zhoujie
800883b6ec
- Implemented a function to fetch futures data from the API with error handling and response validation. - Added example usage for fetching and saving K-line data to CSV. - Updated CSV files with new data entries for specified date ranges. - Enhanced the structure of the data retrieval function to include parameters for depth and adjust type.
1736 lines
77 KiB
Python
1736 lines
77 KiB
Python
"""
|
||
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。F
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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 CtpPlus.CTP.MdApi import run_tick_engine
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from CtpPlus.CTP.FutureAccount import get_simulate_account
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from CtpPlus.CTP.FutureAccount import FutureAccount
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from CtpPlus.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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# import talib as tb
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import akshare as ak
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import ast
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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,
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)
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# from email.mime.application import MIMEApplication
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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 = "TD_Simnow_Signal" # 设置邮件主题 订单流策略交易信号
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# 配置邮件服务器信息
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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" # zrmpcgttataabhjh，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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time_period = 30
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delta_rate = 0.8
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dj_rate = 0.8
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clearing_time_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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}
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fees_df = pd.read_csv('./futures_fees_info.csv', header = 0, usecols= [1, 3, 5, 13, 15],names=['合约代码', '品种代码', '合约乘数', '做多保证金率（按金额）', '做空保证金率（按金额）'])
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contacts_df = pd.read_csv('./main_contacts.csv', header = 0, usecols= [16, 17],names=['主连代码', '品种代码'])
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def get_main_contact_on_time(main_symbol_code,contacts_df):
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main_symbol = contacts_df[contacts_df['品种代码'] == main_symbol_code]['主连代码'].iloc[0]
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# print("最终使用的主连代码：",main_symbol)
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return main_symbol#.encode('ascii')
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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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# def get_otm_put_strike_price(option_finance_board_df, future_price):
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# # 计算距离当前期货价格最近的行权价
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# option_finance_board_df['strike_diff'] = abs(option_finance_board_df['行权价'] - future_price)
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# closest_row = option_finance_board_df.loc[option_finance_board_df['strike_diff'].idxmin()]
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# otm_put_strike_price = closest_row['行权价']
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# return otm_put_strike_price
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def send_feishu_message(text):
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headers = {
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"Content-Type": "application/json"
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}
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data = {
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"msg_type": "text",
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"content": {
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"text": text
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}
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}
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# response = requests.post("https://open.feishu.cn/open-apis/bot/v2/hook/8608dfa4-e599-462a-8dba-6ac72873dd27", headers=headers, json=data) #AI策略飞书地址
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response = requests.post("https://open.feishu.cn/open-apis/bot/v2/hook/fae322eb-1ff7-4133-ba00-0ca4895d205e", headers=headers, json=data) #订单流策略飞书地址
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if response.status_code != 200:
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print(f"飞书消息发送失败，状态码: {response.status_code}, 响应内容: {response.text}")
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# def futures_main_day(future_symbol, delta_days):
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# # 获取当前日期的数据
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# today = datetime.now().strftime("%Y%m%d")
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# # 计算多少日前的日期
