{"id":1050083,"date":"2024-12-31T14:04:44","date_gmt":"2024-12-31T06:04:44","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1050083.html"},"modified":"2024-12-31T14:04:47","modified_gmt":"2024-12-31T06:04:47","slug":"python%e5%a6%82%e4%bd%95%e5%88%b6%e4%bd%9c5%e6%97%a5%e5%9d%87%e7%ba%bf","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1050083.html","title":{"rendered":"python\u5982\u4f55\u5236\u4f5c5\u65e5\u5747\u7ebf"},"content":{"rendered":"

\"python\u5982\u4f55\u5236\u4f5c5\u65e5\u5747\u7ebf\"<\/p>\n

Python\u5236\u4f5c5\u65e5\u5747\u7ebf\u7684\u65b9\u6cd5<\/strong><\/p>\n<\/p>\n

\u8981\u5728Python\u4e2d\u5236\u4f5c5\u65e5\u5747\u7ebf\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u8ba1\u7b97\u3002\u83b7\u53d6\u80a1\u7968\u6570\u636e\u3001\u8ba1\u7b975\u65e5\u5747\u7ebf\u3001\u7ed8\u5236\u5747\u7ebf\u56fe\u8868<\/strong>\u662f\u4e3b\u8981\u6b65\u9aa4\u3002\u5177\u4f53\u6765\u8bf4\uff0c5\u65e5\u5747\u7ebf\u7684\u8ba1\u7b97\u65b9\u6cd5\u662f\u5c06\u6700\u8fd15\u5929\u7684\u6536\u76d8\u4ef7\u76f8\u52a0\uff0c\u7136\u540e\u9664\u4ee55\u3002\u8fd9\u79cd\u5747\u7ebf\u53ef\u4ee5\u5e2e\u52a9\u6295\u8d44\u8005\u5e73\u6ed1\u4ef7\u683c\u6ce2\u52a8\uff0c\u8bc6\u522b\u4ef7\u683c\u8d8b\u52bf\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u8be6\u7ec6\u5b9e\u73b0\u8fd9\u4e9b\u6b65\u9aa4\u7684\u4ecb\u7ecd\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/h3>\n<\/p>\n

\u8981\u5236\u4f5c5\u65e5\u5747\u7ebf\uff0c\u9996\u5148\u9700\u8981\u83b7\u53d6\u80a1\u7968\u7684\u5386\u53f2\u6570\u636e\u3002\u53ef\u4ee5\u901a\u8fc7\u4e00\u4e9b\u91d1\u878d\u6570\u636eAPI\u5982Yahoo Finance\u3001Alpha Vantage\u6216\u8005\u76f4\u63a5\u4f7f\u7528Pandas\u7684pandas_datareader<\/code>\u6a21\u5757\u6765\u83b7\u53d6\u6570\u636e\u3002\u4e0b\u9762\u662f\u4f7f\u7528pandas_datareader<\/code>\u83b7\u53d6\u6570\u636e\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

