Win_home
1326 lines
55 KiB
Python
1326 lines
55 KiB
Python
"""
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该代码的主要目的是处理Tick数据并生成交易信号。代码中定义了一个tickcome函数，它接收到Tick数据后会进行一系列的处理，包括构建Tick字典、更新上一个Tick的成交量、保存Tick数据、生成K线数据等。其中涉及到的一些函数有：
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on_tick(tick): 处理单个Tick数据，根据Tick数据生成K线数据。
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tickdata(df, symbol): 处理Tick数据，生成K线数据。
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orderflow_df_new(df_tick, df_min, symbol): 处理Tick和K线数据，生成订单流数据。
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GetOrderFlow_dj(kData): 计算订单流的信号指标。
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除此之外，代码中还定义了一个MyTrader类，继承自TraderApiBase，用于实现交易相关的功能。
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"""
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# from concurrent.futures import ThreadPoolExecutor
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from multiprocessing import Process, Queue
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import queue
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import threading
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from AlgoPlus.CTP.MdApi import run_tick_engine
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# from AlgoPlus.CTP.FutureAccount import get_simulate_account
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from AlgoPlus.CTP.FutureAccount import FutureAccount
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from AlgoPlus.CTP.TraderApiBase import TraderApiBase
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# from AlgoPlus.ta.time_bar import tick_to_bar
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import pandas as pd
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from datetime import datetime # timedelta
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from datetime import time as s_time
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import operator
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import time
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import numpy as np
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import os
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import re
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# 加入邮件通知
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import smtplib
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from email.mime.text import MIMEText # 导入 MIMEText 类发送纯文本邮件
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from email.mime.multipart import (
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MIMEMultipart, ) # 导入 MIMEMultipart 类发送带有附件的邮件
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# from email.mime.application import MIMEApplication # 导入 MIMEApplication 类发送二进制附件
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# 配置邮件信息
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receivers = ["240884432@qq.com"] # 设置邮件接收人地址
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subject = "订单流策略交易信号" # 设置邮件主题
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# text = " " # 设置邮件正文
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# file_path = "test.txt" # 设置邮件附件文件路径
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smtp_server = "smtp.qq.com" # 设置发送邮件的 SMTP 服务器地址
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smtp_port = 465 # 设置发送邮件的 SMTP 服务器端口号，一般为 25 端口 465
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sender = "240884432@qq.com" # 设置发送邮件的邮箱地址
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username = "240884432@qq.com" # 设置发送邮件的邮箱用户名
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password = "osjyjmbqrzxtbjbf" # 设置发送邮件的邮箱密码或授权码
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tickdatadict = {} # 存储Tick数据的字典
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quotedict = {} # 存储行情数据的字典
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ofdatadict = {} # 存储K线数据的字典
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trade_dfs = {} # pd.DataFrame({}) 存储交易数据的DataFrame对象
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previous_volume = {} # 上一个Tick的成交量
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tsymbollist = {}
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commodity_night_dict = {
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"sc": s_time(2, 30),
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"bc": s_time(1, 0),
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"lu": s_time(23, 0),
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"nr": s_time(23, 0),
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"au": s_time(2, 30),
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"ag": s_time(2, 30),
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"ss": s_time(1, 0),
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"sn": s_time(1, 0),
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"ni": s_time(1, 0),
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"pb": s_time(1, 0),
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"zn": s_time(1, 0),
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"al": s_time(1, 0),
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"cu": s_time(1, 0),
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"ru": s_time(23, 0),
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"rb": s_time(23, 0),
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"hc": s_time(23, 0),
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"fu": s_time(23, 0),
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"bu": s_time(23, 0),
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"sp": s_time(23, 0),
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"PF": s_time(23, 0),
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"SR": s_time(23, 0),
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"CF": s_time(23, 0),
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"CY": s_time(23, 0),
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"RM": s_time(23, 0),
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"MA": s_time(23, 0),
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"TA": s_time(23, 0),
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"ZC": s_time(23, 0),
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"FG": s_time(23, 0),
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"OI": s_time(23, 0),
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"SA": s_time(23, 0),
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"p": s_time(23, 0),
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"j": s_time(23, 0),
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"jm": s_time(23, 0),
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"i": s_time(23, 0),
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"l": s_time(23, 0),
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"v": s_time(23, 0),
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"pp": s_time(23, 0),
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"eg": s_time(23, 0),
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"c": s_time(23, 0),
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"cs": s_time(23, 0),
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"y": s_time(23, 0),
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"m": s_time(23, 0),
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"a": s_time(23, 0),
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"b": s_time(23, 0),
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"rr": s_time(23, 0),
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"eb": s_time(23, 0),
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"pg": s_time(23, 0),
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"SH": s_time(23, 00),
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}
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def send_mail(text):
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msg = MIMEMultipart()
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msg["From"] = sender