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# start_day = (datetime.now() - timedelta(days=delta_days)).strftime("%Y%m%d")
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# futures_main_sina_hist = ak.futures_main_sina(
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# symbol=future_symbol, start_date=start_day, end_date=today
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# )
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# return futures_main_sina_hist
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# 交易程序---------------------------------------------------------------------------------------------------------------------------------------------------------------------
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class ParamObj:
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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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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 = (
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ActionDay[:4]
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+ "-"
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+ ActionDay[4:6]
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+ "-"
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+ ActionDay[6:]
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+ " "
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+ update_time
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+ "."
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+ 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": instrument_id, # 品种代码和交易所ID
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"created_at": datetime.strptime(created_at, "%Y-%m-%d %H:%M:%S.%f"),
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# "created_at": datetime.strptime(created_at, "-- %H:%M:%S.%f"),
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"price": 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
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else 0
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), # 瞬时成交量
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"bid_p": float(data["BidPrice1"]), # 买价
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"bid_v": int(data["BidVolume1"]), # 买量
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"ask_p": float(data["AskPrice1"]), # 卖价
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"ask_v": int(data["AskVolume1"]), # 卖量
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"UpperLimitPrice": float(data["UpperLimitPrice"]), # 涨停价
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"LowerLimitPrice": float(data["LowerLimitPrice"]), # 跌停价
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"TradingDay": data["TradingDay"].decode(), # 交易日日期
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"cum_volume": int(data["Volume"]), # 最新总成交量
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"cum_amount": float(data["Turnover"]), # 最新总成交额
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"cum_position": int(data["OpenInterest"]), # 合约持仓量
|
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}
|
||
|
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previous_volume[instrument_id] = int(data["Volume"])
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if tick["last_volume"] > 0:
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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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if tick["last_volume"] == 0:
|
||
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:
|
||
# 获取上一个tick的买卖价和买卖量
|
||
rquotes = tsymbollist[tsymbol]
|
||
bid_p = rquotes["bid_p"]
|
||
ask_p = rquotes["ask_p"]
|
||
bid_v = rquotes["bid_v"]
|
||
ask_v = rquotes["ask_v"]
|
||
tsymbollist[tsymbol] = tick
|
||
tick_dt = pd.DataFrame(
|
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{
|
||
"datetime": timetick,
|
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"symbol": tick["symbol"],
|
||
"mainsym": tick["symbol"].rstrip("0123456789").upper(),
|
||
"lastprice": tick["price"],
|
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"vol": tick["last_volume"],
|
||
"bid_p": bid_p,
|
||
"ask_p": ask_p,
|
||
"bid_v": bid_v,
|
||
"ask_v": ask_v,
|
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},
|
||
index=[0],
|
||
)
|
||
sym = tick_dt["symbol"][0]
|
||
self.tickdata(tick_dt, sym)
|
||
|
||
def data_of(self, symbol, df):
|
||
global trade_dfs
|
||
trade_dfs[symbol] = pd.concat([trade_dfs[symbol], df], ignore_index=True)
|
||
|
||
def process(self, bidDict, askDict, symbol):
|
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try:
|
||
# 尝试从quotedict中获取对应品种的报价数据
|
||
dic = quotedict[symbol]
|
||
bidDictResult = dic["bidDictResult"]
|
||
askDictResult = dic["askDictResult"]
|
||
except Exception:
|
||
# 如果获取失败，则初始化bidDictResult和askDictResult为空字典
|
||
bidDictResult, askDictResult = {}, {}
|
||
|
||
# 将所有买盘字典和卖盘字典的key合并，并按升序排序
|
||
sList = sorted(set(list(bidDict.keys()) + list(askDict.keys())))
|
||
|
||
# 遍历所有的key，将相同key的值进行累加
|
||
for s in sList:
|
||
if s in bidDict:
|
||
if s in bidDictResult:
|
||
bidDictResult[s] = int(bidDict[s]) + bidDictResult[s]
|
||
else:
|
||
bidDictResult[s] = int(bidDict[s])
|
||
if s not in askDictResult:
|
||
askDictResult[s] = 0
