# -*- coding: utf-8 -*-
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#
# 除非遵守当前许可，否则不得使用本软件。
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# 详细的授权流程，请联系 public@ricequant.com 获取。
import datetime
from decimal import Decimal, getcontext
from itertools import chain
from typing import Dict, List, Optional, Union, Tuple
import numpy as np
import pandas as pd
from rqalpha.api import export_as_api
from rqalpha.apis.api_abstract import (order, order_percent, order_shares,
order_target_percent,
order_target_value, order_to,
order_value,
common_rules, TUPLE_PRICE_OR_STYLE_TYPE, PRICE_OR_STYLE_TYPE)
from rqalpha.apis.api_base import (assure_instrument, assure_order_book_id,
cal_style, calc_open_close_style)
from rqalpha.const import (DEFAULT_ACCOUNT_TYPE, EXECUTION_PHASE,
INSTRUMENT_TYPE, ORDER_TYPE, POSITION_DIRECTION,
POSITION_EFFECT, SIDE)
from rqalpha.core.execution_context import ExecutionContext
from rqalpha.environment import Environment
from rqalpha.mod.rqalpha_mod_sys_risk.validators.cash_validator import \
is_cash_enough
from rqalpha.model.instrument import IndustryCode as industry_code
from rqalpha.model.instrument import IndustryCodeItem, Instrument
from rqalpha.model.instrument import SectorCode as sector_code
from rqalpha.model.instrument import SectorCodeItem
from rqalpha.model.order import LimitOrder, MarketOrder, Order, OrderStyle, ALGO_ORDER_STYLES
from rqalpha.utils import INST_TYPE_IN_STOCK_ACCOUNT, is_valid_price
from rqalpha.utils.arg_checker import apply_rules, verify_that
from rqalpha.utils.datetime_func import to_date
from rqalpha.utils.exception import RQInvalidArgument
from rqalpha.utils.i18n import gettext as _
from rqalpha.utils.logger import user_system_log
# 使用Decimal 解决浮点数运算精度问题
getcontext().prec = 10
export_as_api(industry_code, name='industry_code')
export_as_api(sector_code, name='sector_code')
KSH_MIN_AMOUNT = 200
def _get_account_position_ins(id_or_ins):
ins = assure_instrument(id_or_ins)
try:
account = Environment.get_instance().portfolio.accounts[DEFAULT_ACCOUNT_TYPE.STOCK]
except KeyError:
raise KeyError(_(
u"order_book_id: {order_book_id} needs stock account, please set and try again!"
).format(order_book_id=ins.order_book_id))
position = account.get_position(ins.order_book_id, POSITION_DIRECTION.LONG)
return account, position, ins
def _round_order_quantity(ins, quantity) -> int:
if ins.type == "CS" and ins.board_type == "KSH":
# KSH can buy(sell) 201, 202 shares
return 0 if abs(quantity) < KSH_MIN_AMOUNT else int(quantity)
else:
round_lot = ins.round_lot
try:
return int(Decimal(quantity) / Decimal(round_lot)) * round_lot
except ValueError:
raise
def _get_order_style_price(order_book_id, style):
if isinstance(style, LimitOrder):
return style.get_limit_price()
env = Environment.get_instance()
if isinstance(style, MarketOrder):
return env.data_proxy.get_last_price(order_book_id)
if isinstance(style, ALGO_ORDER_STYLES):
price, _ = env.data_proxy.get_algo_bar(order_book_id, style, env.calendar_dt)
return price
raise RuntimeError(f"no support {style} order style")
def _submit_order(ins, amount, side, position_effect, style, current_quantity, auto_switch_order_value):
env = Environment.get_instance()
if isinstance(style, LimitOrder) and np.isnan(style.get_limit_price()):
raise RQInvalidArgument(_(u"Limit order price should not be nan."))
