{"id":1179862,"date":"2025-01-15T18:29:39","date_gmt":"2025-01-15T10:29:39","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1179862.html"},"modified":"2025-01-15T18:29:42","modified_gmt":"2025-01-15T10:29:42","slug":"python%e5%a6%82%e4%bd%95%e8%ae%a1%e7%ae%97%e8%82%a1%e7%a5%a8%e6%8c%81%e4%bb%93%e6%97%b6%e9%97%b4","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1179862.html","title":{"rendered":"python\u5982\u4f55\u8ba1\u7b97\u80a1\u7968\u6301\u4ed3\u65f6\u95f4"},"content":{"rendered":"

\"python\u5982\u4f55\u8ba1\u7b97\u80a1\u7968\u6301\u4ed3\u65f6\u95f4\"<\/p>\n

Python\u8ba1\u7b97\u80a1\u7968\u6301\u4ed3\u65f6\u95f4\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u901a\u8fc7\u4ea4\u6613\u6570\u636e\u8ba1\u7b97\u3001\u4f7f\u7528Python\u5e93\u5982Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u3001\u4ee5\u53ca\u7ed3\u5408\u65e5\u671f\u65f6\u95f4\u6a21\u5757\u5904\u7406\u65f6\u95f4\u6570\u636e\u3002<\/strong> \u5176\u4e2d\uff0c\u5229\u7528Pandas\u5e93\u5904\u7406\u4ea4\u6613\u6570\u636e\u662f\u6700\u5e38\u7528\u4e14\u9ad8\u6548\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3aPandas\u80fd\u591f\u975e\u5e38\u65b9\u4fbf\u5730\u5904\u7406\u548c\u5206\u6790\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u4ecb\u7ecd\uff1a<\/p>\n<\/p>\n

\u4e00\u3001\u901a\u8fc7\u4ea4\u6613\u6570\u636e\u8ba1\u7b97<\/h3>\n<\/p>\n

\u901a\u8fc7\u8bfb\u53d6\u4ea4\u6613\u6570\u636e\uff0c\u53ef\u4ee5\u8ba1\u7b97\u6301\u4ed3\u7684\u5f00\u59cb\u548c\u7ed3\u675f\u65f6\u95f4\uff0c\u5e76\u8fdb\u800c\u8ba1\u7b97\u6301\u4ed3\u65f6\u95f4\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u4ea4\u6613\u8bb0\u5f55\u7684CSV\u6587\u4ef6\uff0c\u5176\u4e2d\u5305\u62ec\u4ea4\u6613\u65e5\u671f\u3001\u4e70\u5356\u7c7b\u578b\u3001\u80a1\u7968\u4ee3\u7801\u7b49\u4fe1\u606f\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u8bfb\u53d6\u4ea4\u6613\u6570\u636e<\/strong><\/h2>\n
data = pd.read_csv('trading_data.csv')<\/p>\n
\u7b5b\u9009\u51fa\u4e70\u5165\u8bb0\u5f55<\/strong><\/h2>\n
buy_data = data[data['type'] == 'buy']<\/p>\n
\u7b5b\u9009\u51fa\u5356\u51fa\u8bb0\u5f55<\/strong><\/h2>\n
sell_data = data[data['type'] == 'sell']<\/p>\n
\u5408\u5e76\u4e70\u5165\u548c\u5356\u51fa\u8bb0\u5f55\uff0c\u8ba1\u7b97\u6301\u4ed3\u65f6\u95f4<\/strong><\/h2>\n
holding_periods = pd.merge(buy_data, sell_data, on='stock_code', suffixes=('_buy', '_sell'))<\/p>\n
holding_periods['holding_time'] = pd.to_datetime(holding_periods['date_sell']) - pd.to_datetime(holding_periods['date_buy'])<\/p>\n
print(holding_periods[['stock_code', 'holding_time']])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406<\/h3>\n<\/p>\n
Pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u65f6\u95f4\u5e8f\u5217\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u7528\u6765\u65b9\u4fbf\u5730\u5904\u7406\u548c\u8ba1\u7b97\u6301\u4ed3\u65f6\u95f4\u3002<\/p>\n<\/p>\n
1. \u8bfb\u53d6\u548c\u9884\u5904\u7406\u6570\u636e<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u8bfb\u53d6\u4ea4\u6613\u6570\u636e\uff0c\u5e76\u8fdb\u884c\u57fa\u672c\u7684\u9884\u5904\u7406\u3002\u5047\u8bbe\u6570\u636e\u5305\u542b\u4ea4\u6613\u65e5\u671f\u3001\u4ea4\u6613\u7c7b\u578b\uff08\u4e70\u5165\/\u5356\u51fa\uff09\u3001\u80a1\u7968\u4ee3\u7801\u7b49\u5b57\u6bb5\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6\u4ea4\u6613\u6570\u636e<\/strong><\/h2>\n