import pandas_datareader.data as web<\/p>\n
import datetime<\/p>\n
\u5b9a\u4e49\u65f6\u95f4\u8303\u56f4<\/strong><\/h2>\n
start = datetime.datetime(2022, 1, 1)<\/p>\n
end = datetime.datetime(2022, 12, 31)<\/p>\n
\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/strong><\/h2>\n
df = web.DataReader('AAPL', 'yahoo', start, end)<\/p>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u8ba1\u7b975\u65e5\u5747\u7ebf<\/h3>\n<\/p>\n
\u83b7\u53d6\u6570\u636e\u540e\uff0c\u63a5\u4e0b\u6765\u5c31\u662f\u8ba1\u7b975\u65e5\u5747\u7ebf\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u7684rolling<\/code>\u65b9\u6cd5\u6765\u8ba1\u7b97\u79fb\u52a8\u5e73\u5747\u7ebf\u3002\u4ee5\u4e0b\u662f\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b975\u65e5\u5747\u7ebf<\/p>\n
df['5D_MA'] = df['Close'].rolling(window=5).mean()<\/p>\n
print(df[['Close', '5D_MA']].head(10))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u7ed8\u5236\u5747\u7ebf\u56fe\u8868<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u76f4\u89c2\u5730\u5c55\u793a5\u65e5\u5747\u7ebf\u7684\u6548\u679c\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u6765\u7ed8\u5236\u5747\u7ebf\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u7ed8\u5236\u56fe\u8868\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u6536\u76d8\u4ef7\u548c5\u65e5\u5747\u7ebf<\/strong><\/h2>\n
plt.figure(figsize=(14, 7))<\/p>\n
plt.plot(df['Close'], label='Close Price')<\/p>\n
plt.plot(df['5D_MA'], label='5 Day Moving Average')<\/p>\n
plt.title('5 Day Moving Average of AAPL')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Price')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6838\u5fc3\u91cd\u70b9\u5185\u5bb9\u8be6\u89e3<\/h3>\n<\/p>\n
1\u3001\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/h4>\n<\/p>\n
\u91d1\u878d\u6570\u636eAPI\u5982Yahoo Finance\u548cAlpha Vantage\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5386\u53f2\u6570\u636e\uff0c\u53ef\u4ee5\u7528\u6765\u8fdb\u884c\u5404\u79cd\u5206\u6790\u3002pandas_datareader<\/code>\u662f\u4e00\u4e2a\u5f88\u65b9\u4fbf\u7684\u5e93\uff0c\u53ef\u4ee5\u76f4\u63a5\u4ece\u8fd9\u4e9bAPI\u4e2d\u83b7\u53d6\u6570\u636e\u3002\u83b7\u53d6\u7684\u6570\u636e\u901a\u5e38\u5305\u62ec\u5f00\u76d8\u4ef7\u3001\u6536\u76d8\u4ef7\u3001\u6700\u9ad8\u4ef7\u3001\u6700\u4f4e\u4ef7\u548c\u4ea4\u6613\u91cf\u7b49\u3002<\/p>\n<\/p>\n
import pandas_datareader.data as web<\/p>\n
import datetime<\/p>\n
\u5b9a\u4e49\u65f6\u95f4\u8303\u56f4<\/strong><\/h2>\n
start = datetime.datetime(2022, 1, 1)<\/p>\n
end = datetime.datetime(2022, 12, 31)<\/p>\n
\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/strong><\/h2>\n
df = web.DataReader('AAPL', 'yahoo', start, end)<\/p>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u65f6\u95f4\u8303\u56f4\u4e3a2022\u5e741\u67081\u65e5\u81f32022\u5e7412\u670831\u65e5\uff0c\u5e76\u83b7\u53d6\u4e86\u82f9\u679c\u516c\u53f8\uff08AAPL\uff09\u7684\u80a1\u7968\u6570\u636e\u3002<\/p>\n<\/p>\n
2\u3001\u8ba1\u7b975\u65e5\u5747\u7ebf<\/h4>\n<\/p>\n
5\u65e5\u5747\u7ebf\u7684\u8ba1\u7b97\u901a\u8fc7Pandas\u7684rolling<\/code>\u65b9\u6cd5\u5b9e\u73b0\uff0c\u8fd9\u4e2a\u65b9\u6cd5\u53ef\u4ee5\u5bf9\u6307\u5b9a\u7a97\u53e3\u5185\u7684\u6570\u636e\u8fdb\u884c\u6ed1\u52a8\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b975\u65e5\u5747\u7ebf<\/p>\n
df['5D_MA'] = df['Close'].rolling(window=5).mean()<\/p>\n
print(df[['Close', '5D_MA']].head(10))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5bf9\u6536\u76d8\u4ef7\u5217\u8fdb\u884c\u6ed1\u52a8\u8ba1\u7b97\uff0c\u7a97\u53e3\u5927\u5c0f\u4e3a5\u3002\u8ba1\u7b97\u7ed3\u679c\u4fdd\u5b58\u5728\u65b0\u7684\u52175D_MA<\/code>\u4e2d\u3002<\/p>\n<\/p>\n
3\u3001\u7ed8\u5236\u5747\u7ebf\u56fe\u8868<\/h4>\n<\/p>\n
\u56fe\u8868\u7684\u7ed8\u5236\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u76f4\u89c2\u5730\u7406\u89e3\u6570\u636e\u548c\u5747\u7ebf\u7684\u53d8\u5316\u3002Matplotlib\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u7528\u6765\u7ed8\u5236\u5404\u79cd\u56fe\u8868\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u6536\u76d8\u4ef7\u548c5\u65e5\u5747\u7ebf<\/strong><\/h2>\n
plt.figure(figsize=(14, 7))<\/p>\n
plt.plot(df['Close'], label='Close Price')<\/p>\n
plt.plot(df['5D_MA'], label='5 Day Moving Average')<\/p>\n
