901 lines
47 KiB
Python
901 lines
47 KiB
Python
'''
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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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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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#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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'''
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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 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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## 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 = "ifjgwlnzdvrfbjgf" #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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clearing_time_dict = {'sc': s_time(2,30), 'bc': s_time(1,0), 'lu': s_time(23,0), 'nr': s_time(23,0),'au': s_time(2,30), 'ag': s_time(2,30),
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'ss': s_time(1,0), 'sn': s_time(1,0), 'ni': s_time(1,0), 'pb': s_time(1,0),'zn': s_time(1,0), 'al': s_time(1,0), 'cu': s_time(1,0),
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'ru': s_time(23,0), 'rb': s_time(23,0), 'hc': s_time(23,0), 'fu': s_time(23,0), 'bu': s_time(23,0), 'sp': s_time(23,0),
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'PF': s_time(23,0), 'SR': s_time(23,0), 'CF': s_time(23,0), 'CY': s_time(23,0), 'RM': s_time(23,0), 'MA': s_time(23,0),
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'TA': s_time(23,0), 'ZC': s_time(23,0), 'FG': s_time(23,0), 'OI': s_time(23,0), 'SA': s_time(23,0),
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'p': s_time(23,0), 'j': s_time(23,0), 'jm': s_time(23,0), 'i': s_time(23,0), 'l': s_time(23,0), 'v': s_time(23,0),
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'pp': s_time(23,0), 'eg': s_time(23,0), 'c': s_time(23,0), 'cs': s_time(23,0), 'y': s_time(23,0), 'm': s_time(23,0),
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'a': s_time(23,0), 'b': s_time(23,0), 'rr': s_time(23,0), 'eb': s_time(23,0), 'pg': s_time(23,0)}
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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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# 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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# 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__(self, symbol, Lots, py, trailing_stop_percent, fixed_stop_loss_percent, dj_X, delta, sum_delta,失衡,堆积,周期):
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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__(self, broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
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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] + '-' + ActionDay[6:] + ' ' + update_time + '.' + update_millisec #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': created_at, # 创建时间
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'price': float(data['LastPrice']), # 最新价
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'last_volume': int(data['Volume']) - previous_volume.get(instrument_id, 0) if previous_volume.get(instrument_id, 0) != 0 else 0, # 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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# 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({'datetime':timetick,'symbol':tick['symbol'],'mainsym':tick['symbol'].rstrip('0123456789').upper(),'lastprice':tick['price'],
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'vol':tick['last_volume'],
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'bid_p':bid_p,'ask_p':ask_p,'bid_v':bid_v,'ask_v':ask_v},index=[0])
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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], 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({'datetime':df['datetime'],'symbol':df['symbol'],'lastprice':df['lastprice'],
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'volume':df['vol'],'bid_p':df['bid_p'],'bid_v':df['bid_v'],'ask_p':df['ask_p'],'ask_v':df['ask_v']})
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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(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,rdf['high'].values)
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tickdata['low'] = min(tickdata['lastprice'].values,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',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)
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bardata =bardata.dropna().reset_index(drop = True)
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bardata['bartime'] = pd.to_datetime(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:
|
||
# break
|
||
# elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] < tEnd):
|
||
Bp = round(bp1TickArray[0],4)
|
||
Ap = round(ap1TickArray[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()))
|
||
#print(prinslist,asklist,bidlist)
|
||
#print(len(prinslist),len(bidDictResult),len(askDictResult))
|
||
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
|
||
|
||
#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。
|
||
|
||
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'] == 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'] == 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==True:
|
||
# 选择最后一行数据
|
||
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", 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 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)
|
||
# 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{data['AskPrice1']+self.py}"
|
||
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']+self.py}"
|
||
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']-self.py}"
|
||
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']-self.py}"
|
||
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。
|
||
# current_directory = os.getcwd()
|
||
# print("当前工作目录：", current_directory)
|
||
# # 设置新的工作目录
|
||
# new_directory = 'C:/simnow_trader'
|
||
# os.chdir(new_directory)
|
||
# # 验证新的工作目录
|
||
# updated_directory = os.getcwd()
|
||
# print("已更改为新的工作目录：", updated_directory)
|
||
|
||
#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['ni2406'] = ParamObj(symbol='ni2405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||
param_dict['j2409'] = 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['TA409'] = 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['ag24g08'] = 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['sc2406'] = 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['lu2407'] = 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,A:7*24股票模拟环境交易前置 tcp://210.14.72.15:44007*24股票模拟环境行情前置 tcp://210.14.72.15:4402;
|
||
#B:7*24股票模拟环境交易前置 tcp://210.14.72.16:9500 7*24股票模拟环境行情前置 tcp://210.14.72.16:9402
|
||
future_account = get_simulate_account(
|
||
investor_id='223828', # simnow账户:'135858'&'223828'，注意是登录账户的ID，SIMNOW个人首页查看，N视界:00641394，TTS:1148
|
||
password='Zj1234!@#%', # simnow密码:'Zj82334475'&'Zj1234!@#$',N视界:29109937,TTS:123456
|
||
server_name='TEST', # 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='***', # 账户
|
||
# password='***', # 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()
|
||
|
||
|