知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
623 lines
22 KiB
Python
623 lines
22 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2025/1/13 22:30
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Desc: 东方财富网-数据中心-大宗交易-市场统计
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https://data.eastmoney.com/dzjy/
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"""
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import pandas as pd
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import requests
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def stock_dzjy_sctj() -> pd.DataFrame:
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"""
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东方财富网-数据中心-大宗交易-市场统计
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https://data.eastmoney.com/dzjy/dzjy_sctj.html
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:return: 市场统计表
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:rtype: pandas.DataFrame
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"""
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url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
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params = {
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"sortColumns": "TRADE_DATE",
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"sortTypes": "-1",
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"pageSize": "500",
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"pageNumber": "1",
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"reportName": "PRT_BLOCKTRADE_MARKET_STA",
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"columns": "TRADE_DATE,SZ_INDEX,SZ_CHANGE_RATE,BLOCKTRADE_DEAL_AMT,PREMIUM_DEAL_AMT,"
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"PREMIUM_RATIO,DISCOUNT_DEAL_AMT,DISCOUNT_RATIO",
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"source": "WEB",
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"client": "WEB",
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}
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r = requests.get(url, params=params)
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data_json = r.json()
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total_page = int(data_json["result"]["pages"])
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big_df = pd.DataFrame()
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for page in range(1, total_page + 1):
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params.update({"pageNumber": page})
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["result"]["data"])
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big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
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big_df.reset_index(inplace=True)
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big_df["index"] = big_df["index"] + 1
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big_df.columns = [
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"序号",
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"交易日期",
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"上证指数",
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"上证指数涨跌幅",
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"大宗交易成交总额",
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"溢价成交总额",
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"溢价成交总额占比",
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"折价成交总额",
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"折价成交总额占比",
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]
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big_df["交易日期"] = pd.to_datetime(big_df["交易日期"], errors="coerce").dt.date
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big_df["上证指数"] = pd.to_numeric(big_df["上证指数"], errors="coerce")
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big_df["上证指数涨跌幅"] = pd.to_numeric(big_df["上证指数涨跌幅"], errors="coerce")
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big_df["大宗交易成交总额"] = pd.to_numeric(
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big_df["大宗交易成交总额"], errors="coerce"
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)
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big_df["溢价成交总额"] = pd.to_numeric(big_df["溢价成交总额"], errors="coerce")
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big_df["溢价成交总额占比"] = pd.to_numeric(
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big_df["溢价成交总额占比"], errors="coerce"
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)
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big_df["折价成交总额"] = pd.to_numeric(big_df["折价成交总额"], errors="coerce")
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big_df["折价成交总额占比"] = pd.to_numeric(
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big_df["折价成交总额占比"], errors="coerce"
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)
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return big_df
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def stock_dzjy_mrmx(
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symbol: str = "基金", start_date: str = "20220104", end_date: str = "20220104"
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) -> pd.DataFrame:
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"""
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东方财富网-数据中心-大宗交易-每日明细
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https://data.eastmoney.com/dzjy/dzjy_mrmx.html
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:param symbol: choice of {'A股', 'B股', '基金', '债券'}
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:type symbol: str
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:param start_date: 开始日期
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:type start_date: str
