#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from typing import List, Union
import datetime
import numpy as np
import pandas as pd
from simonsc.client import SimonsClient
from simonsc.utils import *
__all__ = []
[docs]@assert_auth
@export_as_api
def all_instruments(type: str=None, date: Union[str, datetime.datetime, datetime.date]=None) ->pd.DataFrame:
"""
获取simons目前支持的所有合约信息
:param type: 需要查询合约类型，例如：type='CS'代表股票。默认是所有类型
:param date: 查询时间点
其中type参数传入的合约类型和对应的解释如下
========================= ====================================================
合约类型 说明
========================= ====================================================
CS Common Stock, 即股票
ETF Exchange Traded Fund, 即交易所交易基金
LOF Listed Open-Ended Fund，即上市型开放式基金
INDX Index, 即指数
Future Futures，即期货，包含股指、国债和商品期货
========================= ====================================================
:example:
.. code-block:: python3
>>> instrument_df = all_instruments(type="CS")
>>> instrument_df.head()
order_book_id symbol industry_code exchange status type listed_date
0 000001.XSHE 平安银行 J66 XSHE Active CS 1991-04-03
1 000002.XSHE 万科A K70 XSHE Active CS 1991-01-29
2 000004.XSHE 国农科技 I65 XSHE Active CS 1991-01-14
3 000005.XSHE 世纪星源 N77 XSHE Active CS 1990-12-10
4 000006.XSHE 深振业A K70 XSHE Active CS 1992-04-27
"""
date = convert_datetime_to_str(date)
return SimonsClient.instance().all_instruments(**locals())
[docs]@assert_auth
@export_as_api
def history_bars(
order_book_ids: str,
bar_count: int,
frequency: str,
dt: datetime.datetime,
fields: List[str]=None,
skip_suspended: bool=True,
include_now: bool=False,
adjust_type: str="pre",
adjust_orig:datetime.datetime = None,
) -> pd.DataFrame:
"""获取指定合约的历史 k 线行情，支持任意日频率xd(1d,5d)和任意分钟频率xm(1m,3m,5m,15m)的历史数据。
:param order_book_ids: 多个标的合约代码
: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）
========================= ===================================================
Example1::
获取中国平安和万科 2020-04-20之前10天的交易数据
.. code-block:: python3
import pandas as pd
from simons.api import history_bars
#
>>> dt = pd.Timestamp("2020-04-20")
>>> fields=["datetime","open","high","low","close","volume"]
>>> data = history_bars(order_book_ids=["000001.XSHE", "000002.XSHE"], dt=dt, bar_count=10, frequency="1d", fields=fields)
>>> print(data)
open high low close volume
order_book_id datetime
000001.XSHE 2020-04-07 12.89 12.94 12.81 12.88 87031371.0
2020-04-08 12.88 12.92 12.72 12.78 52871614.0
2020-04-09 12.88 12.89 12.72 12.74 40855377.0
2020-04-10 12.76 12.98 12.65 12.79 66667495.0
2020-04-13 12.67 12.71 12.47 12.59 44621440.0
2020-04-14 12.65 12.86 12.57 12.86 68608687.0
2020-04-15 12.86 12.93 12.78 12.87 65639640.0
2020-04-16 12.79 12.79 12.54 12.68 78915498.0
2020-04-17 12.77 13.04 12.65 12.89 133116477.0
2020-04-20 12.86 13.05 12.77 12.99 81845583.0
000002.XSHE 2020-04-07 27.34 27.42 26.80 27.07 67154006.0
2020-04-08 26.90 27.25 26.75 26.96 41251395.0
2020-04-09 27.10 27.16 26.60 26.69 38726254.0
2020-04-10 26.84 27.34 26.59 26.88 62460322.0
2020-04-13 26.74 27.13 26.61 27.04 43264902.0
2020-04-14 27.10 27.75 27.02 27.35 64241868.0
2020-04-15 27.20 27.23 26.55 26.70 70359257.0
2020-04-16 26.52 26.76 26.40 26.58 50238931.0
2020-04-17 26.78 27.03 26.55 26.72 83813322.0
2020-04-20 26.78 26.81 26.05 26.58 85012343.0
"""
dt = convert_datetime_to_str(dt)
return SimonsClient.instance().history_bars(**locals())
