{"id":1041627,"date":"2024-12-31T12:50:43","date_gmt":"2024-12-31T04:50:43","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1041627.html"},"modified":"2024-12-31T12:50:45","modified_gmt":"2024-12-31T04:50:45","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e8%ae%a1%e7%ae%97%e8%82%a1%e7%a5%a8%e6%af%8f%e6%ac%a1%e8%bf%9e%e6%b6%a8%e8%b7%8c","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1041627.html","title":{"rendered":"\u5982\u4f55\u7528python\u8ba1\u7b97\u80a1\u7968\u6bcf\u6b21\u8fde\u6da8\u8dcc"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u8ba1\u7b97\u80a1\u7968\u6bcf\u6b21\u8fde\u6da8\u8dcc\"<\/p>\n

\u5982\u4f55\u7528Python\u8ba1\u7b97\u80a1\u7968\u6bcf\u6b21\u8fde\u6da8\u8dcc<\/strong><\/p>\n<\/p>\n

\u8981\u7528Python\u8ba1\u7b97\u80a1\u7968\u6bcf\u6b21\u8fde\u6da8\u8dcc\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\u5b9e\u73b0\uff1a\u83b7\u53d6\u80a1\u7968\u6570\u636e\u3001\u8ba1\u7b97\u6bcf\u65e5\u6da8\u8dcc\u5e45\u3001\u8bc6\u522b\u8fde\u7eed\u6da8\u8dcc\u3001\u7edf\u8ba1\u8fde\u6da8\u8dcc\u6b21\u6570<\/strong>\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u63cf\u8ff0\u5176\u4e2d\u4e00\u4e2a\u6b65\u9aa4\uff1a\u83b7\u53d6\u80a1\u7968\u6570\u636e\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u7684pandas<\/code>\u548cyfinance<\/code>\u5e93\u4eceYahoo Finance\u83b7\u53d6\u80a1\u7968\u5386\u53f2\u6570\u636e\u3002\u7136\u540e\u901a\u8fc7\u8ba1\u7b97\u6bcf\u4e2a\u4ea4\u6613\u65e5\u7684\u6da8\u8dcc\u5e45\uff0c\u8bc6\u522b\u51fa\u8fde\u7eed\u6da8\u8dcc\u7684\u533a\u95f4\u5e76\u8bb0\u5f55\u6bcf\u6b21\u8fde\u6da8\u8dcc\u7684\u5929\u6570\u3002<\/p>\n<\/p>\n

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

\u9996\u5148\uff0c\u9700\u8981\u83b7\u53d6\u80a1\u7968\u7684\u5386\u53f2\u6570\u636e\u3002\u53ef\u4ee5\u4f7f\u7528yfinance<\/code>\u5e93\u4eceYahoo Finance\u83b7\u53d6\u6570\u636e\u3002yfinance<\/code>\u662f\u4e00\u4e2aPython\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u83b7\u53d6\u91d1\u878d\u6570\u636e\uff0c\u5c24\u5176\u662f\u80a1\u7968\u6570\u636e\u3002<\/p>\n<\/p>\n

import yfinance as yf<\/p>\n
\u5b9a\u4e49\u80a1\u7968\u4ee3\u7801\u548c\u65f6\u95f4\u8303\u56f4<\/strong><\/h2>\n
stock_code = 'AAPL'<\/p>\n
start_date = '2020-01-01'<\/p>\n
end_date = '2023-01-01'<\/p>\n
\u4e0b\u8f7d\u80a1\u7968\u6570\u636e<\/strong><\/h2>\n
data = yf.download(stock_code, start=start_date, end=end_date)<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0a\u8ff0\u4ee3\u7801\u5c06\u4e0b\u8f7d\u82f9\u679c\u516c\u53f8\uff08AAPL\uff09\u4ece2020\u5e741\u67081\u65e5\u52302023\u5e741\u67081\u65e5\u7684\u80a1\u7968\u6570\u636e\uff0c\u5e76\u8f93\u51fa\u524d\u51e0\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u8ba1\u7b97\u6bcf\u65e5\u6da8\u8dcc\u5e45<\/p>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u9700\u8981\u8ba1\u7b97\u6bcf\u65e5\u7684\u6da8\u8dcc\u5e45\u3002\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u6765\u8ba1\u7b97\u6bcf\u65e5\u7684\u6da8\u8dcc\u5e45\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8ba1\u7b97\u6bcf\u65e5\u6da8\u8dcc\u5e45<\/strong><\/h2>\n