plt.title('5 Day Moving Average of AAPL')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Price')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u7ed8\u5236\u4e86\u6536\u76d8\u4ef7\u548c5\u65e5\u5747\u7ebf\u7684\u56fe\u8868\uff0c\u5e76\u6dfb\u52a0\u4e86\u56fe\u4f8b\u3001\u6807\u9898\u548c\u5750\u6807\u8f74\u6807\u7b7e\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4e2d\u5236\u4f5c5\u65e5\u5747\u7ebf\uff0c\u5e76\u7ed8\u5236\u76f8\u5e94\u7684\u56fe\u8868\u3002\u83b7\u53d6\u80a1\u7968\u6570\u636e\u3001\u8ba1\u7b975\u65e5\u5747\u7ebf\u3001\u7ed8\u5236\u5747\u7ebf\u56fe\u8868<\/strong>\u662f\u5173\u952e\u6b65\u9aa4\u3002\u5229\u7528Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\uff0c\u5229\u7528Matplotlib\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6295\u8d44\u8005\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u80a1\u7968\u4ef7\u683c\u8d8b\u52bf\u3002\u5e0c\u671b\u8fd9\u4e9b\u5185\u5bb9\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u83b7\u53d6\u80a1\u7968\u6570\u636e\u4ee5\u8ba1\u7b975\u65e5\u5747\u7ebf\uff1f<\/strong>
\u8981\u8ba1\u7b975\u65e5\u5747\u7ebf\uff0c\u9996\u5148\u9700\u8981\u83b7\u53d6\u80a1\u7968\u7684\u5386\u53f2\u4ef7\u683c\u6570\u636e\u3002\u53ef\u4ee5\u4f7f\u7528Python\u7684\u5e93\uff0c\u5982pandas<\/code>\u548cyfinance<\/code>\uff0c\u6765\u8f7b\u677e\u83b7\u53d6\u8fd9\u4e9b\u6570\u636e\u3002\u4f60\u53ea\u9700\u5b89\u88c5yfinance<\/code>\u5e93\uff0c\u5e76\u4f7f\u7528\u5b83\u4e0b\u8f7d\u6240\u9700\u7684\u80a1\u7968\u6570\u636e\uff0c\u7136\u540e\u5229\u7528pandas<\/code>\u8ba1\u7b975\u65e5\u5747\u7ebf\u3002<\/p>\n
\u8ba1\u7b975\u65e5\u5747\u7ebf\u7684\u4ee3\u7801\u793a\u4f8b\u662f\u4ec0\u4e48\uff1f<\/strong>
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4ee3\u7801\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u8ba1\u7b975\u65e5\u5747\u7ebf\uff1a<\/p>\n
import pandas as pd\nimport yfinance as yf\n\n# \u83b7\u53d6\u80a1\u7968\u6570\u636e\nstock_data = yf.download('AAPL', start='2023-01-01', end='2023-10-01')\n\n# \u8ba1\u7b975\u65e5\u5747\u7ebf\nstock_data['5\u65e5\u5747\u7ebf'] = stock_data['Close'].rolling(window=5).mean()\n\nprint(stock_data[['Close', '5\u65e5\u5747\u7ebf']])\n<\/code><\/pre>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cClose<\/code>\u5217\u4ee3\u8868\u80a1\u7968\u7684\u6536\u76d8\u4ef7\uff0c\u901a\u8fc7rolling(window=5).mean()<\/code>\u8ba1\u7b97\u51fa5\u65e5\u5747\u7ebf\u3002<\/p>\n
\u5982\u4f55\u53ef\u89c6\u53165\u65e5\u5747\u7ebf\u548c\u80a1\u7968\u4ef7\u683c\u7684\u5173\u7cfb\uff1f<\/strong>
\u4e3a\u4e86\u66f4\u76f4\u89c2\u5730\u89c2\u5bdf5\u65e5\u5747\u7ebf\u4e0e\u80a1\u7968\u4ef7\u683c\u7684\u5173\u7cfb\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528matplotlib<\/code>\u5e93\u8fdb\u884c\u53ef\u89c6\u5316\u3002\u901a\u8fc7\u7ed8\u5236\u6536\u76d8\u4ef7\u548c5\u65e5\u5747\u7ebf\uff0c\u60a8\u53ef\u4ee5\u6e05\u695a\u5730\u770b\u5230\u8d8b\u52bf\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u53ef\u89c6\u5316\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n
import matplotlib.pyplot as plt\n\nplt.figure(figsize=(12,6))\nplt.plot(stock_data['Close'], label='\u6536\u76d8\u4ef7', color='blue')\nplt.plot(stock_data['5\u65e5\u5747\u7ebf'], label='5\u65e5\u5747\u7ebf', color='orange')\nplt.title('\u80a1\u7968\u6536\u76d8\u4ef7\u4e0e5\u65e5\u5747\u7ebf')\nplt.xlabel('\u65e5\u671f')\nplt.ylabel('\u4ef7\u683c')\nplt.legend()\nplt.show()\n<\/code><\/pre>\n
\u4f7f\u7528\u8be5\u4ee3\u7801\uff0c\u60a8\u5c06\u5f97\u5230\u4e00\u5f20\u56fe\u8868\uff0c\u6e05\u6670\u5730\u663e\u793a\u51fa\u80a1\u7968\u7684\u6536\u76d8\u4ef7\u53ca\u51765\u65e5\u5747\u7ebf\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5236\u4f5c5\u65e5\u5747\u7ebf\u7684\u65b9\u6cd5 \u8981\u5728Python\u4e2d\u5236\u4f5c5\u65e5\u5747\u7ebf\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u8ba1\u7b97\u3002\u83b7\u53d6 […]","protected":false},"author":3,"featured_media":1050091,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1050083"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1050083"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1050083\/revisions"}],"predecessor-version":[{"id":1050097,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1050083\/revisions\/1050097"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1050091"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1050083"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1050083"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1050083"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}