def get_sectorconstituent(index_code, index_name, target_date) -> pd.DataFrame:
"""
通过 wind 获取指数成分股及权重
:param index_code:
:param index_name:
:param target_date:
:return:
"""
target_date_str = date_2_str(target_date)
logger.info('获取 %s %s %s 板块信息', index_code, index_name, target_date)
sec_df = invoker.wset(
"indexconstituent",
"date=%s;windcode=%s" % (target_date_str, index_code))
if sec_df is not None and sec_df.shape[0] > 0:
# 发现部分情况下返回数据的日期与 target_date 日期不匹配
sec_df = sec_df[sec_df['date'].apply(
lambda x: str_2_date(x) == target_date)]
if sec_df is None or sec_df.shape[0] == 0:
return None
sec_df["index_code"] = index_code
sec_df["index_name"] = index_name
sec_df.rename(columns={
'date': 'trade_date',
'sec_name': 'stock_name',
'i_weight': 'weight',
},
inplace=True)
return sec_df
def get_cb_set(date_fetch):
date_fetch_str = date_fetch.strftime(STR_FORMAT_DATE)
data_df = invoker.wset("sectorconstituent", "date=%s;sectorid=1000021892000000" % date_fetch_str)
if data_df is None:
logging.warning('%s 获取股票代码失败', date_fetch_str)
return None
data_count = data_df.shape[0]
logging.info('get %d convertible bond on %s', data_count, date_fetch_str)
return set(data_df['wind_code'])
def get_stock_code_set(date_fetch):
"""
:param date_fetch:
:return:
"""
date_fetch_str = date_fetch.strftime(STR_FORMAT_DATE)
stock_df = invoker.wset("sectorconstituent", "date=%s;sectorid=a002010100000000" % date_fetch_str) # 全部港股
if stock_df is None:
logging.warning('%s 获取股票代码失败', date_fetch_str)
return None
stock_count = stock_df.shape[0]
logging.info('get %d wind_code on %s', stock_count, date_fetch_str)
return set(stock_df['wind_code'])
def get_wind_code_set(date_fetch):
date_fetch_str = date_fetch.strftime(STR_FORMAT_DATE)
# 纯股票型基金 混合型
sectorid_list = ['2001010101000000', '2001010200000000']
for sector_id in sectorid_list:
data_df = invoker.wset("sectorconstituent", "date=%s;sectorid=%s" % (date_fetch_str, sector_id))
if data_df is None:
logging.warning('%s 获取股票代码失败', date_fetch_str)
return None
data_count = data_df.shape[0]
logging.info('get %d public offering fund on %s', data_count, date_fetch_str)
wind_code_s = data_df['wind_code']
wind_code_s = wind_code_s[wind_code_s.apply(lambda x: x.find('!') == -1)]
return set(wind_code_s)
def get_sectorconstituent(sector_code, sector_name, target_date) -> pd.DataFrame:
"""
通过 wind 获取板块成分股
:param sector_code:
:param sector_name:
:param target_date:
:return:
"""
target_date_str = date_2_str(target_date)
logger.info('获取 %s %s %s 板块信息', sector_code, sector_name, target_date)
sec_df = invoker.wset("sectorconstituent", "date=%s;sectorid=%s" % (target_date_str, sector_code))
sec_df["sector_code"] = sector_code
sec_df["sector_name"] = sector_name
sec_df.rename(columns={
'date': 'trade_date',
'sec_name': 'stock_name',
}, inplace=True)
return sec_df
def import_smfund_info(chain_param=None):
"""
:param chain_param: 在celery 中將前面結果做爲參數傳給後面的任務
:return:
"""
table_name = "wind_smfund_info"
has_table = engine_md.has_table(table_name)
# w.start()
types = {
u'主动股票型分级母基金': 1000007766000000,
u'被动股票型分级母基金': 1000007767000000,
u'纯债券型分级母基金': 1000007768000000,
u'混合债券型分级母基金': 1000007769000000,
u'混合型分级母基金': 1000026143000000,
u'QDII分级母基金': 1000019779000000
}
col_name_param_list = [
('wind_code', String(20)),
('fund_type', String(20)),
('sec_name', String(50)),
('class_a_code', String(20)),
('class_a_name', String(50)),
('class_b_code', String(20)),
('class_b_name', String(50)),
('track_indexcode', String(20)),
('track_indexname', String(50)),
('a_pct', DOUBLE),
('b_pct', DOUBLE),
('upcv_nav', DOUBLE),
('downcv_nav', DOUBLE),
('max_purchasefee', DOUBLE),
('max_redemptionfee', DOUBLE),
]
col_name = ",".join([col_name for col_name, _ in col_name_param_list])
# 获取各个历史时段的分级基金列表，并汇总全部基金代码
dates = [
'2011-01-01', '2013-01-01', '2015-01-01', '2017-01-01', '2018-01-01'
] # 分三个时间点获取市场上所有分级基金产品
df = pd.DataFrame()
# 获取接数据
for date_p in dates:
temp_df = invoker.wset("sectorconstituent",
"date=%s;sectorid=1000006545000000" % date_p)
df = df.append(temp_df)
wind_code_all = df['wind_code'].unique()
# 设置dtype
dtype = {key: val for key, val in col_name_param_list}
dtype['wind_code'] = String(20)
dtype["tradable"] = String(20)
dtype["fund_setupdate"] = Date
dtype["fund_maturitydate"] = Date
if has_table:
with with_db_session(engine_md) as session:
table = session.execute("SELECT wind_code FROM wind_smfund_info")
wind_code_existed = set(
[content[0] for content in table.fetchall()])
wind_code_new = list(set(wind_code_all) - wind_code_existed)
else:
wind_code_new = list(set(wind_code_all))
# if len(wind_code_new) == 0:
# print('no sm fund imported')
# 查询数据库，剔除已存在的基金代码
wind_code_new = [code for code in wind_code_new if code.find('!') < 0]
info_df = invoker.wss(wind_code_new, 'fund_setupdate, fund_maturitydate')
if info_df is None:
raise Exception('no data')
info_df['FUND_SETUPDATE'] = info_df['FUND_SETUPDATE'].apply(
lambda x: str_2_date(x))
info_df['FUND_MATURITYDATE'] = info_df['FUND_MATURITYDATE'].apply(
lambda x: str_2_date(x))
info_df.rename(columns={
'FUND_SETUPDATE': 'fund_setupdate',
'FUND_MATURITYDATE': 'fund_maturitydate'
},
inplace=True)
field = col_name
# field = "fund_type,wind_code,sec_name,class_a_code,class_a_name,class_b_code,class_b_name,a_pct,b_pct,upcv_nav,downcv_nav,track_indexcode,track_indexname,max_purchasefee,max_redemptionfee"
for code in info_df.index:
beginDate = info_df.loc[code, 'fund_setupdate'].strftime('%Y-%m-%d')
temp_df = invoker.wset("leveragedfundinfo",
"date=%s;windcode=%s;field=%s" %
(beginDate, code, field)) # ;field=%s , field
df = df.append(temp_df)
if DEBUG and len(df) > 10:
break
df.set_index('wind_code', inplace=True)
df['tradable'] = df.index.map(lambda x: x if 'S' in x else None)
# df.index = df.index.map(lambda x: x[:-2] + 'OF')
info_df = info_df.join(df, how='outer')
# TODO: 需要检查一下代码
info_df.rename(
columns={
'a_nav': 'nav_a',
'b_nav': 'nav_b',
'a_fs_inc': 'fs_inc_a',
'b_fs_inc': 'fs_inc_b'
})
info_df.index.rename('wind_code', inplace=True)
info_df.reset_index(inplace=True)
bunch_insert_on_duplicate_update(info_df,
table_name,
engine_md,
dtype=dtype)
logging.info("更新 %s 完成 存量数据 %d 条", table_name, len(info_df))
if not has_table and engine_md.has_table(table_name):
alter_table_2_myisam(engine_md, [table_name])
build_primary_key([table_name])
# 更新 code_mapping 表
update_from_info_table(table_name)
def import_smfund_daily(chain_param=None):
"""
:param chain_param: 在celery 中將前面結果做爲參數傳給後面的任務
:return:
"""
table_name = "wind_smfund_daily"
has_table = engine_md.has_table(table_name)
col_name_param_list = [
('next_pcvdate', Date),
('a_nav', DOUBLE),
('b_nav', DOUBLE),
('a_fs_inc', DOUBLE),
('b_fs_inc', DOUBLE),
('cur_interest', DOUBLE),
('next_interest', DOUBLE),
('ptm_year', DOUBLE),
('anal_pricelever', DOUBLE),
('anal_navlevel', DOUBLE),
('t1_premium', DOUBLE),
('t2_premium', DOUBLE),
('dq_status', String(50)),
('tm_type', TEXT),
('code_p', String(20)),
('trade_date', Date),
('open', DOUBLE),
('high', DOUBLE),
('low', DOUBLE),
('close', DOUBLE),
('volume', DOUBLE),
('amt', DOUBLE),
('pct_chg', DOUBLE),
('open_a', DOUBLE),
('high_a', DOUBLE),
('low_a', DOUBLE),
('close_a', DOUBLE),
('volume_a', DOUBLE),
('amt_a', DOUBLE),
('pct_chg_a', DOUBLE),
('open_b', DOUBLE),
('high_b', DOUBLE),
('low_b', DOUBLE),
('close_b', DOUBLE),
('volume_b', DOUBLE),
('amt_b', DOUBLE),
('pct_chg_b', DOUBLE),
]
# wset的调用参数
wind_indictor_str = ",".join([key for key, _ in col_name_param_list[:14]])
# 设置dtype类型
dtype = {key: val for key, val in col_name_param_list}
date_ending = date.today() - ONE_DAY if datetime.now(
).hour < BASE_LINE_HOUR else date.today()
date_ending_str = date_ending.strftime('%Y-%m-%d')
# 对于 表格是否存在进行判断，取值
if has_table:
sql_str = """
SELECT wind_code, ifnull(date, fund_setupdate) date_start, class_a_code, class_b_code
FROM wind_smfund_info fi LEFT OUTER JOIN
(SELECT code_p, adddate(max(trade_date), 1) trade_date_max FROM wind_smfund_daily GROUP BY code_p) smd
ON fi.wind_code = smd.code_p
WHERE fund_setupdate IS NOT NULL
AND class_a_code IS NOT NULL
AND class_b_code IS NOT NULL"""
else:
sql_str = """
SELECT wind_code, ifnull(date, fund_setupdate) date_start, class_a_code, class_b_code
FROM wind_smfund_info
WHERE fund_setupdate IS NOT NULL
AND class_a_code IS NOT NULL
AND class_b_code IS NOT NULL"""
df = pd.read_sql(sql_str, engine_md)
df.set_index('wind_code', inplace=True)
data_len = df.shape[0]
logger.info('分级基金数量: %d', data_len)
index_start = 1
# 获取data_from
for data_num, wind_code in enumerate(
df.index, start=1): # 可调整 # [100:min([df_count, 200])]
if data_num < index_start:
continue
logger.info('%d/%d) %s start to import', data_num, data_len, wind_code)
date_from = df.loc[wind_code, 'date_start']
date_from = str_2_date(date_from)
if type(date_from) not in (date, datetime, Timestamp):
logger.info('%d/%d) %s has no fund_setupdate will be ignored',
data_num, data_len, wind_code)
# print(df.iloc[i, :])
continue
date_from_str = date_from.strftime('%Y-%m-%d')
if date_from > date_ending:
logger.info('%d/%d) %s %s %s 跳过', data_num, data_len, wind_code,
date_from_str, date_ending_str)
continue
# 设置wsd接口参数
field = "open,high,low,close,volume,amt,pct_chg"
# wsd_cache(w, code, field, beginTime, today, "")
try:
df_p = invoker.wsd(wind_code, field, date_from_str,
date_ending_str, "")
except APIError as exp:
logger.exception("%d/%d) %s 执行异常", data_num, data_len, wind_code)
if exp.ret_dic.setdefault('error_code', 0) in (
-40520007, # 没有可用数据
-40521009, # 数据解码失败。检查输入参数是否正确，如：日期参数注意大小月月末及短二月
):
continue
else:
break
if df_p is None:
continue
df_p.rename(columns=lambda x: x.swapcase(), inplace=True)
df_p['code_p'] = wind_code
code_a = df.loc[wind_code, 'class_a_code']
if code_a is None:
print('%d %s has no code_a will be ignored' %
(data_num, wind_code))
# print(df.iloc[i, :])
continue
# 获得数据存储到df_a里面
# df_a = wsd_cache(w, code_a, field, beginTime, today, "")
df_a = invoker.wsd(code_a, field, date_from_str, date_ending_str, "")
df_a.rename(columns=lambda x: x.swapcase() + '_a', inplace=True)
code_b = df.loc[wind_code, 'class_b_code']
# df_b = wsd_cache(w, code_b, field, beginTime, today, "")
# 获取接口数据 获得df_b
df_b = invoker.wsd(code_b, field, date_from_str, date_ending_str, "")
df_b.columns = df_b.columns.map(lambda x: x.swapcase() + '_b')
new_df = pd.DataFrame()
for date_str in df_p.index:
# time = date_str.date().strftime('%Y-%m-%d')
field = "date=%s;windcode=%s;field=%s" % (date_str, wind_code,
wind_indictor_str)
# wset_cache(w, "leveragedfundinfo", field)
temp = invoker.wset("leveragedfundinfo", field)