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msg["To"] = ";".join(receivers)
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msg["Subject"] = subject
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msg.attach(MIMEText(text, "plain", "utf-8"))
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smtp = smtplib.SMTP_SSL(smtp_server, smtp_port)
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smtp.login(username, password)
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smtp.sendmail(sender, receivers, msg.as_string())
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smtp.quit()
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# 交易程序---------------------------------------------------------------------------------------------------------------------------------------------------------------------
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class ParamObj:
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# 策略需要用到的参数，在新建合约对象的时候传入！！
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symbol = None # 合约名称
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Lots = None # 下单手数
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py = None # 设置委托价格的偏移，更加容易促成成交
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trailing_stop_percent = None # 跟踪出场参数
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fixed_stop_loss_percent = None # 固定出场参数
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dj_X = None # 开仓的堆积参数
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delta = None # 开仓的delta参数
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sum_delta = None # 开仓的delta累积参数
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失衡 = None
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堆积 = None
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周期 = None
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# 策略需要用到的变量
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cont_df = 0
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pos = 0
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short_trailing_stop_price = 0
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long_trailing_stop_price = 0
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sl_long_price = 0
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sl_shor_price = 0
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out_long = 0
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out_short = 0
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clearing_executed = False
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kgdata = True
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def __init__(
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self,
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symbol,
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Lots,
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py,
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trailing_stop_percent,
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fixed_stop_loss_percent,
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dj_X,
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delta,
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sum_delta,
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失衡,
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堆积,
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周期,
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):
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self.symbol = symbol
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self.Lots = Lots
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self.py = py
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self.trailing_stop_percent = trailing_stop_percent
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self.fixed_stop_loss_percent = fixed_stop_loss_percent
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self.dj_X = dj_X
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self.delta = delta
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self.sum_delta = sum_delta
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self.失衡 = 失衡
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self.堆积 = 堆积
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self.周期 = 周期
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class MyTrader(TraderApiBase):
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def __init__(
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self,
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broker_id,
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td_server,
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investor_id,
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password,
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app_id,
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auth_code,
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md_queue=None,
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page_dir="",
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private_resume_type=2,
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public_resume_type=2,
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):
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self.param_dict = {}
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self.queue_dict = {}
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self.品种 = " "
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# 邮件通知模块
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def tickcome(self, md_queue):
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global previous_volume
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data = md_queue
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instrument_id = data["InstrumentID"].decode() # 品种代码
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ActionDay = data["ActionDay"].decode() # 交易日日期
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update_time = data["UpdateTime"].decode() # 更新时间
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update_millisec = str(data["UpdateMillisec"]) # 更新毫秒数
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created_at = (ActionDay[:4] + "-" + ActionDay[4:6] + "-" +
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ActionDay[6:] + " " + update_time + "." + update_millisec
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) # 创建时间
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# 构建tick字典
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tick = {
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"symbol":
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instrument_id, # 品种代码和交易所ID
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"created_at":
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datetime.strptime(created_at, "%Y-%m-%d %H:%M:%S.%f"),
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# 'created_at': created_at, # 创建时间
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"price":
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float(data["LastPrice"]), # 最新价
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"last_volume":
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(int(data["Volume"]) - previous_volume.get(instrument_id, 0)
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if previous_volume.get(instrument_id, 0) != 0 else 0), # 瞬时成交量
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"bid_p":
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float(data["BidPrice1"]), # 买价
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"bid_v":
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int(data["BidVolume1"]), # 买量
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"ask_p":
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float(data["AskPrice1"]), # 卖价
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"ask_v":
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int(data["AskVolume1"]), # 卖量
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"UpperLimitPrice":
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float(data["UpperLimitPrice"]), # 涨停价
