{"id":1099425,"date":"2025-01-08T15:30:38","date_gmt":"2025-01-08T07:30:38","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1099425.html"},"modified":"2025-01-08T15:30:41","modified_gmt":"2025-01-08T07:30:41","slug":"%e5%a6%82%e4%bd%95%e5%88%a9%e7%94%a8python%e5%81%9a%e9%87%91%e8%9e%8d%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1099425.html","title":{"rendered":"\u5982\u4f55\u5229\u7528python\u505a\u91d1\u878d\u6570\u636e\u5206\u6790"},"content":{"rendered":"

\"\u5982\u4f55\u5229\u7528python\u505a\u91d1\u878d\u6570\u636e\u5206\u6790\"<\/p>\n

\u5229\u7528Python\u505a\u91d1\u878d\u6570\u636e\u5206\u6790\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\u5b9e\u73b0\uff1a\u6570\u636e\u83b7\u53d6\u3001\u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406\u3001\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316\u3001\u6a21\u578b\u6784\u5efa\u4e0e\u9884\u6d4b\u3001\u7ed3\u679c\u8bc4\u4f30\u4e0e\u4f18\u5316<\/strong>\u3002\u5176\u4e2d\uff0c\u6570\u636e\u83b7\u53d6<\/strong>\u662f\u7b2c\u4e00\u6b65\u4e14\u975e\u5e38\u91cd\u8981\uff0c\u672c\u6587\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u5229\u7528Python\u8fdb\u884c\u91d1\u878d\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n

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

\u83b7\u53d6\u91d1\u878d\u6570\u636e\u662f\u91d1\u878d\u6570\u636e\u5206\u6790\u7684\u57fa\u7840\u3002\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u83b7\u53d6\u91d1\u878d\u6570\u636e\uff0c\u4f8b\u5982\u4f7f\u7528API\u63a5\u53e3\u3001\u722c\u866b\u6280\u672f\u4ee5\u53ca\u5bfc\u5165\u5df2\u6709\u7684\u91d1\u878d\u6570\u636e\u96c6\u3002\u4ee5\u4e0b\u662f\u51e0\u79cd\u5e38\u89c1\u7684\u65b9\u5f0f\uff1a<\/p>\n<\/p>\n

    \n
  1. \u4f7f\u7528API\u63a5\u53e3<\/strong><\/li>\n<\/ol>\n

    \u91d1\u878d\u6570\u636e\u63d0\u4f9b\u5546\u5982Alpha Vantage\u3001Yahoo Finance\u3001Quandl\u7b49\u63d0\u4f9bAPI\u63a5\u53e3\uff0c\u4f7f\u7528\u8fd9\u4e9bAPI\u53ef\u4ee5\u65b9\u4fbf\u5730\u83b7\u53d6\u80a1\u7968\u3001\u5916\u6c47\u3001\u57fa\u91d1\u7b49\u5404\u7c7b\u91d1\u878d\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Alpha Vantage API\u83b7\u53d6\u80a1\u7968\u6570\u636e\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

