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cls
2025-11-06 10:26:02 +08:00
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聚宽跟单/索普量化聚宽交易服务器/data/实时数据/123456实时数据.csv
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状态,订单添加时间,买卖,下单数量,已经成交,股票代码,订单ID,平均成交价格,持仓成本,多空,交易费用,交易日,数据状态,证券代码
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,1111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,1111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,11111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,11111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,111111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,111111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,1111111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,1111111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,11111111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,11111111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,111111111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,111111111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,1111111111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,1111111111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,11111111111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,11111111111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,111111111111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,111111111111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,1111111111111112.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,1111111111111112.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,1.111111111111112e+16,1732208241,10.5,10.59,long,128.31,2025-10-10,True,1.111111111111112e+16
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,,1732208241,10.5,10.59,long,128.31,2025-10-10,True,
1 状态 订单添加时间 买卖 下单数量 已经成交 股票代码 订单ID 平均成交价格 持仓成本 多空 交易费用 交易日 数据状态 证券代码
2 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 111111.0
3 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 1111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 1111111.0
4 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 11111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 11111111.0
5 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 111111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 111111111.0
6 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 1111111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 1111111111.0
7 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 11111111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 11111111111.0
8 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 111111111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 111111111111.0
9 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 1111111111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 1111111111111.0
10 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 11111111111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 11111111111111.0
11 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 111111111111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 111111111111111.0
12 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 1111111111111112.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 1111111111111112.0
13 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 1.111111111111112e+16 1732208241 10.5 10.59 long 128.31 2025-10-10 True 1.111111111111112e+16
14 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 1732208241 10.5 10.59 long 128.31 2025-10-10 True
聚宽跟单/索普量化聚宽交易服务器/xg_qmt_jq.py
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'''
索普聚宽交易系统
作者:索普量化
微信:xms_quants1
原理替代,继承聚宽的交易函数类
读取下单类的函数参数把交易数据发送到服务器
把下面的全部源代码复制到聚宽策略的开头就可以
实盘前先模拟盘测试一下数据
把下面的内容全部复制到策略的开头就可以
'''
import requests
import json
import pandas as pd
from jqdata import *
url='http://101.34.65.108'
port=8888
#自定义服务器编码
url_code='63d85b6189e42cba63feea36381da615c31ad8e36ae420ed67f60f3598efc9ad'
#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
password='123456'
class joinquant_trader:
def __init__(self,url='http://124.220.32.224',
port=8025,
url_code='63d85b6189e42cba63feea36381da615c31ad8e36ae420ed67f60f3598efc9ad',
password='123456'):
'''
获取服务器数据
'''
self.url=url
self.port=port
self.url_code=url_code
self.password=password
def get_user_data(self,data_type='用户信息'):
'''
获取使用的数据
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'
'''
url='{}:{}/_dash-update-component'.format(self.url,self.port)
headers={'Content-Type':'application/json'}
data={"output":"joinquant_trader_table.data@{}".format(self.url_code),
"outputs":{"id":"joinquant_trader_table","property":"data@{}".format(self.url_code)},
"inputs":[{"id":"joinquant_trader_password","property":"value","value":self.password},
{"id":"joinquant_trader_data_type","property":"value","value":data_type},
{"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 "},
{"id":"joinquant_trader_run","property":"value","value":"运行"},
{"id":"joinquant_trader_down_data","property":"value","value":"不下载数据"}],
"changedPropIds":["joinquant_trader_run.value"],"parsedChangedPropsIds":["joinquant_trader_run.value"]}
res=requests.post(url=url,data=json.dumps(data),headers=headers)
text=res.json()
df=pd.DataFrame(text['response']['joinquant_trader_table']['data'])
return df
def send_order(self,result):
'''
发送交易数据
'''
url='{}:{}/_dash-update-component'.format(self.url,self.port)
headers={'Content-Type':'application/json'}
data={"output":"joinquant_trader_table.data@{}".format(self.url_code),
"outputs":{"id":"joinquant_trader_table","property":"data@{}".format(self.url_code)},
"inputs":[{"id":"joinquant_trader_password","property":"value","value":self.password},
{"id":"joinquant_trader_data_type","property":"value","value":'发送信号'},
{"id":"joinquant_trader_text","property":"value","value":result},
{"id":"joinquant_trader_run","property":"value","value":"运行"},
{"id":"joinquant_trader_down_data","property":"value","value":"不下载数据"}],
"changedPropIds":["joinquant_trader_run.value"],"parsedChangedPropsIds":["joinquant_trader_run.value"]}
res=requests.post(url=url,data=json.dumps(data),headers=headers)
text=res.json()
df=pd.DataFrame(text['response']['joinquant_trader_table']['data'])
return df
#继承类
xg_data=joinquant_trader(url=url,port=port,password=password,)
def send_order(result):
'''
发送函数
status: 状态, 一个OrderStatus值
add_time: 订单添加时间, [datetime.datetime]对象
is_buy: bool值, 买还是卖，对于期货:
开多/平空 -> 买
开空/平多 -> 卖
amount: 下单数量, 不管是买还是卖, 都是正数
filled: 已经成交的股票数量, 正数
security: 股票代码
order_id: 订单ID
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)
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).
