251 lines
9.1 KiB
Python
251 lines
9.1 KiB
Python
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from datetime import timedelta
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import numpy as np
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import pandas
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from utils.comm import *
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class AllIndex:
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# 中国的指数
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ZH_INDEX = pd.DataFrame(
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[["上证指数", "000001.SH"], ["深证成指", "399001.SZ"], ["创业板指", "399006.SZ"], ["中证100", "000903.SH"],
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["中证100", "399903.SZ"]],
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columns=['name', 'value'])
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# 世界的指数
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GLOBAL_INDEX = ['道琼斯', '标普500', '纳斯达克', '恒生指数', '日经225', '韩国KOSPI', '美元指数']
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METAL_INDEX = pd.DataFrame(
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[["黄金", "Au99.99"]
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# , ["白银", "Ag99.99"]
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], columns=['name', 'value'])
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def __init__(self, trade_date):
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self.trade_date = trade_date.strftime('%Y%m%d')
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self.start_date = return_trading_day(trade_date, -29)
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self.end_date = self.trade_date
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def calendar_page_json(self):
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"""
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日历页面数据，只要A股指数
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获取1年内的全部数控，生成一个json文件
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:return:
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"""
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# 计算一年前的日期
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start_date = datetime.strptime(self.end_date, '%Y%m%d') - timedelta(days=365)
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start_date = start_date.strftime("%Y%m%d")
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# 在首页显示 这三个的当日最新涨跌情况
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index_page_display = ["000001.SH", "399001.SZ", "399006.SZ"]
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res_pandas = pandas.DataFrame(columns=['title', 'start', 'textColor', 'backgroundColor'])
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for stock in index_page_display:
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for _ in range(5):
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try:
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# 获取单个指数日线行情
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df = xcsc_pro.index_daily(
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ts_code=stock,
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start_date=start_date,
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end_date=self.end_date,
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fields="ts_code,trade_date,pct_chg")
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break
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except Exception as e:
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print("获取单个指数日线行情 出现问题：", e)
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time.sleep(1)
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continue
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else:
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print("重试5次依然没有解决问题，请重视~！！！")
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return None
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# 获取对应的中文名称
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name = self.ZH_INDEX.loc[self.ZH_INDEX['value'] == stock, 'name'].iloc[0]
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df['pct_chg_format'] = (round(df['pct_chg'], 2)).astype("str") + "%"
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df['title'] = name + " --> " + df['pct_chg_format']
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# 将字符串转换为日期时间对象
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df['trade_date'] = pd.to_datetime(df['trade_date'])
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# 格式化日期时间列为 "YYYY-MM-DD" 形式
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df['start'] = df['trade_date'].dt.strftime("%Y-%m-%d")
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# 设置条件
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conditions = [
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(df['pct_chg'] > 1),
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(df['pct_chg'] < -1),
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((-1 <= df['pct_chg']) & (df['pct_chg'] < 0)),
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((0 < df['pct_chg']) & (df['pct_chg'] <= 1))
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]
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# 字体颜色 当涨跌幅大于1的时候，将字体颜色设置成白色 #e60000
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text_colors = ['#ffffff', '#ffffff', '#5fcf57', '#e60000']
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df['textColor'] = np.select(conditions, text_colors)
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# 背景颜色
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background_colors = ['#e60000', '#5fcf57', '#ffffff', '#ffffff']
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df['backgroundColor'] = np.select(conditions, background_colors)
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df = df[['title', 'start', 'textColor', 'backgroundColor']]
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res_pandas = pd.concat([res_pandas, df], ignore_index=True)
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return res_pandas
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def html_page_data(self):
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# A股指数
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a_all_df, a_table_date, a_home_page_df = self.a_data()
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# 全球部分指数
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g_all_df, g_table_date, g_home_page_df = self.global_index()
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# 贵金属行情
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s_all_df, s_table_date, s_home_page_df = self.spot_hist_sge()
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# 生成首页的徽章
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badge = ""
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badge += self.__create_badge(a_home_page_df)
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badge += self.__create_badge(g_home_page_df)
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badge += self.__create_badge(s_home_page_df)
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# 生成 指数数据 页
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card_deck_df = pd.concat([a_table_date, g_table_date], axis=0)
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card_deck_df = pd.concat([card_deck_df, s_table_date], axis=0)
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card_deck = self.__create_card_deck(card_deck_df)
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return badge, card_deck
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def __create_badge(self, df):
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# 生成首页的徽章
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badge = ""
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for index, row in df.iterrows():
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badge_color = "badge-success"
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if row['pct_chg'] > 0:
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badge_color = "badge-danger"
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badge += f"<div class='col-md-auto'><span class='badge badge-pill {badge_color}'>{row['name']}：{row['pct_chg']}</span></div>"
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return badge
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def __create_card_deck(self, df):
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grouped = df.groupby('name')
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card_deck = '<div class="card-deck">'
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row = 1
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for group_name, group_df in grouped:
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card_deck_color = "border-success"
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if group_df.head(1)["pct_chg"].values[0] > 0:
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card_deck_color = "border-danger"
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card_deck += f'<div class="card {card_deck_color} mb-3" style="max-width: 14rem;">' \
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f'<div class="card-header">{group_name}</div>' \
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f'<div class="card-body {"text-danger" if group_df.head(1)["pct_chg"].values[0] > 0 else "text-success"}">' \
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f'<h4 class="card-title">{round(group_df.head(1)["close"].values[0], 2)}</h4>' \
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f'<p class="card-text row">' \
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f'<span class="{"text-danger" if group_df.head(2)["pct_chg"].values[1] > 0 else "text-success"} ml-3">' \
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f'{round(group_df.head(2)["close"].values[1], 2)}' \