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:param end_date: 结束日期
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:type end_date: str
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:return: 每日明细
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:rtype: pandas.DataFrame
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"""
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symbol_map = {
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"A股": "1",
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"B股": "2",
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"基金": "3",
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"债券": "4",
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}
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url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
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params = {
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"sortColumns": "SECURITY_CODE",
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"sortTypes": "1",
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"pageSize": "5000",
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"pageNumber": "1",
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"reportName": "RPT_DATA_BLOCKTRADE",
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"columns": "TRADE_DATE,SECURITY_CODE,SECUCODE,SECURITY_NAME_ABBR,CHANGE_RATE,CLOSE_PRICE,"
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"DEAL_PRICE,PREMIUM_RATIO,DEAL_VOLUME,DEAL_AMT,TURNOVER_RATE,BUYER_NAME,SELLER_NAME,"
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"CHANGE_RATE_1DAYS,CHANGE_RATE_5DAYS,CHANGE_RATE_10DAYS,CHANGE_RATE_20DAYS,BUYER_CODE,SELLER_CODE",
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"source": "WEB",
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"client": "WEB",
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"filter": f"""(SECURITY_TYPE_WEB={symbol_map[symbol]})(TRADE_DATE>=
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'{"-".join([start_date[:4], start_date[4:6], start_date[6:]])}')(TRADE_DATE<=
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'{"-".join([end_date[:4], end_date[4:6], end_date[6:]])}')""",
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}
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r = requests.get(url, params=params)
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data_json = r.json()
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if not data_json["result"]["data"]:
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return pd.DataFrame()
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temp_df = pd.DataFrame(data_json["result"]["data"])
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temp_df.reset_index(inplace=True)
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temp_df["index"] = temp_df.index + 1
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if symbol in {"A股"}:
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temp_df.columns = [
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"序号",
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"交易日期",
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"证券代码",
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"-",
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"证券简称",
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"涨跌幅",
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"收盘价",
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"成交价",
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"折溢率",
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"成交量",
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"成交额",
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"成交额/流通市值",
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"买方营业部",
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"卖方营业部",
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"_",
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"_",
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"_",
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"_",
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"_",
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"_",
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]
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temp_df["交易日期"] = pd.to_datetime(
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temp_df["交易日期"], errors="coerce"
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).dt.date
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temp_df = temp_df[
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[
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"序号",
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"交易日期",
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"证券代码",
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"证券简称",
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"涨跌幅",
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"收盘价",
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"成交价",
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"折溢率",
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"成交量",
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"成交额",
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"成交额/流通市值",
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"买方营业部",
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"卖方营业部",
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]
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]
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temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
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temp_df["收盘价"] = pd.to_numeric(temp_df["收盘价"], errors="coerce")
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temp_df["成交价"] = pd.to_numeric(temp_df["成交价"], errors="coerce")
