def get_adj_rate(code, fuquan_df):
frow = fuquan_df.head(1)
rt = td.get_realtime_quotes(code)
if rt is None:
return None
if ((float(rt['high']) == 0) & (float(rt['low']) == 0)):
preClose = float(rt['pre_close'])
else:
if du.is_holiday(du.today()):
preClose = float(rt['price'])
else:
if (du.get_hour() > 9) & (du.get_hour() < 18):
preClose = float(rt['pre_close'])
else:
preClose = float(rt['price'])
rate = float(frow['fqprice']) / preClose
return rate
def get_adj_rate(code,stock_list,fuquan_df):
frow = fuquan_df.head(1)
rt = stock_list[stock_list.code == code]
if rt.empty:
return None
if ((float(rt['high']) == 0) & (float(rt['low']) == 0)):
preClose = float(rt['settlement'])
else:
if du.is_holiday(du.today()):
preClose = float(rt['trade'])
else:
if (du.get_hour() > 9) & (du.get_hour() < 18):
preClose = float(rt['settlement'])
else:
preClose = float(rt['trade'])
rate = float(frow['fqprice']) / preClose
return rate
def get_adj_rate(code, stock_list, fuquan_df):
frow = fuquan_df.head(1)
rt = stock_list[stock_list.code == code]
if rt.empty:
return None
if ((float(rt['high']) == 0) & (float(rt['low']) == 0)):
preClose = float(rt['settlement'])
else:
if du.is_holiday(du.today()):
preClose = float(rt['trade'])
else:
if (du.get_hour() > 9) & (du.get_hour() < 18):
preClose = float(rt['settlement'])
else:
preClose = float(rt['trade'])
rate = float(frow['fqprice']) / preClose
return rate
def get_adj_rate(code,fuquan_df):
frow = fuquan_df.head(1)
rt = td.get_realtime_quotes(code)
if rt is None:
return None
if ((float(rt['high']) == 0) & (float(rt['low']) == 0)):
preClose = float(rt['pre_close'])
else:
if du.is_holiday(du.today()):
preClose = float(rt['price'])
else:
if (du.get_hour() > 9) & (du.get_hour() < 18):
preClose = float(rt['pre_close'])
else:
preClose = float(rt['price'])
rate = float(frow['fqprice']) / preClose
return rate
def get_today_ticks(code=None, retry_count=3, pause=0.001):
"""
获取当日分笔明细数据
Parameters
------
code:string
股票代码 e.g. 600848
retry_count : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
return
-------
DataFrame 当日所有股票交易数据(DataFrame)
属性:成交时间、成交价格、价格变动，成交手、成交金额(元)，买卖类型
"""
if code is None or len(code) != 6:
return None
symbol = _code_to_symbol(code)
date = du.today()
try:
request = Request(ct.TODAY_TICKS_PAGE_URL % (date, symbol))
data_str = urlopen(request, timeout=10).read()
data_str = data_str.decode('GBK')
data_str = data_str[1:-1]
data_str = eval(
data_str,
type('Dummy', (dict, ), dict(__getitem__=lambda s, n: n))())
data_str = json.dumps(data_str)
data_str = json.loads(data_str)
pages = len(data_str['detailPages'])
data = pd.DataFrame()
ct._write_head()
for pNo in range(1, pages):
data = data.append(_today_ticks(symbol, date, pNo, retry_count,
pause),
ignore_index=True)
except Exception as er:
print(str(er))
return data
def get_today_ticks(code=None, retry_count=3, pause=0.001):
"""
获取当日分笔明细数据
Parameters
------
code:string
股票代码 e.g. 600848
retry_count : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
return
-------
DataFrame 当日所有股票交易数据(DataFrame)
属性:成交时间、成交价格、价格变动，成交手、成交金额(元)，买卖类型
"""
if code is None or len(code)!=6 :
return None
symbol = _code_to_symbol(code)
date = du.today()
try:
request = Request(ct.TODAY_TICKS_PAGE_URL % ( date,symbol))
data_str = urlopen(request, timeout=10).read()
data_str = data_str.decode('GBK')
data_str = data_str[1:-1]
data_str = eval(data_str, type('Dummy', (dict,),
dict(__getitem__ = lambda s, n:n))())
data_str = json.dumps(data_str)
data_str = json.loads(data_str)
pages = len(data_str['detailPages'])
data = pd.DataFrame()
ct._write_head()
for pNo in range(1, pages):
data = data.append(_today_ticks(symbol, date, pNo,
retry_count, pause), ignore_index=True)
except Exception as er:
print(str(er))
return data
def get_h_data(code, start=None, end=None, autype='qfq',
index=False, retry_count=3, pause=0.001):
'''
获取历史复权数据
Parameters
------
code:string
股票代码 e.g. 600848
start:string
开始日期 format：YYYY-MM-DD 为空时取当前日期
end:string
结束日期 format：YYYY-MM-DD 为空时取去年今日
autype:string
复权类型，qfq-前复权 hfq-后复权 None-不复权，默认为qfq
retry_count : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
return
-------
DataFrame
date 交易日期 (index)
open 开盘价
high 最高价
close 收盘价
low 最低价
volume 成交量
amount 成交金额
factor 后复权因子
'''
start = du.today_last_year() if start is None else start
end = du.today() if end is None else end
qs = du.get_quarts(start, end)
qt = qs[0]
ct._write_head()
data = _parse_fq_data(_get_index_url(index, code, qt), index,
retry_count, pause)
if len(qs)>1:
for d in range(1, len(qs)):
qt = qs[d]
ct._write_console()
df = _parse_fq_data(_get_index_url(index, code, qt), index,