side: 多/空，'long'/'short'
action: 开/平， 'open'/'close'
commission交易费用（佣金、税费等）
'''
data={}
data['状态']=str(result.status)
data['订单添加时间']=str(result.add_time)
data['买卖']=str(result.is_buy)
data['下单数量']=str(result.amount)
data['已经成交']=str(result.filled)
data['股票代码']=str(result.security)
data['订单ID']=str(result.order_id)
data['平均成交价格']=str(result.price)
data['持仓成本']=str(result.avg_cost)
data['多空']=str(result.side)
data['交易费用']=str(result.commission)
result=str(data)
xg_data.send_order(result)
return data
def xg_order(func):
'''
继承order对象 数据交易函数
'''
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
if result == None:
return
send_order(result)
return result
return wrapper
def xg_order_target(func):
'''
继承order_target对象 百分比
'''
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
if result == None:
return
send_order(result)
return result
return wrapper
def xg_order_value(func):
'''
继承order_value对象 数量
'''
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
if result == None:
return
send_order(result)
return result
return wrapper
def xg_order_target_value(func):
'''
继承order_target_value对象 数量
'''
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
if result == None:
return
send_order(result)
return result
return wrapper
order = xg_order(order)
order_target = xg_order_target(order_target)
order_value = xg_order_value(order_value)
order_target_value = xg_order_target_value(order_target_value)
聚宽跟单/索普量化聚宽交易服务器/启动服务器.bat
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py 索普量化聚宽交易服务器.py
聚宽跟单/索普量化聚宽交易服务器/安装第三方库原始.bat
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py -m pip install pip==23.2
py -m pip install akshare
py -m pip install easyquotation
py -m pip install yagmail
py -m pip install pywinauto==0.6.6
py -m pip install matplotlib
py -m pip install mplfinance
py -m pip install finta
py -m pip install pyinstaller
py -m pip install pyautogui
py -m pip install schedule
py -m pip install pywin32
py -m pip install pyexecjs
py -m pip install pytdx
py -m pip install pydash
py -m pip install empyrical
py -m pip install pywencai
py -m pip install pandas==1.5.3
py -m pip install numpy==1.26.2
py -m pip install scipy==1.11.4
py -m pip install ffn
py -m pip install quantstats==0.0.60
py -m pip install dash
聚宽跟单/索普量化聚宽交易服务器/安装第三方库清华.bat
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py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pip==23.2
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple akshare
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple easyquotation
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple yagmail
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pywinauto==0.6.6
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple matplotlib
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple mplfinance
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple finta
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pyinstaller
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pyautogui
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple schedule
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pywin32
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pyexecjs
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pytdx
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pydash
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple empyrical
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pywencai
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pandas==1.5.3
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple numpy==1.26.2
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple scipy==1.11.4
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple ffn
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple quantstats==0.0.60
py -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple dash
聚宽跟单/索普量化聚宽交易服务器/安装第三方库阿里云.bat
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py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ pip==23.2
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ akshare
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ easyquotation
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ yagmail
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ pywinauto==0.6.6
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ matplotlib
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ mplfinance
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ finta
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ pyinstaller
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ pyautogui
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ schedule
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ pywin32
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ pyexecjs