temp['date'] = date_str
new_df = new_df.append(temp)
if DEBUG and len(new_df) > 8:
break
# 将获取信息进行表格联立 合并
new_df['next_pcvdate'] = new_df['next_pcvdate'].map(
lambda x: str_2_date(x) if x is not None else x)
new_df.set_index('date', inplace=True)
one_df = pd.concat([df_p, df_a, df_b, new_df], axis=1)
one_df.index.rename('trade_date', inplace=True)
one_df.reset_index(inplace=True)
# one_df['date'] = one_df['date'].map(lambda x: x.date())
one_df.rename(columns={'date': 'trade_date'}, inplace=True)
# one_df.rename(columns={"index":'trade_date'},inplace=True)
# one_df.set_index(['code_p', 'trade_date'], inplace=True)
bunch_insert_on_duplicate_update(one_df,
table_name,
engine_md,
dtype=dtype)
logger.info('%d/%d) %s import success', data_num, data_len, wind_code)
if not has_table and engine_md.has_table(table_name):
alter_table_2_myisam(engine_md, [table_name])
# build_primary_key([table_name])
# 手动创建主键， 主键不是wind_code
create_pk_str = """ALTER TABLE {table_name}
CHANGE COLUMN `code_p` `code_p` VARCHAR(20) NOT NULL ,
CHANGE COLUMN `trade_date` `trade_date` DATE NOT NULL ,
ADD PRIMARY KEY (`code_p`, `trade_date`)""".format(
table_name=table_name)
with with_db_session(engine_md) as session:
session.execute(create_pk_str)
def import_future_info(chain_param=None):
"""
更新期货合约列表信息
:param chain_param: 在celery 中將前面結果做爲參數傳給後面的任務
:return:
"""
table_name = "wind_future_info"
has_table = engine_md.has_table(table_name)
logger.info("更新 %s 开始", table_name)
# 获取已存在合约列表
if has_table:
sql_str = 'select wind_code, ipo_date from {table_name}'.format(
table_name=table_name)
with with_db_session(engine_md) as session:
table = session.execute(sql_str)
wind_code_ipo_date_dic = dict(table.fetchall())
else:
wind_code_ipo_date_dic = {}
# 通过wind获取合约列表
# w.start()
# 初始化服务器接口，用于下载万得数据
future_sectorid_dic_list = [
#中金所期货合约
# {'subject_name': 'CFE 沪深300', 'regex': r"IF\d{4}\.CFE",
# 'sectorid': 'a599010102000000', 'date_establish': '2010-4-16'},
# {'subject_name': 'CFE 上证50', 'regex': r"IH\d{4}\.CFE",
# 'sectorid': '1000014871000000', 'date_establish': '2015-4-16'},
# {'subject_name': 'CFE 中证500', 'regex': r"IC\d{4}\.CFE",
# 'sectorid': '1000014872000000', 'date_establish': '2015-4-16'},
# {'subject_name': '2年期国债', 'regex': r"TS\d{4}\.CFE",
# 'sectorid': '1000014880000000', 'date_establish': '2018-08-17'},
# {'subject_name': '5年期国债', 'regex': r"TF\d{4}\.CFE",
# 'sectorid': '1000010299000000', 'date_establish': '2013-09-06'},
# {'subject_name': '10年期国债', 'regex': r"T\d{4}\.CFE",
# 'sectorid': '1000014874000000', 'date_establish': '2015-03-20'},
#中金所指数
{
'subject_name': 'CFE 沪深300',
'regex': r"IF\.CFE",
'sectorid': 'a599010102000000',
'date_establish': '2010-4-16'
},
{
'subject_name': 'CFE 上证50',
'regex': r"IH\.CFE",
'sectorid': '1000014871000000',
'date_establish': '2015-4-16'
},
{
'subject_name': 'CFE 中证500',
'regex': r"IC\.CFE",
'sectorid': '1000014872000000',
'date_establish': '2015-4-16'
},
{
'subject_name': '2年期国债',
'regex': r"TS\.CFE",
'sectorid': '1000014880000000',
'date_establish': '2018-08-17'
},
{
'subject_name': '5年期国债',
'regex': r"TF\.CFE",
'sectorid': '1000010299000000',
'date_establish': '2013-09-06'
},
{
'subject_name': '10年期国债',
'regex': r"T\.CFE",
'sectorid': '1000014874000000',
'date_establish': '2015-03-20'
},
# 大连商品交易所合约
# {'subject_name': 'DCE 焦炭', 'regex': r"J\d{4}\.DCE",
# 'sectorid': '1000002976000000', 'date_establish': '2011-04-15'},
# {'subject_name': 'DCE 焦煤', 'regex': r"JM\d{4}\.DCE",
# 'sectorid': '1000009338000000', 'date_establish': '2013-03-22'},
# {'subject_name': '铁矿石', 'regex': r"I\d{4}\.DCE",
# 'sectorid': '1000006502000000', 'date_establish': '2013-10-18'},
# {'subject_name': '豆粕', 'regex': r"M\d{4}\.DCE",
# 'sectorid': 'a599010304000000', 'date_establish': '2000-07-17'},
# {'subject_name': '豆油', 'regex': r"Y\d{4}\.DCE",
# 'sectorid': 'a599010306000000', 'date_establish': '2006-01-09'},
# {'subject_name': '棕榈油', 'regex': r"P\d{4}\.DCE",
# 'sectorid': 'a599010307000000', 'date_establish': '2007-10-29'},
# {'subject_name': '豆一', 'regex': r"A\d{4}\.DCE",
# 'sectorid': 'a599010302000000', 'date_establish': '2004-07-15'},
# {'subject_name': '豆二', 'regex': r"B\d{4}\.DCE",
# 'sectorid': 'a599010303000000', 'date_establish': '2004-12-22'},
# {'subject_name': '玉米', 'regex': r"C\d{4}\.DCE",
# 'sectorid': 'a599010305000000', 'date_establish': '2004-09-22'},
# {'subject_name': '玉米淀粉', 'regex': r"CS\d{4}\.DCE",
# 'sectorid': '1000011469000000', 'date_establish': '2014-12-19'},
# {'subject_name': '鸡蛋', 'regex': r"JD\d{4}\.DCE",
# 'sectorid': '1000011464000000', 'date_establish': '2013-11-08'},
# {'subject_name': '线型低密度聚乙烯', 'regex': r"L\d{4}\.DCE",
# 'sectorid': 'a599010308000000', 'date_establish': '2007-07-31'},
# {'subject_name': '聚氯乙烯', 'regex': r"V\d{4}\.DCE",
# 'sectorid': 'a599010309000000', 'date_establish': '2009-05-25'},
# {'subject_name': '聚丙烯', 'regex': r"PP\d{4}\.DCE",
# 'sectorid': '1000011468000000', 'date_establish': '2014-02-28'},
#大连指数
{
'subject_name': 'DCE 焦炭',
'regex': r"J\.DCE",
'sectorid': '1000002976000000',
'date_establish': '2011-04-15'
},
{
'subject_name': 'DCE 焦煤',
'regex': r"JM\.DCE",
'sectorid': '1000009338000000',
'date_establish': '2013-03-22'
},
{
'subject_name': '铁矿石',
'regex': r"I\.DCE",
'sectorid': '1000006502000000',