    import requests<\/p>\n
    import pandas as pd<\/p>\n
    def get_stock_data(symbol, api_key):<\/p>\n
        url = f'https:\/\/www.alphavantage.co\/query?function=TIME_SERIES_DAI<\/a>LY&symbol={symbol}&apikey={api_key}'<\/p>\n
        response = requests.get(url)<\/p>\n
        data = response.json()<\/p>\n
        df = pd.DataFrame.from_dict(data['Time Series (Daily)'], orient='index')<\/p>\n
        df = df.astype(float)<\/p>\n
        return df<\/p>\n
    api_key = 'your_api_key_here'<\/p>\n
    symbol = 'AAPL'<\/p>\n
    stock_data = get_stock_data(symbol, api_key)<\/p>\n
    print(stock_data.head())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u4f7f\u7528\u722c\u866b\u6280\u672f<\/strong><\/li>\n<\/ol>\n
      \u5bf9\u4e8e\u4e00\u4e9b\u4e0d\u63d0\u4f9bAPI\u63a5\u53e3\u7684\u7f51\u7ad9\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u722c\u866b\u6280\u672f\u83b7\u53d6\u6570\u636e\u3002\u4f8b\u5982\uff0c\u4f7f\u7528BeautifulSoup\u5e93\u4ece\u7f51\u9875\u4e2d\u63d0\u53d6\u91d1\u878d\u6570\u636e\uff1a<\/p>\n<\/p>\n
      import requests<\/p>\n
      from bs4 import BeautifulSoup<\/p>\n
      def get_financial_data(url):<\/p>\n
          response = requests.get(url)<\/p>\n
          soup = BeautifulSoup(response.content, 'html.parser')<\/p>\n
          table = soup.find('table', {'class': 'financials'})<\/p>\n
          data = []<\/p>\n
          for row in table.find_all('tr'):<\/p>\n
              cols = row.find_all('td')<\/p>\n
              cols = [col.text.strip() for col in cols]<\/p>\n
              data.append(cols)<\/p>\n
          return data<\/p>\n
      url = 'your_target_url_here'<\/p>\n
      financial_data = get_financial_data(url)<\/p>\n
      print(financial_data)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u5bfc\u5165\u5df2\u6709\u7684\u6570\u636e\u96c6<\/strong><\/li>\n<\/ol>\n
        \u91d1\u878d\u6570\u636e\u5206\u6790\u5e38\u7528\u7684\u6570\u636e\u96c6\u53ef\u4ee5\u4ece\u7f51\u4e0a\u4e0b\u8f7d\uff0c\u5bfc\u5165\u5230Python\u8fdb\u884c\u5904\u7406\u3002\u5e38\u89c1\u7684\u683c\u5f0f\u6709CSV\u3001Excel\u7b49\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
        import pandas as pd<\/p>\n
        file_path = 'path_to_your_dataset.csv'<\/p>\n
        financial_data = pd.read_csv(file_path)<\/p>\n
        print(financial_data.head())<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u4e8c\u3001\u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406<\/p>\n<\/p>\n
        \u83b7\u53d6\u5230\u6570\u636e\u540e\uff0c\u901a\u5e38\u9700\u8981\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u8d28\u91cf\u548c\u5206\u6790\u7ed3\u679c\u7684\u53ef\u9760\u6027\u3002\u5e38\u89c1\u7684\u6570\u636e\u6e05\u6d17\u6b65\u9aa4\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u6570\u636e\u683c\u5f0f\u8f6c\u6362\u3001\u5f02\u5e38\u503c\u5904\u7406\u7b49\u3002<\/p>\n<\/p>\n
          \n
        1. \u5904\u7406\u7f3a\u5931\u503c<\/strong><\/li>\n<\/ol>\n
          \u7f3a\u5931\u503c\u662f\u91d1\u878d\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e38\u89c1\u95ee\u9898\uff0c\u53ef\u4ee5\u9009\u62e9\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u6216\u4f7f\u7528\u63d2\u503c\u6cd5\u586b\u8865\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n
          # \u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c<\/p>\n
          cleaned_data = financial_data.dropna()<\/p>\n
          \u4f7f\u7528\u63d2\u503c\u6cd5\u586b\u8865\u7f3a\u5931\u503c<\/strong><\/h2>\n
          cleaned_data = financial_data.interpolate()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u6570\u636e\u683c\u5f0f\u8f6c\u6362<\/strong><\/li>\n<\/ol>\n
            \u786e\u4fdd\u6570\u636e\u7684\u683c\u5f0f\u6b63\u786e\uff0c\u4f8b\u5982\u5c06\u65e5\u671f\u5217\u8f6c\u6362\u4e3adatetime\u7c7b\u578b\uff1a<\/p>\n<\/p>\n
            financial_data['Date'] = pd.to_datetime(financial_data['Date'])<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u5f02\u5e38\u503c\u5904\u7406<\/strong><\/li>\n<\/ol>\n
              \u68c0\u6d4b\u5e76\u5904\u7406\u6570\u636e\u4e2d\u7684\u5f02\u5e38\u503c\uff0c\u4f8b\u5982\u4f7f\u7528\u7bb1\u7ebf\u56fe\u8fdb\u884c\u53ef\u89c6\u5316\u68c0\u6d4b\u5f02\u5e38\u503c\uff1a<\/p>\n<\/p>\n
