def batch_flush(self, codes):
"""刷新批量股票"""
codes = ensure_list(codes)
for code in codes:
sess = get_session()
self.flush(sess, code)
sess.close()
def flush_gpgk(codes=None):
"""
刷新股票概况信息
说明：
如初始化则包含所有曾经上市的股票代码，含已经退市
否则仅包含当前在市的股票代码
"""
def get_to_do(x):
p1 = not_yet_updated(x, SpecialTreatment)
p2 = not_yet_updated(x, ShortName)
return sorted(list(p1.union(p2)))
if codes is None:
codes = get_all_codes(False)
else:
codes = ensure_list(codes)
to_do = get_to_do(codes)
for i in range(1, 10):
logger.info('股票代码数量：{}'.format(len(to_do)))
to_do = batch_flush(to_do)
to_do = get_to_do(to_do)
if len(to_do) == 0:
break
time.sleep(1)
if i > 1:
logger.info('第{}次尝试，其中{}个股票代码失败'.format(i, len(to_do)))
def __init__(self,
asset,
inds,
start=pd.datetime.today() - pd.Timedelta(days=60),
end=pd.datetime.today()):
self._asset = asset
self._inds = ensure_list(inds)
self._start = start
self._end = end
self._fig = None
def flush_adjustment(codes=None):
"""
刷新股票分红派息数据
说明：
如初始化则包含所有曾经上市的股票代码，含已经退市
否则仅包含当前在市的股票代码
"""
if codes is None:
codes = get_all_codes(False)
else:
codes = ensure_list(codes)
flush(codes)
def flush_reports(codes=None):
"""
刷新财务报告
说明：
如初始化则包含所有曾经上市的股票代码，含已经退市
否则仅包含当前在市的股票代码
"""
if codes is None:
codes = get_all_codes(False)
else:
codes = ensure_list(codes)
for item in VALID_ITEMS:
fresher = ReportRefresher(item)
fresher.batch_flush(codes)
def _gen_fig(asset, inds, start, end):
start = pd.Timestamp(start)
end = pd.Timestamp(end)
assert end > start, '结束日期必须大于开始日期'
inds = ensure_list(inds)
asset = symbols([asset])[0]
asset_name = asset.asset_name
# 前移半年
d_start = pd.Timestamp(start) - pd.Timedelta(days=180)
df = ohlcv(asset, d_start, end)
limit_start = df.index[df.index >= start][0]
limit_end = df.index[df.index <= end][-1]
# 颜色
# colors = ['red' if x >= 0 else 'green' for x in df.close.pct_change()]
diff = (df.close - df.open).loc[limit_start:limit_end]
colors = ['red' if x >= 0 else 'green' for x in diff]
title = '{}技术分析图({}到{})'.format(asset_name,
limit_start.strftime(r'%Y-%m-%d'),
limit_end.strftime(r'%Y-%m-%d'))
fig_setting = _fig_default_setting()
kwargs = {
'df': df,
'limit_start': limit_start,
'limit_end': limit_end,
'colors': colors,
}
kwargs.update(fig_setting)
traces = []
# 主图
traces.extend(_Candlestick(kwargs))
traces.extend(_BBANDS(kwargs))
# 成交量图
traces.extend(_Volume(kwargs))
traces.extend(_SMA(kwargs))
# 其他技术图
for ind in inds:
if ind.upper() not in ALLOWED:
raise NotImplementedError('不支持指标{}\n，可接受的指标：{}'.format(
ind, ALLOWED))
traces.extend(_indicators_factory(ind, kwargs))
fig = _relayout(traces)
# 不使用Rangeslider及不显示x刻度
layout = dict(
title=title,
showlegend=False,
xaxis=dict(rangeslider=dict(visible=False), showticklabels=False))
fig['layout'].update(layout)
return fig
def write_dynamic_data_to_bcolz(tables=None):
"""
将每日变动数据以bcolz格式存储，提高数据集加载速度
"""
if not tables:
to_does = DYNAMIC_TABLES
else:
to_does = ensure_list(to_does)
for table in to_does:
if table in DYNAMIC_TABLES:
ndays = 0
logger.info('预处理表：{}'.format(table))
expr = STOCK_DB[table]
_write_by_expr(expr, ndays)
else:
raise ValueError('要写入的数据表{}不在"{}"范围'.format(table, DYNAMIC_TABLES))
def symbols(symbols_, symbol_reference_date=None, handle_missing='log'):
"""
Convert a or a list of str and int into a list of Asset objects.
Parameters:
symbols_ (str, int or iterable of str and int)
Passed strings are interpreted as ticker symbols and
resolved relative to the date specified by symbol_reference_date.
symbol_reference_date (str or pd.Timestamp, optional)
String or Timestamp representing a date used to resolve symbols
that have been held by multiple companies. Defaults to the current time.
handle_missing ({'raise', 'log', 'ignore'}, optional)
String specifying how to handle unmatched securities. Defaults to ‘log’.
