476 lines
21 KiB
Plaintext
476 lines
21 KiB
Plaintext
from jqdata import *
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from jqfactor import *
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import numpy as np
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import pandas as pd
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from datetime import time,date
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from jqdata import finance
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'''
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索普聚宽交易系统
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作者:索普量化
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微信:xms_quants1
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原理替代,继承聚宽的交易函数类
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读取下单类的函数参数把交易数据发送到服务器
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把下面的全部源代码复制到聚宽策略的开头就可以
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实盘前先模拟盘测试一下数据
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把下面的内容全部复制到策略的开头就可以
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'''
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import requests
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import json
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import pandas as pd
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from jqdata import *
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url='http://101.34.65.108'
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port=8888
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#自定义服务器编码
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url_code='63d85b6189e42cba63feea36381da615c31ad8e36ae420ed67f60f3598efc9ad'
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#记得把这个改成自己的一个策略一个,策略的名称找作者建立
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password='国九条后中小板微盘小改'
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class joinquant_trader:
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def __init__(self,url='http://124.220.32.224',
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port=8025,
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url_code='63d85b6189e42cba63feea36381da615c31ad8e36ae420ed67f60f3598efc9ad',
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password='123456'):
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'''
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获取服务器数据
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'''
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self.url=url
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self.port=port
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self.url_code=url_code
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self.password=password
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def get_user_data(self,data_type='用户信息'):
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'''
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获取使用的数据
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data_type='用户信息','实时数据',历史数据','清空实时数据','清空历史数据'
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'''
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url='{}:{}/_dash-update-component'.format(self.url,self.port)
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headers={'Content-Type':'application/json'}
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data={"output":"joinquant_trader_table.data@{}".format(self.url_code),
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"outputs":{"id":"joinquant_trader_table","property":"data@{}".format(self.url_code)},
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"inputs":[{"id":"joinquant_trader_password","property":"value","value":self.password},
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{"id":"joinquant_trader_data_type","property":"value","value":data_type},
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{"id":"joinquant_trader_text","property":"value","value":"\n {'状态': 'held', '订单添加时间': 'datetime.datetime(2024, 4, 23, 9, 30)', '买卖': 'False', '下单数量': '9400', '已经成交': '9400', '股票代码': '001.XSHE', '订单ID': '1732208241', '平均成交价格': '10.5', '持仓成本': '10.59', '多空': 'long', '交易费用': '128.31'}\n "},
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{"id":"joinquant_trader_run","property":"value","value":"运行"},
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{"id":"joinquant_trader_down_data","property":"value","value":"不下载数据"}],
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"changedPropIds":["joinquant_trader_run.value"],"parsedChangedPropsIds":["joinquant_trader_run.value"]}
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res=requests.post(url=url,data=json.dumps(data),headers=headers)
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text=res.json()
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df=pd.DataFrame(text['response']['joinquant_trader_table']['data'])
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return df
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def send_order(self,result):
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'''
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发送交易数据
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'''
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url='{}:{}/_dash-update-component'.format(self.url,self.port)
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headers={'Content-Type':'application/json'}
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data={"output":"joinquant_trader_table.data@{}".format(self.url_code),
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"outputs":{"id":"joinquant_trader_table","property":"data@{}".format(self.url_code)},
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"inputs":[{"id":"joinquant_trader_password","property":"value","value":self.password},
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{"id":"joinquant_trader_data_type","property":"value","value":'发送信号'},
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{"id":"joinquant_trader_text","property":"value","value":result},
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{"id":"joinquant_trader_run","property":"value","value":"运行"},
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{"id":"joinquant_trader_down_data","property":"value","value":"不下载数据"}],
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"changedPropIds":["joinquant_trader_run.value"],"parsedChangedPropsIds":["joinquant_trader_run.value"]}
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res=requests.post(url=url,data=json.dumps(data),headers=headers)
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text=res.json()
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df=pd.DataFrame(text['response']['joinquant_trader_table']['data'])
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return df
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#继承类
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xg_data=joinquant_trader(url=url,port=port,password=password,)
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def send_order(result):
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'''
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发送函数
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status: 状态, 一个OrderStatus值
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add_time: 订单添加时间, [datetime.datetime]对象
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is_buy: bool值, 买还是卖，对于期货:
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开多/平空 -> 买
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开空/平多 -> 卖
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amount: 下单数量, 不管是买还是卖, 都是正数
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filled: 已经成交的股票数量, 正数
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security: 股票代码
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order_id: 订单ID
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price: 平均成交价格, 已经成交的股票的平均成交价格(一个订单可能分多次成交)
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avg_cost: 卖出时表示下卖单前的此股票的持仓成本, 用来计算此次卖出的收益. 买入时表示此次买入的均价(等同于price).
