<h2><strong>\u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n<p>y_pred = model.predict(X_test)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>3. \u8bc4\u4f30\u6a21\u578b<\/h3>\n<\/p>\n<p><p>\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\uff0c\u4ee5\u786e\u5b9a\u5176\u9884\u6d4b\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.metrics import mean_squared_error, r2_score<\/p>\n<h2><strong>\u8ba1\u7b97\u5747\u65b9\u8bef\u5dee<\/strong><\/h2>\n<p>mse = mean_squared_error(y_test, y_pred)<\/p>\n<h2><strong>\u8ba1\u7b97R\u65b9\u503c<\/strong><\/h2>\n<p>r2 = r2_score(y_test, y_pred)<\/p>\n<p>print(f&#39;Mean Squared Error: {mse}&#39;)<\/p>\n<p>print(f&#39;R^2 Score: {r2}&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528Python\u6bd4\u5bf9\u6240\u6709A\u80a1\u7684\u8868\u73b0\uff0c\u5e76\u8fdb\u884c\u6df1\u5165\u7684\u5206\u6790\u548c\u9884\u6d4b\u3002\u65e0\u8bba\u662f\u83b7\u53d6\u6570\u636e\u3001\u6e05\u6d17\u6570\u636e\u3001\u5206\u6790\u6570\u636e\u8fd8\u662f\u53ef\u89c6\u5316\u548c\u9884\u6d4b\uff0cPython\u90fd\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u5de5\u5177\u548c\u5e93\uff0c\u80fd\u591f\u5e2e\u52a9\u60a8\u9ad8\u6548\u5730\u5b8c\u6210\u8fd9\u4e9b\u4efb\u52a1\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u4f7f\u7528Python\u83b7\u53d6A\u80a1\u7684\u5b9e\u65f6\u6570\u636e\u8fdb\u884c\u6bd4\u5bf9\uff1f<\/strong><br \/>\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684\u91d1\u878d\u6570\u636e\u5305\uff0c\u5982<code>pandas<\/code>\u548c<code>yfinance<\/code>\uff0c\u7ed3\u5408API\u63a5\u53e3\uff08\u5982TuShare\u3001\u805a\u5bbd\u7b49\uff09\uff0c\u83b7\u53d6A\u80a1\u7684\u5b9e\u65f6\u6570\u636e\u3002\u901a\u8fc7\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5e93\uff0c\u60a8\u53ef\u4ee5\u5bf9\u4e0d\u540c\u80a1\u7968\u8fdb\u884c\u6bd4\u5bf9\uff0c\u5206\u6790\u5176\u4ef7\u683c\u3001\u6210\u4ea4\u91cf\u7b49\u591a\u79cd\u6307\u6807\u3002<\/p>\n<p><strong>\u5728Python\u4e2d\u5982\u4f55\u5b9e\u73b0A\u80a1\u4e4b\u95f4\u7684\u8d22\u52a1\u6307\u6807\u6bd4\u5bf9\uff1f<\/strong><br \/>\u60a8\u53ef\u4ee5\u5229\u7528Python\u4e2d\u7684<code>pandas<\/code>\u5e93\u5bf9A\u80a1\u516c\u53f8\u7684\u8d22\u52a1\u6570\u636e\u8fdb\u884c\u5904\u7406\u3002\u901a\u8fc7\u8bfb\u53d6\u8d22\u52a1\u62a5\u8868\u6570\u636e\uff08\u5982\u51c0\u5229\u6da6\u3001\u8425\u6536\u7b49\uff09\uff0c\u53ef\u4ee5\u5c06\u4e0d\u540c\u516c\u53f8\u7684\u8d22\u52a1\u6307\u6807\u653e\u5728\u4e00\u8d77\u8fdb\u884c\u6bd4\u8f83\uff0c\u4ece\u800c\u5e2e\u52a9\u6295\u8d44\u51b3\u7b56\u3002\u53ef\u901a\u8fc7API\u83b7\u53d6\u8fd9\u4e9b\u6570\u636e\uff0c\u5e76\u5229\u7528\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff08\u5982Matplotlib\u3001Seaborn\uff09\u8fdb\u884c\u5c55\u793a\u3002<\/p>\n<p><strong>\u662f\u5426\u53ef\u4ee5\u901a\u8fc7Python\u8fdb\u884cA\u80a1\u7684\u5386\u53f2\u6570\u636e\u5206\u6790\uff1f<\/strong><br 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