price = env.data_proxy.get_last_price(ins.order_book_id)
if not is_valid_price(price):
user_system_log.warn(
_(u"Order Creation Failed: [{order_book_id}] No market data").format(order_book_id=ins.order_book_id))
return
if (side == SIDE.BUY) or (side == SIDE.SELL and current_quantity != abs(amount)):
amount = _round_order_quantity(ins, amount)
if amount == 0:
user_system_log.warn(_(
u"Order Creation Failed: 0 order quantity, order_book_id={order_book_id}"
).format(order_book_id=ins.order_book_id))
return
order = Order.__from_create__(ins.order_book_id, abs(amount), side, style, position_effect)
if side == SIDE.BUY and auto_switch_order_value:
account, position, ins = _get_account_position_ins(ins)
if not is_cash_enough(env, order, account.cash):
user_system_log.warn(_(
"insufficient cash, use all remaining cash({}) to create order"
).format(account.cash))
return _order_value(account, position, ins, account.cash, style)
return env.submit_order(order)
def _order_shares(ins, amount, style, quantity, auto_switch_order_value):
side, position_effect = (SIDE.BUY, POSITION_EFFECT.OPEN) if amount > 0 else (SIDE.SELL, POSITION_EFFECT.CLOSE)
return _submit_order(ins, amount, side, position_effect, style, quantity, auto_switch_order_value)
def _order_value(account, position, ins, cash_amount, style):
env = Environment.get_instance()
if cash_amount > 0:
cash_amount = min(cash_amount, account.cash)
if isinstance(style, LimitOrder):
price = style.get_limit_price()
else:
price = env.data_proxy.get_last_price(ins.order_book_id)
if not is_valid_price(price):
user_system_log.warn(
_(u"Order Creation Failed: [{order_book_id}] No market data").format(order_book_id=ins.order_book_id)
)
return
amount = int(Decimal(cash_amount) / Decimal(price))
round_lot = int(ins.round_lot)
if cash_amount > 0:
amount = _round_order_quantity(ins, amount)
while amount > 0:
expected_transaction_cost = env.get_order_transaction_cost(Order.__from_create__(
ins.order_book_id, amount, SIDE.BUY, LimitOrder(price), POSITION_EFFECT.OPEN
))
if amount * price + expected_transaction_cost <= cash_amount:
break
amount -= round_lot
else:
user_system_log.warn(_(
u"Order Creation Failed: 0 order quantity, order_book_id={order_book_id}"
).format(order_book_id=ins.order_book_id))
return
if amount < 0:
amount = max(amount, -position.closable)
return _order_shares(ins, amount, style, position.quantity, auto_switch_order_value=False)
@order_shares.register(INST_TYPE_IN_STOCK_ACCOUNT)
def stock_order_shares(id_or_ins, amount, price=None, style=None, price_or_style=None):
auto_switch_order_value = Environment.get_instance().config.mod.sys_accounts.auto_switch_order_value
account, position, ins = _get_account_position_ins(id_or_ins)
return _order_shares(
assure_instrument(id_or_ins), amount, cal_style(price, style, price_or_style), position.quantity,
auto_switch_order_value
)
@order_value.register(INST_TYPE_IN_STOCK_ACCOUNT)
def stock_order_value(id_or_ins, cash_amount, price=None, style=None, price_or_style=None):
account, position, ins = _get_account_position_ins(id_or_ins)
return _order_value(account, position, ins, cash_amount, cal_style(price, style, price_or_style))
@order_percent.register(INST_TYPE_IN_STOCK_ACCOUNT)
def stock_order_percent(id_or_ins, percent, price=None, style=None, price_or_style=None):