df = pd.read_csv('trading_data.csv')<\/p>\n
\u8f6c\u6362\u4ea4\u6613\u65e5\u671f\u4e3adatetime\u7c7b\u578b<\/strong><\/h2>\n
df['date'] = pd.to_datetime(df['date'])<\/p>\n
\u6839\u636e\u80a1\u7968\u4ee3\u7801\u548c\u65e5\u671f\u6392\u5e8f<\/strong><\/h2>\n
df = df.sort_values(by=['stock_code', 'date'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8ba1\u7b97\u6301\u4ed3\u65f6\u95f4<\/h4>\n<\/p>\n
\u901a\u8fc7\u5bf9\u6bcf\u53ea\u80a1\u7968\u7684\u4ea4\u6613\u8bb0\u5f55\u8fdb\u884c\u904d\u5386\uff0c\u8ba1\u7b97\u6bcf\u6b21\u4e70\u5165\u548c\u5bf9\u5e94\u7684\u5356\u51fa\u4e4b\u95f4\u7684\u6301\u4ed3\u65f6\u95f4\u3002<\/p>\n<\/p>\n
holding_times = []<\/p>\n
for stock_code in df['stock_code'].unique():<\/p>\n
    stock_data = df[df['stock_code'] == stock_code]<\/p>\n
    buy_dates = stock_data[stock_data['type'] == 'buy']['date']<\/p>\n
    sell_dates = stock_data[stock_data['type'] == 'sell']['date']<\/p>\n
    for buy_date, sell_date in zip(buy_dates, sell_dates):<\/p>\n
        holding_time = sell_date - buy_date<\/p>\n
        holding_times.append({'stock_code': stock_code, 'holding_time': holding_time})<\/p>\n
holding_times_df = pd.DataFrame(holding_times)<\/p>\n
print(holding_times_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u7ed3\u5408\u65e5\u671f\u65f6\u95f4\u6a21\u5757\u5904\u7406\u65f6\u95f4\u6570\u636e<\/h3>\n<\/p>\n
Python\u7684datetime<\/code>\u6a21\u5757\u4e5f\u53ef\u4ee5\u7528\u6765\u5904\u7406\u548c\u8ba1\u7b97\u6301\u4ed3\u65f6\u95f4\u3002<\/p>\n<\/p>\n
from datetime import datetime<\/p>\n
\u793a\u4f8b\u4ea4\u6613\u6570\u636e<\/strong><\/h2>\n
trades = [<\/p>\n
    {'date': '2023-01-01', 'type': 'buy', 'stock_code': 'AAPL'},<\/p>\n
    {'date': '2023-01-15', 'type': 'sell', 'stock_code': 'AAPL'},<\/p>\n
    {'date': '2023-02-01', 'type': 'buy', 'stock_code': 'GOOGL'},<\/p>\n
    {'date': '2023-02-20', 'type': 'sell', 'stock_code': 'GOOGL'}<\/p>\n
]<\/p>\n
\u8f6c\u6362\u65e5\u671f\u5b57\u7b26\u4e32\u4e3adatetime\u5bf9\u8c61<\/strong><\/h2>\n
for trade in trades:<\/p>\n
    trade['date'] = datetime.strptime(trade['date'], '%Y-%m-%d')<\/p>\n
\u8ba1\u7b97\u6301\u4ed3\u65f6\u95f4<\/strong><\/h2>\n
holding_times = []<\/p>\n
for i in range(0, len(trades), 2):<\/p>\n
    buy_date = trades[i]['date']<\/p>\n
    sell_date = trades[i+1]['date']<\/p>\n
    holding_time = sell_date - buy_date<\/p>\n
    holding_times.append({'stock_code': trades[i]['stock_code'], 'holding_time': holding_time})<\/p>\n
for ht in holding_times:<\/p>\n
    print(ht)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f18\u5316\u548c\u6269\u5c55<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u80fd\u9700\u8981\u5904\u7406\u66f4\u591a\u7684\u4ea4\u6613\u6570\u636e\uff0c\u5e76\u4e14\u9700\u8981\u66f4\u590d\u6742\u7684\u903b\u8f91\u6765\u5904\u7406\u90e8\u5206\u4e70\u5356\u5339\u914d\u7684\u95ee\u9898\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u4f18\u5316\u548c\u6269\u5c55\u7684\u5efa\u8bae\uff1a<\/p>\n<\/p>\n
1. \u5904\u7406\u90e8\u5206\u4e70\u5356\u5339\u914d<\/h4>\n<\/p>\n