[docs]@assert_auth
@export_as_api
def history_snapshot(
order_book_id: str,
bar_count: int,
dt: datetime.datetime,
fields: List[str]=None,
skip_suspended: bool=True,
include_now: bool=False,
adjust_type: str="none",
adjust_orig:datetime = None,
) -> pd.DataFrame:
"""获取指定合约的历史快照数据
:param order_book_id: 合约代码
:param bar_count: 获取的历史数据数量，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
.. admonition:: 可支持的数据字段
:class: dropdown, note
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| fields | 中文名 | dtype | 是否是原始字段 | 注释 |
+========================================+==========================+=========+================+=======================================================+
| date | 交易归属日期 | <i8 | Y | yyyymmdd |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| datetime | 交易发生时间 | <i8 | C | yyyymmddhhmmss，由交易日当天日期和数据生成时间（交易s |
| | | | |（交易所直接下发的）合成。 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| last | 最新成交价 | <f8 | Y | |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| sell_price_10 ~ sell_price_1 | 第10 ~ 1档委托卖出价 | <f8 | Y | |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| buy_price_1 ~ buy_price_10 | 第1 ~ 10档委托买入价 | <f8 | Y | |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| sell_volume_10 ~ sell_volume_1 | 第10 ~ 1档申卖量 | <f8 | Y | |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| buy_volume_1 ~ buy_volume_10 | 第1 ~ 10档申买量 | <f8 | Y | |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| num_sell_trades_10 ~ num_sell_trades_1 | 委卖笔数10 ~ 委卖笔数1 | <f8 | Y | 委卖价1 ~ 10的委托总比数 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| num_buy_trades_1 ~ num_buy_trades_10 | 委买笔数1 ~ 委买笔数10 | <f8 | Y | 委买价1 ~ 10的委托总比数 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| total_num_trades | 成交总笔数 | <f8 | Y | 开盘至当前时刻的累计成交笔数 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| current_num_trades | 分笔期间成交笔数 | <f8 | 上交所：N | 当前成交总比数(total_num_trades_t) - 上一记录的成 |
| | | | 深交所：Y | 交总比数(total_num_trades_t-1);首条记录取当前成交 |
| | | | | 总比 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| total_volume | 成交总量 | <f8 | Y | 开盘至当前时刻的累计成交量 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| current_volume | 分笔期间成交量 | <f8 | 上交所：N | 当前成交总量(total_volume_t ) - 上一记录的成 |
| | | | 深交所：Y | 交总量(total_volume_t-1);首条记录取当前成交总量 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| total_turnover | 成交总额 | <f8 | Y | 开盘至当前时刻的累计成交额 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| current_turnover | 分笔期间成交额 | <f8 | 上交所：N | 当前成交总额(total_turnover_t) - 上一记录的成交总额 |
| | | | 深交所：Y | (total_turnover_t-1);首条记录取当前成交总额 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| total_sell_order_volume | 委托卖出总量 | <f8 | Y | 是指直接到切片时间的还存在的, 所有委托卖单总量 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| total_buy_order_volume | 委托买入总量 | <f8 | Y | 是指直接到切片时间的还存在的，所有委托买单总量 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| wt_avg_sell_price | 加权平均委卖价格 | <f8 | Y | 单位：元 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| wt_avg_buy_price | 加权平均委买价格 | <f8 | Y | 单位：元 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| prev_close | 昨收盘价 | <f8 | Y | 上一交易日的收盘价，上交所的收盘价格是最后一分钟的成 |