|
||
else:
|
||
if s in askDictResult:
|
||
askDictResult[s] = int(askDict[s]) + askDictResult[s]
|
||
else:
|
||
askDictResult[s] = int(askDict[s])
|
||
if s not in bidDictResult:
|
||
bidDictResult[s] = 0
|
||
|
||
# 构建包含bidDictResult和askDictResult的字典，并存入quotedict中
|
||
df = {"bidDictResult": bidDictResult, "askDictResult": askDictResult}
|
||
quotedict[symbol] = df
|
||
return bidDictResult, askDictResult
|
||
|
||
def tickdata(self, df, symbol):
|
||
tickdata = pd.DataFrame(
|
||
{
|
||
"datetime": df["datetime"],
|
||
"symbol": df["symbol"],
|
||
"lastprice": df["lastprice"],
|
||
"volume": df["vol"],
|
||
"bid_p": df["bid_p"],
|
||
"bid_v": df["bid_v"],
|
||
"ask_p": df["ask_p"],
|
||
"ask_v": df["ask_v"],
|
||
}
|
||
)
|
||
try:
|
||
if symbol in tickdatadict.keys():
|
||
rdf = tickdatadict[symbol]
|
||
rdftm = pd.to_datetime(rdf["bartime"][0]).strftime("%Y-%m-%d %H:%M:%S")
|
||
now = str(tickdata["datetime"][0])
|
||
if now > rdftm:
|
||
try:
|
||
oo = ofdatadict[symbol]
|
||
self.data_of(symbol, oo)
|
||
if symbol in quotedict.keys():
|
||
quotedict.pop(symbol)
|
||
if symbol in tickdatadict.keys():
|
||
tickdatadict.pop(symbol)
|
||
if symbol in ofdatadict.keys():
|
||
ofdatadict.pop(symbol)
|
||
except IOError as e:
|
||
print("rdftm捕获到异常", e)
|
||
tickdata["bartime"] = pd.to_datetime(tickdata["datetime"])
|
||
tickdata["open"] = tickdata["lastprice"]
|
||
tickdata["high"] = tickdata["lastprice"]
|
||
tickdata["low"] = tickdata["lastprice"]
|
||
tickdata["close"] = tickdata["lastprice"]
|
||
tickdata["starttime"] = tickdata["datetime"]
|
||
else:
|
||
tickdata["bartime"] = rdf["bartime"]
|
||
tickdata["open"] = rdf["open"]
|
||
tickdata["high"] = max(
|
||
tickdata["lastprice"].values, rdf["high"].values
|
||
)
|
||
tickdata["low"] = min(
|
||
tickdata["lastprice"].values, rdf["low"].values
|
||
)
|
||
tickdata["close"] = tickdata["lastprice"]
|
||
tickdata["volume"] = df["vol"] + rdf["volume"].values
|
||
tickdata["starttime"] = rdf["starttime"]
|
||
else:
|
||
print("新bar的第一个tick进入")
|
||
tickdata["bartime"] = pd.to_datetime(tickdata["datetime"])
|
||
tickdata["open"] = tickdata["lastprice"]
|
||
tickdata["high"] = tickdata["lastprice"]
|
||
tickdata["low"] = tickdata["lastprice"]
|
||
tickdata["close"] = tickdata["lastprice"]
|
||
tickdata["starttime"] = tickdata["datetime"]
|
||
except IOError as e:
|
||
print("捕获到异常", e)
|
||
|
||
tickdata["bartime"] = pd.to_datetime(tickdata["bartime"])
|
||
param = self.param_dict[self.品种]
|
||
bardata = (
|
||
tickdata.resample(
|
||
on="bartime", rule=param.周期, label="right", closed="right"
|
||
)
|
||
.agg(
|
||
{
|
||
"starttime": "first",
|
||
"symbol": "last",
|
||
"open": "first",
|
||
"high": "max",
|
||
"low": "min",
|
||
"close": "last",
|
||
"volume": "sum",
|
||
}
|
||
)
|
||
.reset_index(drop=False)
|
||
)
|
||
bardata = bardata.dropna().reset_index(drop=True)
|
||
bardata["bartime"] = pd.to_datetime(bardata["bartime"][0]).strftime(
|
||
"%Y-%m-%d %H:%M:%S"
|
||
)
|
||
tickdatadict[symbol] = bardata
|
||
tickdata["volume"] = df["vol"].values
|
||
self.orderflow_df_new(tickdata, bardata, symbol)
|
||
|
||
def orderflow_df_new(self, df_tick, df_min, symbol):
|
||
# startArray = pd.to_datetime(df_min["starttime"]).values
|
||
voluememin = df_min["volume"].values
|
||
highs = df_min["high"].values
|
||
lows = df_min["low"].values
|
||
opens = df_min["open"].values
|
||
closes = df_min["close"].values
|
||
# endArray = pd.to_datetime(df_min['bartime']).values
|
||
endArray = df_min["bartime"].values
|
||
# print(endArray)
|
||
# deltaArray = np.zeros((len(endArray),))
|
||
# tTickArray = pd.to_datetime(df_tick["datetime"]).values
|
||
bp1minickArray = df_tick["bid_p"].values
|
||
ap1minickArray = df_tick["ask_p"].values
|
||
lastTickArray = df_tick["lastprice"].values
|
||
volumeTickArray = df_tick["volume"].values
|
||
symbolarray = df_tick["symbol"].values
|
||
# indexFinal = 0
|
||
for index, tEnd in enumerate(endArray):
|
||
dt = endArray[index]
|
||
# start = startArray[index]
|
||
bidDict = {}
|
||
askDict = {}
|
||
bar_vol = voluememin[index]
|
||
bar_close = closes[index]
|
||
bar_open = opens[index]
|
||
bar_low = lows[index]
|
||
bar_high = highs[index]
|
||
bar_symbol = symbolarray[index]
|
||
Bp = round(bp1minickArray[0], 4)
|
||
Ap = round(ap1minickArray[0], 4)
|
||
LastPrice = round(lastTickArray[0], 4)
|
||
Volume = volumeTickArray[0]
|
||
if LastPrice >= Ap:
|
||
if str(LastPrice) in askDict.keys():
|
||
askDict[str(LastPrice)] += Volume
|
||
else:
|
||
askDict[str(LastPrice)] = Volume
|
||
if LastPrice <= Bp:
|
||
if str(LastPrice) in bidDict.keys():
|
||
bidDict[str(LastPrice)] += Volume
|
||
else:
|
||
bidDict[str(LastPrice)] = Volume
|
||
# indexFinal = indexTick
|
||
bidDictResult, askDictResult = self.process(bidDict, askDict, symbol)
|
||
bidDictResult = dict(
|
||
sorted(bidDictResult.items(), key=operator.itemgetter(0))
|
||
)
|
||
askDictResult = dict(
|
||
sorted(askDictResult.items(), key=operator.itemgetter(0))
|
||
)
|
||
prinslist = list(bidDictResult.keys())
|
||
asklist = list(askDictResult.values())
|
||
bidlist = list(bidDictResult.values())
|
||
delta = sum(askDictResult.values()) - sum(bidDictResult.values())
|
||
df = pd.DataFrame(
|
||
{
|
||
"price": pd.Series([prinslist]),
|
||
"Ask": pd.Series([asklist]),
|
||
"Bid": pd.Series([bidlist]),
|
||
}
|
||
)
|
||
