def _historical_trading_dates(start, end):
"""
以指数历史数据计算期间的所有交易日期
网络数据存在延迟，如当天交易，历史数据可能在次日或次次日
才提供，导致近期交易日的计算存在误差。
"""
df = fetch_history('000001', start, end, True)
return df.index
def get_data(code):
"""获取单个股票的日线数据"""
sess = get_session(db_dir_name)
d_ = last_date(sess, StockDaily, code)
sess.close()
if d_ is None:
s = get_ipo_date(code)
else:
s = d_ + pd.Timedelta(days=1)
if s > pd.Timestamp('today').normalize():
return pd.DataFrame()
df = _fix_data(fetch_history(code, s))
return df
def _refresh_data(code, sess):
"""刷新或者添加单个股票的日线数据"""
d_ = last_date(sess, StockDaily, code)
if d_ is None:
s = get_ipo_date(code)
if s is None:
s = MARKET_START.tz_convert(None)
else:
s = d_ + pd.Timedelta(days=1)
if s > pd.Timestamp('today').normalize():
logger.info('{} 数据已经是最新状态'.format(code))
return
df = _fix_data(fetch_history(code, s))
if len(df):
table_name = 'stock_dailies'
df.to_sql(table_name, sess.bind.engine, if_exists='append')
logger.info('添加{}数据共{}行'.format(code, df.shape[0]))
else:
logger.info('{} {} 无数据添加'.format(code, s))
def flush(codes, end):
for code in codes:
sess = get_session(db_dir_name)
start = _get_start_date(sess, code)
if start is not None and start > end:
logger.info('代码：{} 无需刷新'.format(code))
continue
try:
df = fetch_history(code=code, start=start, end=end, is_index=True)
# 按日期排序（升序）
df.sort_index(inplace=True)
except ValueError:
# 当开始日期大于结束日期时，触发值异常
logger.info('无法获取网页数据。代码：{}，开始日期：{}, 结束日期：{}'.format(
code, start, end))
continue
objs = _gen(df)
sess.add_all(objs)
sess.commit()
logger.info('代码：{}, 新增{}行'.format(
code, len(objs)))
sess.close()
def test_fetch_history_1():
"""测试历史股价"""
df = fetch_history('000001', '1990-01-01', '2019-11-24')
assert df.shape == (7041, 15)
assert df.iat[0, 0] == "'000001"
assert df.index[0] == pd.Timestamp('2019-11-22 00:00:00')
def test_fetch_history_2():
"""测试历史股价(退市股票)"""
df = fetch_history('000033', '1990-01-01', '2019-11-24')
assert df.shape == (5221, 15)
assert df.iat[0, 0] == "'000033"
assert df.index[0] == pd.Timestamp('2017-07-06 00:00:00')