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f'</span>' \
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f'<span class="{"text-danger" if group_df.head(3)["pct_chg"].values[2] > 0 else "text-success"} ml-auto mr-3">' \
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f'{round(group_df.head(3)["close"].values[2], 2)}' \
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f'</span>' \
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f'</p>' \
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f'</div>' \
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f'</div>'
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if row % 5 == 0:
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card_deck += '</div>'
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print(f"{row - 1} -- >> {len(grouped)}")
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if row != len(grouped):
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card_deck += '<div class="card-deck">'
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if row % 5 != 0 and row == len(grouped):
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card_deck += '</div>'
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row += 1
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return card_deck
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def a_data(self):
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"""
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A股指数数据
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返回 1个月内，3日，1日
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:return:
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"""
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# 在首页显示 这三个的当日最新涨跌情况
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index_page_display = ["000001.SH", "399001.SZ", "399006.SZ"]
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all_df, home_page_df, table_df = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
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for index, row in self.ZH_INDEX.iterrows():
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print(self.start_date, " --- >>", self.end_date)
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# 获取单个指数日线行情
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for _ in range(5):
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try:
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df = xcsc_pro.index_daily(ts_code=row['value'], start_date=self.start_date,
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end_date=self.end_date,
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fields="ts_code,trade_date,pct_chg,close")
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break
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except Exception as e:
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print("获取单个指数日线行情 出现问题：", e)
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continue
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else:
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print("重试5次依然没有解决问题，请重视~！！！")
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return None
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df['pct_chg'] = df['pct_chg'].round(2)
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df['name'] = self.ZH_INDEX.loc[self.ZH_INDEX['value'] == row['value'], 'name'].iloc[0]
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# 一个月的数据
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all_df = pd.concat([all_df, df], axis=0)
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if index_page_display.count(row['value']):
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print(df)
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# 一日的数据
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home_page_date = df.head(1)
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home_page_df = pd.concat([home_page_df, home_page_date], axis=0)
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# 三日的数据
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table_df_date = df.head(3)
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table_df = pd.concat([table_df, table_df_date], axis=0)
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return all_df, table_df, home_page_df
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def global_index(self):
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"""
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国外部分指数数据
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3日内的涨跌幅
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1个月内的折线图
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:return:
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"""
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all_df, table_df, home_page_df = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
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for i in self.GLOBAL_INDEX:
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for _ in range(5):
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try:
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df = qs.get_data(i)
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break
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except Exception as e:
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print(f"国外部分指数数据【{i}】拉取出现错误，", e)
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continue
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else:
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print("重试5次依然没有解决问题，请重视~！！！")
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return None
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df['pct_chg'] = round(((df['close'] - df['open']) / df['open']) * 100, 2)
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all_df_date, table_df_date, home_page_data = df.tail(30), df.tail(3), df.tail(1)
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all_df = pd.concat([all_df, all_df_date], axis=0)
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table_df = pd.concat([table_df, table_df_date], axis=0)
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home_page_df = pd.concat([home_page_df, home_page_data], axis=0)
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# pct_chg = global_df.groupby('name')['pct_chg'].apply(list).reset_index()
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# print(pct_chg)
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return all_df, table_df, home_page_df
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def oil_index(self): # 油价
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# 调价日期
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energy_oil_hist_df = ak.energy_oil_hist()
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print(energy_oil_hist_df)
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formatted_date = energy_oil_hist_df['调整日期'].iloc[-3].strftime('%Y%m%d')
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# 地区油价
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energy_oil_detail_df = ak.energy_oil_detail(date=formatted_date)
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energy_oil_detail_df = energy_oil_detail_df[energy_oil_detail_df['地区'] == '北京']
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print(energy_oil_detail_df)
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def spot_hist_sge(self):
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all_df, table_df, home_page_df = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
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for index, row in self.METAL_INDEX.iterrows():
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for _ in range(5):
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try:
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df = ak.spot_hist_sge(symbol=row['value'])
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break
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except Exception as e:
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print(f"上海黄金交易所-数据资讯-行情走势-历史数据，", e)
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continue
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else:
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print("重试5次依然没有解决问题，请重视~！！！")
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return None
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df['name'] = self.METAL_INDEX.loc[self.METAL_INDEX['value'] == row['value'], 'name'].iloc[0]
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df['pct_chg'] = round(((df['close'] - df['open']) / df['open']) * 100, 2)
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all_df_date, table_df_date, home_page_data = df.tail(30), df.tail(3), df.tail(1)
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all_df = pd.concat([all_df, all_df_date], axis=0)
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table_df = pd.concat([table_df, table_df_date], axis=0)
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home_page_df = pd.concat([home_page_df, home_page_data], axis=0)
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return all_df, table_df, home_page_df
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if __name__ == '__main__':
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# current_date = datetime.now().strftime('%Y%m%d')
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current_date = datetime.strptime('20230918', "%Y%m%d")
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# if if_run(current_date):
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index = AllIndex(trade_date=current_date)
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# index.a_index()
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badge, card_deck = index.html_page_data()
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print(badge)
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