# df=pd.DataFrame({'price':pd.Series(bidDictResult.keys()),'Ask':pd.Series(askDictResult.values()),'Bid':pd.Series(bidDictResult.values())})
|
||
df["symbol"] = bar_symbol
|
||
df["datetime"] = dt
|
||
df["delta"] = str(delta)
|
||
df["close"] = bar_close
|
||
df["open"] = bar_open
|
||
df["high"] = bar_high
|
||
df["low"] = bar_low
|
||
df["volume"] = bar_vol
|
||
# df['ticktime']=tTickArray[0]
|
||
df["dj"] = self.GetOrderFlow_dj(df)
|
||
ofdatadict[symbol] = df
|
||
|
||
def GetOrderFlow_dj(self, kData):
|
||
param = self.param_dict[self.品种]
|
||
Config = {
|
||
"Value1": param.失衡,
|
||
"Value2": param.堆积,
|
||
"Value4": True,
|
||
}
|
||
aryData = kData
|
||
djcout = 0
|
||
|
||
# 遍历kData中的每一行，计算djcout指标
|
||
for index, row in aryData.iterrows():
|
||
kItem = aryData.iloc[index]
|
||
# high = kItem["high"]
|
||
# low = kItem["low"]
|
||
# close = kItem["close"]
|
||
# open = kItem["open"]
|
||
dtime = kItem["datetime"]
|
||
price_s = kItem["price"]
|
||
Ask_s = kItem["Ask"]
|
||
Bid_s = kItem["Bid"]
|
||
delta = kItem["delta"]
|
||
|
||
price_s = price_s
|
||
Ask_s = Ask_s
|
||
Bid_s = Bid_s
|
||
|
||
gj = 0
|
||
xq = 0
|
||
gxx = 0
|
||
xxx = 0
|
||
|
||
# 遍历price_s中的每一个元素，计算相关指标
|
||
for i in np.arange(0, len(price_s), 1):
|
||
duiji = {
|
||
"price": 0,
|
||
"time": 0,
|
||
"longshort": 0,
|
||
}
|
||
|
||
if i == 0:
|
||
delta = delta
|
||
|
||
order = {
|
||
"Price": price_s[i],
|
||
"Bid": {"Value": Bid_s[i]},
|
||
"Ask": {"Value": Ask_s[i]},
|
||
}
|
||
# 空头堆积
|
||
if i >= 0 and i < len(price_s) - 1:
|
||
if order["Bid"]["Value"] > Ask_s[i + 1] * int(Config["Value1"]):
|
||
gxx += 1
|
||
gj += 1
|
||
if gj >= int(Config["Value2"]) and Config["Value4"] is True:
|
||
duiji["price"] = price_s[i]
|
||
duiji["time"] = dtime
|
||
duiji["longshort"] = -1
|
||
if float(duiji["price"]) > 0:
|
||
djcout += -1
|
||
else:
|
||
gj = 0
|
||
# 多头堆积
|
||
if i >= 1 and i < len(price_s) - 1:
|
||
if order["Ask"]["Value"] > Bid_s[i - 1] * int(Config["Value1"]):
|
||
xq += 1
|
||
xxx += 1
|
||
if xq >= int(Config["Value2"]) and Config["Value4"] is True:
|
||
duiji["price"] = price_s[i]
|
||
duiji["time"] = dtime
|
||
duiji["longshort"] = 1
|
||
if float(duiji["price"]) > 0:
|
||
djcout += 1
|
||
else:
|
||
xq = 0
|
||
|
||
# 返回计算得到的djcout值
|
||
return djcout
|
||
|
||
# 读取保存的数据
|
||
def read_to_csv(self, symbol):
|
||
# 文件夹路径和文件路径
|
||
# 使用正则表达式提取英文字母并重新赋值给symbol
|
||
param = self.param_dict[symbol]
|
||
# symbol = ''.join(re.findall('[a-zA-Z]', str(symbol)))
|
||
folder_path = "traderdata"
|
||
file_path = os.path.join(folder_path, f"{str(symbol)}_traderdata.csv")
|
||
# 如果文件夹不存在则创建
|
||
if not os.path.exists(folder_path):
|
||
os.makedirs(folder_path)
|
||
|
||
# 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 is True:
|
||
# 选择最后一行数据
|
||
# df = df._append(df.iloc[-1], ignore_index=True)
|
||
row = df.iloc[-1]
|
||
|
||
# 根据CSV文件的列名将数据赋值给相应的属性
|
||
param.pos = int(row["pos"])
|
||
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
|
||
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
|
||
|
||
# 保存数据
|
||
def save_to_csv(self, symbol):
|
||
param = self.param_dict[symbol]
|
||
# 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",
|
||
# mode="a",
|
||
# index=False,
|
||
# header=False,
|
||
# )
|
||
|
||
traderdata_file_path = f"traderdata/{str(symbol)}_traderdata.csv"
|
||
if os.path.exists(traderdata_file_path):
|
||
# 仅保存最后一行数据
|
||
# csv_df = pd.read_csv(traderdata_file_path)
|
||
# if df["pos"].iloc[-1] != csv_df["pos"].iloc[-1]:
|
||
df.to_csv(traderdata_file_path, mode="a", header=False, index=False)
|
||
else:
|
||
# 创建新文件并保存整个DataFrame
|
||
df.to_csv(traderdata_file_path, index=False)
|
||
# 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, 5)
|
||
clearing_time1_end = s_time(15, 10)
|
||
|
||
# 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 clearing_time_dict.keys():
|
||
clearing_time2_start = clearing_time_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)
|
||
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):
|
||
# #jerome :保存数增加'delta累计'、POC、、终极平滑值、趋势方向
|
||
# # 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)
|
||
# 检查是否存在重复行
|
||
if os.path.exists(csv_file_path):
|
||
existing_df = pd.read_csv(csv_file_path, usecols=range(12))
|
||
# 获取要写入的新数据
|
||
new_data = trade_df.tail(1)
|
||
|
||
# 检查新数据是否与现有数据重复
|
||
is_duplicate = False
|
||
for _, row in existing_df.iterrows():
|
||
if (row['datetime'] == new_data['datetime'].iloc[0] and
|
||
row['price'] == new_data['price'].iloc[0] and
|
||
row['Ask'] == new_data['Ask'].iloc[0] and
|
||
row['Bid'] == new_data['Bid'].iloc[0] and
|
||
row['symbol'] == new_data['symbol'].iloc[0] and
|
||
row['delta'] == new_data['delta'].iloc[0] and
|
||
row['close'] == new_data['close'].iloc[0] and
|
||
row['open'] == new_data['open'].iloc[0] and
|
||
row['high'] == new_data['high'].iloc[0] and
|
||
row['low'] == new_data['low'].iloc[0] and
|
||
row['volume'] == new_data['volume'].iloc[0] and
|
||
row['dj'] == new_data['dj'].iloc[0]):
|
||
is_duplicate = True
|
||
break
|
||
|
||
# 检查Ask和Bid的值是否为空或全为0
|
||
ask_value = new_data['Ask'].iloc[0]
|
||
bid_value = new_data['Bid'].iloc[0]
|
||
is_valid_data = (
|
||
ask_value != [] and
|
||
ask_value != [0] and
|
||
bid_value != [] and
|
||
bid_value != [0]
|
||
)
|
||
|
||
if not is_duplicate and is_valid_data:
|
||
# 如果没有重复且数据有效，则写入新数据
|
||