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ pytdx
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ pydash
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ empyrical
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ pywencai
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ pandas==1.5.3
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ numpy==1.26.2
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ scipy==1.11.4
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ ffn
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ quantstats==0.0.60
py -m pip install -i http://mirrors.aliyun.com/pypi/simple/ dash
聚宽跟单/索普量化聚宽交易服务器/实时数据/历史数据/123456历史数据.csv
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状态,订单添加时间,买卖,下单数量,已经成交,股票代码,订单ID,平均成交价格,持仓成本,多空,交易费用,交易日,数据状态,证券代码
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,1111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,1111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,11111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,11111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,111111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,111111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,1111111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,1111111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,11111111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,11111111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,111111111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,111111111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,1111111111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,1111111111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,11111111111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,11111111111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,111111111111111.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,111111111111111.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,1111111111111112.0,1732208241,10.5,10.59,long,128.31,2025-10-10,True,1111111111111112.0
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,1.111111111111112e+16,1732208241,10.5,10.59,long,128.31,2025-10-10,True,1.111111111111112e+16
held,"datetime.datetime(2024, 4, 23, 9, 30)",False,9400,9400,,1732208241,10.5,10.59,long,128.31,2025-10-10,True,
1 状态 订单添加时间 买卖 下单数量 已经成交 股票代码 订单ID 平均成交价格 持仓成本 多空 交易费用 交易日 数据状态 证券代码
2 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 111111.0
3 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 1111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 1111111.0
4 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 11111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 11111111.0
5 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 111111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 111111111.0
6 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 1111111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 1111111111.0
7 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 11111111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 11111111111.0
8 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 111111111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 111111111111.0
9 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 1111111111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 1111111111111.0
10 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 11111111111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 11111111111111.0
11 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 111111111111111.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 111111111111111.0
12 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 1111111111111112.0 1732208241 10.5 10.59 long 128.31 2025-10-10 True 1111111111111112.0
13 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 1.111111111111112e+16 1732208241 10.5 10.59 long 128.31 2025-10-10 True 1.111111111111112e+16
14 held datetime.datetime(2024, 4, 23, 9, 30) False 9400 9400 1732208241 10.5 10.59 long 128.31 2025-10-10 True
聚宽跟单/索普量化聚宽交易服务器/授权表/授权表.xlsx
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聚宽跟单/索普量化聚宽交易服务器/索普量化聚宽交易服务器.py
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'''
索普量化聚宽交易服务器2.0
作者索普量化
微信:xms_quants1
'''
from dash import html, dcc, Input, Output, dash_table, dash
import pandas as pd
import os
from datetime import datetime
from pathlib import Path
import logging
from typing import Dict, Any, Optional
# 配置日志
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class TradingDataManager:
"""交易数据管理类"""
def __init__(self, base_path: str):
self.base_path = Path(base_path)
self.realtime_data_dir = self.base_path / "data" / "实时数据"
self.historical_data_dir = self.base_path / "实时数据" / "历史数据"