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temp_df["折溢率"] = pd.to_numeric(temp_df["折溢率"], errors="coerce")
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temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
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temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
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temp_df["成交额/流通市值"] = pd.to_numeric(
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temp_df["成交额/流通市值"], errors="coerce"
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)
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if symbol in {"B股", "基金", "债券"}:
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temp_df.columns = [
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"序号",
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"交易日期",
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"证券代码",
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"-",
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"证券简称",
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"-",
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"-",
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"成交价",
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"-",
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"成交量",
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"成交额",
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"-",
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"买方营业部",
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"卖方营业部",
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"_",
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"_",
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"_",
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"_",
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"_",
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"_",
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]
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temp_df["交易日期"] = pd.to_datetime(
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temp_df["交易日期"], errors="coerce"
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).dt.date
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temp_df = temp_df[
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[
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"序号",
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"交易日期",
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"证券代码",
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"证券简称",
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"成交价",
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"成交量",
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"成交额",
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"买方营业部",
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"卖方营业部",
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]
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]
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temp_df["成交价"] = pd.to_numeric(temp_df["成交价"], errors="coerce")
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temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
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temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
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return temp_df
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def stock_dzjy_mrtj(
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start_date: str = "20220105", end_date: str = "20220105"
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) -> pd.DataFrame:
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"""
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东方财富网-数据中心-大宗交易-每日统计
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https://data.eastmoney.com/dzjy/dzjy_mrtj.html
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:param start_date: 开始日期
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:type start_date: str
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:param end_date: 结束日期
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:type end_date: str
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:return: 每日统计
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:rtype: pandas.DataFrame
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"""
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url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
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params = {
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"sortColumns": "TURNOVERRATE",
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"sortTypes": "-1",
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"pageSize": "5000",
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"pageNumber": "1",
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"reportName": "RPT_BLOCKTRADE_STA",
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"columns": "TRADE_DATE,SECURITY_CODE,SECUCODE,SECURITY_NAME_ABBR,CHANGE_RATE,"
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"CLOSE_PRICE,AVERAGE_PRICE,PREMIUM_RATIO,DEAL_NUM,VOLUME,DEAL_AMT,"
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"TURNOVERRATE,D1_CLOSE_ADJCHRATE,D5_CLOSE_ADJCHRATE,D10_CLOSE_ADJCHRATE,D20_CLOSE_ADJCHRATE",
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"source": "WEB",
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"client": "WEB",
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"filter": f"(TRADE_DATE>='{'-'.join([start_date[:4], start_date[4:6], start_date[6:]])}')(TRADE_DATE<="
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f"'{'-'.join([end_date[:4], end_date[4:6], end_date[6:]])}')",