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side: 多/空，'long'/'short'
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action: 开/平， 'open'/'close'
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commission交易费用（佣金、税费等）
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'''
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data={}
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data['状态']=str(result.status)
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data['订单添加时间']=str(result.add_time)
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data['买卖']=str(result.is_buy)
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data['下单数量']=str(result.amount)
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data['已经成交']=str(result.filled)
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data['股票代码']=str(result.security)
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data['订单ID']=str(result.order_id)
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data['平均成交价格']=str(result.price)
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data['持仓成本']=str(result.avg_cost)
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data['多空']=str(result.side)
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data['交易费用']=str(result.commission)
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result=str(data)
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xg_data.send_order(result)
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return data
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def xg_order(func):
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'''
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继承order对象 数据交易函数
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'''
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def wrapper(*args, **kwargs):
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result = func(*args, **kwargs)
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if result == None:
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return
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send_order(result)
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return result
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return wrapper
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def xg_order_target(func):
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'''
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继承order_target对象 百分比
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'''
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def wrapper(*args, **kwargs):
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result = func(*args, **kwargs)
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if result == None:
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return
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send_order(result)
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return result
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return wrapper
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def xg_order_value(func):
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'''
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继承order_value对象 数量
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'''
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def wrapper(*args, **kwargs):
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result = func(*args, **kwargs)
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if result == None:
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return
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send_order(result)
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return result
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return wrapper
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def xg_order_target_value(func):
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'''
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继承order_target_value对象 数量
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'''
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def wrapper(*args, **kwargs):
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result = func(*args, **kwargs)
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if result == None:
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return
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send_order(result)
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return result
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return wrapper
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order = xg_order(order)
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order_target = xg_order_target(order_target)
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order_value = xg_order_value(order_value)
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order_target_value = xg_order_target_value(order_target_value)
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#初始化函数
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def initialize(context):
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# 开启防未来函数
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set_option('avoid_future_data', True)
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# 成交量设置
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#set_option('order_volume_ratio', 0.10)
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# 设定基准
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set_benchmark('399101.XSHE')
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# 用真实价格交易
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set_option('use_real_price', True)
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# 将滑点设置为0
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set_slippage(FixedSlippage(3/10000))
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# 设置交易成本万分之三，不同滑点影响可在归因分析中查看
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set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=2.5/10000, close_commission=2.5/10000, close_today_commission=0, min_commission=5),type='stock')
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# 过滤order中低于error级别的日志
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log.set_level('order', 'error')
