def submit_order(self, order):
if order.position_effect == POSITION_EFFECT.MATCH:
raise NotImplementedError(
_("unsupported position_effect {}").format(
order.position_effect))
account = self._env.get_account(order.order_book_id)
self._env.event_bus.publish_event(
Event(EVENT.ORDER_PENDING_NEW, account=account, order=order))
if order.position_effect == POSITION_EFFECT.EXERCISE:
return self._open_exercise_orders.append(order)
if order.is_final():
return
if self._env.config.base.frequency == '1d' and not self._match_immediately:
if ExecutionContext.phase() == EXECUTION_PHASE.OPEN_AUCTION:
self._open_orders.append((account, order))
order.active()
else:
self._delayed_orders.append((account, order))
return
if ExecutionContext.phase() == EXECUTION_PHASE.OPEN_AUCTION:
self._open_auction_orders.append((account, order))
else:
self._open_orders.append((account, order))
order.active()
self._env.event_bus.publish_event(
Event(EVENT.ORDER_CREATION_PASS, account=account, order=order))
if self._match_immediately:
self._match()
def __getitem__(self, key):
if not isinstance(key, six.string_types):
raise patch_user_exc(ValueError('invalid key {} (use order_book_id please)'.format(key)))
instrument = self._data_proxy.instruments(key)
if instrument is None:
raise patch_user_exc(ValueError('invalid order book id or symbol: {}'.format(key)))
order_book_id = instrument.order_book_id
try:
return self._cache[order_book_id]
except KeyError:
try:
if not self._dt:
return BarObject(instrument, NANDict, self._dt)
if ExecutionContext.phase() == EXECUTION_PHASE.OPEN_AUCTION:
bar = self._data_proxy.get_open_auction_bar(order_book_id, self._dt)
else:
bar = self._data_proxy.get_bar(order_book_id, self._dt, self._frequency)
except PermissionError:
raise
except Exception as e:
system_log.exception(e)
raise patch_user_exc(KeyError(_(u"id_or_symbols {} does not exist").format(key)))
if bar is None:
return BarObject(instrument, NANDict, self._dt)
else:
self._cache[order_book_id] = bar
return bar
def current_snapshot(id_or_symbol):
"""
获得当前市场快照数据。只能在日内交易阶段调用，获取当日调用时点的市场快照数据。
市场快照数据记录了每日从开盘到当前的数据信息，可以理解为一个动态的day bar数据。
在目前分钟回测中，快照数据为当日所有分钟线累积而成，一般情况下，最后一个分钟线获取到的快照数据应当与当日的日线行情保持一致。
需要注意，在实盘模拟中，该函数返回的是调用当时的市场快照情况，所以在同一个handle_bar中不同时点调用可能返回的数据不同。
如果当日截止到调用时候对应股票没有任何成交，那么snapshot中的close, high, low, last几个价格水平都将以0表示。
:param str order_book_id: 合约代码或简称
:return: :class:`~Snapshot`
:example:
在handle_bar中调用该函数，假设策略当前时间是20160104 09:33:
.. code-block:: python3
:linenos:
[In]
logger.info(current_snapshot('000001.XSHE'))
[Out]
2016-01-04 09:33:00.00 INFO
Snapshot(order_book_id: '000001.XSHE', datetime: datetime.datetime(2016, 1, 4, 9, 33), open: 10.0, high: 10.025, low: 9.9667, last: 9.9917, volume: 2050320, total_turnover: 20485195, prev_close: 9.99)
"""
env = Environment.get_instance()
frequency = env.config.base.frequency
order_book_id = assure_order_book_id(id_or_symbol)
dt = env.calendar_dt
if env.config.base.run_type == RUN_TYPE.BACKTEST:
if ExecutionContext.phase() == EXECUTION_PHASE.BEFORE_TRADING:
dt = env.data_proxy.get_previous_trading_date(
env.trading_dt.date())
return env.data_proxy.current_snapshot(order_book_id, "1d", dt)
elif ExecutionContext.phase() == EXECUTION_PHASE.AFTER_TRADING:
return env.data_proxy.current_snapshot(order_book_id, "1d", dt)
# PT、实盘直接取最新快照，忽略 frequency, dt 参数
return env.data_proxy.current_snapshot(order_book_id, frequency, dt)
def current_snapshot(id_or_symbol):
"""
获得当前市场快照数据。只能在日内交易阶段调用，获取当日调用时点的市场快照数据。
市场快照数据记录了每日从开盘到当前的数据信息，可以理解为一个动态的day bar数据。
在目前分钟回测中，快照数据为当日所有分钟线累积而成，一般情况下，最后一个分钟线获取到的快照数据应当与当日的日线行情保持一致。
需要注意，在实盘模拟中，该函数返回的是调用当时的市场快照情况，所以在同一个handle_bar中不同时点调用可能返回的数据不同。
如果当日截止到调用时候对应股票没有任何成交，那么snapshot中的close, high, low, last几个价格水平都将以0表示。
:param str order_book_id: 合约代码或简称
:return: :class:`~Snapshot`
:example:
在handle_bar中调用该函数，假设策略当前时间是20160104 09:33:
.. code-block:: python3
:linenos:
[In]
logger.info(current_snapshot('000001.XSHE'))
[Out]
2016-01-04 09:33:00.00 INFO
Snapshot(order_book_id: '000001.XSHE', datetime: datetime.datetime(2016, 1, 4, 9, 33), open: 10.0, high: 10.025, low: 9.9667, last: 9.9917, volume: 2050320, total_turnover: 20485195, prev_close: 9.99)
"""
