ricequant / Rqopen Client
rqopen-client：本地拿到Ricequant上实盘模拟交易策略的交易信号和持仓
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RQOpen-Client - RQOpen客户端
简介
通过这个简单的python库，你可以拿到你在 www.ricequant.com 运行的实盘、模拟交易或回测的运行数据。包含交易列表，仓位信息，投资组合信息。
RQOpen 获取到的信号，会比系统时间稍微延迟一些。Ricequant Gateway 在切分钟线的时候会延迟10多秒，因为数据源的问题。而 RQAlpha 产生信号后，保存在数据库中。由 RQOpen 轮训，轮训的间隔，也是信号的延迟时间。
安装
pip install rqopen-client
简单的范例
from pprint import pprint
from rqopen_client import RQOpenClient
username = "your ricequant username"
password = "your ricequant password"
run_id = your_run_id
client = RQOpenClient(username, password)
# client = RQOpenClient(username, password, return_df=Fales)
pprint(client.trades(run_id))
pprint(client.current_positions(run_id))
上面代码的一些展示结果（随便一个范例）：
初始化&登录: RQOpenClient(username, password, return_df=True)
:param username: 登录账户
:param password: 密码
:param logger: 日志
:param log_level: 日志级别
:param base_url: 服务地址，默认web端 rqpro2.0需要单独配置
:param timeout: 超时时间
:param return_df: 返回数据是否为DataFrame False返回dict
查询当日交易记录: current_trades(run_id)
返回示例 - 今天没有任何下单：
# return_df=False
{'code': 200,
'resp': {'name': '改进版小盘股 2016-08-28 16:15:06', 'run_id': 968101, 'trades': []}}
# return_df=True
Empty DataFrame
Columns: []
Index: []
返回示例 - 当日有交易：
# return_df=False
{'code': 200,
'resp': {'data': [{'commission': 5.0,
'date': '2019-01-04 09:31:00',
'exec_id': '15641121150000',
'last_price': 9.29,
'last_quantity': 100.0,
'order_book_id': '000001.XSHE',
'order_id': 15641121140000,
'position_effect': 'OPEN',
'side': 'BUY',
'tax': 0.0,
'trade_id': '15641121150000',
'transaction_cost': 5.0},
{'commission': 5.0,
'date': '2019-01-04 09:31:00',
'exec_id': '15641121150002',
'last_price': 16.29,
'last_quantity': 100.0,
'order_book_id': '000004.XSHE',
'order_id': 15641121140002,
'position_effect': 'OPEN',
'side': 'BUY',
'tax': 0.0,
'trade_id': '15641121150002',
'transaction_cost': 5.0}],
'name': 'SVM大法好',
'run_id': 6281}}
# return_df=True
commission ... transaction_cost
date ...
2018-05-04 09:31:00 8.568 ... 8.568
2018-05-04 09:32:00 8.608 ... 8.608
2018-05-04 09:33:00 8.592 ... 8.592
2018-05-04 09:34:00 8.600 ... 8.600
2018-05-04 09:35:00 8.592 ... 8.592
查询最新持仓: current_positions(run_id)
返回示 - 有持仓：
# return_df=False
{'code': 200,
'resp': {'name': 'SVM大法好',
'run_id': 6281,
'positions': [{'order_book_id': '000001.XSHE',
'last_price': 13.86,
'quantity': 5062.0},
{'order_book_id': '000002.XSHE', 'last_price': 32.22, 'quantity': 4939.0},
{'order_book_id': '000004.XSHE', 'last_price': 21.67, 'quantity': 4341.0},
{'order_book_id': '000005.XSHE', 'last_price': 3.97, 'quantity': 4903.0}}]}}
# return_df=True
last_price order_book_id quantity
0 13.86 000001.XSHE 5062.0
1 32.22 000002.XSHE 4939.0
2 21.67 000004.XSHE 4341.0
3 3.97 000005.XSHE 4903.0
4 7.13 000006.XSHE 5011.0
5 7.57 000007.XSHE 4955.0
6 4.97 000008.XSHE 4906.0
7 7.19 000009.XSHE 4995.0
8 3.76 000010.XSHE 4996.0
9 12.19 000011.XSHE 4931.0
10 5.81 000012.XSHE 4984.0
11 12.75 000014.XSHE 4974.0
查询全部交易: trades(run_id)
返回示 - 有交易：
# return_df=False
{'code': 200,
'resp': {'data': [{'commission': 5.0,
'date': '2019-01-07 11:05:00',
'exec_id': '15641121150040',
'last_price': 3.36,
'last_quantity': 1.0,
'order_book_id': '000010.XSHE',
'order_id': 15641121140202,
'position_effect': 'CLOSE',
'side': 'SELL',
'tax': 0.0033599999999998076,
'trade_id': '15641121150040',
'transaction_cost': 5.00336},
'name': '横琴比赛',
'run_id': 6281}}
# return_df=True
commission ... transaction_cost
date ...
2019-01-04 09:31:00 5.0 ... 5.00000
2019-01-04 09:31:00 5.0 ... 5.00000
... ... ...
2019-04-04 14:44:00 5.0 ... 5.10176
2019-04-04 14:54:00 5.0 ... 5.06417
[3102 rows x 11 columns]
查询全部持仓: positions(run_id)
返回示 - 有持仓：
# return_df=False
{'code': 200,
'resp': {'data': [{'000001.XSHE': 975.0, # market_value
'000002.XSHE': 2493.0,
'000004.XSHE': 1660.0000000000002,
'000005.XSHE': 275.0,
'000006.XSHE': 526.0,
'000007.XSHE': 800.0,
'000008.XSHE': 391.0,
'000009.XSHE': 437.0,
'000010.XSHE': 332.0,
'000011.XSHE': 956.0,
'000012.XSHE': 409.99999999999994,
'000014.XSHE': 969.0,
'cash': 1990076.0,
'date': '2019-01-04 00:00:00'},
'name': '横琴比赛',
'run_id': 6281}}
# return_df=True
000001.XSHE 000002.XSHE ... 000014.XSHE cash
date ...
2019-01-04 975.00 2493.00 ... 969.00 1.990076e+06
2019-01-07 1801.90 4534.05 ... 1664.99 1.981082e+06
2019-01-08 2675.82 6950.00 ... 2388.66 1.972075e+06
2019-01-09 3618.16 9144.13 ... 3246.15 1.963209e+06
[4rows x 13 columns]
查询投资组合: portfolio(run_id)
# return_df=False
{'code': 200,
'resp': {'data': [{'annualized_returns': 0.038520563489603976,
'benchmark_daily_returns': 0.023957447956719332,
'benchmark_total_returns': 0.023957447956719332,
'cash': 1990076.0,
'daily_pnl': 300.000000000189,
'daily_returns': 0.0001500000000000945,
'date': '2019-01-04 00:00:00',
'market_value': 10224.0,
'portfolio_value': 2000300.0,
'starting_cash': 2000000.0,
'total_returns': 0.0001500000000000945,
'transaction_cost': 60.0},],
'name': '横琴比赛',
'run_id': 6281}}
# return_df=True
annualized_returns ... transaction_cost
date ...
2019-01-04 0.038521 ... 60.00000
2019-01-07 0.022092 ... 226.54735
2019-01-08 -0.005048 ... 251.59331
2019-01-09 -0.004138 ... 266.77119=
[4 rows x 11 columns]
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