new_data.to_csv(
|
||
csv_file_path, mode="a", header=False, index=False
|
||
)
|
||
else:
|
||
# 如果文件不存在，直接写入新数据
|
||
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:
|
||
|
||
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)
|
||
|
||
if param.short_trailing_stop_price > 0 and param.pos < 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)
|
||
|
||
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
|
||
)
|
||
# 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)
|
||
|
||
|
||
# 计算累积的delta值datetime.strptime(str_time, "%Y-%m-%d %H:%M:%S")
|
||
trade_df["delta"] = trade_df["delta"].astype(float)
|
||
# trade_df['datetime'] = pd.to_datetime(trade_df['datetime'], format='mixed')
|
||
trade_df['datetime'] = pd.to_datetime(trade_df['datetime'], format='%Y-%m-%d %H:%M:%S')
|
||
|
||
# 自定义分组逻辑：前一日21:00至当日15:00为一天
|
||
def get_trading_day(dt):
|
||
# 如果时间在21:00之后，属于下一个交易日
|
||
if dt.hour >= 21:
|
||
return (dt + pd.Timedelta(days=1)).date()
|
||
# 如果时间在15:00之前，属于当前交易日
|
||
elif dt.hour < 15:
|
||
return dt.date()
|
||
# 15:00-21:00之间的数据属于当前交易日
|
||
else:
|
||
return dt.date()
|
||
|
||
trade_df['trading_day'] = trade_df['datetime'].apply(get_trading_day)
|
||
|
||
# 将日期转换为字符串
|
||
trade_df['trading_day'] = trade_df['trading_day'].astype(str)
|
||
|
||
# 按交易日计算delta累计
|
||
trade_df['delta累计'] = trade_df.groupby('trading_day')['delta'].cumsum()
|
||
|
||
trade_df = trade_df.fillna('缺值')#fillna(value=0)
|
||
|
||
def ultimate_smoother(price,period):
|
||
# 初始化变量（修正角度单位为弧度）
|
||
a1 = np.exp(-1.414 * np.pi / period)
|
||
b1 = 2 * a1 * np.cos(1.414 * np.pi / period) # 将180改为np.pi
|
||
c2 = b1
|
||
c3 = -a1 ** 2
|
||
c1 = (1 + c2 - c3) / 4
|
||
|
||
# 准备输出序列
|
||
us = np.zeros(len(price))
|
||
us_new = np.zeros(len(price))
|
||
trend = [None]*(len(price))
|
||
ma_close = np.zeros(len(price))
|
||
|
||
# 前4个点用原始价格初始化
|
||
for i in range(len(price)):
|
||
if i < 4:
|
||
us[i] = price.iloc[i]
|
||
else:
|
||
# 应用递归公式
|
||
us[i] = (1 - c1) * price.iloc[i] + (2 * c1 - c2) * price.iloc[i-1] \
|
||
- (c1 + c3) * price.iloc[i-2] + c2 * us[i-1] + c3 * us[i-2]
|
||
|
||
us_new = np.around(us, decimals=2)
|
||
ma_close = price.rolling(window=4*period).mean()#5*
|
||
|
||
# if us_new[i]>price[i] and ma_close[i]>price[i]:
|
||
# trend[i] = '空头趋势'
|
||
# elif us_new[i]<price[i] and ma_close[i]<price[i]:
|
||
# trend[i] = '多头趋势'
|
||
# else:
|
||
# trend[i] = '无趋势'
|
||
|
||
if us_new[i] < ma_close.iloc[i]:
|
||
trend[i] = '空头趋势'
|
||
elif us_new[i] > ma_close.iloc[i]:
|
||
trend[i] = '多头趋势'
|
||
else:
|
||
trend[i] = '无趋势'
|
||
|
||
|
||
return us_new,trend
|
||
|
||
trade_df['终极平滑值'],trade_df['趋势方向'] = ultimate_smoother(trade_df['close'],time_period)#,df['ma_close']
|
||
|
||
trade_df['datetime'] = trade_df['datetime'].dt.strftime("%Y-%m-%d %H:%M:%S")
|
||
|
||
|
||
def safe_literal_eval(x):
|
||
"""带异常处理的安全转换"""
|
||
try:
|
||
return ast.literal_eval(x)
|
||
except ValueError:
|
||
return [] # 返回空列表作为占位符
|
||
|
||
def add_poc_column(df):
|
||
# 安全转换列数据
|
||
df['price'] = df['price'].apply(safe_literal_eval)
|
||
df['Ask'] = df['Ask'].apply(lambda x: list(map(int, safe_literal_eval(x))))
|
||
df['Bid'] = df['Bid'].apply(lambda x: list(map(int, safe_literal_eval(x))))
|
||
|
||
# 定义处理函数（带数据验证）
|
||
def find_poc(row):
|
||
# 验证三个列表长度一致且非空
|
||
if not (len(row['price']) == len(row['Ask']) == len(row['Bid']) > 0):
|
||
return '缺值' # 返回空值标记异常数据
|
||
|
||
sums = [a + b for a, b in zip(row['Ask'], row['Bid'])]
|
||
try:
|
||
max_index = sums.index(max(sums))
|
||
return row['price'][max_index]
|
||
except ValueError:
|
||
return '缺值' # 处理空求和列表情况
|
||
|
||
# 应用处理函数
|
||
df['POC'] = df.apply(find_poc, axis=1)
|
||
|
||
# 可选：统计异常数据
|
||
error_count = df['POC'].isnull().sum()
|
||
if error_count > 0:
|
||
print(f"警告：发现 {error_count} 行异常数据（已标记为NaN）")
|
||
|
||
return df['POC']
|
||
|
||
trade_df['POC'] = add_poc_column(trade_df)
|
||
|
||
def finall_trend(delta_sum,trend):
|
||
f_trend = [None]*(len(delta_sum))
|
||
# delta_sum = delta_sum.astype(float)
|
||
for i in range(len(delta_sum)):
|
||
if (delta_sum[i] == '缺值') or (trend[i] == '缺值'):
|
||
f_trend[i] = '方向不明'
|
||
# return f_trend
|
||
else:
|
||
if delta_sum[i] > 0 and (trend[i] == '多头趋势'):
|
||
f_trend[i] = '强多头'
|
||
elif delta_sum[i] < 0 and (trend[i] == '空头趋势'):
|
||
f_trend[i] = '强空头'
|
||
else:
|
||
f_trend[i] = '方向不明'
|
||
return f_trend
|
||
|
||
trade_df['最终趋势'] = finall_trend(trade_df['delta累计'],trade_df['趋势方向'])
|
||
|
||
# table_text = f"品种：{trade_df['symbol'].iloc[-1]}, 时间：{trade_df['datetime'].iloc[-1]},close:{trade_df['close'].iloc[-1]},open:{trade_df['open'].iloc[-1]},high:{trade_df['high'].iloc[-1]},low:{trade_df['low'].iloc[-1]},delta:{trade_df['delta'].iloc[-1]}, delta累计:{trade_df['delta累计'].iloc[-1]}, dj:{trade_df['dj'].iloc[-1]},POC:{trade_df['POC'].iloc[-1]}, 终极平滑值:{trade_df['终极平滑值'].iloc[-1]}, 趋势方向:{trade_df['趋势方向'].iloc[-1]},最终趋势:{trade_df['最终趋势'].iloc[-1]}"
|
||
# if data["InstrumentID"]:
|
||
# option_buy_symbol,option_buy_price= get_otm_option(data["InstrumentID"], 'C')
|
||
# option_sell_symbol,option_sell_price = get_otm_option(data["InstrumentID"], 'P')
|
||
# print("买入平值期权为:", option_buy_symbol, "，价格为:", option_buy_price)
|
||
# print("卖出平值期权为:", option_sell_symbol, "，价格为:", option_sell_price)
|
||
|
||
def get_otm_option(future_symbol, trade_type):
|
||
def get_option_symbol(future_symbol):