env = Environment.get_instance()
frequency = env.config.base.frequency
order_book_id = assure_order_book_id(id_or_symbol)
dt = env.calendar_dt
if env.config.base.run_type == RUN_TYPE.BACKTEST:
if ExecutionContext.phase() == EXECUTION_PHASE.BEFORE_TRADING:
dt = env.data_proxy.get_previous_trading_date(env.trading_dt.date())
return env.data_proxy.current_snapshot(order_book_id, "1d", dt)
elif ExecutionContext.phase() == EXECUTION_PHASE.AFTER_TRADING:
return env.data_proxy.current_snapshot(order_book_id, "1d", dt)
# PT、实盘直接取最新快照，忽略 frequency, dt 参数
return env.data_proxy.current_snapshot(order_book_id, frequency, dt)
def mavg(self, intervals, frequency='1d'):
if frequency == 'day':
frequency = '1d'
if frequency == 'minute':
frequency = '1m'
# copy form history
env = Environment.get_instance()
dt = env.calendar_dt
if (env.config.base.frequency == '1m' and frequency == '1d') or ExecutionContext.phase() == EXECUTION_PHASE.BEFORE_TRADING:
# 在分钟回测获取日线数据, 应该推前一天
dt = env.data_proxy.get_previous_trading_date(env.calendar_dt.date())
bars = env.data_proxy.fast_history(self._instrument.order_book_id, intervals, frequency, 'close', dt)
return bars.mean()
def mavg(self, intervals, frequency='1d'):
if frequency == 'day':
frequency = '1d'
if frequency == 'minute':
frequency = '1m'
# copy form history
env = Environment.get_instance()
dt = env.calendar_dt
if (env.config.base.frequency == '1m' and frequency == '1d') or ExecutionContext.phase() == EXECUTION_PHASE.BEFORE_TRADING:
# 在分钟回测获取日线数据, 应该推前一天
dt = env.data_proxy.get_previous_trading_date(env.calendar_dt.date())
bars = env.data_proxy.fast_history(self._instrument.order_book_id, intervals, frequency, 'close', dt)
return bars.mean()
def _history_bars(self, order_book_id, bar_count, freq, dt):
if self.fetch_data_by_api and ExecutionContext.phase() in (
EXECUTION_PHASE.BEFORE_TRADING,
EXECUTION_PHASE.ON_BAR,
EXECUTION_PHASE.ON_TICK,
EXECUTION_PHASE.AFTER_TRADING,
EXECUTION_PHASE.SCHEDULED):
bars = history_bars(
order_book_id, bar_count, freq, fields=None)
else:
bars = self.rqalpha_env.data_proxy.history_bars(
order_book_id, bar_count, freq,
field=["datetime", "open", "high", "low", "close", "volume"],
dt=dt)
return bars
def _history_bars(self, order_book_id, bar_count, freq, dt):
if self.fetch_data_by_api and ExecutionContext.phase() in (
EXECUTION_PHASE.BEFORE_TRADING,
EXECUTION_PHASE.ON_BAR,
EXECUTION_PHASE.ON_TICK,
EXECUTION_PHASE.AFTER_TRADING,
EXECUTION_PHASE.SCHEDULED):
bars = history_bars(
order_book_id, bar_count, freq, fields=None)
else:
bars = self.rqalpha_env.data_proxy.history_bars(
order_book_id, bar_count, freq,
field=["datetime", "open", "high", "low", "close", "volume"],
dt=dt)
return bars
def vwap(self, intervals, frequency='1d'):
if frequency == 'day':
frequency = '1d'
if frequency == 'minute':
frequency = '1m'
# copy form history
env = Environment.get_instance()
dt = env.calendar_dt
if (env.config.base.frequency == '1m' and frequency == '1d') or ExecutionContext.phase() == EXECUTION_PHASE.BEFORE_TRADING:
# 在分钟回测获取日线数据, 应该推前一天
dt = env.data_proxy.get_previous_trading_date(env.calendar_dt.date())
bars = env.data_proxy.fast_history(self._instrument.order_book_id, intervals, frequency, ['close', 'volume'], dt)
sum = bars['volume'].sum()
if sum == 0:
# 全部停牌
return 0
return np.dot(bars['close'], bars['volume']) / sum
def vwap(self, intervals, frequency='1d'):
if frequency == 'day':
frequency = '1d'
if frequency == 'minute':
frequency = '1m'
# copy form history
env = Environment.get_instance()
dt = env.calendar_dt
if (env.config.base.frequency == '1m' and frequency == '1d') or ExecutionContext.phase() == EXECUTION_PHASE.BEFORE_TRADING:
# 在分钟回测获取日线数据, 应该推前一天
dt = env.data_proxy.get_previous_trading_date(env.calendar_dt.date())
bars = env.data_proxy.fast_history(self._instrument.order_book_id, intervals, frequency, ['close', 'volume'], dt)
sum = bars['volume'].sum()
if sum == 0:
# 全部停牌
return 0
return np.dot(bars['close'], bars['volume']) / sum
def history_bars(order_book_id, bar_count, frequency, fields=None, skip_suspended=True,
include_now=False, adjust_type='pre'):
"""
获取指定合约的历史行情，同时支持日以及分钟历史数据。不能在init中调用。 注意，该API会自动跳过停牌数据。