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"LowerLimitPrice":
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float(data["LowerLimitPrice"]), # 跌停价
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"TradingDay":
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data["TradingDay"].decode(), # 交易日日期
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"cum_volume":
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int(data["Volume"]), # 最新总成交量
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"cum_amount":
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float(data["Turnover"]), # 最新总成交额
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"cum_position":
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int(data["OpenInterest"]), # 合约持仓量
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}
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# print('&&&&&&&&',instrument_id, tick['created_at'],'vol:',tick['last_volume'])
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# 更新上一个Tick的成交量
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previous_volume[instrument_id] = int(data["Volume"])
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if tick["last_volume"] > 0:
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# print(tick['created_at'],'vol:',tick['last_volume'])
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# 处理Tick数据
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self.on_tick(tick)
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def can_time(self, hour, minute):
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hour = str(hour)
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minute = str(minute)
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if len(minute) == 1:
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minute = "0" + minute
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return int(hour + minute)
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def on_tick(self, tick):
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tm = self.can_time(tick["created_at"].hour, tick["created_at"].minute)
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# print(tick['symbol'])
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# print(1)
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# if tm>1500 and tm<2100 :
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# return
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if tick["last_volume"] == 0:
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return
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quotes = tick
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timetick = str(tick["created_at"]).replace("+08:00", "")
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tsymbol = tick["symbol"]
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if tsymbol not in tsymbollist.keys():
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# 获取tick的买卖价和买卖量
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tsymbollist[tsymbol] = tick
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bid_p = quotes["bid_p"]
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ask_p = quotes["ask_p"]
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bid_v = quotes["bid_v"]
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ask_v = quotes["ask_v"]
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else:
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# 获取上一个tick的买卖价和买卖量
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rquotes = tsymbollist[tsymbol]
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bid_p = rquotes["bid_p"]
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ask_p = rquotes["ask_p"]
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bid_v = rquotes["bid_v"]
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ask_v = rquotes["ask_v"]
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tsymbollist[tsymbol] = tick
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tick_dt = pd.DataFrame(
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{
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"datetime": timetick,
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"symbol": tick["symbol"],
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"mainsym": tick["symbol"].rstrip("0123456789").upper(),
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"lastprice": tick["price"],
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"vol": tick["last_volume"],
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"bid_p": bid_p,
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"ask_p": ask_p,
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"bid_v": bid_v,
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"ask_v": ask_v,
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},
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index=[0],
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)
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sym = tick_dt["symbol"][0]
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# print(tick_dt)
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self.tickdata(tick_dt, sym)
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# 公众号：松鼠Quant
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# 主页：www.quant789.com
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# 本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
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# 版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
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def data_of(self, symbol, df):
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global trade_dfs
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# 将df数据合并到trader_df中
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# if symbol not in trade_dfs.keys():
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# trade_df = pd.DataFrame({})
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# else:
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# trade_df = trade_dfs[symbol]
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trade_dfs[symbol] = pd.concat([trade_dfs[symbol], df],
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ignore_index=True)
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# print('!!!!!!!!!!!trader_df: ', symbol, df['datetime'].iloc[-1])
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# print(trader_df)
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def process(self, bidDict, askDict, symbol):
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try:
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# 尝试从quotedict中获取对应品种的报价数据
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dic = quotedict[symbol]
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bidDictResult = dic["bidDictResult"]
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askDictResult = dic["askDictResult"]
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except:
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# 如果获取失败，则初始化bidDictResult和askDictResult为空字典
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bidDictResult, askDictResult = {}, {}
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# 将所有买盘字典和卖盘字典的key合并，并按升序排序
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sList = sorted(set(list(bidDict.keys()) + list(askDict.keys())))
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# 遍历所有的key，将相同key的值进行累加
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for s in sList:
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if s in bidDict:
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if s in bidDictResult:
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bidDictResult[s] = int(bidDict[s]) + bidDictResult[s]
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else:
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bidDictResult[s] = int(bidDict[s])
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if s not in askDictResult:
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askDictResult[s] = 0