|
||
# 创建一个字典,将期货值futuresymbol映射到对应的option symbol .
|
||
option_dict = {
|
||
"IH": "上证50股指期权",
|
||
"IF": "沪深300股指期权",
|
||
"IC": "中证500股指期权",
|
||
"IM": "中证1000股指期权"
|
||
}
|
||
# 解析 future_symbol 获取期货代码和到期月份
|
||
m = re.match(r'([A-Za-z]+)(\d+)', future_symbol)
|
||
if not m:
|
||
raise ValueError(f"future_symbol 格式不正确: {future_symbol}")
|
||
future_code, future_end_month = m.groups()
|
||
option_symbol = option_dict.get(future_code)
|
||
return option_symbol, future_end_month
|
||
|
||
try:
|
||
option_symbol, future_end_month = get_option_symbol(future_symbol)
|
||
option_finance_board_df = ak.option_finance_board(symbol=option_symbol, end_month=future_end_month)
|
||
|
||
len(option_finance_board_df)
|
||
|
||
half = len(option_finance_board_df) // 2
|
||
|
||
first_half_df = option_finance_board_df.iloc[:half]
|
||
first_half_df.columns = [f"{col}_C" for col in first_half_df.columns]
|
||
first = first_half_df.reset_index(drop=True)
|
||
|
||
second_half_df = option_finance_board_df.iloc[half:]
|
||
second_half_df.columns = [f"{col}_P" for col in second_half_df.columns]
|
||
second = second_half_df.reset_index(drop=True)
|
||
|
||
df = pd.concat([first, second], axis=1)
|
||
df['lastprice_(C-P)'] = df['lastprice_C'] - df['lastprice_P']
|
||
|
||
idx = df['lastprice_(C-P)'].abs().idxmin()
|
||
row = df.loc[idx, ['instrument_C', 'instrument_P', 'lastprice_C','lastprice_P','lastprice_(C-P)']]
|
||
|
||
# print(f"index={idx}, instrument_C={row['instrument_C']}, instrument_P={row['instrument_P']}, lastprice_(C-P)={row['lastprice_(C-P)']}")
|
||
|
||
# return df.loc[idx, ['instrument_C', 'instrument_P']]
|
||
if trade_type == 'C':
|
||
return row['instrument_C'], float(row['lastprice_C'])
|
||
elif trade_type == 'P':
|
||
return row['instrument_P'], float(row['lastprice_P'])
|
||
else:
|
||
raise ValueError(f"未知的 trade_type: {trade_type}")
|
||
except Exception as e:
|
||
print(f"get_otm_option error for {future_symbol}: {e}")
|
||
return None
|
||
|
||
def get_otm_pirce(future_symbol, trade_option_symbol):
|
||
def get_option_symbol(future_symbol):
|
||
# 创建一个字典,将期货值futuresymbol映射到对应的option symbol .
|
||
option_dict = {
|
||
"IH": "上证50股指期权",
|
||
"IF": "沪深300股指期权",
|
||
"IC": "中证500股指期权",
|
||
"IM": "中证1000股指期权"
|
||
}
|
||
# 解析 future_symbol 获取期货代码和到期月份
|
||
m = re.match(r'([A-Za-z]+)(\d+)', future_symbol)
|
||
if not m:
|
||
raise ValueError(f"future_symbol 格式不正确: {future_symbol}")
|
||
future_code, future_end_month = m.groups()
|
||
option_symbol = option_dict.get(future_code)
|
||
return option_symbol, future_end_month
|
||
|
||
try:
|
||
option_symbol, future_end_month = get_option_symbol(future_symbol)
|
||
option_finance_board_df = ak.option_finance_board(symbol=option_symbol, end_month=future_end_month)
|
||
|
||
mask = option_finance_board_df['instrument'] == trade_option_symbol
|
||
if mask.any():
|
||
lastprice_value = option_finance_board_df.loc[mask, 'lastprice'].iloc[0]
|
||
return lastprice_value
|
||
# print(lastprice_value)
|
||
else:
|
||
print("未找到对应的行")
|
||
except Exception as e:
|
||
print(f"get_otm_option error for {future_symbol}: {e}")
|
||
return None
|
||
|
||
print("trade_df['symbol'].iloc[-1]}:", trade_df['symbol'].iloc[-1])
|
||
print("trade_df['symbol'].iloc[-1]}的类型:", type(trade_df['symbol'].iloc[-1]))
|
||
|
||
|
||
# 开多、空前置条件
|
||
开多条件 = (trade_df['趋势方向'].iloc[-1] == '多头趋势')
|
||
开空条件 = (trade_df['趋势方向'].iloc[-1] == '空头趋势')
|
||
|
||
if len(trade_df) >= 4*time_period:
|
||
#开多
|
||
开多1 = (trade_df['dj'].iloc[-1] >= 2) #max(0.8 * max(trade_df['dj'].iloc[-4*time_period-1:-1]), 10))
|
||
# print("开多1:",开多1)
|
||
开多2 = (trade_df['delta'].iloc[-1] >= 10)# max(0.8 * max(trade_df['delta'].iloc[-4*time_period-1:-1]), 350))
|
||
开多3 = (trade_df['delta累计'].iloc[-2] < 0 and trade_df['delta累计'].iloc[-1] > 0)
|
||
|
||
# 开空
|
||
开空1 = (trade_df['dj'].iloc[-1] <= -2) #min(0.8 * min(trade_df['dj'].iloc[-4*time_period-1:-1]), -10))
|
||
开空2 = (trade_df['delta'].iloc[-1] <= -10) #min(0.8 * min(trade_df['delta'].iloc[-4*time_period-1:-1]),-350))
|
||
# print("开空2:",开空2)
|
||
开空3 = (trade_df['delta累计'].iloc[-2] > 0 and trade_df['delta累计'].iloc[-1] < 0)
|
||
|
||
# 开多组合 = (开多条件 and (开多1 or 开多2 or 开多3))
|
||
# 开空组合 = (开空条件 and (开空1 or 开空2 or 开空3))
|
||
|
||
# 平多组合 = (开空条件 or 开空1 or 开空2 or 开空3)
|
||
# 平空组合 = (开多条件 or 开多1 or 开多2 or 开多3)
|
||
|
||
开多组合 = 开多1 and 开多2
|
||
开空组合 = 开空1 and 开空2
|
||
|
||
平多组合 = 开空1 or 开空2
|
||
平空组合 = 开多1 or 开多2
|
||
|
||
option_buy_symbol = ''
|
||
option_buy_price = 0
|
||
option_sell_symbol = ''
