def QA_fetch_get_future_day(code,
start_date,
end_date,
frequence='day',
ip=best_ip,
port=7727):
'期货数据 日线'
apix = TdxExHq_API()
start_date = str(start_date)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
global extension_market_info
extension_market_info = QA_fetch_get_future_list(
) if extension_market_info is None else extension_market_info
with apix.connect(ip['future'], port):
code_market = extension_market_info.query('code=="{}"'.format(code))
data = pd.concat([
apix.to_df(
apix.get_instrument_bars(_select_type(frequence),
int(code_market.market), str(code),
(int(lens / 700) - i) * 700, 700))
for i in range(int(lens / 700) + 1)
],
axis=0)
data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10]))).set_index('date', drop=False, inplace=False)
return data.drop(
['year', 'month', 'day', 'hour', 'minute', 'datetime'],
axis=1)[start_date:end_date].assign(
date=data['date'].apply(lambda x: str(x)[0:10]))
def QA_fetch_get_index_day(code,
start_date,
end_date,
frequence='day',
ip=best_ip["stock"],
port=7709):
'指数日线'
api = TdxHq_API()
if frequence in ['day', 'd', 'D', 'DAY', 'Day']:
frequence = 9
elif frequence in ['w', 'W', 'Week', 'week']:
frequence = 5
elif frequence in ['month', 'M', 'm', 'Month']:
frequence = 6
elif frequence in ['Q', 'Quarter', 'q']:
frequence = 10
elif frequence in ['y', 'Y', 'year', 'Year']:
frequence = 11
with api.connect(ip, port):
start_date = str(start_date)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
if str(code)[0] in ['5', '1']: # ETF
data = pd.concat([
api.to_df(
api.get_security_bars(
frequence,
1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code,
(int(lens / 800) - i) * 800, 800))
for i in range(int(lens / 800) + 1)
],
axis=0)
else:
data = pd.concat([
api.to_df(
api.get_index_bars(
frequence,
1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code,
(int(lens / 800) - i) * 800, 800))
for i in range(int(lens / 800) + 1)
],
axis=0)
data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.assign(code=code)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)[start_date:end_date]
return data.assign(date=data['date'].apply(lambda x: str(x)[0:10]))
def QA_fetch_get_future_min(code,
start,
end,
frequence='1min',
ip=best_ip,
port=7727):
'期货数据 分钟线'
apix = TdxExHq_API()
type_ = ''
start_date = str(start)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
global extension_market_info
extension_market_info = QA_fetch_get_future_list(
) if extension_market_info is None else extension_market_info
if str(frequence) in ['5', '5m', '5min', 'five']:
frequence, type_ = 0, '5min'
lens = 48 * lens
elif str(frequence) in ['1', '1m', '1min', 'one']:
frequence, type_ = 8, '1min'
lens = 240 * lens
elif str(frequence) in ['15', '15m', '15min', 'fifteen']:
frequence, type_ = 1, '15min'
lens = 16 * lens
elif str(frequence) in ['30', '30m', '30min', 'half']:
frequence, type_ = 2, '30min'
lens = 8 * lens
elif str(frequence) in ['60', '60m', '60min', '1h']:
frequence, type_ = 3, '60min'
lens = 4 * lens
if lens > 20800:
lens = 20800
with apix.connect(ip['future'], port):
code_market = extension_market_info.query('code=="{}"'.format(code))
data = pd.concat([
apix.to_df(
apix.get_instrument_bars(frequence, int(code_market.market),
str(code),
(int(lens / 700) - i) * 700, 700))
for i in range(int(lens / 700) + 1)
],
axis=0)
data = data\
.assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\
.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\
.assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\
.assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\
.assign(type=type_).set_index('datetime', drop=False, inplace=False)[start:end]
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))
def QA_fetch_get_stock_min(code,
start,
end,
frequence='1min',
ip=None,
port=7709):
api = TdxHq_API()
type_ = ''
start_date = str(start)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
if str(frequence) in ['5', '5m', '5min', 'five']:
frequence, type_ = 0, '5min'
lens = 48 * lens