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else:
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if s in askDictResult:
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askDictResult[s] = int(askDict[s]) + askDictResult[s]
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else:
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askDictResult[s] = int(askDict[s])
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if s not in bidDictResult:
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bidDictResult[s] = 0
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# 构建包含bidDictResult和askDictResult的字典，并存入quotedict中
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df = {"bidDictResult": bidDictResult, "askDictResult": askDictResult}
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quotedict[symbol] = df
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return bidDictResult, askDictResult
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# 公众号：松鼠Quant
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# 主页：www.quant789.com
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# 本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
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# 版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
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def tickdata(self, df, symbol):
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tickdata = pd.DataFrame({
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"datetime": df["datetime"],
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"symbol": df["symbol"],
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"lastprice": df["lastprice"],
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"volume": df["vol"],
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"bid_p": df["bid_p"],
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"bid_v": df["bid_v"],
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"ask_p": df["ask_p"],
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"ask_v": df["ask_v"],
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})
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try:
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if symbol in tickdatadict.keys():
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rdf = tickdatadict[symbol]
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rdftm = pd.to_datetime(
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rdf["bartime"][0]).strftime("%Y-%m-%d %H:%M:%S")
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now = str(tickdata["datetime"][0])
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if now > rdftm:
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try:
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oo = ofdatadict[symbol]
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self.data_of(symbol, oo)
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# print('oo',oo)
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if symbol in quotedict.keys():
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quotedict.pop(symbol)
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if symbol in tickdatadict.keys():
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tickdatadict.pop(symbol)
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if symbol in ofdatadict.keys():
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ofdatadict.pop(symbol)
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except IOError as e:
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print("rdftm捕获到异常", e)
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tickdata["bartime"] = pd.to_datetime(tickdata["datetime"])
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tickdata["open"] = tickdata["lastprice"]
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tickdata["high"] = tickdata["lastprice"]
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tickdata["low"] = tickdata["lastprice"]
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tickdata["close"] = tickdata["lastprice"]
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tickdata["starttime"] = tickdata["datetime"]
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else:
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tickdata["bartime"] = rdf["bartime"]
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tickdata["open"] = rdf["open"]
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tickdata["high"] = max(tickdata["lastprice"].values,
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rdf["high"].values)
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tickdata["low"] = min(tickdata["lastprice"].values,
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rdf["low"].values)
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tickdata["close"] = tickdata["lastprice"]
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tickdata["volume"] = df["vol"] + rdf["volume"].values
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tickdata["starttime"] = rdf["starttime"]
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else:
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print("新bar的第一个tick进入")
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tickdata["bartime"] = pd.to_datetime(tickdata["datetime"])
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tickdata["open"] = tickdata["lastprice"]
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tickdata["high"] = tickdata["lastprice"]
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tickdata["low"] = tickdata["lastprice"]
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tickdata["close"] = tickdata["lastprice"]
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tickdata["starttime"] = tickdata["datetime"]
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except IOError as e:
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print("捕获到异常", e)
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tickdata["bartime"] = pd.to_datetime(tickdata["bartime"])
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param = self.param_dict[self.品种]
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bardata = (tickdata.resample(on="bartime",
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rule=param.周期,
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label="right",
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closed="right").agg({
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"starttime": "first",
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"symbol": "last",
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"open": "first",
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"high": "max",
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"low": "min",
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"close": "last",
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"volume": "sum",
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}).reset_index(drop=False))
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bardata = bardata.dropna().reset_index(drop=True)
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bardata["bartime"] = pd.to_datetime(
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bardata["bartime"][0]).strftime("%Y-%m-%d %H:%M:%S")
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tickdatadict[symbol] = bardata
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tickdata["volume"] = df["vol"].values
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# print(bardata['symbol'].values,bardata['bartime'].values)
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self.orderflow_df_new(tickdata, bardata, symbol)
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# time.sleep(0.5)
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# 公众号：松鼠Quant
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# 主页：www.quant789.com
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# 本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
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# 版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