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}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["result"]["data"])
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temp_df.reset_index(inplace=True)
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temp_df["index"] = temp_df.index + 1
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temp_df.columns = [
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"序号",
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"交易日期",
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"证券代码",
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"-",
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"证券简称",
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"涨跌幅",
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"收盘价",
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"成交价",
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"折溢率",
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"成交笔数",
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"成交总量",
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"成交总额",
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"成交总额/流通市值",
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"_",
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"_",
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"_",
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"_",
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]
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temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"], errors="coerce").dt.date
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temp_df = temp_df[
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[
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"序号",
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"交易日期",
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"证券代码",
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"证券简称",
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"涨跌幅",
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"收盘价",
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"成交价",
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"折溢率",
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"成交笔数",
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"成交总量",
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"成交总额",
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"成交总额/流通市值",
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]
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]
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temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
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temp_df["收盘价"] = pd.to_numeric(temp_df["收盘价"], errors="coerce")
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temp_df["成交价"] = pd.to_numeric(temp_df["成交价"], errors="coerce")
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temp_df["折溢率"] = pd.to_numeric(temp_df["折溢率"], errors="coerce")
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temp_df["成交笔数"] = pd.to_numeric(temp_df["成交笔数"], errors="coerce")
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temp_df["成交总量"] = pd.to_numeric(temp_df["成交总量"], errors="coerce")
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temp_df["成交总额"] = pd.to_numeric(temp_df["成交总额"], errors="coerce")
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temp_df["成交总额/流通市值"] = pd.to_numeric(
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temp_df["成交总额/流通市值"], errors="coerce"
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)
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return temp_df
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def stock_dzjy_hygtj(symbol: str = "近三月") -> pd.DataFrame:
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"""
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东方财富网-数据中心-大宗交易-活跃 A 股统计
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https://data.eastmoney.com/dzjy/dzjy_hygtj.html
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:param symbol: choice of {'近一月', '近三月', '近六月', '近一年'}
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:type symbol: str
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:return: 活跃 A 股统计
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:rtype: pandas.DataFrame
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"""
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period_map = {
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"近一月": "1",
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"近三月": "3",
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"近六月": "6",
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"近一年": "12",
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}
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url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
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params = {
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"sortColumns": "DEAL_NUM,SECURITY_CODE",
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"sortTypes": "-1,-1",
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"pageSize": "5000",
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"pageNumber": "1",
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"reportName": "RPT_BLOCKTRADE_ACSTA",
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"columns": "SECURITY_CODE,SECUCODE,SECURITY_NAME_ABBR,CLOSE_PRICE,CHANGE_RATE,TRADE_DATE,"
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"DEAL_AMT,PREMIUM_RATIO,SUM_TURNOVERRATE,DEAL_NUM,PREMIUM_TIMES,DISCOUNT_TIMES,"
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"D1_AVG_ADJCHRATE,D5_AVG_ADJCHRATE,D10_AVG_ADJCHRATE,D20_AVG_ADJCHRATE,DATE_TYPE_CODE",
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"source": "WEB",