\u5728\u5b9e\u9645\u4ea4\u6613\u4e2d\uff0c\u53ef\u80fd\u4f1a\u6709\u90e8\u5206\u4e70\u5356\u5339\u914d\u7684\u60c5\u51b5\uff0c\u5373\u4e00\u6b21\u4e70\u5165\u53ef\u80fd\u5bf9\u5e94\u591a\u6b21\u5356\u51fa\uff0c\u6216\u8005\u4e00\u6b21\u5356\u51fa\u5bf9\u5e94\u591a\u6b21\u4e70\u5165\u3002\u53ef\u4ee5\u901a\u8fc7\u904d\u5386\u4ea4\u6613\u8bb0\u5f55\uff0c\u6839\u636e\u6570\u91cf\u8fdb\u884c\u90e8\u5206\u5339\u914d\u6765\u8ba1\u7b97\u6301\u4ed3\u65f6\u95f4\u3002<\/p>\n<\/p>\n
2. \u5904\u7406\u672a\u5e73\u4ed3\u7684\u60c5\u51b5<\/h4>\n<\/p>\n
\u5728\u5b9e\u9645\u4ea4\u6613\u4e2d\uff0c\u53ef\u80fd\u4f1a\u6709\u672a\u5e73\u4ed3\u7684\u60c5\u51b5\uff0c\u5373\u4e00\u4e9b\u4e70\u5165\u4ea4\u6613\u6ca1\u6709\u5bf9\u5e94\u7684\u5356\u51fa\u4ea4\u6613\u3002\u53ef\u4ee5\u6839\u636e\u5f53\u524d\u65e5\u671f\u6765\u8ba1\u7b97\u6301\u4ed3\u65f6\u95f4\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
from datetime import datetime<\/p>\n
\u793a\u4f8b\u4ea4\u6613\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    'date': ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-20', '2023-03-01'],<\/p>\n
    'type': ['buy', 'sell', 'buy', 'sell', 'buy'],<\/p>\n
    'stock_code': ['AAPL', 'AAPL', 'GOOGL', 'GOOGL', 'AAPL'],<\/p>\n
    'quantity': [10, 10, 5, 5, 15]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
df['date'] = pd.to_datetime(df['date'])<\/p>\n
\u5904\u7406\u672a\u5e73\u4ed3\u7684\u60c5\u51b5<\/strong><\/h2>\n
current_date = datetime.now()<\/p>\n
holding_times = []<\/p>\n
for stock_code in df['stock_code'].unique():<\/p>\n
    stock_data = df[df['stock_code'] == stock_code]<\/p>\n
    quantity = 0<\/p>\n
    buy_dates = []<\/p>\n
    for _, row in stock_data.iterrows():<\/p>\n
        if row['type'] == 'buy':<\/p>\n
            quantity += row['quantity']<\/p>\n
            buy_dates.append(row['date'])<\/p>\n
        elif row['type'] == 'sell':<\/p>\n
            quantity -= row['quantity']<\/p>\n
            sell_date = row['date']<\/p>\n
            while row['quantity'] > 0:<\/p>\n
                buy_date = buy_dates.pop(0)<\/p>\n
                holding_time = sell_date - buy_date<\/p>\n
                holding_times.append({'stock_code': stock_code, 'holding_time': holding_time})<\/p>\n
                row['quantity'] -= 1<\/p>\n
    # \u5904\u7406\u672a\u5e73\u4ed3\u7684\u60c5\u51b5<\/p>\n
    while quantity > 0:<\/p>\n
        buy_date = buy_dates.pop(0)<\/p>\n
        holding_time = current_date - buy_date<\/p>\n
        holding_times.append({'stock_code': stock_code, 'holding_time': holding_time})<\/p>\n
        quantity -= 1<\/p>\n
holding_times_df = pd.DataFrame(holding_times)<\/p>\n
print(holding_times_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u5229\u7528Python\u6765\u8ba1\u7b97\u80a1\u7968\u7684\u6301\u4ed3\u65f6\u95f4\u3002Pandas\u5e93\u7684\u5f3a\u5927\u6570\u636e\u5904\u7406\u80fd\u529b\u548cPython\u7684\u65e5\u671f\u65f6\u95f4\u6a21\u5757\u7ed3\u5408\u4f7f\u7528\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u5904\u7406\u548c\u8ba1\u7b97\u80a1\u7968\u4ea4\u6613\u6570\u636e\u4e2d\u7684\u6301\u4ed3\u65f6\u95f4\u3002<\/strong> \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6839\u636e\u5177\u4f53\u9700\u6c42\uff0c\u53ef\u4ee5\u5bf9\u8fd9\u4e9b\u65b9\u6cd5\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u4f18\u5316\u548c\u6269\u5c