| | | | | 交均价 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| open | 开盘价 | <f8 | Y | 当日开盘价 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| high | 最高价 | <f8 | Y | 开盘至当前时刻所出现的最高成交价 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| low | 最低价 | <f8 | Y | 开盘至当前时刻所出现的最低成交价 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| daily_close | 今日收盘价 | <f8 | Y | 该交易日的收盘价，上交所的收盘价格是最后一分钟的成交均|
| | | | | 价（在最后一笔行情上更新，其余行值为0） |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| sell_level_no | 申卖价格档位数 | <f8 | Y | 表示揭示的档位数，取值（0,10） |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| buy_level_no | 申买价格档位数 | <f8 | Y | 表示揭示的档位数，取值（0,10） |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
bbla
.. code-block:: python3
from simonsc.api import history_snapshot
>>> dt = pd.Timestamp("2020-07-24 14:55:00")
>>> fields=["datetime","last","buy_price_1","buy_volume_1","sell_price_1","sell_volume_1","sell_price_10"]
>>> data = history_snapshot(order_book_id="600446.XSHG", dt=dt, bar_count=10, fields=fields)
>>> print(data)
last buy_price_1 buy_volume_1 sell_price_1 sell_volume_1 sell_price_10
order_book_id datetime
600446.XSHG 2020-07-24 14:54:32 19.12 19.12 1100. 19.13, 1500. 19.26
2020-07-24 14:54:35 19.12 19.11 6600. 19.12, 57900. 19.25
2020-07-24 14:54:38 19.12 19.11 6800. 19.12, 57800. 19.25
2020-07-24 14:54:41 19.12 19.11 36400. 19.12, 57200. 19.25
2020-07-24 14:54:44 19.11 19.11 21200. 19.12, 55900. 19.25
2020-07-24 14:54:47 19.11 19.11 7400. 19.12, 52200. 19.25
2020-07-24 14:54:50 19.12 19.11 4700. 19.12, 40800. 19.25
2020-07-24 14:54:53 19.12 19.12 41800. 19.13, 9700. 19.26
2020-07-24 14:54:56 19.12 19.12 40900. 19.13, 9700. 19.26
2020-07-24 14:54:59 19.13 19.12 44000. 19.13, 9600. 19.26
"""
dt = convert_datetime_to_str(dt)
return SimonsClient.instance().history_snapshot(**locals())
@assert_auth
@export_as_api
def history_transaction(
order_book_id: str,
tick_count: int,
start_dt: datetime,
end_dt: datetime,
fields: List[str]=None,
include_prehours: bool=False
) -> np.ndarray:
"""获取指定合约的历史快照数据
:param order_book_id: 合约代码
:param tick_count: 获取的逐笔成交条数, 与start_dt, end_dt, 三者必填两者
:param start_dt: 获取数据的起始日期时间，e.g. “2017-01-12 09:33:05”
:param end_dt: 获取数据的截止日期时间，e.g. “2017-01-12 09:33:05”
:param fields: 返回数据字段。必填项。见下方列表。
:param include_prehours: 是否包含盘前数据
.. admonition:: 可支持的数据字段
:class: dropdown, note
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| fields | 中文名 | dtype | 是否是原始字段 | 注释 |
+========================================+==========================+=========+================+=======================================================+
| date | 交易归属日期 | <i8 | Y | yyyymmdd |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| datetime | 交易发生时间 | <i8 | C | yyyymmddhhmmssmmm，由交易日当天日期和数据生成时间 |
| | | | |（交易所直接下发的）合成。精确到10毫秒级 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| trade_price | 成交价格 | <f8 | Y | |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| trade_volume | 成交数量 | <f8 | Y | |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| trade_turnover | 成交金额 | <f8 | 上交所：Y | 成交价格 X 成交量 |
| | | | 深交所：N | |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
| buy_sell_flag | 内外盘标志 | O | 上交所：Y | 上交所：（深交所全部为NULL） |
| | | | 深交所：N | - 2013-04-15前，没有下发该字段，值为NULL； |
| | | | | - 2013-04-15至今，下发了该字段，字段值含义分别如下： |
| | | | | B:外盘，主动买；S:内盘，主动卖；N:未知 |
+----------------------------------------+--------------------------+---------+----------------+-------------------------------------------------------+
bbla
.. code-block:: python3