elif str(frequence) in ['1', '1m', '1min', 'one']:
frequence, type_ = 8, '1min'
lens = 240 * lens
elif str(frequence) in ['15', '15m', '15min', 'fifteen']:
frequence, type_ = 1, '15min'
lens = 16 * lens
elif str(frequence) in ['30', '30m', '30min', 'half']:
frequence, type_ = 2, '30min'
lens = 8 * lens
elif str(frequence) in ['60', '60m', '60min', '1h']:
frequence, type_ = 3, '60min'
lens = 4 * lens
if lens > 20800:
lens = 20800
with api.connect(select_best_ip()['stock'], port):
data = pd.concat([
api.to_df(
api.get_security_bars(frequence, _select_market_code(
str(code)), str(code), (int(lens / 800) - i) * 800, 800))
for i in range(int(lens / 800) + 1)
],
axis=0)
data = data\
.assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\
.drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\
.assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\
.assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\
.assign(type=type_).set_index('datetime', drop=False, inplace=False)[start:end]
return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))
def QA_fetch_get_stock_day(code,
start_date,
end_date,
if_fq='00',
frequence='day',
ip=best_ip,
port=7709):
"""获取日线及以上级别的数据
Arguments:
code {str:6} -- code 是一个单独的code 6位长度的str
start_date {str:10} -- 10位长度的日期 比如'2017-01-01'
end_date {str:10} -- 10位长度的日期 比如'2018-01-01'
Keyword Arguments:
if_fq {str} -- '00'/'bfq' -- 不复权 '01'/'qfq' -- 前复权 '02'/'hfq' -- 后复权 '03'/'ddqfq' -- 定点前复权 '04'/'ddhfq' --定点后复权
frequency {str} -- day/week/month/quarter/year 也可以是简写 D/W/M/Q/Y
ip {str} -- [description] (default: best_ip['stock']) ip可以通过select_best_ip()函数重新获取
port {int} -- [description] (default: {7709})
Returns:
pd.DataFrame/None -- 返回的是dataframe,如果出错比如只获取了一天,而当天停牌,返回None
Exception:
如果出现网络问题/服务器拒绝, 会出现socket:time out 尝试再次获取/更换ip即可, 本函数不做处理
"""
api = TdxHq_API()
with api.connect(ip['stock'], port):
if frequence in ['day', 'd', 'D', 'DAY', 'Day']:
frequence = 9
elif frequence in ['w', 'W', 'Week', 'week']:
frequence = 5
elif frequence in ['month', 'M', 'm', 'Month']:
frequence = 6
elif frequence in ['quarter', 'Q', 'Quarter', 'q']:
frequence = 10
elif frequence in ['y', 'Y', 'year', 'Year']:
frequence = 11
start_date = str(start_date)[0:10]
today_ = datetime.date.today()
lens = QA_util_get_trade_gap(start_date, today_)
data = pd.concat([
api.to_df(
api.get_security_bars(frequence, _select_market_code(code),
code, (int(lens / 800) - i) * 800, 800))
for i in range(int(lens / 800) + 1)
],
axis=0)
# 这里的问题是: 如果只取了一天的股票,而当天停牌, 那么就直接返回None了
if len(data) < 1:
return None
data = data[data['open'] != 0]
if if_fq in ['00', 'bfq']:
data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10]))).set_index('date', drop=False, inplace=False)
return data.drop(
['year', 'month', 'day', 'hour', 'minute', 'datetime'],
axis=1)[start_date:end_date].assign(
date=data['date'].apply(lambda x: str(x)[0:10]))
elif if_fq in ['01', 'qfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
bfq_data = data.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: str(x[0:10])))).assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10]))).set_index('date', drop=False, inplace=False)
bfq_data = bfq_data.drop(
['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)
#
if xdxr_data is not None:
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data['if_trade'] = True
data = pd.concat(
[bfq_data, info[['category']][bfq_data.index[0]:]], axis=1)
data['date'] = data.index
data['if_trade'].fillna(value=False, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat([
data, info[['fenhong', 'peigu', 'peigujia', 'songzhuangu'
]][bfq_data.index[0]:]
],
axis=1)
data = data.fillna(0)
data['preclose'] = (
data['close'].shift(1) * 10 - data['fenhong'] +
data['peigu'] * data['peigujia']) / (10 + data['peigu'] +
data['songzhuangu'])
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1)[::-1].cumprod()
data['open'] = data['open'] * data['adj']
data['high'] = data['high'] * data['adj']
data['low'] = data['low'] * data['adj']
