def _history_bars_fundamentals_jq(order_book_ids,
bar_count,
fields=None,
dt=None):
#
start_date = DataProxy.get_instance().get_previous_trading_date(
dt, n=bar_count)
start_date = convert_timestamp_to_str(start_date)
trading_dates = DataProxy.get_instance().get_trading_dates(
start_date=start_date, end_date=dt)
date_str_list = [
convert_timestamp_to_str(timestamp) for timestamp in trading_dates
]
data_df = get_fundamental_data(order_book_ids=order_book_ids,
fields=fields,
dt_list=date_str_list)
return data_df
def history_bars(order_book_ids,
bar_count,
frequency,
fields=None,
dt=None,
skip_suspended=False,
include_now=True,
adjust_type='pre',
adjust_orig=None):
"""
:param order_book_id: 合约代码
:type order_book_id: `str`
:param int bar_count: 获取的历史数据数量，必填项
:param str frequency: 获取数据什么样的频率进行。'1d'或'1m'分别表示每日和每分钟，必填项
:param str fields: 返回数据字段。必填项。见下方列表。
========================= ===================================================
fields 字段名
========================= ===================================================
datetime 时间戳
open 开盘价
high 最高价
low 最低价
close 收盘价
volume 成交量
total_turnover 成交额
open_interest 持仓量（期货专用）
basis_spread 期现差（股指期货专用）
settlement 结算价（期货日线专用）
prev_settlement 结算价（期货日线专用）
========================= ===================================================
:param bool skip_suspended: 是否跳过停牌数据
:param bool include_now: 是否包含当前数据
:param str adjust_type: 复权类型，默认为前复权 pre；可选 pre, none, post
:return: `ndarray`, 方便直接与talib等计算库对接，效率较history返回的DataFrame更高。
"""
data_proxy = DataProxy.get_instance()
if dt is None:
dt = datetime.datetime.now()
if frequency[-1] not in {'d', "w", "M"}:
raise TypeError("only support 'd','w' and 'M' frequency data type")
if fields is None:
fields = ["date", "open", "high", "low", "close", "volume"]
bars = data_proxy.history_bars(order_book_ids=order_book_ids,
bar_count=bar_count,
frequency=frequency,
fields=fields,
dt=dt,
skip_suspended=skip_suspended,
include_now=include_now,
adjust_type=adjust_type,
adjust_orig=adjust_orig)
return bars
def get_trading_dates(start_date, end_date):
"""
获取某个国家市场的交易日列表（起止日期加入判断）。目前仅支持中国市场。
:param start_date: 开始日期
:type start_date: `str` | `date` | `datetime` | `pandas.Timestamp`
:param end_date: 结束如期
:type end_date: `str` | `date` | `datetime` | `pandas.Timestamp`
:return: list[`datetime.date`]
:example:
.. code-block:: python3
:linenos:
[In]get_trading_dates(start_date='2016-05-05', end_date='20160505')
[Out]
[datetime.date(2016, 5, 5)]
"""
return DataProxy.get_instance().get_trading_dates(start_date, end_date)
def history_tradings(order_book_ids,
bar_count,
frequency,
dt,
fields=['date', 'open', 'high', 'low', 'close'],
skip_suspended=False,
include_now=True,
adjust_type="pre",
adjust_orig=None):
#get_bars(security, count, unit='1d', fields=['date','open','high','low','close'], include_now=False, end_dt=None, fq_ref_date=None)
dt = DataProxy.get_instance().get_next_trading_date(dt, n=1)
if frequency == "1d":
data = _get_price(order_book_ids=order_book_ids,
bar_count=bar_count,
dt=dt,
frequency="daily",
fields=fields,
skip_suspended=skip_suspended,
include_now=include_now,
adjust_type=adjust_type,
adjust_orig=adjust_orig)
return data
if "date" not in fields:
fields.append("date")
data = get_bars(security=order_book_ids,
count=bar_count,
unit=frequency,
end_dt=dt,
fields=fields,
include_now=include_now,
fq_ref_date=adjust_orig)
data["datetime"] = data["date"].apply(convert_to_timestamp)
data = data.reset_index().rename(columns={"level_0": "order_book_id"})
data = data.set_index(["order_book_id", "datetime"])
data = data.drop(columns=["level_1", "date"])
return data
def history_bars_fundamentals_jq(order_book_ids,
bar_count,
frequency,
fields=None,
dt=None):
"""
return: [pd.Panel]
Items: date
Major: order_boook_id
Minor: fields
"""
if (frequency[-1] not in ['d']) and (frequency not in ['1w', '1M']):
raise Exception("only support xd, 1w and 1M data")
if frequency == '1d':
data_df = _history_bars_fundamentals_jq(order_book_ids=order_book_ids,
bar_count=bar_count,
fields=fields,
dt=dt)
return data_df
dt = DataProxy.get_instance().get_next_trading_date(dt, n=1)
trading_data = get_bars(security='000001.XSHG',
count=bar_count,
unit=frequency,
fields=['date'],
include_now=True,
end_dt=dt,
fq_ref_date=None)
date_list = trading_data['date'].tolist()
date_str_list = [date.strftime("%Y-%m-%d") for date in date_list]
#pdb.set_trace()
if 'turnover_ratio' not in fields:
data_df = get_fundamental_data(order_book_ids=order_book_ids,
fields=fields,
dt_list=date_str_list)
return data_df
else:
fields.remove('turnover_ratio')
data_df = get_fundamental_data(
order_book_ids=order_book_ids,
fields=fields,
dt_list=date_str_list) # difference frequency
# scale
scale = 1
if frequency[-1] == 'd':
scale *= int(frequency[:-1])
elif frequency == "1w":
scale *= 5
elif frequency == '1M':
scale *= 20
bar_count_ = bar_count * scale
# get turnover_ratio data
turnover_df = _history_bars_fundamentals_jq(
order_book_ids=order_book_ids,
bar_count=bar_count_,
fields=['turnover_ratio'],
dt=dt)['turnover_ratio']
timestamp_list = [
convert_to_timestamp(date_str) for date_str in date_str_list
]
frequency_date_df = pd.DataFrame(timestamp_list,
index=timestamp_list,
columns=['datetime'])
frequency_date_df.index.name = "datetime"
#pdb.set_trace()
turnover = frequency_date_df.join(turnover_df, how='outer')
turnover['datetime1'] = turnover.groupby(
level="order_book_id")['datetime'].fillna(method="bfill")
turnover = turnover.reset_index(level="order_book_id").groupby(
by=['order_book_id', 'datetime1'])["turnover_ratio"].sum()
turnover.rename_axis(index={"datetime1": "datetime"}, inplace=True)
data = turnover.to_frame().join(data_df, how='outer')
return data
def get_trading_calendar():
"""
获取中国A股当年所有的交易日期列表
return pd.Series
"""
return DataProxy.get_instance().get_trading_calendar()
def get_next_trading_date(date, n=1):
return DataProxy.get_instance().get_next_trading_date(date, n)
def get_previous_trading_date(date, n=1):
return DataProxy.get_instance().get_previous_trading_date(date, n)
def get_instrument_industry(order_book_ids, dt=None):
return DataProxy.get_instance().get_instrument_industry(
order_book_ids=order_book_ids, dt=dt)
def index_components(order_book_id, dt=None):
return DataProxy.get_instance().index_components(
order_book_id=order_book_id, dt=dt)