data['DAI<\/a>ly Change'] = data['Close'].pct_change()<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
pct_change()<\/code>\u51fd\u6570\u8ba1\u7b97\u6bcf\u4e2a\u4ea4\u6613\u65e5\u7684\u767e\u5206\u6bd4\u53d8\u5316\uff0c\u751f\u6210\u65b0\u5217'Daily Change'\uff0c\u8868\u793a\u6bcf\u65e5\u7684\u6da8\u8dcc\u5e45\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u8bc6\u522b\u8fde\u7eed\u6da8\u8dcc<\/p>\n<\/p>\n
\u8bc6\u522b\u8fde\u7eed\u6da8\u8dcc\u9700\u8981\u901a\u8fc7\u904d\u5386\u6bcf\u65e5\u6da8\u8dcc\u5e45\u6570\u636e\u6765\u5b9e\u73b0\u3002\u5982\u679c\u5f53\u524d\u65e5\u7684\u6da8\u8dcc\u5e45\u4e0e\u524d\u4e00\u65e5\u76f8\u540c\u65b9\u5411\uff08\u6b63\u6216\u8d1f\uff09\uff0c\u5219\u8ba4\u4e3a\u662f\u8fde\u7eed\u6da8\u8dcc\uff1b\u5426\u5219\uff0c\u8ba4\u4e3a\u662f\u65b0\u7684\u6da8\u8dcc\u5468\u671f\u3002<\/p>\n<\/p>\n
data['Trend'] = data['Daily Change'].apply(lambda x: 'up' if x > 0 else ('down' if x < 0 else 'flat'))<\/p>\n
\u521d\u59cb\u5316\u53d8\u91cf<\/strong><\/h2>\n
previous_trend = None<\/p>\n
streak_count = 0<\/p>\n
streaks = []<\/p>\n
\u904d\u5386\u6bcf\u65e5\u6570\u636e<\/strong><\/h2>\n
for index, row in data.iterrows():<\/p>\n
    current_trend = row['Trend']<\/p>\n
    if current_trend == previous_trend:<\/p>\n
        streak_count += 1<\/p>\n
    else:<\/p>\n
        if previous_trend is not None:<\/p>\n
            streaks.append((previous_trend, streak_count))<\/p>\n
        streak_count = 1<\/p>\n
        previous_trend = current_trend<\/p>\n
\u6dfb\u52a0\u6700\u540e\u4e00\u4e2a\u8d8b\u52bf<\/strong><\/h2>\n
if previous_trend is not None:<\/p>\n
    streaks.append((previous_trend, streak_count))<\/p>\n
print(streaks)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6b64\u4ee3\u7801\u904d\u5386\u6bcf\u65e5\u7684\u6da8\u8dcc\u5e45\u6570\u636e\uff0c\u8bc6\u522b\u51fa\u8fde\u7eed\u6da8\u8dcc\u7684\u5929\u6570\uff0c\u5e76\u8bb0\u5f55\u5230\u5217\u8868streaks<\/code>\u4e2d\u3002<\/p>\n<\/p>\n
\u56db\u3001\u7edf\u8ba1\u8fde\u6da8\u8dcc\u6b21\u6570<\/p>\n<\/p>\n
\u6700\u540e\uff0c\u53ef\u4ee5\u7edf\u8ba1\u6bcf\u79cd\u8fde\u6da8\u8dcc\u7684\u6b21\u6570\u548c\u6700\u5927\u8fde\u6da8\u8dcc\u5929\u6570\u3002<\/p>\n<\/p>\n
from collections import Counter<\/p>\n
\u7edf\u8ba1\u8fde\u6da8\u8dcc\u6b21\u6570<\/strong><\/h2>\n
streak_counter = Counter(streaks)<\/p>\n
\u8ba1\u7b97\u6700\u5927\u8fde\u6da8\u8dcc\u5929\u6570<\/strong><\/h2>\n
max_up_streak = max([streak[1] for streak in streaks if streak[0] == 'up'], default=0)<\/p>\n
max_down_streak = max([streak[1] for streak in streaks if streak[0] == 'down'], default=0)<\/p>\n