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def orderflow_df_new(self, df_tick, df_min, symbol):
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startArray = pd.to_datetime(df_min["starttime"]).values
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voluememin = df_min["volume"].values
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highs = df_min["high"].values
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lows = df_min["low"].values
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opens = df_min["open"].values
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closes = df_min["close"].values
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# endArray = pd.to_datetime(df_min['bartime']).values
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endArray = df_min["bartime"].values
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# print(endArray)
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deltaArray = np.zeros((len(endArray), ))
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tTickArray = pd.to_datetime(df_tick["datetime"]).values
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bp1TickArray = df_tick["bid_p"].values
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ap1TickArray = df_tick["ask_p"].values
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lastTickArray = df_tick["lastprice"].values
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volumeTickArray = df_tick["volume"].values
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symbolarray = df_tick["symbol"].values
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indexFinal = 0
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for index, tEnd in enumerate(endArray):
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dt = endArray[index]
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start = startArray[index]
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bidDict = {}
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askDict = {}
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bar_vol = voluememin[index]
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bar_close = closes[index]
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bar_open = opens[index]
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bar_low = lows[index]
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bar_high = highs[index]
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bar_symbol = symbolarray[index]
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# for indexTick in range(indexFinal,len(df_tick)):
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# if tTickArray[indexTick] >= tEnd:
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# break
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# elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] < tEnd):
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Bp = round(bp1TickArray[0], 4)
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Ap = round(ap1TickArray[0], 4)
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LastPrice = round(lastTickArray[0], 4)
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Volume = volumeTickArray[0]
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if LastPrice >= Ap:
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if str(LastPrice) in askDict.keys():
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askDict[str(LastPrice)] += Volume
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else:
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askDict[str(LastPrice)] = Volume
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if LastPrice <= Bp:
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if str(LastPrice) in bidDict.keys():
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bidDict[str(LastPrice)] += Volume
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else:
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bidDict[str(LastPrice)] = Volume
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# indexFinal = indexTick
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bidDictResult, askDictResult = self.process(
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bidDict, askDict, symbol)
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bidDictResult = dict(
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sorted(bidDictResult.items(), key=operator.itemgetter(0)))
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askDictResult = dict(
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sorted(askDictResult.items(), key=operator.itemgetter(0)))
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prinslist = list(bidDictResult.keys())
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asklist = list(askDictResult.values())
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bidlist = list(bidDictResult.values())
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delta = sum(askDictResult.values()) - sum(bidDictResult.values())
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# print(prinslist,asklist,bidlist)
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# print(len(prinslist),len(bidDictResult),len(askDictResult))
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df = pd.DataFrame({
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"price": pd.Series([prinslist]),
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"Ask": pd.Series([asklist]),
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"Bid": pd.Series([bidlist]),
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})
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# df=pd.DataFrame({'price':pd.Series(bidDictResult.keys()),'Ask':pd.Series(askDictResult.values()),'Bid':pd.Series(bidDictResult.values())})
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df["symbol"] = bar_symbol
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df["datetime"] = dt
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df["delta"] = str(delta)
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df["close"] = bar_close
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df["open"] = bar_open
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df["high"] = bar_high
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df["low"] = bar_low
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df["volume"] = bar_vol
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# df['ticktime']=tTickArray[0]
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df["dj"] = self.GetOrderFlow_dj(df)
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ofdatadict[symbol] = df
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# 公众号：松鼠Quant
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# 主页：www.quant789.com
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# 本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
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# 版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
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def GetOrderFlow_dj(self, kData):
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param = self.param_dict[self.品种]
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Config = {
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"Value1": param.失衡,
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"Value2": param.堆积,
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"Value4": True,
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}
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aryData = kData
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djcout = 0
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# 遍历kData中的每一行，计算djcout指标
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for index, row in aryData.iterrows():
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kItem = aryData.iloc[index]
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high = kItem["high"]
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low = kItem["low"]
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close = kItem["close"]