Returns:
list of Asset objects – The symbols that were requested.
"""
symbols_ = ensure_list(symbols_)
allowed_dtype = [str, int, Asset, Equity]
res = {0: [], 1: [], 2: [], 3: []}
for s in symbols_:
try:
pos = allowed_dtype.index(type(s))
res[pos].append(s)
except ValueError as e:
raise Exception(
'{} is not str、int or zipline.assets.Asset'.format(s))
if symbol_reference_date is not None:
asof_date = pd.Timestamp(symbol_reference_date, tz='UTC')
else:
asof_date = pd.Timestamp('today', tz='UTC')
res[0] = finder.lookup_symbols(res[0], asof_date)
res[1] = finder.retrieve_all(res[1])
ret = []
for s in res.values():
ret.extend(s)
return ret
def flush_stockdaily(codes=None, init=False):
"""
刷新股票日线数据
说明：
如初始化则包含所有曾经上市的股票代码，含已经退市
否则仅包含当前在市的股票代码
"""
sess = get_session()
end = sess.query(func.max(TradingCalendar.date)).filter(
TradingCalendar.is_trading == True).scalar()
sess.close()
if end is None:
raise NotImplementedError('尚未初始化交易日历数据！')
if init or codes is None:
codes = get_all_codes(init)
else:
codes = ensure_list(codes)
# 删除无效数据
_delete()
flush(codes, end)
def write_financial_data_to_bcolz(tables=None):
"""
将财务报告相关表以bcolz格式存储，提高数据集加载速度
Notes:
------
写入财务报告数据时，默认财务报告公告日期为报告期后45天
"""
# 首先要刷新数据
flush_report_dates()
if not tables:
to_does = QUARTERLY_TABLES
else:
to_does = ensure_list(to_does)
for table in to_does:
if table in QUARTERLY_TABLES:
logger.info('预处理表：{}'.format(table))
expr = STOCK_DB[table]
_write_by_expr(expr)
else:
raise ValueError('要写入的数据表{}不在"{}"范围'.format(
table, QUARTERLY_TABLES))
def flush_shareholder(codes=None):
"""
刷新股票股东信息
说明：
如初始化则包含所有曾经上市的股票代码，含已经退市
否则仅包含当前在市的股票代码
"""
# if init:
# insert_preprocessed_data()
if codes is None:
codes = get_all_codes(False)
else:
codes = ensure_list(codes)
# 按代码循环
for code in codes:
with session_scope() as sess:
# 按类型循环
for type_ in (ShareholderType.main,
ShareholderType.circulating,
ShareholderType.fund):
_flush_by(sess, code, type_)
def flush_dealdetail(codes=None, init=False):
"""
刷新股票分时交易数据
说明：
如初始化则包含所有曾经上市的股票代码，含已经退市
否则仅包含当前在市的股票代码
"""
sess = get_session()
if init:
# 最多前溯2周?
start = datetime.today().date() - timedelta(days=14)
else:
start = datetime.today().date() - timedelta(days=1)
if init or codes is None:
codes = get_all_codes(init)
else:
codes = ensure_list(codes)
dates = sess.query(
TradingCalendar.date).filter(TradingCalendar.date >= start).filter(
TradingCalendar.is_trading == True).all()
sess.close()
flush(codes, dates)
def symbols(symbols_, symbol_reference_date=None, handle_missing='log'):
"""
Convert a string or a list of strings into Asset objects.
Parameters:
symbols_ (String or iterable of strings.)
Passed strings are interpreted as ticker symbols and
resolved relative to the date specified by symbol_reference_date.
symbol_reference_date (str or pd.Timestamp, optional)
String or Timestamp representing a date used to resolve symbols
that have been held by multiple companies. Defaults to the current time.
handle_missing ({'raise', 'log', 'ignore'}, optional)
String specifying how to handle unmatched securities. Defaults to ‘log’.
Returns:
list of Asset objects – The symbols that were requested.
"""
finder = bundle_data.asset_finder
symbols_ = ensure_list(symbols_)
first_type = type(symbols_[0])
for s in symbols_:
type_ = type(s)
if type_ != first_type:
raise TypeError('symbols_列表不得存在混合类型')
if first_type is Equity:
return symbols_
elif first_type is int:
symbols_ = [str(s).zfill(6) for s in symbols_]
if symbol_reference_date is not None:
asof_date = pd.Timestamp(symbol_reference_date, tz='UTC')
else:
asof_date = pd.Timestamp('today', tz='UTC')
res = finder.lookup_symbols(symbols_, asof_date)
# 返回的是列表
return res
def test_ensure_list(self):
x = '1'
self.assertTrue(type(ensure_list(x)), list)
x = (1, 2, 3)
self.assertTrue(type(ensure_list(x)), list)