account, position, ins = _get_account_position_ins(id_or_ins)
return _order_value(account, position, ins, account.total_value * percent, cal_style(price, style, price_or_style))
@order_target_value.register(INST_TYPE_IN_STOCK_ACCOUNT)
def stock_order_target_value(id_or_ins, cash_amount, price=None, style=None, price_or_style=None):
account, position, ins = _get_account_position_ins(id_or_ins)
open_style, close_style = calc_open_close_style(price, style, price_or_style)
if cash_amount == 0:
return _submit_order(
ins, position.closable, SIDE.SELL, POSITION_EFFECT.CLOSE, close_style, position.quantity, False
)
_delta = cash_amount - position.market_value
_style = open_style if _delta > 0 else close_style
return _order_value(account, position, ins, _delta, _style)
@order_target_percent.register(INST_TYPE_IN_STOCK_ACCOUNT)
def stock_order_target_percent(id_or_ins, percent, price=None, style=None, price_or_style=None):
account, position, ins = _get_account_position_ins(id_or_ins)
open_style, close_style = calc_open_close_style(price, style, price_or_style)
if percent == 0:
return _submit_order(
ins, position.closable, SIDE.SELL, POSITION_EFFECT.CLOSE, close_style, position.quantity, False
)
_delta = account.total_value * percent - position.market_value
_style = open_style if _delta > 0 else close_style
return _order_value(account, position, ins, _delta, _style)
@order.register(INST_TYPE_IN_STOCK_ACCOUNT)
def stock_order(order_book_id, quantity, price=None, style=None, price_or_style=None):
result_order = stock_order_shares(order_book_id, quantity, price, style, price_or_style)
if result_order:
return [result_order]
return []
@order_to.register(INST_TYPE_IN_STOCK_ACCOUNT)
def stock_order_to(order_book_id, quantity, price=None, style=None, price_or_style=None):
position = Environment.get_instance().portfolio.get_position(order_book_id, POSITION_DIRECTION.LONG)
open_style, close_style = calc_open_close_style(price, style, price_or_style)
quantity = quantity - position.quantity
_style = open_style if quantity > 0 else close_style
result_order = stock_order_shares(order_book_id, quantity, price, _style, price_or_style)
if result_order:
return [result_order]
return []
[文档]@export_as_api
@ExecutionContext.enforce_phase(
EXECUTION_PHASE.OPEN_AUCTION,
EXECUTION_PHASE.ON_BAR,
EXECUTION_PHASE.ON_TICK,
EXECUTION_PHASE.SCHEDULED,
EXECUTION_PHASE.GLOBAL
)
@apply_rules(verify_that('id_or_ins').is_valid_stock(), verify_that('amount').is_number(), *common_rules)
def order_lots(id_or_ins, amount, price=None, style=None, price_or_style=None):
# type: (Union[str, Instrument], int, Optional[float], Optional[OrderStyle], PRICE_OR_STYLE_TYPE) -> Optional[Order]
"""
指定手数发送买/卖单。如有需要落单类型当做一个参量传入，如果忽略掉落单类型，那么默认是市价单（market order）。
:param id_or_ins: 下单标的物
:param int amount: 下单量, 正数代表买入，负数代表卖出。将会根据一手xx股来向下调整到一手的倍数，比如中国A股就是调整成100股的倍数。
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要用于简化 `style` 参数。
:param style: 下单类型, 默认是市价单。目前支持的订单类型有 :class:`~LimitOrder` 和 :class:`~MarketOrder`
:param price_or_style: 默认为None，表示市价单，可设置价格，表示限价单，也可以直接设置订单类型，有如下选项：MarketOrder、LimitOrder、
TWAPOrder、VWAPOrder
:example:
.. code-block:: python
#买入20手的平安银行股票，并且发送市价单：
order_lots('000001.XSHE', 20)
#买入10手平安银行股票，并且发送限价单，价格为￥10：
order_lots('000001.XSHE', 10, style=LimitOrder(10))
"""