55\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8ba1\u7b97\u80a1\u7968\u6301\u4ed3\u65f6\u95f4\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u5229\u7528datetime\u6a21\u5757\u6765\u8ba1\u7b97\u80a1\u7968\u7684\u6301\u4ed3\u65f6\u95f4\u3002\u9996\u5148\uff0c\u8bb0\u5f55\u80a1\u7968\u7684\u8d2d\u4e70\u65e5\u671f\u548c\u5f53\u524d\u65e5\u671f\uff0c\u7136\u540e\u901a\u8fc7\u76f8\u51cf\u8ba1\u7b97\u51fa\u6301\u4ed3\u5929\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n
from datetime import datetime\n\n# \u5047\u8bbe\u8d2d\u4e70\u65e5\u671f\u4e3a2022\u5e741\u67081\u65e5\npurchase_date = datetime(2022, 1, 1)\ncurrent_date = datetime.now()\n\nholding_period = current_date - purchase_date\nprint(f"\u6301\u4ed3\u65f6\u95f4\u4e3a: {holding_period.days} \u5929")\n<\/code><\/pre>\n
\u662f\u5426\u53ef\u4ee5\u901a\u8fc7Python\u5e93\u6765\u83b7\u53d6\u80a1\u7968\u7684\u5386\u53f2\u6570\u636e\uff1f<\/strong>
\u662f\u7684\uff0cPython\u6709\u8bb8\u591a\u5f3a\u5927\u7684\u5e93\u53ef\u4ee5\u83b7\u53d6\u80a1\u7968\u7684\u5386\u53f2\u6570\u636e\uff0c\u4f8b\u5982pandas_datareader<\/code>\u548cyfinance<\/code>\u3002\u8fd9\u4e9b\u5e93\u53ef\u4ee5\u8f7b\u677e\u83b7\u53d6\u80a1\u7968\u7684\u4ef7\u683c\u548c\u4ea4\u6613\u91cf\u7b49\u4fe1\u606f\uff0c\u5e2e\u52a9\u7528\u6237\u5206\u6790\u6301\u4ed3\u65f6\u95f4\u4e0e\u5e02\u573a\u8868\u73b0\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n
\u5982\u4f55\u5728\u6301\u4ed3\u65f6\u95f4\u5185\u8ba1\u7b97\u80a1\u7968\u7684\u6536\u76ca\u7387\uff1f<\/strong>
\u53ef\u4ee5\u901a\u8fc7\u8bb0\u5f55\u8d2d\u4e70\u4ef7\u683c\u548c\u5f53\u524d\u4ef7\u683c\u6765\u8ba1\u7b97\u6536\u76ca\u7387\u3002\u6536\u76ca\u7387\u7684\u516c\u5f0f\u4e3a\uff1a(\u5f53\u524d\u4ef7\u683c – \u8d2d\u4e70\u4ef7\u683c) \/ \u8d2d\u4e70\u4ef7\u683c * 100%\u3002\u7ed3\u5408\u6301\u4ed3\u65f6\u95f4\uff0c\u53ef\u4ee5\u66f4\u6df1\u5165\u5730\u5206\u6790\u6295\u8d44\u8868\u73b0\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4ee3\u7801\u793a\u4f8b\uff1a  <\/p>\n
purchase_price = 100  # \u5047\u8bbe\u8d2d\u4e70\u4ef7\u683c\u4e3a100\ncurrent_price = 120   # \u5047\u8bbe\u5f53\u524d\u4ef7\u683c\u4e3a120\n\nreturn_rate = (current_price - purchase_price) \/ purchase_price * 100\nprint(f"\u6301\u4ed3\u671f\u95f4\u7684\u6536\u76ca\u7387\u4e3a: {return_rate:.2f}%")\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"Python\u8ba1\u7b97\u80a1\u7968\u6301\u4ed3\u65f6\u95f4\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u901a\u8fc7\u4ea4\u6613\u6570\u636e\u8ba1\u7b97\u3001\u4f7f\u7528Python\u5e93\u5982Pandas\u8fdb […]","protected":false},"author":3,"featured_media":1179866,"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\/1179862"}],"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=1179862"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1179862\/revisions"}],"predecessor-version":[{"id":1179870,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1179862\/revisions\/1179870"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1179866"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1179862"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1179862"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1179862"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}