self.auth_file = self.base_path / "授权表" / "授权表.xlsx"
# 创建必要的目录
self.realtime_data_dir.mkdir(parents=True, exist_ok=True)
self.historical_data_dir.mkdir(parents=True, exist_ok=True)
self.auth_file.parent.mkdir(parents=True, exist_ok=True)
def get_user_file_path(self, user_id: str, data_type: str) -> Path:
"""获取用户数据文件路径"""
if data_type == "实时数据":
return self.realtime_data_dir / f"{user_id}实时数据.csv"
elif data_type == "历史数据":
return self.historical_data_dir / f"{user_id}历史数据.csv"
else:
raise ValueError(f"不支持的数据类型: {data_type}")
def load_user_data(self, user_id: str, data_type: str) -> pd.DataFrame:
"""加载用户数据"""
file_path = self.get_user_file_path(user_id, data_type)
try:
if file_path.exists():
df = pd.read_csv(file_path)
# 清理可能的索引列
df = df.drop(columns=['Unnamed: 0'], errors='ignore')
return df
return pd.DataFrame()
except Exception as e:
logger.error(f"加载用户数据失败: {e}")
return pd.DataFrame()
def save_user_data(self, user_id: str, data_type: str, df: pd.DataFrame):
"""保存用户数据"""
file_path = self.get_user_file_path(user_id, data_type)
try:
df.to_csv(file_path, index=False)
except Exception as e:
logger.error(f"保存用户数据失败: {e}")
def load_auth_data(self) -> pd.DataFrame:
"""加载授权数据"""
try:
if self.auth_file.exists():
df = pd.read_excel(self.auth_file, dtype='object')
df = df.drop(columns=['Unnamed: 0'], errors='ignore')
return df
return pd.DataFrame(columns=['用户名称', '到期时间'])
except Exception as e:
logger.error(f"加载授权数据失败: {e}")
return pd.DataFrame(columns=['用户名称', '到期时间'])
class AuthManager:
"""授权管理类"""
@staticmethod
def calculate_days_remaining(start_date: str, end_date: str) -> int:
"""计算剩余天数"""
try:
start = datetime.strptime(start_date, '%Y-%m-%d')
end = datetime.strptime(end_date, '%Y-%m-%d')
return (end - start).days
except Exception as e:
logger.error(f"计算天数失败: {e}")
return -1
@staticmethod
def validate_user(auth_df: pd.DataFrame, user_id: str) -> Dict[str, Any]:
"""验证用户授权"""
try:
auth_df['用户名称'] = auth_df['用户名称'].astype(str)
auth_df['到期时间'] = auth_df['到期时间'].astype(str)
if user_id not in auth_df['用户名称'].values:
return {"valid": False, "message": "用户没有注册请注册/联系作者微信15117320079"}
user_data = auth_df[auth_df['用户名称'] == user_id].iloc[0]
end_date = str(user_data['到期时间'])[:10]
current_date = datetime.now().strftime('%Y-%m-%d')
days_remaining = AuthManager.calculate_days_remaining(current_date, end_date)
if days_remaining >= 1:
return {
"valid": True,
"days_remaining": days_remaining,
"user_data": user_data
}
else:
return {"valid": False, "message": "授权码到期找作者获取"}
except Exception as e:
logger.error(f"用户验证失败: {e}")
return {"valid": False, "message": "授权验证失败"}
class DataProcessor:
"""数据处理类"""
@staticmethod
def parse_signal_text(text: str) -> Dict[str, Any]:
"""解析信号文本"""
try:
cleaned_text = '\n'.join([
line.strip().lstrip()
for line in text.split('\n')
if line.strip()
])
return eval(cleaned_text)
except Exception as e:
logger.error(f"解析信号文本失败: {e}")
return {}
@staticmethod
def process_stock_data(df: pd.DataFrame) -> pd.DataFrame:
"""处理股票数据"""
if df.empty:
return df
# 清理股票代码
if '股票代码' in df.columns:
df['证券代码'] = df['股票代码'].apply(
lambda x: str(x).split('.XSHE')[0].split('.XSHG')[0]
)
df['数据长度'] = df['证券代码'].apply(lambda x: len(str(x)))
df = df[df['数据长度'] >= 6]
df = df.drop(columns=['数据长度'], errors='ignore')
return df
@staticmethod
def create_signal_data(signal_dict: Dict[str, Any]) -> pd.DataFrame:
"""创建信号数据"""
return pd.DataFrame([signal_dict])
class TradingApp:
"""交易应用主类"""
def __init__(self):
self.path = os.path.dirname(os.path.abspath(__file__))
self.data_manager = TradingDataManager(self.path)
self.auth_manager = AuthManager()
self.data_processor = DataProcessor()
self.select_options = [
'用户信息', '发送信号', '实时数据', '历史数据',
'清空实时数据', '清空历史数据'
]
self.app = dash.Dash(__name__)
self.setup_layout()
self.setup_callbacks()
def setup_layout(self):
"""设置应用布局"""
self.app.layout = html.Div([
html.H1([html.A('索普聚宽交易使用教程2.0',href='https://gitee.com/li-xingguo11111/big_qmt_joinquant_trader',style={'textAlign': 'center'}),]),
html.H3('索普量化,找作者获取授权码 微信xms_quants1,交易函数数据函数分离,平台只是一个信号的中转平台,聚宽的策略仔细研究避免未来函数,投资有风险,平台不做投资参考',style={'textAlign': 'center'}),
self._create_control_table(),
self._create_text_area(),
html.H3('数据展示'),
self._create_data_table(),
dcc.Download(id='joinquant_trader_table_down')
])
def _create_control_table(self):
"""创建控制表格"""
return html.Table([
html.Tr([
html.Td('用户名称', style={'border': '1px solid', 'width': '200px'}),
html.Td('数据类型', style={'border': '1px solid', 'width': '200px'}),
html.Td('运行程序', style={'border': '1px solid', 'width': '200px'}),
html.Td('下载数据', style={'border': '1px solid', 'width': '200px'})
]),
html.Tr([
html.Td(
dcc.Input(value='123456', id='joinquant_trader_password'),
style={'border': '1px solid', 'width': '200px'}
),
html.Td(
dcc.Dropdown(
options=self.select_options,