auto_switch_order_value = Environment.get_instance().config.mod.sys_accounts.auto_switch_order_value
account, position, ins = _get_account_position_ins(id_or_ins)
return _order_shares(
ins, amount * int(ins.round_lot), cal_style(price, style, price_or_style), position.quantity,
auto_switch_order_value
)
ORDER_TARGET_PORTFOLIO_SUPPORTED_INS_TYPES = {
INSTRUMENT_TYPE.CS, INSTRUMENT_TYPE.ETF, INSTRUMENT_TYPE.LOF, INSTRUMENT_TYPE.INDX
}
[文档]@export_as_api
@ExecutionContext.enforce_phase(
EXECUTION_PHASE.OPEN_AUCTION,
EXECUTION_PHASE.ON_BAR,
EXECUTION_PHASE.ON_TICK,
EXECUTION_PHASE.SCHEDULED,
EXECUTION_PHASE.GLOBAL
)
def order_target_portfolio(
target_portfolio: Dict[str, float], price_or_styles: Dict[str, TUPLE_PRICE_OR_STYLE_TYPE] = dict({}),
) -> List[Order]:
"""
批量调整股票仓位至目标权重。注意：股票账户中未出现在 target_portfolio 中的资产将被平仓！
该 API 的参数 target_portfolio 为字典，key 为 order_book_id 或 instrument，value 为权重。
此时将根据参数 price_or_styles 中设置的价格来计算目标持仓数量并调仓。
:param target_portfolio: 目标权重字典，key 为 order_book_id，value 为权重。
:param price_or_styles: 目标下单价格字典，key 为 order_book_id, value 为价格或订单类型或订单类型和价格组成的 tuple
:example:
.. code-block:: python
# 调整仓位，以使平安银行和万科 A 的持仓占比分别达到 10% 和 15%, 同时发送市价单
order_target_portfolio({
'000001.XSHE': 0.1,
'000002.XSHE': 0.15
})
# 调整仓位，分别以 14 和 26 元发出限价单，目标是使平安银行和万科 A 的持仓占比分别达到 10% 和 15%
order_target_portfolio({
'000001.XSHE': 0.1,
'000002.XSHE': 0.15
}, {
'000001.XSHE': 14,
'000002.XSHE': 26,
})
# 调整仓位，使平安银行和万科 A 的持仓占比分别达到 10% 和 15%。
# 其中平安银行的平仓价为 14 元，开仓价为 15 元；万科 A 的平仓价为 26 元，开仓价为 27 元。
order_target_portfolio({
'000001.XSHE': 0.1,
'000002.XSHE': 0.15
}, {
'000001.XSHE': (15, 14),
'000002.XSHE': (27, 26)
})
"""
env = Environment.get_instance()
target: Dict[str, Tuple[float, float, float, float]] = {}
for id_or_ins, percent in target_portfolio.items():
ins = assure_instrument(id_or_ins)
if not ins:
raise RQInvalidArgument(_(
"function order_target_portfolio: invalid keys of target_portfolio, "
"expected order_book_ids or Instrument objects, got {} (type: {})"
).format(id_or_ins, type(id_or_ins)))
if ins.type not in ORDER_TARGET_PORTFOLIO_SUPPORTED_INS_TYPES:
raise RQInvalidArgument(_(
"function order_target_portfolio: invalid instrument type, excepted CS/ETF/LOF/INDX, got {}"
).format(ins.order_book_id))
order_book_id = ins.order_book_id
last_price = env.data_proxy.get_last_price(order_book_id)
if not is_valid_price(last_price):
user_system_log.warn(
_(u"Order Creation Failed: [{order_book_id}] No market data").format(order_book_id=order_book_id)
)
continue
price_or_style = price_or_styles.get(ins.order_book_id)
open_style, close_style = calc_open_close_style(price=None, style=None, price_or_style=price_or_style)
if percent < 0:
raise RQInvalidArgument(_(
"function order_target_portfolio: invalid values of target_portfolio, "
"excepted float between 0 and 1, got {} (key: {})"
).format(percent, id_or_ins))
target[order_book_id] = percent, open_style, close_style, last_price
total_percent = sum(p for p, *__ in target.values())
if total_percent > 1 and not np.isclose(total_percent, 1):
raise RQInvalidArgument(_("total percent should be lower than 1, current: {}").format(total_percent))
account = env.portfolio.accounts[DEFAULT_ACCOUNT_TYPE.STOCK]
# 先把不在目标权重中的仓位平掉
current_quantities = {
p.order_book_id: p.quantity for p in account.get_positions() if p.direction == POSITION_DIRECTION.LONG