日回测获取分钟历史数据：不支持
日回测获取日历史数据
========================= ===================================================
调用时间 返回数据
========================= ===================================================
T日before_trading T-1日day bar
T日handle_bar T日day bar
========================= ===================================================
分钟回测获取日历史数据
========================= ===================================================
调用时间 返回数据
========================= ===================================================
T日before_trading T-1日day bar
T日handle_bar T-1日day bar
========================= ===================================================
分钟回测获取分钟历史数据
========================= ===================================================
调用时间 返回数据
========================= ===================================================
T日before_trading T-1日最后一个minute bar
T日handle_bar T日当前minute bar
========================= ===================================================
: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。
:example:
获取最近5天的日线收盘价序列（策略当前日期为20160706）:
.. code-block:: python3
:linenos:
[In]
logger.info(history_bars('000002.XSHE', 5, '1d', 'close'))
[Out]
[ 8.69 8.7 8.71 8.81 8.81]
"""
order_book_id = assure_order_book_id(order_book_id)
env = Environment.get_instance()
dt = env.calendar_dt
if frequency[-1] not in {'m', 'd'}:
raise RQInvalidArgument('invalid frequency {}'.format(frequency))
if frequency[-1] == 'm' and env.config.base.frequency == '1d':
raise RQInvalidArgument('can not get minute history in day back test')
if frequency[-1] == 'd' and frequency != '1d':
raise RQInvalidArgument('invalid frequency')
if adjust_type not in {'pre', 'post', 'none'}:
raise RuntimeError('invalid adjust_type')
if frequency == '1d':
sys_frequency = Environment.get_instance().config.base.frequency
if ((sys_frequency in ['1m', 'tick'] and not include_now) or ExecutionContext.phase() == EXECUTION_PHASE.BEFORE_TRADING):
dt = env.data_proxy.get_previous_trading_date(env.trading_dt.date())
# 当 EXECUTION_PHASE.BEFORE_TRADING 的时候，强制 include_now 为 False
include_now = False
if sys_frequency == "1d":
# 日回测不支持 include_now
include_now = False
if fields is None:
fields = ["datetime", "open", "high", "low", "close", "volume"]
return env.data_proxy.history_bars(order_book_id, bar_count, frequency, fields, dt,
skip_suspended=skip_suspended, include_now=include_now,
adjust_type=adjust_type, adjust_orig=env.trading_dt)
def history_bars(order_book_id,
bar_count,
frequency,
fields=None,
skip_suspended=True,
include_now=False,
adjust_type='pre'):
"""
获取指定合约的历史行情，同时支持日以及分钟历史数据。不能在init中调用。 注意，该API会自动跳过停牌数据。
日回测获取分钟历史数据：不支持
日回测获取日历史数据
========================= ===================================================
调用时间 返回数据
========================= ===================================================
T日before_trading T-1日day bar
T日handle_bar T日day bar
========================= ===================================================
分钟回测获取日历史数据
========================= ===================================================
调用时间 返回数据
========================= ===================================================
T日before_trading T-1日day bar
T日handle_bar T-1日day bar
========================= ===================================================
分钟回测获取分钟历史数据
========================= ===================================================
调用时间 返回数据
========================= ===================================================
T日before_trading T-1日最后一个minute bar
T日handle_bar T日当前minute bar
========================= ===================================================
: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。
:example:
获取最近5天的日线收盘价序列（策略当前日期为20160706）:
.. code-block:: python3
:linenos:
[In]
logger.info(history_bars('000002.XSHE', 5, '1d', 'close'))
[Out]
[ 8.69 8.7 8.71 8.81 8.81]
"""
order_book_id = assure_order_book_id(order_book_id)
env = Environment.get_instance()
dt = env.calendar_dt
if frequency[-1] == 'm' and env.config.base.frequency == '1d':
raise RQInvalidArgument('can not get minute history in day back test')
if adjust_type not in {'pre', 'post', 'none'}:
raise RuntimeError('invalid adjust_type')