value='用户信息',
id='joinquant_trader_data_type'
),
style={'border': '1px solid', 'width': '200px'}
),
html.Td(
dcc.Dropdown(
options=['运行', '不运行'],
id='joinquant_trader_run',
value='不运行'
),
style={'border': '1px solid', 'width': '200px'}
),
html.Td(
dcc.RadioItems(
options={'下载数据': "下载数据", "不下载数据": "不下载数据"},
id='joinquant_trader_down_data',
value='不下载数据'
),
style={'border': '1px solid', 'width': '200px'}
)
]),
])
def _create_text_area(self):
"""创建文本区域"""
return dcc.Textarea(
value="""
{'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': '', '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'}
""",
id='joinquant_trader_text',
style={'width': '80%', 'height': 200, "text-align": "left"}
)
def _create_data_table(self):
"""创建数据表格"""
return dash_table.DataTable(
id='joinquant_trader_table',
page_size=10,
style_table={'font-size': 15},
sort_action='native'
)
def setup_callbacks(self):
"""设置回调函数"""
# 主数据回调
@self.app.callback(
Output('joinquant_trader_table', 'data', allow_duplicate=True),
Input('joinquant_trader_password', 'value'),
Input('joinquant_trader_data_type', 'value'),
Input('joinquant_trader_text', 'value'),
Input('joinquant_trader_run', 'value'),
Input('joinquant_trader_down_data', 'value'),
prevent_initial_call=True
)
def update_table(password, data_type, text, run, down_data):
return self._handle_data_update(password, data_type, text, run, down_data)
# 下载回调
@self.app.callback(
Output('joinquant_trader_table_down', 'data', allow_duplicate=True),
Input('joinquant_trader_password', 'value'),
Input('joinquant_trader_data_type', 'value'),
Input('joinquant_trader_text', 'value'),
Input('joinquant_trader_run', 'value'),
Input('joinquant_trader_down_data', 'value'),
prevent_initial_call=True
)
def download_data(password, data_type, text, run, down_data):
return self._handle_download(password, data_type, text, run, down_data)
def _handle_data_update(self, password: str, data_type: str, text: str,
run: str, down_data: str) -> list:
"""处理数据更新"""
if run != '运行':
return self._create_message_df("没有点击运行选择", False).to_dict('records')
auth_df = self.data_manager.load_auth_data()
auth_result = self.auth_manager.validate_user(auth_df, str(password))
if not auth_result["valid"]:
return self._create_message_df(auth_result["message"], False).to_dict('records')
return self._process_data_type(
password, data_type, text, auth_result
).to_dict('records')
def _process_data_type(self, user_id: str, data_type: str, text: str,
auth_result: Dict[str, Any]) -> pd.DataFrame:
"""处理不同类型的数据请求"""
current_date = datetime.now().strftime('%Y-%m-%d')
if data_type == '用户信息':
return self._handle_user_info(auth_result)
elif data_type == '发送信号':
return self._handle_send_signal(user_id, text, current_date)
elif data_type in ['实时数据', '历史数据']:
return self._handle_data_query(user_id, data_type)
elif data_type in ['清空实时数据', '清空历史数据']:
return self._handle_clear_data(user_id, data_type)
else:
return self._create_message_df("未知的数据类型", False)
def _handle_user_info(self, auth_result: Dict[str, Any]) -> pd.DataFrame:
"""处理用户信息请求"""
user_data = auth_result["user_data"].copy()
user_data['到期天数'] = auth_result["days_remaining"]
user_data['信息推送'] = '授权码正常'
user_data['数据状态'] = True
return pd.DataFrame([user_data])
def _handle_send_signal(self, user_id: str, text: str, current_date: str) -> pd.DataFrame:
"""处理发送信号"""
signal_data = self.data_processor.parse_signal_text(text)
if not signal_data:
return self._create_message_df("信号数据解析失败", False)
# 处理实时数据
realtime_df = self.data_manager.load_user_data(user_id, "实时数据")
realtime_df = self.data_processor.process_stock_data(realtime_df)
# 处理历史数据
historical_df = self.data_manager.load_user_data(user_id, "历史数据")
historical_df = self.data_processor.process_stock_data(historical_df)
# 创建新信号数据
new_signal = self.data_processor.create_signal_data(signal_data)
new_signal['交易日'] = current_date
new_signal['数据状态'] = True
# 合并数据并去重
realtime_updated = self._merge_and_deduplicate(realtime_df, new_signal, current_date)
historical_updated = self._merge_and_deduplicate(historical_df, new_signal, current_date)
# 保存更新后的数据
self.data_manager.save_user_data(user_id, "实时数据", realtime_updated)
self.data_manager.save_user_data(user_id, "历史数据", historical_updated)
return new_signal
def _handle_data_query(self, user_id: str, data_type: str) -> pd.DataFrame:
"""处理数据查询"""
df = self.data_manager.load_user_data(user_id, data_type)
df = self.data_processor.process_stock_data(df)
return df if not df.empty else self._create_message_df(f"{data_type}为空", True)
def _handle_clear_data(self, user_id: str, data_type: str) -> pd.DataFrame:
"""处理清空数据"""
clear_type = "实时数据" if "实时" in data_type else "历史数据"
self.data_manager.save_user_data(user_id, clear_type, pd.DataFrame())
return self._create_message_df(f"{clear_type}已清空", True)