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open = kItem["open"]
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dtime = kItem["datetime"]
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price_s = kItem["price"]
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Ask_s = kItem["Ask"]
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Bid_s = kItem["Bid"]
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delta = kItem["delta"]
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price_s = price_s
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Ask_s = Ask_s
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Bid_s = Bid_s
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gj = 0
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xq = 0
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gxx = 0
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xxx = 0
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# 遍历price_s中的每一个元素，计算相关指标
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for i in np.arange(0, len(price_s), 1):
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duiji = {
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"price": 0,
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"time": 0,
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"longshort": 0,
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}
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if i == 0:
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delta = delta
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order = {
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"Price": price_s[i],
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"Bid": {
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"Value": Bid_s[i]
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},
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"Ask": {
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"Value": Ask_s[i]
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},
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}
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# 空头堆积
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if i >= 0 and i < len(price_s) - 1:
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if order["Bid"]["Value"] > Ask_s[i + 1] * int(
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Config["Value1"]):
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gxx += 1
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gj += 1
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if gj >= int(
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Config["Value2"]) and Config["Value4"] == True:
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duiji["price"] = price_s[i]
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duiji["time"] = dtime
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duiji["longshort"] = -1
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if float(duiji["price"]) > 0:
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djcout += -1
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else:
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gj = 0
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# 多头堆积
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if i >= 1 and i < len(price_s) - 1:
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if order["Ask"]["Value"] > Bid_s[i - 1] * int(
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Config["Value1"]):
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xq += 1
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xxx += 1
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if xq >= int(
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Config["Value2"]) and Config["Value4"] == True:
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duiji["price"] = price_s[i]
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duiji["time"] = dtime
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duiji["longshort"] = 1
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if float(duiji["price"]) > 0:
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djcout += 1
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else:
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xq = 0
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# 返回计算得到的djcout值
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return djcout
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# 读取保存的数据
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def read_to_csv(self, symbol):
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# 文件夹路径和文件路径
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# 使用正则表达式提取英文字母并重新赋值给symbol
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param = self.param_dict[symbol]
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# symbol = ''.join(re.findall('[a-zA-Z]', str(symbol)))
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folder_path = "traderdata"
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file_path = os.path.join(folder_path, f"{str(symbol)}_traderdata.csv")
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# 如果文件夹不存在则创建
|
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if not os.path.exists(folder_path):
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|
os.makedirs(folder_path)
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|
# 读取保留的模型数据CSV文件
|
|
if os.path.exists(file_path):
|
|
df = pd.read_csv(file_path)
|
|
if not df.empty and param.kgdata == True:
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|
# 选择最后一行数据
|
|
row = df.iloc[-1]
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|
|
# 根据CSV文件的列名将数据赋值给相应的属性
|
|
param.pos = int(row["pos"])
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|
param.short_trailing_stop_price = float(
|
|
row["short_trailing_stop_price"])
|
|
param.long_trailing_stop_price = float(
|
|
row["long_trailing_stop_price"])
|
|
param.sl_long_price = float(row["sl_long_price"])
|
|
param.sl_shor_price = float(row["sl_shor_price"])
|
|
# param.out_long = int(row['out_long'])
|
|
# param.out_short = int(row['out_short'])
|
|
print("找到历史交易数据文件,已经更新持仓，止损止盈数据", df.iloc[-1])
|
|
param.kgdata = False
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|
else:
|
|
pass
|
|
# print("没有找到历史交易数据文件", file_path)
|
|
# 如果没有找到CSV，则初始化变量
|
|
|
|
pass
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# 保存数据
|
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def save_to_csv(self, symbol):
|
|
param = self.param_dict[symbol]
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|
# 使用正则表达式提取英文字母并重新赋值给symbol
|
|
# symbol = ''.join(re.findall('[a-zA-Z]', str(symbol)))
|
|
# 创建DataFrame
|
|
|
|
data = {
|
|
"datetime": [trade_dfs[symbol]["datetime"].iloc[-1]],
|
|
"pos": [param.pos],
|
|
"short_trailing_stop_price": [param.short_trailing_stop_price],
|
|
"long_trailing_stop_price": [param.long_trailing_stop_price],
|
|
"sl_long_price": [param.sl_long_price],
|
|
"sl_shor_price": [param.sl_shor_price],
|
|
# 'out_long': [param.out_long],
|
|
# 'out_short': [param.out_short]
|
|
}
|
|
|
|
df = pd.DataFrame(data)
|
|
|
|
# 将DataFrame保存到CSV文件
|
|
df.to_csv(f"traderdata/{str(symbol)}_traderdata.csv", index=False)
|
|
|
|
# 每日收盘重置数据
|
|
def day_data_reset(self, symbol):
|
|
param = self.param_dict[symbol]
|
|
sec = "".join(re.findall("[a-zA-Z]", str(symbol)))
|
|
# 获取当前时间
|
|
current_time = datetime.now().time()
|
|
|
|
# 第一时间范围（日盘收盘）
|
|
clearing_time1_start = s_time(15, 00)
|
|
clearing_time1_end = s_time(15, 15)
|
|
|
|
# 创建一个标志变量，用于记录是否已经执行过
|
|
param.clearing_executed = False
|
|
# 检查当前时间第一个操作的时间范围内
|
|
if (clearing_time1_start <= current_time <= clearing_time1_end
|
|
and not param.clearing_executed):
|
|
param.clearing_executed = True # 设置标志变量为已执行
|
|
trade_dfs[symbol].drop(trade_dfs[symbol].index,
|
|
inplace=True) # 清除当天的行情数据
|
|
|
|
# 检查当前时间是否在第二个操作的时间范围内(夜盘收盘)
|
|
elif sec in commodity_night_dict.keys():
|
|
clearing_time2_start = commodity_night_dict[sec]
|
|
clearing_time2_end = s_time(clearing_time2_start.hour,
|
|
clearing_time2_start.minute + 15)
|
|