}
for order_book_id, quantity in current_quantities.items():
if order_book_id not in target:
env.submit_order(Order.__from_create__(
order_book_id, quantity, SIDE.SELL, MarketOrder(), POSITION_EFFECT.CLOSE
))
account_value = account.total_value
close_orders, open_orders = [], []
for order_book_id, (target_percent, open_style, close_style, last_price) in target.items():
open_price = _get_order_style_price(order_book_id, open_style)
close_price = _get_order_style_price(order_book_id, close_style)
if not (is_valid_price(close_price) and is_valid_price(open_price)):
user_system_log.warn(_(
"Adjust position of {id_or_ins} Failed: "
"Invalid close/open price {close_price}/{open_price}").format(
id_or_ins=order_book_id, close_price=close_price, open_price=open_price
)
)
continue
delta_quantity = (account_value * target_percent / close_price) - current_quantities.get(order_book_id, 0)
delta_quantity = _round_order_quantity(env.data_proxy.instrument(order_book_id), delta_quantity)
if delta_quantity == 0:
continue
elif delta_quantity > 0:
quantity, side, position_effect = delta_quantity, SIDE.BUY, POSITION_EFFECT.OPEN
order_list = open_orders
target_style = open_style
else:
quantity, side, position_effect = abs(delta_quantity), SIDE.SELL, POSITION_EFFECT.CLOSE
order_list = close_orders
target_style = close_style
order = Order.__from_create__(order_book_id, quantity, side, target_style, position_effect)
if isinstance(target_style, MarketOrder):
order.set_frozen_price(last_price)
order_list.append(order)
return list(env.submit_order(o) for o in chain(close_orders, open_orders))
[文档]@export_as_api
@ExecutionContext.enforce_phase(EXECUTION_PHASE.ON_INIT,
EXECUTION_PHASE.BEFORE_TRADING,
EXECUTION_PHASE.OPEN_AUCTION,
EXECUTION_PHASE.ON_BAR,
EXECUTION_PHASE.ON_TICK,
EXECUTION_PHASE.AFTER_TRADING,
EXECUTION_PHASE.SCHEDULED)
@apply_rules(verify_that('order_book_id').is_valid_instrument(),
verify_that('count').is_greater_than(0))
def is_suspended(order_book_id, count=1):
# type: (str, Optional[int]) -> Union[bool, pd.DataFrame]
"""
判断某只股票是否全天停牌。
:param order_book_id: 某只股票的代码或股票代码，可传入单只股票的order_book_id, symbol
:param count: 回溯获取的数据个数。默认为当前能够获取到的最近的数据
"""
dt = Environment.get_instance().calendar_dt.date()
order_book_id = assure_order_book_id(order_book_id)
return Environment.get_instance().data_proxy.is_suspended(order_book_id, dt, count)
[文档]@export_as_api
@ExecutionContext.enforce_phase(EXECUTION_PHASE.ON_INIT,
EXECUTION_PHASE.BEFORE_TRADING,
EXECUTION_PHASE.OPEN_AUCTION,
EXECUTION_PHASE.ON_BAR,
EXECUTION_PHASE.ON_TICK,
EXECUTION_PHASE.AFTER_TRADING,
EXECUTION_PHASE.SCHEDULED)
@apply_rules(verify_that('order_book_id').is_valid_instrument())
def is_st_stock(order_book_id, count=1):
# type: (str, Optional[int]) -> Union[bool, pd.DataFrame]
"""
判断股票在一段时间内是否为ST股（包括ST与*ST）。
ST股是有退市风险因此风险比较大的股票，很多时候您也会希望判断自己使用的股票是否是'ST'股来避开这些风险大的股票。另外，我们目前的策略比赛也禁止了使用'ST'股。
:param order_book_id: 某只股票的代码，可传入单只股票的order_book_id, symbol
:param count: 回溯获取的数据个数。默认为当前能够获取到的最近的数据
"""
dt = Environment.get_instance().calendar_dt.date()
order_book_id = assure_order_book_id(order_book_id)
return Environment.get_instance().data_proxy.is_st_stock(order_book_id, dt, count)
[文档]@export_as_api
@ExecutionContext.enforce_phase(
EXECUTION_PHASE.ON_INIT,
EXECUTION_PHASE.BEFORE_TRADING,