              import matplotlib.pyplot as plt<\/p>\n
              plt.boxplot(financial_data['Close'])<\/p>\n
              plt.show()<\/p>\n
              \u4f7f\u7528IQR\u65b9\u6cd5\u5904\u7406\u5f02\u5e38\u503c<\/strong><\/h2>\n
              Q1 = financial_data['Close'].quantile(0.25)<\/p>\n
              Q3 = financial_data['Close'].quantile(0.75)<\/p>\n
              IQR = Q3 - Q1<\/p>\n
              lower_bound = Q1 - 1.5 * IQR<\/p>\n
              upper_bound = Q3 + 1.5 * IQR<\/p>\n
              cleaned_data = financial_data[(financial_data['Close'] >= lower_bound) & (financial_data['Close'] <= upper_bound)]<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u4e09\u3001\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316<\/p>\n<\/p>\n
              \u6e05\u6d17\u540e\u7684\u6570\u636e\u53ef\u4ee5\u8fdb\u884c\u5206\u6790\u4e0e\u53ef\u89c6\u5316\uff0c\u5e38\u89c1\u7684\u91d1\u878d\u6570\u636e\u5206\u6790\u65b9\u6cd5\u5305\u62ec\u57fa\u672c\u7edf\u8ba1\u5206\u6790\u3001\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u7b49\u3002<\/p>\n<\/p>\n
                \n
              1. \u57fa\u672c\u7edf\u8ba1\u5206\u6790<\/strong><\/li>\n<\/ol>\n
                \u901a\u8fc7\u63cf\u8ff0\u6027\u7edf\u8ba1\u5206\u6790\u4e86\u89e3\u6570\u636e\u7684\u57fa\u672c\u7279\u5f81\uff1a<\/p>\n<\/p>\n
                print(cleaned_data.describe())<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u65f6\u95f4\u5e8f\u5217\u5206\u6790<\/strong><\/li>\n<\/ol>\n
                  \u65f6\u95f4\u5e8f\u5217\u5206\u6790\u662f\u91d1\u878d\u6570\u636e\u5206\u6790\u4e2d\u7684\u91cd\u8981\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u7b80\u5355\u7684\u65f6\u95f4\u5e8f\u5217\u5206\u6790\uff1a<\/p>\n<\/p>\n
                  # \u7ed8\u5236\u6536\u76d8\u4ef7\u65f6\u95f4\u5e8f\u5217\u56fe<\/p>\n
                  plt.plot(cleaned_data['Date'], cleaned_data['Close'])<\/p>\n
                  plt.xlabel('Date')<\/p>\n
                  plt.ylabel('Close Price')<\/p>\n
                  plt.title('Time Series of Close Price')<\/p>\n
                  plt.show()<\/p>\n
                  \u8ba1\u7b97\u79fb\u52a8\u5e73\u5747<\/strong><\/h2>\n
                  cleaned_data['MA50'] = cleaned_data['Close'].rolling(window=50).mean()<\/p>\n
                  cleaned_data['MA200'] = cleaned_data['Close'].rolling(window=200).mean()<\/p>\n
                  plt.plot(cleaned_data['Date'], cleaned_data['Close'], label='Close Price')<\/p>\n
                  plt.plot(cleaned_data['Date'], cleaned_data['MA50'], label='50-Day Moving Average')<\/p>\n
                  plt.plot(cleaned_data['Date'], cleaned_data['MA200'], label='200-Day Moving Average')<\/p>\n
                  plt.xlabel('Date')<\/p>\n
                  plt.ylabel('Price')<\/p>\n
                  plt.title('Moving Averages')<\/p>\n
                  plt.legend()<\/p>\n
                  plt.show()<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u56db\u3001\u6a21\u578b\u6784\u5efa\u4e0e\u9884\u6d4b<\/p>\n<\/p>\n
                  \u5728\u91d1\u878d\u6570\u636e\u5206\u6790\u4e2d\uff0c\u5e38\u7528\u7684\u6a21\u578b\u5305\u62ec\u7ebf\u6027\u56de\u5f52\u3001\u65f6\u95f4\u5e8f\u5217\u6a21\u578b\uff08\u5982ARIMA\uff09\u3001\u673a\u5668\u5b66\u4e60<\/a>\u6a21\u578b\uff08\u5982\u968f\u673a\u68ee\u6797\u3001\u795e\u7ecf\u7f51\u7edc\uff09\u7b49\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/strong><\/li>\n<\/ol>\n
                    \u7ebf\u6027\u56de\u5f52\u6a21\u578b\u53ef\u4ee5\u7528\u4e8e\u9884\u6d4b\u80a1\u7968\u4ef7\u683c\u7684\u8d8b\u52bf\uff1a<\/p>\n<\/p>\n
                    from sklearn.model_selection import train_test_split<\/p>\n
                    from sklearn.linear_model import LinearRegression<\/p>\n
                    \u51c6\u5907\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf<\/strong><\/h2>\n
                    X = cleaned_data[['MA50', 'MA200']]<\/p>\n
                    y = cleaned_data['Close']<\/p>\n
                    \u62c6\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
                    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
                    \u6784\u5efa\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