'date_establish': '2013-10-18'
},
{
'subject_name': '豆粕',
'regex': r"M\.DCE",
'sectorid': 'a599010304000000',
'date_establish': '2000-07-17'
},
{
'subject_name': '豆油',
'regex': r"Y\.DCE",
'sectorid': 'a599010306000000',
'date_establish': '2006-01-09'
},
{
'subject_name': '棕榈油',
'regex': r"P\.DCE",
'sectorid': 'a599010307000000',
'date_establish': '2007-10-29'
},
{
'subject_name': '豆一',
'regex': r"A\.DCE",
'sectorid': 'a599010302000000',
'date_establish': '2004-07-15'
},
{
'subject_name': '豆二',
'regex': r"B\.DCE",
'sectorid': 'a599010303000000',
'date_establish': '2004-12-22'
},
{
'subject_name': '玉米',
'regex': r"C\.DCE",
'sectorid': 'a599010305000000',
'date_establish': '2004-09-22'
},
{
'subject_name': '玉米淀粉',
'regex': r"CS\.DCE",
'sectorid': '1000011469000000',
'date_establish': '2014-12-19'
},
{
'subject_name': '鸡蛋',
'regex': r"JD\.DCE",
'sectorid': '1000011464000000',
'date_establish': '2013-11-08'
},
{
'subject_name': '线型低密度聚乙烯',
'regex': r"L\.DCE",
'sectorid': 'a599010308000000',
'date_establish': '2007-07-31'
},
{
'subject_name': '聚氯乙烯',
'regex': r"V\.DCE",
'sectorid': 'a599010309000000',
'date_establish': '2009-05-25'
},
{
'subject_name': '聚丙烯',
'regex': r"PP\.DCE",
'sectorid': '1000011468000000',
'date_establish': '2014-02-28'
},
#上海期货交易所合约
# {'subject_name': '天然橡胶', 'regex': r"RU\d{4}\.SHF",
# 'sectorid': 'a599010208000000', 'date_establish': '1995-06-01'},
# {'subject_name': '铜', 'regex': r"CU\d{4}\.SHF",
# 'sectorid': 'a599010202000000', 'date_establish': '1995-05-01'},
# {'subject_name': '铝', 'regex': r"AL\d{4}\.SHF",
# 'sectorid': 'a599010203000000', 'date_establish': '1995-05-01'},
# {'subject_name': '锌', 'regex': r"ZN\d{4}\.SHF",
# 'sectorid': 'a599010204000000', 'date_establish': '2007-03-26'},
# {'subject_name': '铅', 'regex': r"PB\d{4}\.SHF",
# 'sectorid': '1000002892000000', 'date_establish': '2011-03-24'},
# {'subject_name': '镍', 'regex': r"NI\d{4}\.SHF",
# 'sectorid': '1000011457000000', 'date_establish': '2015-03-27'},
# {'subject_name': '锡', 'regex': r"SN\d{4}\.SHF",
# 'sectorid': '1000011458000000', 'date_establish': '2015-03-27'},
# {'subject_name': 'SHFE 黄金', 'regex': r"AU\d{4}\.SHF",
# 'sectorid': 'a599010205000000', 'date_establish': '2008-01-09'},
# {'subject_name': 'SHFE 沪银', 'regex': r"AG\d{4}\.SHF",
# 'sectorid': '1000006502000000', 'date_establish': '2012-05-10'},
# {'subject_name': 'SHFE 螺纹钢', 'regex': r"RB\d{4}\.SHF",
# 'sectorid': 'a599010206000000', 'date_establish': '2009-03-27'},
# {'subject_name': 'SHFE 热卷', 'regex': r"HC\d{4}\.SHF",
# 'sectorid': '1000011455000000', 'date_establish': '2014-03-21'},
# {'subject_name': 'SHFE 沥青', 'regex': r"BU\d{4}\.SHF",
# 'sectorid': '1000011013000000', 'date_establish': '2013-10-09'},
# {'subject_name': '原油', 'regex': r"SC\d{4}\.SHF",
# 'sectorid': '1000011463000000', 'date_establish': '2018-03-26'},
#上海期货交易所指数
{
'subject_name': '天然橡胶',
'regex': r"RU\.SHF",
'sectorid': 'a599010208000000',
'date_establish': '1995-06-01'
},
{
'subject_name': '铜',
'regex': r"CU\.SHF",
'sectorid': 'a599010202000000',
'date_establish': '1995-05-01'
},
{
'subject_name': '铝',
'regex': r"AL\.SHF",
'sectorid': 'a599010203000000',
'date_establish': '1995-05-01'
},
{
'subject_name': '锌',
'regex': r"ZN\.SHF",
'sectorid': 'a599010204000000',
'date_establish': '2007-03-26'
},
{
'subject_name': '铅',
'regex': r"PB\.SHF",
'sectorid': '1000002892000000',
'date_establish': '2011-03-24'
},
{
'subject_name': '镍',
'regex': r"NI\.SHF",
'sectorid': '1000011457000000',
'date_establish': '2015-03-27'
},
{
'subject_name': '锡',
'regex': r"SN\.SHF",
'sectorid': '1000011458000000',
'date_establish': '2015-03-27'
},
{
'subject_name': 'SHFE 黄金',
'regex': r"AU\.SHF",
'sectorid': 'a599010205000000',
'date_establish': '2008-01-09'
},
{
'subject_name': 'SHFE 沪银',
'regex': r"AG\.SHF",
'sectorid': '1000006502000000',
'date_establish': '2012-05-10'
},
{
'subject_name': 'SHFE 螺纹钢',
'regex': r"RB\.SHF",
'sectorid': 'a599010206000000',
'date_establish': '2009-03-27'
},
{
'subject_name': 'SHFE 热卷',
'regex': r"HC\.SHF",
'sectorid': '1000011455000000',
'date_establish': '2014-03-21'
},
{
'subject_name': 'SHFE 沥青',
'regex': r"BU\\.SHF",
'sectorid': '1000011013000000',
'date_establish': '2013-10-09'
},
{
'subject_name': '原油',
'regex': r"SC\.SHF",
'sectorid': '1000011463000000',
'date_establish': '2018-03-26'
},
#郑商所合约
# {'subject_name': '白糖', 'regex': r"SR\d{3,4}\.CZC",
# 'sectorid': 'a599010405000000', 'date_establish': '2006-01-06'},
# {'subject_name': '棉花', 'regex': r"CF\d{3,4}\.CZC",
# 'sectorid': 'a599010404000000', 'date_establish': '2004-06-01'},
# {'subject_name': '动力煤', 'regex': r"(ZC|TC)\d{3,4}\.CZC",
# 'sectorid': '1000011012000000', 'date_establish': '2013-09-26'},
# {'subject_name': '玻璃', 'regex': r"FG\d{3,4}\.CZC",
# 'sectorid': '1000008549000000', 'date_establish': '2013-12-03'},
# {'subject_name': '精对苯二甲酸', 'regex': r"TA\d{3,4}\.CZC",
# 'sectorid': 'a599010407000000', 'date_establish': '2006-12-18'},
# {'subject_name': '甲醇', 'regex': r"(ME|MA)\d{3,4}\.CZC",
# 'sectorid': '1000005981000000', 'date_establish': '2011-10-28'},
# {'subject_name': '菜籽油', 'regex': r"OI\d{3,4}\.CZC",
# 'sectorid': 'a599010408000000', 'date_establish': '2007-06-08'},
# {'subject_name': '菜籽粕', 'regex': r"RM\d{3,4}\.CZC",
# 'sectorid': '1000008622000000', 'date_establish': '2012-12-28'},
#郑商所指数