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"client": "WEB",
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"filter": f"(DATE_TYPE_CODE={period_map[symbol]})",
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}
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r = requests.get(url, params=params)
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data_json = r.json()
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total_page = data_json["result"]["pages"]
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big_df = pd.DataFrame()
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for page in range(1, int(total_page) + 1):
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params.update({"pageNumber": page})
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["result"]["data"])
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big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
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big_df.reset_index(inplace=True)
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big_df["index"] = big_df.index + 1
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big_df.columns = [
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"序号",
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"证券代码",
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"_",
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"证券简称",
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"最新价",
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"涨跌幅",
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"最近上榜日",
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"总成交额",
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"折溢率",
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"成交总额/流通市值",
|
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"上榜次数-总计",
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"上榜次数-溢价",
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"上榜次数-折价",
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"上榜日后平均涨跌幅-1日",
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"上榜日后平均涨跌幅-5日",
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"上榜日后平均涨跌幅-10日",
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"上榜日后平均涨跌幅-20日",
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"_",
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]
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big_df = big_df[
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[
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"序号",
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"证券代码",
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"证券简称",
|
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"最新价",
|
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"涨跌幅",
|
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"最近上榜日",
|
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"上榜次数-总计",
|
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"上榜次数-溢价",
|
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"上榜次数-折价",
|
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"总成交额",
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"折溢率",
|
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"成交总额/流通市值",
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"上榜日后平均涨跌幅-1日",
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"上榜日后平均涨跌幅-5日",
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"上榜日后平均涨跌幅-10日",
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"上榜日后平均涨跌幅-20日",
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]
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]
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big_df["最近上榜日"] = pd.to_datetime(big_df["最近上榜日"], errors="coerce").dt.date
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big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
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big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce")
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big_df["上榜次数-总计"] = pd.to_numeric(big_df["上榜次数-总计"], errors="coerce")
|
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big_df["上榜次数-溢价"] = pd.to_numeric(big_df["上榜次数-溢价"], errors="coerce")
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big_df["上榜次数-折价"] = pd.to_numeric(big_df["上榜次数-折价"], errors="coerce")
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big_df["总成交额"] = pd.to_numeric(big_df["总成交额"], errors="coerce")
|
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big_df["折溢率"] = pd.to_numeric(big_df["折溢率"], errors="coerce")
|
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big_df["成交总额/流通市值"] = pd.to_numeric(
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big_df["成交总额/流通市值"], errors="coerce"
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)
|
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big_df["上榜日后平均涨跌幅-1日"] = pd.to_numeric(
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big_df["上榜日后平均涨跌幅-1日"], errors="coerce"
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)
|
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big_df["上榜日后平均涨跌幅-5日"] = pd.to_numeric(
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big_df["上榜日后平均涨跌幅-5日"], errors="coerce"
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)
|
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big_df["上榜日后平均涨跌幅-10日"] = pd.to_numeric(
|
|
big_df["上榜日后平均涨跌幅-10日"], errors="coerce"
|
|
)
|
|
big_df["上榜日后平均涨跌幅-20日"] = pd.to_numeric(
|
|
big_df["上榜日后平均涨跌幅-20日"], errors="coerce"
|
|
)
|
|
return big_df
|
|
|
|
|
|
def stock_dzjy_hyyybtj(symbol: str = "近3日") -> pd.DataFrame:
|
|
"""
|
|
东方财富网-数据中心-大宗交易-活跃营业部统计
|
|
https://data.eastmoney.com/dzjy/dzjy_hyyybtj.html
|
|
:param symbol: choice of {'当前交易日', '近3日', '近5日', '近10日', '近30日'}
|
|
:type symbol: str
|
|
:return: 活跃营业部统计
|
|
:rtype: pandas.DataFrame