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log.set_level('system', 'error')
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log.set_level('strategy', 'debug')
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#初始化全局变量 bool
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g.trading_signal = True # 是否为可交易日
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g.run_stoploss = True # 是否进行止损
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g.filter_audit = False # 是否筛选审计意见
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g.adjust_num = True # 是否调整持仓数量
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#全局变量list
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g.hold_list = [] #当前持仓的全部股票
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g.yesterday_HL_list = [] #记录持仓中昨日涨停的股票
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g.target_list = []
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g.pass_months = [1, 4] # 空仓的月份
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g.limitup_stocks = [] # 记录涨停的股票避免再次买入
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#全局变量float/str
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g.min_mv = 10 # 股票最小市值要求
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g.max_mv = 100 # 股票最大市值要求
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g.stock_num = 4 # 持股数量
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g.stoploss_list = [] # 止损卖出列表
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g.other_sale = [] # 其他卖出列表
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g.stoploss_strategy = 3 # 1为止损线止损，2为市场趋势止损, 3为联合1、2策略
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g.stoploss_limit = 0.09 # 止损线
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g.stoploss_market = 0.05 # 市场趋势止损参数
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g.highest = 50 # 股票单价上限设置
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g.money_etf = '511880.XSHG' # 空仓月份持有银华日利ETF
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# 设置交易运行时间
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run_daily(prepare_stock_list, '9:05')
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run_daily(trade_afternoon, time='14:00', reference_security='399101.XSHE') #检查持仓中的涨停股是否需要卖出
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run_daily(stop_loss, time='10:00') # 止损函数
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run_daily(close_account, '14:50')
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run_weekly(weekly_adjustment,2,'10:00')
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#run_weekly(print_position_info, 5, time='15:10', reference_security='000300.XSHG')
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#1-1 准备股票池
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def prepare_stock_list(context):
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#获取已持有列表
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g.limitup_stocks = []
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g.hold_list = list(context.portfolio.positions)
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#获取昨日涨停列表
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if g.hold_list:
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df = get_price(g.hold_list, end_date=context.previous_date, frequency='daily', fields=['close','high_limit','low_limit'], count=1, panel=False, fill_paused=False)
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df = df[df['close'] == df['high_limit']]
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g.yesterday_HL_list = df['code'].tolist()
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else:
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g.yesterday_HL_list = []
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#判断今天是否为账户资金再平衡的日期
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g.trading_signal = today_is_between(context)
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#1-2 选股模块
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def get_stock_list(context):
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final_list = []
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MKT_index = '399101.XSHE'
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initial_list = filter_stocks(context, get_index_stocks(MKT_index))
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# 国九更新：过滤近一年净利润为负且营业收入小于1亿的
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# 国九更新：过滤近一年期末净资产为负的 (经查询没有为负数的，所以直接pass这条)
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# 国九更新：过滤近一年审计建议无法出具或者为负面建议的 (经过净利润等筛选，审计意见几乎不会存在异常)
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q = query(
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valuation.code,
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).filter(
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valuation.code.in_(initial_list),
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valuation.market_cap.between(g.min_mv,g.max_mv), # 总市值 circulating_market_cap/market_cap 单位：亿元
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income.np_parent_company_owners > 0, # 归属于母公司所有者的净利润(元)
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income.net_profit > 0, # 净利润(元)
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income.operating_revenue > 1e8 # 营业收入 (元)
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).order_by(valuation.market_cap.asc()).limit(g.stock_num*3)
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df = get_fundamentals(q)
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# 如果筛选审计意见会大幅度增加回测时长，实测增加此项筛选不影响选股
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if g.filter_audit:
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before_audit_filter = len(df)
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df['audit'] = df['code'].apply(lambda x: filter_audit(context, x))
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df_audit = df[df['audit'] == True]
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log.info('去除掉了存在审计问题的股票{}只'.format(len(df)-before_audit_filter))
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final_list = df['code'].tolist()
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if final_list:
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last_prices = history(1, unit='1d', field='close', security_list=final_list)
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return [stock for stock in final_list if stock in g.hold_list or last_prices[stock][-1] <= g.highest]
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else:
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# 由于有时候选股条件苛刻，所以会没有股票入选，这时买入银华日利ETF
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log.info('无适合股票，买入ETF')
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return [g.money_etf]
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#1-3 整体调整持仓
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def weekly_adjustment(context):
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if g.trading_signal:
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if g.adjust_num:
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new_num = adjust_stock_num(context)
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g.stock_num = new_num
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log.info(f'持仓数量修改为{new_num}')
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g.target_list = get_stock_list(context)[:g.stock_num]
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log.info(str(g.target_list))
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sell_list = [stock for stock in g.hold_list if stock not in g.target_list and stock not in g.yesterday_HL_list]
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hold_list = [stock for stock in g.hold_list if stock in g.target_list or stock in g.yesterday_HL_list]
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log.info("卖出[%s]" % (str(sell_list)))
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log.info("已持有[%s]" % (str(hold_list)))
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for stock in sell_list:
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order_target_value(stock, 0)
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buy_list = [stock for stock in g.target_list if stock not in g.hold_list]
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buy_security(context, buy_list,len(buy_list))
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else:
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buy_security(context, [g.money_etf],1)
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log.info('该月份为空仓月份，持有银华日利ETF')
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#1-4 调整昨日涨停股票
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def check_limit_up(context):
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now_time = context.current_dt
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if g.yesterday_HL_list != []:
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#对昨日涨停股票观察到尾盘如不涨停则提前卖出，如果涨停即使不在应买入列表仍暂时持有
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for stock in g.yesterday_HL_list:
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current_data = get_price(stock, end_date=now_time, frequency='1m', fields=['close','high_limit'], skip_paused=False, fq='pre', count=1, panel=False, fill_paused=True)
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if current_data.iloc[0,0] < current_data.iloc[0,1]:
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log.info("[%s]涨停打开，卖出" % (stock))
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order_target_value(stock, 0)
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g.other_sale.append(stock)
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g.limitup_stocks.append(stock)
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else:
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log.info("[%s]涨停，继续持有" % (stock))
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#1-5 如果昨天有股票卖出或者买入失败造成空仓，剩余的金额当日买入
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def check_remain_amount(context):
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addstock_num = len(g.other_sale)
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loss_num = len(g.stoploss_list)
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empty_num = addstock_num + loss_num
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g.hold_list = context.portfolio.positions
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if len(g.hold_list) < g.stock_num:
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# 计算需要买入的股票数量，止损仓位补足货币etf
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# 可替换下一行代码以更换逻辑：改为将清空仓位全部补足股票，而非原作中止损仓位补充货币etf
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# num_stocks_to_buy = min(empty_num,g.stock_num-len(g.hold_list))
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num_stocks_to_buy = min(addstock_num,g.stock_num-len(g.hold_list))
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target_list = [stock for stock in g.target_list if stock not in g.limitup_stocks][:num_stocks_to_buy]
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log.info('有余额可用'+str(round((context.portfolio.cash),2))+'元。买入'+ str(target_list))
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buy_security(context,target_list,len(target_list))
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if loss_num !=0:
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log.info('有余额可用'+str(round((context.portfolio.cash),2))+'元。买入货币基金'+ str(g.money_etf))
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buy_security(context,[g.money_etf],loss_num)
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g.stoploss_list = []
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g.other_sale = []
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#1-6 下午检查交易
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def trade_afternoon(context):
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if g.trading_signal:
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check_limit_up(context)
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check_remain_amount(context)
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buy_security(context,[g.money_etf],1)
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#1-7 止盈止损
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def stop_loss(context):
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if g.run_stoploss:
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current_positions = context.portfolio.positions