EXECUTION_PHASE.OPEN_AUCTION,
EXECUTION_PHASE.ON_BAR,
EXECUTION_PHASE.ON_TICK,
EXECUTION_PHASE.AFTER_TRADING,
EXECUTION_PHASE.SCHEDULED,
)
@apply_rules(verify_that("code").is_instance_of((str, IndustryCodeItem)))
def industry(code):
# type: (str) -> List[str]
"""
获得属于某一行业的所有股票列表。
:param code: 行业名称或行业代码。例如，农业可填写industry_code.A01 或 'A01'
我们目前使用的行业分类来自于中国国家统计局的 `国民经济行业分类 <http://www.stats.gov.cn/tjsj/tjbz/hyflbz/>`_ ，可以使用这里的任何一个行业代码来调用行业的股票列表：
========================= ===================================================
行业代码 行业名称
========================= ===================================================
A01 农业
A02 林业
A03 畜牧业
A04 渔业
A05 农、林、牧、渔服务业
B06 煤炭开采和洗选业
B07 石油和天然气开采业
B08 黑色金属矿采选业
B09 有色金属矿采选业
B10 非金属矿采选业
B11 开采辅助活动
B12 其他采矿业
C13 农副食品加工业
C14 食品制造业
C15 酒、饮料和精制茶制造业
C16 烟草制品业
C17 纺织业
C18 纺织服装、服饰业
C19 皮革、毛皮、羽毛及其制品和制鞋业
C20 木材加工及木、竹、藤、棕、草制品业
C21 家具制造业
C22 造纸及纸制品业
C23 印刷和记录媒介复制业
C24 文教、工美、体育和娱乐用品制造业
C25 石油加工、炼焦及核燃料加工业
C26 化学原料及化学制品制造业
C27 医药制造业
C28 化学纤维制造业
C29 橡胶和塑料制品业
C30 非金属矿物制品业
C31 黑色金属冶炼及压延加工业
C32 有色金属冶炼和压延加工业
C33 金属制品业
C34 通用设备制造业
C35 专用设备制造业
C36 汽车制造业
C37 铁路、船舶、航空航天和其它运输设备制造业
C38 电气机械及器材制造业
C39 计算机、通信和其他电子设备制造业
C40 仪器仪表制造业
C41 其他制造业
C42 废弃资源综合利用业
C43 金属制品、机械和设备修理业
D44 电力、热力生产和供应业
D45 燃气生产和供应业
D46 水的生产和供应业
E47 房屋建筑业
E48 土木工程建筑业
E49 建筑安装业
E50 建筑装饰和其他建筑业
F51 批发业
F52 零售业
G53 铁路运输业
G54 道路运输业
G55 水上运输业
G56 航空运输业
G57 管道运输业
G58 装卸搬运和运输代理业
G59 仓储业
G60 邮政业
H61 住宿业
H62 餐饮业
I63 电信、广播电视和卫星传输服务
I64 互联网和相关服务
I65 软件和信息技术服务业
J66 货币金融服务
J67 资本市场服务
J68 保险业
J69 其他金融业
K70 房地产业
L71 租赁业
L72 商务服务业
M73 研究和试验发展
M74 专业技术服务业
M75 科技推广和应用服务业
N76 水利管理业
N77 生态保护和环境治理业
N78 公共设施管理业
O79 居民服务业
O80 机动车、电子产品和日用产品修理业
O81 其他服务业
P82 教育
Q83 卫生
Q84 社会工作
R85 新闻和出版业
R86 广播、电视、电影和影视录音制作业
R87 文化艺术业
R88 体育
R89 娱乐业
S90 综合
========================= ===================================================
:example:
.. code-block:: python3
:linenos:
def init(context):
stock_list = industry('A01')
logger.info("农业股票列表：" + str(stock_list))
#INITINFO 农业股票列表：['600354.XSHG', '601118.XSHG', '002772.XSHE', '600371.XSHG', '600313.XSHG', '600672.XSHG', '600359.XSHG', '300143.XSHE', '002041.XSHE', '600762.XSHG', '600540.XSHG', '300189.XSHE', '600108.XSHG', '300087.XSHE', '600598.XSHG', '000998.XSHE', '600506.XSHG']
"""
if isinstance(code, IndustryCodeItem):
code = code.code
else:
code = to_industry_code(code)
cs_instruments = Environment.get_instance().data_proxy.all_instruments((INSTRUMENT_TYPE.CS, ))
return [i.order_book_id for i in cs_instruments if i.industry_code == code]
[文档]@export_as_api
@ExecutionContext.enforce_phase(
EXECUTION_PHASE.ON_INIT,
EXECUTION_PHASE.BEFORE_TRADING,
EXECUTION_PHASE.OPEN_AUCTION,
EXECUTION_PHASE.ON_BAR,
EXECUTION_PHASE.ON_TICK,
EXECUTION_PHASE.AFTER_TRADING,
EXECUTION_PHASE.SCHEDULED,
)
@apply_rules(verify_that("code").is_instance_of((str, SectorCodeItem)))
def sector(code):
# type: (str) -> List[str]
"""
获得属于某一板块的所有股票列表。
:param code: 板块名称或板块代码。例如，能源板块可填写'Energy'、'能源'或sector_code.Energy
目前支持的板块分类如下，其取值参考自MSCI发布的全球行业标准分类:
========================= ========================= ==============================================================================
板块代码 中文板块名称 英文板块名称
========================= ========================= ==============================================================================
Energy 能源 energy
Materials 原材料 materials