print('\u8fde\u6da8\u6b21\u6570:', streak_counter[('up', max_up_streak)])<\/p>\n
print('\u6700\u5927\u8fde\u6da8\u5929\u6570:', max_up_streak)<\/p>\n
print('\u8fde\u8dcc\u6b21\u6570:', streak_counter[('down', max_down_streak)])<\/p>\n
print('\u6700\u5927\u8fde\u8dcc\u5929\u6570:', max_down_streak)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6b64\u4ee3\u7801\u4f7f\u7528Counter<\/code>\u7c7b\u7edf\u8ba1\u8fde\u6da8\u8dcc\u7684\u6b21\u6570\uff0c\u5e76\u8ba1\u7b97\u6700\u5927\u8fde\u6da8\u548c\u8fde\u8dcc\u7684\u5929\u6570\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u53ef\u89c6\u5316\u7ed3\u679c<\/p>\n<\/p>\n
\u4e3a\u4e86\u66f4\u76f4\u89c2\u5730\u5c55\u793a\u7ed3\u679c\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib<\/code>\u5e93\u5bf9\u7ed3\u679c\u8fdb\u884c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u80a1\u7968\u6536\u76d8\u4ef7<\/strong><\/h2>\n
plt.figure(figsize=(14, 7))<\/p>\n
plt.plot(data.index, data['Close'], label='Close Price')<\/p>\n
plt.title('Stock Close Price')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Close Price')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u6bcf\u65e5\u6da8\u8dcc\u5e45<\/strong><\/h2>\n
plt.figure(figsize=(14, 7))<\/p>\n
plt.plot(data.index, data['Daily Change'], label='Daily Change', color='orange')<\/p>\n
plt.title('Daily Change')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Percentage Change')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6b64\u4ee3\u7801\u4f7f\u7528matplotlib<\/code>\u5e93\u7ed8\u5236\u80a1\u7968\u6536\u76d8\u4ef7\u548c\u6bcf\u65e5\u6da8\u8dcc\u5e45\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u7528Python\u8ba1\u7b97\u80a1\u7968\u6bcf\u6b21\u8fde\u6da8\u8dcc\uff0c\u5e76\u7edf\u8ba1\u548c\u53ef\u89c6\u5316\u7ed3\u679c\u3002\u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u6295\u8d44\u8005\u66f4\u597d\u5730\u7406\u89e3\u80a1\u7968\u7684\u6da8\u8dcc\u8d8b\u52bf\uff0c\u4ece\u800c\u505a\u51fa\u66f4\u660e\u667a\u7684\u6295\u8d44\u51b3\u7b56\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u7528Python\u83b7\u53d6\u80a1\u7968\u7684\u5386\u53f2\u4ef7\u683c\u6570\u636e\uff1f<\/strong>
\u53ef\u4ee5\u4f7f\u7528\u591a\u4e2a\u5e93\u6765\u83b7\u53d6\u80a1\u7968\u7684\u5386\u53f2\u4ef7\u683c\u6570\u636e\u3002\u6700\u5e38\u7528\u7684\u5e93\u662fpandas_datareader<\/code>\uff0c\u5b83\u53ef\u4ee5\u4eceYahoo Finance\u3001Alpha Vantage\u7b49\u6765\u6e90\u83b7\u53d6\u6570\u636e\u3002\u6b64\u5916\uff0cyfinance<\/code>\u5e93\u4e5f\u662f\u4e00\u4e2a\u975e\u5e38\u6d41\u884c\u7684\u9009\u62e9\uff0c\u5141\u8bb8\u7528\u6237\u8f7b\u677e\u4e0b\u8f7d\u80a1\u7968\u6570\u636e\u3002\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u53ef\u4ee5\u83b7\u53d6\u67d0\u53ea\u80a1\u7968\u7684\u5386\u53f2\u6570\u636e\uff1a<\/p>\n