{
'subject_name': '白糖',
'regex': r"SR\.CZC",
'sectorid': 'a599010405000000',
'date_establish': '2006-01-06'
},
{
'subject_name': '棉花',
'regex': r"CF\.CZC",
'sectorid': 'a599010404000000',
'date_establish': '2004-06-01'
},
{
'subject_name': '动力煤',
'regex': r"(ZC|TC)\.CZC",
'sectorid': '1000011012000000',
'date_establish': '2013-09-26'
},
{
'subject_name': '玻璃',
'regex': r"FG\.CZC",
'sectorid': '1000008549000000',
'date_establish': '2013-12-03'
},
{
'subject_name': '精对苯二甲酸',
'regex': r"TA\.CZC",
'sectorid': 'a599010407000000',
'date_establish': '2006-12-18'
},
{
'subject_name': '甲醇',
'regex': r"(ME|MA)\.CZC",
'sectorid': '1000005981000000',
'date_establish': '2011-10-28'
},
{
'subject_name': '菜籽油',
'regex': r"OI\.CZC",
'sectorid': 'a599010408000000',
'date_establish': '2007-06-08'
},
{
'subject_name': '菜籽粕',
'regex': r"RM\.CZC",
'sectorid': '1000008622000000',
'date_establish': '2012-12-28'
},
]
wind_code_set = set()
ndays_per_update = 60
# 获取接口参数以及参数列表
col_name_param_list = [
("ipo_date", Date),
("sec_name", String(50)),
("sec_englishname", String(200)),
("exch_eng", String(200)),
("lasttrade_date", Date),
("lastdelivery_date", Date),
("dlmonth", String(20)),
("lprice", DOUBLE),
("sccode", String(20)),
("margin", DOUBLE),
("punit", String(200)),
("changelt", DOUBLE),
("mfprice", DOUBLE),
("contractmultiplier", DOUBLE),
("ftmargins", String(100)),
("trade_code", String(200)),
]
wind_indictor_str = ",".join(col_name
for col_name, _ in col_name_param_list)
dtype = {key: val for key, val in col_name_param_list}
dtype['wind_code'] = String(20)
# 获取历史期货合约列表信息
logger.info("获取历史期货合约列表信息")
for future_sectorid_dic in future_sectorid_dic_list:
subject_name = future_sectorid_dic['subject_name']
sector_id = future_sectorid_dic['sectorid']
regex_str = future_sectorid_dic['regex']
date_establish = datetime.strptime(
future_sectorid_dic['date_establish'], STR_FORMAT_DATE).date()
date_since = get_date_since(wind_code_ipo_date_dic, regex_str,
date_establish)
date_yestoday = date.today() - timedelta(days=1)
logger.info("%s[%s] %s ~ %s", subject_name, sector_id, date_since,
date_yestoday)
while date_since <= date_yestoday:
date_since_str = date_since.strftime(STR_FORMAT_DATE)
future_info_df = invoker.wset(
"sectorconstituent",
"date=%s;sectorid=%s" % (date_since_str, sector_id))
data_count = 0 if future_info_df is None else future_info_df.shape[
0]
logger.info("subject_name=%s[%s] %s 返回 %d 条数据", subject_name,
sector_id, date_since_str, data_count)
if data_count > 0:
wind_code_set |= set(future_info_df['wind_code'])
if date_since >= date_yestoday:
break
else:
date_since += timedelta(days=ndays_per_update)
if date_since > date_yestoday:
date_since = date_yestoday
# 获取合约列表
wind_code_list = [
wc for wc in wind_code_set if wc not in wind_code_ipo_date_dic
]
# 获取合约基本信息
# w.wss("AU1706.SHF,AG1612.SHF,AU0806.SHF", "ipo_date,sec_name,sec_englishname,exch_eng,lasttrade_date,lastdelivery_date,dlmonth,lprice,sccode,margin,punit,changelt,mfprice,contractmultiplier,ftmargins,trade_code")
if len(wind_code_list) > 0:
logger.info("%d wind_code will be invoked by wss, wind_code_list=%s",
len(wind_code_list), wind_code_list)
future_info_df = invoker.wss(wind_code_list, wind_indictor_str)
future_info_df['MFPRICE'] = future_info_df['MFPRICE'].apply(
mfprice_2_num)
future_info_count = future_info_df.shape[0]
future_info_df.rename(
columns={c: str.lower(c)
for c in future_info_df.columns},
inplace=True)
future_info_df.index.rename('wind_code', inplace=True)
future_info_df.reset_index(inplace=True)
data_count = bunch_insert_on_duplicate_update(future_info_df,
table_name,
engine_md,
dtype=dtype)
logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
if not has_table and engine_md.has_table(table_name):
alter_table_2_myisam(engine_md, [table_name])
build_primary_key([table_name])
logger.info("更新 wind_future_info 结束 %d 条记录被更新", future_info_count)
update_from_info_table(table_name)
def import_private_fund_info(table_name, chain_param=None, get_df=False):
# 初始化服务器接口，用于下载万得数据
# table_name = 'fund_info'
has_table = engine_md.has_table(table_name)
types = {u'股票多头策略': 1000023122000000,
u'股票多空策略': 1000023123000000,
u'其他股票策略': 1000023124000000,
u'阿尔法策略': 1000023125000000,
u'其他市场中性策略': 1000023126000000,
u'事件驱动策略': 1000023113000000,
u'债券策略': 1000023114000000,
u'套利策略': 1000023115000000,
u'宏观策略': 1000023116000000,
u'管理期货': 1000023117000000,
u'组合基金策略': 1000023118000000,
u'货币市场策略': 1000023119000000,
u'多策略': 100002312000000,
u'其他策略': 1000023121000000}
df = pd.DataFrame()
today = date.today().strftime('%Y-%m-%d')
param_list = [
('FUND_SETUPDATE', Date),
('FUND_MATURITYDATE', Date),
('FUND_MGRCOMP', String(800)),
('FUND_EXISTINGYEAR', String(500)),
('FUND_PTMYEAR', String(30)),
('FUND_TYPE', String(20)),
('FUND_FUNDMANAGER', String(500)),
('SEC_NAME', String(2000)),
('STRATEGY_TYPE', String(200)),
('TRADE_DATE_LATEST', String(200)),
]
col_name_dic = {col_name.upper(): col_name.lower() for col_name, _ in param_list}
# 获取列表名
col_name_list = [col_name.lower() for col_name in col_name_dic.keys()]
param_str = ",".join(col_name_list[:8])
# 设置dtype类型
dtype = {key.lower(): val for key, val in param_list}
dtype['wind_code'] = String(20)
for i in types.keys():