data['close'] = data['close'] * data['adj']
data['preclose'] = data['preclose'] * data['adj']
data = data[data['if_trade']]
return data.drop(
[
'fenhong', 'peigu', 'peigujia', 'songzhuangu',
'if_trade', 'category'
],
axis=1)[data['open'] != 0].assign(date=data['date'].apply(
lambda x: str(x)[0:10]))[start_date:end_date]
else:
bfq_data['preclose'] = bfq_data['close'].shift(1)
bfq_data['adj'] = 1
return bfq_data[start_date:end_date]
elif if_fq in ['03', 'ddqfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade'] = True
data = pd.concat(
[bfq_data, info[['category']][bfq_data.index[0]:end_date]],
axis=1)
data['date'] = data.index
data['if_trade'].fillna(value=False, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat([
data, info[['fenhong', 'peigu', 'peigujia', 'songzhuangu'
]][bfq_data.index[0]:end_date]
],
axis=1)
data = data.fillna(0)
data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] +
data['peigu'] * data['peigujia']) / (
10 + data['peigu'] + data['songzhuangu'])
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1)[::-1].cumprod()
data['open'] = data['open'] * data['adj']
data['high'] = data['high'] * data['adj']
data['low'] = data['low'] * data['adj']
data['close'] = data['close'] * data['adj']
data['preclose'] = data['preclose'] * data['adj']
data = data[data['if_trade']]
return data.drop(
[
'fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade',
'category'
],
axis=1)[data['open'] != 0].assign(date=data['date'].apply(
lambda x: str(x)[0:10]))[start_date:end_date]
elif if_fq in ['02', 'hfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade'] = True
data = pd.concat(
[bfq_data, info[['category']][bfq_data.index[0]:]], axis=1)
data['date'] = data.index
data['if_trade'].fillna(value=False, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat([
data, info[['fenhong', 'peigu', 'peigujia', 'songzhuangu'
]][bfq_data.index[0]:]
],
axis=1)
data = data.fillna(0)
data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] +
data['peigu'] * data['peigujia']) / (
10 + data['peigu'] + data['songzhuangu'])
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1).cumprod()
data['open'] = data['open'] / data['adj']
data['high'] = data['high'] / data['adj']
data['low'] = data['low'] / data['adj']
data['close'] = data['close'] / data['adj']
data['preclose'] = data['preclose'] / data['adj']
data = data[data['if_trade']]
return data.drop(
[
'fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade',
'category'
],
axis=1)[data['open'] != 0].assign(date=data['date'].apply(
lambda x: str(x)[0:10]))[start_date:end_date]
elif if_fq in ['04', 'ddhfq']:
xdxr_data = QA_fetch_get_stock_xdxr(code)
info = xdxr_data[xdxr_data['category'] == 1]
bfq_data = data\
.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\
.assign(code=str(code))\
.assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\
.set_index('date', drop=False, inplace=False)\
.drop(['year', 'month', 'day', 'hour',
'minute', 'datetime'], axis=1)
bfq_data['if_trade'] = True
data = pd.concat(
[bfq_data, info[['category']][bfq_data.index[0]:end_date]],
axis=1)
data['date'] = data.index
data['if_trade'].fillna(value=False, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat([
data, info[['fenhong', 'peigu', 'peigujia', 'songzhuangu'
]][bfq_data.index[0]:end_date]
],
axis=1)
data = data.fillna(0)
data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] +
data['peigu'] * data['peigujia']) / (
10 + data['peigu'] + data['songzhuangu'])
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1).cumprod()
data['open'] = data['open'] / data['adj']
data['high'] = data['high'] / data['adj']
data['low'] = data['low'] / data['adj']
data['close'] = data['close'] / data['adj']
data['preclose'] = data['preclose'] / data['adj']
data = data[data['if_trade']]
return data.drop(
[
'fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade',
'category'
],
axis=1)[data['open'] != 0].assign(date=data['date'].apply(
lambda x: str(x)[0:10]))[start_date:end_date]