import yfinance as yf\n\n# \u83b7\u53d6\u7279\u5b9a\u80a1\u7968\u7684\u5386\u53f2\u6570\u636e\nstock_data = yf.download('AAPL', start='2020-01-01', end='2023-01-01')\n<\/code><\/pre>\n
\u5728Python\u4e2d\u5982\u4f55\u8bc6\u522b\u80a1\u7968\u7684\u8fde\u6da8\u548c\u8fde\u8dcc\uff1f<\/strong>
\u8bc6\u522b\u80a1\u7968\u7684\u8fde\u6da8\u548c\u8fde\u8dcc\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u6bcf\u65e5\u6536\u76d8\u4ef7\u7684\u53d8\u5316\u7387\u6765\u5b9e\u73b0\u3002\u53ef\u4ee5\u904d\u5386\u4ef7\u683c\u6570\u636e\uff0c\u68c0\u67e5\u8fde\u7eed\u51e0\u5929\u7684\u4ef7\u683c\u53d8\u5316\u662f\u5426\u90fd\u662f\u4e0a\u6da8\u6216\u4e0b\u8dcc\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n
import pandas as pd\n\n# \u8ba1\u7b97\u6bcf\u65e5\u6536\u76ca\u7387\nstock_data['Returns'] = stock_data['Close'].pct_change()\n# \u6807\u8bb0\u8fde\u6da8\u548c\u8fde\u8dcc\nstock_data['Trend'] = stock_data['Returns'].apply(lambda x: '\u4e0a\u6da8' if x > 0 else ('\u4e0b\u8dcc' if x < 0 else '\u5e73'))\n<\/code><\/pre>\n
\u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u80a1\u7968\u4ef7\u683c\u7684\u53d8\u5316\u8d8b\u52bf\u56fe\uff1f<\/strong>
\u5229\u7528matplotlib<\/code>\u5e93\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u7ed8\u5236\u80a1\u7968\u4ef7\u683c\u7684\u53d8\u5316\u8d8b\u52bf\u56fe\u3002\u60a8\u53ea\u9700\u5c06\u65e5\u671f\u8bbe\u7f6e\u4e3aX\u8f74\uff0c\u5c06\u6536\u76d8\u4ef7\u8bbe\u7f6e\u4e3aY\u8f74\u3002\u4ee5\u4e0b\u662f\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n
import matplotlib.pyplot as plt\n\n# \u7ed8\u5236\u80a1\u7968\u6536\u76d8\u4ef7\u53d8\u5316\u8d8b\u52bf\u56fe\nplt.figure(figsize=(12, 6))\nplt.plot(stock_data.index, stock_data['Close'], label='\u6536\u76d8\u4ef7')\nplt.title('\u80a1\u7968\u6536\u76d8\u4ef7\u53d8\u5316\u8d8b\u52bf')\nplt.xlabel('\u65e5\u671f')\nplt.ylabel('\u4ef7\u683c')\nplt.legend()\nplt.show()\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"\u5982\u4f55\u7528Python\u8ba1\u7b97\u80a1\u7968\u6bcf\u6b21\u8fde\u6da8\u8dcc \u8981\u7528Python\u8ba1\u7b97\u80a1\u7968\u6bcf\u6b21\u8fde\u6da8\u8dcc\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\u5b9e\u73b0\uff1a\u83b7\u53d6\u80a1\u7968\u6570\u636e\u3001 […]","protected":false},"author":3,"featured_media":1041630,"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\/1041627"}],"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=1041627"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1041627\/revisions"}],"predecessor-version":[{"id":1041632,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1041627\/revisions\/1041632"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1041630"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1041627"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1041627"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1041627"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}