|
||
option_sell_price = 0
|
||
|
||
# 平空
|
||
if param.pos < 0 and 平空组合:
|
||
# close_sell_price = get_otm_pirce(data["InstrumentID"].decode(), option_sell_symbol)
|
||
print(
|
||
"平空： ",
|
||
"ExchangeID: ",
|
||
data["ExchangeID"],
|
||
"InstrumentID",
|
||
option_sell_symbol,
|
||
"AskPrice1",
|
||
800,#close_sell_price + param.py,
|
||
)
|
||
# 平空
|
||
self.insert_order(
|
||
data["ExchangeID"],
|
||
option_sell_symbol,
|
||
800,#close_sell_price + param.py,
|
||
param.Lots,
|
||
b"0",
|
||
b"1",
|
||
)
|
||
self.insert_order(
|
||
data["ExchangeID"],
|
||
option_sell_symbol,
|
||
800,#close_sell_price + 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}"
|
||
text = f"C_S_T: ID:{option_sell_symbol}, datetime:{trade_df['datetime'].iloc[-1]}, C_S_T_Price:{data['AskPrice1'] + param.py}, T_Lots:{param.Lots}"
|
||
send_mail(text)
|
||
|
||
# 开多
|
||
if param.pos == 0 and 开多组合:
|
||
print("trade_df_last:",trade_df['symbol'].iloc[-1])
|
||
option_buy_symbol,option_buy_price= get_otm_option(data["InstrumentID"].decode(), 'C')
|
||
print("买入看涨平值期权为:", option_buy_symbol, "，价格为:", option_buy_price)
|
||
print(
|
||
"开多： ",
|
||
"ExchangeID: ",
|
||
data["ExchangeID"],
|
||
"InstrumentID",
|
||
option_buy_symbol,
|
||
"AskPrice1",
|
||
option_buy_price + param.py,
|
||
)
|
||
# 开多
|
||
self.insert_order(
|
||
data["ExchangeID"],
|
||
option_buy_symbol,
|
||
option_buy_price + param.py,
|
||
param.Lots,
|
||
b"0",
|
||
b"0",
|
||
)
|
||
print(
|
||
"datetime+sig: ",
|
||
trade_df["datetime"].iloc[-1],
|
||
"多头开仓",
|
||
"开仓价格：",
|
||
option_buy_price + 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"O_L_T ID:{option_buy_symbol}, datetime:{trade_df['datetime'].iloc[-1]}, O_L_T_Price:{option_buy_price + param.py}, T_Lots:{param.Lots}"
|
||
send_mail(text)
|
||
|
||
# 平多
|
||
if param.pos > 0 and 平多组合:
|
||
print('option_buy_symbol',option_buy_symbol)
|
||
# close_buy_price = get_otm_pirce(data["InstrumentID"].decode(), option_buy_symbol)
|
||
# print('close_buy_price:',close_buy_price)
|
||
print(
|
||
"平多： ",
|
||
"ExchangeID: ",
|
||
data["ExchangeID"],
|
||
"InstrumentID",
|
||
option_buy_symbol,
|
||
"BidPrice1",
|
||
1,#close_buy_price - param.py,
|
||
)
|
||
# 平多
|
||
self.insert_order(
|
||
data["ExchangeID"],
|
||
option_buy_symbol,
|
||
1,#close_buy_price - param.py,
|
||
param.Lots,
|
||
b"1",
|
||
b"1",
|
||
)
|
||
self.insert_order(
|
||
data["ExchangeID"],
|
||
option_buy_symbol,
|
||
1,#close_buy_price - 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}"
|
||
text = f"C_L_T: ID:{option_buy_symbol}, datetime:{trade_df['datetime'].iloc[-1]}, C_L_T_Price:{data['BidPrice1'] - param.py}, T_Lots:{param.Lots}"
|
||
send_mail(text)
|
||
|
||
# 开空
|
||
if param.pos == 0 and 开空组合:
|
||
option_sell_symbol,option_sell_price = get_otm_option(data["InstrumentID"].decode(), 'P')
|
||
print("买入看跌平值期权为:", option_sell_symbol, "，价格为:", option_sell_price)
|
||
print(
|
||
"开空： ",
|
||
"ExchangeID: ",
|
||
data["ExchangeID"],
|
||
"InstrumentID",
|
||
option_sell_symbol,
|
||
"BidPrice1",
|
||
data["BidPrice1"],
|
||
)
|
||
# 开空
|
||
self.insert_order(
|
||
data["ExchangeID"],
|
||
option_sell_symbol,
|
||
option_sell_price - param.py,
|
||
param.Lots,
|
||
b"1",
|
||
b"0",
|
||
)
|
||
print(
|
||
"datetime+sig: ",
|
||
trade_df["datetime"].iloc[-1],
|
||
"空头开仓",
|
||
"开仓价格：",
|
||
option_sell_price - 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"O_S_T: ID:{option_sell_symbol}, datetime:{trade_df['datetime'].iloc[-1]}, O_S_T_Price:{option_sell_price - param.py}, T_Lots:{param.Lots}"
|
||
send_mail(text)
|
||
|
||
print(trade_df)
|
||
symbol_label = trade_df["symbol"].iloc[-1]
|
||
trade_df.to_csv(f"trade_df_{symbol_label}.csv", index=False, encoding='utf-8')
|
||
# with open('trade_df.txt', 'w', encoding='utf-8') as file:
|
||
# print(trade_df, file=file)
|
||
print("------------------------------------------------")
|
||
# print(trade_df.iloc[0])
|
||
# print(trade_df.iloc[-1])
|
||
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():
|
||
# folder_path = "traderdata"
|
||
# ofdata_file_path = os.path.join("traderdata", f"{str(symbol)}_ofdata.csv")
|
||
if os.path.exists(f"traderdata/{symbol}_ofdata.csv"):
|
||
columns = [
|
||
"price",
|
||
"Ask",
|
||
"Bid",
|
||
"symbol",
|
||
"datetime",
|
||
"delta",
|
||
"close",
|
||
"open",
|
||
"high",
|
||
"low",
|
||
"volume",
|
||
"dj",
|
||
]
|
||
# import csv
|
||
# with open(f"traderdata/{symbol}_ofdata.csv", "r") as f:
|
||
# reader = csv.reader(f)
|
||