from simons.api import history_transaction
>>> dt = pd.Timestamp("2020-07-24 14:55:00")
>>> fields=["datetime","trade_price","trade_volume","trade_turnover"]
>>> data = history_transaction(order_book_id="600446.XSHG", start_dt=dt, bar_count=10, fields=fields)
>>> print(data)
"""
[docs]@assert_auth
@export_as_api
def get_factor_exposure(
order_book_ids: List[str],
bar_count: int,
dt: datetime.datetime,
fields: List[str]=None,
frequency: str='1d',
) -> pd.DataFrame:
"""获取指定合约的历史因子数据，支持任意日频率xd(1d,5d)和分钟频率xm(1m,3m,5m,15m)的历史因子数据（当前只支持1d）。
:param order_book_id: 合约代码
:param bar_count: 获取的历史数据数量，必填项
:param dt: 获取数据的截止日期时间，e.g. “2020-09-18”
:param fields: 返回数据字段。必填项。见因子数据数据字典。
: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，默认为1d
Example1::
获取中国平安和万科A(order_book_ids), 2020-09-18(dt)之前10(bar_count)天的return_mean_5和returns_skew_20(fields)因子数据
.. code-block:: python3
import pandas as pd
from simonsc.api import get_factor_exposure
#
>>> dt = pd.Timestamp("2020-09-18")
>>> fields=["datetime","return_mean_5","returns_skew_20"]
>>> order_book_id_list = ["000001.XSHE","000002.XSHE"]
>>> factor_exposure = get_factor_exposure(order_book_ids=order_book_id_list, dt=dt, bar_count=10, fields=fields, frequency="1d")
>>> print(factor_exposure)
return_mean_5 returns_skew_20
order_book_id datetime
000001.XSHE 2020-09-07 -0.001771 0.585366
2020-09-08 0.003992 0.499581
2020-09-09 -0.001237 0.683064
2020-09-10 0.005955 0.571358
2020-09-11 0.000848 0.744097
2020-09-14 0.004979 0.487314
2020-09-15 -0.000927 0.436575
2020-09-16 0.003097 0.357694
2020-09-17 0.003072 0.572409
2020-09-18 0.013797 0.365532
000002.XSHE 2020-09-07 0.008380 -0.107046
2020-09-08 0.007832 -0.028114
2020-09-09 0.003366 -0.196695
2020-09-10 0.004690 -0.285299
2020-09-11 -0.000413 0.327043
2020-09-14 -0.005120 0.512526
2020-09-15 -0.002978 0.489892
2020-09-16 0.001579 0.271366
2020-09-17 -0.002774 0.348217
2020-09-18 0.009297 1.139321
"""
dt = convert_datetime_to_str(dt)
return SimonsClient.instance().get_factor_exposure(**locals())
[docs]@assert_auth
@export_as_api
def get_factor_returns(
bar_count: int,
dt: datetime.datetime,
fields: List[str]=None,
frequency: str='1d',
) -> pd.DataFrame:
"""获取指定合约的历史因子收益率，支持频率1d(日), 1w(周), 1M(月)历史因子数据
:param bar_count: 获取的历史数据数量，必填项
:param dt: 获取数据的截止日期时间，e.g. “2020-09-18”
:param fields: 返回数据字段。必填项。见因子收益率数据字典。
: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，默认为1d
Example1::
获取Fama-French 5因子 2020-09-18之前10天的SMB_5和HML_5因子收益率
.. code-block:: python3
import pandas as pd
from simonsc.api import get_factor_returns
#
>>> dt = pd.Timestamp("2020-09-18")
>>> fields=["SMB_5","HML_5"]
>>> factor_returns = get_factor_returns(dt=dt, bar_count=10, fields=fields, frequency="1d")
>>> print(factor_returns)
SMB_5 HML_5
datetime
20200907000000 0.036871 0.015527
20200908000000 0.020943 0.016531
20200909000000 -0.002040 0.024283
20200910000000 -0.085072 -0.000579
20200911000000 0.012589 -0.012224
20200914000000 0.033144 -0.007565
20200915000000 -0.014592 -0.006376
20200916000000 -0.005519 0.008924
20200917000000 0.012206 0.000091
20200918000000 -0.019927 0.004799
"""
dt = convert_datetime_to_str(dt)
return SimonsClient.instance().get_factor_returns(**locals())
[docs]@assert_auth
@export_as_api
def available_factor_names(frequency="1d"):
"""获取所有因子字段名
"""
return SimonsClient.instance().available_factor_names(**locals())