if (clearing_time2_start <= current_time <= clearing_time2_end
|
|
and not param.clearing_executed):
|
|
param.clearing_executed = True # 设置标志变量为已执行
|
|
trade_dfs[symbol].drop(trade_dfs[symbol].index,
|
|
inplace=True) # 清除当天的行情数据
|
|
else:
|
|
param.clearing_executed = False
|
|
pass
|
|
return param.clearing_executed
|
|
|
|
def OnRtnTrade(self, pTrade):
|
|
print("||成交回报||", pTrade)
|
|
|
|
def OnRspOrderInsert(self, pInputOrder, pRspInfo, nRequestID, bIsLast):
|
|
print("||OnRspOrderInsert||", pInputOrder, pRspInfo, nRequestID,
|
|
bIsLast)
|
|
|
|
# 订单状态通知
|
|
def OnRtnOrder(self, pOrder):
|
|
print("||订单回报||", pOrder)
|
|
|
|
def cal_sig(self, symbol_queue):
|
|
while True:
|
|
try:
|
|
data = symbol_queue.get(block=True,
|
|
timeout=5) # 如果5秒没收到新的tick行情，则抛出异常
|
|
instrument_id = data["InstrumentID"].decode() # 品种代码
|
|
size = symbol_queue.qsize()
|
|
if size > 1:
|
|
print(f"当前{instrument_id}共享队列长度为{size}, 有点阻塞！！！！！")
|
|
self.read_to_csv(instrument_id)
|
|
self.day_data_reset(instrument_id)
|
|
param = self.param_dict[instrument_id]
|
|
self.品种 = instrument_id
|
|
self.tickcome(data)
|
|
trade_df = trade_dfs[instrument_id]
|
|
# 新K线开始，启动交易程序 and 保存行情数据
|
|
self.read_to_csv(instrument_id)
|
|
# size = symbol_queue.qsize()
|
|
# if size > 2:
|
|
# print(f'！！！！！当前{instrument_id}共享队列长度为：',size)
|
|
if len(trade_df) > param.cont_df:
|
|
# 检查文件是否存在
|
|
csv_file_path = f"traderdata/{instrument_id}_ofdata.csv"
|
|
if os.path.exists(csv_file_path):
|
|
# 仅保存最后一行数据
|
|
trade_df.tail(1).to_csv(csv_file_path,
|
|
mode="a",
|
|
header=False,
|
|
index=False)
|
|
else:
|
|
# 创建新文件并保存整个DataFrame
|
|
trade_df.to_csv(csv_file_path, index=False)
|
|
|
|
# 更新跟踪止损价格
|
|
if param.long_trailing_stop_price > 0 and param.pos > 0:
|
|
|
|
# print('datetime+sig: ',dt,'旧多头出线',param.long_trailing_stop_price,'low',self.low[0])
|
|
|
|
param.long_trailing_stop_price = (
|
|
trade_df["low"].iloc[-1]
|
|
if param.long_trailing_stop_price
|
|
< trade_df["low"].iloc[-1] else
|
|
param.long_trailing_stop_price)
|
|
self.save_to_csv(instrument_id)
|
|
|
|
# print('datetime+sig: ',dt,'多头出线',param.long_trailing_stop_price)
|
|
if param.short_trailing_stop_price > 0 and param.pos < 0:
|
|
|
|
# print('datetime+sig: ',dt,'旧空头出线',param.short_trailing_stop_price,'high',self.high[0])
|
|
|
|
param.short_trailing_stop_price = (
|
|
trade_df["high"].iloc[-1]
|
|
if trade_df["high"].iloc[-1]
|
|
< param.short_trailing_stop_price else
|
|
param.short_trailing_stop_price)
|
|
self.save_to_csv(instrument_id)
|
|
|
|
# print('datetime+sig: ',dt,'空头出线',param.short_trailing_stop_price)
|
|
|
|
param.out_long = param.long_trailing_stop_price * (
|
|
1 - param.trailing_stop_percent)
|
|
param.out_short = param.short_trailing_stop_price * (
|
|
1 + param.trailing_stop_percent)
|
|
# print('datetime+sig: ',dt,'空头出线',param.out_short)
|
|
# print('datetime+sig: ',dt,'多头出线',param.out_long)
|
|
# 跟踪出场
|
|
if param.out_long > 0:
|
|
print(
|
|
"datetime+sig: ",
|
|
trade_df["datetime"].iloc[-1],
|
|
"预设——多头止盈——",
|
|
"TR",
|
|
param.out_long,
|
|
"low",
|
|
trade_df["low"].iloc[-1],
|
|
)
|
|
if (trade_df["low"].iloc[-1] < param.out_long
|
|
and param.pos > 0 and param.sl_long_price > 0
|
|
and trade_df["low"].iloc[-1]
|
|
> param.sl_long_price):
|
|
print(
|
|
"datetime+sig: ",
|
|
trade_df["datetime"].iloc[-1],
|
|
"多头止盈",
|
|
"TR",
|
|
param.out_long,
|
|
"low",
|
|
trade_df["low"].iloc[-1],
|
|
)
|
|
# 平多
|
|
self.insert_order(
|
|
data["ExchangeID"],
|
|
data["InstrumentID"],
|
|
data["BidPrice1"] - param.py,
|
|
param.Lots,
|
|
b"1",
|
|
b"1",
|
|
)
|
|
self.insert_order(
|
|
data["ExchangeID"],
|
|
data["InstrumentID"],
|
|
data["BidPrice1"] - param.py,
|
|
param.Lots,
|
|
b"1",
|
|
b"3",
|
|
)
|
|
param.long_trailing_stop_price = 0
|
|
param.out_long = 0
|
|
param.sl_long_price = 0
|
|
param.pos = 0
|
|
self.save_to_csv(instrument_id)
|
|
|
|
if param.out_short > 0:
|
|
print(
|
|
"datetime+sig: ",
|
|
trade_df["datetime"].iloc[-1],
|
|
"预设——空头止盈——: ",
|
|
"TR",
|
|
param.out_short,
|
|
"high",
|
|
trade_df["high"].iloc[-1],
|
|
)
|
|
if (trade_df["high"].iloc[-1] > param.out_short
|
|
and param.pos < 0 and param.sl_shor_price > 0
|
|
and trade_df["high"].iloc[-1]
|
|
< param.sl_shor_price):
|
|
print(
|
|
"datetime+sig: ",
|
|
trade_df["datetime"].iloc[-1],
|
|
"空头止盈: ",
|
|
"TR",
|
|
param.out_short,
|
|
"high",
|
|
trade_df["high"].iloc[-1],
|
|
)
|
|
# 平空
|
|
self.insert_order(
|
|
data["ExchangeID"],
|
|
data["InstrumentID"],
|
|
data["AskPrice1"] + param.py,
|
|
param.Lots,
|
|
b"0",
|
|
b"1",
|
|
)
|
|
self.insert_order(
|
|
data["ExchangeID"],
|
|
data["InstrumentID"],
|
|
data["AskPrice1"] + param.py,
|
|
param.Lots,
|
|
b"0",
|
|
b"3",
|
|
)
|
|
param.short_trailing_stop_price = 0
|
|
param.sl_shor_price = 0
|
|
self.out_shor = 0
|
|
param.pos = 0
|
|
self.save_to_csv(instrument_id)
|
|
|
|
# 固定止损
|
|
fixed_stop_loss_L = param.sl_long_price * (
|
|
1 - param.fixed_stop_loss_percent)
|
|
if param.pos > 0:
|
|
print(
|
|
"datetime+sig: ",
|
|
trade_df["datetime"].iloc[-1],
|
|
"预设——多头止损",
|
|
"SL",
|
|
fixed_stop_loss_L,
|
|
"close",
|
|
trade_df["close"].iloc[-1],
|
|
)
|
|
if (param.sl_long_price > 0 and fixed_stop_loss_L > 0
|
|
and param.pos > 0 and trade_df["close"].iloc[-1]
|
|
< fixed_stop_loss_L):
|
|
print(
|
|
"datetime+sig: ",
|
|
trade_df["datetime"].iloc[-1],
|
|
"多头止损",
|
|
"SL",
|
|
fixed_stop_loss_L,
|
|
"close",
|
|
trade_df["close"].iloc[-1],
|
|
)
|
|
# 平多
|
|
self.insert_order(
|
|
data["ExchangeID"],
|
|
data["InstrumentID"],
|
|
data["BidPrice1"] - param.py,
|
|
param.Lots,
|
|
b"1",
|
|
b"1",
|
|
)
|
|
self.insert_order(
|
|
data["ExchangeID"],
|
|
data["InstrumentID"],
|
|
data["BidPrice1"] - param.py,
|
|
param.Lots,
|
|
b"1",
|
|
b"3",
|
|
)
|
|
param.long_trailing_stop_price = 0
|
|
param.sl_long_price = 0
|
|
param.out_long = 0
|
|
param.pos = 0
|
|
self.save_to_csv(instrument_id)
|
|
|
|
fixed_stop_loss_S = param.sl_shor_price * (
|
|
1 + param.fixed_stop_loss_percent)
|
|
if param.pos < 0:
|
|
print(
|
|
"datetime+sig: ",
|
|
trade_df["datetime"].iloc[-1],
|
|
"预设——空头止损",
|
|
"SL",
|
|
fixed_stop_loss_S,
|
|
"close",
|
|
trade_df["close"].iloc[-1],
|
|
)
|
|
if (param.sl_shor_price > 0 and fixed_stop_loss_S > 0
|
|
and param.pos < 0 and trade_df["close"].iloc[-1]
|
|
> fixed_stop_loss_S):
|
|
print(
|
|
"datetime+sig: ",
|
|
trade_df["datetime"].iloc[-1],
|
|
"空头止损",
|
|
"SL",
|
|
fixed_stop_loss_S,
|
|
"close",
|
|
trade_df["close"].iloc[-1],
|
|
)
|
|
# 平空
|
|
self.insert_order(
|
|
data["ExchangeID"],
|
|
data["InstrumentID"],
|
|
data["AskPrice1"] + param.py,
|
|
param.Lots,
|
|
b"0",
|
|
b"1",
|
|
)
|
|
self.insert_order(
|
|
data["ExchangeID"],
|
|
data["InstrumentID"],
|
|
data["AskPrice1"] + param.py,
|
|
param.Lots,
|
|
b"0",
|
|
b"3",
|
|
)
|
|
param.short_trailing_stop_price = 0
|
|
param.sl_shor_price = 0
|
|
param.out_short = 0
|
|
param.pos = 0
|
|
self.save_to_csv(instrument_id)
|
|
|
|
# 日均线
|
|
trade_df["dayma"] = trade_df["close"].mean()
|
|
|
|
# 计算累积的delta值
|
|
trade_df["delta"] = trade_df["delta"].astype(float)
|
|
trade_df["delta累计"] = trade_df["delta"].cumsum()
|
|
|
|
# 大于日均线
|
|
开多1 = (trade_df["dayma"].iloc[-1] > 0
|
|
and trade_df["close"].iloc[-1]
|
|
> trade_df["dayma"].iloc[-1])
|
|
|
|
# 累计多空净量大于X
|
|
开多4 = (trade_df["delta累计"].iloc[-1] > param.sum_delta
|
|
and trade_df["delta"].iloc[-1] > param.delta)
|
|
|
|
# 小于日均线
|
|
开空1 = (trade_df["dayma"].iloc[-1] > 0
|
|
and trade_df["close"].iloc[-1]
|
|
< trade_df["dayma"].iloc[-1])
|
|
|
|
# 累计多空净量小于X
|
|
开空4 = (trade_df["delta累计"].iloc[-1] < -param.sum_delta
|
|
and trade_df["delta"].iloc[-1] < -param.delta)
|
|
开多组合 = 开多1 and 开多4 and trade_df["dj"].iloc[-1] > param.dj_X
|
|
开空条件 = 开空1 and 开空4 and trade_df["dj"].iloc[-1] < -param.dj_X
|
|
|
|
平多条件 = trade_df["dj"].iloc[-1] < -param.dj_X
|
|
平空条件 = trade_df["dj"].iloc[-1] > param.dj_X
|
|
# 开仓
|
|
# 多头开仓条件
|
|
if param.pos < 0 and 平空条件:
|
|
print(
|
|
"平空： ",
|
|
"ExchangeID: ",
|
|
data["ExchangeID"],
|
|
"InstrumentID",
|
|
data["InstrumentID"],
|
|
"AskPrice1",
|
|
data["AskPrice1"] + param.py,
|
|
)
|
|
# 平空
|
|
self.insert_order(
|
|
data["ExchangeID"],
|
|
data["InstrumentID"],
|
|