temp = invoker.wset("sectorconstituent", "date=%s;sectorid=%s" % (today, str(types[i])))
temp['strategy_type'] = i
df = pd.concat([df, temp], axis=0)
if DEBUG and len(df) > 1000:
break
# 插入数据库
# 初始化数据库engine
# 整理数据
fund_types_df = df[['wind_code', 'sec_name', 'strategy_type']]
fund_types_df.set_index('wind_code', inplace=True)
# 获取基金基本面信息
code_list = list(fund_types_df.index) # df['wind_code']
code_count = len(code_list)
seg_count = 5000
info_df = pd.DataFrame()
for n in range(int(code_count / seg_count) + 1):
num_start = n * seg_count
num_end = (n + 1) * seg_count
num_end = num_end if num_end <= code_count else code_count
if num_start <= code_count:
codes = ','.join(code_list[num_start:num_end])
# 分段获取基金成立日期数据
info2_df = invoker.wss(codes, param_str)
logging.info('%05d ) [%d %d]' % (n, num_start, num_end))
info_df = info_df.append(info2_df)
if DEBUG and len(info_df) > 1000:
break
else:
break
# 整理数据插入数据库)
info_df['FUND_SETUPDATE'] = info_df['FUND_SETUPDATE'].apply(lambda x: str_2_date(x))
info_df['FUND_MATURITYDATE'] = info_df['FUND_MATURITYDATE'].apply(lambda x: str_2_date(x))
info_df = fund_types_df.join(info_df, how='right')
info_df.rename(columns=col_name_dic, inplace=True)
info_df['trade_date_latest'] = None
info_df.index.names = ['wind_code']
info_df.reset_index(inplace=True)
info_df.drop_duplicates(inplace=True)
bunch_insert_on_duplicate_update(info_df, table_name, engine_md, dtype=dtype)
logging.info('%d funds inserted' % len(info_df))
if not has_table and engine_md.has_table(table_name):
alter_table_2_myisam(engine_md, [table_name])
build_primary_key([table_name])
# 更新 code_mapping 表
update_from_info_table(table_name)
if get_df:
return info_df
def import_private_fund_info(chain_param=None):
"""
:param chain_param: 在celery 中將前面結果做爲參數傳給後面的任務
:return:
"""
# 更新 基金信息
table_name = 'wind_fund_info'
# 初始化服务器接口，用于下载万得数据
# 初始化数据库，并获取旧表信息
old = pd.read_sql_query('select wind_code from %s' % table_name, engine_md)
old_set = set(old['wind_code'])
# 从万得获取最新基金列表
types = {
# u'股票多头策略': 1000023122000000,
# u'股票多空策略': 1000023123000000,
u'其他股票策略': 1000023124000000,
# u'阿尔法策略': 1000023125000000,
u'其他市场中性策略': 1000023126000000,
# u'事件驱动策略': 1000023113000000,
# u'债券策略': 1000023114000000,
u'套利策略': 1000023115000000,
# u'宏观策略': 1000023116000000,
# u'管理期货策略': 1000023117000000,
# u'组合基金策略': 1000023118000000,
# u'货币市场策略': 1000023119000000,
# u'多策略': 100002312000000,
u'其他策略': 1000023121000000}
param_list = [
("sec_name", String(200)),
("fund_setupdate", Date),
("fund_maturitydate", Date),
("fund_mgrcomp", Date),
("fund_existingyear", Date),
("fund_ptmyear", Date),
("fund_type", Date),
("fund_fundmanager", Date),
]
wind_indictor_str = ",".join([key for key, _ in param_list])
dtype = {col_name: val for col_name, val in param_list}
dtype['wind_code'] = String(200)
df = pd.DataFrame()
yestday = (date.today() - timedelta(days=1)).strftime('%Y-%m-%d')
for i in types.keys():
temp = invoker.wset("sectorconstituent", "date=%s;sectorid=%s" % (yestday, str(types[i])))
temp['strategy_type'] = i
logging.info('%s sectorconstituent %s df.shape:%s', yestday, i, temp.shape)
df = pd.concat([df, temp], axis=0)
if DEBUG and len(df) > 1:
break
fund_types_df = df[['wind_code', 'strategy_type']] # , 'sec_name' 后续wss接口可以获得
new_set = set(fund_types_df['wind_code'])
target_set = new_set.difference(old_set) # in new_set but not old_set
# fund_types_df.set_index('wind_code', inplace=True)
# fund_info_df.index.names = ['wind_code']
# # 获取新成立基金各项基本面信息
fund_info_df = get_fund_info_df_by_wind(list(target_set), wind_indictor_str) # 115
fund_info_df = fund_types_df.join(fund_info_df, how='right')
return save_fund_info(fund_info_df, dtype)
def import_future_info(chain_param=None):
"""
更新期货合约列表信息
:param chain_param: 在celery 中將前面結果做爲參數傳給後面的任務
:return:
"""
table_name = "wind_future_info"
has_table = engine_md.has_table(table_name)
logger.info("更新 %s 开始", table_name)
# 获取已存在合约列表
if has_table:
sql_str = 'select wind_code, ipo_date from {table_name}'.format(
table_name=table_name)
with with_db_session(engine_md) as session:
table = session.execute(sql_str)
wind_code_ipo_date_dic = dict(table.fetchall())
else:
wind_code_ipo_date_dic = {}
# 通过wind获取合约列表
# w.start()
# 初始化服务器接口，用于下载万得数据
future_sectorid_dic_list = [
{
'subject_name': 'CFE 沪深300',
'regex': r"IF\d{4}\.CFE",
'sectorid': 'a599010102000000',
'date_establish': '2010-4-16'
},
{
'subject_name': 'CFE 上证50',
'regex': r"IH\d{4}\.CFE",
'sectorid': '1000014871000000',
'date_establish': '2015-4-16'
},
{
'subject_name': 'CFE 中证500',
'regex': r"IC\d{4}\.CFE",
'sectorid': '1000014872000000',
'date_establish': '2015-4-16'
},
{
'subject_name': 'SHFE 黄金',
'regex': r"AU\d{4}\.SHF",
'sectorid': 'a599010205000000',
'date_establish': '2008-01-09'
},
{
'subject_name': 'SHFE 沪银',
'regex': r"AG\d{4}\.SHF",
'sectorid': '1000006502000000',
'date_establish': '2012-05-10'
},
{
'subject_name': 'SHFE 螺纹钢',
'regex': r"RB\d{4}\.SHF",
'sectorid': 'a599010206000000',
'date_establish': '2009-03-27'
},
{
'subject_name': 'SHFE 热卷',
'regex': r"HC\d{4}\.SHF",
'sectorid': '1000011455000000',
'date_establish': '2014-03-21'
},
{
'subject_name': 'DCE 焦炭',
'regex': r"J\d{4}\.SHF",
'sectorid': '1000002976000000',
'date_establish': '2011-04-15'
},
{
'subject_name': 'DCE 焦煤',
'regex': r"JM\d{4}\.SHF",