if frequency == '1d':
sys_frequency = Environment.get_instance().config.base.frequency
if ((sys_frequency in ['1m', 'tick'] and not include_now)
or ExecutionContext.phase() == EXECUTION_PHASE.BEFORE_TRADING):
dt = env.data_proxy.get_previous_trading_date(
env.trading_dt.date())
# 当 EXECUTION_PHASE.BEFORE_TRADING 的时候，强制 include_now 为 False
include_now = False
if sys_frequency == "1d":
# 日回测不支持 include_now
include_now = False
if fields is None:
fields = ["datetime", "open", "high", "low", "close", "volume"]
return env.data_proxy.history_bars(order_book_id,
bar_count,
frequency,
fields,
dt,
skip_suspended=skip_suspended,
include_now=include_now,
adjust_type=adjust_type,
adjust_orig=env.trading_dt)
def history_bars(
order_book_id,
bar_count,
frequency,
fields=None,
skip_suspended=True,
include_now=False,
adjust_type="pre",
):
# type: (str, int, str, Optional[str], Optional[bool], Optional[bool], Optional[str]) -> np.ndarray
"""
获取指定合约的历史 k 线行情，同时支持日以及分钟历史数据。不能在init中调用。
日回测获取分钟历史数据：不支持
日回测获取日历史数据
========================= ===================================================
调用时间 返回数据
========================= ===================================================
T日before_trading T-1日day bar
T日handle_bar T日day bar
========================= ===================================================
分钟回测获取日历史数据
========================= ===================================================
调用时间 返回数据
========================= ===================================================
T日before_trading T-1日day bar
T日handle_bar T-1日day bar
========================= ===================================================
分钟回测获取分钟历史数据
========================= ===================================================
调用时间 返回数据
========================= ===================================================
T日before_trading T-1日最后一个minute bar
T日handle_bar T日当前minute bar
========================= ===================================================
:param order_book_id: 合约代码
:param bar_count: 获取的历史数据数量，必填项
:param 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 fields: 返回数据字段。必填项。见下方列表。
:param skip_suspended: 是否跳过停牌数据
:param include_now: 是否包含当前数据
:param adjust_type: 复权类型，默认为前复权 pre；可选 pre, none, post
========================= ===================================================
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）
========================= ===================================================
:example:
获取最近5天的日线收盘价序列（策略当前日期为20160706）:
.. code-block:: python3
:linenos:
[In]
logger.info(history_bars('000002.XSHE', 5, '1d', 'close'))
[Out]
[ 8.69 8.7 8.71 8.81 8.81]
"""
order_book_id = assure_order_book_id(order_book_id)
env = Environment.get_instance()
dt = env.calendar_dt
if frequency[-1] not in {"m", "d"}:
raise RQInvalidArgument("invalid frequency {}".format(frequency))
if frequency[-1] == "m" and env.config.base.frequency == "1d":
raise RQInvalidArgument("can not get minute history in day back test")
if frequency[-1] == "d" and frequency != "1d":
raise RQInvalidArgument("invalid frequency")
if adjust_type not in {"pre", "post", "none"}:
raise RuntimeError("invalid adjust_type")
if frequency == "1d":
sys_frequency = Environment.get_instance().config.base.frequency
if (sys_frequency in ["1m", "tick"] and not include_now
and ExecutionContext.phase() != EXECUTION_PHASE.AFTER_TRADING
) or (ExecutionContext.phase() in (EXECUTION_PHASE.BEFORE_TRADING,
EXECUTION_PHASE.OPEN_AUCTION)):
dt = env.data_proxy.get_previous_trading_date(
env.trading_dt.date())
# 当 EXECUTION_PHASE.BEFORE_TRADING 的时候，强制 include_now 为 False
include_now = False
if sys_frequency == "1d":
# 日回测不支持 include_now
include_now = False
if fields is None:
fields = ["datetime", "open", "high", "low", "close", "volume"]
return env.data_proxy.history_bars(
order_book_id,
bar_count,
frequency,
fields,
dt,
skip_suspended=skip_suspended,
include_now=include_now,
adjust_type=adjust_type,
adjust_orig=env.trading_dt,
)
def _get_bar(self, order_book_id):
if ExecutionContext.phase() == EXECUTION_PHASE.OPEN_AUCTION:
return self._env.data_proxy.get_open_auction_bar(
order_book_id, self._env.calendar_dt)
return self._env.get_bar(order_book_id)