|
|
"""
|
|
period_map = {
|
|
"当前交易日": "1",
|
|
"近3日": "3",
|
|
"近5日": "5",
|
|
"近10日": "10",
|
|
"近30日": "30",
|
|
}
|
|
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
|
params = {
|
|
"sortColumns": "BUYER_NUM,TOTAL_BUYAMT",
|
|
"sortTypes": "-1,-1",
|
|
"pageSize": "5000",
|
|
"pageNumber": "1",
|
|
"reportName": "RPT_BLOCKTRADE_OPERATEDEPTSTATISTICS",
|
|
"columns": "OPERATEDEPT_CODE,OPERATEDEPT_NAME,ONLIST_DATE,STOCK_DETAILS,"
|
|
"BUYER_NUM,SELLER_NUM,TOTAL_BUYAMT,TOTAL_SELLAMT,TOTAL_NETAMT,N_DATE",
|
|
"source": "WEB",
|
|
"client": "WEB",
|
|
"filter": f"(N_DATE=-{period_map[symbol]})",
|
|
}
|
|
r = requests.get(url, params=params)
|
|
data_json = r.json()
|
|
total_page = data_json["result"]["pages"]
|
|
big_df = pd.DataFrame()
|
|
for page in range(1, int(total_page) + 1):
|
|
params.update({"pageNumber": page})
|
|
r = requests.get(url, params=params)
|
|
data_json = r.json()
|
|
temp_df = pd.DataFrame(data_json["result"]["data"])
|
|
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
|
big_df.reset_index(inplace=True)
|
|
big_df["index"] = big_df.index + 1
|
|
big_df.columns = [
|
|
"序号",
|
|
"_",
|
|
"营业部名称",
|
|
"最近上榜日",
|
|
"买入的股票",
|
|
"次数总计-买入",
|
|
"次数总计-卖出",
|
|
"成交金额统计-买入",
|
|
"成交金额统计-卖出",
|
|
"成交金额统计-净买入额",
|
|
"_",
|
|
]
|
|
big_df = big_df[
|
|
[
|
|
"序号",
|
|
"最近上榜日",
|
|
"营业部名称",
|
|
"次数总计-买入",
|
|
"次数总计-卖出",
|
|
"成交金额统计-买入",
|
|
"成交金额统计-卖出",
|
|
"成交金额统计-净买入额",
|
|
"买入的股票",
|
|
]
|
|
]
|
|
big_df["最近上榜日"] = pd.to_datetime(big_df["最近上榜日"], errors="coerce").dt.date
|
|
big_df["次数总计-买入"] = pd.to_numeric(big_df["次数总计-买入"], errors="coerce")
|
|
big_df["次数总计-卖出"] = pd.to_numeric(big_df["次数总计-卖出"], errors="coerce")
|
|
big_df["成交金额统计-买入"] = pd.to_numeric(
|
|
big_df["成交金额统计-买入"], errors="coerce"
|
|
)
|
|
big_df["成交金额统计-卖出"] = pd.to_numeric(
|
|
big_df["成交金额统计-卖出"], errors="coerce"
|
|
)
|
|
big_df["成交金额统计-净买入额"] = pd.to_numeric(
|
|
big_df["成交金额统计-净买入额"], errors="coerce"
|
|
)
|
|
return big_df
|
|
|
|
|
|
def stock_dzjy_yybph(symbol: str = "近三月") -> pd.DataFrame:
|
|
"""
|
|
东方财富网-数据中心-大宗交易-营业部排行
|
|
https://data.eastmoney.com/dzjy/dzjy_yybph.html
|
|
:param symbol: choice of {'近一月', '近三月', '近六月', '近一年'}
|
|
:type symbol: str
|
|
:return: 营业部排行
|
|
:rtype: pandas.DataFrame
|
|
"""
|
|
period_map = {
|
|
"近一月": "30",
|
|
"近三月": "90",
|
|
"近六月": "180",
|
|
"近一年": "360",
|
|
}
|
|
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
|
params = {
|
|
"sortColumns": "D5_BUYER_NUM,D1_AVERAGE_INCREASE",
|
|
"sortTypes": "-1,-1",
|
|
"pageSize": "5000",
|
|
"pageNumber": "1",
|
|
"reportName": "RPT_BLOCKTRADE_OPERATEDEPT_RANK",
|
|
"columns": "OPERATEDEPT_CODE,OPERATEDEPT_NAME,D1_BUYER_NUM,D1_AVERAGE_INCREASE,"
|
|
"D1_RISE_PROBABILITY,D5_BUYER_NUM,D5_AVERAGE_INCREASE,D5_RISE_PROBABILITY,"
|
|
"D10_BUYER_NUM,D10_AVERAGE_INCREASE,D10_RISE_PROBABILITY,D20_BUYER_NUM,"
|
|
"D20_AVERAGE_INCREASE,D20_RISE_PROBABILITY,N_DATE,RELATED_ORG_CODE",
|
|
"source": "WEB",
|
|
"client": "WEB",
|
|
"filter": f"(N_DATE=-{period_map[symbol]})",
|
|
}
|
|
r = requests.get(url, params=params)
|
|
data_json = r.json()
|
|
total_page = data_json["result"]["pages"]
|
|
big_df = pd.DataFrame()
|
|
for page in range(1, int(total_page) + 1):
|
|
params.update({"pageNumber": page})
|
|
r = requests.get(url, params=params)
|
|
data_json = r.json()
|
|
temp_df = pd.DataFrame(data_json["result"]["data"])
|
|
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
|
|
|
big_df.reset_index(inplace=True)
|
|
big_df["index"] = big_df.index + 1
|
|
big_df.columns = [
|
|
"序号",
|
|
"_",
|
|
"营业部名称",
|
|
"上榜后1天-买入次数",
|
|
"上榜后1天-平均涨幅",
|
|
"上榜后1天-上涨概率",
|
|
"上榜后5天-买入次数",
|
|
"上榜后5天-平均涨幅",
|
|
"上榜后5天-上涨概率",
|
|
"上榜后10天-买入次数",
|
|
"上榜后10天-平均涨幅",
|
|
"上榜后10天-上涨概率",
|
|
"上榜后20天-买入次数",
|
|
"上榜后20天-平均涨幅",
|
|
"上榜后20天-上涨概率",
|
|
"_",
|
|
"_",
|
|
]
|
|
big_df = big_df[
|
|
[
|
|
"序号",
|
|
"营业部名称",
|
|
"上榜后1天-买入次数",
|
|
"上榜后1天-平均涨幅",
|
|
"上榜后1天-上涨概率",
|
|
"上榜后5天-买入次数",
|
|
"上榜后5天-平均涨幅",
|
|
"上榜后5天-上涨概率",
|
|
"上榜后10天-买入次数",
|
|
"上榜后10天-平均涨幅",
|
|
"上榜后10天-上涨概率",
|
|
"上榜后20天-买入次数",
|
|
"上榜后20天-平均涨幅",
|
|
"上榜后20天-上涨概率",
|
|
]
|
|
]
|
|
big_df["上榜后1天-买入次数"] = pd.to_numeric(
|
|
big_df["上榜后1天-买入次数"], errors="coerce"
|
|
)
|
|
big_df["上榜后1天-平均涨幅"] = pd.to_numeric(
|
|
big_df["上榜后1天-平均涨幅"], errors="coerce"
|
|
)
|
|
big_df["上榜后1天-上涨概率"] = pd.to_numeric(
|
|
big_df["上榜后1天-上涨概率"], errors="coerce"
|
|
)
|
|
big_df["上榜后5天-买入次数"] = pd.to_numeric(
|
|
big_df["上榜后5天-买入次数"], errors="coerce"
|
|
)
|
|
big_df["上榜后5天-平均涨幅"] = pd.to_numeric(
|
|
big_df["上榜后5天-平均涨幅"], errors="coerce"
|
|
)
|
|
big_df["上榜后5天-上涨概率"] = pd.to_numeric(
|
|
big_df["上榜后5天-上涨概率"], errors="coerce"
|
|
)
|
|
big_df["上榜后10天-买入次数"] = pd.to_numeric(
|
|
big_df["上榜后10天-买入次数"], errors="coerce"
|
|
)
|
|
big_df["上榜后10天-平均涨幅"] = pd.to_numeric(
|
|
big_df["上榜后10天-平均涨幅"], errors="coerce"
|
|
)
|
|
big_df["上榜后10天-上涨概率"] = pd.to_numeric(
|
|
big_df["上榜后10天-上涨概率"], errors="coerce"
|
|
)
|
|
big_df["上榜后20天-买入次数"] = pd.to_numeric(
|
|
big_df["上榜后20天-买入次数"], errors="coerce"
|
|
)
|
|
big_df["上榜后20天-平均涨幅"] = pd.to_numeric(
|
|
big_df["上榜后20天-平均涨幅"], errors="coerce"
|
|
)
|
|
big_df["上榜后20天-上涨概率"] = pd.to_numeric(
|
|
big_df["上榜后20天-上涨概率"], errors="coerce"
|
|
)
|
|
return big_df
|
|
|
|
|
|
if __name__ == "__main__":
|
|
stock_dzjy_sctj_df = stock_dzjy_sctj()
|
|
print(stock_dzjy_sctj_df)
|
|
|
|
stock_dzjy_mrmx_df = stock_dzjy_mrmx(
|
|
symbol="债券", start_date="20220104", end_date="20220104"
|
|
)
|
|
print(stock_dzjy_mrmx_df)
|
|
|
|
stock_dzjy_mrtj_df = stock_dzjy_mrtj(start_date="20220105", end_date="20220105")
|
|
print(stock_dzjy_mrtj_df)
|
|
|
|
stock_dzjy_hygtj_df = stock_dzjy_hygtj(symbol="近三月")
|
|
print(stock_dzjy_hygtj_df)
|
|
|
|
stock_dzjy_hyyybtj_df = stock_dzjy_hyyybtj(symbol="近3日")
|
|
print(stock_dzjy_hyyybtj_df)
|
|
|
|
stock_dzjy_yybph_df = stock_dzjy_yybph(symbol="近三月")
|
|
print(stock_dzjy_yybph_df)
|