def _merge_and_deduplicate(self, existing_df: pd.DataFrame,
new_df: pd.DataFrame, current_date: str) -> pd.DataFrame:
"""合并数据并去重"""
merged_df = pd.concat([existing_df, new_df], ignore_index=True)
merged_df['交易日'] = current_date
# 去重逻辑
dup_cols = ['股票代码', '下单数量', '买卖', '多空']
if all(col in merged_df.columns for col in dup_cols):
merged_df = merged_df.drop_duplicates(subset=dup_cols, keep='last')
return merged_df
def _handle_download(self, password: str, data_type: str, text: str,
run: str, down_data: str):
"""处理数据下载"""
if down_data != '下载数据':
return None
if data_type not in ['实时数据', '历史数据']:
return None
auth_df = self.data_manager.load_auth_data()
auth_result = self.auth_manager.validate_user(auth_df, str(password))
if not auth_result["valid"]:
return None
df = self.data_manager.load_user_data(str(password), data_type)
current_date = datetime.now().strftime('%Y-%m-%d')
return dcc.send_data_frame(
df.to_excel,
filename=f'{current_date}{data_type}.xlsx'
)
@staticmethod
def _create_message_df(message: str, status: bool) -> pd.DataFrame:
"""创建消息DataFrame"""
return pd.DataFrame({'信息提示': [message], '数据状态': [status]})
def run(self, host: str = '127.0.0.1', port: str = '8025'):
"""运行应用"""
try:
self.app.run(debug=True, host=host, port=port)
except Exception as e:
logger.error(f"启动应用失败: {e}")
# 尝试备用端口
try:
self.app.run_server(debug=True, host=host, port=port)
except Exception as e:
logger.error(f"备用端口启动也失败: {e}")
if __name__ == '__main__':
host = '127.0.0.1'
port = '8025'
app = TradingApp()
app.run(host=host,port=port)
聚宽跟单/索普量化聚宽交易系统源代码版/国九条后中小板微盘小改.txt
+476
View File
@@ -0,0 +1,476 @@
from jqdata import *
from jqfactor import *
import numpy as np
import pandas as pd
from datetime import time,date
from jqdata import finance
'''
索普聚宽交易系统
作者:索普量化
微信:xms_quants1
原理替代,继承聚宽的交易函数类
读取下单类的函数参数把交易数据发送到服务器
把下面的全部源代码复制到聚宽策略的开头就可以
实盘前先模拟盘测试一下数据
把下面的内容全部复制到策略的开头就可以
'''
import requests
import json
import pandas as pd
from jqdata import *
url='http://101.34.65.108'
port=8888
#自定义服务器编码
url_code='63d85b6189e42cba63feea36381da615c31ad8e36ae420ed67f60f3598efc9ad'
#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
password='国九条后中小板微盘小改'
class joinquant_trader:
def __init__(self,url='http://124.220.32.224',
port=8025,
url_code='63d85b6189e42cba63feea36381da615c31ad8e36ae420ed67f60f3598efc9ad',
password='123456'):
'''
获取服务器数据
'''
self.url=url
self.port=port
self.url_code=url_code
self.password=password
def get_user_data(self,data_type='用户信息'):
'''
获取使用的数据
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'
'''
url='{}:{}/_dash-update-component'.format(self.url,self.port)
headers={'Content-Type':'application/json'}
data={"output":"joinquant_trader_table.data@{}".format(self.url_code),
"outputs":{"id":"joinquant_trader_table","property":"data@{}".format(self.url_code)},
"inputs":[{"id":"joinquant_trader_password","property":"value","value":self.password},
{"id":"joinquant_trader_data_type","property":"value","value":data_type},
{"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 "},
{"id":"joinquant_trader_run","property":"value","value":"运行"},
{"id":"joinquant_trader_down_data","property":"value","value":"不下载数据"}],
"changedPropIds":["joinquant_trader_run.value"],"parsedChangedPropsIds":["joinquant_trader_run.value"]}
res=requests.post(url=url,data=json.dumps(data),headers=headers)
text=res.json()
df=pd.DataFrame(text['response']['joinquant_trader_table']['data'])
return df
def send_order(self,result):
'''
发送交易数据
'''
url='{}:{}/_dash-update-component'.format(self.url,self.port)
headers={'Content-Type':'application/json'}
data={"output":"joinquant_trader_table.data@{}".format(self.url_code),
"outputs":{"id":"joinquant_trader_table","property":"data@{}".format(self.url_code)},
"inputs":[{"id":"joinquant_trader_password","property":"value","value":self.password},
{"id":"joinquant_trader_data_type","property":"value","value":'发送信号'},
{"id":"joinquant_trader_text","property":"value","value":result},
{"id":"joinquant_trader_run","property":"value","value":"运行"},
{"id":"joinquant_trader_down_data","property":"value","value":"不下载数据"}],
"changedPropIds":["joinquant_trader_run.value"],"parsedChangedPropsIds":["joinquant_trader_run.value"]}
res=requests.post(url=url,data=json.dumps(data),headers=headers)
text=res.json()
df=pd.DataFrame(text['response']['joinquant_trader_table']['data'])
return df
#继承类
xg_data=joinquant_trader(url=url,port=port,password=password,)
def send_order(result):
'''
发送函数
status: 状态, 一个OrderStatus值
add_time: 订单添加时间, [datetime.datetime]对象
is_buy: bool值, 买还是卖，对于期货:
开多/平空 -> 买
开空/平多 -> 卖
amount: 下单数量, 不管是买还是卖, 都是正数
filled: 已经成交的股票数量, 正数
security: 股票代码
order_id: 订单ID
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)
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).