retry_count, pause)
data = data.append(df, ignore_index=True)
if len(data) == 0 or len(data[(data.date>=start)&(data.date<=end)]) == 0:
return None
data = data.drop_duplicates('date')
if index:
data = data[(data.date>=start) & (data.date<=end)]
data = data.set_index('date')
data = data.sort_index(ascending=False)
return data
if autype == 'hfq':
#data = data.drop('factor', axis=1)
data = data[(data.date>=start) & (data.date<=end)]
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label].map(ct.FORMAT)
data[label] = data[label].astype(float)
data = data.set_index('date')
data = data.sort_index(ascending = False)
return data
else:
if autype == 'qfq':
#data = data.drop('factor', axis=1)
df = _parase_fq_factor(code, start, end)
df = df.drop_duplicates('date')
df = df.sort('date', ascending=False)
frow = df.head(1)
rt = get_realtime_quotes(code)
if rt is None:
return None
if ((float(rt['high']) == 0) & (float(rt['low']) == 0)):
preClose = float(rt['pre_close'])
else:
if du.is_holiday(du.today()):
preClose = float(rt['price'])
else:
if (du.get_hour() > 9) & (du.get_hour() < 18):
preClose = float(rt['pre_close'])
else:
preClose = float(rt['price'])
rate = float(frow['factor']) / preClose
data = data[(data.date >= start) & (data.date <= end)]
for label in ['open', 'high', 'low', 'close']:
data[label] = data[label] / rate
data[label] = data[label].map(ct.FORMAT)
data[label] = data[label].astype(float)
data = data.set_index('date')
data = data.sort_index(ascending = False)
return data
else:
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label] / data['factor']
#data = data.drop('factor', axis=1)
data = data[(data.date>=start) & (data.date<=end)]
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label].map(ct.FORMAT)
data = data.set_index('date')
data = data.sort_index(ascending=False)
data = data.astype(float)
return data
def get_h_data(code,
start=None,
end=None,
autype='qfq',
index=False,
retry_count=3,
pause=0.001):
'''
获取历史复权数据
Parameters
------
code:string
股票代码 e.g. 600848
start:string
开始日期 format：YYYY-MM-DD 为空时取当前日期
end:string
结束日期 format：YYYY-MM-DD 为空时取去年今日
autype:string
复权类型，qfq-前复权 hfq-后复权 None-不复权，默认为qfq
retry_count : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
return
-------
DataFrame
date 交易日期 (index)
open 开盘价
high 最高价
close 收盘价
low 最低价
volume 成交量
amount 成交金额
factor 后复权因子
'''
start = du.today_last_year() if start is None else start
end = du.today() if end is None else end
qs = du.get_quarts(start, end)
qt = qs[0]
ct._write_head()
data = _parse_fq_data(_get_index_url(index, code, qt), index, retry_count,
pause)
if len(qs) > 1:
for d in range(1, len(qs)):
qt = qs[d]
ct._write_console()
df = _parse_fq_data(_get_index_url(index, code, qt), index,
retry_count, pause)
data = data.append(df, ignore_index=True)
if len(data) == 0 or len(
data[(data.date >= start) & (data.date <= end)]) == 0:
return None
data = data.drop_duplicates('date')
if index:
data = data[(data.date >= start) & (data.date <= end)]
data = data.set_index('date')
data = data.sort_index(ascending=False)
return data
if autype == 'hfq':
#data = data.drop('factor', axis=1)
data = data[(data.date >= start) & (data.date <= end)]
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label].map(ct.FORMAT)
data[label] = data[label].astype(float)
data = data.set_index('date')
data = data.sort_index(ascending=False)
return data
else:
if autype == 'qfq':
#data = data.drop('factor', axis=1)
df = _parase_fq_factor(code, start, end)
df = df.drop_duplicates('date')
df = df.sort('date', ascending=False)
frow = df.head(1)
rt = get_realtime_quotes(code)
if rt is None:
return None
if ((float(rt['high']) == 0) & (float(rt['low']) == 0)):
preClose = float(rt['pre_close'])
else:
if du.is_holiday(du.today()):
preClose = float(rt['price'])
else:
if (du.get_hour() > 9) & (du.get_hour() < 18):
preClose = float(rt['pre_close'])
else:
preClose = float(rt['price'])
rate = float(frow['factor']) / preClose
data = data[(data.date >= start) & (data.date <= end)]
for label in ['open', 'high', 'low', 'close']:
data[label] = data[label] / rate
data[label] = data[label].map(ct.FORMAT)
data[label] = data[label].astype(float)
data = data.set_index('date')
data = data.sort_index(ascending=False)
return data
else:
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label] / data['factor']
#data = data.drop('factor', axis=1)
data = data[(data.date >= start) & (data.date <= end)]
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label].map(ct.FORMAT)
data = data.set_index('date')
data = data.sort_index(ascending=False)
data = data.astype(float)
return data