# for i, row in enumerate(reader, 1):
|
||
# if len(row) != 12:
|
||
# print(f"Line {i} has {len(row)} columns: {row}")
|
||
trade_dfs[symbol] = pd.read_csv(
|
||
f"traderdata/{symbol}_ofdata.csv", usecols=columns
|
||
)
|
||
|
||
else:
|
||
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__":
|
||
# 注意：运行前请先安装好algoplus,
|
||
# pip install AlgoPlus
|
||
# http://www.algo.plus/ctp/python/0103001.html
|
||
|
||
param_dict = {}
|
||
|
||
## 交易一个品种，手动设置合约代码
|
||
|
||
# param_dict["TF2509"] = ParamObj(
|
||
# symbol="TF2509",
|
||
# Lots=1,
|
||
# py=5,
|
||
# trailing_stop_percent=0.01,
|
||
# fixed_stop_loss_percent=0.02,
|
||
# dj_X=8,
|
||
# delta=500,
|
||
# sum_delta=800,
|
||
# 失衡=3,
|
||
# 堆积=3,
|
||
# 周期="2min",
|
||
# )
|
||
|
||
## 交易所有品种，自动设置合约代码,
|
||
# 交易指定品种时，symbols = ['IM','IC','ag']
|
||
# symbols = ['IM','IC','ag']
|
||
symbols = contacts_df['品种代码'].tolist()
|
||
# for i, symbol in enumerate(symbols, start=1):
|
||
# globals()[f'sb_{i}'] = get_main_contact_on_time(symbol, contacts_df)
|
||
# symbol = globals()[f'sb_{i}']
|
||
# # print("最终使用的主连代码：",symbol)
|
||
# param_dict[str(symbol)] = ParamObj(symbol=symbol.encode('ascii'),Lots=1,py=5,trailing_stop_percent=0.01,fixed_stop_loss_percent=0.02,dj_X=8,delta=500,sum_delta=800,失衡=3,堆积=3,周期="5min")
|
||
for i, symbol in enumerate(symbols, start=1):
|
||
if symbol in ['wr', 'RS' , 'bb', 'WH', 'fb', 'rr', 'PL']:
|
||
continue
|
||
elif symbol in ['IM',]: #symbol in ['IH', 'IF', 'IC', 'IM', 'au', 'sc']
|
||
globals()[f'sb_{i}'] = get_main_contact_on_time(symbol, contacts_df)
|
||
symbol = globals()[f'sb_{i}']
|
||
param_dict[str(symbol)] = ParamObj(symbol=symbol.encode('ascii'),Lots=1,py=5,trailing_stop_percent=0.02,fixed_stop_loss_percent=0.04,dj_X=8,delta=500,sum_delta=800,失衡=3,堆积=3,周期="2min")
|
||
|
||
|
||
## 交易多个指定品种，自动设置合约代码,手动设置其他参数
|
||
# param_dict[symbol] = ParamObj(symbol=get_main_contact_on_time('IM', contacts_df),Lots=1,py=5,trailing_stop_percent=0.01,fixed_stop_loss_percent=0.02,dj_X=8,delta=500,sum_delta=800,失衡=3,堆积=3,周期="1min")
|
||
# param_dict[symbol] = ParamObj(symbol=get_main_contact_on_time('IC', contacts_df),Lots=1,py=5,trailing_stop_percent=0.02,fixed_stop_loss_percent=0.04,dj_X=8,delta=500,sum_delta=800,失衡=3,堆积=3,周期="1min")
|
||
# print(param_dict.keys())
|
||
|
||
# 用simnow模拟，不要忘记屏蔽下方实盘的future_account字典
|
||
# SIMULATE_SERVER = {
|
||
# '电信1': {'BrokerID': 9999, 'TDServer': "180.168.146.187:10201", 'MDServer': '180.168.146.187:10211', 'AppID': 'simnow_client_test', 'AuthCode': '0000000000000000'},
|
||
# '电信2': {'BrokerID': 9999, 'TDServer': "180.168.146.187:10202", 'MDServer': '180.168.146.187:10212', 'AppID': 'simnow_client_test', 'AuthCode': '0000000000000000'},
|
||
# '移动': {'BrokerID': 9999, 'TDServer': "218.202.237.33:10203", 'MDServer': '218.202.237.33:10213', 'AppID': 'simnow_client_test', 'AuthCode': '0000000000000000'},
|
||
# 'TEST': {'BrokerID': 9999, 'TDServer': "180.168.146.187:10130", 'MDServer': '180.168.146.187:10131', 'AppID': 'simnow_client_test', 'AuthCode': '0000000000000000'},
|
||
# 'N视界': {'BrokerID': 10010, 'TDServer': "210.14.72.12:4600", 'MDServer': '210.14.72.12:4602', 'AppID': '', 'AuthCode': ''},
|
||
# }
|
||
# BrokerID统一为：9999
|
||
# 支持上期所期权、能源中心期权、中金所期权、广期所期权、郑商所期权、大商所期权
|
||
# 第一组
|
||
# Trade Front：180.168.146.187:10201，Market Front：180.168.146.187:10211；【电信】（看穿式前置，使用监控中心生产秘钥）
|
||
|
||
# 第二组
|
||
# Trade Front：180.168.146.187:10202，Market Front：180.168.146.187:10212；【电信】（看穿式前置，使用监控中心生产秘钥）
|
||
|
||
# 第三组
|
||
# Trade Front：218.202.237.33:10203，Market Front：218.202.237.33:10213；【移动】（看穿式前置，使用监控中心生产秘钥）
|
||
|
||
# 用户注册后，默认的APPID为simnow_client_test，认证码为0000000000000000（16个0），默认开启终端认证，程序化用户可以选择不开终端认证接入。
|
||
|
||
future_account = get_simulate_account(
|
||
investor_id="227508", # simnow账户，注意是登录账户的ID，SIMNOW个人首页查看
|
||
password="Zj1234!@#%", # 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
|
||
)
|
||
# future_account = get_simulate_account(
|
||
# investor_id="00033556", # simnow账户，注意是登录账户的ID，SIMNOW个人首页查看
|
||
# password="27138169", # simnow密码
|
||
# server_name="N视界", # 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='', # 期货公司BrokerID
|
||
# server_dict={'TDServer': "121.37.80.177:20002", 'MDServer': '121.37.80.177:20004'}, # 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='1114', # 账户
|
||
# password='123456', # 密码
|
||
# app_id='', # 认证使用AppID
|
||
# auth_code='', # 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
|
||
# )
|
||
|
||
# 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()
|