side: 多/空，'long'/'short'
action: 开/平， 'open'/'close'
commission交易费用（佣金、税费等）
'''
data={}
data['状态']=str(result.status)
data['订单添加时间']=str(result.add_time)
data['买卖']=str(result.is_buy)
data['下单数量']=str(result.amount)
data['已经成交']=str(result.filled)
data['股票代码']=str(result.security)
data['订单ID']=str(result.order_id)
data['平均成交价格']=str(result.price)
data['持仓成本']=str(result.avg_cost)
data['多空']=str(result.side)
data['交易费用']=str(result.commission)
result=str(data)
xg_data.send_order(result)
return data
def xg_order(func):
'''
继承order对象 数据交易函数
'''
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
if result == None:
return
send_order(result)
return result
return wrapper
def xg_order_target(func):
'''
继承order_target对象 百分比
'''
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
if result == None:
return
send_order(result)
return result
return wrapper
def xg_order_value(func):
'''
继承order_value对象 数量
'''
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
if result == None:
return
send_order(result)
return result
return wrapper
def xg_order_target_value(func):
'''
继承order_target_value对象 数量
'''
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
if result == None:
return
send_order(result)
return result
return wrapper
order = xg_order(order)
order_target = xg_order_target(order_target)
order_value = xg_order_value(order_value)
order_target_value = xg_order_target_value(order_target_value)
#初始化函数
def initialize(context):
# 开启防未来函数
set_option('avoid_future_data', True)
# 成交量设置
#set_option('order_volume_ratio', 0.10)
# 设定基准
set_benchmark('399101.XSHE')
# 用真实价格交易
set_option('use_real_price', True)
# 将滑点设置为0
set_slippage(FixedSlippage(3/10000))
# 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
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')
# 过滤order中低于error级别的日志
log.set_level('order', 'error')
log.set_level('system', 'error')
log.set_level('strategy', 'debug')
#初始化全局变量 bool
g.trading_signal = True # 是否为可交易日
g.run_stoploss = True # 是否进行止损
g.filter_audit = False # 是否筛选审计意见
g.adjust_num = True # 是否调整持仓数量
#全局变量list
g.hold_list = [] #当前持仓的全部股票
g.yesterday_HL_list = [] #记录持仓中昨日涨停的股票
g.target_list = []
g.pass_months = [1, 4] # 空仓的月份
g.limitup_stocks = [] # 记录涨停的股票避免再次买入
#全局变量float/str
g.min_mv = 10 # 股票最小市值要求
g.max_mv = 100 # 股票最大市值要求
g.stock_num = 4 # 持股数量
g.stoploss_list = [] # 止损卖出列表
g.other_sale = [] # 其他卖出列表
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
g.stoploss_limit = 0.09 # 止损线
g.stoploss_market = 0.05 # 市场趋势止损参数
g.highest = 50 # 股票单价上限设置
g.money_etf = '511880.XSHG' # 空仓月份持有银华日利ETF
# 设置交易运行时间
run_daily(prepare_stock_list, '9:05')
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
run_daily(stop_loss, time='10:00') # 止损函数
run_daily(close_account, '14:50')
run_weekly(weekly_adjustment,2,'10:00')
#run_weekly(print_position_info, 5, time='15:10', reference_security='000300.XSHG')
#1-1 准备股票池
def prepare_stock_list(context):
#获取已持有列表
g.limitup_stocks = []
g.hold_list = list(context.portfolio.positions)
#获取昨日涨停列表
if g.hold_list:
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)
df = df[df['close'] == df['high_limit']]
g.yesterday_HL_list = df['code'].tolist()
else:
g.yesterday_HL_list = []
#判断今天是否为账户资金再平衡的日期
g.trading_signal = today_is_between(context)
#1-2 选股模块
def get_stock_list(context):
final_list = []
MKT_index = '399101.XSHE'
initial_list = filter_stocks(context, get_index_stocks(MKT_index))
# 国九更新：过滤近一年净利润为负且营业收入小于1亿的
# 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)
# 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)
q = query(
valuation.code,
).filter(
valuation.code.in_(initial_list),
valuation.market_cap.between(g.min_mv,g.max_mv), # 总市值 circulating_market_cap/market_cap 单位：亿元
income.np_parent_company_owners > 0, # 归属于母公司所有者的净利润(元)
income.net_profit > 0, # 净利润(元)
income.operating_revenue > 1e8 # 营业收入 (元)
).order_by(valuation.market_cap.asc()).limit(g.stock_num*3)
df = get_fundamentals(q)
# 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
if g.filter_audit:
before_audit_filter = len(df)
df['audit'] = df['code'].apply(lambda x: filter_audit(context, x))
df_audit = df[df['audit'] == True]
log.info('去除掉了存在审计问题的股票{}只'.format(len(df)-before_audit_filter))
final_list = df['code'].tolist()
if final_list:
last_prices = history(1, unit='1d', field='close', security_list=final_list)
return [stock for stock in final_list if stock in g.hold_list or last_prices[stock][-1] <= g.highest]
else:
# 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
log.info('无适合股票，买入ETF')
return [g.money_etf]
#1-3 整体调整持仓
def weekly_adjustment(context):
if g.trading_signal:
if g.adjust_num:
new_num = adjust_stock_num(context)
g.stock_num = new_num
log.info(f'持仓数量修改为{new_num}')
g.target_list = get_stock_list(context)[:g.stock_num]
log.info(str(g.target_list))
sell_list = [stock for stock in g.hold_list if stock not in g.target_list and stock not in g.yesterday_HL_list]
hold_list = [stock for stock in g.hold_list if stock in g.target_list or stock in g.yesterday_HL_list]
log.info("卖出[%s]" % (str(sell_list)))
log.info("已持有[%s]" % (str(hold_list)))
for stock in sell_list:
order_target_value(stock, 0)
buy_list = [stock for stock in g.target_list if stock not in g.hold_list]
buy_security(context, buy_list,len(buy_list))
else:
buy_security(context, [g.money_etf],1)
log.info('该月份为空仓月份，持有银华日利ETF')
#1-4 调整昨日涨停股票
def check_limit_up(context):
now_time = context.current_dt
if g.yesterday_HL_list != []:
#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
for stock in g.yesterday_HL_list:
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)
if current_data.iloc[0,0] < current_data.iloc[0,1]:
log.info("[%s]涨停打开，卖出" % (stock))
order_target_value(stock, 0)
g.other_sale.append(stock)
g.limitup_stocks.append(stock)
else:
log.info("[%s]涨停，继续持有" % (stock))
#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
def check_remain_amount(context):
addstock_num = len(g.other_sale)
loss_num = len(g.stoploss_list)
empty_num = addstock_num + loss_num
g.hold_list = context.portfolio.positions
if len(g.hold_list) < g.stock_num:
# 计算需要买入的股票数量，止损仓位补足货币etf
# 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
# num_stocks_to_buy = min(empty_num,g.stock_num-len(g.hold_list))
num_stocks_to_buy = min(addstock_num,g.stock_num-len(g.hold_list))
target_list = [stock for stock in g.target_list if stock not in g.limitup_stocks][:num_stocks_to_buy]
log.info('有余额可用'+str(round((context.portfolio.cash),2))+'元。买入'+ str(target_list))
buy_security(context,target_list,len(target_list))
if loss_num !=0:
log.info('有余额可用'+str(round((context.portfolio.cash),2))+'元。买入货币基金'+ str(g.money_etf))
buy_security(context,[g.money_etf],loss_num)
g.stoploss_list = []
g.other_sale = []
#1-6 下午检查交易
def trade_afternoon(context):
if g.trading_signal:
check_limit_up(context)
check_remain_amount(context)
buy_security(context,[g.money_etf],1)
#1-7 止盈止损
def stop_loss(context):
if g.run_stoploss:
current_positions = context.portfolio.positions
if g.stoploss_strategy == 1 or g.stoploss_strategy == 3:
for stock in current_positions.keys():
price = current_positions[stock].price
avg_cost = current_positions[stock].avg_cost
# 个股盈利止盈
if price >= avg_cost * 2:
order_target_value(stock, 0)
log.debug("收益100%止盈,卖出{}".format(stock))
g.other_sale.append(stock)
# 个股止损
elif price < avg_cost * (1 - g.stoploss_limit):
order_target_value(stock, 0)
log.debug("收益止损,卖出{}".format(stock))
g.stoploss_list.append(stock)
if g.stoploss_strategy == 2 or g.stoploss_strategy == 3:
stock_df = get_price(security=get_index_stocks('399101.XSHE')
,end_date=context.previous_date, frequency='daily'
,fields=['close', 'open'], count=1, panel=False)
# 计算成分股平均涨跌，即指数涨跌幅
down_ratio = (1 - stock_df['close'] / stock_df['open']).mean()
# 市场大跌止损
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————————————————————————————————————————')
聚宽跟单/索普量化聚宽交易系统源代码版/索普量化聚宽交易系统源代码版.rzrk
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