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if g.stoploss_strategy == 1 or g.stoploss_strategy == 3:
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for stock in current_positions.keys():
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price = current_positions[stock].price
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avg_cost = current_positions[stock].avg_cost
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# 个股盈利止盈
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if price >= avg_cost * 2:
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order_target_value(stock, 0)
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log.debug("收益100%止盈,卖出{}".format(stock))
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g.other_sale.append(stock)
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# 个股止损
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elif price < avg_cost * (1 - g.stoploss_limit):
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order_target_value(stock, 0)
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log.debug("收益止损,卖出{}".format(stock))
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g.stoploss_list.append(stock)
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if g.stoploss_strategy == 2 or g.stoploss_strategy == 3:
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stock_df = get_price(security=get_index_stocks('399101.XSHE')
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,end_date=context.previous_date, frequency='daily'
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,fields=['close', 'open'], count=1, panel=False)
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# 计算成分股平均涨跌，即指数涨跌幅
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down_ratio = (1 - stock_df['close'] / stock_df['open']).mean()
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# 市场大跌止损
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if down_ratio >= g.stoploss_market:
|
||
g.stoploss_list.append(stock)
|
||
log.debug("大盘惨跌,平均降幅{:.2%}".format(down_ratio))
|
||
for stock in current_positions.keys():
|
||
order_target_value(stock, 0)
|
||
|
||
|
||
#1-8 动态调仓代码
|
||
def adjust_stock_num(context):
|
||
ma_para = 10 # 设置MA参数
|
||
today = context.previous_date
|
||
index_df = get_price('399101.XSHE', end_date=today,count = ma_para,fields = 'close', frequency='daily')
|
||
ma = index_df['close'].mean()
|
||
last_row = index_df['close'].iloc[-1]
|
||
diff = last_row - ma
|
||
# 根据差值结果返回数字
|
||
result = 3 if diff >= 500 else \
|
||
3 if 200 <= diff < 500 else \
|
||
4 if -200 <= diff < 200 else \
|
||
5 if -500 <= diff < -200 else \
|
||
6
|
||
return result
|
||
|
||
|
||
#2 过滤各种股票
|
||
def filter_stocks(context, stock_list):
|
||
current_data = get_current_data()
|
||
# 涨跌停和最近价格的判断
|
||
last_prices = history(1, unit='1m', field='close', security_list=stock_list)
|
||
# 过滤标准
|
||
filtered_stocks = []
|
||
for stock in stock_list:
|
||
if current_data[stock].paused: # 停牌
|
||
continue
|
||
if current_data[stock].is_st: # ST
|
||
continue
|
||
if '退' in current_data[stock].name: # 退市
|
||
continue
|
||
if stock.startswith('30') or stock.startswith('68') or stock.startswith('8') or stock.startswith('4'): # 市场类型
|
||
continue
|
||
if not (stock in context.portfolio.positions or last_prices[stock][-1] < current_data[stock].high_limit): # 涨停
|
||
continue
|
||
if not (stock in context.portfolio.positions or last_prices[stock][-1] > current_data[stock].low_limit): # 跌停
|
||
continue
|
||
# 次新股过滤
|
||
start_date = get_security_info(stock).start_date
|
||
if context.previous_date - start_date < timedelta(days=375):
|
||
continue
|
||
filtered_stocks.append(stock)
|
||
return filtered_stocks
|
||
|
||
#2.1 筛选审计意见
|
||
def filter_audit(context, code):
|
||
# 获取审计意见，近三年内如果有不合格(report_type为2、3、4、5)的审计意见则返回False，否则返回True
|
||
lstd = context.previous_date
|
||
last_year = lstd.replace(year=lstd.year - 3, month=1, day=1)
|
||
q=query(finance.STK_AUDIT_OPINION.code, finance.STK_AUDIT_OPINION.report_type
|
||
).filter(finance.STK_AUDIT_OPINION.code==code,finance.STK_AUDIT_OPINION.pub_date>=last_year)
|
||
df=finance.run_query(q)
|
||
df['report_type'] = df['report_type'].astype(str)
|
||
contains_nums = df['report_type'].str.contains(r'2|3|4|5')
|
||
return not contains_nums.any()
|
||
|
||
|
||
#3-4 买入模块
|
||
def buy_security(context,target_list,num):
|
||
#调仓买入
|
||
position_count = len(context.portfolio.positions)
|
||
target_num = num
|
||
if target_num !=0:
|
||
value = context.portfolio.cash / target_num
|
||
for stock in target_list:
|
||
order_target_value(stock, value)
|
||
log.info("买入[%s]（%s元）" % (stock,value))
|
||
if len(context.portfolio.positions) == g.stock_num:
|
||
break
|
||
|
||
|
||
#4-1 判断今天是否跳过月份
|
||
def today_is_between(context):
|
||
# 根据g.pass_month跳过指定月份
|
||
month = context.current_dt.month
|
||
# 判断当前月份是否在指定月份范围内
|
||
if month in g.pass_months:
|
||
code = '399303.XSHE'
|
||
close = history(count = 3, unit='1d', field='close', security_list= [code], df = False, skip_paused = False, fq = 'none')[code]
|
||
if close[-1] > close[-2] * 0.995 and close[-1] > close[-3] * 0.994:
|
||
return True
|
||
# 判断当前日期是否在指定日期范围内
|
||
return False
|
||
else:
|
||
return True
|
||
|
||
def close_account(context):
|
||
if not g.trading_signal:
|
||
curr_data = get_current_data()
|
||
if len(g.hold_list) != 0 and g.hold_list != [g.money_etf]:
|
||
for stock in g.hold_list:
|
||
if stock == g.money_etf:
|
||
continue
|
||
if curr_data[stock].last_price == curr_data[stock].low_limit or curr_data[stock].paused:
|
||
continue
|
||
order_target_value(stock, 0)
|
||
log.info("卖出[%s]" % (stock))
|
||
|
||
def print_position_info(context):
|
||
for position in list(context.portfolio.positions.values()):
|
||
securities=position.security
|
||
cost=position.avg_cost
|
||
price=position.price
|
||
ret=100*(price/cost-1)
|
||
value=position.value
|
||
amount=position.total_amount
|
||
print('代码:{}'.format(securities))
|
||
print('成本价:{}'.format(format(cost,'.2f')))
|
||
print('现价:{}'.format(price))
|
||
print('收益率:{}%'.format(format(ret,'.2f')))
|
||
print('持仓(股):{}'.format(amount))
|
||
print('市值:{}'.format(format(value,'.2f')))
|
||
print('———————————————————————————————————————分割线————————————————————————————————————————') |