'sectorid': '1000009338000000',
'date_establish': '2013-03-22'
},
{
'subject_name': '铁矿石',
'regex': r"I\d{4}\.SHF",
'sectorid': '1000006502000000',
'date_establish': '2013-10-18'
},
{
'subject_name': '天然橡胶',
'regex': r"RU\d{4}\.SHF",
'sectorid': 'a599010208000000',
'date_establish': '1995-06-01'
},
{
'subject_name': '铜',
'regex': r"CU\d{4}\.SHF",
'sectorid': 'a599010202000000',
'date_establish': '1995-05-01'
},
{
'subject_name': '铝',
'regex': r"AL\d{4}\.SHF",
'sectorid': 'a599010203000000',
'date_establish': '1995-05-01'
},
{
'subject_name': '锌',
'regex': r"ZN\d{4}\.SHF",
'sectorid': 'a599010204000000',
'date_establish': '2007-03-26'
},
{
'subject_name': '铅',
'regex': r"PB\d{4}\.SHF",
'sectorid': '1000002892000000',
'date_establish': '2011-03-24'
},
{
'subject_name': '镍',
'regex': r"NI\d{4}\.SHF",
'sectorid': '1000011457000000',
'date_establish': '2015-03-27'
},
{
'subject_name': '锡',
'regex': r"SN\d{4}\.SHF",
'sectorid': '1000011458000000',
'date_establish': '2015-03-27'
},
{
'subject_name': '白糖',
'regex': r"SR\d{4}\.CZC",
'sectorid': 'a599010405000000',
'date_establish': '2006-01-06'
},
{
'subject_name': '棉花',
'regex': r"CF\d{4}\.CZC",
'sectorid': 'a599010404000000',
'date_establish': '2004-06-01'
},
{
'subject_name': '棉花',
'regex': r"CF\d{4}\.CZC",
'sectorid': 'a599010404000000',
'date_establish': '2004-06-01'
},
]
wind_code_set = set()
ndays_per_update = 60
# 获取接口参数以及参数列表
col_name_param_list = [
("ipo_date", Date),
("sec_name", String(50)),
("sec_englishname", String(200)),
("exch_eng", String(200)),
("lasttrade_date", Date),
("lastdelivery_date", Date),
("dlmonth", String(20)),
("lprice", DOUBLE),
("sccode", String(20)),
("margin", DOUBLE),
("punit", String(200)),
("changelt", DOUBLE),
("mfprice", DOUBLE),
("contractmultiplier", DOUBLE),
("ftmargins", String(100)),
("trade_code", String(200)),
]
wind_indictor_str = ",".join(col_name
for col_name, _ in col_name_param_list)
dtype = {key: val for key, val in col_name_param_list}
dtype['wind_code'] = String(20)
# 获取历史期货合约列表信息
for future_sectorid_dic in future_sectorid_dic_list:
subject_name = future_sectorid_dic['subject_name']
sector_id = future_sectorid_dic['sectorid']
regex_str = future_sectorid_dic['regex']
date_establish = datetime.strptime(
future_sectorid_dic['date_establish'], STR_FORMAT_DATE).date()
date_since = get_date_since(wind_code_ipo_date_dic, regex_str,
date_establish)
date_yestoday = date.today() - timedelta(days=1)
while date_since <= date_yestoday:
date_since_str = date_since.strftime(STR_FORMAT_DATE)
future_info_df = invoker.wset(
"sectorconstituent",
"date=%s;sectorid=%s" % (date_since_str, sector_id))
wind_code_set |= set(future_info_df['wind_code'])
if date_since >= date_yestoday:
break
else:
date_since += timedelta(days=ndays_per_update)
if date_since > date_yestoday:
date_since = date_yestoday
# 获取合约列表
wind_code_list = [
wc for wc in wind_code_set if wc not in wind_code_ipo_date_dic
]
# 获取合约基本信息
# w.wss("AU1706.SHF,AG1612.SHF,AU0806.SHF", "ipo_date,sec_name,sec_englishname,exch_eng,lasttrade_date,lastdelivery_date,dlmonth,lprice,sccode,margin,punit,changelt,mfprice,contractmultiplier,ftmargins,trade_code")
if len(wind_code_list) > 0:
future_info_df = invoker.wss(wind_code_list, wind_indictor_str)
future_info_df['MFPRICE'] = future_info_df['MFPRICE'].apply(
mfprice_2_num)
future_info_count = future_info_df.shape[0]
future_info_df.rename(
columns={c: str.lower(c)
for c in future_info_df.columns},
inplace=True)
future_info_df.index.rename('wind_code', inplace=True)
future_info_df.reset_index(inplace=True)
data_count = bunch_insert_on_duplicate_update(future_info_df,
table_name,
engine_md,
dtype=dtype)
logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
if not has_table and engine_md.has_table(table_name):
alter_table_2_myisam(engine_md, [table_name])
build_primary_key([table_name])
logger.info("更新 wind_future_info_hk 结束 %d 条记录被更新", future_info_count)
update_from_info_table(table_name)
def wind_CS_sector_update(chain_param=None):
"""
:param chain_param: 在celery 中將前面結果做爲參數傳給後面的任務
:return:
"""
dic = {
u'CS石油石化': 'b101000000000000',
u'CS煤炭': 'b102000000000000',
u'CS有色金属': 'b103000000000000',
u'CS电力及公用事业': 'b104000000000000',
u'CS钢铁': 'b105000000000000',
u'CS基础化工': 'b106000000000000',
u'CS建筑': 'b107000000000000',
u'CS建材': 'b108000000000000',
u'CS轻工制造': 'b109000000000000',
u'CS机械': 'b10a000000000000',
u'CS电力设备': 'b10b000000000000',
u'CS国防军工': 'b10c000000000000',
u'CS汽车': 'b10d000000000000',
u'CS商贸零售': 'b10e000000000000',
u'CS餐饮旅游': 'b10f000000000000',
u'CS家电': 'b10g000000000000',
u'CS纺织服装': 'b10h000000000000',
u'CS医药': 'b10i000000000000',
u'CS食品饮料': 'b10j000000000000',
u'CS农林牧鱼': 'b10k000000000000',
u'CS银行': 'b10l000000000000',
u'CS证券II': 'b10m010000000000',
u'CS保险II': 'b10m020000000000',
u'CS信托及其他': 'b10m030000000000',
u'CS房地产': 'b10n000000000000',
u'CS交通运输': 'b10o000000000000',
u'CS电子元器件': 'b10p000000000000',
u'CS通信': 'b10q000000000000',
u'CS计算机': 'b10r000000000000',
u'CS传媒': 'b10s000000000000',
u'CS综合': 'b10t000000000000'
}
table_name = 'wind_CS_sector'
has_table = engine_md.has_table(table_name)
param_list = [
('sector', String(20)),
('date', Date),
]
dtype = {col_name: val for col_name, val in param_list}
dtype['wind_code'] = String(20)
info = pd.read_sql_query(
