def hist_line(pepbs, start=origin):
h = pd.DataFrame()
for date in pd.DatetimeIndex(jqdata.get_trade_days(start_date=start)):
h[date] = pd.Series(pos_hist(pepbs,
dt.datetime.strftime(date, '%Y-%m-%d')),
index=['pe', 'pb'])
return h.transpose()
def hist_data(code, start_date=None, end_date=None):
# 参数检查
today_str = (datetime.now().date() - timedelta(1)).strftime('%Y-%m-%d')
if start_date is None or start_date < _start_date:
start_date = _start_date
if end_date is None or end_date < _start_date or end_date > today_str:
end_date = today_str
# 交易日
trade_days = get_trade_days(start_date=start_date, end_date=end_date)
col = _codes[code].get('position')
name = _codes[code].get('name')
def data_list():
for day in trade_days:
datas = []
for page in [2, 3, 4]:
#链接
url = '%stype=zy%s&date=%s' % (_url_base, page, day)
#页面请求
content = requests.get(url).text
soup = BeautifulSoup(content, 'html.parser')
data = soup.select('tbody[class="tc"] td')
if len(data) == 0:
break
datas.append(data[col].get_text())
print '\r数据更新：板块%s %s %s ' % (code, name, day),
yield [day] + datas
# 组织数据（迭代数据生成器）
df = pd.DataFrame(data=[item for item in data_list()],
columns=['date', 'pe', 'pb', 'dyr'])
df.set_index('date', inplace=True)
df.index.name = None
df.index = pd.to_datetime(df.index, format='%Y-%m-%d')
return df.astype('float')
def pl_load_change_pct_data(context,pl_codes):
pl_change_pct_dict_list = []
# 计算涨跌幅需要用到前一日收盘价，所以需要多加载一天的数据，
# 计算前一个交易日收盘后的数据，所以需要再多加载一天
# 使用固定的25个交易日，而非25个bar计算涨跌幅
pl_count = PL_CHANGE_PCT_DAY_NUMBER + 1
# 获取25个交易日的日期
pl_pre_25_dates = jqdata.get_trade_days(start_date=None, end_date=context.current_dt, count=pl_count)
pl_pre_25_date = pl_pre_25_dates[0]
pl_pre_1_date = pl_pre_25_dates[-1]
for pl_code in pl_codes:
pl_pre_25_data = get_price(pl_code, start_date=None, end_date=pl_pre_25_date, frequency='daily', fields=['close'], skip_paused=True, fq='post', count=1)
pl_pre_1_data = get_price(pl_code, start_date=None, end_date=pl_pre_1_date, frequency='daily', fields=['close'], skip_paused=True, fq='post', count=1)
pl_pre_25_close = None
pl_pre_1_close = None
if str(pl_pre_25_date) == str(pl_pre_25_data.index[0])[:10]:
pl_pre_25_close = pl_pre_25_data['close'][0]
if str(pl_pre_1_date) == str(pl_pre_1_data.index[0])[:10]:
pl_pre_1_close = pl_pre_1_data['close'][0]
if pl_pre_25_close != None and pl_pre_1_close != None and not math.isnan(pl_pre_25_close) and not math.isnan(pl_pre_1_close):
pl_change_pct = (pl_pre_1_close - pl_pre_25_close) / pl_pre_25_close
pl_item = {'code':pl_code, 'change_pct': pl_change_pct}
pl_change_pct_dict_list.append(pl_item)
return pl_change_pct_dict_list
def get_pepb_range(index_code, start=origin, end=today, weight='C'):
pebs = pd.DataFrame()
for date in pd.DatetimeIndex(
jqdata.get_trade_days(start_date=start, end_date=end)):
s = get_pepb(index_code, dt.datetime.strftime(date, '%Y-%m-%d'),
weight)
if s != [0, 0]:
pebs[dt.datetime.strftime(date, '%Y-%m-%d')] = pd.Series(
s, index=['pe', 'pb'])
return pebs.dropna()
def update(self, codes):
"""
更新数据，子类可重载
codes：代码列表，list
返回：无
"""
# 更新数量
n = 0
# 更新日期
update_date = None
# 最近一个交易日
trade_date = get_trade_days(end_date=pd.datetime.today(),
count=10)[-2].strftime('%Y-%m-%d')
# 数据保存模式
append = False
# 遍历代码列表
for code in codes:
print '\r数据更新：%s' % (self.pool.name(code)),
# 判断数据文件是否存在
# 不存在则初次获取，存在则追加数据
try:
# 读取数据
temp_df = self.read(code, years=1)
# 获取上次更新日期
update_date = temp_df.index[-1].date()
# 本次更新日期
clac_date = (update_date + timedelta(1)).strftime('%Y-%m-%d')
# 本次更新日期大于最近交易日期，则无需更新
if clac_date > trade_date:
continue
# 增量获取
df = self.get_data(code, start_date=clac_date)
# 追加模式
append = True
except IOError:
# 替换模式
append = False
# 初次获取
df = self.get_data(code)
except Exception as e:
#print '%s：%s'%(self.pool.name(code),e)
continue
if not df is None:
# 保存数据
self.save(code, df, append=append)
# 获取更新日期
#update_date=df.index[-1].date()
print code
n += 1
if n > 0:
print '\r数据更新：%s，已更新 %s 个' % (self.project.note, n)
else:
print '\r数据更新：%s，无需更新' % (self.project.note)
def get_current_tradeday(context=None,
end_date=None,
days: int = 0,
ret_first=True):
"""
获取最近N个交易日
:return:
"""
if context: end_date = context.current_dt.date()
trade_days = jq.get_trade_days(end_date=end_date, count=days + 1)
return trade_days[0] if ret_first else trade_days
def load_change_pct_data(context, codes):
'''
计算标的的25日涨跌幅。
Args:
context 上下文
codes 标的的代码列表
Returns:
标的的涨跌幅列表。列表中的每一项数据时一个字典：
code:标的代码
change_pct: 标的的涨跌幅
'''
change_pct_dict_list = []
# 计算涨跌幅需要用到前一日收盘价，所以需要多加载一天的数据，
# 而这里在第二日的开盘前运行，计算前一个交易日收盘后的数据，所以需要再多加载一天
# 使用固定的25个交易日，而非25个bar计算涨跌幅
count = CHANGE_PCT_DAY_NUMBER + 1
# 获取25个交易日的日期
pre_25_dates = jqdata.get_trade_days(start_date=None,
end_date=context.current_dt,
count=count)
pre_25_date = pre_25_dates[0]
pre_1_date = pre_25_dates[-1]
for code in codes:
pre_25_data = get_price(code,
start_date=None,
end_date=pre_25_date,
frequency='daily',
fields=['close'],
skip_paused=True,
fq='post',
count=1)
pre_1_data = get_price(code,
start_date=None,
end_date=pre_1_date,
frequency='daily',
fields=['close'],
skip_paused=True,
fq='post',
count=1)
pre_25_close = None
pre_1_close = None
if str(pre_25_date) == str(pre_25_data.index[0])[:10]:
pre_25_close = pre_25_data['close'][0]
if str(pre_1_date) == str(pre_1_data.index[0])[:10]:
pre_1_close = pre_1_data['close'][0]
if pre_25_close != None and pre_1_close != None and not math.isnan(
pre_25_close) and not math.isnan(pre_1_close):
change_pct = (pre_1_close - pre_25_close) / pre_25_close
item = {'code': code, 'change_pct': change_pct}
change_pct_dict_list.append(item)
return change_pct_dict_list
