import os
from WindPy import w
from kquant_data.xio.csv import read_datetime_dataframe
from kquant_data.api import all_instruments
from kquant_data.wind.tdays import read_tdays
from kquant_data.wind_resume.wsd import resume_download_daily_to_many_files, \
resume_download_financial_report_data_daily_latest
from kquant_data.config import __CONFIG_H5_STK_FACTOR_DIR__, __CONFIG_H5_STK_DIR__, __CONFIG_TDAYS_SSE_FILE__
if __name__ == '__main__':
w.start()
path = os.path.join(__CONFIG_H5_STK_DIR__, '1day', 'Symbol.csv')
Symbols = all_instruments(path)
wind_codes = list(Symbols['wind_code'])
trading_days = read_tdays(__CONFIG_TDAYS_SSE_FILE__)
# 读取ipo_date，提前做好准备
path = os.path.join(__CONFIG_H5_STK_FACTOR_DIR__, 'ipo_date.csv')
ipo_date = read_datetime_dataframe(path)
# 下载最新一天的数据
field = 'total_shares'
# 下载最新一天，并且合并历史
resume_download_financial_report_data_daily_latest(
w, wind_codes, trading_days, __CONFIG_H5_STK_FACTOR_DIR__, field)
resume_download_daily_to_many_files(w, trading_days, ipo_date,
__CONFIG_H5_STK_FACTOR_DIR__, field)
df = wind_code_2_InstrumentID(df_csv, 'wind_code')
df = df.apply(CZCE_3to4, axis=1)
df = df.set_index(['InstrumentID'])
return df
if __name__ == '__main__':
ipo_last_trade = load_ipo_last_trade_trading()
ipo_last_trade['ipo_date'] = ipo_last_trade['ipo_date'].apply(lambda x: yyyyMMdd_2_datetime(x))
ipo_last_trade['lasttrade_date'] = ipo_last_trade['lasttrade_date'].apply(lambda x: yyyyMMdd_2_datetime(x))
ipo_last_trade['ipo_date'] = pd.to_datetime(ipo_last_trade['ipo_date'])
ipo_last_trade['lasttrade_date'] = pd.to_datetime(ipo_last_trade['lasttrade_date'])
root_path = r'D:\DATA_FUT_HDF5\Data_Wind\60'
csv_path = os.path.join(root_path, 'first_last.csv')
first_last = read_datetime_dataframe(csv_path)
if first_last is None:
first_last = pd.DataFrame(columns=['first', 'last'])
date_table = ipo_last_trade.merge(first_last, how='inner', left_index=True, right_index=True)
tic()
df = get_first_last_min(root_path, date_table, first_last)
toc()
# 如何实现
root_path = r'D:\DATA_FUT_HDF5\Data_Wind\86400_Wind'
csv_path = os.path.join(root_path, 'first_last.csv')
first_last = read_datetime_dataframe(csv_path)
if first_last is None:
first_last = pd.DataFrame(columns=['first', 'last'])
from kquant_data.config import __CONFIG_H5_FUT_FACTOR_DIR__
from kquant_data.xio.csv import read_datetime_dataframe
from kquant_data.wind.tdays import read_tdays
from kquant_data.wind_resume.wsd import resume_download_daily_to_many_files, \
resume_download_financial_report_data_daily_latest
from kquant_data.config import __CONFIG_TDAYS_SHFE_FILE__
if __name__ == '__main__':
w.start()
trading_days = read_tdays(__CONFIG_TDAYS_SHFE_FILE__)
# 读取合约上市日期，提前做好准备
path = os.path.join(__CONFIG_H5_FUT_FACTOR_DIR__, 'contract_issuedate.csv')
contract_issuedate = read_datetime_dataframe(path)
wind_codes = list(contract_issuedate.columns)
# 下载最新一天的数据
field = 'trade_hiscode'
if True:
# 下载最新一天，并且合并历史
date_str = (datetime.today() + timedelta(days=0)).strftime('%Y-%m-%d')
resume_download_financial_report_data_daily_latest(
w, wind_codes, trading_days, __CONFIG_H5_FUT_FACTOR_DIR__, field,
date_str)
# 使用下一个交易日
new_trading_days = trading_days[date_str:]
# 也可选择全下
# 分钟数据是按天分割，还是放在一个文件中呢？
# 下载是从哪下到哪呢？
w.start()
# 注意，数据中有可能出现0
path_ipo_last_trade = os.path.join(__CONFIG_H5_FUT_SECTOR_DIR__, 'ipo_last_trade_trading.csv')
ipo_last_trade = pd.read_csv(path_ipo_last_trade)
ipo_last_trade['ipo_date'] = ipo_last_trade['ipo_date'].apply(lambda x: yyyyMMdd_2_datetime(x))
ipo_last_trade['lasttrade_date'] = ipo_last_trade['lasttrade_date'].apply(lambda x: yyyyMMdd_2_datetime(x))
root_path = r'D:\DATA_FUT\60'
path_first_last = os.path.join(root_path, 'first_last.csv')
first_last = read_datetime_dataframe(path_first_last)
date_str = (datetime.today() - timedelta(days=1)).strftime('%Y-%m-%d')
# 设置至少30天比较靠谱
start_str = (datetime.today() - timedelta(days=30)).strftime('%Y-%m-%d')
trading_days = read_tdays(__CONFIG_TDAYS_SHFE_FILE__)
trading_days_slice = trading_days[start_str:date_str]
wind_code_set = set()
dirpath = os.path.join(__CONFIG_H5_FUT_SECTOR_DIR__, '中金所全部品种')
for i in range(len(trading_days_slice)):
wind_code_set = download_constituent_min(w, dirpath, trading_days_slice['date'][i], ipo_last_trade, first_last,
wind_code_set, trading_days)