'select sector, max(date) as last_date from wind_CS_sector group by sector',
engine_md)
info.set_index('sector', inplace=True)
for sector in info.index:
begin_date = info.loc[sector, 'last_date'] + timedelta(days=1)
week_ends = invoker.tdays(beginTime=begin_date,
endTime=date.today(),
options='Period=W').Times ##
for week_end in week_ends:
for sector in dic.keys():
df = invoker.wset(
"sectorconstituent", "date=%s;sectorid=%s" %
(week_end.date().strftime('%Y-%m-%d'), dic[sector]))
if len(df) == 0:
continue
df['sector'] = sector
df.set_index(['sector', 'date', 'wind_code'], inplace=True)
bunch_insert_on_duplicate_update(df,
table_name,
engine_md,
dtype=dtype)
logging.info('Success import %s - %s' % (week_end, sector))
if not has_table and engine_md.has_table(table_name):
alter_table_2_myisam(engine_md, [table_name])
build_primary_key([table_name])
def import_future_info(chain_param=None):
"""
更新期货合约列表信息
:param chain_param: 在celery 中將前面結果做爲參數傳給後面的任務
:return:
"""
table_name = "wind_future_info"
has_table = engine_md.has_table(table_name)
logger.info("更新 %s 开始", table_name)
# 获取已存在合约列表
if has_table:
sql_str = 'select wind_code, ipo_date from {table_name}'.format(
table_name=table_name)
with with_db_session(engine_md) as session:
table = session.execute(sql_str)
wind_code_ipo_date_dic = dict(table.fetchall())
else:
wind_code_ipo_date_dic = {}
# 按交易所获取合约列表
# 上期所
# w.wset("sectorconstituent","date=1995-05-10;sectorid=a599010201000000")
# 金交所
# w.wset("sectorconstituent","date=2013-09-10;sectorid=a599010101000000")
# 大商所
# w.wset("sectorconstituent","date=1999-01-10;sectorid=a599010301000000")
# 郑商所
# w.wset("sectorconstituent","date=1999-01-10;sectorid=a599010401000000")
exchange_sectorid_dic_list = [
{
'exch_eng': 'SHFE',
'exchange_name': '上期所',
'sectorid': 'a599010201000000',
'date_establish': '1995-05-10'
},
{
'exch_eng': 'CFFEX',
'exchange_name': '金交所',
'sectorid': 'a599010101000000',
'date_establish': '2013-09-10'
},
{
'exch_eng': 'DCE',
'exchange_name': '大商所',
'sectorid': 'a599010301000000',
'date_establish': '1999-01-10'
},
{
'exch_eng': 'CZCE',
'exchange_name': '郑商所',
'sectorid': 'a599010401000000',
'date_establish': '1999-01-10'
},
]
exchange_latest_ipo_date_dic = get_exchange_latest_data()
wind_code_set = set()
ndays_per_update = 90
# 获取接口参数以及参数列表
col_name_param_list = [
("ipo_date", Date),
("sec_name", String(50)),
("sec_englishname", String(200)),
("exch_eng", String(200)),
("lasttrade_date", Date),
("lastdelivery_date", Date),
("dlmonth", String(20)),
("lprice", DOUBLE),
("sccode", String(20)),
("margin", DOUBLE),
("punit", String(200)),
("changelt", DOUBLE),
("mfprice", DOUBLE),
("contractmultiplier", DOUBLE),
("ftmargins", String(100)),
("trade_code", String(200)),
]
wind_indictor_str = ",".join(col_name
for col_name, _ in col_name_param_list)
dtype = {key: val for key, val in col_name_param_list}
dtype['wind_code'] = String(20)
# 获取历史期货合约列表信息
logger.info("获取历史期货合约列表信息")
for exchange_sectorid_dic in exchange_sectorid_dic_list:
exchange_name = exchange_sectorid_dic['exchange_name']
exch_eng = exchange_sectorid_dic['exch_eng']
sector_id = exchange_sectorid_dic['sectorid']
date_establish = exchange_sectorid_dic['date_establish']
date_since = str_2_date(
exchange_latest_ipo_date_dic.setdefault(exch_eng, date_establish))
date_yestoday = date.today() - timedelta(days=1)
logger.info("%s[%s][%s] %s ~ %s", exchange_name, exch_eng, sector_id,
date_since, date_yestoday)
while date_since <= date_yestoday:
date_since_str = date_since.strftime(STR_FORMAT_DATE)
future_info_df = invoker.wset(
"sectorconstituent",
"date=%s;sectorid=%s" % (date_since_str, sector_id))
data_count = 0 if future_info_df is None else future_info_df.shape[
0]
logger.info("subject_name=%s[%s] %s 返回 %d 条数据", exchange_name,
sector_id, date_since_str, data_count)
if data_count > 0:
wind_code_set |= set(future_info_df['wind_code'])
if date_since >= date_yestoday:
break
else:
date_since += timedelta(days=ndays_per_update)
if date_since > date_yestoday:
date_since = date_yestoday
# 获取合约列表
wind_code_list = [
wc for wc in wind_code_set if wc not in wind_code_ipo_date_dic
]
# 获取合约基本信息
# w.wss("AU1706.SHF,AG1612.SHF,AU0806.SHF", "ipo_date,sec_name,sec_englishname,exch_eng,lasttrade_date,lastdelivery_date,dlmonth,lprice,sccode,margin,punit,changelt,mfprice,contractmultiplier,ftmargins,trade_code")
if len(wind_code_list) > 0:
logger.info("%d wind_code will be invoked by wss, wind_code_list=%s",
len(wind_code_list), wind_code_list)
future_info_df = invoker.wss(wind_code_list, wind_indictor_str)
future_info_df['MFPRICE'] = future_info_df['MFPRICE'].apply(
mfprice_2_num)
future_info_count = future_info_df.shape[0]
future_info_df.rename(
columns={c: str.lower(c)
for c in future_info_df.columns},
inplace=True)
future_info_df.index.rename('wind_code', inplace=True)
future_info_df.reset_index(inplace=True)
data_count = bunch_insert_on_duplicate_update(future_info_df,
table_name,
engine_md,
dtype=dtype)
logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
if not has_table and engine_md.has_table(table_name):
# alter_table_2_myisam(engine_md, [table_name])
build_primary_key([table_name])
logger.info("更新 wind_future_info 结束 %d 条记录被更新", future_info_count)
update_from_info_table(table_name)