data["AskPrice1"] + param.py,
|
|
param.Lots,
|
|
b"0",
|
|
b"1",
|
|
)
|
|
self.insert_order(
|
|
data["ExchangeID"],
|
|
data["InstrumentID"],
|
|
data["AskPrice1"] + param.py,
|
|
param.Lots,
|
|
b"0",
|
|
b"3",
|
|
)
|
|
param.pos = 0
|
|
param.sl_shor_price = 0
|
|
param.short_trailing_stop_price = 0
|
|
print(
|
|
"datetime+sig: ",
|
|
trade_df["datetime"].iloc[-1],
|
|
"反手平空：",
|
|
"平仓价格：",
|
|
data["AskPrice1"] + param.py,
|
|
"堆积数：",
|
|
trade_df["dj"].iloc[-1],
|
|
)
|
|
self.save_to_csv(instrument_id)
|
|
|
|
# 发送邮件
|
|
text = f"平空交易： 交易品种为{data['InstrumentID']}, 交易时间为{trade_df['datetime'].iloc[-1]}, 反手平空的平仓价格为{data['AskPrice1']+param.py}, 交易手数位{param.Lots}"
|
|
send_mail(text)
|
|
|
|
if param.pos == 0 and 开多组合:
|
|
print(
|
|
"开多： ",
|
|
"ExchangeID: ",
|
|
data["ExchangeID"],
|
|
"InstrumentID",
|
|
data["InstrumentID"],
|
|
"AskPrice1",
|
|
data["AskPrice1"] + param.py,
|
|
)
|
|
# 开多
|
|
self.insert_order(
|
|
data["ExchangeID"],
|
|
data["InstrumentID"],
|
|
data["AskPrice1"] + param.py,
|
|
param.Lots,
|
|
b"0",
|
|
b"0",
|
|
)
|
|
print(
|
|
"datetime+sig: ",
|
|
trade_df["datetime"].iloc[-1],
|
|
"多头开仓",
|
|
"开仓价格：",
|
|
data["AskPrice1"] + param.py,
|
|
"堆积数：",
|
|
trade_df["dj"].iloc[-1],
|
|
)
|
|
param.pos = 1
|
|
param.long_trailing_stop_price = data["AskPrice1"]
|
|
param.sl_long_price = data["AskPrice1"]
|
|
self.save_to_csv(instrument_id)
|
|
|
|
# 发送邮件
|
|
text = f"开多交易： 交易品种为{data['InstrumentID']}, 交易时间为{trade_df['datetime'].iloc[-1]}, 多头开仓的开仓价格{data['AskPrice1']+param.py}, 交易手数位{param.Lots}"
|
|
send_mail(text)
|
|
|
|
if param.pos > 0 and 平多条件:
|
|
print(
|
|
"平多： ",
|
|
"ExchangeID: ",
|
|
data["ExchangeID"],
|
|
"InstrumentID",
|
|
data["InstrumentID"],
|
|
"BidPrice1",
|
|
data["BidPrice1"] - param.py,
|
|
)
|
|
# 平多
|
|
self.insert_order(
|
|
data["ExchangeID"],
|
|
data["InstrumentID"],
|
|
data["BidPrice1"] - param.py,
|
|
param.Lots,
|
|
b"1",
|
|
b"1",
|
|
)
|
|
self.insert_order(
|
|
data["ExchangeID"],
|
|
data["InstrumentID"],
|
|
data["BidPrice1"] - param.py,
|
|
param.Lots,
|
|
b"1",
|
|
b"3",
|
|
)
|
|
param.pos = 0
|
|
param.long_trailing_stop_price = 0
|
|
param.sl_long_price = 0
|
|
print(
|
|
"datetime+sig: ",
|
|
trade_df["datetime"].iloc[-1],
|
|
"反手平多",
|
|
"平仓价格：",
|
|
data["BidPrice1"] - param.py,
|
|
"堆积数：",
|
|
trade_df["dj"].iloc[-1],
|
|
)
|
|
self.save_to_csv(instrument_id)
|
|
|
|
# 发送邮件
|
|
text = f"平多交易： 交易品种为{data['InstrumentID']}, 交易时间为{trade_df['datetime'].iloc[-1]}, 反手平多的平仓价格{data['BidPrice1']-param.py}, 交易手数位{param.Lots}"
|
|
send_mail(text)
|
|
|
|
if param.pos == 0 and 开空条件:
|
|
print(
|
|
"开空： ",
|
|
"ExchangeID: ",
|
|
data["ExchangeID"],
|
|
"InstrumentID",
|
|
data["InstrumentID"],
|
|
"BidPrice1",
|
|
data["BidPrice1"],
|
|
)
|
|
# 开空
|
|
self.insert_order(
|
|
data["ExchangeID"],
|
|
data["InstrumentID"],
|
|
data["BidPrice1"] - param.py,
|
|
param.Lots,
|
|
b"1",
|
|
b"0",
|
|
)
|
|
print(
|
|
"datetime+sig: ",
|
|
trade_df["datetime"].iloc[-1],
|
|
"空头开仓",
|
|
"开仓价格：",
|
|
data["BidPrice1"] - param.py,
|
|
"堆积数：",
|
|
trade_df["dj"].iloc[-1],
|
|
)
|
|
param.pos = -1
|
|
param.short_trailing_stop_price = data["BidPrice1"]
|
|
param.sl_shor_price = data["BidPrice1"]
|
|
self.save_to_csv(instrument_id)
|
|
|
|
# 发送邮件
|
|
text = f"开空交易： 交易品种为{data['InstrumentID']}, 交易时间为{trade_df['datetime'].iloc[-1]}, 空头开仓的开仓价格{data['BidPrice1']-param.py}, 交易手数位{param.Lots}"
|
|
send_mail(text)
|
|
|
|
print(trade_df)
|
|
param.cont_df = len(trade_df)
|
|
except queue.Empty:
|
|
# print(f"当前合约队列为空，等待新数据插入。")
|
|
pass
|
|
|
|
# 将CTP推送的行情数据分发给对应线程队列去执行
|
|
def distribute_tick(self):
|
|
while True:
|
|
if self.status == 0:
|
|
data = None
|
|
while not self.md_queue.empty():
|
|
data = self.md_queue.get(block=False)
|
|
instrument_id = data["InstrumentID"].decode() # 品种代码
|
|
try:
|
|
self.queue_dict[instrument_id].put(
|
|
data, block=False) # 往对应合约队列中插入行情
|
|
# print(f"{instrument_id}合约数据插入。")
|
|
except queue.Full:
|
|
# 当某个线程阻塞导致对应队列容量超限时抛出异常，不会影响其他合约的信号计算
|
|
print(f"{instrument_id}合约信号计算阻塞导致对应队列已满，请检查对应代码逻辑后重启。")
|
|
else:
|
|
time.sleep(1)
|
|
|
|
def start(self, param_dict):
|
|
threads = []
|
|
self.param_dict = param_dict
|
|
for symbol in param_dict.keys():
|
|
trade_dfs[symbol] = pd.DataFrame({})
|
|
self.queue_dict[symbol] = queue.Queue(
|
|
20) # 为每个合约创建一个限制数为10的队列，当计算发生阻塞导致队列达到限制数时会抛出异常
|
|
t = threading.Thread(
|
|
target=self.cal_sig,
|
|
args=(self.queue_dict[symbol], )) # 为每个合约单独创建一个线程计算开仓逻辑
|
|
threads.append(t)
|
|
t.start()
|
|
self.distribute_tick()
|
|
for t in threads:
|
|
t.join()
|
|
|
|
|
|
def run_trader(
|
|
param_dict,
|
|
broker_id,
|
|
td_server,
|
|
investor_id,
|
|
password,
|
|
app_id,
|
|
auth_code,
|
|
md_queue=None,
|
|
page_dir="",
|
|
private_resume_type=2,
|
|
public_resume_type=2,
|
|
):
|
|
my_trader = MyTrader(
|
|
broker_id,
|
|
td_server,
|
|
investor_id,
|
|
password,
|
|
app_id,
|
|
auth_code,
|
|
md_queue,
|
|
page_dir,
|
|
private_resume_type,
|
|
public_resume_type,
|
|
)
|
|
my_trader.start(param_dict)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# global symbol
|
|
# 公众号：松鼠Quant
|
|
# 主页：www.quant789.com
|
|
# 本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
|
# 版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
|
|
|
# 注意：运行前请先安装好algoplus,
|
|
# pip install AlgoPlus
|
|
# http://www.algo.plus/ctp/python/0103001.html
|
|
|
|
# 实盘参数字典，需要实盘交易的合约，新建对应的参数对象即可，以下参数仅供测试使用，不作为实盘参考！！！！
|
|
# 实盘参数字典，需要实盘交易的合约，新建对应的参数对象即可，以下参数仅供测试使用，不作为实盘参考！！！！
|
|
# 实盘参数字典，需要实盘交易的合约，新建对应的参数对象即可，以下参数仅供测试使用，不作为实盘参考！！！！
|
|
param_dict = {}
|
|
param_dict["rb2410"] = ParamObj(
|
|
symbol="rb2410",
|
|
Lots=1,
|
|
py=5,
|
|
trailing_stop_percent=0.02,
|
|
fixed_stop_loss_percent=0.01,
|
|
dj_X=1,
|
|
delta=1500,
|
|
sum_delta=2000,
|
|
失衡=3,
|
|
堆积=3,
|
|
周期="1T",
|
|
)
|
|
# param_dict['ag2408'] = ParamObj(symbol='ag2408', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=500,sum_delta=1000,失衡=3,堆积=3,周期='5T')
|
|
# param_dict['j2405'] = ParamObj(symbol='j2405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
|
# param_dict['TA405'] = ParamObj(symbol='TA405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
|
# param_dict['au2406'] = ParamObj(symbol='au2406', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
|
# param_dict['sc2405'] = ParamObj(symbol='sc2405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
|
# param_dict['bc2406'] = ParamObj(symbol='bc2406', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
|
# param_dict['lu2406'] = ParamObj(symbol='lu2406', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
|
|
|
# 用simnow模拟，不要忘记屏蔽下方实盘的future_account字典
|
|
# future_account = get_simulate_account(
|
|
# investor_id='135858', # simnow账户，注意是登录账户的ID，SIMNOW个人首页查看
|
|
# password='Zj82334475', # simnow密码
|
|
# server_name='电信1', # 电信1、电信2、移动、TEST、N视界
|
|
# subscribe_list=list(param_dict.keys()), # 合约列表
|
|
# )
|
|
|
|
# 实盘用这个，不要忘记屏蔽上方simnow的future_account字典
|
|
future_account = FutureAccount(
|
|
broker_id="8888", # 期货公司BrokerID
|
|
server_dict={
|
|
"TDServer": "103.140.14.210:43205",
|
|
"MDServer": "103.140.14.210:43173",
|
|
}, # TDServer为交易服务器，MDServer为行情服务器。服务器地址格式为"ip:port。"
|
|
reserve_server_dict={}, # 备用服务器地址
|
|
investor_id="155878", # 账户
|
|
password="Zj82334475", # 密码
|
|
app_id="vntech_vnpy_2.0", # 认证使用AppID
|
|
auth_code="N46EKN6TJ9U7V06V", # 认证使用授权码
|
|
subscribe_list=list(param_dict.keys()), # 订阅合约列表
|
|
md_flow_path="./log", # MdApi流文件存储地址，默认MD_LOCATION
|
|
td_flow_path="./log", # TraderApi流文件存储地址，默认TD_LOCATION
|
|
)
|
|
|
|
print("开始", len(future_account.subscribe_list))
|
|
# 共享队列
|
|
share_queue = Queue(maxsize=200)
|
|
|
|
# 行情进程
|
|
md_process = Process(target=run_tick_engine,
|
|
args=(future_account, [share_queue]))
|
|
|
|
# 交易进程
|
|
trader_process = Process(
|
|
target=run_trader,
|
|
args=(
|
|
param_dict,
|
|
future_account.broker_id,
|
|
future_account.server_dict["TDServer"],
|
|
future_account.investor_id,
|
|
future_account.password,
|
|
future_account.app_id,
|
|
future_account.auth_code,
|
|
share_queue, # 队列
|
|
future_account.td_flow_path,
|
|
),
|
|
)
|
|
|
|
md_process.start()
|
|
trader_process.start()
|
|
|
|
md_process.join()
|
|
trader_process.join()
|