ConsumerDiscretionary 非必需消费品 consumer discretionary
ConsumerStaples 必需消费品 consumer staples
HealthCare 医疗保健 health care
Financials 金融 financials
InformationTechnology 信息技术 information technology
TelecommunicationServices 电信服务 telecommunication services
Utilities 公共服务 utilities
Industrials 工业 industrials
========================= ========================= ==============================================================================
:example:
.. code-block:: python3
:linenos:
def init(context):
ids1 = sector("consumer discretionary")
ids2 = sector("非必需消费品")
ids3 = sector("ConsumerDiscretionary")
assert ids1 == ids2 and ids1 == ids3
logger.info(ids1)
#INIT INFO
#['002045.XSHE', '603099.XSHG', '002486.XSHE', '002536.XSHE', '300100.XSHE', '600633.XSHG', '002291.XSHE', ..., '600233.XSHG']
"""
if isinstance(code, SectorCodeItem):
code = code.name
else:
code = to_sector_name(code)
cs_instruments = Environment.get_instance().data_proxy.all_instruments((INSTRUMENT_TYPE.CS,))
return [i.order_book_id for i in cs_instruments if i.sector_code == code]
[文档]@export_as_api
@apply_rules(
verify_that("order_book_id").is_valid_instrument(),
verify_that("start_date").is_valid_date(ignore_none=False),
)
def get_dividend(order_book_id, start_date):
# type: (str, Union[str, datetime.date, datetime.datetime, pd.Timestamp]) -> Optional[np.ndarray]
"""
获取某只股票到策略当前日期前一天的分红情况（包含起止日期）。
:param order_book_id: 股票代码
:param start_date: 开始日期，需要早于策略当前日期
========================= ===================================================
fields 字段名
========================= ===================================================
announcement_date 分红宣布日
book_closure_date 股权登记日
dividend_cash_before_tax 税前分红
ex_dividend_date 除权除息日
payable_date 分红到帐日
round_lot 分红最小单位
========================= ===================================================
:example:
获取平安银行2013-01-04 到策略当前日期前一天的分红数据:
.. code-block:: python3
:linenos:
get_dividend('000001.XSHE', start_date='20130104')
#[Out]
#array([(20130614, 20130619, 20130620, 20130620, 1.7 , 10),
# (20140606, 20140611, 20140612, 20140612, 1.6 , 10),
# (20150407, 20150410, 20150413, 20150413, 1.74, 10),
# (20160608, 20160615, 20160616, 20160616, 1.53, 10)],
# dtype=[('announcement_date', '<u4'), ('book_closure_date', '<u4'), ('ex_dividend_date', '<u4'), ('payable_date', '<u4'), ('dividend_cash_before_tax', '<f8'), ('round_lot', '<u4')])
"""
# adjusted 参数在不复权数据回测时不再提供
env = Environment.get_instance()
dt = env.trading_dt.date() - datetime.timedelta(days=1)
start_date = to_date(start_date)
if start_date > dt:
raise RQInvalidArgument(
_(
u"in get_dividend, start_date {} is later than the previous test day {}"
).format(start_date, dt)
)
order_book_id = assure_order_book_id(order_book_id)
array = env.data_proxy.get_dividend(order_book_id)
if array is None:
return None
sd = start_date.year * 10000 + start_date.month * 100 + start_date.day
ed = dt.year * 10000 + dt.month * 100 + dt.day
return array[
(array["announcement_date"] >= sd) & (array["announcement_date"] <= ed)
]
def to_industry_code(s):
for __, v in industry_code.__dict__.items():
if isinstance(v, IndustryCodeItem):
if v.name == s:
return v.code
return s
def to_sector_name(s):
for __, v in sector_code.__dict__.items():
if isinstance(v, SectorCodeItem):
if v.cn == s or v.en == s or v.name == s:
return v.name
# not found
return s