                    model = LinearRegression()<\/p>\n
                    model.fit(X_train, y_train)<\/p>\n
                    \u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n
                    y_pred = model.predict(X_test)<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u65f6\u95f4\u5e8f\u5217\u6a21\u578b\uff08ARIMA\uff09<\/strong><\/li>\n<\/ol>\n
                      ARIMA\u6a21\u578b\u5e38\u7528\u4e8e\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u9884\u6d4b\uff1a<\/p>\n<\/p>\n
                      from statsmodels.tsa.arima_model import ARIMA<\/p>\n
                      \u51c6\u5907\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/strong><\/h2>\n
                      ts_data = cleaned_data.set_index('Date')['Close']<\/p>\n
                      \u62c6\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
                      train_data = ts_data[:int(0.8*len(ts_data))]<\/p>\n
                      test_data = ts_data[int(0.8*len(ts_data)):]<\/p>\n
                      \u6784\u5efaARIMA\u6a21\u578b<\/strong><\/h2>\n
                      model = ARIMA(train_data, order=(5, 1, 0))<\/p>\n
                      model_fit = model.fit(disp=0)<\/p>\n
                      \u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n
                      forecast = model_fit.forecast(steps=len(test_data))[0]<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u4e94\u3001\u7ed3\u679c\u8bc4\u4f30\u4e0e\u4f18\u5316<\/p>\n<\/p>\n
                      \u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\uff0c\u5e76\u6839\u636e\u8bc4\u4f30\u7ed3\u679c\u8fdb\u884c\u4f18\u5316\u3002\u5e38\u7528\u7684\u8bc4\u4f30\u6307\u6807\u5305\u62ec\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u3001\u5747\u65b9\u6839\u8bef\u5dee\uff08RMSE\uff09\u3001\u5e73\u5747\u7edd\u5bf9\u8bef\u5dee\uff08MAE\uff09\u7b49\u3002<\/p>\n<\/p>\n
                        \n
                      1. \u8bc4\u4f30\u6a21\u578b\u6027\u80fd<\/strong><\/li>\n<\/ol>\n
                        \u8ba1\u7b97\u6a21\u578b\u7684\u8bc4\u4f30\u6307\u6807\uff1a<\/p>\n<\/p>\n
                        from sklearn.metrics import mean_squared_error, mean_absolute_error<\/p>\n
                        \u8ba1\u7b97\u5747\u65b9\u8bef\u5dee<\/strong><\/h2>\n
                        mse = mean_squared_error(y_test, y_pred)<\/p>\n
                        print(f'MSE: {mse}')<\/p>\n
                        \u8ba1\u7b97\u5747\u65b9\u6839\u8bef\u5dee<\/strong><\/h2>\n
                        rmse = mean_squared_error(y_test, y_pred, squared=False)<\/p>\n
                        print(f'RMSE: {rmse}')<\/p>\n
                        \u8ba1\u7b97\u5e73\u5747\u7edd\u5bf9\u8bef\u5dee<\/strong><\/h2>\n
                        mae = mean_absolute_error(y_test, y_pred)<\/p>\n
                        print(f'MAE: {mae}')<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                          \n
                        1. \u6a21\u578b\u4f18\u5316<\/strong><\/li>\n<\/ol>\n
                          \u6839\u636e\u8bc4\u4f30\u7ed3\u679c\uff0c\u5bf9\u6a21\u578b\u8fdb\u884c\u4f18\u5316\uff0c\u4f8b\u5982\u8c03\u6574\u6a21\u578b\u53c2\u6570\u3001\u9009\u62e9\u5176\u4ed6\u6a21\u578b\u7b49\u3002<\/p>\n<\/p>\n
                          \u7efc\u4e0a\u6240\u8ff0\uff0c\u5229\u7528Python\u8fdb\u884c\u91d1\u878d\u6570\u636e\u5206\u6790\u7684\u6b65\u9aa4\u5305\u62ec\u6570\u636e\u83b7\u53d6\u3001\u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406\u3001\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316\u3001\u6a21\u578b\u6784\u5efa\u4e0e\u9884\u6d4b\u3001\u7ed3\u679c\u8bc4\u4f30\u4e0e\u4f18\u5316\u3002\u6bcf\u4e00\u6b65\u90fd\u81f3\u5173\u91cd\u8981\uff0c\u901a\u8fc7\u5408\u7406\u7684\u5206\u6790\u65b9\u6cd5\u548c\u6a21\u578b\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u4ece\u91d1\u878d\u6570\u636e\u4e2d\u63d0\u53d6\u6709\u4ef7\u503c\u7684\u4fe1\u606f\uff0c\u4e3a\u6295\u8d44\u51b3\u7b56\u63d0\u4f9b\u652f\u6301\u3002<\/p>\n<\/p>\n
                          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                           \u5982\u4f55\u5f00\u59cb\u4f7f\u7528Python\u8fdb\u884c\u91d1\u878d\u6570\u636e\u5206\u6790\uff1f<\/strong>
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